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Face Processing Impairments in Schizophrenia and Other Psychiatric Disorders

Hayley Darke (519351)

ORCID ID: 0000-0003-0262-5330

Submitted in partial fulfilment of the requirements of the combined degree of Master of Psychology

(Clinical Neuropsychology) and Doctor of Philosophy.

May 2018

Melbourne School of Psychological Sciences

Faculty of Medicine, Dentistry and Health Sciences

University of Melbourne

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Abstract

The ability to recognise and interpret the facial expressions of others is shown to be impaired in

individuals with schizophrenia and may contribute to poor social functioning. In contrast, it remains

unclear whether other aspects of face processing (such as identity recognition) are also impaired, and

whether such deficits correlate with specific symptoms. This thesis explores face processing in

schizophrenia and other psychiatric disorders. To test this, a novel set of video-based tasks were

designed to improve upon traditional image-based tasks, as these have been criticised for lacking

ecological validity. We show that these video-based tasks are recognised more easily than image-

based versions and are sensitive to schizophrenia-like experiences in a study of healthy individuals

(n=82). Eighty-six psychiatric inpatients and an additional twenty healthy controls completed a series

of five video-based tasks: Emotion Discrimination (same or different), Emotion Labelling (fear or

disgust), Identity Discrimination (same or different), Sex Labelling (male or female), and a non-face

control task – Car Discrimination (same or different). Schizophrenia patients (n=36) were impaired

compared to healthy controls across all tasks except for Identity Discrimination (which showed

marginal impairment), and Sex Labelling. When all patient groups were compared, it was revealed

that the schizophrenia (n=36) and bipolar disorder groups (n=15) were significantly impaired on the

emotion-processing tasks and Car Discrimination compared to both healthy controls (n=20) and

patients with non-psychotic diagnoses (n=18). There were no significant differences between patients

and healthy controls on the general face tasks (Identity Discrimination and Sex Labelling). These

findings hint that the “emotion-specific” processing deficits reported in previous studies may

represent a more general cognitive or perceptual impairment. Correlational analyses revealed that

non-cognitive positive symptoms – such as delusions and suspiciousness – were negatively correlated

with emotion-processing ability and car discrimination, but not identity-processing ability. In contrast,

more severe cognitive symptoms were associated with generally reduced performance across tasks.

Overall, results suggest that deficits in emotion processing reflect symptom pathology independent of

diagnosis and support the idea of a generalised cognitive deficit that is particularly prominent in

patients showing positive symptoms of psychosis. These findings are discussed in terms of

dimensional models of psychosis. Furthermore, they highlight the importance of including appropriate

control tasks when assessing allegedly specific deficits.

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Declaration

This is to certify that:

i. the thesis comprises only my original work towards the PhD except where indicated in the

Preface,

ii. due acknowledgement has been made in the text to all other material used,

iii. the thesis is fewer than 100 000 words in length, exclusive of tables, maps, bibliographies and

appendices,

iv. the research reported in this thesis was conducted in accordance with the principles of the

ethical treatment of human subjects as approved for research by Human Research Ethics

Committee at the University of Melbourne and Northern Health.

Hayley Darke 20th May 2018

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Preface

Acknowledgement of contributors and funding

Data for the experiment described in Chapters 5 and 6 were collected as part of a larger

battery which was administered by a visiting master’s student, Lola Kourouma. Data for the

additional experiment described in Chapter 9 were collected with the assistance of an honours student,

Letian Wang. However, all task development, programming, data cleaning and analyses, and

interpretation of results were conducted by the PhD candidate alone, and therefore have been included

as a part of this thesis.

Parts of this thesis have appeared in peer-reviewed manuscripts with multiple authors (listed

below). However, all chapters of this thesis were drafted by the PhD candidate alone. This research

was funded by an Australian Research Council Grant to A/Prof Olivia Carter: FT140100807.

Publications arising from this thesis

Darke, H., Sundram, S., Cropper, S. J., Carter, O. (In preparation). Deficits in facial emotion

processing are associated with psychotic symptoms and are not limited to schizophrenia.

Darke, H., Cropper, S. & Carter, O. (Manuscript under review). Using morphing to vary person

and emotion attributes in face video stimuli. Behavior Research Methods.

Peterman, J. S., Darke, H., Carter, O, Sundram, S. & Park, S. (2017). M124. Perception of socio-

emotional information in dynamic gait in inpatients with schizophrenia spectrum disorders.

Schizophrenia Bulletin, 43(suppl_1), S255-S256.

Darke, H., Peterman, J. S., Park, S., Sundram, S. & Carter, O. (2013). Are patients with

schizophrenia impaired in processing non-emotional features of human faces? Frontiers in

Psychology, 4, 529.

Darke, H. Dynamic stimulus database. The emotional faces, non-emotional faces, and car stimuli

developed for this thesis can be downloaded from http://go.unimelb.edu.au/e3t6.

Posters and presentations

Darke, H., Sundram, S. & Carter, O. (2014). Face processing in schizophrenia and other

psychiatric disorders. Talk presented at the Department of Psychiatry Research Symposium,

University of Melbourne, Australia. Winner of the Best Presenter Award for Early Career

Researcher.

Darke, H., Carter, O. & Sundram, S. (2014). Deficits in facial expression and identity recognition

in schizophrenia. Poster presented at the Biological Psychiatry Australia Conference, Melbourne,

Australia.

Darke, H., Sundram, S. & Carter, O. (2014). Facial expression and identity recognition

impairments in schizophrenia and other disorders. Poster presented at the Integrative Brain

Function Workshop, MBI, Monash University, Melbourne, Australia.

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Acknowledgements

Holy moly, I can’t believe I’m writing this. We did it! And I must say “we”, because this

thesis could never have existed without the many generous, patient, and ridiculously clever people

who have supported me through this journey. Foremost, I want to thank my primary supervisor A/Prof

Olivia Carter for her tireless guidance and encouragement throughout my candidature. I also want to

thank my co-supervisors Professor Suresh Sundram and Dr Simon Cropper for their excellent input at

all stages of the project. I am indebted to my partner, friends, colleagues, and family for their

constant encouragement, assistance and, of course, gentle prodding to stop procrastinating and finish

this thesis. I am also extremely grateful to the staff at the School of Psychological Sciences and the

Northern Hospital for supporting my data collection efforts.

Finally, I want to thank the participants who took part in this study. I know it was boring

(sorry!), but it is thanks to your generosity that research like this is made possible.

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Table of Contents

Chapter 1: Introduction ......................................................................................................................... 10

Chapter 2: Face Processing in Healthy versus Schizophrenia Populations .......................................... 14

Chapter 3: Is it really all about emotion? Non-emotional Face Processing is (Maybe) Impaired in

Schizophrenia ........................................................................................................................................ 29

Chapter 4: What do we really want to know about face processing in schizophrenia? Rationale for the

Inpatient Study ...................................................................................................................................... 40

Chapter 5: Is our data only as good as our tools? Issues of Static versus Dynamic Faces, and

Development of a Novel Stimuli Set ...................................................................................................... 44

Chapter 6: Are these tasks sensitive to schizophrenia-like traits? Schizotypy in Healthy Controls and

the Relation to Emotion-processing Performance ................................................................................ 70

Chapter 7: Method for Inpatient Study ................................................................................................. 78

Chapter 8: So what kind of deficit is it, really? Characterising Face Processing Deficits in Inpatients

with Schizophrenia ................................................................................................................................ 87

Chapter 9: Could face-processing impairments in schizophrenia be mediated by deficits in allocating

visuospatial attention? ........................................................................................................................... 98

Chapter 10: Are these deficits specific to schizophrenia? Overlap with Face Processing Deficits in

Other Psychiatric Disorders ............................................................................................................... 115

Chapter 11: What else accompanies these deficits? Symptom Correlates of Face-processing Deficits

in Schizophrenia and Other Disorders ............................................................................................... 131

Chapter 12: General Discussion .......................................................................................................... 162

References ........................................................................................................................................... 178

Appendices .......................................................................................................................................... 208

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List of Tables

Table 3. Identity processing tasks used in schizophrenia research from the last 25 years. .................. 32

Table 5.1. Behavioural studies comparing emotional processing of dynamic and static face stimuli in

healthy controls. .................................................................................................................................... 49

Table 5.2. Behavioural studies using dynamic stimuli to investigate emotion processing in

schizophrenia. ....................................................................................................................................... 50

Table 5.3. Participants’ (n=82) demographic information. ................................................................. 57

Table 5.4. Pearson correlations between task performance and demographic factors. ...................... 67

Table 6.1. Healthy controls’ (n=82) demographics and mean raw scores for the O-LIFE scales. ...... 74

Table 6.2. Spearman rank correlations between face-processing performance and O-LIFE scores for

healthy controls (n=82). ....................................................................................................................... 75

Table 7.1. Breakdown of patient diagnoses by group. ......................................................................... 79

Table 7.2. Mean participant demographics and questionnaire scores by group (SD in parentheses). 84

Table 7.3. Medication status for inpatient groups. ............................................................................... 85

Table 8. Mean participant demographics by group (SD in parentheses). ............................................ 88

Table 9.1. Mean participant demographics and questionnaire scores by group (SD in parentheses).

............................................................................................................................................................ 104

Table 9.2. Uncorrected Pearson correlations between demographic factors and task performance. 105

Table 9.3. Mean c scores for patients with schizophrenia and healthy controls on the five tasks. ..... 105

Table 9.4. Bonferroni-corrected Pearson correlations for performance across different tasks. ........ 110

Table 9.5. Uncorrected partial correlations for performance across different tasks, controlling for

Age, years of Education, and IQ. ........................................................................................................ 111

Table 10. Mean d’ scores for each group on the five tasks. ................................................................ 121

Table 11.1. Studies reporting symptom correlates with reduced performance on emotion tasks in

schizophrenia spectrum disorders. ..................................................................................................... 138

Table 11.2. Studies reporting symptom correlates with reduced performance on non-emotion face

tasks in schizophrenia spectrum disorders. ........................................................................................ 146

Table 11.3. Pearson correlations between PANSS scores and performance (d’) on the five dynamic

tasks. ................................................................................................................................................... 154

Table 11.4. Spearman rank correlations between individual PANSS items and performance (d’) on the

five dynamic tasks ............................................................................................................................... 154

Table 11.5. Characteristics of four clusters based on patient performance. ...................................... 156

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List of Figures

Figure 2.1. A visual representation showing the separation of emotion and identity processing during

face perception. ..................................................................................................................................... 15

Figure 2.2. Bruce and Young’s theory of face perception. ................................................................... 16

Figure 2.3. The distributed neural system for face perception. ............................................................ 17

Figure 2.4. Examples of labelling and discrimination tasks. ................................................................ 21

Figure 3. Examples of tasks used to assess the perception of identity. ................................................ 31

Figure 5.1. Example of morphed stimuli created by Norton and colleagues (2009, p.1095) used in an

emotion discrimination task. ................................................................................................................. 46

Figure 5.2. Partial image sequences (every other frame) from five of the video stimuli ranging in

intensity of emotion. ............................................................................................................................. 54

Figure 5.3. Partial image sequences from six different non-emotional face video stimuli. ................. 55

Figure 5.4. Partial image sequences from two different 3D video stimuli used in the Car

Discrimination task. .............................................................................................................................. 56

Figure 5.5. Trial sequences for Emotion Discrimination (A) and Emotion Labelling (B .................... 59

Figure 5.6. Comparison of percent accuracy by emotional intensity for dynamic and static conditions

on the Discrimination task (A) and the Labelling task (B). Error bars indicate 95% confidence

intervals around means. ........................................................................................................................ 61

Figure 5.7. Raw accuracy rates (A) and d prime scores (B) for Dynamic and Static stimuli for the two

tasks: Emotion Discrimination and Emotion Labelling. ....................................................................... 62

Figure 5.8. Mean response bias as evidenced by c rates across the four task conditions. Error bars

indicate 95% confidence intervals. ....................................................................................................... 63

Figure 7. Example trials for each of the five tasks: Emotion Discrimination, Emotion Labelling,

Identity Discrimination, Sex Labelling, and Car Discrimination. ........................................................ 81

Figure 8.1. Performance (d’) of healthy controls and patients with schizophrenia across the five

dynamic tasks. ....................................................................................................................................... 88

Figure 8.2. Performance (d’) of healthy controls across the five dynamic tasks. ................................. 90

Figure 8.3. Performance (d’) of patient with schizophrenia across the five dynamic tasks. ................ 90

Figure 8.4. Mean response bias as evidenced by c rates across the five tasks for schizophrenia patients

versus controls. ..................................................................................................................................... 91

Figure 8.5. Mean accuracy performance for the schizophrenia spectrum (SZ) and control groups for

(A) emotion discrimination and (B) emotion labelling. ........................................................................ 92

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Figure 8.6. Mean accuracy performance for the schizophrenia spectrum (SZ) and healthy control

groups for (A) identity discrimination and (B) sex labelling. ............................................................... 94

Figure 9.1. Example of hierarchical stimuli used by Johnson and colleagues (2005, p.938). .............. 99

Figure 9.2. The Navon task ................................................................................................................ 102

Figure 9.3. Performance (d’) of healthy controls, psychiatric controls and schizophrenia spectrum

patients for the identity discrimination and emotion discrimination tasks .......................................... 106

Figure 9.4. Performance (d’) of healthy controls, psychiatric controls and schizophrenia spectrum

patients for the global and local attentional tasks. .............................................................................. 107

Figure 9.5. Mean local processing bias for healthy controls, psychiatric controls and schizophrenia

spectrum patients. ............................................................................................................................... 108

Figure 9.6. Reaction times (ms) of healthy controls, psychiatric controls and schizophrenia spectrum

patients for the global and local attentional tasks. .............................................................................. 109

Figure 10.1. Mean d’ performance for the schizophrenia spectrum (SZ), bipolar disorder (BPAD),

other psychosis (Other), non-psychosis (NP) and healthy control groups across the five tasks ......... 122

Figure 10.2. Performance (d’) of patients with bipolar disorder across the five dynamic tasks. ....... 123

Figure 10.3. Performance (d’) of patients with non-schizophrenia psychosis across the five dynamic

tasks. ................................................................................................................................................... 124

Figure 10.4. Performance (d’) of patients with non-psychotic disorders across the five dynamic tasks.

............................................................................................................................................................ 125

Figure 10.5. Mean accuracy performance for the schizophrenia spectrum, bipolar disorder, non-

schizophrenia psychosis (Other psychosis), non-psychotic disorders and control groups across the four

morphed face tasks: Emotion Discrimination (A), Emotion Labelling for disgust faces (B), Emotion

Labelling for fear faces (C), Identity Discrimination (D), and Sex Labelling (E). ............................. 127

Figure 11. Mean performance across five dynamic tasks for the four patient clusters. ..................... 157

Figure 12.1. Pentagonal bifactor model of psychosis. ........................................................................ 171

Figure 12.2. A stress-diathesis model demonstrating the interaction between exposure and genetic

vulnerability to psychosis. .................................................................................................................. 173

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Chapter 1: Introduction

Schizophrenia is a debilitating psychiatric disorder affecting an estimated 0.3 -.66% of

individuals globally (McGrath, Saha, Chant, & Welham, 2008). It is classically characterised by a

triad of symptoms: positive symptoms, including hallucinations and delusions; negative symptoms,

such as diminished emotional expression; and disorganised symptoms, which describe disordered

thoughts and behaviour (Tandon, Nasrallah, & Keshavan, 2009). With onset of the disorder usually

occurring in early adulthood, the course of schizophrenia typically involves fluctuating or episodic

positive and disorganised symptoms accompanied by persisting negative symptoms (Keshavan,

Nasrallah, & Tandon, 2011). In addition, schizophrenia is associated with persisting cognitive

impairment in areas such as basic attention, processing speed, working memory, verbal learning and

memory, and reasoning (Keefe & Harvey, 2012). Although cognitive impairment is not included in

the DSM-V description of schizophrenia, such impairments are increasingly recognised as a core

feature of this disorder, with the average patient performing two standard deviations below the mean

for healthy individuals (Keefe et al., 2011).

Despite their relatively low prevalence, schizophrenia and other psychotic disorders account

for the majority of spending on specialist mental health services in Australia (Australian Institute of

Health and Welfare, 2015). It was estimated that in 2014, psychotic disorders such as schizophrenia

cost the Australia population approximately $10 billion in medical costs, disability support, and lost

productivity (Victoria Institute of Strategic Economic Studies, 2016). A national psychosis survey

conducted in 2010 highlights the extent of disability and disadvantage associated with psychotic

disorders in Australia (Morgan et al., 2012). It was found that almost 20% of patients accessing

mental health services had difficulties with reading and writing, two-thirds had not completed high

school, and only one-third had held some form of employment in the past 12 months. Half of

respondents reported attempting suicide in the past, and 13% had been homeless in the past 12

months. Moreover, the life expectancy of individuals with schizophrenia is 14.5 years below the

average, and has not improved over recent years (Hjorthoj, Sturup, McGrath, & Nordentoft, 2017).

Suggested reasons for premature death include an increased risk of cardiovascular and metabolic

disease, substance use, and reduced health-seeking behaviour. However, risk of death from accidents

and homicide is also increased, and the risk of suicide is 22 times higher in schizophrenia compared to

the general population (McGrath et al., 2008).

The etiology of schizophrenia remains unknown, and is likely complex and multifactorial. It

is known that the disease is highly heritable (approximately 80%; Van Os, 2009) and has also been

linked to a variety of environmental factors such as maternal malnutrition, prenatal infection, urban

birth, obstetric complications, trauma and cannabis exposure (Keshavan et al., 2011). While a range of

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different models have been proposed to explain the pathophysiology of schizophrenia (e.g.: aberrant

salience, oxidative stress, dopamine imbalance) there is not yet a unified explanation that can account

for all aspects of the disorder. One major obstacle to clinical research is the high level of

heterogeneity within schizophrenia. For example, it is unclear why one individual presents with

disorganised behaviour and significant cognitive impairment, while another presents with complex

persecutory delusion. Historically, this heterogeneity was explained in terms of four clinical subtypes:

disorganised or hebephrenic, catatonic, paranoid, and undifferentiated (i.e.: none of the above)

schizophrenia. However, studies have consistently failed to show support for these subtypes, instead

finding that the vast majority of patients fall into the undifferentiated category (Tandon et al., 2013).

For this reason, clinical subtypes have since been removed from the DSM-V.

A second obstacle to clinical research is the substantial overlap between schizophrenia and

other psychiatric disorders. Clinically, schizophrenia can be difficult to differentiate from other

diagnoses with psychotic features, such as bipolar disorder and psychotic depression (Dacquino, De

Rossi, & Spalletta, 2015). This is partly because individuals with psychosis show high rates of mood

symptoms which may or may not overlap with non-affective psychosis at different times (Malaspina

et al., 2013). To address this overlap, schizoaffective disorder was first introduced to the DSM in

1980 to serve as an intermediary diagnosis between schizophrenia and bipolar disorder. However, the

reliability and stability of schizoaffective disorder over time is poor, meaning that diagnostic

boundaries between these disorders are still often blurred in clinical practice. In fact, there is

increasing evidence that these disorders are not only similar in symptoms, course, and prognosis, but

are also underlain by a shared polygenic vulnerability (Purcell et al., 2009). An alternative

conceptualisation of these disorders is that they represent a spectrum of psychosis, rather than discrete

nosological categories. This approach shifts the focus of study from diagnostic categories to specific

symptoms, such as depression or hallucinations, and how these relate to neurobiological and

behavioural markers. Although some authors have questioned the utility of applying a dimensional

scale to clinical practice (e.g.: Lawrie et al., 2010) , this approach allows researchers to identify

specific patterns of symptomatology and deficit from an otherwise highly heterogeneous population.

A particular area of deficit in psychosis that has gained interest in recent years is social

cognition. Social cognition broadly includes abilities such as reacting emotionally to others,

experiencing empathy, and inferring the emotions of others (Green, Horan, & Lee, 2015). One critical

aspect of social cognition is the ability to extract emotional cues from faces. Interchangeably referred

to as facial affect recognition, expression recognition, and facial emotion processing, this ability has

been studied extensively in schizophrenia, and to a lesser degree in other psychotic disorders.

Research indicates that facial affect recognition is strongly associated with social, occupational, and

community functioning in patients with schizophrenia, and may also mediate the relationship between

social functioning and broader neurocognitive deficits (Irani, Seligman, Kamath, Kohler, & Gur,

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2012; Meyer & Kurtz, 2009; Tsunoda et al., 2012). Furthermore, deficits in facial affect recognition

may predict the conversion to schizophrenia in clinically high-risk populations, suggesting that these

deficits precede the formal onset of psychosis (Corcoran et al., 2015).

The exact mechanics that may underlie impairments in facial emotion processing are an

ongoing source of debate. However, there is consensus that individuals with schizophrenia are

significantly impaired in their ability to recognise emotions compared to healthy controls and certain

other psychiatric disorders (Kohler, Walker, Martin, Healey, & Moberg, 2010; Ventura, Wood,

Jimenez, & Hellemann, 2013). Meta-analyses estimate these effect sizes to be quite large, e.g.: d =

−.91, d = −.85, and g = .89 respectively (Chan, Li, Cheung, & Gong, 2010; Kohler et al., 2010; Savla,

Vella, Armstrong, Penn, & Twamley, 2013). Deficits in emotion recognition have been demonstrated

reliably across a wide range of behavioural studies. Furthermore, there is a mounting body of

evidence that individuals with schizophrenia show differences from healthy controls using other

methodologies such as analysis of visual scanpaths during face viewing, neuroimaging, and

electrophysiological studies.

It remains unclear whether these emotion processing deficits reported in schizophrenia are

indeed specific to facial expressions, or whether they could be due to more general impairments in

processing non-emotional faces, or processing visual stimuli more generally. For instance, there is

evidence to suggest that patients with schizophrenia may have impairments in recognising the identity

of a face (i.e.: non-affective face processing). Like emotion-processing, the ability to recognise

identity is a crucial aspect of social cognition, and deficits may contribute to poorer functional

outcome in this disorder (Chen, Norton, McBain, Ongur, & Heckers, 2009). As emotion-processing

and identity-processing are believed to be underlain by different neural routes, the comparison of

these abilities allows us to better characterise these impairments and their relevant pathophysiology.

Through improved characterisation of impairment, it is hoped that we can eventually improve

interventions aimed at remediating these deficits in individuals before the formal onset of psychotic

illness.

The goal of this thesis, therefore, is to better understand facial affect processing impairments

in schizophrenia by addressing the following research questions:

I. Can we better characterise face processing deficits in schizophrenia? i.e.: Can impairments in

emotion-processing be explained by more general deficits in non-emotional face processing or

non-face processing?

II. Are face processing impairments specific to schizophrenia, or are they shared by other psychiatric

disorders on the psychosis continuum?

III. Do specific symptoms correlate with face-processing impairments?

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These questions will be explored through experiments using a set of original dynamic (video-

based) tasks to better overcome some of the methodological issues raised in previous studies.

Outline of chapters

Chapter 2 of this thesis will begin by introducing the dominant theories of face-processing in

healthy individuals. It will then review behavioural, imaging, and ERP research investigating

emotion-processing impairments in schizophrenia. Chapter 3 will review studies examining non-

emotional face processing in schizophrenia and presents the argument that putative ‘emotion-specific’

deficits may not be so specific after all. Chapter 4 will present the rationale and aims of the

experiments that were conducted for this thesis. Chapter 5 will discuss methodological issues raised

in previous research and outline the development of a novel set of video-based tasks for the

assessment of face processing in schizophrenia. It will then present the results of an experiment

evaluating the efficacy of these video-based tasks in healthy controls. Chapter 6 will introduce the

concept of schizotypy (schizophrenia-like traits) and discuss how these experiences related to task

performance in the previous healthy control study. Chapter 7 will present the methods and initial

results for a large inpatient study examining face-processing in psychiatric inpatients with

schizophrenia, bipolar disorder, other forms of psychosis, and non-psychotic disorders. Chapter 8

will discuss selected results with reference to the first aim of the study: to better characterise face-

processing deficits in schizophrenia using a range of dynamic tasks. Chapter 9 will present the results

of an additional inpatient study designed to explore the possible relationship between emotion-

processing deficits and visuospatial attention in schizophrenia. In Chapter 10, results of the larger

inpatient study will be discussed with reference to the second aim: to determine whether face-

processing deficits are specific to schizophrenia or shared across other psychiatric disorders with and

without psychotic features. Chapter 11 will present a review of research examining associations

between symptomatology and face-processing impairments in schizophrenia. It will also discuss the

results of correlational analyses between symptoms and task performance in the current study. Finally,

the general discussion and practical implications of this thesis will be presented in Chapter 12.

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Chapter 2: Face Processing in Healthy versus Schizophrenia

Populations

The perception of human faces is arguably one of the most complex and specialized visual

processes that we possess. As a critical aspect of social communication, faces are a unique stimulus

class that bridges the gap between visual and social cognitive sciences. Healthy individuals are able to

extract a wealth of information from faces – such as identity, sex, attractiveness, emotion, and focus

of attention – with almost effortless ease. This chapter will outline the dominant theories of face

processing in healthy adults, highlighting the neural and behavioural dissociation between emotional

(expression) and non-emotional (facial identity) face information (See Figure 2.1). It will then present

a broad review of emotion-processing impairments in schizophrenia, drawing on research from

behavioural, visual scanpath, functional imaging, and electrophysiological studies.

Face processing in the healthy brain

Bruce & Young’s theory of face recognition (1986)

An early iteration of modern theories of face processing was produced by Bruce and Young

(1986), who proposed that different types of information are extracted from a face via both

hierarchical and parallel functions (see Figure 2.2). The first step involves structural encoding, which

involves extracting two types of information from a face: view-centred descriptions such as transient

facial movements and expressions, and expression-independent descriptions which are more abstract,

view-independent information, such as the distance between features. After encoding, this information

then feeds into three parallel routes: the face-recognition route (i.e.: processing the identity of a face)

which relies on expression-independent information, and the expression-analysis and facial speech

analysis routes which both rely on view-centred descriptions. A fourth route, directed visual

processing, describes strategic goal-directed visual processing, such as searching for a friend’s face in

a crowd. In addition to making the critical distinction between processing the identity of a face and

processing emotional expressions, Bruce and Young’s (1986) theory also included a framework for

recognising familiar faces. This hierarchical process involved first the activation of Facial

Recognition Units (triggering a sense of familiarity), then Person Identity Nodes (accessing memories

about the individual) and finally Name Generation (accessing the specific name of the individual).

The distinction between Person Identity Nodes and Name Generation aims to explain the common

‘tip-of-the-tongue phenomenon’ where individuals can recall information about a person without

being able to recall their name (e.g. “that’s whats-his-name who taught my psychology course last

year”). Bruce and Young’s functional theory was compatible with a range of psychological research,

including behavioural studies, observation of everyday errors, and studies of cerebral injury (Hanley,

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2011). However, this theory made no assumptions about the anatomical correlates that may underpin

these functional routes.

Figure 2.1. A visual representation showing the separation of emotion and identity processing during

face perception.

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Figure 2.2. Bruce and Young’s theory of face perception. Image adapted from Bruce and Young

(1986, p.312).

The distributed neural system for face perception

Bruce and Young’s (1986) theoretical model was later expanded upon by Haxby and colleagues

(2000), who proposed the distributed neural model or dual-route model of face perception. This

model proposed a neuroanatomical framework for face recognition derived from neuroimaging

research. This framework consists of a Core System of extrastriate areas where visual identity and

non-identity information are analysed separately, then fed into a shared Extended System (see Figure

2.2). Analogous to Bruce and Young’s (1986) expression-independent and view-centred descriptions,

Haxby and colleagues distinguish between two types of information: invariant and changeable.

Invariant information refers to properties that are consistent across different views and facial

expressions, and are necessary for recognising the identity of a face. Changeable information includes

eye gaze, expression, and movements of the eyes and mouth, and is necessary for the recognition of

facial affect. At the level of the Core System, the most basic processing of facial features is thought to

be mediated by neurons in the inferior occipital gyri (the Occipital Face Area; Haxby & Gobbini,

2011). From here, the analysis of changeable visual features is processed largely via a route involving

the posterior superior temporal sulcus (pSTS). In contrast, invariant information is processed via a

ventral temporal route which is thought to include the inferior occipital and fusiform gyri, including

the Fusiform Face Area (FFA). These two routes then feed into the Extended System for higher level

interpretation, which is composed of structures outside the visual extrastriate cortex. This includes

functions such as retrieval of personal knowledge (associated with the medial prefrontal cortex,

anterior temporal cortex, and posterior cingulate gyrus, among others), motor simulation of facial

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expressions (associated with putative mirror neurons in the inferior parietal lobe and frontal

operculum) and emotion recognition (associated with the limbic system).

Figure 2.3. The distributed neural system for face perception. Image adapted from Haxby and

Gobbini (2011, p.105).

Evidence in support of this model, with particular attention to the separation of identity and

expression recognition, is discussed at length elsewhere (see Calder & Young, 2005, for a helpful

review). Briefly, this support comes from a broad range of sources including behavioural research

(Bruce, 1986; Calder, Young, Keane, & Dean, 2000; Campbell, Brooks, de Haan, & Roberts, 1996)

functional imaging studies (George et al., 1993; Sergent, Ohta, Macdonald, & Zuck, 1994; Winston,

Henson, Fine-Goulden, & Dolan, 2004), and in dissociations exhibited by brain injured individuals

(Hornak, Rolls, & Wade, 1996; Tranel, Damasio, & Damasio, 1988; Young, Newcombe, de Haan,

Small, & Hay, 1993). For instance, it has been demonstrated in healthy participants that the familiarity

of a face does not affect reaction times during an expression recognition task, and vice versa (e.g.:

Calder et al., 2000; Campbell, et al., 1996). Accounts of individuals with prosopagnosia – severe

deficits in recognising the identity of familiar faces, despite intact low-level vision and general

cognitive function – have described preserved ability to recognise facial expressions despite impaired

identity recognition (Baudouin & Humphreys, 2006; Riddoch, Johnston, Bracewell, Boutsen, &

Humphreys, 2008).

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Compelling evidence for this dissociation has also come from studies of nonhuman primates.

In humans, fMRI has revealed several specialised regions in the temporal lobe that respond much

more strongly to faces than to other objects (Kanwisher, McDermott, & Chun, 1997; Puce, Allison,

Asgari, Gore, & McCarthy, 1996). Similar regions have been found in the temporal lobes of macaques

(Pinsk, DeSimone, Moore, Gross, & Kastner, 2005; Rajimehr, Young, & Tootell, 2009; Tsao,

Freiwald, Knutsen, Mandeville, & Tootell, 2003). In one study, Tsao and colleagues (2008) scanned

humans and macaques while showing images of faces and nonface objects. In macaques, six ‘patches’

of face-selective cortex were identified in the temporal lobe. The locations of these patches were

consistent across 9 of the 10 animals scanned. In humans, the number of these patches in the temporal

lobe varied between three and five. These were found primarily in the occipital face area (Kanwisher

& Yovel, 2006), the superior temporal sulcus (Hoffman & Haxby, 2000), the fusiform face area

(Kanwisher et al., 1997) and one or two regions in the anterior collateral sulcus (Tsao, Moeller, &

Freiwald, 2008). In addition to the temporal lobe face patches, Tsao and colleagues (2008) also

identified two face-selective patches in the macaque frontal lobe. Face-responsive regions have also

been found in the human frontal lobe, particularly in the orbitofrontal cortex (Haxby et al., 1996). The

orbitofrontal cortex has been shown to play a role in the regulation of mood, emotion processing, and

social reinforcement (Bechara, Damasio, & Damasio, 2000), raising the possibility that this brain

region may be selective to facial expressions. Tsao and colleagues (2008) found that when neutral and

expressive faces were shown to macaques, the frontal lobe face patches responded much more

strongly to expressive faces than neutral faces. Conversely, the responses of the temporal lobe patches

were only weakly modulated by expression. This result suggests that in macaques, frontal lobe face

patches are involved in facial expression recognition, while the temporal lobe patches are involved in

non-expressive face processing, i.e. identity recognition.

While it has been established that identity and expression recognition involve separate

processes, the level at which this bifurcation occurs, and the degree to which these parallel processes

interact, are yet to be resolved. A convincing double dissociation between identity and expression

recognition is yet to be demonstrated, and while evidence for some dissociation exists, it is not clear

whether this represents a separation at the visuoperceptual level or at a higher stage of processing

(Calder, 2011). Furthermore, it is likely that some degree of crossover exists at the Core level. For

example, both changeable and invariant cues may be used to identify an individual based on

characteristic facial expressions or face movements (Fitousi & Wenger, 2013). Considering the scope

of this PhD, it will adopt the prevailing view that facial identity and facial expression recognition are

largely, but not entirely dissociable processes that nevertheless provide useful information about the

relative health of two different but interrelated neural routes.

19

Summary

The dominant theory in the face literature is that the two major components of face processing

– identity recognition and emotion recognition – represent largely independent processes. This view

reflects the two most prominent and widely-accepted models of face perception. The first is Bruce and

Young's (1986) classic functional model, which asserts that visual information pertaining to identity

and information pertaining to emotion recognition and speech are processed by two separate routes.

The second is Haxby and colleagues' (2000) dual-route model, which provides a neuroanatomical

framework to support the separation between identity recognition, emotion recognition, and other

face-related information, such as visual processing of speech. While the precise level at which

identity- and emotion-processing routes bifurcate remains an issue of contention, there is

overwhelming evidence from behavioural, neuroimaging, nonhuman primate, and brain-injury studies

to support the division of these neural functions.

The universality of facial emotions

The study of facial emotion recognition has for the most part focused on what is referred to as

the six basic emotions: happiness, sadness, fear, disgust, anger, and surprise (Kohler & Martin, 2006).

While there remains some debate as to whether additional emotions (such as contempt) should also be

included in this list, there is strong evidence to suggest that these six emotions are biologically

determined, rather than learned through social interactions.

The idea that emotional expressions may be innate was first attributed to Charles Darwin.

Upon observing similarities in facial expressions shared by phylogenetically similar species (such as

different primates) Darwin suggested that expressions are an evolutionary adaptation that is

biologically determined (Darwin, 1872, reprinted with commentary in Darwin, Ekman, & Prodger,

1998) . A century later, this idea was revisited in a series of studies designed to examine the

‘universality’ of emotional expressions across different cultures. Revived by three independent

researchers, Tomkin, Ekman and Izard, these studies revealed high consistency in both the expression

and recognition of six primary emotions: joy, sadness, fear, disgust, anger and surprise. This was

demonstrated across a range of literate cultures including Japan, Brazil, USA, Chile and Argentina

(Ekman & Friesen, 1971; Ekman et al., 1987; Izard, 1971; Tomkins & McCarter, 1964), as well as in

a socially isolated, pre-literate cultural group in Papua New Guinea (Ekman & Friesen, 1971; Ekman,

Sorenson, & Friesen, 1969). In the decades since, more than 30 studies have replicated the finding

that emotional expressions are universally recognised across cultures (see Matsumoto, 2001) , and

over 75 studies have shown consistency in the way that these basic emotions are expressed in

response to emotion-eliciting stimuli, such as films (Matsumoto, Keltner, Shiota, Frank, & O'Sullivan,

2008).

20

Other support for the idea that certain emotional expressions are biologically determined

comes from studies of congenitally blind individuals, who also produce the same six facial

expressions despite lacking the ability to perceive others’ expressions visually (Galati, Sini, Schmidt,

& Tinti, 2003; Matsumoto & Willingham, 2009). Considered as a whole, the literature strongly

suggests that the six basic emotional expressions are hard-wired in humans, therefore major

impairment in the identification and expression of these emotions is likely to be underpinned by

neural differences, rather than differences in culture or socialisation.

Behavioural studies of emotion processing in schizophrenia

The most widely used method of evaluating facial emotion processing in schizophrenia is

through behavioural tasks that involve viewing static images of faces. There is an extensive body of

research demonstrating that patients with schizophrenia perform more poorly on expression

processing tasks compared to healthy controls (see Kohler et al., 2010 for a meta-analytic review) as

well as other psychiatric populations such as patients with depression (Bediou, Krolak-Salmon, et al.,

2005; Weniger, Lange, Ruther, & Irle, 2004), schizoaffective disorder (Chen, Cataldo, Norton, &

Ongur, 2012) and affective psychoses (Edwards, Pattison, Jackson, & Wales, 2001). This deficit has

been demonstrated across a wide variety of tasks. These tasks can be broadly divided into two

categories: recognition/labelling tasks, and discrimination tasks. Recognition/labelling tasks involve

selecting the most appropriate label for a given stimulus. Figure 2.4A shows an example which

requires the participant to decide whether each face more closely resembles happiness, sadness, anger,

fear or disgust. In contrast, discrimination tasks involve viewing pairs of faces and making some sort

of judgement, such as deciding whether they show the same or different expression, or deciding which

expression has greater intensity (see Figure 2.4B and C).

The benefit of discrimination tasks is that, unlike labelling tasks, they do not require

participants to explicitly identify the expression shown; they are simply identifying whether the

expressions are the same or not. For this reason they are thought to be less affected by difficulties with

language or semantic retrieval (Macmillan & Creelman, 1991). However, because discrimination

tasks require comparison between two faces – either two presented simultaneously, or serially,

meaning that the first face must be held in iconic memory – they arguably place a greater load on

working memory compared to labelling tasks (Macmillan & Creelman, 1991). This is an important

consideration when assessing clinical samples such as schizophrenia, as working memory deficits are

increasingly recognised as a central feature of this disorder (Forbes, Carrick, McIntosh, & Lawrie,

2009). In recognition of the differing demands of these two types of tasks, many researchers include

both labelling and discrimination tasks in their studies to account for the possibility that they may be

sensitive to different impairments (Kohler et al., 2010). For example, one meta-analysis reported that

performance on only labelling tasks, not discrimination tasks, significantly predicted functional

21

outcomes in schizophrenia, although it is possible that this reflected a lack of power in the analyses

(Irani et al., 2012). Nevertheless, significant emotion perception impairments in schizophrenia have

been repeatedly and reliably demonstrated across a broad range of both labelling (Bediou et al., 2007;

Fakra, Salgado-Pineda, Delaveau, Hariri, & Blin, 2008; Kucharska-Pietura, David, Masiak, &

Phillips, 2005; Martin, Slessor, Allen, Phillips, & Darling, 2012; Penn et al., 2000; Whittaker, Deakin,

& Tomenson, 2001) and discrimination tasks (Addington, Saeedi, & Addington, 2006; Martin,

Baudouin, Tiberghien, & Franck, 2005; Weniger et al., 2004). Together, these results suggest that

these deficits exist even when memory and language demands are minimised.

Figure 2.4. Examples of labelling and discrimination tasks. A: A typical labelling task using Ekman

face stimuli (Ekman & Friesen, 1976). B: An example same-or-different task, also using Ekman

stimuli. C: An intensity discrimination task created by Bediou and colleagues (2005, p.528).

Emotional valence in behavioural studies

Overall, there is a consensus that individuals with schizophrenia show impairment in the

perception of affect in static facial expressions. However, a number of issues remain. One is whether

this impairment may be specific to different emotions. In general, studies have found stronger

evidence for impairment in recognising negative emotions such as fear, disgust, anger and sadness

(Bediou, Franck, et al., 2005; Brune, 2005; Comparelli et al., 2014; Edwards et al., 2001; Kohler et

al., 2003; Lahera et al., 2014; Pinkham, Penn, Perkins, & Lieberman, 2003). In contrast, these studies

have reported either unimpaired, or less impaired processing of positive emotions such as happiness.

It is possible that this distinction indicates differential processing of positive and negative emotions in

22

schizophrenia. However, authors have also pointed out that this finding may simply reflect inherent

differences in the difficulty of identifying these emotions, as even healthy controls typically find

happy faces to be the easiest emotion to identify and fearful faces the most difficult (Marwick & Hall,

2008).

Another interesting trend in the behavioural literature is that patients with schizophrenia also

show impairments in correctly identifying neutral facial expressions. For instance, Kohler and

colleagues (2003) noted that patients with schizophrenia were more likely to mislabel neutral faces as

“fearful” or “disgusted” compared to healthy controls. According to another study, this tendency to

miscategorise neutral faces appears to be more prominent in schizophrenia patients with paranoid

symptoms compared to those without (Pinkham, Brensinger, Kohler, Gur, & Gur, 2011). These

authors found that paranoid patients were significantly more likely to attribute neutral faces as

“angry”, despite performing similarly to non-paranoid patients on all other conditions. It was

suggested that this finding may reflect the broader tendency to misattribute salience to ambiguous

stimuli in schizophrenia, with paranoid patients in particular showing heightened sensitivity to

potentially threatening stimuli (Holt, Titone, et al., 2006). Furthermore, this implies that

schizophrenia is associated with difficulty distinguishing between socially-salient emotional

information and non-salient information, coupled with a tendency to assign meaning to ambiguous or

unimportant details (Phillips, 2011).

Visual scanpath studies in schizophrenia

Another approach to studying facial affect processing is through the recording of visual

scanpaths. A visual scanpath refers to the spatial sequence of eye gaze fixations and saccades

(movements) that occur when viewing a scene or object (Noton & Stark, 1971). This is thought to

represent the real-time allocation of visual attention required for encoding visual information.

Typically, visual scanpaths are measured using paradigms that record eye movements while faces are

shown under free-viewing conditions. When viewing faces, healthy individuals show a characteristic

pattern of fixations across the face, with particular focus on salient features such as the eyes, nose, and

mouth (Radua et al., 2010). In contrast, individuals with schizophrenia tend to show a comparatively

restricted scanning pattern characterised by shorter saccades, more time spent on each fixation, and a

tendency to avoid salient facial features (Toh, Rossell, & Castle, 2011). This pattern has been

demonstrated reliably in studies using neutral faces (Gordon et al., 1992; Loughland, Williams, &

Gordon, 2002b; Manor et al., 1999; Phillips & David, 1998; Rosse, Schwartz, Johri, & Deutsch, 1998;

Williams, Loughland, Gordon, & Davidson, 1999) as well as across the six universal emotions

(Bestelmeyer et al., 2006; Delerue, Laprevote, Verfaillie, & Boucart, 2010; Green & Phillips, 2004;

Green, Williams, & Davidson, 2003; Loughland, Williams, & Gordon, 2002a; Shimizu et al., 2000;

Streit, Wolwer, & Gaebel, 1997). This pattern also corresponds to poorer accuracy for recognising

23

emotions in a behavioural task (Williams et al., 1999), and is more pronounced in schizophrenia

compared to affective disorders (Loughland et al., 2002a).

Importantly, this pattern of fewer, longer fixations is also shown when viewing non-face

stimuli such as complex scenes, geometric shapes, and ambiguous stimuli (Benson, Leonards,

Lothian, St Clair, & Merlo, 2007; Bestelmeyer et al., 2006; Green, Waldron, Simpson, & Coltheart,

2008; Hori, Fukuzako, Sugimoto, & Takigawa, 2002; Obayashi et al., 2001), suggesting that this

scanning strategy is not simply in reaction to the class of object being viewed (Beedie, Benson, & St

Clair, 2011). However, the tendency to avoid salient facial features such as the eyes is also shared by

individuals with autism and social phobia (Horley, Williams, Gonsalvez, & Gordon, 2003; Radua et

al., 2010). It has been suggested patients with these disorders may find faces (especially eyes)

particularly anxiety-inducing and simply prefer to avoid attending to these features (Morris, Weickert,

& Loughland, 2009). For instance, it has been shown that people with schizophrenia choose to stand

further away from emotional faces compared to healthy controls (Mandal, Pandey, & Prasad, 1998).

Does this mean that the avoidance of salient facial features is simply an individual preference? The

majority of past research examined scanpaths under passive viewing conditions, meaning that

participants are requested to look at the face without performing any specific judgements. To

determine whether scanpath abnormalities persist in active viewing conditions, Delerue and

colleagues (2010) examined the visual scanpaths of 20 patients with schizophrenia while completing a

variety of active tasks. These included judging the age or sex of the face, identifying the emotion

shown, and deciding if the face is familiar on unfamiliar. Interestingly, they found that although the

patients with schizophrenia showed the typical restricted gaze pattern when viewing faces passively,

there was no significant difference in gaze pattern between healthy controls and patients on the four

active tasks. That is, patients were no longer avoiding salient features when they were explicitly asked

to make judgements about the face they are viewing. These results suggest that scanpath abnormalities

can be eliminated when task demands require active attention, and are unlikely to account for the

facial processing deficits identified on active behavioural tasks (Delerue et al., 2010).

In conclusion, visual scanpath abnormalities under passive viewing conditions are a persistent

feature of schizophrenia. However, patients show scanpaths that are identical to healthy controls when

performing active behavioural tasks, such as judging the gender of a face. Therefore, abnormalities in

visual scanning are unlikely to explain the pronounced facial emotion processing impairments shown

in schizophrenia across different behavioural tasks in the literature. It is possible that anxiety relating

to eye contact with faces may account for the unusual scanpath patterns produced in studies using

passive (undirected) viewing conditions.

24

Neuroimaging studies in schizophrenia

The neural network activated in healthy adults when viewing emotional faces has been well

established in the literature (Kret & Ploeger, 2015). This network has been shown to include the

amygdala (Haxby et al., 2000) fusiform face area (Kanwisher et al., 1997), occipital face area

(Gauthier et al., 2000), basal ganglia, and regions of the medial and inferior frontal cortex, such as the

anterior cingulate cortex (Morris et al., 2009). In particular, the amygdala appears to play a key role in

modulating the activity of the fusiform face area when viewing emotional faces, especially fearful

expressions (Adams, Gordon, Baird, Ambady, & Kleck, 2003; Demos, Kelley, Ryan, Davis, &

Whalen, 2008; Rotshtein, Malach, Hadar, Graif, & Hendler, 2001; Vuilleumier & Pourtois, 2007).

Accordingly, individuals with bilateral amygdala damage show particular impairment in recognising

fear from faces, and show reduced attention to the eye region (Adolphs et al., 2005; Calder, 1996;

Spezio, Huang, Castelli, & Adolphs, 2007).

Schizophrenia is associated with both structural and functional abnormalities of the amygdala.

On average, the amygdala of individuals with schizophrenia are 6% smaller in volume compared to

healthy controls (Wright et al., 2000). Several meta-analyses of functional imaging studies in

schizophrenia have shown reduced activation of the bilateral amygdala when viewing emotional faces

compared to healthy controls (Anticevic et al., 2012; Li, Chan, McAlonan, & Gong, 2010; Taylor et

al., 2012). Studies suggest that this hypoactivation is particularly pronounced when viewing fearful

faces (R. E. Gur et al., 2002; Michalopoulou et al., 2008). However, these studies typically report a

reduction in amygdala activation compared to activation for neutral faces (Morris et al., 2009). Other

studies suggest that patients instead show increased amygdala activation for neutral faces compared to

healthy controls (Habel et al., 2010; Hall et al., 2008; Holt, Kunkel, et al., 2006; Surguladze et al.,

2006). Therefore, it remains unclear whether this difference indicates true hypoactivation during

fearful face processing, or is simply due to hyperactivation to the comparison stimulus. It is possible

that this increased activation to neutral faces reflects the tendency of patients to mistakenly ascribe

emotional significance to non-emotional stimuli (Mothersill et al., 2014). In further support of this

idea, several studies using non-face baseline comparisons have reported hyperactivation of the

amygdala when viewing emotional faces (Holt, Kunkel, et al., 2006; Kosaka et al., 2002; Rauch et al.,

2010), or have failed to find differences in activation altogether (Holt et al., 2005; Sachs et al., 2012).

It is also possible that these discrepancies represent differences in tasks used (e.g.: passive viewing

versus activate emotion discrimination), differences in analytic techniques used, or heterogeneity in

patient clinical symptoms (Pankow et al., 2013).

Schizophrenia has also been associated with abnormalities in other brain regions involved in

emotion processing. For instance, the insular cortex and basal ganglia are shown to play an important

role in recognising expressions of disgust in healthy controls (Phan, Wager, Taylor, & Liberzon,

2002), and studies indicate that both of these regions have reduced volume in patients with

25

schizophrenia (Honea, Crow, Passingham, & Mackay, 2005; Shenton, Dickey, Frumin, & McCarley,

2001). Furthermore, the insula and basal ganglia also show reduced blood flow in patients compared

to healthy controls while viewing expressions of disgust (Phillips et al., 1999). Similarly, structural

abnormalities in the fusiform face area have been reported in schizophrenia (Lawrence, Calder,

McGowan, & Grasby, 2002), which correspond to reduced activation of this area when viewing

emotional faces (Li et al., 2010; Maher, Ekstrom, Holt, Ongur, & Chen, 2016; Taylor et al., 2012).

Still other regions, including the hippocampal and parahippocampal areas, appear to show increased

activity in response to both emotional (Holt, Kunkel, et al., 2006; Holt et al., 2005; Russell et al.,

2007) and neutral faces (Surguladze et al., 2006) in patients with schizophrenia compared to controls.

Another key brain region implicated in emotion processing is the anterior cingulate cortex

(ACC). The ACC is a region of the prefrontal cortex that is shown to be activated during emotion

perception tasks (Kober et al., 2008). It is believed to play an important role in coordinating emotional

responses to salient environmental stimuli, and is involved in emotional control, motivation, arousal,

and performance monitoring (Kennerley, Walton, Behrens, Buckley, & Rushworth, 2006; Patterson,

Ungerleider, & Bandettini, 2002; Ridderinkhof, Ullsperger, Crone, & Nieuwenhuis, 2004). While

some studies report decreased activity of the ACC in schizophrenia when viewing emotional faces

(Habel et al., 2010; Hempel, Hempel, Schonknecht, Stippich, & Schroder, 2003), others report

overactivity, particularly in response to neutral faces (Hall et al., 2008). Like the amygdala, it has

been suggested that activation of this region may reflect inappropriate attribution of salience to

ambiguous or neutral face stimuli (Mothersill et al., 2014). Dysfunction of this region is also

supported by structural (Baiano et al., 2007; Fornito, Yucel, Patti, Wood, & Pantelis, 2009) and post-

mortem (Benes, 1998) findings that indicate abnormal volumes in individuals with schizophrenia.

Exploring these abnormalities from a different angle, several authors have examined patterns

of neural connectivity in schizophrenia while processing emotional faces. Fakra and colleagues (2008)

found that, on an emotion-matching task, patients with schizophrenia did not show the typical pattern

of functional connectivity between the amygdala and prefrontal cortex as seen in healthy controls. The

authors suggest that this may reflect a tendency to rely on more cognitive strategies to perform this

task, rather than engaging the usual limbic structures. Similarly, other studies have reported patterns

of reduced connectivity between the amygdala and the fusiform gyrus (Hall et al., 2008) and between

the amygdala and regions of the parietal lobe and precuneus (Mukherjee et al., 2012). Importantly,

these regions are shown to be involved in emotion regulation in healthy controls (Mak, Hu, Zhang,

Xiao, & Lee, 2009). Furthermore, one study reported increased activation of these areas in

schizophrenia when emotional load interferes with performance on a working memory task

suggesting that patients are disproportionately recruiting these areas to compensate for abnormal

functioning of emotion processing networks (Becerril & Barch, 2011).

Taken together, functional imaging studies indicate that schizophrenia is associated with

abnormal activation of regions of the temperomedial lobe that are typically involved in emotion

26

processing, including the amygdala, insula, hippocampal and parahippocampal gyri. This includes

patterns of hypoactivation to emotional stimuli as well as hyperactivation to ambiguous or

emotionally-neutral stimuli, suggesting that this dysfunctional activation may reflect a tendency to

misattribute salience to emotional stimuli in schizophrenia. In addition, studies have also reported

altered activation and connectivity of prefrontal areas that are involved in cognitive control, such as

the anterior cingulate cortex. It has been suggested that unusual activation of prefrontal regions may

be indicative of compensatory cognitive strategies to actively overcome deficits in emotion

processing.

EEG studies in schizophrenia

Event-related potential (ERP) studies have identified several electrophysiological markers

that may be linked to face processing deficits in schizophrenia. The most widely studied of these is

the N170, an ERP component that peaks negatively at 170 milliseconds after presentation of a

stimulus (Bentin, Allison, Puce, Perez, & McCarthy, 1996). The N170 is found primarily at

occipitotemporal sites in the right hemisphere, and is thought to reflect activity of the fusiform face

area (Deffke et al., 2007), occipital cortex (Herrmann, Ehlis, Muehlberger, & Fallgatter, 2005; Utama,

Takemoto, Koike, & Nakamura, 2009), and superior temporal gyrus (Nguyen & Cunnington, 2014).

In healthy individuals, this waveform is significantly larger in response to faces and face-related

stimuli such as line drawings of faces, individual facial features, inverted faces and distorted faces

compared to other visual stimuli (Bentin et al., 1996; Rossion & Caharel, 2011; Rossion et al., 1999;

Wynn, Lee, Horan, & Green, 2008). For this reason, it has been suggested that the N170 represents

face detection: the early categorisation of faces as a perceptual category, rather than processing of

specific aspects such as facial identity or expression (Bentin et al., 1996; Maher, Mashhoon, Ekstrom,

Lukas, & Chen, 2016). A meta-analysis by McCleerey and colleagues (2015) noted that of 21

identified studies examining N170 amplitudes in schizophrenia, 15 reported significant deficits in

patients. Specifically, this meta-analysis reported a mean effect size of .64 (a medium effect) with

patients showing reduced N170 waveforms in response to face stimuli across a range of tasks

including both passive and active viewing (McCleery et al., 2015).

Another ERP implicated in face processing is the N250, a negative waveform occurring 250

milliseconds after stimulus presentation that is observed at fronto-central sites (Brenner, Rumak, &

Burns, 2016). This waveform is thought to represent the accumulation and integration of face-related

information from different regions, including basic structural information associated with the N170

(Streit, Wolwer, Brinkmeyer, Ihl, & Gaebel, 2000; Wynn, Jahshan, Altshuler, Glahn, & Green, 2013).

In healthy controls, the N250 is shown to be modulated by properties such as familiarity (Pierce et al.,

2011; Tanaka, Curran, Porterfield, & Collins, 2006) and facial affect (Streit, Wolwer, Brinkmeyer,

Ihl, & Gaebel, 2001), suggesting that this ERP may be involved in processing more complex face

27

information such as identity, gender, and emotional expression. Deficits in the N250 are not as well

documented in schizophrenia compared to the N170. While several studies have found reduced

amplitude of the N250 in response to faces (Streit et al., 2001; Wynn et al., 2013; Wynn et al., 2008),

others reported intact N250 waveforms compared to controls (Jung, Kim, Kim, Im, & Lee, 2012; Lee,

Kim, Kim, & Bae, 2010; Turetsky et al., 2007). However, the previously cited meta-analysis by

McCleery and colleagues (2015) indicated significant reductions in the N250 in patients overall, with

a mean effect size of 0.49 (a medium effect size). Taken together, the authors suggest that the

reduction of both N170 and N250 waveforms in schizophrenia signify impairments in the early stages

of visual processing of faces.

In addition to specific time-locked waveforms such as the N170, researchers have also

examined links between facial emotion processing and sustained activity in the theta frequency range

(4-7 Hz). For instance, healthy controls tend to show increased theta oscillations when viewing

emotional faces compared to neutral faces (Aftanas, Varlamov, Pavlov, Makhnev, & Reva, 2001;

Balconi & Pozzoli, 2008; Knyazev, Slobodskoj-Plusnin, & Bocharov, 2009). However, this activity

may reflect greater neural recruitment in response to biologically meaningful stimuli, rather than a

purely emotion-specific process (Krombholz, Schaefer, & Boucsein, 2007; Palermo & Rhodes, 2007).

In addition to emotion processing, theta oscillations have been implicated in other complex cognitive

processes including maintenance of information in memory (Lee, Simpson, Logothetis, & Rainer,

2005; Reinhart et al., 2012; Tsoneva, Baldo, Lema, & Garcia-Molina, 2011), as well as focussed

attention under low working memory load (Chang & Huang, 2012). It has also been suggested that

increases in theta activity may represent different functions at different time points, with oscillations

within 500 milliseconds of stimulus presentation indicating directed attention and early visual

processing, and oscillations after 950 milliseconds indicating maintenance in working memory

(Brenner et al., 2016; Deiber et al., 2007). In schizophrenia, reduced theta oscillations have been

demonstrated across various several face-related tasks (Missonnier et al., 2012; Schmiedt, Brand,

Hildebrandt, & Basar-Eroglu, 2005). One study found that individuals with schizophrenia showed

reduced theta activity despite intact behavioural performance on a facial emotion recognition task

(Ramos-Loyo, Gonzalez-Garrido, Sanchez-Loyo, Medina, & Basar-Eroglu, 2009). Another study

reported reduced theta activity (specifically between 140-200 ms) in schizophrenia, which was

correlated with performance deficits in an emotion recognition task (Csukly, Stefanics, Komlosi,

Czigler, & Czobor, 2014). Together, these studies suggest that early theta activity (overlapping with

the N170 and N250) following stimulus onset appears to be sensitive to emotion deficits in

schizophrenia. To examine the relationship between N170, N250 and P100 (an ERP associated with

early visual processing), and later-stage theta oscillations in schizophrenia, Brenner and colleagues

(2016) recorded electrophysiological activity during a same-or-different facial emotion discrimination

task. They found that, while both patients and healthy controls showed increased N170 peaks in

response to negative facial expressions compared to neutral, patients showed increased theta

28

oscillations in the inter-stimulus interval. Interestingly, increased theta oscillations during this period

predicted poorer behavioural performance for both patients and healthy controls. The authors propose

that this increased activity may reflect prolonged processing of emotional content that impedes task

performance in schizophrenia (Brenner et al., 2016).

In summary, electrophysiological studies have highlighted dysfunction at different stages of

face and emotion processing in schizophrenia. Evidence suggests that patients have reduced N170 and

N250 responses to emotional faces compared to healthy controls, indicating irregularity at the early

stages of perceptual processing. While these waveforms are stronger for emotional faces, this does

not rule out the possibility of a more generalised deficit in non-emotional face processing.

Furthermore, increased later-stage theta oscillations in response to emotional faces may indicate

dysfunction at the later stages of emotion processing as well.

29

Chapter 3: Is it really all about emotion? Non-emotional Face

Processing is (Maybe) Impaired in Schizophrenia

The previous chapter outlined a large body of literature indicating impaired emotion-

processing schizophrenia. However, modern theories of face-processing in healthy individuals tell us

that information relating to facial emotion is processed largely independently from information

relating to facial identity. This raises the question: Is identity-processing also impaired in

schizophrenia? Answering this question may allow us to determine in which stage of perceptual

processing these abnormalities lie. If found, these deficits challenge the idea that the ‘emotion-

processing’ deficit is truly specific to emotion. This chapter will review the extant research

investigating non-emotional face processing in schizophrenia.

Non-emotional face processing in schizophrenia

Deficits in non-emotional face processing have been explored in schizophrenia using a variety

of behavioural tasks. However, heterogeneity across tasks types and participants has produced mixed

results (see Table 3). Tasks that assess true identity processing are primarily matching tasks, where

the participant views static photographs of faces (with non-face identifying features removed, such as

hair and spectacles), either serially or concurrently. The participant must match the identity of the first

face to one of several options (Addington & Addington, 1998; Chen et al., 2012; Kucharska-Pietura et

al., 2005; Penn et al., 2000). The most commonly used face-matching task is the Benton Test of Facial

Recognition (Benton, 1983; see Figure 3A). Individuals with schizophrenia have shown impaired

performance on the Benton test in many (Addington & Addington, 1998; Evangeli & Broks, 2000;

Kucharska-Pietura et al., 2005; Soria Bauser et al., 2012; Whittaker et al., 2001), but not all studies

(Hall et al., 2004; Pomarol-Clotet et al., 2010; Sachse et al., 2014; Scholten, Aleman, Montagne, &

Kahn, 2005; van 't Wout et al., 2007). Four studies using face-matching tasks with similar properties

to the Benton – that is, matching one image to one of two or three options displayed simultaneously –

also reported no impairment compared to healthy controls (Kosmidis et al., 2007; Quintana et al.,

2011; Quintana, Wong, Ortiz-Portillo, Marder, & Mazziotta, 2003; Williams et al., 1999). One study

using an identity matching task with seven options reported impaired performance in patients with

schizophrenia, suggesting that increasing task difficulty is more likely to precipitate group differences

(Williams et al., 1999). However, another study using a similar 7-option matching design showed no

impairments in patients (Hooker & Park, 2002). Inconsistent findings have also been produced by

studies using a morphed identity-matching task, in which participants choose which of a pair of faces

of varying similarity matches a briefly presented target (see Figure 3B). Two studies using this task

reported significant impairments in patients with schizophrenia (Chen & Ekstrom, 2016; Chen,

McBain, & Norton, 2015), while one only approached a significant difference (Chen et al., 2009), and

30

two reported intact functioning (Chen et al., 2012; Norton, McBain, Holt, Ongur, & Chen, 2009). The

reason for these inconsistent findings is unclear. Given the relatively heterogeneous patient samples,

however, the influence of factors such as age, illness duration and gender may be worth exploring

through a formal meta-analysis in the future.

An alternative measure of identity recognition is the two-alternative forced-choice identity

discrimination paradigm, in which the participant judges whether two serially presented faces are the

same or different. Again, some studies reported significant impairment in schizophrenia (Butler et al.,

2008; Kim et al., 2010; Martin et al., 2005; Shin et al., 2008; Soria Bauser et al., 2012) while others

found no impairment relative to controls (Edwards et al., 2001; Fakra, Jouve, Guillaume, Azorin, &

Blin, 2015; Johnston et al., 2010; Soria Bauser et al., 2012). Another study requiring participants to

distinguish between photographs of two learned identities, person A and person B, also reported

significant impairment (Baudouin, Martin, Tiberghien, Verlut, & Franck, 2002). Again, the reason for

these inconsistent findings is unclear, but given that mean age and illness duration of the patients

(Kucharska-Pietura et al., 2005), and the stimuli used in these tasks (e.g.: Soria Bauser et al., 2012)

are known to influence performance, it is possible these factors contributed here. While it is outside

the scope of the current review it would be interesting to consider the relative severity of face

perception deficits in a non-clinical population of individuals high in psychosis-proneness and

whether these deficits predict transition to psychosis. While impairments have been seen in some non-

emotional aspects of face processing in these populations before (Poreh, Whitman, Weber, & Ross,

1994), they are not reported as frequently as deficits in face emotion perception (Germine & Hooker,

2011; Waldeck & Miller, 2000; Williams, Henry, & Green, 2007).

31

Figure 3. Examples of tasks used to assess the perception of identity. A: Plate from the Benton Facial

Recognition Test (Benton et al., 1983). Participants indicate which of the six images match the target.

(Published in Busigny & Rossion, 2010, p 969). B: Identity matching task used by Norton and

colleagues (2009). C: Example of morphed images ranging from ‘no sex’ (50% male, 50% female) to

100% male face (from Bediou et al., 2005, p 528). D: Examples of upright and inverted stimuli used

in Soria Bauser and colleagues (2012). E: Example of semi-successive frames of the point-light

displays (walking) used in Kim and colleagues (2005).

32

Table 3. Identity processing tasks used in schizophrenia research from the last 25 years. Results indicate performance of patients relative to controls.

Task Results References

Tasks Assessing Identity Discrimination

Benton Test of Facial Recognition

[see Figure 3A]

Impaired

Kerr & Neale, 19931; Borod et al., 19931; Bellack et al., 19962; Mueser et al., 19961; Salem et al. 19961;

Addington & Addington, 19981; Evangeli & Broks, 20001; Penn et al., 20001; Whittaker et al., 20012;

Hooker & Park, 20022; Kucharska-Pietura et al, 20052; Soria Bauser et al., 20123

NOT impaired Hall et al., 20041; Scholten, et al., 20053; Van ’t Wout et al., 20071; Pomarol-Clotet et al., 20103; Sachse

et al., 20141

Identity Matching

[Match target to 1 of 2-3 choices] NOT impaired Williams et al., 1999; Quintana et al., 2003; Kosmidis et al., 2007; Quintana 2011

Identity Matching

[Match target to 1 of 7 choices]

Impaired Williams et al., 1999

NOT impaired Hooker & Park, 2002

Morphed-Faces Identity Matching

[Match target to 1 of 2 choices]

Impaired Chen et al., 2015; Chen & Ekstrom, 2016

Approaching significance Chen et al., 2009

NOT impaired Chen et al., 2012; Norton et al., 2009

Identity Discrimination [same/different

judgements of serially presented faces]

Impaired Martin et al., 2005; Butler et al., 2008; Shin et al., 2008; Kim et al., 2010; Soria Bauser et al., 2012

NOT impaired Edwards et al., 2001; Johnston et al., 2010; Soria Bauser et al., 2012; Fakra et al., 2015

Identity Recognition Task

[Is this Person A or B?]

Slower only (accuracy at

ceiling) Baudouin et al., 2002

Tasks Assessing One Aspect of Identity

Sex recognition task [Male or female?] Impaired Hall et al., 2008

NOT impaired Bigelow et al., 2006; Michalopoulou et al., 2008; Delerue 2010

Morphed sex recognition task Impaired Bigelow et al., 2006

NOT impaired Bediou et al., 2005; Bediou et al., 2007; Bediou et al., 2012

33

Age Discrimination task [indicate the age by

decade: 1=teens... 7= seventies]

Impaired Schneider et al., 1995; Kohler et al., 2000; Habel et al., 2000

NOT impaired

(speed/accuracy trade-off) Schneider et al., 1998

Age Discrimination task

[Judge whether face is older or younger than 30]

Impaired Schneider et al., 2006

NOT impaired Gur et al., 2002a, Gur et al., 2002b; Pinkham et al., 2008; Goghari et al., 2011

Age Discrimination [Child, teen, adult or elder?] Impaired Delerue et al., 2010

Tasks Assessing Familiarity/Memory for Faces

Penn Face Memory Test Impaired Sachs et al., 2004; Calkins et al., 2005; Silver et al., 2009

The Warrington Recognition Memory Test–Faces

subtest

Impaired Soria Bauser et al., 2012; Whittaker, et al., 2001

NOT impaired Evangeli & Broks, 2000

Delayed identity matching (3s and 10s) Impaired Chen et al., 2009

Encoding and recognition task Impaired Walther et al., 2009

Famous Faces

Impaired Pomarol-Clotet et al., 2010

NOT impaired Evangeli & Broks, 2000; Whittaker et al., 2001; Joshua & Rossell, 2009; Lee et al., 2007; Walther et al.,

2009; Heinisch et al., 2013; Yun et al., 2014

Familiarity task [is this face familiar or

unknown?]

Impaired Irani et al., 2006; Caharel et al., 2007; Kircher et al., 2007

NOT impaired Delerue et al., 2010

1Short form, 2Long form, 3Not specified.

34

Tasks assessing a single aspect of face identity

Tasks that do not assess true identity discrimination, but just one aspect of facial identity have

produced slightly more consistent results. For instance, three studies reported that patients with

schizophrenia have an intact ability to identify the sex of a face (Bigelow et al., 2006; Delerue et al.,

2010; Michalopoulou et al., 2008), although one study reported reduced performance compared to

controls (Hall et al., 2008). Bediou and colleagues (2007; 2005, 2012) found that patients were able to

accurately identify the sex of a face even when stimuli were digitally morphed to increase ambiguity

(see Figure 3C). However, Bigelow and colleagues (2006) found that patients were significantly

poorer at identifying the sex of digitally-morphed faces but not non-morphed faces.

Three studies (Habel et al., 2000; Kohler, Bilker, Hagendoorn, Gur, & Gur, 2000; Schneider,

Gur, Gur, & Shtasel, 1995) found that patients were significantly impaired in judging the age of a face

in decades (i.e.: teens, twenties, thirties, etc), while one reported poorer accuracy, but faster reaction

times than controls (Schneider et al., 1998). Another study using a similar paradigm (judge each face

as child, teen, adult or elder) also found impaired performance in patients (Delerue et al., 2010). Yet

another study found that patients were impaired in judging if a face is older or younger than 30

(Schneider et al., 2006), while four studies found no difference using the same paradigm (Goghari,

Macdonald, & Sponheim, 2011; R. C. Gur et al., 2002; R. E. Gur et al., 2002; Pinkham, Hopfinger,

Pelphrey, Piven, & Penn, 2008). Taken together, these findings suggest that patients with

schizophrenia are less likely to show impairments when asked to make gross judgements about the

sex or age of a face, but have greater difficulty on tasks that require more fine-grained judgements,

such as defining the age of a face by decade. This result highlights the importance of selecting the

right measure when assessing face recognition in schizophrenia. True identity perception likely

reflects a judgement based on complex interactions between multiple facial features, so a task

assessing just one aspect of face perception (such as sex) may not be a valid indicator of a true deficit

in identity discrimination.

Identity recollection and familiarity

Finally, tasks that assess memory of – as opposed to discrimination between – faces have

largely revealed significant impairment in schizophrenia (Calkins, Gur, Ragland, & Gur, 2005; Chen

et al., 2009; Sachs, Steger-Wuchse, Kryspin-Exner, Gur, & Katschnig, 2004; Silver, Bilker, &

Goodman, 2009; Soria Bauser et al., 2012; Walther et al., 2009; Whittaker et al., 2001). In contrast,

patients have been shown to have an intact ability to recognise famous faces (Evangeli & Broks,

2000; Whittaker et al., 2001; Lee, Kwon, Shin, Lee, & Park, 2007; Joshua & Rossell, 2009; Walther

et al., 2009; Heinisch, Wiens, Grundl, Juckel, & Brune, 2013; Yun et al., 2014; although one study

reported significant impairment: Pomarol-Clotet et al., 2010). Three studies suggest that schizophrenia

35

patients tend to be less accurate in familiarity judgment of photographs of strangers and known people

(e.g., their doctor’s face) (Caharel et al., 2007; Irani et al., 2006; Kircher, Seiferth, Plewnia, Baar, &

Schwabe, 2007), while another reported intact performance (Delerue et al., 2010). However, these

tasks are not necessarily an indicator of pure face processing in schizophrenia because this disorder is

associated with general impairments in memory and new learning (Boyer, Phillips, Rousseau, &

Ilivitsky, 2007). Again, care should be taken when employing memory-based face recognition tasks to

ensure that general impairments in memory are taken into account.

The impact of expression variability on identity processing

Although models of face perception argue that facial identity processing is largely

independent from facial emotion processing, research clearly indicates that these two types of

information interact. Depending on conditions, emotional information may either interfere with or

enhance the perception of identity. For instance, Redford and Benton (2017b) examined the impact of

expression variability on identity discrimination in healthy controls using a card-sorting paradigm.

They found that participants made significantly more errors in grouping cards by identity when

emotionally-expressive faces were used, compared to cards which showed neutral faces. This suggests

that the additional variability introduced by emotional expressions interfered with the ability to

distinguish identity. In contrast, two studies (Redfern & Benton, 2017a, 2018) showed that increased

exposure to expression variability may enhance the learning of individual identities. In these studies,

participants who were exposed to neutral or low-expressiveness faces during the learning period were

less accurate at identifying identities when they were portrayed in high-expressiveness images.

However, participants who were exposed to high-expressiveness faces during learning were equally

accurate at identifying identities under low-expressiveness and high-expressiveness conditions. This

finding is in line with established literature which posits that greater exposure to variability

(expressive or otherwise) enhances our ability to learn new face identities (Andrews, Jenkins,

Cursiter, & Burton, 2015; Murphy, Ipser, Gaigg, & Cook, 2015; Ritchie & Burton, 2017). To date, no

published studies have specifically examined the impact of expression variability on identity learning

in schizophrenia. However, several have demonstrated that patients with schizophrenia are more

likely than healthy controls to show interference from identity information on an emotion recognition

task (Baudouin et al., 2002; Martin et al., 2005) and vice-versa (Zvyagintsev, Parisi, Chechko,

Nikolaev, & Mathiak, 2013). It is possible that impairments in the recognition of emotional

information may disproportionately impede face learning in day-to-day-life for people with

schizophrenia, despite intact processing of identity-specific information. Further research is needed to

clarify the impact of expression variability in populations with schizophrenia and other psychiatric

disorders.

36

Other aspects of impaired face processing in schizophrenia

Face information is extracted from the environment using both face-specific and more general

perceptual processes (McKone & Robbins, 2011). Some argue that face processing deficits in

schizophrenia indeed represent dysfunction in face-specific perceptual processes - generally referred

to as ‘holistic face processing.’ This represents a rapid, involuntary face-specific perceptual process

that integrates information across the face as a whole. It includes such information as the shapes of

individual features, the relative distances between them, and the contour of the cheeks and jaw

(Maurer, Grand, & Mondloch, 2002; McKone & Yovel, 2009). This process is specific to invariant

face information, and is therefore critical for perceiving identity (McKone & Robbins, 2011). For

instance, it has been demonstrated in healthy controls that holistic processing predicts an individual's

ability to remember and distinguish between faces (Richler, Cheung, & Gauthier, 2011). Similarly, it

has been shown that individuals with congenital prosopagnosia (inability to recognise faces) perform

poorly on holistic processing tasks (Palermo et al., 2011).

One common means of evaluating holistic processing is with the Face Inversion Effect -

observed as a reduction in face discrimination performance for inverted faces compared to upright

faces (see Figure 3D). The magnitude of this effect is thought to represent a loss of holistic

information crucial to face discrimination, and is disproportionately larger for faces compared to other

non-face stimuli (Yin, 1969). Studies of holistic processing in schizophrenia produced varied results,

with some studies reporting normal inversion effects for faces (Butler et al., 2008; Chambon,

Baudouin, & Franck, 2006; Schwartz, Marvel, Drapalski, Rosse, & Deutsch, 2002) while others

report reduced inversion effects compared to controls (Kim et al., 2010; Shin et al., 2008; Soria

Bauser et al., 2012). In particular, Shin and colleagues (2008) reported that patients with

schizophrenia were more impaired when discriminating faces that differed in configural information,

rather than featural information. An electrophysiological indicator of the face inversion effect is the

N170 (Eimer, 2000), a negative potential seen using electroencephalography (EEG). The N170 is

reduced in patients with schizophrenia while viewing inverted faces (Ibanez et al., 2012; Onitsuka et

al., 2006), and is associated with lower scores on measures of social functioning (Obayashi et al.,

2009; Tsunoda et al., 2012). These findings suggest there may be an underlying face processing

abnormality that may go undetected by commonly used behavioural measures.

In a related behavioural study, Schwartz and colleagues (2002) employed the composite face

task, which is considered to provide a more rigorous measure of holistic processing than other

inversion tasks (McKone, 2009). In this task, participants are required to make decisions about the

upper halves of faces while ignoring the lower halves. These face halves are either aligned to form a

complete face (producing an interference effect) or misaligned (removing the interference). When the

stimuli are inverted, however, the aligned faces no longer produce strong interference effects. It was

found that patients with schizophrenia showed typical patterns of interference for upright faces and

37

not inverted faces. While this study has not been repeated, it provides support for the argument that

holistic processing is largely preserved in schizophrenia and appears to contradict some of the results

using the face inversion effect.

Evidence for identity processing deficits using non-face stimuli

As outlined above, a number of studies have shown impaired performance on tasks designed

to assess face-specific processing. However, a number of similar deficits seem to be apparent on tasks

using non-face stimuli. For example, Soria Bauser and colleagues (2012) reported reduced inversion

effects for cars and bodies (see Figure 3D) that mirrored their findings using face stimuli, suggesting

an impairment that encompasses more than just face-specific holistic processing. An interesting

comparison is also provided by research looking at gait perception. Previous research has indicated

that the identity of an individual can also be extracted from an individual’s gait pattern (Cutting &

Kozlowski, 1977). Through the use of point-light displays (Johansson, 1973), visually impoverished

stimuli provide body form and structure solely through motion cues of coordinated dots (see Figure

3E). Similar to the ERP findings regarding face processing, the N170 component has also been found

in healthy individuals during visual processing of inverted point-light displays and static images of

bodies (Jokisch, Daum, Suchan, & Troje, 2005; Stekelenburg & de Gelder, 2004). Loula and

colleagues (2005) demonstrated that healthy subjects exhibited superior performance in identifying

self and friend’s movement when compared to a stranger’s movement. Furthermore, inverting the

point-light display resulted in chance performance across all three conditions.

There is some evidence to suggest that the ability to recognise identity cues from body

movements in impaired in schizophrenia. For instance, patients with schizophrenia are impaired in

discriminating point-light displayed body movements (biological motion) from scrambled point-light

display body movements (Kim, Doop, Blake, & Park, 2005). A study by Peterman and colleagues

(2017) used dynamic walking avatars (i.e. simplified 3D animated models) to investigate emotion and

gender recognition in patients with schizophrenia. It was found that patients were less able to extract

cues relating to gender and emotion from an avatars gait compared to healthy controls. They also

reported that patients were less likely to take the avatars emotion into account when making social

judgements, such as rating the angry avatar as more approachable compared to controls.

Does impaired identity processing reflect a generalised attentional deficit?

One possible account for face processing deficits in schizophrenia is that they are the result of a more

general impairment in allocating visuospatial attention (Baudouin et al., 2002). One suggestion is an

impairment in global versus local visual processing. ‘Global processing’ refers to the ability to attend

to any visual stimulus as a 'whole', as opposed to its component features (Tan, Jones, & Watson,

2009). Studies of schizophrenia have revealed impairments in global processing, but largely preserved

local processing both for static (Goodarzi, Wykes, & Hemsley, 2000; Johnson, Lowery, Kohler, &

38

Turetsky, 2005; Poirel et al., 2010; Silverstein, Kovacs, Corry, & Valone, 2000) and dynamic stimuli

(Chen, Nakayama, Levy, Matthysse, & Holzman, 2003). In addition, patients with schizophrenia

demonstrate a bias towards attending to the local level of a stimulus, even when task demands favour

a global strategy (Landgraf et al., 2011).

It is possible that a global processing deficit could contribute to impairments in identity

recognition because the important global-level information is not being processed efficiently. For

instance, it has been shown that identity recognition performance is improved when healthy

participants are primed to adopt a global processing strategy, and impaired when primed with a local

processing strategy (Macrae & Lewis, 2002; Perfect, 2003). Patients with schizophrenia similarly

showed less of a reduction in identity recognition performance compared to controls when configural

cues were removed from a face (Joshua & Rossell, 2009), indicating that these individuals relied more

strongly on local features when identifying famous faces. Global processing deficits could also

explain the expected deficits in identity recognition from gait in individuals with schizophrenia. Kim

and colleagues (2005) argued that deficits in biological motion perception in individuals with

schizophrenia may arise due to their well-documented difficulties in global motion perception (for

review see Chen, 2011) . This research question will be investigated further in Chapter 9.

Does impaired face identity processing reflect a general visual perceptual

difficulty?

Individuals with schizophrenia show a gamut of visual perceptual impairments (Butler et al., 2008).

These difficulties include form processing such as object recognition, grouping, perceptual closure,

and visual context (Brenner, Wilt, Lysaker, Koyfman, & O'Donnell, 2003; Doniger, Foxe, Murray,

Higgins, & Javitt, 2002; Kerr & Neale, 1993; Kohler et al., 2000; Kurylo, Pasternak, Silipo, Javitt, &

Butler, 2007; Place & Gilmore, 1980; Rabinowicz, Opler, Owen, & Knight, 1996; Rief, 1991;

Silverstein et al., 2000; Uhlhaas, Phillips, Mitchell, & Silverstein, 2006; Yang et al., 2013). Moreover,

neuroanatomical data indicate that the visual cortex in schizophrenia is abnormal with respect to the

density of neurons (Selemon, Rajkowska, & Goldman-Rakic, 1995), total number of neurons (Dorph-

Petersen, Pierri, Wu, Sampson, & Lewis, 2007) and GABA concentration in the visual cortex that is

associated with orientation-specific center-surround suppression (Yoon et al., 2010). Interestingly, the

face fusiform area (FFA) seems relatively intact, at least functionally (Yoon, D'Esposito, & Carter,

2006). Given the exhaustive list of basic visual perceptual deficits in schizophrenia, it seems likely

that processing of complex visual stimuli such as faces would also be compromised. Thus, it is likely

that at least some aspects of face processing deficits observed in schizophrenia arise from visual

cortical abnormalities.

Conclusions

39

Deficits in face processing have frequently been observed in patients with schizophrenia. In

order to fully understand the mechanisms underlying these impairments it is important to consider the

relative contribution of the multiple factors that may be involved. The fact that deficits have been seen

in face identity tasks without an emotional/ expression recognition component suggests that these

deficits are unlikely to be limited to emotion processing. Moreover, the observation of more

generalised impairments in visual and attentional function in these patients also raises questions about

whether there is indeed anything special about faces at all. Lastly, the potential role of medication in

these impairments has yet to be clearly determined. It is only through future controlled studies that

balance difficulty across memory, attentional and perceptual demands – or directly assess the

capacities – that we will begin to understand how face processing deficits emerge in these patients.

40

Chapter 4: What do we really want to know about face processing

in schizophrenia? Rationale for the Inpatient Study

Impairments in face processing have been demonstrated in patients with schizophrenia, and

are shown to correlate significantly with aspects of social functioning and overall quality of life

(Addington et al., 2006; Hooker & Park, 2002; Irani et al., 2012). Two components of accurate face

processing are the perception of emotion (perceiving transient emotional expressions) and identity

(perceiving the invariant features which distinguish an individual; Calder, 2011). Deficits in both of

these components have been demonstrated to a varying degree in schizophrenia (Chen et al., 2012;

Martin et al., 2005). However, it remains unclear whether these functional deficits reflect a single

face-specific deficit, distinct impairments in emotion and identity processing, or some other

generalised impairment. In healthy individuals, emotion and identity processing are thought to be

relatively independent processes which are mediated via different neural routes (Haxby & Gobbini,

2011; Haxby et al., 2000). It is therefore important to determine whether different subtypes of

impairments can be identified in schizophrenia, and whether distinct processes can be targeted for

intervention to improve functional outcomes (Marwick & Hall, 2008). The current experiment aims to

investigate these questions using dynamic (video-based) stimuli, which are shown to have greater

ecological validity and sensitivity to deficit compared to traditional static (image-based) emotional

stimuli.

Emotion processing deficits

There is extensive research demonstrating that patients with schizophrenia perform poorly on

tasks of facial emotion processing compared to healthy controls (see Kohler et al., 2010 for meta-

analytic review). This deficit has been demonstrated across a variety of tasks involving either

recognising or labelling the emotion portrayed (Bediou et al., 2007; Fakra et al., 2008; Johnston,

Devir, & Karayanidis, 2006; Kucharska-Pietura et al., 2005; Penn et al., 2000; Simpson, Pinkham,

Kelsven, & Sasson, 2013; Strauss, Jetha, Ross, Duke, & Allen, 2010; Whittaker et al., 2001) or

discriminating between pairs of static faces showing the same or different expression (Addington et

al., 2006; Fakra et al., 2015; Martin et al., 2005; Weniger et al., 2004). Together, these results suggest

that this deficit is robust and persists even when memory and language demands of the task are

minimised. Moreover, longitudinal studies suggest that these deficits are stable up to at least one year,

and do not improve with the resolution of symptoms (Addington & Addington, 1998; Kee, Green,

Mintz, & Brekke, 2003). Despite these impairments being demonstrated repeatedly across a range of

studies, it remains unclear whether these deficits in emotion processing exist above and beyond more

general perceptual or attentional impairments.

41

Identity processing deficits

Studies investigating identity processing in schizophrenia, however, have not been as

consistent (Darke, Peterman, Park, Sundram, & Carter, 2013). Significant deficits have been

demonstrated in schizophrenia across a variety of identity discrimination tasks (Butler et al., 2008;

Chen et al., 2009; Shin et al., 2008; Soria Bauser et al., 2012) but not others (Chen et al., 2012;

Johnston et al., 2010; Norton et al., 2009; Pomarol-Clotet et al., 2010). An important distinction can

be drawn between face discrimination tasks (considering overall identity of a face) and single-feature

labelling tasks (considering one aspect of identity, such as age or sex). Namely, studies assessing just

one feature of identity are less likely to show impairments in schizophrenia, particularly when the task

only requires broad judgments (e.g.: male or female?) rather than fine-grained judgements (e.g.:

estimate age by decade) (Bediou et al., 2007; Bediou et al., 2012; R. E. Gur et al., 2002).

Consequently, it has been suggested that the deficits shown in previous studies (Butler et al., 2008;

Chen et al., 2009; Shin et al., 2008; Soria Bauser et al., 2012) may reflect general task demands, rather

than actual impairment in face-specific processes (Marwick & Hall, 2008). For instance, face

measures with a considerable memory component typically show deficits (Caharel et al., 2007; Silver

et al., 2009; Soria Bauser et al., 2012) and may result from broader working memory impairments

associated with schizophrenia (Piskulic, Olver, Norman, & Maruff, 2007).

Are these face processing deficits specific to schizophrenia?

A further question is whether any face processing deficits observed are shared by other

psychiatric disorders. The diagnostic specificity of these deficits is unclear as the majority of face

processing studies compare only patients with schizophrenia to healthy controls. For the smaller

number of studies that have included other patient groups, individuals with schizophrenia have been

found to show significantly greater face processing impairments relative to individuals with

depression (Bediou, Krolak-Salmon, et al., 2005; Weniger et al., 2004), bipolar disorder (Addington

& Addington, 1998; Lembke & Ketter, 2002; Venn et al., 2004) and affective psychoses (Edwards et

al., 2001). However, other studies have reported equivalent impairments in emotion processing in

patients with bipolar disorder (Bozikas, Tonia, Fokas, Karavatos, & Kosmidis, 2006; Getz, Shear, &

Strakowski, 2003; Lembke & Ketter, 2002). Face processing impairments in disorders other than

schizophrenia, and the relevant results of the inpatient experiment, will be reviewed further in Chapter

10.

It is also possible that these deficits accompany specific symptoms which are shared across

disorders. For instance, emotion processing deficits have been reliably associated with both positive

and negative symptoms in schizophrenia, as well as other illness factors such as later age of illness

onset and inpatient status (Kohler et al., 2010; Martin et al., 2005; Sachs et al., 2004; van 't Wout et

al., 2007). However, the relationship between identity recognition impairments and symptoms has not

42

been widely studied, and evidence is mixed. Several studies have reported a negative correlation

between identity recognition performance in schizophrenia and negative symptoms (Addington &

Addington, 1998; Baudouin et al., 2002; Chen et al., 2012; Martin et al., 2005), while others report an

association between performance and overall symptom severity (Chen et al., 2009; Penn et al., 2000;

Sachs et al., 2004). This literature will be discussed further in Chapter 11.

The current study aimed to address these outstanding issues by assessing participants with a

range of psychiatric disorders (n=86) and healthy controls (n=20) using four different emotion and

identity processing tasks. Furthermore, to ascertain whether these deficits generalise beyond face

processing, performance was also compared on an equivalent task using non-face stimuli. Dynamic

(video-based) stimuli were used in all tasks, as these are shown to have better ecological validity than

traditional static face images (Atkinson, Dittrich, Gemmell, & Young, 2004; Kilts, Egan, Gideon, Ely,

& Hoffman, 2003). Chapter 5 will review the importance of dynamic versus static emotional stimuli

in greater detail, and will discuss the development and initial testing of the novel stimuli created for

this experiment. Additionally, Chapter 6 will investigate associations between psychosis-like

personality traits in healthy controls (i.e. schizotypy) and emotion-processing in this initial study.

For clarity, the results of the inpatient study have been divided into different chapters aligned

with the following research questions:

I. Characterising the deficit in schizophrenia: Can impairments in emotion-processing be

explained by more general deficits in non-emotional face processing or non-face processing?

The first aim focuses on the comparison of patients with schizophrenia and age-matched healthy

controls on a series of dynamic tasks designed to assess emotion-processing, identity-processing,

and non-face visual processing. These results will be discussed in Chapter 8.

II. Are face processing impairments specific to schizophrenia, or are they shared by other

psychiatric disorders?

The second aim is to compare face processing in patients with schizophrenia with other

psychiatric inpatient groups including bipolar disorder, other psychotic disorders (such as drug-

induced psychosis), and non-psychotic disorders (such as major depressive disorder). These

results will be discussed in Chapter 10.

III. Do specific symptoms correlate with face-processing impairments?

The third aim is to examine the relationship between face processing impairments and specific

symptomatology across psychiatric disorders. These results will be discussed in Chapter 11.

43

Justification of tasks used

To assess emotion processing ability, two different paradigms were employed: an emotion

discrimination task and an emotion labelling task. As mentioned in Chapter 2, these two tasks have

slightly different demands, and may be sensitive to different aspects of cognitive impairment. That is,

discrimination tasks are more dependent on working memory ability, while labelling tasks may be

affected by semantic memory deficits (Macmillan & Creelman, 1991). Furthermore, as these two

types of tasks may be correlated with different impairments in schizophrenia (Irani et al., 2012), both

paradigms were included to see if any kind of differential deficit is seen in patients.

To assess facial identity recognition, two tasks were created that were matched to the task

demands of the two emotion tasks: identity discrimination and sex labelling. Identity discrimination

involves making same-or-different judgements of serially presented faces. Patients with schizophrenia

have shown impairments in some studies (Butler et al., 2008; Martin et al., 2005; Shin et al., 2008;

Soria Bauser et al., 2012) but not others (Edwards et al., 2001; Johnston et al., 2010; Soria Bauser et

al., 2012). However, these studies used static face stimuli only. The current study is the first to use

dynamic face stimuli to assess facial identity processing in schizophrenia. The second task, sex

labelling, involves simply deciding whether a face looks more male or more female. Patients with

schizophrenia typically show no impairments on tasks such as this, which require judgements about

only a single aspect of facial identity (Bediou et al., 2007; Bediou, Franck, et al., 2005; Bediou,

Krolak-Salmon, et al., 2005). For this reason, some authors conclude that identity recognition is

intact in schizophrenia. However, it is unclear if this task is sensitive to true impairments in identity

recognition, which typically involves consideration of multiple aspects of facial identity. Both

paradigms have therefore been included in this experiment.

Finally, a non-face control task was included to determine whether any deficits shown on the

four face tasks may be better accounted for by a more general visual or cognitive impairment. In other

words, if impairments in emotion-processing and identity-processing are specific to faces, we would

expect to see comparatively intact performance on a non-face discrimination task. However, if

impairments are not specific to faces, we would expect to see similar deficits on the non-face task.

44

Chapter 5: Is our data only as good as our tools? Issues of Static

versus Dynamic Faces, and Development of a Novel Stimuli Set

Face-based tasks are used ubiquitously in the study of human perception and cognition.

Video-based (dynamic) face stimuli are increasingly utilised by researchers because they have higher

ecological validity than static images (Fiorentini & Viviani, 2011). However, there are few ready-to-

use dynamic stimulus sets currently available to researchers. This chapter will review the literature

surrounding the use of dynamic versus static stimuli in face research. It will then outline the

development of three original dynamic stimulus sets which were created as part of this PhD. Finally,

this chapter will present an experiment evaluating the utility of these new stimuli sets.

Criticisms of traditional static face stimuli sets

Face processing has been the focus of an enormous variety of research spanning a range of

disciplines, examining both human and non-human subjects. Not only is face processing a rapid,

involuntary, and highly specialised perceptual process, but it also plays a crucial role in

communication and social interaction. Consequently, face-based stimuli sets are used for a huge range

of applications in the study of human perception and cognition, such as investigating low-level visual

processing (Cauchoix, Barragan-Jason, Serre, & Barbeau, 2014), memory (Faces subtest; WMS-III

(Wechsler, 1997), Theory of Mind (van Veluw & Chance, 2014), different aspects of facial identity

perception (Fitousi & Wenger, 2013), and the perception of emotional expression (Fusar-Poli et al.,

2009).

Traditionally, face processing studies are typically carried out using behavioural or

neuroimaging measures that involve viewing and making judgements about static images of faces. For

example, one commonly used measure of identity recognition is the Benton Facial Recognition Task

(Benton, 1983), which requires participants to match the identity of a target face to one of several

possible options. The most widely-used stimulus set for examining emotion processing is the Ekman

faces (Ekman & Friesen, 1976), a set of 60 photographs demonstrating the six main emotions:

happiness, sadness, disgust, fear, anger, and surprise. Other standardised static face sets include the

Japanese and Caucasian Facial Expressions of Emotion (JACFEE; Biehl et al., 1997), the Montreal

Set of Facial Displays of Emotion (MSDEF; Beaupré & Hess, 2005) and the Nim Stim Face Stimulus

Set (Tottenham et al., 2009).

However, the use of stereotyped or exemplar faces such as these have been criticised for

lacking ecological validity. That is, they show only exaggerated, staged expressions which do not

reflect the subtler nuances that we experience in natural social interactions (Davis & Gibson, 2000).

Furthermore, it has been argued that tasks using exemplar faces are prone to ceiling effects and may

not be sensitive to subtler impairments in face-specific processes (Harms, Martin, & Wallace, 2010).

45

To address this, some researchers have utilised morphing software to create stimuli that show varying

intensities of emotion. Figure 5.1 shows an example of a set of morphed faces ranging from high to

low intensity, created by Norton and colleagues (2009). Stimuli such as these permit the study of

threshold differences between clinical populations, which reveal that patients with schizophrenia

require greater intensity to identify expressions such as disgust and fear (Bediou, Franck, et al., 2005;

Chen et al., 2012; Norton et al., 2009).

Another way in which researchers have attempted to improve the ecological validity of face

tasks is through the use of ambient stimuli. Ambient stimuli refer to naturalistic photographs or

videos, typically obtained from films or the internet. It has been argued that reliance on tightly-

controlled, artificially edited stimuli not only limits the generalisability of results to everyday

situations, but may even undermine our theoretical understanding of face recognition (Burton, 2013).

Burton argues that, as face processing involves deriving stable constructs (such as an emotion or an

identity) from constant variability, then by eliminating this variability we may no longer be assessing

the same perceptual process. For instance, research in healthy populations suggests that the inclusion

of body information (i.e.: not just an isolated face) has an effect on both emotion and identity

recognition, even if the participant is unaware of using these cues (O'Toole et al., 2011; Rice, Phillips,

Natu, An, & O'Toole, 2013; Van den Stock, Tamietto, Hervais-Adelman, Pegna, & de Gelder, 2015).

Other studies suggest that more naturalistic images may produce different patterns of deficits in

disorders such as schizophrenia. For example, Davis and Gibson (2000) found that patients with

schizophrenia were significantly impaired when discriminating posed facial expressions, but not

genuine facial expressions (e.g.: photos of surprise obtained by popping a balloon near the actor).

More recently, Regenbogen and colleagues (2015) found that patients with schizophrenia and

depression showed no deficits on an empathy task which involved viewing naturalistic stimuli. This

finding was in contrast to the majority of past literature using traditional stimuli (Li et al., 2010;

Stuhrmann, Suslow, & Dannlowski, 2011), suggesting that the use of non-naturalistic stimuli may be

artificially eliciting deficits that are unrelated to everyday perceptual functioning. Taken together, this

literature suggests that, although heavily edited and standardised stimuli are attractive because they

can be used to minimise unwanted variability, researchers should take extra care to consider the

greater generalisability of their tasks to everyday functioning.

46

In addition to varying intensity, researchers are increasingly utilising dynamic (video-based)

face sets to examine face processing impairments. Again, the ecological validity of static stimuli has

been questioned because they are not representative of the moving, changing facial expressions we

encounter in face-to-face interactions (Atkinson et al., 2004; Kilts et al., 2003). Discerning the

emotional state of an individual in daily life involves detecting and rapidly interpreting temporal

changes in facial movements such as a brief smile, or a narrowing of the eyes. It can be argued,

therefore, that expressions are inherently dynamic, and that static images may be too impoverished to

adequately tap into emotion processing mechanisms (Fiorentini & Viviani, 2011). Several dynamic

face sets have been made available for emotion research, including the Perception of Emotion Test

(POET; Kilts et al., 2003), the Cohn-Kanade Facial Expression Database (Kanade, Cohn, & Tian,

2000), the CMU-Pittsburgh AU-Coded Face Expression Image Database (Pantic, Valstar, Rademaker,

& Maat, 2005), and the Amsterdam Dynamic Facial Expression Set (ADFES; van der Schalk, Hawk,

Fischer, & Doosje, 2011). Research in both healthy and clinical populations suggests that there are a

range of differences in the ways that individuals respond to static and dynamic face stimuli.

Figure 5.1. Example of morphed stimuli created by Norton and colleagues (2009, p.1095) used in an

emotion discrimination task. Participants are asked, “Which face, the first or the second, looks more

afraid?”

Comparison between static and dynamic faces in healthy popu lations

A range of studies in healthy controls have investigated the role of dynamic motion in

emotion processing. These findings are summarised in Table 5.1. In particular, several behavioural

studies have reported advantages for recognising emotion from dynamic faces over traditional static

47

faces. For instance, two studies found increased accuracy rates for dynamic faces across all emotion

compared to matched static faces (Wehrle, Kaiser, Schmidt, & Scherer, 2000; Weyers, Muhlberger,

Hefele, & Pauli, 2006). Similarly, a study using dynamic stimuli that varied in the intensity of

expression found that dynamic stimuli were recognised more easily than static (Montagne, Kessels,

De Haan, & Perrett, 2007). Other studies, however, found no difference between static and dynamic

conditions (Fiorentini & Viviani, 2011) or only found significant effects for certain emotions. For

example, Recio, Schact and Sommer (2011) found that dynamic happy expressions were recognised

with greater accuracy compared to static faces, but reported no such effect for expressions of anger.

Kamachi and colleagues (2001) found that participants were better at recognising static faces of anger

and sadness compared to dynamic faces. When comparing the recognition of facial expressions

presented centrally versus peripherally, Fujimura and Suzuki (2010) reported that dynamic angry

faces were recognised more accurately than static faces in the periphery only, with no differences for

central presentation or other valences of emotion. In addition to differences in recognition between

dynamic and static stimuli, Yoshikawa and Sato (2006) found increased self-reported “emotional

experiences” in response to dynamic faces compared to matched static faces. Similarly, Biele and

Grabowska (2006) found that dynamic faces were perceived as more intense than static versions.

Using a cueing paradigm, Kaufman and Johnston (2014) found that dynamic cues had a greater

impact than static cues on a same-or-different emotion discrimination task.

Different responses to static compared to dynamic facial expressions have also been

demonstrated through imaging studies. In a PET study, Kilts and colleagues (2003) revealed

significantly different patterns of brain activation for dynamic happy and angry faces compared to

static, particularly involving area V5, the superior temporal sulcus, cerebellum, and temperomedial

cortical areas. LaBar and colleagues (2003) found increased fMRI activation in the amygdala and

fusiform gyrus for angry and fearful dynamic expressions compared to static equivalents, indicating

stronger emotional responses to moving stimuli. Similarly, Sato and colleagues (2004) found greater

activation in the fusiform gyrus, medial temporal gyrus, and inferior occipital gyrus in response to

dynamic happy and fearful expressions. More recent fMRI studies and a meta-analysis by Arsalidou,

Morris and Taylor (2011) have similarly reported substantial increases in activation to dynamic faces

in brain areas associated with the processing of emotion, biological motion, and social cues (Kessler

et al., 2011; Trautmann, Fehr, & Herrmann, 2009). Further evidence for a dissociation between static

and dynamic facial expressions has come from studies of clinical populations. At least two case

studies have been published of brain-injured patients who were unable to identify emotions in static

images, but could correctly identify emotions from dynamic faces, or from faces formed of moving

point-light displays (Adolphs, Tranel, & Damasio, 2003; Humphreys, Donnelly, & Riddoch, 1993).

Taken together, these studies suggest that dynamic faces more effectively tap into neural processes

relevant to emotion processing compared to static face images.

Not only do static and dynamic expressions elicit different patterns of behavioural and neural

48

responses, but they also appear to produce differences in viewers’ unconscious muscular reactions.

Electromyography (EMG) studies allow researchers to examine the movements of participants’ facial

muscles in response to viewing static and dynamic facial expressions. For instance, Sato and

colleagues (2008) found that dynamic happy faces prompted stronger activation of the zygomaticus

major muscle (involved in smiling), while dynamic angry faces prompted stronger activation of the

corrugator supercilii muscle (involved in frowning) compared to static faces. A related study using

discreet video recording found similar patterns of muscular movements in response to dynamic faces,

suggesting that moving stimuli are more likely to elicit facial mimicry than static images (Sato &

Yoshikawa, 2007). Two studies measuring EMG responses to happy, angry, and neutral faces asked

participants to rate the intensity of the emotion shown. In both studies, the dynamic stimuli were rated

as more intense, as well as more realistic than static equivalents (Rymarczyk, Biele, Grabowska, &

Majczynski, 2011; Weyers et al., 2006). However, while happy faces elicited stronger activation of

the zygomatic muscles and reduced activation of the corrugator supercilii, no significant EMG

differences were found for dynamic angry faces in either study (Rymarczyk et al., 2011; Weyers et

al., 2006). Overall, EMG studies suggest that dynamic facial emotions are more likely to prompt

spontaneous facial mimicry than static faces, however this finding appears to be more robust for

happy faces than for other emotions.

The studies above provide convincing evidence of an advantage for dynamic stimuli over

static in the investigation of emotion. However, is this advantage due to the presence of motion, or

due to some other characteristic of the stimulus? An obvious confound when comparing dynamic

stimuli to static is that they contain different quantities of visual information. That is, while dynamic

stimuli are comprised of multiple frames, static comprise only one. Therefore, it is possible that

dynamic stimuli simply provide a larger number of visual cues compared to static images, and that

this drives the recognition advantages seen in previous studies. To investigate whether differences

between static and dynamic stimuli still exist when the quantity of information is controlled for,

Ambadar, Schooler and Cohn (2005) conducted an emotion-recognition study using subtle (low

intensity) facial expressions. Performance was compared across four stimulus conditions: dynamic (3

to 6 frame videos, moving from a neutral to emotional expression), multi-static (the same 3 or 6

frames, with masking in between to eliminate the perception of motion), first-last (sequence showing

only the first and final frames of each video), and single-static (showing the final frame only). They

found that both accuracy and confidence ratings were significantly higher for the two moving

conditions (dynamic and first-last) than the two static conditions (single-static and multi-static). This

suggests that the advantage shown for dynamic stimuli is due to the presence of motion, rather than

the quantity of information. As the performance for the first-last sequence was equal to the dynamic

sequence, the authors argue that emotion recognition is likely tuned to the perception of change from

a neutral face to an expressive face, and is not dependent on cues relating to the actual temporal

unfolding of an expression (Ambadar et al., 2005).

49

Table 5.1. Behavioural studies comparing emotional processing of dynamic and static face stimuli in

healthy controls.

Study Task type Dynamic vs static

Kamachi et al. (2001) Rate intensity (7 choice) Static > dynamic

Ambadar, Schooler & Cohn

(2005) Labelling (7 choice) Dynamic > static

Biele & Grabowska (2006)

Rate intensity (4 choice) Dynamic > static

Yoshikawa & Sato (2006) Matching to same intensity

(sliding scale)

No difference, but rapid changes

were perceived as more intense

than slow changes.

Montagne et al. (2007) Labelling (6 choice) Dynamic > static

Fujimura & Suzuki (2010) Labelling (6 choice) Dynamic > static

(anger only)

Fiorentini & Viviani (2011) Labelling (2 choice) No difference

Recio, Schacht & Sommer (2011) Labelling (3 choice) Dynamic > static

(happiness only)

Kaufman & Johnston (2014) Same-or-different discrimination

(static only)

Dynamic cues produced faster

‘same’ responses than static cues

Studies using dynamic faces to examine emotion processing in

schizophrenia

Research in healthy populations suggests that using dynamic stimuli – rather than static

images – to investigate emotion processing confers a range of advantages. These include increased

recognition for both exemplar and more subtle expressions, increased ratings of emotional intensity,

greater neural activation, and higher rates of spontaneous facial mimicry. Despite this, dynamic

stimuli are still not widely used, especially in studies of clinical populations. Only a handful of studies

have employed dynamic stimuli to investigate emotion processing in schizophrenia. These studies are

summarised in Table 5.2.

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Table 5.2. Behavioural studies using dynamic stimuli to investigate emotion processing in

schizophrenia.

Study Task type Dynamic vs static Group difference?

Archer, Hay & Young

(1994)

Match label to face

(2 choice)

Static > dynamic

HC > SZ

Depression > SZ

Match face to face

(3 choice) No difference

HC > SZ

Depression > SZ

Johnston et al. (2010) Labelling (2 choice: fear

or suprise) No difference HC > SZ

Mendoza et al. (2011) Labelling (6 choice)

Judge intensity only N/A HC > SZ

Behere et al. (2011) Labelling (7 choice) Dynamic > static HC > SZ

Souto et al. (2013) Labelling (6 choice) N/A No difference

Hargreaves et al. (2016) Labelling (6 choice) N/A HC > SZ

Note: HC = Healthy control, SZ = Schizophrenia.

One of the earliest studies to use dynamic faces to investigate schizophrenia was conducted

by Archer, Hay and Young (1994) , who compared inpatients and healthy controls using colour videos

of talking faces. They found that inpatients with schizophrenia showed significantly poorer accuracy

for identifying anger, surprise, disgust, sadness and fear compared to healthy controls. They were also

significantly poorer at identifying anger and surprise compared to inpatients with depression.

Another study by Johnston and colleagues (2010) compared dynamic video of facial

expressions with static images in patients with schizophrenia and healthy controls. Participants

viewed one-second videos of neutral expressions morphing into either fearful or surprised

expressions. Within a single block, these videos were randomly interspersed with static images of

these same expressions. Two control tasks were included that used static stimuli: an identity

discrimination task and a non-face discrimination task. It was reported that patients performed poorer

than controls for the emotion discrimination task only (for both static and dynamic stimuli),

suggesting an emotion-specific deficit. Of greater importance, however, was the unique finding that

static and dynamic stimuli correlated with different patterns of symptomatology. High negative

symptom scores were associated with poorer performance for static stimuli but not dynamic stimuli,

while high positive symptom scores were associated more strongly with dynamic stimuli. Links

between performance and symptomatology will be discussed in greater detail in Chapter 11. However,

this finding suggests that tasks using static and dynamic face stimuli may tap into different aspects of

impairment in schizophrenia, and further highlights the need to use ecologically valid stimuli when

investigating face processing deficits in this disorder.

51

A similar study by Behere and colleagues (2011) used dynamic and static expressions to

evaluate threat identification in schizophrenia. Consistent with the results seen in healthy populations

(e.g.: Weyers, Muhlberger, Hefele, & Pauli, 2006; Montagne, Kessels, De Haan, & Perrett, 2007),

patients with schizophrenia were more accurate at identifying dynamic emotions compared to static.

Moreover, they reported that patients experiencing first-rank symptoms (a subset of symptoms

strongly associated with paranoid schizophrenia) were more likely to misidentify non-threatening

emotions as threatening (e.g.: label a neutral face as angry) compared to patients without first-rank

symptoms.

Using a slightly different paradigm, Mendoza and colleagues (2011) compared patients with

schizophrenia, first degree relatives, and unrelated controls on a dynamic morphed stimuli task. In this

experiment, participants were shown a gradual sequence of frames morphing from a neutral face to

one of six emotions, and were required to press a button as soon as they could identify the emotion. It

was found that patients with schizophrenia required significantly more frames (i.e.: greater intensity)

to correctly recognise all six emotions. Unaffected relatives, however, required greater intensity to

recognise disgust and fear only.

A more recent study (n.b.: published since the collection of data for this PhD) examined

emotion processing in schizophrenia using dynamic stimuli which varied in emotional intensity

(Hargreaves et al., 2016). In this paradigm, videos of the six primary emotions were presented in a 6-

option labelling task. The first block showed emotions at 20% intensity, then increased in 20%

intervals until the fifth and final block showed expressions at 100% intensity. It was found that

outpatients with schizophrenia (n=47) performed significantly less accurately than healthy controls

across all emotions except surprise. Accuracy increased with emotional intensity for all participants.

Furthermore, performance in patients correlated with IQ, several measures of memory, attentional

control and social cognition, while performance in healthy controls correlated with a face memory

task and social cognition only. These results concur with research using static stimuli (e.g.: Bediou et

al., 2007) and lend weight to the argument that emotion-processing deficits are associated with the

core cognitive deficits seen in schizophrenia.

In an attempt to develop a paradigm with even greater ecological validity than 2-dimensional

video, Souto and colleagues (2013) created a virtual reality program to evaluate emotion recognition

using 3D computer-generated avatars. Interestingly, preliminary findings revealed no significant

differences in accuracy rates between healthy controls and patients with schizophrenia. However, this

may be due to insufficient sample sizes (n=12 per group). Regardless, the utility of virtual reality

alternatives to 2-dimensional stimuli remains a promising avenue for future study.

In addition to behavioural studies, to date only two imaging studies have been conducted

using dynamic stimuli to investigate emotion processing in schizophrenia. Russell and colleagues

(2007) collected fMRI data while patients with schizophrenia viewed videos of emerging or

dissipating fear (i.e.: neutral-to-fear vs fear-to-neutral). Compared to healthy controls, patients with

52

paranoid symptoms showed abnormal activity in the left and right amygdala while viewing emerging

fearful faces, while non-paranoid patients showed increased activity in the hippocampi only. These

findings are in line with previous imaging research suggesting that amygdala abnormalities may be

involved in the misattribution of emotional salience in schizophrenia (Anticevic et al., 2012). A more

recent fMRI study by Mothersill and colleagues (2014) examined activations in response to passive

viewing of angry and neutral face videos. They found that patients with schizophrenia showed less

deactivation of the medial prefrontal cortex (including the anterior cingulate cortex), and less

activation of the left cerebellum when viewing faces compared to healthy controls. Again, these

results are consistent with research using static face stimuli which have implicated the ACC in

emotion processing difficulties in schizophrenia (Habel et al., 2010; Hall et al., 2008).

In summary, the use of dynamic stimuli to investigate emotion processing in schizophrenia

remains sparse, but is gaining in popularity. In line with studies using static stimuli, dynamic studies

have demonstrated significant differences in emotion identification between patients with

schizophrenia and healthy controls. Furthermore, one study suggests that impairments in recognising

static and dynamic faces are associated with different patterns of symptomatology in schizophrenia.

Overall, the literature suggests that not only are dynamic stimuli more ecologically valid than static,

but they appear to involve quite different neural substrates and therefore may be sensitive to different

types of impairment. Given these differences, there is a strong rationale for employing dynamic

stimuli in future schizophrenia research.

Rationale for the development of original dynamic stimuli

Given the above criticisms of traditional static stimuli, it was decided that dynamic stimuli

would be utilised for this thesis. Several online databases of dynamic face videos already exist and

can be accessed without charge for research purposes (e.g.: ADFES; van der Schalk, Hawk, Fischer,

& Doosje, 2011). For the purpose of our experiments, however, these stimuli were not suitable

without further modification. For instance, the majority of videos contained non-face identifying

features such as hair, glasses, and clothing which could be used by participants to distinguish between

different identities. Given that we wished to evaluate facial identity processing, it was necessary to

eliminate these features to prevent participants from relying on these extraneous cues.

Furthermore, one of our goals was to introduce a new technique: applying morphing software

to video-based stimuli. Just as Norton and colleagues (2009) utilised morphing to modify static

stimuli, we aimed to modify the intensity of emotional faces (or the similarity between two faces) to

create a broader range of stimuli. By varying this intensity, it is expected that the stimuli will be less

sensitive to ceiling effects and more likely to detect subtle impairments in face processing ability

(Hargreaves et al., 2016; Norton et al., 2009).

Three matched dynamic stimuli sets were created: a set of emotional faces varying by

53

emotional intensity (fear and disgust); a set of non-emotional face animations varying by facial

similarity (morphing of same-sex or different-sex face pairs); and a set of rotating car animations.

This third dynamic set was created to serve as a non-face control stimulus that is matched to the same

task parameters as the face sets. All stimuli can be downloaded from http://go.unimelb.edu.au/e3t6.

Step by step creation of a new stimuli set

Preparation of face stimuli for morphing

Raw videos were sourced with permission from the MMI-Facial Expression Database

(https://mmifacedb.eu; Pantic et al., 2005; Valstar & Pantic, 2010) and the Facial Expressions and

Emotion Database (FEED; Wallhoff, 2006) . Each .AVI video file was then converted to a sequence

of frames using VirtualDub (Lee, 2012). From this sequence, 13 frames were selected that showed a

progression from a neutral resting face to a ‘completed’ facial movement or expression. Each frame

was then edited in Adobe Photoshop CS2 to isolate the face against a black background, stabilise any

head movements, and to remove non-face cues such as glasses, hair, and facial hair. All stimuli were

converted to greyscale to eliminate the possibility of participants using colour-matching as an

alternative strategy to discriminate between faces. Each face fitted within 200x200 pixels.

Morphing to vary the intensity of an expression

As a means to assess facial affect processing, videos of facial expressions were edited to vary

the intensity of each expression without altering the identity of the individual. To achieve this, the

first frame of each video (a neutral expression) was morphed with every subsequent frame using

Fantamorph 5 (Abrosoft, 2012). This was accomplished using the ‘Face Locator’ add-in to map out

the main features of each face. Once the maps were manually adjusted to indicate features as precisely

as possible, the morphing slider was used to select the ratio between the neutral and emotive faces,

e.g.: 33% neutral, 67% emotive. This produced an overall effect of ‘relaxing’ or ‘diluting’ the facial

movements in order to create a subtler facial expression in the final video. The final morphed frame

was then exported as a new file, and then the process was repeated for all remaining frames. This

method was used with videos showing fear and disgust to create a series of animations ranging from

100% intensity to 33% intensity (see Figure 5.2 for examples). Original animations lasted 1 second.

After piloting, however, stimuli were slowed down to 2 seconds in order to increase accuracy to an

acceptable level.

For this set, twelve videos of different individuals (6 showing fear, 6 showing disgust) were

morphed to create five levels of expression intensity: 33%, 50%, 67%, 83%, and 100%. A sixth

intensity level, 17%, was also piloted. These were not included in the final set, however, because

healthy controls could not reliably identify the emotion at such a low intensity. The final set

comprised 60 stimuli.

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Figure 5.2. Partial image sequences (every other frame) from five of the video stimuli ranging in

intensity of emotion. The top video shows the original video of an individual’s face changing from

neutral to an expression of disgust. This video was then morphed with the neutral face frame (leftmost

frame) to reduce the intensity of the final expression (rightmost frame). Emotional intensity ranged

from 100% (unedited video) to 33% intensity.

Morphing to vary the identity of a face

To assess non-emotional aspects of face processing, videos were edited to vary the similarity

between two different individuals. To accomplish this, pairs of videos were selected that showed the

same non-emotive facial movement (e.g.: raising the eyebrows, opening the mouth, or sticking out the

tongue). The thirteen individual frames were then matched as closely as possible so that both videos

showed the movement at the same speed. From there, the first frame of one video was morphed with

the first frame of the second video using the ‘Face Locator’ add-in in Fantamorph 5. When repeated

for all frames, this produced a new video showing the new ‘morphed’ individual performing the full

55

facial action. This method was used to create a series of 1 second animations ranging from one

individual to the other via 20% increments.

Two different sets of stimuli were created: one set where the faces in each morphed pair were

the same sex, and one set where each morphed pair were opposite sex. For the same-sex set, six pairs

of unique individuals (3 male, 3 female) were morphed in pairs to create six sets of face animations

ranging from one identity to the other by increments of 20%. Thirty-six stimuli were created in total.

The stimuli for the opposite-sex set were created in the same way as above. Six pairs of

individuals of the opposite sex were morphed together to create six sets of animations ranging from

100% male to 100% female. Thirty-six stimuli were created. Examples from this set are shown in

Figure 5.3.

Figure 5.3. Partial image sequences from six different non-emotional face video stimuli. The top and

bottom videos show two different individuals making the same motion (eyebrow raise). These videos

were then morphed together to create four new videos which vary on a continuum from person A to

person B.

56

Dynamic Car Stimulus Set

In order to evaluate the specificity of face processing deficits, a set of non-face dynamic

stimuli were also created. Side-views of cars were selected because, like faces, they are composed of a

fixed configuration of features (e.g.: wheels, windows, headlights), but do not appear to invoke face-

specific processing networks nor tap into emotional responses in the same way as faces. Previous

research suggests that, unlike viewing faces, viewing cars is not associated with activity in the

fusiform face area (Grill-Spector, Knouf, & Kanwisher, 2004), nor does it elicit the elevated MEG

response component M170 (Xu, Liu, & Kanwisher, 2005).

To create dynamic car videos, 3D meshes of various car models were obtained online via a

free 3D modelling website (Studio ArchiDOM, 2011). These meshes were then recoloured to match

vehicle colour and animated using 3DS Max Design (Autodesk Inc., 2012). Each model was animated

rotating from a side-view to a 45-degree view. Attempts to use morphing to vary the similarity

between cars were unsuccessful. Instead, models were paired with similar looking models in order to

avoid ceiling effects in distinguishing different cars.

In total, six pairs of one-second rotating car animations were created. Each of the 12 cars

appears in two different animations, once rotating left, and once rotating right. Twenty-four stimuli

were created in total. See Figure 5.4 for examples.

Figure 5.4. Partial image sequences from two different 3D video stimuli used in the Car

Discrimination task. In each video, cars rotate from a side view to a 45-degree view. Car 1 and Car 2

are different models that are similar in appearance.

57

Experiment: Static versus Dynamic Emotion Stimuli

The aims of the current experiment were to determine a) whether the newly developed set of

emotional face stimuli will be identified more easily in dynamic form compared to static form, and b)

whether the type of paradigm used (either labelling or discrimination) will interact with the type of

stimulus viewed (dynamic or static). To assess this, a group of healthy undergraduate students

completed an emotion labelling task and an emotion discrimination task which were each composed

of randomly interspersed static and dynamic faces.

Method

Participants

Eighty-two first-year undergraduate psychology students were recruited through the Research

Experience Program (REP) at the Melbourne School of Psychological Sciences, University of

Melbourne. Written informed consent was obtained from all participants, who received course credit

in exchange for their participation. According to self-report all participants were free from

neurological injury, psychological disorder and substance use, and were not taking psychotropic

medications (See Appendices A & B). The study was approved by the University of Melbourne

Human Research Ethics Committee (No. 1135478.6). Due to incomplete or missing data, three

participants were excluded from analysis for the Discrimination task (n=79) and one was excluded

from analyses for the Labelling task (n=81). As all participants performed above 50% accuracy for

both tasks, no individuals were excluded on the basis of poor performance. Demographic data are

shown in Table 5.3.

Table 5.3. Participants’ (n=82) demographic information.

M SD

Age (years) 19.60 3.54 (Range 17-38)

Males/Females (% male) 18/64 (22%) -

Education (years) 12.23 0.76 (9 months)

Estimated FSIQ from NART

(n=54)* 112.88 5.43

Digit span – forward

(mean standard scores) 10.02 2.72

Digit span – backward

(mean standard scores) 11.65 2.92

*28 participants were excluded from this analysis due to poor English (All English as Second Language).

58

Questionnaires and demographics

Participants completed the National Adult Reading Test (NART): a brief reading task that

produces a broad estimate of general intellectual functioning (Crawford et al., 1992; See Appendix C)

. NART error scores were used to calculate Wechsler Adult Intelligence Scale Revised (WAIS-R) IQ

estimates, which are shown in Table 5.3. Participants also completed the Digit Span Forwards and

Backwards subtests from the WAIS-IV (Wechsler, 2008) as a measure of working memory ability.

Demographic information regarding age, sex, and years of education were also collected. As part of a

larger study, The Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE; Mason &

Claridge, 2006) was also administered to assess schizotypy. Correlations between these scores and

performance on these tasks will be described in Chapter 6.

Emotion Discrimination Task

This task was created using the 60 dynamic emotional face stimuli described above. Each 2-

second animation showed a neutral expression changing into either a fearful or disgusted expression.

Each final expression varied in intensity, showing either 33%, 50%, 67%, 83% or 100% intensity of

emotion. Half of the stimuli were presented in original dynamic form, and half were changed to static

images of the final frame only, presented for 2 seconds continuously. Stimuli were 5 x 4cm and were

viewed at a distance of approximately 50cm (5.7 x 4.6° of visual angle).

In each trial one expression was shown for 2 seconds, then a second face of a different

individual showing either the same or different expression was shown for 2 seconds, followed by a

blank screen with the words “Same or different?” (Figure 5.5A). Participants were instructed to press

‘S’ on a keyboard if the two faces showed the same emotion, and ‘D’ if the two faces showed a

different emotion. Pairs of expressions were always shown at the same intensity level, and always

showed the same stimulus type (either static or dynamic). Each stimulus was shown twice: once in a

‘same’ trial and once in a ‘different’ trial, for a total of 120 trials altogether.

To attempt to control for the possibility that certain stimuli might produce different effects in

the static and dynamic conditions, two different versions were created. In version A half of the faces

were presented as dynamic, and half as static. In version B, the stimuli were reversed, with the

dynamic stimuli shown as static and the static shown as dynamic. Half of the participants completed

version A and half completed version B.

Emotion Labelling Task

Stimuli used were the same as those used in Emotion Discrimination. In each trial an

expression was shown for 2 seconds, followed by a blank screen with the words “Fear or Disgust?”

(Figure 5.5B). Participants were instructed to press ‘A’ on a keyboard if the face showed fear and ‘S’

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if the face showed disgust. Each stimulus was shown once, for a total of 60 trials altogether. Half of

the trials were static and half were dynamic. Participants completed either version A or B to coincide

with version A or B of the Discrimination task. That is, for each participant, the faces that appeared as

static for the Discrimination task were dynamic for the Labelling task, and vice versa.

Figure 5.5. Trial sequences for Emotion Discrimination (A) and Emotion Labelling (B). Static and

dynamic stimuli were interspersed randomly within each task. Note that for Emotion Discrimination,

both faces within each trial were always the same stimulus type (either static or dynamic). Correct

answers are: A: different, and B: disgust.

General procedure

Participants completed the two tasks in one of four counterbalanced orders (Labelling or

Discrimination first; either version A or B). Prior to each task, participants were shown two easy

practice trials with feedback. If the participant did not answer the two trials correctly, instructions

were repeated and the incorrect trial shown again until the participant understood the task.

Testing took approximately 30 minutes to complete, and participants were permitted to take

as many breaks as desired. Computerised tasks were completed on a laptop computer (60 Hz, 16-inch

screen size, resolution 1280 x 1024 pixels) at a comfortable viewing distance of approximately 50cm

in a quiet, distraction-free environment.

Analytical methods

Accuracy rates and reaction times were analysed for each group. Reaction times were

measure in milliseconds, where t=0 was the onset of the final frame of each trial (i.e.: “Same or

different” or “Disgust or fear”, see Figure 5.5). Participants had an unlimited time to respond. While

percentage correct is a straightforward measure of task performance, group differences may be

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obfuscated by differences in response bias. For instance, it is often reported that clinical populations

show higher rates of false alarms compared to healthy controls. To minimise response bias, we

converted percent correct on each task to d’ scores using formulae recommended by MacMillan and

Creelman (1991). A higher d’ value indicates more accurate performance. Before analysis, all

participants performing below chance (50% overall accuracy) were excluded.

Prior to calculating d’, hit rates and false alarms were calculated using formulae suggested by

Corwin (1994), which are adjusted to avoid dividing by zero. Hit rates were calculated as: (Correct

hits +.05)/ (Total targets + 1); and false alarm rates were calculated as: (False alarm + .05)/(Total

distracters + 1).

For the Labelling task, d prime was simply calculated as: d’ = z(Hit rate) – z(False alarms).

For the Discrimination task, this value was then converted to a modified d’ using table A5.3 from

MacMillan and Creelman (1991). This was because same-or-different tasks are shown to contain an

inherent bias to say ‘same’ more often than ‘different’ (MacMillan & Creelman, 1991), therefore a

higher level of adjustment is necessary.

A measure of response bias, c, was also calculated using the formula: c = -0.5 [z(Hit

rate)+z(False alarms)](Macmillan & Creelman, 1991). A value of 0 indicates no bias, while a positive

or negative value of c indicates an increasing tendency to favour one response option over the other.

D prime and c values for each group were then compared using t-tests or Repeated-Measures

ANOVA, where appropriate. Uncorrected reaction times (in ms) for correct trials were also analysed.

Results

Impact of emotional intensity

Analyses were conducted to determine whether varying emotional intensity had a differential

impact on static and dynamic conditions in either task. A 2 x 2 x 5 Repeated-Measures ANOVA was

conducted with percent accuracy as the dependent variable and Task (Discrimination versus

Labelling), Stimuli (Dynamic versus Static) and Emotion Intensity (33%, 50%, 67%, 83%, and 100%)

as within-subjects factors. Shapiro-Wilks tests revealed that assumptions of normality were violated

(p<.05) for all 20 conditions. However, upon visual inspection of histograms these were not found to

be extreme violations. Given equal sample sizes and the robustness of ANOVAs to violations,

parametric tests were used for the following analyses. Mauchly’s test indicated that the assumption of

sphericity was violated for Emotion Intensity x2(9)=31.49, p<.001, therefore the Greenhouse-Geisser

correction was used for all analyses involving this factor. Significant main effects were found for

Task, F(1,77) = 138.1, p<.001, ŋp2=.64; Stimuli, F(1,77) = 36.6, p<.001, ŋp

2=.32; and Emotion

Intensity, F(4,261.3) = 51.3, p<.001, ŋp2=.40. Overall, accuracy was higher for the Labelling task

(M=87.4%) than the Discrimination Task (M=72.9%, difference=14.5%, see Figure 5.7A). For both

tasks, accuracy increased as Emotional Intensity increased (See Figure 5.6). Bonferroni-adjusted post-

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hoc pairwise comparisons revealed a significant jump in accuracy from the 33% intensity condition to

the 50% intensity condition (p<.001) and an almost significant jump from the 50% intensity condition

to the 67% intensity condition (p=.056). There was no significant difference in accuracy between the

67%, 83%, and 100% intensity conditions (p values>.99).

A significant interaction between Task and Stimuli was also found, F(1,77) = 11.0, p=.001,

ŋp2=.13. Post-hoc pairwise t-tests revealed that accuracy was significantly higher for Dynamic stimuli

compared to Static stimuli (difference=7.7%) on the Discrimination Task only, t(78)=6.43, p<.001.

No significant difference in accuracy was found between Dynamic and Static stimuli conditions on

the Labelling task, t(78)=1.46, p=.15. No other interactions approached significance.

Figure 5.6. Comparison of percent accuracy by emotional intensity for dynamic and static conditions

on the Discrimination task (A) and the Labelling task (B). Error bars indicate 95% confidence

intervals around means.

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Figure 5.7. Raw accuracy rates (A) and d prime scores (B) for Dynamic and Static stimuli for the two

tasks: Emotion Discrimination and Emotion Labelling. Error bars indicate 95% confidence intervals.

Response bias – c

Mean values of c (a measure of response bias) ranged from -.10 to .14 across the four

conditions (see Figure 5.8). One-sample t-tests revealed that c was significantly different from zero

for Static conditions on the Discrimination task, t(78)=3.42, p=.001, 95% CI [.06, .23], and the

Labelling task, t(80)=-2.60, p=.01, 95% CI [-.18, -.02]. Mean c values were not significantly different

from zero for either of the Dynamic conditions: Discrimination task, t(78)=-1.2, p=.22, 95% CI [-.13,

.03], Labelling task, t(80)=-1.34, p=.19, 95% CI [-.11, .02]. A 2 x 2 repeated-measures ANOVA with

c as the dependent variable and Task (Discrimination vs Labelling) and Stimuli (Dynamic vs Static)

as within-subject factors was conducted to ascertain whether c values differed significantly between

the four conditions. A significant main effect was found for Task, F(1,77) = 8.17, p=.005, ŋp2=.10; but

not Stimuli F(1,77) = 3.58, p=.06, ŋp2=.04. The interaction between Task and Stimuli was also

significant, F(1,77) = 12.24, p=.001, ŋp2=.14. Post-hoc pairwise t-tests revealed that response bias was

significantly greater for Static compared to Dynamic stimuli on the Discrimination task, t(78)=-3.87,

p<.001; but no difference was found between Stimuli conditions on the Labelling task, t(80)=1.06,

p=.29. For the Discrimination task, participants were more likely to say ‘different’ (rather than

‘same’) when viewing Static faces, but showed no such bias for Dynamic faces. In contrast, although

there was a slight tendency to say ‘disgust’ (rather than ‘fear’) when viewing Static faces on the

Labelling task, this was not significantly different for Dynamic stimuli, which showed zero bias.

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These results indicate that response criteria differed significantly between conditions. This is

a problematic finding because unequal response criteria may differentially impact accuracy rates

between tasks. For instance, a large response bias (such as a greater tendency to say ‘same’ rather than

different) can cause overall percent accuracy to appear much lower than a task with very low response

bias (Harvey Jr, 2003). For this reason, d prime scores were calculated as an alternative to raw

accuracy rates, and included in all subsequent analyses. D prime is a sensitivity measure which takes

both true-positive and false-positive responses into account, and therefore provides a measure of

performance which is unaffected by response bias (Macmillan & Creelman, 1991).

Figure 5.8. Mean response bias as evidenced by c rates across the four task conditions. Error bars

indicate 95% confidence intervals.

Repeated-measures ANOVA – d prime performance

Given that c values differed between conditions, d prime scores were calculated as a measure

of performance because they are believed to be less affected by response bias than raw accuracy rates.

Therefore, to examine the impact of task and stimulus type on bias-corrected performance rates, a 2 x

2 Repeated Measures ANOVA was conducted with d prime scores as the dependent variable. Task

(Discrimination versus Labelling) and Stimuli (Dynamic versus Static) were included as within-

subjects factors. As no significant interactions were found involving Emotion Intensity, the data was

collapsed across all levels of intensity for the following comparisons. Shapiro-Wilks tests revealed

that assumptions of normality were violated (p<.05) for two of the four conditions, however, once

again visual inspection of histograms suggested that these were not extreme violations, and parametric

tests were continued.

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Significant main effects were found for Task, F(1,77) = 14.5, p<.001, ŋp2=.16, and Stimuli,

F(1,77) = 38.2, p<.001, ŋp2=.33. Overall, d prime scores were significantly higher for the Labelling

task (M=2.38) compared to the Discrimination task (M=2.02), and were also significantly higher for

Dynamic stimuli (M=2.39) compared to Static stimuli (M=2.03; see Figure 5.7B). A significant

interaction between Task and Stimuli was also revealed, F(1,77) = 6.2, p=.015, ŋp2=.08. This was

explored further using post-hoc pairwise t-tests which showed that, while d prime scores were

significantly higher for Dynamic stimuli compared to Static stimuli on both tasks, this difference was

larger for the Discrimination task (difference = .53), t(78)=6.20, p<.001, than for the Labelling task

(difference = .21), t(80)=2.14, p=.04. There was no difference in performance between dynamic

conditions on the Discrimination and Labelling tasks, t(78)=1.60, p=.11.

Repeated-measures ANOVA – reaction time data

A 2 x 2 Repeated Measures ANOVA was conducted with reaction times in milliseconds as

the dependent variable, and Task (Discrimination versus Labelling) and Stimuli (Dynamic versus

Static) as within-subjects factors. No significant interactions were found involving Emotion Intensity,

therefore the data was collapsed across all levels of intensity. Shapiro-Wilks tests revealed that

assumptions of normality were violated (p<.05) for all four conditions, however, once again

parametric tests were continued because visual inspection of histograms revealed no extreme

violations. Reaction time data are shown in Figure 5.9.

Figure 5.9. Comparison of reaction times for static and dynamic conditions on the two tasks: Emotion

Discrimination and Emotion Labelling.

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Main effects did not approach significance for either Task, F(1,77) = .32, p=.57, or Stimuli,

F(1,77) = 2.34, p=.13. However, a significant interaction was found between Task and Stimuli,

F(1,77) = 9.9, p<.001, ŋp2=.21. Post-hoc pairwise t-tests showed that reaction times were significantly

slower for Dynamic stimuli compared to Static stimuli on the Discrimination task (difference = 167

ms), t(78)=4.65, p<.001. In contrast, there was no significant difference between Dynamic and Static

conditions on the Labelling task, (difference = 69ms), t(80)=1.70, p=.09.

Taken together with the results of the d’ analyses, this pattern of results suggests that

performance on the Discrimination task was slower, but more accurate for Dynamic stimuli compared

to Static stimuli. To explore the possibility of a speed-accuracy trade-off, four uncorrected Pearson

correlations were conducted (see Figure 5.10). These revealed no significant linear relationship

between performance (d’) and reaction time for any of the four conditions (p values=0.7-75). Visual

inspection of scatterplots (see Figure 5.10) show no evidence of a non-linear relationship between

accuracy and speed on any of the four conditions. Therefore, it appears unlikely that participants who

were faster on the tasks were likely to make more errors.

Impact of task version

To assess whether the version of tasks completed had any impact on task performance (d’) or

reaction times, a series of independent t-tests were conducted. It was found that participants who

completed version B of the tasks were significantly more accurate at labelling static faces than

participants who completed version A, t(79)=3.77, p<.001. No other comparisons approached

significance (p values=.21-.97). This suggests that the faces shown as static in version A were more

difficult to label than the faces shown as static in version B. However, there was no difference in

version A or version B faces on the dynamic conditions, or in the Discrimination task for either

accuracy or reaction times.

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Figure 5.10. Scatterplots plotting the relationships between accuracy (d’) and reaction times (ms) on

each of the four task conditions: Dynamic Discrimination (A), Static Discrimination (B), Dynamic

Labelling (C) and Static Labelling (D). Uncorrected Pearson correlations are shown in the upper right

corner of each plot.

Correlations with demographics

Pearson correlations were conducted to determine whether performance (d’) on either the

Discrimination or Labelling tasks were influenced by demographic factors. Correlations were initially

run separately for static and dynamic conditions. As coefficients did not differ, data was collapsed

across conditions. Coefficients are summarised in Table 5.4. No significant correlations were found

between age, years of education, FSIQ estimates, Digit Span scores, and performance on either of the

emotion tasks (p values=.28-.99). However, it was found that performance on the two tasks was

positively and significantly correlated, r=.30, p=.008, CI[.08, .49]. This finding indicates that

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participants who performed well on the Emotion Discrimination task also performed well on the

Emotion Labelling task.

Table 5.4. Pearson correlations between task performance and demographic factors.

Emotion Discrimination (d’) Emotion Labelling (d’)

r (p) [95% CI] r (p) [95% CI]

Age -.07 (.53) [-.29, .15] .02 (.85) [-.20, .24]

Years of Education .004 (.97) [-.22, .22] .05 (.66) [-.17, .27]

Estimated FSIQ from

NART* -.002 (.99) [-.28, .28] .10 (.48) [-.18, .36]

Digit span - forward .08 (.47) [-.14, .30] .02 (.86) [-.20, .24]

Digit span - backward .005 (.96) [-.22, .23] -.12 (.28)

[-.33, .10]

Emotion Discrimination

(d’) - - .30 (.008)* [.08, .49]

Emotion Labelling (d’) .30 (.008)* [.08, .49] - -

*28 participants were excluded from this analysis due to poor English (All English as Second Language),

leaving n=54; **p<.01.

Discussion

The goal of this experiment was to investigate whether a newly developed face stimulus set

would be recognised more accurately in dynamic form compared to static form, using two different

types of emotion processing tasks: discrimination and labelling. Results showed that, as anticipated,

performance was significantly higher for dynamic stimuli across both tasks. While performance was

higher overall for the labelling task, there was no difference in performance between Labelling and

Discrimination when comparing the two dynamic conditions. Both tasks produced a similar increase

in accuracy for stimuli shown at a higher emotional intensity, and this effect was comparable for both

dynamic and static stimuli. Interestingly, task performance did not correlate with any demographic

factors such as age, years of education, working memory ability, or estimated IQ.

Dynamic versus static emotions

The finding that dynamic facial expressions were recognised more easily than static

equivalents is consistent with a number of past studies comparing these stimuli in healthy populations

(Ambadar et al., 2005; Biele & Grabowska, 2006; Fujimura & Suzuki, 2010; Montagne et al., 2007;

Recio et al., 2011). This result indicates that participants recognise fear and disgust more readily when

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it is presented as a moving stimulus rather than a static image. This is in line with the notion that

perceived motion is a central component of emotion recognition, and that studies relying on static

stimuli alone may be overlooking the importance of this contribution (Alves, 2013). However, an

alternative explanation is that the dynamic advantage is not due to the motion itself, but the simple

fact that the dynamic stimulus provides more information than a single frame. While this explanation

cannot be disregarded, a study by Ambadar and colleagues (2005) suggests that the dynamic

advantage remains even when the quantity of information is controlled for (i.e. by comparing a video

to a sequence of static images interspersed with masks to disrupt the perception of motion). Therefore,

it does not seem likely that the results seen in the current study are due to factors unrelated to the

perception of motion.

Discrimination versus labelling paradigm

To the best of my knowledge, this is the first study to compare static and dynamic faces on an

emotion discrimination task. Interestingly, this task not only showed a dynamic advantage, but this

advantage was more pronounced than that shown in the labelling task. It could be argued that this

discrepancy is due to an underlying difference in difficulty between the two tasks, as accuracy was

higher for the labelling task overall. However, there was no difference in accuracy rates between the

dynamic conditions for each task, which suggests that the two tasks were reasonably well matched in

difficulty for the dynamic stimuli. One could argue, instead, that performance on the discrimination

task was more affected by a loss of motion than performance on the labelling task. In other words,

same-or-different discrimination tasks may simply be a better measure of motion-sensitive emotion

processing ability. Further research is clearly required to investigate this idea, and is beyond the scope

of this thesis. Nevertheless, it would be informative to know if this result is replicated in other studies.

For instance, the dynamic advantage has not been unanimously found across studies, and it could be

due to the paradigm used. In contrast to the experiments cited above, some studies using dynamic

labelling paradigms have reported no dynamic advantage (Fiorentini & Viviani, 2011) or only found

an advantage for certain emotions, such as anger or happiness (Fujimura & Suzuki, 2010; Recio et al.,

2011). It is possible that labelling tasks may simply be less effective at tapping into emotion

processing deficits than other paradigms, such as discrimination.

Impact of emotional intensity

This study showed that accuracy increased with higher emotional intensity for both the

discrimination and labelling tasks. Similar patterns were shown for both dynamic and static stimuli,

with no evidence of an interaction between intensity and type of stimulus. This finding is consistent

with the study by Hargreaves and colleagues (2016) who reported similar patterns using dynamic

expressions in an emotion labelling task. It is also consistent with studies using static stimuli of

varying emotional intensity (Bediou, Krolak-Salmon, et al., 2005; Chen et al., 2012; Norton et al.,

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2009). On closer inspection, this increase in accuracy in the current study appears to be driven by

jumps between the 33% and 50% intensity conditions, and between the 50% and 67% conditions.

Accuracy did not differ between the 67%, 83% and 100% conditions for either static or dynamic

stimuli. This result is unlikely to be due to ceiling effects, as raw accuracy in these conditions was

below 80% for the discrimination task and below 90% for the labelling task. Therefore, it is possible

that participants did not obtain more useful visual information from the most intense expressions.

Study limitations: A possible speed-accuracy trade-off for emotion discrimination?

The results of this experiment revealed that participants were more accurate at discriminating

between dynamic facial expressions than static expressions. However, they were also significantly

slower at distinguishing dynamic expressions compared to static. This raises the possibility of a

speed-accuracy trade off; namely, that participants sacrificed accuracy for speed of response on static

trials. However, our analyses suggest that this was not the case: Individuals who responded faster

were not less accurate compared to individuals who responded more slowly. So, what could account

for the slower response times for dynamic stimuli? One possible explanation is that participants

simply required more time to process dynamic stimuli before making a decision. Static stimuli

presented identical information for the entire 2-second presentation time, and (while participants were

not able to respond until after the second stimulus had disappeared), they may well have already made

their decision before the presentation ended. In contrast, dynamic stimuli present their information

more gradually, and participants may have needed to view the entire video before obtaining enough

information to make their decision with certainty. This difference in the timing of information may

possibly lead to a significant delay in making same-or-different decisions about dynamic stimuli

compared to static stimuli.

Conclusions and implications

This experiment compared dynamic and static versions of face stimuli across two different

emotion processing tasks. Using a newly developed face stimulus set, it was found that dynamic

stimuli were processed more accurately than static faces for both the same-or-different discrimination

task and an emotion labelling task. These results showed that the lesser-utilised discrimination task

may in fact be a better measure of motion-sensitive emotional information than the typical emotion

labelling task. Given the demonstrated ecological validity of dynamic stimuli (e.g. Alves, 2013), there

is a strong motivation to use video-based stimuli in future emotion processing studies. The results of

the current experiment indicate a clear dynamic advantage in a newly-developed set of video-based

expressions that vary across emotional intensities. This suggests that these new stimuli are a more

effective and more ecologically-valid tool to assess emotion processing deficits compared to

traditional measures.

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Chapter 6: Are these tasks sensitive to schizophrenia-like traits?

Schizotypy in Healthy Controls and the Relation to Emotion-

processing Performance

“Schizotypy” is a personality construct that indicates the presence and degree of sub-clinical

behavioural traits similar to those in schizophrenia – such as hallucinations, unusual beliefs or

affective difficulties – in otherwise healthy individuals (Mason, Claridge, & Jackson, 1995). These

schizophrenia-like traits have been associated with emotion-processing deficits in previous literature.

In the previous chapter, a new set of dynamic tasks were developed and verified in order to overcome

some of the limitations of traditional face tasks. A subsequent aim was to determine whether the

newly-developed dynamic stimuli are in fact sensitive to schizophrenia-like traits in healthy controls.

The following chapter will review studies examining face and emotion-processing impairments

associated with schizotypal traits. It will then present an experiment investigating whether different

aspects of schizotypy are associated with performance on the tasks introduced in the previous chapter.

Schizotypy in the general population

Considerable controversy surrounds the question of whether psychosis is better described as a

categorical or dimensional phenomenon. Studies show that the symptoms of psychosis, such as

hallucinations (Lincoln & Keller, 2008), delusions (Rossler et al., 2007), and social anhedonia

(Hanssen, Bak, Bijl, Vollebergh, & van Os, 2005), are not limited to patients, but are distributed

continuously throughout the general population. Therefore, many argue that schizophrenia represents

an extreme position on a continuum (or multiple continua) of normal experience (Claridge, 1997;

DeRosse & Karlsgodt, 2015). Others, however, argue for a quasi-dimensional view in which the

schizophrenia spectrum disorders exist as a discrete category separate from healthy individuals

(Lawrie et al., 2010; Meehl, 1990). These authors argue that the existence of a continuous distribution

of symptoms in the general populations does not erase the qualitative boundaries between clinical

illness and healthy functioning (Lawrie et al., 2010). Regardless of which standpoint is adopted, the

study of subthreshold symptoms in clinically healthy samples permits researchers to explore the

relationships between symptoms, and to identify possible predictors of conversion to schizophrenia

(Phillips & Seidman, 2008). Furthermore, studies of healthy populations can eliminate common

confounds in clinical research such as the impacts of antipsychotic and other psychotropic medication,

illness chronicity, adversity and disease stigma.

The presence of sub-clinical features of psychosis in otherwise healthy individuals has been

variously termed ‘schizotypy’, ‘psychosis-proneness’, and ‘psychoticism’ (Mason & Claridge, 2006).

A distinction can be drawn between three main types of samples: those with genetic or familial risk

(such as unaffected relatives of persons with schizophrenia), those exhibiting subthreshold

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behavioural symptoms of psychosis (i.e.: schizotypal personality traits), and those that meet formal

diagnostic criteria for Schizotypal Personality Disorder (Kohler et al., 2014). Schizotypal personality

traits include a variety of characteristics such as magical ideation, superstitiousness, referential

thinking, paranoia, and social anxiety (Vollema & Hoijtink, 2000).

Schizotypy is typically measured using self-report questionnaires such as the Schizotypal

Personality Questionnaire (SPQ, Raine, 1991). On the basis of factor analytic studies, there is a

general consensus that schizotypy consists of three factors: positive schizotypy (cognitive-perceptual

features), negative schizotypy (affective and interpersonal difficulties), and cognitive disorganisation

(Venables & Rector, 2000; Vollema & Hoijtink, 2000). This mirrors the three-factor model of

schizophrenia (i.e.: positive, negative and disorganised symptoms; Mason & Claridge, 2006), and

lends further support to the idea that schizotypy and schizophrenia spectrum disorders exist on a

shared continuum. Furthermore, individuals that score highly on measures of schizotypy have a

heightened risk of developing a formal psychotic disorder (Kelleher & Cannon, 2011). For instance,

the Dunedin birth cohort study found that children who reported psychotic experiences at age 11 were

5-16 times more likely to be diagnosed with a schizophrenia spectrum disorder (Poulton et al., 2000).

Similarly, a large-scale longitudinal study by Hanssen and colleagues (2005) found that of the 2% of

people who reported a recent psychosis-like experience, 8% were diagnosed with a schizophrenia

spectrum disorder within the next two years. Such experiences alone cannot be considered a

prodromal stage for clinical psychosis, however, as the overwhelming majority of individuals do not

go on to meet diagnostic criteria (Hanssen et al., 2005). Rather, other factors must play a role in the

development of, or conversion to, clinical psychosis.

Emotion-processing impairments in schizotypy

Research suggests that impairment in emotion processing is associated with illness severity in

schizophrenia (Couture, Penn, & Roberts, 2006; Kohler et al., 2010). This raises the question, could

emotion processing impairments serve as an endophenotypic marker of future illness in healthy

individuals? A range of studies support the presence of an emotion processing deficit in individuals

who score highly on schizotypy measures, albeit to a lesser degree than in clinical populations

(Kohler et al., 2014). For instance, high schizotypy in adults has been associated with a reduced

ability to name or distinguish between facial expressions, particularly for “threat” expressions such as

anger and disgust (Brown & Cohen, 2010; van 't Wout, Aleman, Kessels, Laroi, & Kahn, 2004;

Williams et al., 2007). One large scale study (n>1500) found that scores on the Schizotypal

Personality Questionnaire-Brief predicted performance on a facial emotion labelling task, but not

performance on an identity discrimination task or sex labelling task (Germine & Hooker, 2011). This

negative relationship persisted even when the highest scorers (≥16 out of a maximum 22) were

excluded, indicating that the relationship was unlikely to be driven by a small group of individuals at

very high risk of psychosis. Overall, these findings suggest that trait schizotypy is related to facial

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emotion processing, and cannot be accounted for by more general face processing abilities. Studies of

adolescent populations have shown a similar pattern, with high psychosis-proneness scores found to

be associated with poorer facial emotion recognition, as well as difficulties recognising their own

emotions, independent of individual differences in intellectual functioning (Roddy et al., 2012; van

Rijn et al., 2011). However, it should be noted that some studies failed to find any relationship

between schizotypy and emotional processing in healthy adults (Jahshan & Sergi, 2007; Shean, Bell,

& Cameron, 2007; Toomey, Seidman, Lyons, Faraone, & Tsuang, 1999) or children (Thompson et al.,

2011).

Aside from behavioural performance on emotion recognition tasks, schizotypy has also been

associated with a range of other indicators of anomalous emotion processing. For instance, adults with

high total schizotypy scores are more likely to show an unusual fixation on the left side of a face

during initial saccades (Leonards & Mohr, 2009) and show reduced right-hemisphere performance

during a lateralised emotion-naming task (Mason & Claridge, 1999). Seiferth and colleagues (2008)

found that high schizotypy individuals showed atypical patterns of neural activation during an

emotion recognition task, including increased activation for neutral faces. These differences were

significant despite normal behavioural performance in these participants. Similarly, abnormal

neurophysiological responses have also been reported. Batty and colleagues (2014) found

significantly reduced N170 amplitude in high schizotypy individuals when viewing inverted faces,

suggesting abnormalities in the holistic processing of face stimuli. Taken together, these studies offer

broad support for a schizophrenia-like neural processing deficit in healthy individuals with

subthreshold symptoms of psychosis.

Researchers have also examined the relationship between emotion processing and specific

factors of schizotypy. Results are highly varied, however, possibly due to a lack of consistency in the

measures used to assess both emotion processing and schizotypy. Three studies using static emotion

tasks reported a significant relationship between emotion processing deficits and the positive

schizotypy factor (Kerns, 2005; Shean et al., 2007; van 't Wout et al., 2004), while two others found

associations with the negative schizotypy factor only (Abbott & Green, 2013; Williams et al., 2007).

Another study by Brown and Cohen (2010) found that both disorganised symptoms and lower overall

quality of life were associated with reduced accuracy for identifying neutral faces. At present, only

one study has examined individual factors on schizotypy using a dynamic (video-based) emotion

processing task. Abbott and Byrne (2013) found that only positive schizotypy was associated with

overall task performance, however, negative schizotypy was associated with the number of errors

made when recognising positive emotions.

It has been suggested that positive (cognitive-perceptual), negative (affective and

interpersonal) and disorganised factors of schizotypy may impact emotion processing in two different

ways. That is, positive symptoms may produce impairment through misattribution of emotion (i.e.: a

73

bias towards perceiving negative emotions) while negative and disorganised symptoms may produce

errors of omission (i.e.: a failure to recognise positive emotions) (Abbott & Byrne, 2013).

The Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE)

As alluded to above, schizotypy has been previously measured using a range of different self-

report questionnaires. A likely contributor to the inconsistency in past studies is that these

questionnaires vary considerable in content and scope. For instance, the SPQ (Raine, 1991) covers a

broad range of items and is modelled closely on the DSM symptoms of schizotypal personality

disorder. In contrast, other scales such as the Perceptual Aberration Scale (Chapman, Chapman, &

Raulin, 1978) focus only on specific aspects of schizotypy. One criticism of the SPQ is that it relies

heavily on the reporting of clinical symptoms, and may be less sensitive to detecting trait-based

features of schizotypy (Mason & Claridge, 2006). In order to create a measure intended specifically

for use in non-clinical populations, Mason and colleagues (1995) developed the Oxford-Liverpool

Inventory of Feelings and Experiences (O-LIFE). The items of this questionnaire were based on

extensive factor analysis of fifteen pre-existing schizotypy or psychosis-proneness scales (Claridge et

al., 1996). The O-LIFE comprises four scales: Unusual Experiences (perceptual and cognitive

features, i.e. the positive schizotypy factor), Cognitive Disorganisation (relating to attention,

concentration and decision-making), Introvertive Anhedonia (broadly relating to the negative

schizotypy factor), and a new fourth factor, Impulsive Nonconformity (including features such as

disinhibited behaviour, recklessness, and a desire to avoid a conforming lifestyle) (Mason & Claridge,

2006).

The O-LIFE has been widely used in clinical and non-clinical samples across a range of areas

including neuropsychological functioning (Avons, Nunn, Chan, & Armstrong, 2003; Rawlings &

Goldberg, 2001), latent inhibition (Schmidt-Hansen, Killcross, & Honey, 2009; Shrira & Tsakanikos,

2009), and emotion processing (Hoshi, Scoales, Mason, & Kamboj, 2011; Najt, Bayer, & Hausmann,

2012). Other benefits of the O-LIFE scale are that it has well established normative data (Mason &

Claridge, 2006; Mason et al., 1995), high internal consistency (α=.72-.89 for all scales; (Claridge,

1997; Mason et al., 1995), and good test-retest reliability (>.70 for all scales; Burch, Steel & Hemsley,

1998) . For these reasons, the O-LIFE was considered the most appropriate instrument for this study.

Experiment: Schizotypy and processing of dynamic versus static faces

An additional goal of this thesis was to examine whether schizotypy was associated with face-

processing performance in healthy controls using our dynamic emotion processing tasks. To

accomplish this, data from the 82 healthy controls who participated in the experiment described in

Chapter 5 was analysed in relation to their scores on the O-LIFE scale. It was predicted that both

74

positive and negative aspects of schizotypy would be negatively correlated with emotion processing

performance.

Method

Protocol and method for this study can be found in Chapter 5. Demographics for the 82

healthy controls are repeated below in Table 6.1. After finishing the computerised tasks, participants

completed a slightly modified version of the O-LIFE (See Appendix D). This version consists of the

same 147 items introduced by Mason and colleagues (1995), but rather than responding “yes” or “no”,

participants were instructed to rate each item on a Likert scale ranging from 1 (“Not at all”) to 6

(“Very much”). The purpose of this modification is to increase the sensitivity of the measure, as it

allows participants to partially endorse some of the more unconventional items that they would

otherwise respond “no” to in a binary response format. For example, the item “Do you believe in

telepathy?” has a low rate of positive endorsements. This response format has been used successfully

in previous studies (Corcoran, Cropper, Wong, Rutherford, & Groot, 2016; Cropper, Groot, Corcoran,

Bruno, & Johnston, 2017; Cropper, Johnston, & Groot, 2015). Additionally, these Likert scores can be

easily converted to typical scores to be compared to Mason and colleagues’ (1995, 2006) normative

data.

Table 6.1. Healthy controls’ (n=82) demographics and mean raw scores for the O-LIFE scales.

M SD

Age (years) 19.60 3.54 (Range 17-38)

Males/Females (% male) 18/64 (22%) -

Education (years) 12.23 0.76 (9 months)

Estimated FSIQ* 112.88 5.43

Digit span - forward 10.02 2.72

Digit span - backward 11.65 2.92

OLIFE scales

Unusual Experiences 77.21 26.20

Cognitive Disorganisation 79.91 18.11

Introvertive Anhedonia 92.54 12.40

Impulsive Nonconformity 66.89 10.42

Total 316.55 52.50

*28 participants were excluded from this analysis due to poor English (All English as Second Language),

leaving n=54.

75

Results

Correlations with demographics

Mean scores for the O-LIFE and participant demographics are shown in Table 6.1. Responses

for two of the four O-LIFE scales, Unusual Experiences and Introvertive Anhedonia, were not

normally distributed (Shapiro-Wilks test p<.05) therefore Spearman rank correlations were conducted

in these instances. Bootstrapping was used to calculate bias corrected and accelerated (BCa)

confidence intervals using 1000 resamples (DiCiccio & Efron, 1996).

Spearman’s rank correlations revealed that none of the O-LIFE scales correlated significantly

with Age (rs=.-.17 to .02) or Years of Education (rs=-.04 to .10). However, Estimated IQ scores were

negatively correlated with both Unusual Experiences (rs=-.28, 95% CI[-.50, -.04], p<.05) and

Cognitive Disorganisation (rs=-.29, 95% CI[-.54, -.04], p<.05). As FSIQ was not significantly

associated with task performance (see Results in Chapter 5), however, this was unlikely to affect the

interpretation of these results.

Table 6.2. Spearman rank correlations between face-processing performance and O-LIFE scores for

healthy controls (n=82).

Unusual

Experiences

Cognitive

Disorganisation

Introvertive

Anhedonia

Impulsive

Nonconformity

rs

[95% CI]

rs

[95% CI]

rs

[95% CI]

rs

[95% CI]

Static Emotion

Discrimination

.-.05

[-.29, .18]

.10

[-.14, .32]

-.16

[-.38, .10]

.12

[-.12, .36]

Dynamic Emotion

Discrimination

-.02

[-.26, .21]

.04

[-.21, .27]

.03

[-.22, .30]

.14

[-.12 .39]

Static Emotion

Labelling

-.21

[-.42, .03]

-.20

[-.43,.04]

-.06

[-.31, .20]

-.18

[-.39, .05]

Dynamic Emotion

Labelling

-.31*

[-.49, -.11]

-.08

[-.30, .15]

-.06

[-.30, .18]

-.17

[-.40, .08]

Note: * indicates a significant correlation, where the 95% confidence interval for rs does not include 0.

Correlations with task performance

Pearson correlations revealed significant negative correlations between total schizotypy

scores on the O-LIFE and performance on the Emotion Labelling task, both for static (r=,-.28, 95%

76

CI[-.45, -.08]) and dynamic conditions (r=-.22, 95% CI[-.39, -.05]). Correlations with Emotion

Discrimination did not approach significance (rs =-.01 to .05).

Table 6.2 shows correlations between O-LIFE scales and performance across each of the four

task conditions. The only significant result was a negative correlation between Unusual Experiences

and Emotion Labelling for the dynamic condition only. No other associations approached

significance.

Discussion

The aim of this experiment was to determine whether certain aspects of schizotypy would be

associated with emotion processing ability in a sample of healthy individuals. It was found that total

schizotypy scores were associated with performance on Emotion Labelling, but not Emotion

Discrimination. Contrary to expectations, only the positive schizotypy subscale, Unusual Experiences,

was correlated with task performance, and only for one condition of the four: dynamic Emotion

Labelling.

The finding that individuals with higher total schizotypy scores performed more poorly on a

test of emotion perception is consistent with several past studies using a range of schizotypy scales

(Brown & Cohen, 2010; Germine & Hooker, 2011; van 't Wout et al., 2004; Williams et al., 2007).

Interestingly, this correlation was found for an Emotion Labelling task only, and was not reflected in

an Emotion Discrimination task. This discrepancy may be due to differences in task demand between

the Labelling and Discrimination paradigms. For instance, one previous study also reported

significant correlations between schizotypy and performance on a labelling task, but not a

discrimination task (Williams et al., 2007), while two prominent studies reporting significant findings

used labelling tasks only (Brown & Cohen, 2010; van 't Wout et al., 2004). Only one study could be

found which reported significant correlations with schizotypy using an emotion discrimination

paradigm (Germine & Hooker, 2010), although this may be due to the much larger sample size used

in their study (n>1500). Nevertheless, our data indicates that individuals high in schizotypy are more

likely to show impairments in the ability to correctly identify emotional expressions, but are less likely

to show an impaired ability to distinguish between two different expressions.

The current study showed that the positive schizotypy subscale (the Unusual Experiences

scale from the O-LIFE) was negatively correlated with performance on the Emotion Labelling task,

but only for dynamic faces, not static faces. This finding is broadly consistent with previous research

indicating associations between the positive schizotypy factor and emotion perception performance

(Abbott & Byrne, 2013; Kerns, 2005; Shean et al., 2007; van 't Wout et al., 2004), however the

majority of these studies used static stimuli. It is unclear why a difference would be found between

the static and dynamic conditions. A possible explanation is that, in line with the literature discussed

77

in the previous chapter, dynamic faces are simply a ‘purer’ measure of emotion processing ability, and

therefore were more sensitive to true emotion processing deficits. Contrary to previous research, the

current study found no evidence of a correlation between negative schizotypy scores (i.e.: the

Introvertive Anhedonia and Impulsive Noncomformity scales from the O-LIFE) and emotion

processing performance (Abbott & Green, 2013; Williams et al., 2007). However, this finding is in

line with other studies which similarly reported no correlation with either negative or cognitive

disorganisation factors of schizotypy (Kerns, 2005; Shean et al., 2007; van 't Wout et al., 2004).

In conclusion, the current study found a negative correlation between total schizotypy scores

and the ability to correctly label emotional expressions. In particular, the positive dimension of

schizotypy was associated with poorer performance for dynamic faces. However, schizotypy was not

associated with the ability to discriminate between different emotions (i.e.: indicate whether two

emotions are same or different). These findings are broadly in line with previous research using static

stimuli. Furthermore, it reaffirms the idea that individuals with high trait schizotypy tend to show

deficits in emotion processing similar to those shown in schizophrenia. These deficits may be more

closely associated with the positive schizotypy factor – analogous to the positive symptoms of

schizophrenia – rather than generalised cognitive symptoms, age, or intellectual ability.

78

Chapter 7: Method for Inpatient Study

The previous chapters outlined the development of a new, more ecologically-valid set of

video-based stimuli for examining face processing abilities in schizophrenia. They then presented a

study demonstrating their effectiveness and sensitivity to schizophrenia-like traits in healthy controls.

The current chapter will now present the Method section for a large inpatient study designed to

investigate face-processing in schizophrenia using these new stimuli. General results, including the

impact of patient demographics on task performance, will also be presented and discussed here.

Method

Participants

Eighty-six psychiatric inpatients were recruited from an acute psychiatry inpatient unit in

Melbourne, Australia. Participants were recruited over an 18-month period to ensure a wide range of

diagnoses were represented. Final diagnoses were obtained from separation reports provided by each

patient’s treating psychiatrist. Based on this diagnosis, patients were categorised into four groups: 36

schizophrenia-spectrum (including schizophrenia, schizoaffective disorder, and first episode

psychosis), 15 bipolar disorder (bipolar-affective disorder with a history of psychotic symptoms), 18

non-psychotic disorders (including bipolar II without psychotic symptoms, major depression,

generalised anxiety disorder, borderline personality disorder [PD], and situational crisis – none of

whom had ever previously experienced symptoms of psychosis) and 17 non-schizophrenia psychotic

disorders (including drug induced-psychosis, major depression with psychosis, borderline PD with

hallucinations, and schizotypal PD). A breakdown of each group by diagnosis is shown in Table 7.1.

Twenty non-clinical controls were recruited via online advertising. All were free from

neurological injury, psychiatric illness and substance use disorder by self-report, and were not taking

psychoactive medication.

Demographics

Prior to testing, each participant completed the Edinburgh Handedness Inventory (Oldfield,

1971), the National Adult Reading Test (NART; Crawford et al., 1992) , and a demographic

questionnaire with questions regarding age, gender, years of education, and additionally for patients,

current medication and illness duration (See Appendices C, E-G). Patients were also assessed using

the Positive and Negative Syndrome Scale (PANSS; Kay, Fiszbein, & Opler, 1987; see Appendix H) .

Healthy controls also completed the Oxford-Liverpool Inventory of Feelings and Experiences (O-

LIFE) as a measure of schizotypy (Mason & Claridge, 2006).

79

Table 7.1. Breakdown of patient diagnoses by group.

Schizophrenia

spectrum disorders

n=36

Bipolar affective

disorder

n=15

Non-schizophrenia

psychosis

n=17

Non-psychotic

disorders

n=18

Schizophrenia

n=20 Bipolar Type I, with

previous or current

psychotic symptoms

n=15

Drug-induced psychosis

n=9

Major depression

n=12

Schizoaffective

n=9

Depression with

psychotic features

n=6

Situational crisis

n=4

First-episode psychosis

n=7

Schizotypal personality

disorder n=1

Dysthymia

n=1

Atypical psychosis

n=1

Bipolar Type II

n=1

(Comorbid borderline

personality disorder n=4)

Tasks

Emotion Discrimination Task

Stimuli used were 2-second videos of faces changing from neutral expressions to expressions

of either disgust or fear. Development of these stimuli is explained in detail in Chapter 5. Original

videos consisted of five unique individuals (3 male, 2 female) each showing one expression of disgust

and one of fear. These ten videos were morphed to create five levels of expression intensity (33%,

50%, 67%, 83%, and 100%), totalling 50 stimuli.

For each trial one expression was shown, then followed by a second face of a different

individual showing either the same or different expression, then followed by a blank screen with the

words “Same or different?” shown until response (Figure 7.1A). Pairs of expressions were always

shown at the same intensity level. One hundred trials were shown in total.

Emotion Labelling Task

Stimuli used were the same as those in Emotion Discrimination, with an additional ten videos

to make a total of 60 animated stimuli. Each expression was shown for 2 seconds, then followed by a

blank screen with the words “Fear or disgust?” shown until response (Figure 7.1B). Sixty trials were

shown in total.

80

Identity Discrimination Task

Stimuli used were one-second videos of faces showing non-emotive facial movements, such

as opening the mouth, raising an eyebrow, or poking out the tongue. The development of these stimuli

was described in Chapter 5. Stimuli subtended approximately 5.72 x 4.58° of visual angle at a

viewing distance of approximately 50 cm. In order to vary the similarity between pairs of models,

video of different individuals (of the same sex) were “morphed” together to create new faces. Six

pairs of unique individuals (3 male, 3 female) were used. Each pair was morphed to create six new

animations ranging from one identity to the other at 20% increments, totalling 36 stimuli.

For each trial, a ‘pure’ face (either 100% person 1 or 100% person 2) was shown, followed by

a second face from the same set that was either 0%, 20%, 40%, 60%, 80%, or 100% different, then

followed by a blank screen with the words “Same or different?” shown until response (See Figure

7C). One hundred and twenty trials were shown in total.

Sex Labelling Task

Stimuli used were identical to Identity Discrimination above, with the exception that each of

the six identities was morphed with an opposite-sex identity instead of a same-sex identity. Six sets of

6 face animations were created, ranging from male to female. Half of the trials were “male” (i.e.:

60%, 80%, or 100% male) and half were “female” (0%, 20%, and 40% male). For each trial, a single

face was shown for 1 second, followed by a blank screen with the words “Male or female?” shown

until response (Figure 7D). Each of the 36 faces was shown twice, totalling 72 trials.

Car Discrimination Task

Stimuli used were 1-second videos of 3D car models rotating from a side view to a 45-degree

view (see Chapter 5 for more information on stimulus development). Twelve unique cars were

animated and paired with similar looking models. For each trial, one car video was shown, then a

second car video was shown, followed by a blank screen with the words “Same or different?” shown

until response (Figure 7E). One hundred and twenty trials were shown in total.

81

Figure 7. Example trials for each of the five tasks: Emotion Discrimination, Emotion Labelling,

Identity Discrimination, Sex Labelling, and Car Discrimination. Correct responses are A: different, B:

disgust, C: different, D: male, E: different.

82

General procedure

Participants completed all five computerised tasks in one of four counterbalanced orders.

Prior to completing each of these five tasks, participants were shown two practice trials with

feedback. If the participant did not answer the two trials correctly, instructions were repeated and the

incorrect trial shown again until the participant understood the task.

For the three Discrimination tasks, participants were instructed to say whether each stimulus

(either face, car or emotion, respectively) was the same or different (see Figures 7A, 7C, & 7E). Half

of these trials were ‘same’ and half were ‘different’. For the two Labelling tasks, participants were

instructed to state whether each face more closely resembled “male” or “female” (Sex) or “fear” or

“disgust” (Emotion), respectively (Figures 7B & 7D). The experimenter logged all verbal responses

using a keyboard, in order to reduce any impact of impulsive or impaired motor response mapping.

Testing took approximately two hours to complete, and participants were permitted to take as

many breaks as desired. Computerised tasks were completed on a laptop computer (60 Hz, 16-inch

screen size, 1280 x 1024 resolution) at a comfortable viewing distance in a quiet distraction-free

environment.

Results were analysed using the software package SPSS version 20. To control for response

bias, percentage correct on each task was converted to d’ scores using formulae recommended by

MacMillan and Creelman (1991). For Sex Labelling and Emotion Labelling (yes/no style) tasks, this

was calculated as: d’ = z(Hit rate) – z(False alarms). For the three Discrimination tasks this value was

then converted to a modified d’ using table A5.3 (Macmillan & Creelman, 1991). To eliminate

problems raised by dividing by zero, Hit Rate and False Alarms were adjusted (Corwin, 1994). A

higher d’ value represents better performance. A measure of response bias, c, was also calculated

using the formula: c = -0.5 [z(Hit rate)+z(False alarms)] (Macmillan & Creelman, 1991). A value of 0

indicates no bias, while a positive or negative value of c indicates increasing tendency to favour one

response option over the other.

Results

The main analyses of this inpatient study will be presented in Chapters 8, 10 and 11. First,

however, this section will compare participant groups on clinico-demographic factors such as age,

sex, and estimated intellectual ability, and illness factors such as duration of illness. This is to ensure

that differences in task performance between diagnostic groups cannot simply be attributed to

differences in these factors.

83

Demographics

Demographic information and symptom ratings are shown in Table 7.2. Pearson’s chi-square

test revealed that the gender makeup of groups did not differ significantly as a function of diagnosis,

X2(4) = 6.18, p=.186. One-way ANOVAs were performed with Group as a between-subjects factor

and Age, Years of Education, and estimated FSIQ as within-subjects factors. A significant effect was

found for Years of Education, F(4,101)=2.50, p=.048. Post-hoc t-tests (Bonferroni corrected) revealed

a significant difference of 2.11 years between the Control and Schizophrenia group (p=.02). FSIQ

estimates were also found to differ between groups, F(4,93)=2.52, p=.047. Post-hoc t-tests

(Bonferroni corrected) revealed a significant difference of 8.38 points between the Control and

Schizophrenia spectrum groups (p=.03). Age did not differ significantly between groups,

F(4,101)=.75, p=.56.

One-way ANOVAs conducted with the four inpatient groups only revealed no significant

group differences in mean duration of illness, F(3,82)=1.42, p=.25, mean daily dose of antipsychotics,

F(3,61)=1.68, p=.18, or daily benzodiazepine dose, F(2,13)=.63, p=.55. Medication status for each

group is shown in Table 7.2.

PANSS subscales

One-way ANOVAs were run with group as IV (excluding healthy controls), and Positive,

Negative, and General Psychopathology scores as DVs. A significant main effect was found for

Positive Symptoms, F(3,82)=18.76, p<.001. Bonferroni corrected post hoc tests revealed, not

surprisingly, that the Non-psychosis group had significantly lower Positive symptom scores than all

other groups (p=.002 to <.001). The Other group trended towards having significantly lower Positive

symptom scores compared with the bipolar group (p=.055). No other group differences approached

significance.

Correlations between performance and demographic factors

Pearson correlations were conducted to determine if age, years of education, or FSIQ

estimates predicted performance on any of the tasks. It was found that increasing age correlated with

worsening d’ for Emotion Labelling (r=-.197, p=.04), but not other tasks. FSIQ estimates produced no

significant correlations. Years of education correlated positively with performance on Emotion

Discrimination (r=.260, p=.007) only. To determine if years of education could account for

differences in performance on the Emotion Discrimination task, the analyses were re-run excluding

participants with fewer than 11 years of education (eliminating 13 patients in the schizophrenia

spectrum group, 5 Bipolar disorder, 6 Other and 6 Non-psychosis). One-way ANOVA revealed a

84

significant mean effect of group F(4,70), p<.001. Bonferroni-corrected post hoc tests showed that the

schizophrenia spectrum group performed significantly lower than the control (p<.001), Non-psychosis

(p=.001) and Other groups (p=.02). The control group also outperformed the Bipolar group (p=.01).

Thus, limited years of education was unlikely to account for the group differences shown on these

tasks.

Table 7.2. Mean participant demographics and questionnaire scores by group (SD in parentheses).

Schizophrenia

spectrum

n = 36

Bipolar

disorder

n = 15

Other

psychotic

disorders

n = 17

Non-psychotic

disorders

n = 18

Healthy

controls

n = 20

Age (years)

34.44 (9.44)

range 19-53

36.60 (14.80)

range 19-65

30.65 (7.61)

range 18-43

36.17 (13.57)

range 19-59

34.05 (10.72)

range 18-56

Males/females

(% male)

23/13

(65%)

9/6

(60%)

12/5

(71%)

6/12

(33%)

12/8

(60%)

Education

(years) 10.89 (2.55)* 11.40 (1.76) 11.65 (3.28) 11.83 (2.48) 13.00 (1.59)*

Premorbid IQa 100.33

(10.93)* 103.62 (8.83) 102.65 (9.94) 105.69 (10.73) 108.70 (6.57)*

Illness duration

(years) 9.12 (7.56) 8.87 (11.07) 4.15 (6.31) 8.45 (9.97) -

Antipsychotic

daily doseb

367.2 mg

(276.2)

358.9 mg

(52.6)

225.9 mg

(115.6)

155.6 mg

(212.4) -

Benzodiazepine

daily dosec

35.00 mg

(26.30)

44.00mg

(35.25)

-

23.13mg

(17.72) -

PANSS

Positive scale 17.28 (5.45) 18.47 (4.17) 14.24 (4.35) 8.56 (1.76)d

Negative scale 11.53 (3.72) 9.13 (1.89) 9.94 (3.46) 12.28 (4.85)

General

psychopathology 31.53 (6.47) 31.60 (4.98) 31.53 (7.05) 31.67 (4.51)

*Bonferroni-corrected t-tests revealed a significant group difference at p<.05; aDue to dyslexia, illiteracy, or poor English proficiency, IQ estimates were not available for four patients in the

schizophrenia spectrum group, two in the other group, and two in the non-psychosis group. bChlorpromazine equivalent dose. cDiazepam equivalent dose. dPositive symptom scores for the non-psychosis group were significantly lower than all other groups (p

values=.002 to <.001).

85

Table 7.3. Medication status for inpatient groups.

Neither

medication

Antipsychotics

only

Benzodiazepines

only

Both Antipsychotics

and Benzodiazepines

Schizophrenia spectrum 1 28 0 7

Bipolar disorder 0 10 0 5

Other psychotic disorders 5 12 0 0

Non-psychotic disorders 12 2 3 1

Correlations between performance and illness factors

Pearson correlations revealed that illness duration correlated negatively with task

performance on Emotion Discrimination (r=-.337, p=.002), Emotion Labelling (r=-.224, p=.04) and

Car Discrimination (r=-.298, p=.005), and trended negatively with Identity Discrimination (r=-.204,

p=.06). Hierarchical regression analyses revealed that, after controlling for Age, Illness Duration

continued to significantly predict performance on Emotion Discrimination (change in R2=.09,

F(1,83)=8.37, p=.005) and Car Discrimination (change in R2=.13, F(1,83)=12.27, p=.001), but no

longer predicted performance on Emotion Labelling (change in R2=.01, F(1,83)=1.27, p=.26). This

suggests that patients with a longer illness duration performed more poorly on all tasks except for Sex

Labelling, regardless of group, however this cannot account for the group differences observed.

Benzodiazepine daily dose (n=16) correlated negatively with performance on Emotion

Labelling (r=-.519, p=.04) and Car Discrimination (r=-.535, p=.033) and trended towards significance

for Identity Discrimination (r=-.458, p=.07). Mean antipsychotic daily dose (n=84) produced no

significant correlations.

To determine if benzodiazepine use could account for the group differences in task

performance, a MANOVA was run excluding the 17 inpatients who had taken benzodiazepines. As

before (see Chapter 10 for full task analyses), a significant main effect was found for group, F(4,83) =

2.50, p<.001 (Pillai’s Trace) and univariate tests still revealed significant, or trending towards

significant effects of group for four of the five tasks: Identity Discrimination (F(4,83) = 2.07, p=.09),

Emotion Discrimination (F(4,83) = 11.22, p<.001), Emotion Labelling (F(4,83) = 7.01, p<.001), and

Car Discrimination (F(4,83) = 3.96, p=.005). This suggests that although benzodiazepine use was

related to poorer performance on some tasks, it cannot account for the group differences reported in

this study.

Correlations with PANSS symptom scores will be presented with further discussion in

Chapter 11.

86

Discussion

The purpose of this section was to explore the impact of demographic and illness factors on

task performance, and to investigate whether these factors could possibly account for differences

between diagnostic groups. Results indicated that the five participant groups did not differ

significantly in age, and the four patient groups did not differ significantly in duration of illness or

medication dose. Despite attempts to match groups on all factors, however, the schizophrenia group

completed significantly fewer years of education, and achieved lower IQ estimates compared to the

healthy control group. No other group differences approached significance.

This discrepancy is not unusual in studies comparing patients with schizophrenia and

unaffected controls (e.g.: Hargreaves et al., 2016; Mothersill et al., 2014; Bediou, Franck et al., 2005;

Bediou et al., 2007), as lower levels of academic achievement and lower performance on measures of

premorbid IQ are both common features of schizophrenia (Morgan et al., 2012; Woodberry, Giuliano,

& Seidman, 2008). In fact, it has been argued that attempts to IQ-match healthy controls to patients

may be creating a false equivalence, as lower academic achievement due to illness factors may cause

IQ tests to underestimate true premorbid IQ scores (Meehl, 1970; Walker & Standen, 2011). To avoid

this, some authors choose to match groups on parental years of education instead, as this is believed

to be a reliable alternative indicator of premorbid IQ (Silverstein et al., 2012). Again, however, this

approach is less effective if parents of patients are also affected by psychiatric illness (Keefe, Eesley,

& Poe, 2005).

Regardless of the matching techniques used, the issue remains that significant group

differences in education and premorbid IQ may undermine or mimic true group differences on

performance-based tasks. Fortunately, the results of this study suggest that premorbid IQ was not

significantly associated with performance on any of the dynamic tasks. Although years of education

was significantly associated with performance on one task (Emotion Discrimination), further analyses

revealed that group differences remained unchanged when patients with lower education levels were

excluded. Therefore, it is highly unlikely that differences in education level could be driving

differences in task performance between groups.

In conclusion, although the schizophrenia group completed fewer years of education and

received lower premorbid IQ scores compared to the healthy control group, these differences are

unable to fully account for the group differences in performance presented in the following chapters.

Furthermore, there were no significant differences in age, education, premorbid IQ, illness duration,

or medication dosage between the four inpatient groups.

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Chapter 8: So what kind of deficit is it, really? Characterising

Face Processing Deficits in Inpatients with Schizophrenia

As reviewed in Chapter 2, the ability to recognise and interpret facial expressions has been

reliably shown to be impaired in schizophrenia. These impairments have been demonstrated across a

wide range of paradigms, and likely contribute to poorer functional outcome in this disorder.

However, it is unclear whether this deficit is truly specific to emotion-processing, or if it can be better

accounted for by impairments in other domains. For instance, deficits in non-emotional face

processing (identity processing) may also be impaired in schizophrenia, suggesting that the “emotion-

processing” deficit may be better represented as a general deficit in perceiving faces. Alternatively,

deficits on emotion-processing tasks may be due to more basic impairments in visual attention. This

chapter will address the first aim of this thesis: Can we better characterise face processing deficits in

schizophrenia? In other words, can impairments in emotion-processing be explained by more general

deficits in non-emotional face processing or non-face processing? This question will be addressed by

comparing performance in schizophrenia inpatients and healthy controls on emotion-processing,

identity-processing, and non-face processing (car discrimination) using the dynamic tasks introduced

in previous chapters.

Experiment: Main inpatient study

The method for Experiment 1 is described in detail in Chapter 7. This chapter will focus on

results comparing the performance of patients with schizophrenia (n=36) to healthy controls (n=20).

Results including all other patient groups will be presented in Chapters 10 and 11.

Results

Demographics

Demographic information is repeated in Table 8. Detailed analyses regarding demographics

can be found in the previous chapter.

Group differences in task performance

To compare performance of healthy controls and patients with schizophrenia, d’ scores for the five

tasks were compared in a 2 x 5 multivariate ANOVA. As groups showed similar patterns of

performance across morphing levels in the previous analyses, scores were collapsed across morphing

levels for all tasks. A significant multivariate effect of group was found, F(5,48) = 12.01, p<.001,

ŋp2=.56 (Pillai’s Trace). Performance (d’) for patients and healthy controls are shown in Figure 8.1.

Univariate tests revealed significant effects of group for all tasks except Sex Labelling, F(1,52) = .42,

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p=.52. Healthy controls outperformed patients with schizophrenia on Emotion discrimination, F(1,52)

= 39.46, p<.001, ŋp2=.43, Emotion labelling, F(1,52) = 39.23, p<.001, ŋp

2=.43, Identity discrimination,

F(1,52) = 7.26, p=.009, ŋp2=.12, and Non-face discrimination, F(1,52) = 12.37, p=.001, ŋp

2=.19.

Table 8. Mean participant demographics by group (SD in parentheses).

Schizophrenia spectrum

n = 36

Healthy controls

n = 20

Age (years) 34.44 (9.44)

range 19-53

34.05 (10.72)

range 18-56

Males/females

(% male)

23/13

(65%)

12/8

(60%)

Education (years)* 10.89 (2.55) 13.00 (1.59)

Premorbid IQa* 96.44 (10.55) 104.65 (9.32)

Illness duration (years) 9.12 (7.56) -

Antipsychotic daily dose

(Chlorpromazine equivalent) 367.2 mg (276.2) -

Benzodiazepine daily dose

(Diazepam equivalent) 35.00 mg (26.30) -

*Significant group difference at p<.05; aDue to dyslexia, illiteracy, or poor English proficiency, IQ estimates

were not available for two patients in the schizophrenia spectrum group.

Figure 8.1. Performance (d’) of healthy controls and patients with schizophrenia across the five

dynamic tasks. Error bars indicate 95% confidence intervals. *p<.01; ** p<.001.

Task performance in healthy controls

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Figure 8.2 shows the mean performance of healthy controls for the five tasks. To determine

whether difficulty varied across the five dynamic tasks in healthy controls, a repeated-measures

ANOVA was conducted with accuracy (d’) as the dependent variable and task as a within-subjects

factor. Mauchly’s test showed that the assumption of sphericity was not violated, X2(9)=15.64, p=.08.

A significant main effect of task was found, F(4,76)=7.50, p<.001, ŋp2=.28, indicating that despite

attempts to match task demands, difficulty was not uniform across all tasks. Bonferroni-corrected

post-hoc tests revealed that performance on Identity discrimination was significantly higher than Sex

labelling (p<.001, mean difference =.92), and Emotion discrimination (p=.004, mean difference=.79).

No other comparisons were significant (p values=.07-.99). This suggests that the Identity recognition

task was slightly less difficult compared to the Sex labelling and Emotion discrimination tasks,

however performance across all other tasks was of a comparable level.

Task performance in the schizophrenia group

Mean performance across tasks for the schizophrenia spectrum group are shown in Figure 8.3.

As for healthy controls, a repeated-measures ANOVA was conducted with accuracy (d’) as the

dependent variable and task as a within-subjects factor. According to Mauchly’s test, the assumption

of sphericity was not violated, X2(9)=11.29, p=.26. A main effect of task was found, F(4,132)=53.29,

p<.001, ŋp2=.62. Bonferroni-corrected post-hoc comparisons revealed that performance on the two

emotion tasks was not significantly different from one another (p>.99) but were both significantly

lower than the three remaining tasks (p values>.001, mean differences = .76 – 1.58). The identity

discrimination task was significantly higher than all other tasks (p values<.001, mean differences =

.46 – 1.58). Finally, the car discrimination and sex labelling tasks were not significantly different from

one another (p>.99).

Response bias – c

Mean values of c for healthy controls and patients across tasks are shown in Figure 8.4. Mean

values of c ranged from .04 to .81 across groups and tasks. Independent t-tests revealed no significant

differences between healthy controls and patients for any task (p values=.30-.76), suggesting that

response bias did not differ between groups, and are therefore unlikely to account for differences in

task performance in these groups.

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Figure 8.2. Performance (d’) of healthy controls across the five dynamic tasks. Error bars indicate

95% confidence intervals. Dots indicate the performance of individual participants. *p<.01, **p<.001.

Figure 8.3. Performance (d’) of patient with schizophrenia across the five dynamic tasks. Error bars

indicate 95% confidence intervals. Dots indicate the performance of individual participants. *p<.001.

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Figure 8.4. Mean response bias as evidenced by c rates across the five tasks for schizophrenia patients

versus controls. Error bars indicate 95% confidence intervals. For the three Discrimination tasks,

negative values indicate a tendency to say ‘same’. For Emotion Labelling, a positive value indicates a

tendency to say ‘fear’ while a negative value indicates a tendency to say ‘disgust’. For Sex Labelling,

positive values indicate a tendency to say ‘male’.

Impact of morphing: Varying emotional intensity

To examine the impact of varying emotional intensity on the ability to recognise emotions,

and whether this impact differed between groups, a separate 2 x 5 mixed-model ANOVA was

conducted on raw accuracy for each emotion task. Group (control or schizophrenia) was included as a

between-subjects factor and intensity level (33%, 50%, 67%, 83% and 100%) as a within-subjects

factor. Mauchly’s test showed that the assumption of sphericity was violated for both tasks, therefore

the Greenhouse-Geisser correction was used, X2(9)=131.66, p<.001; X2(9)=19.16, p=.02.

Emotion discrimination task

Raw accuracy rates across intensity levels on the emotion discrimination task are shown in

Figure 8.5.A. A significant main effect was found for group, F(1,53)=41.31, p<.001, ŋp2=.44, but not

emotional intensity, F(1.68, 89.40)=2.79, p=.08. The group by intensity interaction did not approach

significance either, F(1.69, 89.40)=.22, p=.93. Bonferroni-corrected post-hoc comparisons revealed

that the control group outperformed the schizophrenia group at every intensity level (p values=.03-

.005, mean differences = 12.9-18.5%).

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Emotion labelling task

Unlike emotion discrimination, the labelling task permits comparison between different

emotions. To determine whether the valence of emotion affected performance on this task, a 2 x 2 x 5

mixed-model ANOVA was conducted including emotion (fear or disgust) as an additional within-

subjects variable. As no significant main effect (p=.87), or interactions involving this variable (p

values =.26-.98) were found, all subsequent analyses were collapsed across fear and disgust trials.

Raw accuracy rates for emotion labelling are shown in Figure 8.5.B. A 2 x 5 mixed-model

ANOVA was run comparing group and intensity level. The main effects of group, F(1,54)=26.07,

p<.001, ŋp2=.33, and intensity of emotion, F(3.37,182.20)=7.13, p<.001, ŋp

2=.12, were both

significant, but not the interaction, F(3.37, 182.20)=.08, p=.98. Unlike the emotion discrimination

task, accuracy for naming emotions increased somewhat with increasing intensity. Bonferroni-

corrected post-hoc comparisons showed that accuracy was significantly lower for 33% intensity faces

compared to both 67% (p=.01, mean difference =9.3%) and 83% intensity faces (p=.001, mean

difference = 10.6%). Additionally, 50% intensity faces were less accurate than 83% faces (p=.04,

mean difference = 6.4%). No other comparisons approached significance. Like the emotion

discrimination task, post-hoc comparisons showed that the control group outperformed the

schizophrenia group at every level of intensity (p values<.001, mean differences = 16.7-19.1%).

Figure 8.5. Mean accuracy performance for the schizophrenia spectrum (SZ) and control groups for

(A) emotion discrimination and (B) emotion labelling. Emotional intensity is presented on the y axis,

where 100% indicates an unedited expression and 50% indicates an expression morphed 50% with a

neutral expression. Error bars indicate 95% confidence intervals around the mean. *Significant group

difference at p<.05; **p<.001.

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Impact of morphing: Varying facial identity

To determine whether varying facial identity (including gender) affected performance on the

non-emotional face tasks, a separate 2 x 6 mixed-model ANOVA was conducted on raw accuracy for

each task. Group (control or schizophrenia) was included as a between-subjects factor and morphing

level (0%, 20%, 40%, 60%, 80% and 100%) as a within-subjects factor. As Mauchly’s test of

sphericity was violated for both tasks, Greenhouse-Geisser corrected values were reported,

X2(14)=56.88, p>.001; X2(14)=190.68, p>.001.

Identity discrimination task

Raw accuracy rates for patients and healthy controls on the identity discrimination task are

shown in Figure 8.6A. Significant main effects were found for morphing level, F(3.58,

193.13)=332.13, p<.001, ŋp2=.86, and group, F(1,54)=5.60, p=.02, ŋp

2=.09. The interaction between

morphing level and group was also significant, F(3.58, 193.13)=4.98, p=.001, ŋp2=.08. Pairwise

comparisons indicated that accuracy for distinguishing between faces increased as facial similarity

increased. Bonferroni-corrected comparisons showed that nearly all morphing levels were

significantly different from one another (p values>.001, mean differences = 11.0-85.4%), with the

exceptions that the 80% and 100% conditions did not differ from each other (p<.001), nor did they

differ from the 0% condition (p values=.09-.30). Both groups performed above chance when faces

were at least 60% different, and at or below chance at 40% different. Bonferroni-corrected post-hoc

independent comparisons showed that the healthy controls outperformed the schizophrenia group

when faces were 60% different (p=.02, mean difference = 14.4%) and 100% different (p=.006, mean

difference = 13.6%). There were no significant differences between groups on any other conditions (p

values=.07-.99).

Sex labelling task

Raw accuracy for sex labelling across different morphing levels are shown in Figure 8.6B. A

repeated-measures ANOVA revealed a significant main effect for morphing level, F(1.71,

90.70)=40.72, p<.001, ŋp2=.43, but not group, F(1, 53)=.03, p=.86. The interaction did not approach

significance, p=.59. Unexpectedly, accuracy was reliably higher for male faces than female faces (see

Figure 8.6B). Pairwise comparisons revealed that accuracy for 60% male faces was significantly

lower than 80% male faces (p<.001, mean difference = 16.4%), which was in turn lower than 100%

male faces (p=.04, mean difference = 3.3%). In contrast, accuracy for 60% female faces was

significantly lower than 80% female faces (p<.001, mean difference = 15.8%) there was no difference

between 80% and 100% female faces (p=.20). These results suggest that, as anticipated, faces that

were a morphed mix of male and female faces were correctly identified less frequently than un-

morphed faces (100% male or female). However, there was no significant difference in accuracy

between healthy controls and patients with schizophrenia at any level of morphing.

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Figure 8.6. Mean accuracy performance for the schizophrenia spectrum (SZ) and healthy control

groups for (A) identity discrimination and (B) sex labelling. Morphing level is shown on the y axis,

where 50% indicates an equal morph between Face 1 and Face 2. Note that for A, accuracy is

calculated as correct ‘different’ responses for all conditions except the 0% condition, where the

correct response is ‘same’. Error bars represent 95% confidence intervals. *Significant group

difference at p<.05.

Discussion

The aim of this experiment was to better characterise face-processing deficits in

schizophrenia using a newly-developed set of dynamic tasks. Specifically, to answer the question:

Can impairments in emotion-processing be explained by more general deficits in non-emotional face

processing or non-face processing? This question was explored by comparing performance of

schizophrenia inpatients and healthy controls on emotion-processing, identity-processing, and non-

face processing (car discrimination) using the dynamic tasks introduced in previous chapters. This

study found that patients with schizophrenia were impaired not only on dynamic emotion-processing

tasks, but also on an identity discrimination task and a non-face discrimination task. In contrast,

performance on a sex labelling task was unimpaired.

Emotion-processing deficits

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Consistent with previous studies using dynamic face stimuli, this study found significant

impairments in the ability to correctly label emotions in patients with schizophrenia (Archer et al.,

1994; Behere, Venkatasubramanian, Arasappa, Reddy, & Gangadhar, 2011; Hargreaves et al., 2016;

Johnston et al., 2010; Mendoza et al., 2011). It was also found that patients were significantly

impaired on a same-or-different emotion discrimination task compared to healthy controls. Although,

to my knowledge, results have not been previously published using dynamic faces in this task, this

finding is in line with studies using static faces to investigate emotion discrimination in schizophrenia

(Addington et al., 2006; Hooker & Park, 2002; Martin et al., 2005; Penn et al., 2000; Sachs et al.,

2004; Weniger et al., 2004). This suggests that our patients were impaired in both consciously naming

and distinguishing between different emotional expressions.

Identity-processing deficits

For the first time, this study examined facial identity processing in schizophrenia using

dynamic face stimuli. It was found that performance was significantly poorer in patients compared to

controls on a same-or-different discrimination task. This is consistent with several studies which

reported identity discrimination impairments using posed static stimuli (Butler et al., 2008; Martin et

al., 2005; Shin et al., 2008; Soria Bauser et al., 2012), however, others found no difference between

patients and healthy controls (Edwards et al., 2001; Johnston et al., 2010; Soria Bauser et al., 2012).

While it is not clear why this variation exists, it is possibly due to differences in the stimuli used or in

the samples tested (such as differing symptoms or duration of illness). It should be noted, however,

that this difference is no longer significant when analysed with all five groups (results in Chapter 10).

Therefore, this difference may be described as marginal at best.

In contrast, the current study found no performance differences between patients and controls

on a sex labelling task. As average accuracy was approximately 81% for both groups, this lack of

difference cannot be attributed to ceiling effects. Tasks such as this, which require participants to

attend to just one aspect of identity, typically do not show impairments in schizophrenia (Bediou et

al., 2007; Bediou, Krolak-Salmon, et al., 2005; Chen et al., 2012). In their original model of face

processing, Bruce and Young (1986) refer to information such as age or sex as visually derived

semantic codes. These codes are readily extracted from unfamiliar faces and may be derived from

featural cues (such as nose shape), configural cues (such as spacing of features), or a combination of

both. In healthy individuals, decisions about sex tend to prioritise the shape of the eyebrows, nose,

and overall face outline (e.g.: “fleshiness” of the face) (O'Toole et al., 1998). In contrast, decisions

regarding identity draw from a wider range of visual cues, including perceiving the face as a holistic

‘whole’ (Richler et al., 2011). It is possible that distinguishing the sex of a face is simply a less

demanding perceptual task and is unimpaired in schizophrenia despite significant deficits in

distinguishing the identity of a face. An alternative interpretation, however, is that difficulties shown

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on the identity discrimination task are driven by the same-or-different paradigm itself, rather than a

true identity-processing deficit. The same-or-different paradigm involves serial presentation of two

stimuli and, therefore, requires the viewer to hold the first stimulus in memory for several seconds in

order to mentally compare it to the second stimulus. Therefore, this paradigm may be more sensitive

to more general cognitive deficits, such as working memory, than labelling paradigms which simply

show one stimulus at a time. Consistent with this idea is the finding that patients were also impaired

on a same-or-different task involving non-face stimuli (cars). Therefore, it is likely that impairment on

these tasks reflect cognitive impairments in areas such as attention, processing speed, or working

memory – all of which are commonly affected in schizophrenia (Keefe & Harvey, 2012). Future

studies might aim to explore this further using a same-or-different paradigm that uses simultaneous

presentation, rather than sequential, in order to reduce the demand on working memory.

Is the emotion-processing deficit in schizophrenia simply the result of more general cognitive deficits?

To better characterise face-processing deficits, it is necessary to establish whether identity

processing is specifically impaired in schizophrenia. Although patients with schizophrenia showed

significant performance deficits on an identity discrimination task, the finding that patients were

similarly impaired on a car discrimination task (yet unimpaired at recognising the sex of a face),

strongly suggest that these deficits do not represent specific impairments in processing face

information. Rather, performance on these tasks may be affected by more general cognitive deficits. It

is therefore possible that previous studies reporting identity-processing deficits in schizophrenia may

have similarly been impacted by more general attentional or perceptual impairments which are not

specific to faces. One suggestion is that these impairments are driven by deficits in global visuospatial

processing: the ability to view the gestalt of a visual object which is critical for face recognition

(Macrae & Lewis, 2002) and is reliably shown to be affected in schizophrenia (Poirel et al., 2010).

This question will be further investigated in Chapter 9.

In contrast, this study found significant impairments in both recognising and distinguishing

between emotions in schizophrenia. As performance was impaired even on a simple labelling

paradigm (i.e.: not comparing serially presented stimuli), these deficits cannot be accounted for by the

greater working memory demands of the same-or-different paradigm. Similarly, these deficits are

unlikely to be explained by more general impairments in processing non-emotional aspects of faces,

such as identity. Therefore, these findings agree with past literature (Edwards, Jackson, & Pattison,

2002; Johnston et al., 2010) that indicates a specific deficit in processing emotional expressions in

schizophrenia.

Conclusions

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This study examined facial processing deficits using a series of novel dynamic tasks. Patients

with schizophrenia showed the expected impairments in both naming and distinguishing between

dynamic emotions. Patients were also poorer at non-emotional face discrimination and non-face

discrimination, but were unimpaired at distinguishing the sex of a face. These findings hint that the

emotion-processing deficits reported in previous studies may represent a more general cognitive or

perceptual impairment that affects performance regardless of stimulus type. Chapter 11 will examine

this idea further by exploring correlations between performance and patterns of symptomatology. The

following section will investigate whether performance on dynamic face tasks could be affected by

more general impairment in the global allocation of visuospatial attention.

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Chapter 9: Could face-processing impairments in schizophrenia be

mediated by deficits in allocating visuospatial attention?

Patients with schizophrenia are shown to have reduced performance on tasks of emotion

recognition as well as non-emotional face recognition (Darke et al., 2013; Kohler et al., 2010).

However, whether these performance deficits represent a face-specific deficit, or a more general

deficit in cognition or perception remains unclear. One possible account for identity and expression

recognition deficits in schizophrenia is that they are the result of a more general impairment in

allocating visuospatial attention. ‘Global’ versus ‘local’ visual attention refers to the ability to attend

to the overall configuration of a stimulus or its individual details, respectively (Navon, 1977).

Previous studies suggest that patients with schizophrenia show impairments in global attention.

Therefore, a corollary question raised in the course of this PhD is: could deficits in face-processing

performance be explained by difficulties in allocating visuospatial attention? This chapter will present

the results of an additional inpatient study (n=45) designed to address this question.

The role of global and local visuospatial attention in face processing

Global and local visuospatial attention is typically evaluated using compound stimuli, which

consist of a large 'global' letter made up of many small 'local' letters (see Figure 9.1; Navon, 1977) .

Studies of schizophrenia using this, and similar paradigms have revealed impairments in global

processing, but largely preserved local processing both for static (Goodarzi et al., 2000; Johnson et al.,

2005; Poirel et al., 2010; Silverstein et al., 2000) and dynamic stimuli (Chen et al., 2003). In addition,

patients with schizophrenia demonstrate a bias towards attending to the local level of a stimulus, and

are more likely to persist with a local processing strategy even when task demands favour a global

strategy (Landgraf et al., 2011).

It is possible that a global processing deficit could contribute impairments in identity

recognition because the important global-level information is not being processed efficiently. For

instance, it has been shown that identity recognition performance is improved when healthy

participants are primed to adopt a global processing strategy, and impaired when primed with a local

processing strategy (Macrae & Lewis, 2002; Perfect, 2003). In a study by Joshua and Rossell (2009) it

was found that patients with schizophrenia showed less of a reduction in identity recognition

performance compared to controls when configural cues were removed from a face, indicating that

these individuals relied more strongly on local features when identifying famous faces, even when

configural information was available.

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Figure 9.1. Example of hierarchical stimuli used by Johnson and colleagues (2005, p.938). In this

version of the task, participants are required to respond to the target 'H' whenever it appeared at the

global or local level of a series of hierarchical figures.

Although global processing appears to play an important role in face identity recognition,

there is evidence to suggest that the recognition of emotional expression is less reliant on configural

information. For instance, individuals with congenital prosopagnosia (‘face blindness’) have been

shown to perform normally on emotion recognition tasks and are able to identify the basic expressions

of happiness, anger, disgust, fear sadness and surprise, despite demonstrating severe impairments in

the ability to process configural face information (Kress & Daum, 2003; Palermo et al., 2011;

Schmalzl, Palermo, & Coltheart, 2008). While it has been argued that these individuals may rely more

heavily on compensatory strategies than healthy controls – for example, identifying anger based on

eye shape alone (Palermo et al., 2011) – intact performance has been reported even for subtle

expressions that are difficult to categorise (Duchaine, Parker, & Nakayama, 2003; Humphreys,

Avidan, & Behrmann, 2007).

It has been argued that healthy individuals rely primarily on a few feature-based cues when

identifying emotions (Morris et al., 2009). For example, viewing the eyes alone is a reliable indicator

of sadness, fear and anger (Calder et al., 2000; Martin et al., 2012). Similarly, viewing only the mouth

of a face is adequate for identifying basic expressions of happiness and disgust, and results in greater

accuracy than viewing the eye region (Calder et al., 2000). A study by Bimler and colleagues (2013)

demonstrated that inversion has little impact on discriminating between subtly morphed facial

expressions, suggesting that while holistic processing is critical to identity recognition, it is not

required for distinguishing complex expressions. Similarly, a study by Martin and colleagues (2012)

showed that expression recognition performance was enhanced when participants were primed to

attend to individual features using a locally-directed hierarchical figures task and was impaired when

primed to attend globally.

Taken together it appears that, unlike identity recognition which requires the integration of

global information, expression recognition may be improved when a piecemeal, local processing

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strategy is employed. However, it must be noted that although a local strategy is advantageous for

certain experimental tasks with very specific instructions (e.g.: is this face happy or angry?), it does

not mean that global processing is not engaged in expression recognition. Martin and colleagues

(2012) note previous research that suggests that local strategies are more advantageous in situations

where conditions are especially challenging, such as when faces are presented very briefly (e.g.: 125

ms) or when image quality is degraded (Macrae & Martin, 2007; Martin & Macrae, 2007). When

these conditions are not imposed however, global processing plays a greater role (Macrae & Martin,

2007). For this reason, the authors state that expression recognition is likely not a solely feature-based

process. A similar stance was expressed by Calder and colleagues (2000). They reported that

interference effects were found when participants were asked to identify the expression shown in the

top half of a face which was joined with a lower face half showing a different expression. In contrast,

when the bottom face half showed a different identity but the same expression, no interference was

found. The authors argue that this interference occurs because each facial expression is associated

with its own average configuration that is extracted in addition to featural cues (Calder et al., 2000).

From the research discussed above, it appears that a feature-based local processing strategy is

adequate, and even advantageous, for many expression recognition tasks. However, this does not

necessarily reflect normal or complete functioning. It is likely that global information also plays an

important role in recognising expressions, particularly for stimuli with longer presentation times

(Martin et al., 2012). At this point, it is unclear whether global processing dysfunction could

adequately account for expression recognition deficits in schizophrenia.

The aim of the present study is to investigate whether global visual processing correlates with

performance on facial identity or emotion recognition in schizophrenia. A group of inpatients with

(n=17) and without schizophrenia (n=14) and a group of healthy controls (n=14) completed three

tasks: an identity discrimination task, an emotion discrimination task, and a Navon task to assess

global and local visual attention. It was hypothesised that performance on the global attention task

would correlate with performance on the facial identity task, while performance on the local task

would correlate with performance on the emotion task. It was also predicted that, in line with previous

studies, patients with schizophrenia would show reduced performance on the global attention task, but

not the local attention task.

Method

Participants

A total of 31 inpatients (12 female, 19 male) were recruited from a psychiatric unit in

Melbourne, Australia. Demographic information is shown in Table 9.1. Based on final diagnoses

made by each patient’s treating psychiatrist, patients were grouped into a schizophrenia spectrum

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group (comprising 14 with schizophrenia, 1 with schizoaffective disorder, and 2 first-episode

psychosis) and a psychiatric control group (comprising 9 with bipolar disorder, 4 with depression and

1 dysthymia). An additional 14 healthy controls, all without past or present psychiatric illness, were

also tested. All participants were free from visual impairment, traumatic brain injury, or neurological

disease.

Emotion discrimination task

Emotion discrimination was assessed using an identical task and procedure to that described

in the main inpatient experiment (see Chapter 7).

Identity discrimination task

See the Chapter 7 for method and procedure.

Navon task (global vs local attention)

Stimuli

Stimuli were used with permission from McKone and colleagues (2010) . Sixteen

hierarchically-arranged letter stimuli created, each consisting of a large global letter (2.3°x 3.9°)

composed of 7 – 14 small, local letters (0.4°x 0.5°). Target letters were H and T, and non-target letters

were D, E, U, and V. Half of the targets were valid, with a target letter at either the global or local

level, and half were invalid, without a target letter at either the global or local level (see Figure 9.2).

Each stimulus had different letters at the global and local levels, and none contained both targets. A

further six hierarchical stimuli, not presented in the test phases, were shown to participants in a

familiarisation task prior to testing.

Stimuli were presented in black against a white background. The medial edge of each

stimulus appeared at an eccentricity of 0.5° from a fixation cross (0.2°x 0.2°) shown in the centre of

the screen throughout testing. A post-stimulus mask was constructed from small letters (0.25° x 0.25°)

(see Figure 9.2C). The letters were presented in black on a white background. These stimuli were

arranged in a ‘brick wall’ (2.8°x 4.2°). This mask was presented simultaneously to the left and right

portions of the screen, with the medial edges at an eccentricity of 0.5° from the fixation cross.

Design

Participants completed two blocks of 256 trials: one global block and one local block. Block

order was counterbalanced across participants. A short break was permitted halfway through each

block. Each block was preceded by 16 practice trials, which included feedback. The practice trials

were presented with a long stimulus exposure (500 ms), to ensure compliance with task instructions.

Prior to the practice trials, participants completed a familiarisation task. This involved

viewing a Powerpoint presentation in which the experimenter introduced the six different stimuli for

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each task, with examples demonstrating the global and local form of each. Six example stimuli were

then presented, and the participant was required to identify the two letters present in each stimulus.

Procedure

Participants were instructed to respond, with the index finger of the right hand, every time one

of the targets appeared at either the global or local level of a stimulus. The experimenter emphasised

that the participant was not to respond if a target was not presented. Each trial sequence began with

the appearance of a fixation cross (plus sign, ‘+’) for 1 s, a randomly lateralised stimulus for 150 ms,

followed by the post-stimulus mask for 1.5 s (see Figure 9.2C). The next trial began instantly, and

with no intertrial interval.

Figure 9.2. The Navon task. A: An example of a valid stimulus. The target H is present at the global

level. B: An example of an invalid stimulus. C: Post-stimulus mask used in the Navon task. D:

Sample trial sequence.

General procedure

Prior to testing each participant completed the National Adult Reading Test (NART;

Crawford et al., 1992) , and a demographic questionnaire with questions regarding age, gender, years

of education, and additionally for patients, current medication and illness duration (See Appendix A).

Patients were also assessed using the Positive and Negative Syndrome Scale (PANSS; Kay, Fiszbein,

& Opler, 1987) .

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Participants completed the computerised tasks in one of four counterbalanced orders. Stimuli

were presented in E-Prime 2.0 on a 14 inch (viewing size 13.3 inch) laptop screen (1366 x 768, 60

Hz) at a comfortable viewing distance. This study received ethics approval from the Melbourne

Health Human Research and Ethics Committee (HREC Project no. 2008.657), and the University of

Melbourne Human Research Ethics Committee (Ethics ID no. 1135478.4).

Results

Demographics

Demographic information for the three groups are shown in Table 9.1. A Pearson’s chi-square

test showed that the groups did not differ in gender makeup, X2(2) = .60, p = .74. One-way ANOVAs

revealed significant group differences for age, F(2,42)=4.48, p=.02, years of education, F(2,40)=8.15,

p=.001, and estimated FSIQ, F(2,41)=12.65, p<.001. Post-hoc Bonferroni-corrected comparisons

showed that psychiatric controls were significantly older than healthy controls (p=.02, mean

difference = 11.9 years), but neither group was significantly different from the schizophrenia group (p

values=.08-.99). Furthermore, healthy controls completed significantly more years of education (p

values=.002-.006, mean differences = 2.6-2.9 years) and had higher estimated IQ scores (p

values<.001, mean differences = 15.1-15.7 points) than the two patient groups, which did not differ

from each other (p values>.99).

To investigate whether differences in age, education, or estimated IQ could account for group

differences in task performance, a series of bivariate Pearson correlations were conducted (see Table

9.2). It was found that age correlated negatively with performance on the two same-or-different

discrimination tasks (identity discrimination: r=-.35, p=.02; emotion discrimination: -.46, p=.002).

Years of education and IQ both correlated positively with emotion discrimination (rs=.42-.46, p

values=.01-.001) and global attentional performance (p values=43-.46, rs=.002-.003). In contrast, no

correlations approached significance for local attentional performance.

Independent samples t-tests were used to compare disease-specific factors for the two patient

groups. These revealed that the schizophrenia group had significantly higher positive symptom scores

on the PANSS compared to psychiatric controls, t(29)=2.18, p=.04, mean difference = 4.1. Groups did

not differ in duration of illness (p=.42), daily antipsychotic dosage (p=.73), daily benzodiazepine

dosage (p=.82), negative PANSS scores (p=.41), or general psychopathology PANSS scores (p=.35).

Uncorrected Pearson correlations revealed that duration of illness was negatively associated with

identity discrimination (r=-.49, p=.005), emotion discrimination (r=-.36, p=.046), and local task

performance (r=-41, p=.02), but not global performance (r=-.16, p=.39). None of the tasks correlated

significantly with antipsychotic dosage (p values=.53-.92), benzodiazepine dosage (p values=.32-82),

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positive symptoms (p values=.15-.63), negative symptoms (p values=.19-.99), or general

psychopathology (p values=.21-72).

Table 9.1. Mean participant demographics and questionnaire scores by group (SD in parentheses).

Schizophrenia

n = 17

Psychiatric controls

n = 14

Healthy controls

n = 14

Age (years)*

37.65 (9.45)

range 25-60

40.29 (12.29)

range 26-66

28.36 (11.86)

range 20-55

Males/females

(% male)

10/7

(59%)

9/5

(64%)

7/7

(50%)

Education (years)* 11.12 (2.55) 10.64 (1.45) 13.57 (1.91)

Premorbid IQa* 99.21 (11.00) 98.63 (8.94) 114.28 (7.81)

Illness duration (years) 10.12 (6.85) 12.79 (11.21) -

Antipsychotic daily doseb 301.13 mg (212.68) 338.83 mg (257.78) -

Benzodiazepine daily

dosec 11.43 mg (10.69) 10.36 mg (5.83) -

PANSS

Positive scale* 17.00 (5.55) 12.93 (4.66)

Negative scale 15.47 (6.66) 13.71 (4.60)

General psychopathology 32.12 (5.68) 29.93 (7.14)

*Groups are significantly different at p<.05; aDue to illiteracy, an IQ estimate was not available for one patient in the schizophrenia spectrum group. bChlorpromazine equivalent dose. cDiazepam equivalent dose.

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Table 9.2. Uncorrected Pearson correlations between demographic factors and task performance.

Age Years of Education Estimated IQ

d’ r (p)

[95% CI]

r (p)

[95% CI]

r (p)

[95% CI]

Identity Discrimination -.35, (.02)*

[-.58, -.06]

.21, (.17)

[-.10, .48]

.12, (.46)

[-.18, .40]

Emotion Discrimination -.46, (.002)*

[-.66, -.19]

.41, (.005)*

[.14, .64]

.46, (.001)*

[.19, .67]

Global task -.27, (.08)

[-.27, .03]

.43, (.003)*

[.15, .65]

.46, (.002)*

[.19, .67]

Local task -.28, (.07)

[-.53, .02]

.05, (.77)

[-.25, .35]

.17, (.28)

[-.13, .44]

*Significant correlation at p<.05.

Response bias – c

Values of c were calculated as a measure of response bias for each task. Mean c values for all

groups are shown in Table 9.3. One-way ANOVAs revealed no significant group differences for any

of the tasks, suggesting that differences in response bias cannot account for differences in task

performance between groups. Paired t-tests revealed significantly higher response bias for identity

discrimination compared emotion discrimination, t(44)=13.3, p<.001 (mean difference = .76), but no

difference in response bias between the global and local tasks, t(44)=.03, p=.98 (mean difference =

.01). In order to directly compare performance on the two discrimination tasks, d prime values were

calculated according to formulae by Macmillan and Creelman (1991). These values were used in all

subsequent analysis as they are believed to be less affected by differences in response bias than raw

accuracy rates (Macmillan & Creelman, 1991).

Table 9.3. Mean c scores for patients with schizophrenia and healthy controls on the five tasks.

Schizophrenia

spectrum

n = 17

Psychiatric

controls

n = 14

Healthy

controls

n = 14

M (SD) M (SD) M (SD) One-way ANOVA

c Identity Discrimination -.71 (.40) -.85 (.40) -.77(.40) F(2,42)=.49, p =.62

Emotion Discrimination .03 (.18) -.02 (.11) -.05 (.20) F(2,42)=1.05, p =.36

Global attention .19 (.28) .05 (.30) .14 (.24) F(2,42)=.97, p =.39

Local attention .16 (.18) .14 (.23) .09 (.16) F(2,42)=.43, p =.65

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Face processing tasks

To compare d’ performance across groups on the two face processing tasks, a 2 x 3 mixed

ANOVA was conducted with task as a within-subjects factor (identity vs emotion) and group as a

between-subjects factor (see Figure 9.3). Significant main effects were found for task, F(1,42) =

83.53, p<.001, ŋp2=.67 , and group, F(2,42) = 9.56, p<.001, ŋp

2=.31, while a task x group interaction

trended towards significance, F(2,42) = 3.02, p=.059, ŋp2=.13. Bonferroni-corrected pairwise

comparisons showed that performance was significantly higher for identity discrimination than

emotion discrimination overall (p<.001, mean difference=1.02).

Post-hoc one-way ANOVAs revealed that groups differed significantly on emotion

discrimination, F(2,42) = 18.06, p<.001, but not identity discrimination, F(2,42) = 1.58, p=.22.

Bonferroni-corrected multiple comparisons showed that healthy controls were significantly more

accurate than schizophrenia patients (p<.001, mean difference = 1.1), or psychiatric controls (p=.001,

mean difference = .73), who did not differ from one another (p=.17, mean difference= .35). No other

comparisons approached significance.

Figure 9.3. Performance (d’) of healthy controls, psychiatric controls and schizophrenia spectrum

patients for the identity discrimination and emotion discrimination tasks. Error bars indicate 95%

confidence intervals. *p<.01, **p<.001.

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Navon task (global vs local attention)

Performance (d’) on the two attentional tasks was compared across groups using a 2 x 3

mixed ANOVA with task as a within-subjects factor and group as a between-subjects factor (see

Figure 9.4). Significant main effects were found for task, F(1,42) = 5.64, p=.02, ŋp2=.12, and group,

F(2,42) = 6.39, p=.004, ŋp2=.23. The interaction did not approach significance (p=.12). Overall,

performance was significantly higher for the local task compared to the global task (mean

difference=.32). Post-hoc pairwise comparisons revealed that, across tasks, healthy controls

outperformed both schizophrenia patients (p=.01, mean difference=1.0) and psychiatric controls

(p=.008, mean difference=1.1), who did not differ from one another (p>.99).

To determine whether patients showed a specific global processing deficit, performance for

global and local tasks was compared within each group using paired-samples t-tests. Performance did

not differ significantly between tasks in the healthy control group, suggesting that this group had no

bias to either the global or local level, t(13)=.35, p=.74. Patients with schizophrenia showed

significantly reduced performance on the global task compared to the local task, indicating a global

processing deficit, t(16)=2.56, p=.02. Like healthy controls, psychiatric controls showed no

significant difference between tasks, t(13)=1.46, p=.17, however it is unclear if this was due to the

smaller sample size for this group.

Figure 9.4. Performance (d’) of healthy controls, psychiatric controls and schizophrenia spectrum

patients for the global and local attentional tasks. Error bars indicate 95% confidence intervals.

*p<.01, n.s. = not significant.

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Figure 9.5. Mean local processing bias for healthy controls, psychiatric controls and schizophrenia

spectrum patients. Bias was calculated by subtracting individual performance (d’) on the global task

from performance on the local task. Error bars indicate 95% confidence intervals.

An alternative method to evaluate a global processing deficit is to calculate individual bias

scores. These scores were calculated for each participant by subtracting performance (d’) on the

global task from performance on the local task. Scores are shown in Figure 9.5. A positive score

indicates a local processing bias, while a negative score indicates a global processing bias. One-

sample t-tests revealed that while healthy controls, t(13)=.35, p=.74, and psychiatric controls,

t(13)=1.46, p=.17, showed no significant bias one way or the other, the schizophrenia group showed a

significant local processing bias, t(16)=2.56, p=.02. However, a one-way ANOVA showed no

significant effect of group on bias scores, F(2,42) = 2.26, p=.12. Taken together, there is some

evidence to suggest that the schizophrenia patients tended to perform better on the local task

compared to the global task. However, the group samples may be too small to detect a group level

difference.

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Figure 9.6. Reaction times (ms) of healthy controls, psychiatric controls and schizophrenia spectrum

patients for the global and local attentional tasks. Error bars indicate 95% confidence intervals.

Unlike the two face tasks, which have unlimited responding time, the go/no go design of the

Navon tasks requires rapid responses. Therefore, reaction time data was also analysed to determine

whether the results above were affected by a speed-accuracy trade-off. Reaction times were examined

using a mixed model ANOVA with task as a within-subjects factor (global vs local) and group as a

between-subjects factor. Group means are shown in Figure 9.6. Significant main effects were found

for task, F(1,42) = 6.06, p=.02, ŋp2=.13, and group, F(2,42) = 5.72, p=.006, ŋp

2=.21. The interaction

did not approach significance (p=.47). Results mirrored those of the accuracy data, with significantly

faster performance on the local task compared to the global task (mean difference = 124.7 ms). Post-

hoc pairwise comparisons showed that healthy controls were significantly faster than both psychiatric

controls (p=.007, mean difference = 301.0 ms), and schizophrenia patients (p=.047, mean difference =

223.8 ms), who were not different from one another (p>.99). To examine the possibility of a specific

global processing deficit, paired-samples t-tests were conducted for each group separately. No

significant differences in reaction time for the global or local task were found for either healthy

controls, t(13)=1.58, p=.14, or psychiatric controls, t(13)=1.53, p=.15, although a non-significant

trend was found for schizophrenia patients, t(16)=1.78, p=.09. Overall, the results of the reaction time

analyses closely followed the same patterns as the accuracy analyses, suggesting that groups that were

less accurate were also slower to respond. Therefore, there is no evidence to suggest that the results

reported above were distorted by a speed-accuracy trade-off.

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Correlations between tasks

To determine whether performance on the two face tasks could be accounted for by global or

local performance, a series of Bonferroni-corrected Pearson correlations were conducted (Table 9.4).

Significant positive correlations were found between all tasks (p values=.02-.006) with the exception

that identity discrimination performance was not significantly associated with emotion discrimination

performance (p=.06).

Given that the demographic variables age, years of education, and estimated IQ were all

correlated with task performance, a second analysis was conducted to control for these variables (see

Table 9.5). A series of partial Pearson correlations revealed that, when age, education and IQ were

taken into account, identity discrimination performance was positively correlated with both global

(r=.34, p=.03) and local (r=.53, p<.001) task performance. In contrast, emotion discrimination was

positively correlated with local performance only (r=.35, p=.03). Performance on the global and local

tasks was also positively associated (r=.72, p<.001). Together, these results suggest that reduced

performance on the local task affected performance on both face tasks, whereas reduced performance

on the global task affected identity discrimination only, and not emotion recognition.

Pearson correlations between the face tasks and individual local bias scores were also

calculated. However, neither identity discrimination (r=.14, p=.25) nor emotion discrimination (r=-

.14, p=.37) performance was significantly associated with degree of local bias.

Table 9.4. Bonferroni-corrected Pearson correlations for performance across different tasks.

Identity

Discrimination

Emotion

Discrimination Global task Local task

d’ r (p)

[95% CI]

r (p)

[95% CI]

r (p)

[95% CI]

r (p)

[95% CI]

Identity Discrimination - .37, (.06)

[.09, .60]

.39, (.02)*

[.11, 61.]

.56, (.006)**

[.31, .73]

Emotion Discrimination - .48, (.006)**

[.22, .68]

.42, (.02)*

[.14, .63]

Global task - .68, (.006)**

[.48, .81]

Local task -

*Significant correlation at p<.05., ** p<.01.

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Table 9.5. Uncorrected partial correlations for performance across different tasks, controlling for

Age, years of Education, and IQ.

Identity

Discrimination

Emotion

Discrimination Global task Local task

d’ r (p)

[95% CI]

r (p)

[95% CI]

r (p)

[95% CI]

r (p)

[95% CI]

Identity Discrimination - .26, (.11)

[-.06, .53]

.34, (.03)*

[.03, .59]

.53, (>.001)**

[.26, .72]

Emotion Discrimination - .28, (.08)

[-.04, .55]

.35, (.03)*

[.04, .60]

Global task - .72, (<.001)**

[.52, .84]

Local task -

*Significant correlation at p<.05., ** p<.001.

Discussion

The goal of the present study was to examine whether deficits in global visual attention are

associated with impairments in facial identity or expression recognition in schizophrenia. Consistent

with our predictions, the schizophrenia group, but not healthy controls or psychiatric controls, showed

evidence of a selective deficit in global processing. However, our predictions regarding associations

between facial processing and global/local attentional ability were only partially supported. As

predicted, global processing ability was associated with identity discrimination but not emotion

discrimination. However, local processing ability was associated with both emotion and identity

discrimination.

Identity and emotion recognition in schizophrenia

Consistent with the results of the main inpatient study (Chapter 8), patients with

schizophrenia were significantly impaired on a dynamic emotion discrimination task compared to

healthy controls. This result is also consistent with previous studies using similar paradigms

(Addington et al., 2006; Hooker & Park, 2002; Martin et al., 2005; Penn et al., 2000; Sachs et al.,

2004; Weniger et al., 2004). Performance in psychiatric patients without schizophrenia also showed

reduced performance, suggesting that this deficit is not specific to schizophrenia. As the psychiatric

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control group comprised a wide range of diagnoses, however, it is not possible to comment on the role

of emotion processing in specific mental illnesses. Moreover, the possibility that psychotropic

medication may be playing a role in this deficit in patients cannot be ruled out.

However, while the previous study found a significant difference in identity discrimination

for patients compared to healthy controls, no difference in performance was reported in the current

study. Given that mean scores are similar between studies, this likely reflects the smaller sample size

in the current study, resulting in a lack of power to detect more subtle group differences. It is possible

that any true impairments in facial identity processing are simply less pronounced compared to

emotion processing deficits in schizophrenia, and therefore require comparatively larger samples to

detect. This could possibly explain the considerable variation in studies that report (Butler et al., 2008;

Martin et al., 2005; Shin et al., 2008; Soria Bauser et al., 2012) or fail to report (Edwards et al., 2001;

Johnston et al., 2010; Soria Bauser et al., 2012), identity processing impairments in schizophrenia.

Navon tasks (global vs local attention)

In line with previous data in healthy individuals (McKone et al., 2010), the current study

found that healthy controls performed equally well in the global and local tasks, in both accuracy and

reaction time data. This indicates that the tasks employed ‘equal salience’ stimuli, meaning that the

hierarchical stimuli do not promote a bias to attend to either the global or local level (Yovel, Levy, &

Yovel, 2001). Confirmation of this property is important, as an inherent bias to attend globally or

locally may obfuscate individual differences in performance (Aimola Davies, personal

communication, 2010).

It was also found that although patients with schizophrenia performed less accurately than

healthy controls on both the global and local tasks, they showed a relative deficit on the global task.

This finding replicates the global deficit shown in previous similar studies (Goodarzi, Wykes, &

Hemsley, 2000; Johnson, Lowery, Kohler, & Turetsky, 2005; Poirel et al., 2010; Silverstein, Kovacs,

Corry, & Valone, 2000), and provides support for the idea that schizophrenia is associated with a

specific impairment in processing configural or global-level information from a visual stimulus.

Interestingly, while the psychiatric control group showed reduced performance on both tasks, similar

to the schizophrenia group, they did not show evidence of a relative global deficit. Were this finding

robust, it would suggest that the relative global processing deficit may in fact be specific to

schizophrenia. However, the psychiatric control group did show a non-significant trend towards lower

performance in the global task compared to local which means, given the smaller number of patients

in this group (n=14), that the study may have lacked sufficient power to show a significant result.

Furthermore, it is likely that the presence of mood symptoms would have affected performance, as

research suggests that negative affective states temporarily bias participants to preferentially attend to

local details, rather than global (Basso, Schefft, Ris, & Dember, 1996; Gasper & Clore, 2002) . Future

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research is needed to investigate the role of patient symptomatology in global-local attentional

processing.

Is a global-local attentional bias associated with face processing performance?

As predicted, correlational analyses revealed that poorer global processing was associated

with reduced performance on an identity discrimination task, but not an emotion task. This suggests

that the ability to attend to global information affects our ability to discriminate between faces of

different identities, but is not necessary for distinguishing between emotions. This finding agrees with

previous studies that indicate enhanced non-emotional face processing when attention is directed

globally, and poorer performance when attention is directed locally (Macrae & Lewis, 2002; Perfect,

2003), as well as studies that suggest that emotion processing is less reliant on global-level cues

(Bimler et al., 2013; Calder et al., 2000; Martin et al., 2012). Furthermore, this result suggests that the

global processing deficit seen in schizophrenia may partially account for the deficits in facial identity

recognition shown in previous studies (Hooker & Park, 2002; Martin et al., 2005). However, there is

no indication that a global processing deficit could account for the deficits in facial emotion

recognition that are reliably associated with schizophrenia.

In contrast, performance on the local processing task correlated positively with both the

identity discrimination task and the emotion discrimination task. This finding suggests that the ability

to attend selectively to details (such as individual facial features) while ignoring overall configural

details is important for distinguishing both identity and emotion. While previous studies have

highlighted the importance of using feature-based cues in emotion recognition (e.g.: the eyes, Calder

et al., 2000; Martin et al., 2012), the association between local processing and identity discrimination

was unexpected. It is possible that the same-or-different paradigm itself favours a strategy that

involves comparing individual details, such as eyebrow shape, in order to decide if the two videos are

subtly different.

Although patients with schizophrenia were outperformed by healthy controls in the local task,

they still showed a bias to attend locally overall (i.e.: a global deficit). Therefore, it appears that the

profound deficits in emotion discrimination in this group cannot be (fully) explained by a poorer

ability to attend to local details.

Study limitations

The results of this study must be considered in the context of several limitations. First, the

sample sizes for the three groups were small and uneven, which limited the power to detect smaller

group differences and within-group effects. Furthermore, demographic factors such as age and years

of education were not matched between groups. Age, in particular, has an impact on cognitive factors

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such as processing speed (Salthouse, 2000), which may differentially affect performance on tasks with

limited presentation times or limited response windows. Finally, due to the small patient sample, all

inpatients without a diagnosis of schizophrenia were categorised into a single psychiatric control

group. The heterogeneity of this group, which included both psychotic (e.g.: bipolar disorder with

delusions) and non-psychotic patients (e.g.: depression), made it impossible to draw any conclusions

about specific diagnoses.

Conclusions

In line with previous research, the schizophrenia group showed a relative deficit in global

attention compared to local. Both the schizophrenia and psychiatric control groups showed reduced

performance compared to healthy controls on an emotion discrimination task, but not an identity

discrimination task, suggesting an emotion deficit that is not specific to schizophrenia. Correlational

analyses suggest that the ability to attend to global information is an important skill for distinguishing

facial identity, but cannot account for deficits in emotion processing. While the ability to attend to

local details correlates with performance for both identity processing and emotion processing, this

deficit cannot fully explain the profound impairment in emotion discrimination shown by the patient

groups, either. Taken together, these findings indicate that impairments in the allocation of global or

local attention may be adequate to explain deficits in non-emotional facial processing in

schizophrenia. However, deficits in emotion processing may be partly, but not fully, explained by a

reduced ability to attend to local details. Overall, this study suggests that the emotion-processing

deficits seen in schizophrenia cannot be accounted for by impairments in allocating visuospatial

attention.

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Chapter 10: Are these deficits specific to schizophrenia? Overlap

with Face Processing Deficits in Other Psychiatric Disorders

Deficits in emotion processing have been well established in schizophrenia and may even

constitute a core feature of this disorder (Kohler et al., 2010). However, whether these deficits are

diagnostically specific to schizophrenia remains a point of contention. This chapter will address the

second aim of this thesis: To explore whether face processing impairments are specific to

schizophrenia, or whether they are associated with psychosis more generally. This chapter will begin

by reviewing the literature examining face processing deficits in non-schizophrenia psychosis and

non-psychotic disorders. It will then present further results of the inpatient study, which compares

facial emotion and identity processing in schizophrenia with inpatients with bipolar disorder, other

causes of psychosis (such as drug-induced psychosis), and non-psychotic disorders. Finally, these

results will be discussed in terms of theories suggesting a shared aetiology between schizophrenia and

other psychotic disorders.

The current study

The purpose of the Results section in this chapter is to compare face processing in patients

with schizophrenia with other psychiatric inpatient groups, including bipolar I disorder. Unlike

previous studies, the current study included two additional groups: a non-schizophrenia ‘other’

psychosis group (predominantly drug-induced psychosis and psychotic depression), and a non-

psychosis psychiatric control group (predominantly major depressive disorder). Although under-

studied, disorders such as drug-induced psychosis are increasingly viewed as the less extreme end of a

continuum leading to schizophrenia. Drug-induced psychosis is thought to involve similar

neurochemical pathways to schizophrenia (Paparelli, Di Forti, Morrison, & Murray, 2011), with an

estimated 46% of patients with cannabis-induced psychosis and 20-30% of patients with

amphetamine-induced psychosis converting to schizophrenia within 8 years (Niemi-Pynttari et al.,

2013; Starzer, Nordentoft, & Hjorthoj, 2018). Despite this, no studies could be found which

specifically examined emotion processing in non-schizophrenia psychosis. Therefore, the first

research question is: is emotion processing disproportionately impaired in schizophrenia, or are these

deficits seen in other disorders such as a) bipolar I disorder, b) non-schizophrenia psychosis, or c)

non-psychotic disorders? Second, presuming these deficits are found, are these indeed specific to

emotion, or do they generalise to a) non-emotional face processing or b) non-face visual

discrimination?

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Face processing deficits in bipolar disorder

Compared to the extensive schizophrenia literature, far fewer studies have examined face

processing abilities in other disorders, such as bipolar disorder. Nevertheless, there is substantial

evidence pointing to impaired face processing in bipolar disorder, particularly for recognising

emotion, although the degree and persistence of these deficits remain contentious (Van Rheenen &

Rossell, 2013b). Patients with bipolar disorder have shown impaired performance compared to

healthy controls in correctly labelling facial emotions (Getz et al., 2003; Lembke & Ketter, 2002; Van

Rheenen & Rossell, 2014) as well as discriminating between different expressions (Addington &

Addington, 1998; Bozikas, Tonia, et al., 2006; Rossell et al., 2013). Other studies using morphed

static faces have shown that patients with bipolar disorder require greater intensity to recognise

emotions compared to controls (Brotman et al., 2008; Schaefer, Baumann, Rich, Luckenbaugh, &

Zarate, 2010). This emotion processing deficit has not always been found consistently, however, with

several studies reporting intact emotion recognition in bipolar disorder (Edwards et al., 2001; Harmer,

Grayson, & Goodwin, 2002; Vaskinn et al., 2007) or in certain patient subsets, such as euthymic

patients (Lembke & Ketter, 2002).

Comparisons between diagnostic subtypes of bipolar disorder have also produced variable

results. While Summers and colleagues (2006) found significant impairments in labelling dynamic

emotions for both bipolar I and bipolar II patients, a different study using static face stimuli reported

impairments in bipolar I, but intact performance in bipolar II patients (Derntl, Seidel, Kryspin-Exner,

Hasmann, & Dobmeier, 2009). Finally, Lembke and Ketter (2002) found impaired emotion labelling

ability in manic patients with bipolar I, but intact performance in euthymic patients with bipolar I and

II.

One possibility is that emotion processing deficits in bipolar disorder represent a state-

dependent dysfunction. Evidence for this view comes from studies indicating greater impairments in

symptomatic patients compared to remitted or euthymic patients. For instance, poorer performance

has been reported in currently manic patients (Lembke & Ketter, 2002), depressed patients

(Langenecker, Saunders, Kade, Ransom, & McInnis, 2010; Schaefer et al., 2010), and those

experiencing mixed episodes (Gray et al., 2006). However, substantial impairments have also been

reported in remitted patients (Bozikas, Kosmidis, et al., 2006; Vederman et al., 2012), with one study

reporting no difference between remitted and symptomatic patients (Van Rheenen & Rossell, 2013a).

Moreover, emotion recognition deficits have also been reported in individuals at risk of developing

bipolar disorder (Brotman et al., 2008; Rock, Goodwin, & Harmer, 2010). Together, these results

suggest that emotion processing deficits in bipolar disorder may represent a trait marker of the illness

which is likely exacerbated by the resurgence of acute symptoms.

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In addition to behavioural deficits, bipolar disorder has been associated with abnormal

patterns of neural activation. Functional imaging studies have generally shown reduced activation in

regions of the orbitofrontal cortex and ventrolateral prefrontal cortex, accompanied by increased

activation in the amygdala, parahippocampal gyrus, and medial prefrontal gyrus during emotion

processing (Brotman et al., 2014; Delvecchio, Sugranyes, & Frangou, 2013; Keener et al., 2012;

Surguladze et al., 2010; Vizueta et al., 2012). Abnormal functional connectivity has also been

reported in fronto-temporal circuits during an emotion recognition task in bipolar disorder (Versace et

al., 2010; Vizueta et al., 2012). These differences in functional activation broadly point towards

dysfunction in systems typically implicated in social cognition (Kret & Ploeger, 2015).

Several studies have also sought to disentangle trait abnormalities from state-dependent

abnormalities. For instance, Hulvershorn and colleagues (2012) reported that bipolar patients in

depressed mood states showed increased amygdala activity, whereas patients in manic mood states

showed increased insula activity and decreased activity of the left lateral orbitofrontal cortex. In

contrast, Liu and colleagues (2012) found an increase in left orbitofrontal activity in depressed

patients and reduced right anterior prefrontal activity in manic patients, whereas functioning of the

right anterior cingulate cortex, orbitofrontal cortex, and ventral striatum were all found to be abnormal

regardless of mood state. In a functional connectivity study, Versace and colleagues (2010) suggested

that abnormal connectivity between the left amygdala and orbitofrontal cortex may indicate a state

marker of depression. Overall, it is clear that further investigation is required to elucidate the neural

underpinnings of these deficits.

Face processing deficits in major depressive disorder

As for bipolar disorder, a range of studies indicate some degree of impairment in recognising

facial emotion in major depression (MDD), although the extent of these deficits remains unclear.

Several reviews suggest that, on balance, there are mild but significant impairments in emotion

processing in MDD (Bourke, Douglas, & Porter, 2010; Kohler, Hoffman, Eastman, Healey, &

Moberg, 2011; Leppanen, 2006). In particular, MDD patients tend to show a bias towards perceiving

neutral or ambiguous expressions as sadness, and show a reduced ability to recognise all basic

emotions except for sadness (Dalili, Penton-Voak, Harmer, & Munafo, 2015). These deficits have

been demonstrated using emotion labelling tasks (Douglas & Porter, 2010; Joormann & Gotlib, 2006;

Langenecker et al., 2005; Leppanen, Milders, Bell, Terriere, & Hietanen, 2004; Mah & Pollock, 2010;

Surguladze et al., 2004) as well as emotion discrimination or matching tasks (Asthana, Mandal,

Khurana, & Haque-Nizamie, 1998; Gur et al., 1992; Levkovitz, Lamy, Ternochiano, Treves, &

Fennig, 2003; Rubinow & Post, 1992). In contrast, a number of studies have reported no difference in

emotion processing ability between patients with MDD and healthy controls (Anderson et al., 2011;

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Bediou, Krolak-Salmon, et al., 2005; Derntl, Seidel, Schneider, & Habel, 2012; Edwards et al., 2001;

Gaebel & Wolwer, 1992; Gollan, Pane, McCloskey, & Coccaro, 2008; Kan, Mimura, Kamijima, &

Kawamura, 2004; Schaefer et al., 2010). This discrepancy is possibly due to a lack of power in these

studies, as a recent meta-analysis suggests that the overall effect size for these deficits in MDD is

quite small (Hedges g= -.16; Dalili et al., 2015).

Just as for bipolar disorder, these emotion recognition deficits appear to have both state and

trait components. For instance, several studies have shown abnormal emotion processing in MDD

patients in remission (Bouhuys, Geerts, Mersch, & Jenner, 1996; Leppanen et al., 2004; Suslow et al.,

2004), and even in healthy individuals who are genetically at-risk of depression (Le Masurier, Cowen,

& Harmer, 2007). However, other studies have shown significant improvements in expression

recognition following the improvement of symptoms (Bouhuys, Geerts, & Gordijn, 1999; Hale, 1998;

Mikhailova, Vladimirova, Iznak, Tsusulkovskaya, & Sushko, 1996), suggesting that this deficit is

exacerbated by acute symptoms of depression.

Given that bipolar disorder and MDD are both affective disorders with some overlap in both

genetics and symptom expression (Huang, Hsiao, Hwu, & Howng, 2013; Mitchell et al., 2011), could

these emotion processing deficits indicate a shared impairment in these disorders? A meta-analysis by

Kohler and colleagues (2011) found no difference between bipolar disorder and MDD in the extent of

emotion processing deficits on behavioural tasks. However, two individual studies have highlighted

specific differences between these disorders. Schaefer and colleagues (2010) found reduced accuracy

for labelling emotions in both bipolar disorder and MDD, but only the bipolar group required greater

intensity of an expression to identify it correctly. Another study reported that patients with bipolar

disorder were poorer at labelling fear compared to MDD patients, but not other emotions (Vederman

et al., 2012). Neural activations to emotional stimuli in MDD may be different to those seen in bipolar

disorder, with meta-analyses pointing to increased activation of the ventro-rostral anterior cingulate

cortex (ACC), decreased dorsal ACC activation, and increased amygdala activity (Jaworska, Yang,

Knott, & MacQueen, 2015). A meta-analysis by Delvecchio and colleagues (2012) compared fMRI

activations during emotion processing in MDD and bipolar disorder patients. They found that both

disorders showed increased activation of limbic regions, including the amygdala and parahippocampal

gyrus, possibly indicating heightened salience of emotional stimuli in both of these disorders.

However, while bipolar disorder was associated with decreased ventrolateral PFC activity and

increased activity of areas of the thalamus and basal ganglia, MDD was associated with reduced

activation of the sensorimotor cortices. These differences may represent reduced inhibitory control

and higher facial mimicry in bipolar disorder, but reduced emotional reactivity in MDD. Whether

these abnormalities underlie the dysfunction shown on behavioural measures or simply reflect

different patterns of symptoms (such as reduced facial affect in depression versus lability in mania),

however, requires further investigation.

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One study used dynamic causal modelling to investigate differences in MDD and depressed

bipolar patients during an emotion labelling task (Almeida et al., 2009). They found distinct patterns

of connectivity between the amygdala and orbitomedial prefrontal cortex during viewing of happy

faces that distinguished bipolar disorder and MDD. While MDD patients showed increased negative

left-sided top-down connectivity, bipolar disorder patients showed reduced positive left-sided top-

down connectivity and increased negative right-sided bottom-up connectivity. These findings suggest

that, even when bipolar and MDD patients are experiencing similar acute symptoms, they show

different areas of pathophysiology during emotion processing.

Face processing deficits in other non-psychotic disorders

As part of a growing interest in social cognition in psychiatric disorders, a variety of studies

have examined facial emotion processing in disorders such as anxiety and borderline personality

disorder. One meta-analysis examined emotion processing impairments in different anxiety disorders

across 40 studies (Plana, Lavoie, Battaglia, & Achim, 2014). Interestingly, a large weighted mean

effect size was found for post-traumatic stress disorder (Cohen’s d=-1.60), indicating substantial

deficits in emotion recognition. However, only small or negligible effects were found for social

phobia (d=.12), obsessive-compulsive disorder (d=-.16), panic disorder (d=-.25) and generalised

anxiety disorder (d=-.12). However, whether these deficits indicate specific impairments in

recognising emotion, or simply reflect more general difficulties with attentional control that typically

accompany anxiety disorders (Eysenck & Derakshan, 2011), remains to be established. Predictably,

functional imaging studies have showed similar patterns of neural activation in anxiety disorders to

those seen in bipolar disorder and MDD (Kret & Ploeger, 2015). Meta-analyses point towards

increased activity of the amygdala and reduced activity of the ventromedial PFC and thalamus during

emotion processing in patients with anxiety disorders (Etkin & Wager, 2007; Hattingh et al., 2012;

Hayes, Hayes, & Mikedis, 2012) as well as in anxiety-prone healthy individuals (Kret, Denollet,

Grezes, & de Gelder, 2011).

Another disorder of interest is borderline personality disorder (PD). Despite being categorised

as a personality disorder rather than an affective disorder, borderline PD is characterised by powerful

mood swings and emotional instability and has considerable clinical overlap with both bipolar II

disorder and the atypical presentation of major depressive disorder (Perugi, Fornaro, & Akiskal,

2011). Some authors suggest that these three disorders may share a similar ‘cyclothymic’ diathesis

which is supported by substantial familial clustering (Zanarini, Barison, Frankenburg, Reich, &

Hudson, 2009). A review by Kret and Ploeger (2015) suggested that borderline PD is associated with

subtle but significant impairments in recognising emotions, with a particular bias towards

misidentifying neutral emotions as negative. To date, only two meta-analyses have been conducted.

Daros and colleagues (2013) found that patients with borderline PD were less able to recognise

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disgust, fear, and neutral expressions compared to healthy controls. In contrast, Mitchell and

colleagues (2014) found no significant evidence of recognition deficits for negative emotions

compared to healthy controls, but instead found a significant bias to label neutral or ambiguous faces

as negative, consistent with an enhanced sensitivity to threatening stimuli. Functional imaging studies

have produced similarly inconsistent results. Ruocco and colleagues (2013) reported increased

activation of the insula and posterior cingulate cortex and decreased activation of the amygdala and

parts of the anterior cingulate and dorsolateral prefrontal cortex in borderline PD during an emotion

recognition task. In contrast, the meta-analysis by Mitchell and colleagues (2014) reported increased

activation of the amygdala while viewing emotional expressions, a finding consistent with the

amygdala hyperactivation reported in depression (Peluso et al., 2009), bipolar disorder (Surguladze et

al., 2010) and anxiety disorders (Monk et al., 2008). Additionally, they reported altered activation of

areas of the anterior cingulate cortex, inferior frontal gyrus, and superior temporal sulcus, with an

inconsistent mix of hyperactivation and hypoactivation found across individual studies.

Overall, studies of non-psychotic disorders such as anxiety disorders (with the exception of

PTSD) and borderline PD show minor but often significant deficits in recognising facial emotions

compared to healthy controls. These subtle and often inconsistent deficits appear to be similar to those

reported in depression and in bipolar II disorder (without manic episodes). Imaging studies point to

similar areas of altered functioning of the amygdala and areas of the prefrontal cortex, among others,

however it is likely that these differences relate to an attributional bias towards threat sensitivity rather

than dysfunction of areas involved in emotion recognition specifically.

Results: Face processing across psychiatric disorders

Note that the Method for this study can be found in Chapter 7.

Group differences in task performance

Repeated –measures ANOVAs were conducted on raw accuracy data across morphing levels.

As all groups showed the same pattern of performance, data for each task was collapsed across

morphing levels for subsequent analyses.

Graphs showing d’ scores for the five tasks are shown in Figure 10.1. A 5 x 5 MANOVA

revealed a significant main effect for group, F(4,99) = 2.93, p<.001 (Pillai’s Trace). Univariate tests

revealed significant effects of group for all tasks except Sex Labelling. Mean d’ scores for each group

on the five tasks are shown in Table 10.

Emotion Discrimination (Figure 10.1A) also showed a univariate effect of group (F(4,99) =

14.18, p<.001). The Schizophrenia spectrum group performed significantly more poorly compared to

all other groups (p values=.02-<.001) except Bipolar disorder. The healthy control group also

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significantly outperformed the Other psychosis (p=.03) and Bipolar disorder (p<.001) groups. The

Non-psychosis group trended towards significantly outperforming the Bipolar group (p=.07).

A similar pattern of results were found for Emotion Labelling (Figure 10.1B; Univariate

effect of group: F(4,99) = 9.19, p<.001). The healthy control group outperformed the Schizophrenia

spectrum (p<.001), Bipolar (p<.001) and Other psychosis groups (p=.02). The Non-psychosis group

also outperformed the Schizophrenia spectrum (p=.02) and Bipolar (p=.03) groups.

Identity Discrimination (Figure 10.1C) had a main effect of group, (F(4,99) = 2.47, p=.049).

There was a trend for the Schizophrenia spectrum group to perform more poorly than the Control

group (p=.09), however Bonferroni-corrected pairwise comparisons revealed no significant

differences between any groups (p values=.09-.99).

No effect of group was found for Sex Labelling, (F(4,99) = .363, p=.84). An effect of task

difficulty was seen with lowest performance at 40% male faces, however overall performance was

consistent across all groups (see Figure 10.1C).

Unexpectedly, a significant univariate effect of group was also found for Non-face

Discrimination (Figure 10.1E; F(4,99) = 4.31, p=.003). Bonferroni-corrected pairwise comparisons

revealed that this was driven by the Control group significantly outperforming the Schizophrenia

spectrum (p=.01) and Bipolar groups (p=.03). No other comparisons approached significance.

Table 10. Mean d’ scores for each group on the five tasks.

Schizophrenia

n = 36

Bipolar

disorder

n = 15

Other psychotic

disorders

n = 17

Non-psychotic

disorders

n = 18

Healthy

controls

n = 20

M (SD) M (SD) M (SD) M (SD) M (SD)

d’ Emotion

Discrimination .99 (.57)a 1.28 (.52)a 1.55 (.58)a,b 1.86 (.50)b 2.16 (.79)b

Emotion Labelling 1.14 (.73)a 1.00 (.83)a 1.51 (1.19)a 1.96 (1.02)b,c 2.44 (.73)b

Identity

Discrimination 2.57 (.54) 2.67 (.54) 2.59 (.51) 2.87 (.49) 2.95 (.40)

Sex Labelling 1.90 (.65) 2.12 (.60) 1.93 (.71) 2.00 (.36) 2.03 (.70)

Car Discrimination 2.11 (.64)a 2.04 (.79)a 2.45 (.61) 2.61 (.73) 2.73 (.62)b

aSignificantly different from healthy controls, p<.05; bSignificantly different from schizophrenia group, p<.05; cSignificantly different from bipolar disorder group, p<.05. (All bonferroni-corrected post-hoc comparisons).

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Figure 10.1. Mean d’ performance for the schizophrenia spectrum (SZ), bipolar disorder (BPAD),

other psychosis (Other), non-psychosis (NP) and healthy control groups across the five tasks: Emotion

Discrimination (A), Emotion Labelling (B), Identity Discrimination (C), Sex Labelling (D), and Car

Discrimination (E). Significant differences between groups are indicated with dotted lines, **p<.01;

*p<.05. Error bars represent 95% confidence intervals.

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Task performance in the bipolar disorder group

Mean performance across tasks for the bipolar group are shown in Figure 10.2. A repeated-

measures ANOVA was conducted with accuracy (d’) as the dependent variable and task as a within-

subjects factor. According to Mauchly’s test, the assumption of sphericity was violated therefore the

Greenhouse-Geisser correction was used, X2(9)=20.74, p=.02. A main effect of task was found,

F(2.18, 30.55)=26.02, p<.001, ŋp2=.65. Bonferroni-corrected post-hoc comparisons revealed that

performance on the two emotion tasks was not significantly different from one another (p>.99) but

were both significantly lower than the three remaining tasks (p values<.02, mean differences = .76 –

1.67). Performance on the identity discrimination task was significantly higher than all other tasks

except sex labelling (p values<.01, mean differences = .64 – 1.67). Finally, the car discrimination and

sex labelling tasks were not significantly different from one another (p>.99).

Figure 10.2. Performance (d’) of patients with bipolar disorder across the five dynamic tasks. Error

bars indicate 95% confidence intervals. Dots indicate the performance of individual participants.

*p<.01; **p<.001.

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Task performance in the non-schizophrenia psychosis group

Mean performance across tasks for the other-psychosis group are shown in Figure 10.3. A

repeated-measures ANOVA was conducted with accuracy (d’) as the dependent variable and task as a

within-subjects factor. According to Mauchly’s test, the assumption of sphericity was violated

therefore the Greenhouse-Geisser correction was used, X2(9)=21.97, p=.01. A main effect of task was

found, F(2.18, 34.80)=9.45, p<.001, ŋp2=.37. Bonferroni-corrected post-hoc comparisons revealed that

performance on the identity discrimination task was significantly higher than the two emotion tasks

and the sex labelling task (p values <.03, mean differences = .66 – 1.08). Performance on the non-face

task was also significantly higher than the emotion discrimination task (p=.006, mean difference=.91).

No other comparisons approached significance.

Figure 10.3. Performance (d’) of patients with non-schizophrenia psychosis across the five dynamic

tasks. Error bars indicate 95% confidence intervals. Dots indicate the performance of individual

participants. *p<.05; **p<.001.

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Task performance in the non-psychosis group

Mean performance across tasks for the non-psychosis group are shown in Figure 10.4. A

repeated-measures ANOVA was conducted with accuracy (d’) as the dependent variable and task as a

within-subjects factor. According to Mauchly’s test, the assumption of sphericity was not violated,

X2(9)=14.29, p=.11. A main effect of task was found, F(4, 68)=13.34, p<.001, ŋp2=.44. Bonferroni-

corrected post-hoc comparisons revealed that performance on the identity discrimination task was

significantly higher than the two emotion tasks and the sex labelling task (p values<.01, mean

differences = .87 – 1.01). Performance on the non-face task was also significantly higher than the

emotion discrimination task and the sex labelling task (p values<.02, mean difference=.61-.75). No

other comparisons approached significance.

Figure 10.4. Performance (d’) of patients with non-psychotic disorders across the five dynamic tasks.

Error bars indicate 95% confidence intervals. Dots indicate the performance of individual participants.

*p<.05, **p<.001.

Response bias – c

Mean values of c ranged from .02 to .88 across groups and tasks. Multivariate ANOVA

revealed no significant main effect of group. Pairwise comparisons showed no differences between

groups on any task, suggesting that response bias did not differ between groups, and are therefore

unlikely to account for differences in task performance in these groups.

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Impact of morphing: Varying emotional intensity and facial identity

Emotion Discrimination. Repeated-measures ANOVA was conducted for raw accuracy across

the different intensities of expression. The main effect of intensity was significant, F(1.76, 183.44)=

33.13, p=.005. Contrary to expectations, accuracy was lowest for expressions at 67% intensity

compared to all other intensities (p values=.01 to .001), which did not differ significantly from one

another. This finding indicates that, for all groups, decreasing the intensity of a moving expression did

not significantly affect performance, with one exception. The finding that accuracy was lowest for

expressions at 67% intensity likely reflects the increase in visual artefact or “graininess” of the video

as a result of the morphing process at this intensity (Figure 10.5A).

Emotion Labelling. A Repeated measures ANOVA on raw accuracy revealed a significant

main effect for emotional intensity, F(3.46, 363.26)=17.48, p<.001, but not emotion (disgust vs fear).

Performance is shown in Figure 10.5B (disgust faces) and 9.5C (Fear faces). Unlike the Emotion

Discrimination task, accuracy for naming emotions increased somewhat with increasing intensity.

Performance at 33% intensity was significantly lower than all other levels (p values=.006 - .001), and

performance at 50% intensity was lower than 67% intensity (p=.04) and 83% intensity (p=.001). No

other differences in intensity approached significance.

Identity Discrimination. Raw accuracy across different levels of morphing are shown in

Figure 10.5D. Repeated-measures ANOVA revealed a main effect of morphing, F(5,525)= 599.34,

p<.001. Accuracy increased significantly with each level of increasing difference (p values = .04 -

.001) up to 80% difference, which did not differ significantly from 100% difference (p>.999). All

groups performed above chance when faces were 60% different, and at or below chance at 40%

different.

Sex Labelling. Repeated-measures ANOVA revealed significant main effects of sex,

F(1,104)= 33.13, p<.001 and morphing level, F(2,208)= 405.05, p<.001. Accuracy was lowest for the

60/40% morphed faces and highest for 100% (un-morphed) faces. Unexpectedly, accuracy was

reliably higher for identifying male faces than female faces (Figure 10.5E).

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Figure 10.5. Mean accuracy performance for the schizophrenia spectrum, bipolar disorder, non-

schizophrenia psychosis (Other psychosis), non-psychotic disorders and control groups across the four

morphed face tasks: Emotion Discrimination (A), Emotion Labelling for disgust faces (B), Emotion

Labelling for fear faces (C), Identity Discrimination (D), and Sex Labelling (E). For A, B, and C,

morphing level is on the y axis, where 100% indicates an unedited expression and 33% indicates an

expression morphed 50% with a neutral expression. For D and E, morphing level is shown on the y

axis, where 50% indicates an equal morph between Face 1 and Face 2.

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Discussion

The goal of this section was to investigate the extent of facial affect and identity processing

impairments (if any) using dynamic stimuli in patients with a variety of psychiatric disorders. The

purpose of this was to determine whether these deficits are specific to schizophrenia, or if there is

evidence of a shared mechanism of impairment underlying such deficits. The results revealed that

groups with bipolar disorder, schizophrenia, and ‘other’ (non-schizophrenia) psychotic disorders were

all significantly impaired on the facial emotion tasks compared to healthy controls, while patients with

non-psychotic disorders were unimpaired. In contrast, all patient groups showed relatively intact

performance on the two facial identity processing tasks. Unexpectedly, patients with schizophrenia

and bipolar disorder also showed deficits on a non-face comparison task.

Emotion-processing deficits in bipolar disorder

The results of the current experiment revealed significant facial emotion processing deficits in

patients with bipolar I disorder which were comparable to those seen in patients with schizophrenia.

Compared to healthy controls, these groups were impaired both in naming, and distinguishing

between different facial expressions of disgust and fear. These findings are consistent with previous

studies of patients with bipolar disorder using static stimuli, which typically report emotion

processing deficits regardless of task design (Addington & Addington, 1998; Bozikas, Tonia, et al.,

2006; Getz et al., 2003; Lembke & Ketter, 2002; Rossell et al., 2013; Van Rheenen & Rossell, 2014).

Unexpectedly, however, the current study showed no significant difference in performance between

the bipolar and schizophrenia groups. This finding is contrary to previous research indicating that

patients with bipolar disorder have less severe deficits than patients with schizophrenia (Addington &

Addington, 1998; Goghari & Sponheim, 2013; Kohler et al., 2011; Ruocco et al., 2014; Vaskinn et al.,

2007; Wynn et al., 2013), although several other studies have also produced a null result (Bellack,

Blanchard, & Mueser, 1996; Derntl et al., 2012; Edwards et al., 2001). This finding may reflect a lack

of power due to the small number (n=16) of bipolar disorder patients in this sample. However, it is

also possible that this sample was more impaired due to the exclusion of bipolar II disorder (non-

psychotic) patients from this group, a subtype that in some cases has been shown to be less impaired

in emotion processing compared to bipolar I disorder patients (Derntl et al., 2009; Lembke & Ketter,

2002).

Emotion-processing impairments in patients with and without psychotic features

At present, this is the first study to show that facial emotion processing is impaired in non-

schizophrenia psychoses. This finding supports the idea that non-schizophrenia forms of psychosis

produce similar deficits to those seen in schizophrenia, and likely involve similar neural mechanisms

(Paparelli et al., 2011). Moreover, the finding that emotion recognition was unimpaired in patients

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with non-psychotic disorders is consistent with studies reporting broadly intact performance in major

depression (Anderson et al., 2011; Derntl et al., 2012; Gollan et al., 2008; Schaefer et al., 2010),

certain anxiety disorders (Plana et al., 2014), and borderline personality disorder (Mitchell, Dickens,

& Picchioni, 2014). Note that, although some meta-analyses suggest that these disorders are

associated with mild deficits in emotion recognition (particularly misinterpretation of threatening

faces) these are typically of a much smaller effect size than those seen in schizophrenia, and it is

possible that the current study lacked the sample size to detect these subtler deficits (Dalili et al.,

2015; Plana et al., 2014).

Identity-processing deficits and non-face processing performance: Are these deficits specific to

emotion?

The current study revealed no significant impairments in either discriminating between

different identities, or labelling the sex of a face, in any of the groups examined. Although the

analyses conducted in Chapter 8 revealed significantly reduced performance in the schizophrenia

group compared to healthy controls, this result was no longer significant when all groups were

analysed in a multivariate ANOVA. Nevertheless, the finding of intact identity processing is

consistent with previous studies in bipolar disorder (Bozikas, Tonia, et al., 2006; Getz et al., 2003;

Venn et al., 2004), major depression (Bediou, Krolak-Salmon, et al., 2005), and the majority of

studies of schizophrenia (Bediou et al., 2007; Bediou, Krolak-Salmon, et al., 2005; Chen et al., 2012).

An unexpected finding, however, was that the schizophrenia and bipolar I groups were both

significantly impaired on a non-face discrimination task compared to healthy controls. As mentioned

in Chapter 8, this finding suggests that any reduced performance on the identity discrimination task is

unlikely to represent true deficits in mental processes specific to face perception. Rather, this likely

indicates a more generalised deficit – perhaps in attention or working memory – that affects

performance regardless of perceptual category.

Taken together, these results suggest that although some groups showed mild deficits on a

non-face discrimination task, the substantial emotion processing deficits shown in bipolar I disorder,

schizophrenia, and non-schizophrenia psychosis cannot be fully explained by broader deficits in face

processing. However, they may be explained by a more general cognitive impairment. It is unlikely,

therefore, that these disorders have specific impairments in recognising and distinguishing between

dynamic emotions of disgust and fear. This finding is congruent with the idea that emotion processing

deficits are underlain by disturbance to specific frontotemporal networks, which are affected in

psychiatric disorders such as schizophrenia (Hall et al., 2008; Mukherjee et al., 2012), bipolar disorder

(Versace et al., 2010; Vizueta et al., 2012), as well as neurodegenerative disorders involving these

frontotemporal structures (Bediou et al., 2012).

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Conclusions

The aim of this section was to determine whether emotion processing deficits are

disproportionately impaired in schizophrenia compared to other psychiatric disorders. The results

suggest that emotion processing is substantially impaired in disorders with psychotic features –

including schizophrenia spectrum disorders, bipolar I disorder, and non-schizophrenia psychosis – but

not in patients without psychotic symptoms. The finding that emotion-processing impairments

accompany psychosis rather than specific diagnostic categories provides some support for the idea

that the deficits seen in schizophrenia may fall on a continuum with other forms of psychosis.

Associations between specific psychotic symptoms and face processing deficits will be explored

further in Chapter 11.

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Chapter 11: What else accompanies these deficits? Symptom

Correlates of Face-processing Deficits in Schizophrenia and

Other Disorders

In terms of clinical presentation, schizophrenia is a highly heterogeneous disorder. Despite

sharing the same diagnosis, any two patients may present with vastly different constellations of

symptoms which themselves may vary considerably over time (Tandon et al., 2009). In recognition of

this heterogeneity, there is an increasing research focus on associations between symptomatology and

neurocognitive deficits, such as face processing. This chapter will address the third aim of this study:

to explore whether face-processing deficits are associated with specific symptoms. It will begin by

reviewing the literature examining links between clinical symptoms and face processing deficits. It

will then present additional results from the inpatient study, which tested associations between

symptoms and face processing across a varied sample of psychiatric inpatients.

The liability spectrum model of emotion processing in psychiatric disorders

A national survey conducted in the United States indicated that little over half of people with

a mental illness held a single DSM-IV diagnosis; 25% met criteria for two, and 23% had three or

more disorders (Kessler, Chiu, Demler, Merikangas, & Walters, 2005). In recognition of these high

rates of comorbidity among psychiatric disorders, some authors are moving towards a dimensional

approach for conceptualising psychopathology. This approach may be particularly useful for

elucidating trends in otherwise heterogeneous disorders which have substantial symptom overlap with

other disorder categories, such as schizophrenia.

Noting that facial emotion recognition deficits have been reported to varying degrees across a

range of disorders, Kret and Ploeger (2015) proposed their liability continuum model to explain these

deficits. They argue that disrupted emotional processing, including recognition of facial expressions,

forms the basis of a broad range of disorders including affective disorders and psychosis. It is

suggested that disrupted emotional processing creates a vulnerability to mental illness by biasing

individuals to detect fewer positive emotional cues and more negative or emotional cues, leading to

more aversive emotional experiences which may culminate in either internalising or externalising

disorders. This liability spectrum could help to explain the high comorbidity between disorders, and

places importance on the role that emotion perception plays in the precipitation and maintenance of

clinical symptoms.

A major criticism, however, is that this model assumes that emotion perception difficulties

and associated brain changes precede the emergence of psychiatric symptoms. It is not yet clear

whether these deficits genuinely underlie clinical symptoms, or if they simply co-occur as part of the

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disorder presentation. Kret and Ploeger (2015) note that attempts to clarify the causality of these

deficits are challenged by the effects of medication, improper diagnosis, and the reality that

individuals typically experience symptoms for many years prior to receiving a diagnosis.

Clinical symptoms in schizophrenia

Schizophrenia spectrum disorders are characterised by a range of diverse symptom such as

delusions, cognitive impairment, motor symptoms, apathy and perceptual disturbance. Various factor

models have been proposed to classify these symptoms, but the most widely supported of these is the

three-factor model (Blanchard & Cohen, 2006). Although the precise clustering of symptoms for each

factor varies somewhat across studies, it typically comprises a positive factor, a negative factor, and a

disorganised factor. Positive symptoms include distortions of reality such as delusions and

hallucinations (Tandon et al., 2009). The most common delusions are persecutory delusions and

delusions of reference, while the most common hallucinations are auditory in nature (i.e.: voices),

although hallucinations may occur in any modality. In clinical practice, positive symptoms are treated

as the formal onset of a schizophrenia spectrum disorder, however other symptoms often precede

these for many months or years. In contrast, negative symptoms involve a loss or reduction of

function, such as restricted affect, lack of initiative, poverty of speech, social withdrawal, and

anhedonia (Tandon et al., 2009). Unlike positive symptoms, negative symptoms are not ameliorated

by antipsychotic medication, and tend to persist outside acute episodes of psychosis (Stahl & Buckley,

2007). The final factor, disorganised symptoms, refers to both disorganisation of thought and

behaviour. Sometimes referred to as ‘formal thought disorder’, these include symptoms such as

circumstantiality, tangentiality, thought derailment, neologisms (inventing new words), loosening of

associations, and poverty of thought content (Tandon et al., 2009). Behaviourally, disorganised

symptoms can present as inappropriate affect (e.g.: crying in response to a joke), bizarre rituals or

behaviours, and strange attire. Disorganised symptoms are most prominent during acute episodes, and

are more strongly correlated with cognitive impairment than either positive or negative symptoms

(Harvey, Patterson, Potter, Zhong, & Brecher, 2006; Ventura, Thames, Wood, Guzik, & Hellemann,

2010).

Commonly used instruments for assessing symptoms

One of the most popular measures for assessing clinical symptoms in schizophrenia is the

Positive and Negative Syndrome Scale (PANSS) developed by Kay, Fiszbein and Opler (1987). This

scale contains 30 different items rated on a 7-point Likert scale (such as excitement, hallucinations,

and conceptual disorganisation) which are then organised into three subscales: positive symptoms,

negative symptoms, and general psychopathology. Although not directly corresponding to the three

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factor model described above, these subscales are shown to be largely normally distributed,

independent from one another, and robust to the effects of medication, transient mood states, and

cognition (Mortimer, 2007). Due to its sensitivity to symptom changes over time, the PANSS is

considered by some to be the gold standard for measuring treatment efficacy in schizophrenia

(Kumari, Malik, Florival, Manalai, & Sonje, 2017).

Two other widely used instruments are the Scale for the Assessment of Negative Symptoms

(SANS) and the Scale for the Assessment of Positive Symptoms (SAPS) developed by Andreasen

(1984a, 1984b). These were the first standardised scales developed to measure positive and negative

symptoms separately, consisting of 34 (SAPS) and 19 (SANS) items, respectively. Despite the

popularity of these scales, they have been criticised for condensing symptoms into just two scales:

positive and negative. However, subsequent authors have published modified scoring systems for the

SAPS and SANS to include a separate ‘disorganised’ scale, to conform with three-factor models of

schizophrenia (Klimidis, Stuart, Minas, Copolov, & Singh, 1993).

Another instrument that has been used in research with a wide variety of psychiatric

populations is the Brief Psychiatric Rating Scale (BPRS; Overall & Gorham, 1962) . Depending on

the version used it includes either 16, 18, or 24 items covering various psychotic and affective

symptoms. The BPRS had often been favoured for its brief administration time and high sensitivity to

change over time (Mortimer, 2007). However, it lacks subscales and has been criticised for

insufficient coverage of negative symptoms (Mortimer, 2007).

A less frequently used measure is the Krawiecka-Manchester Scale (K-MS; Krawiecka,

Goldberg & Vaughan, 1977), a brief 8-item scale designed for screening for psychosis. While easy to

administer, this scale lacks reliability and validity estimates and was standardised based on a mere 10

psychiatric patients.

The final measure mentioned here is the Royal Park Multidiagnostic Instrument for Psychosis

(RPMIP), developed by McGorry (1989). Developed primarily as a diagnostic tool, the RPMIP

consists of multiple interviews and questionnaires conducted over time. Factor analysis of 92

symptoms drawn from the RPMIP revealed a four factor model, which some researchers have used to

examine correlations with face processing impairments in schizophrenia (McGorry, Bell, Dudgeon, &

Jackson, 1998).

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Associations between symptom ratings and emotion processing in

schizophrenia

Table 11.1 summarises the results from 78 symptom correlation experiments published since

1991. This section will review these findings according to the type of task used to measure emotion

processing. All associations mentioned refer to negative correlations unless otherwise specified.

Emotion labelling tasks using five or more basic emotions

The most widely used emotion labelling task involves simply presenting one image at a time

and requiring participants to select one of five (or more) options that matches the expression shown.

Of the 32 experiments included here, 19 reported no significant correlations with symptoms, or

declined to report due to “no interpretable pattern” of results.

Of those studies employing the PANSS, one reported correlations with both positive and

negative symptoms (Addington & Addington, 1998), one found a correlation with positive symptoms

only (Sachse et al., 2014), and one found a correlation with anxiety only, but also reported lower

accuracy for a group of patients with positive symptoms compared to a group without (Hall et al.,

2004). Of those experiments using the SAPS and SANS, three reported correlations with negative

symptoms, particularly Alogia and Affective Flattening, (Edwards et al., 2001; Johnston et al., 2006;

Sergi et al., 2007), while another reported correlations with positive symptoms and poor attention

(Chambon et al., 2006). One study using the BPRS reported a correlation with Anergia (Mueser et al.,

1996), while another found a correlation with Conceptual Disorganisation only (Kee et al., 2003). A

single experiment employing the K-MS reported no significant correlations with task performance

(Evangeli & Broks, 2000). However, a study using the RPMIP found an association between reduced

accuracy for sad and fearful faces and the “Bleulerian” factor, a factor which incorporates a

combination of negative, disorganised, and catatonic symptoms (Edwards et al., 2001).

In addition, three studies were included here which did not examine correlations with

symptom measures, but instead compared groups of patients with specific symptoms. Strauss and

colleagues (2011) found that patients with high negative symptoms (termed ‘deficit syndrome’)

performed less accurately than patients with minimal negative symptoms. Finally, two older studies

found that patients with paranoid schizophrenia were better at recognising emotions than a non-

paranoid group (Kline, Smith, & Ellis, 1992; Lewis & Garver, 1995), although it is unclear if this was

due to the presence of fewer negative symptoms in the paranoid group.

Overall, there was a high percentage of studies reporting non-significant correlations. This is

unlikely to be due to low power alone, as many of these studies had substantial sample sizes (e.g.;

n=30 or more) which exceeded those used in studies reporting significant findings. Significant

correlations were found for both inpatients and outpatients, with most studies reporting an association

135

with negative symptoms rather than positive or disorganised. It is possible that the high number of

null results is due to the majority of these studies employing Ekman faces, which have been criticised

for lacking ecological validity and having poor sensitivity to subtler face processing impairments

(Harms et al., 2010).

Restricted-choice emotion labelling tasks

Experiments discussed in this section employed tasks which offered only two or three cued

choices per trial (e.g.: anger or disgust?), rather than selecting from the full range of basic emotions.

Only four of the fifteen experiments in this category reported null findings. One study using the

PANSS reported significant correlations with negative symptoms, positive symptoms, and two PCA

factors termed ‘Psychomotor Poverty’ (encompassing blunted affect, social withdrawal, and poor

rapport) and ‘Disorganisation’ (thought disorder) for happy faces only (Loughland et al., 2002b). Of

studies using the SAPS and SANS, six reported correlations with negative symptoms, especially

Alogia and Affect, (Baudouin et al., 2002; Gur et al., 2006; Kohler et al., 2000; Sachs et al., 2004;

Schneider et al., 1995; Turetsky et al., 2007), two found correlations with positive symptoms (Kohler

et al., 2000; Silver, Shlomo, Turner, & Gur, 2002), and two found correlations with disorganised

symptoms (Kohler et al., 2000; Schneider et al., 1995). Finally, one study using the BPRS found a

correlation with “schizophrenia-specific” symptoms only (Schneider et al., 1995). Of those studies

which compared groups based on symptoms, one reported poorer performance in patients with

schizophrenia-specific affective symptoms (compared to high non-specific affective symptoms)

(Heimberg, Gur, Erwin, Shtasel, & Gur, 1992), while another reported poorer performance in a

negative symptom group compared to a positive symptom group (Mandal, Jain, Haque-Nizamie,

Weiss, & Schneider, 1999). Overall, the majority of studies reported correlations between negative

symptoms and face processing deficits, regardless of the symptom scale used.

Same-or-different emotion discrimination tasks

Same-or-different tasks require participants to compare two faces, either serially or

simultaneously, and decide if they are showing the same emotion. Unlike labelling tasks, they do not

require overt categorisation of the emotion and are therefore thought to be less impacted by

impairments in semantic memory. Five of the 15 experiments reviewed here reported no significant

associations with symptoms. Two studies reported correlations between discrimination accuracy and

negative symptoms on the PANSS (Addington & Addington, 1998; Fakra et al., 2015). Two studies

using the SAPS and SANS reported correlations with negative symptoms (Doop & Park, 2009;

Edwards et al., 2001), and one reported a positive correlation with the Delusions item (Martin et al.,

2005). Of studies using the BPRS, one reported correlations with both positive symptoms and

cognitive disorganisation (Leitman et al., 2005) and one found a correlation with overall BPRS scores

only (Doop & Park, 2009). One study utilising the RPMIP reported a correlation between

136

performance and a factor comprised of a combination of negative, disorganised, and catatonic features

(Edwards et al., 2001). Finally, one experiment found that a group of patients with high negative

symptoms was less accurate than a group with low negative symptoms (Strauss et al., 2010), however

no other symptoms were analysed. Overall, the patterns seen in emotion discrimination tasks appear

to mirror those seen in labelling tasks, with poorer performance most frequently associated with worse

negative symptoms.

Judgements of emotional intensity

A handful of studies have examined the relationship between symptoms and performance on

tasks that require judgements of emotional intensity, such as showing two faces and instructing

participants to indicate the face that looks more afraid. It has been argued that these tasks may be

more sensitive to subtle impairments in emotion processing compared to more straightforward

labelling tasks (Norton et al., 2009). One of the four studies reported no significant associations with

scores on the SAPS, SANS or BPRS (Sachs et al., 2004). One study found correlations with both

negative symptoms and general psychopathology on the PANSS, although these were significant for

fearful faces but not happy faces (Norton et al., 2009). Finally, of studies using the SAPS and SANS,

one reported a correlation with negative symptoms and Anhedonia (Silver et al., 2002), while another

found a correlation with Flattened Affect only (Gur et al., 2006). Although only four studies were

included here, they again show a tendency to show more associations with negative symptoms than

other types of symptoms.

Tasks using digitally generated or modified faces

The vast majority of experiments in this area have used photographs to assess emotion

processing. However, three studies have examined associations with symptoms in schizophrenia using

faces that were either entirely computer generated, or digitally ‘degraded’ in order to increase the

difficulty of the task. Hall and colleagues (2004) found that a group of patients with positive

symptoms were less accurate than a group without. However, the only item from the PANSS that

significantly correlated with performance was Anxiety. Van’t Wout and colleagues (2007) reported a

correlation with PANSS negative symptoms only, and only for fearful faces. However, a large-scale

study by Fett and colleagues (2013) found significant correlations with Disorganised, Negative, and

Positive symptoms on the PANSS.

Tasks using dynamic faces

As discussed in Chapter 5, dynamic (video-based) face stimuli provide more emotional

information than traditional static stimuli and are associated with different patterns of neural

activation (Ambadar et al., 2005; Arsalidou et al., 2011). It is therefore possible that these stimuli

would show correlations with different patterns of symptomatology as well. In support of this idea,

137

Johnston and colleagues (2010) found that the ability to distinguish between static faces was

correlated with negative symptoms on the PANSS, while performance for dynamic faces was

correlated with positive symptoms only. In contrast, a study by Mendoza and colleagues (2011) found

significant associations between accuracy for dynamic faces and both positive and negative symptom

scales on the PANSS. Two studies using the SAPS and SANS reported no significant associations

with symptoms (Behere et al., 2011; Bellack et al., 1996). However, one study found a correlation

between performance and conceptual disorganisation on the BPRS (Kee et al., 2003). Finally, a study

by Davis and Gibson (2000) compared two tasks using posed dynamic expressions (i.e. actors) and

genuine dynamic expressions (i.e. elicited by stimuli). They found that a group of patients with

paranoid symptoms were better at distinguishing genuine faces compared to a group without paranoid

symptoms. However, both groups were equally impaired at distinguishing posed emotions. This result

suggests that tasks using more ‘artificial’ expressions may be more difficult to distinguish, and as a

result may be less able to differentiate between the effects of different symptoms. Clearly, more

research is required to explore the relationship between dynamic emotion recognition and

symptomatology in schizophrenia.

Conclusions and summary

Overall, the most frequently reported correlate with emotion processing deficits in studies of

schizophrenia was severity of negative symptoms. This correlation was found across all task types,

and across different symptom measures. Studies using instruments specialised for schizophrenia, such

as the PANSS and SAPS/SANS, produced more significant correlations than more generalised

instruments, such as the BPRS and K-MS, although it is possible that this simply reflects the greater

use of these specialised instruments. Finally, studies using labelling tasks that involved a broad range

of expressions (five or more) appeared to produce null results more often than other task types. While

the reason for this is unclear, it is possible that tasks with only a few response options (e.g.: fear or

disgust?) are more suited to differentiating between levels of ability than tasks with a wider range of

response options. No clear patterns emerged regarding inpatient or outpatient status of the patients

tested.

A smaller number of studies reported significant correlations with positive or disorganised

symptoms. Again, no clear pattern was found regarding task type, symptom measure, sample size, or

population tested. The only possible exception to this was studies using dynamic face stimuli, with

one study suggesting that recognising static faces is affected by negative symptoms, while dynamic

faces is affected by positive symptoms (Johnston et al., 2010). However, this finding has not been

replicated.

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Table 11.1. Studies reporting symptom correlates with reduced performance on emotion tasks in schizophrenia spectrum disorders.

Symptom Measure Task Author (year) Participants Results

Emotion Labelling Tasks – select 1 from 5+ choices (e.g.: happiness, sadness, anger, fear, disgust, surprise, neutral)

PANSS 7-choice, Ekman faces (Ekman &

Friesen, 1976)

Hall et al. (2004) 20 SZ* (-) correlation with Anxiety.

Group with positive symptoms less accurate than group

without.

Lewis & Garver (1995) 18 SZ* N. S.

Addington & Addington (1998) 40 SZ (inpatient, then 3

months later)

(-) correlation with Negative Symptoms and Positive

Symptoms (outpatient phase only).

Amminger et al. (2011) 30 first episode SZ

(outpatient)

79 ultra-high risk of psychosis

N. S.

7-choice task (Bölte et al., 2002) Sachse et al. (2014) 19 paranoid SZ* (-) correlation with Positive Symptoms.

9-choice task (Kucharska-Pietura et

al., 2005)

Kucharska-Pietura et al. (2005) 100 SZ (inpatients) N. S.

5-choice Morphed-intensity task

(Bediou, Krolak-Salmon, et al.,

2005)

Bediou, Krolak-Salmon et al.

(2005)

29 SZ* N. S.

Bediou et al. (2007) 40 drug-naïve first-episode

psychosis (inpatient)

N. S.

SANS/SAPS 7-choice, Ekman faces (Ekman &

Friesen, 1976)

Wölwer et al. (1996) 36 remitted SZ, 32 acute SZ

(inpatient)

No consistent relationship found (SANS only).

Streit et al. (1997) 16 SZ (tested as inpatient and

4 weeks later)

N. S. (SANS only).

139

Green et al. (2007) 20 SZ/ SZA (outpatient) N. S.

Edwards et al. (2001) 29 SZ, 23 affective psychoses,

28 other psychosis (inc. SZA

and schizophreniform)

(first episode, inpatient).

(-) correlation with Negative Symptoms (but

inconsistent across groups).

7-choice task (Johnston et al., 2006) Johnston et al. (2006) 18 SZ (outpatient) (-) correlation with Negative Symptoms (items Alogia

and Affective Flattening).

6-choice FEIT (Kerr & Neale, 1993) Bellack et al. (1996) 35 SZ/SZA (inpatient) N. S. (SANS only)

Silver & Shlomo (2001) 36 chronic SZ (inpatient) N. S.

Leitman et al. (2005) 43 SZ* N. S.

Sergi et al. (2007) 100 SZ/SZA (outpatient) (-) correlation with Affective Flattening.

6-choice task (Chambon et al., 2006) Chambon et al. (2006) 26 SZ (inpatient) (-) correlation with Positive Symptoms (items

Hallucinations, Delusions, and Bizarre Behaviour).

(-) correlation with Attention.

5-choice task (Bediou, Franck et al.

2005)

Bediou, Franck et al. (2005) 30 SZ* N. S.

BPRS 7-choice, Ekman faces (Ekman &

Friesen, 1976)

Lewis & Garver (1995) 18 SZ* N. S.

Wölwer et al. (1996) 36 remitted SZ, 32 acute SZ

(inpatient)

No consistent relationship found.

Streit et al. (1997) 16 SZ (tested as inpatient and

4 weeks later)

N. S.

6-choice FEIT (Kerr & Neale, 1993) Bellack et al. (1996) 35 SZ/SZA (inpatient) N. S.

Mueser et al. (1996) 28 SZ (inpatient) (-) correlation with Anergia.

Salem et al. (1996) 23 SZ (inpatient) N. S.

140

Kee et al. (2003) 81 SZ/ SZA

/schizophreniform (outpatient)

(-) correlation with Conceptual Disorganisation

Note: Used summed scores combining face, voice, and

video tasks.

Leitman et al. (2005) 43 SZ* N. S.

K-MS 7-choice, Ekman faces (Ekman &

Friesen, 1976)

Evangeli & Broks (2000) 12 SZ (inpatient) N. S.

RPMIP 7-choice, Ekman faces (Ekman &

Friesen, 1976)

Edwards et al. (2001) 29 SZ, 23 affective psychoses,

28 other psychosis (inc. SZA

and schizophreniform)

(first episode, inpatient).

(-) correlation with Bleulerian factor (negative,

disorganised, and catatonic features) for sadness/fear

recognition sum scores.

Note: scores summed across multiple affect tasks.

N/A – compared

paranoid and non-

paranoid groups.

7-choice, Ekman faces (Ekman &

Friesen, 1976)

Kline et al. (1992) 14 paranoid SZ, 13 non-

paranoid SZ (outpatient)

Paranoid SZ group distinguished negative emotions

better than non-paranoid group, and equivalent to HCs.

Lewis & Garver (1995) 18 SZ* Paranoid SZ group more accurate than non-paranoid

group.

N/A – compared

deficit syndrome and

non-deficit groups.

7-choice, Ekman faces (Ekman &

Friesen, 1976)

Strauss et al. (2010) 15 deficit-syndrome SZ, 15

non-deficit syndrome SZ.

Deficit syndrome (high negative symptoms) group less

accurate than non-deficit (low negative symptoms)

group.

Emotion Labelling Tasks – Restricted choice (2-3 emotions only)

PANSS 3-choice task (Faces from Mazurski

& Bond, 1993)

(Neutral, happy or sad?)

Loughland et al. (2002) 65 SZ (outpatient) (-) correlation with Negative Symptoms and Positive

Symptoms.

(-) correlation with PCA factors ‘Psychomotor Poverty’

(blunted affect, social withdrawal, rapport) and

‘Disorganisation’ for happy faces only.

Faces Test de Achaval et al. (2010) 20 SZ (outpatient) N. S.

141

(Baron-Cohen, Wheelwright, &

Jolliffe, 1997).

(select 1 of 2 choices; varying

emotion pairs)

SAPS/SANS PEAT (Erwin et al., 1992)

bipolar scale from Happy to Sad

Schneider et al. (1995) 40 SZ (mostly outpatient) (-) correlations with Negative Symptoms and the

SANS/SAPS global factors 1 (Affect, alogia, apathy &

anhedonia) and 2 (Thought disorder, bizarre behaviour

& attention; Gur et al., 1991).

Schneider et al. (1998) 13 SZ* N. S.

Kohler et al. (2000) 35 SZ* (-) correlation with Alogia (SANS), Hallucinations and

Thought Disorder (SAPS).

Silver et al. (2002) 24 chronic SZ (inpatient) (-) correlation with Positive Symptoms for happy faces.

Sachs et al. (2004) 40 SZ (inpatient) (-) correlation with Affect for happy faces.

(-) correlation with Alogia for sad faces.

Gur et al. (2006) 162 SZ (inpatient and

outpatient)

Affect scores (SANS) predicted accuracy.

Group of patients with Flat Affect were less accurate

than group without.

Note:scores were summed across PEAT and

EMODIFF.

Turetsky et al. (2007) 16 SZ (outpatient) (-) correlation with Negative Symptoms and Alogia for

happy faces.

2-choice task

(Anger or Fear?)

Baudouin et al. (2002) 12 SZ (inpatient) (-) correlation with Negative Symptoms.

2-choice task

(Tottenham et al., 2002)

Leppänen et al. (2006) 44 SZ (Xhosa ethnicity)* N. S.

142

(Happy or Angry?)

BPRS PEAT (Erwin et al., 1992)

bipolar scale from Happy to Sad

Schneider et al. (1995) 40 SZ (mostly outpatient) (-) correlation with SZ-specific symptoms.

Schneider et al. (1998) 13 SZ* N. S.

N/A – Compared

groups based on

affective symptom

severity.

PEAT (Erwin et al., 1992)

bipolar scale from Happy to Sad

Heimberg et al. (1992) 20 SZ (inpatient) Group with high SZ-specific affective symptoms was

less accurate than groups with high non-specific

affective symptoms, and both groups with low

symptoms.

N/A – Compared

groups based on

positive or negative

symptoms.

PEAT (Erwin et al., 1992)

bipolar scale from Happy to Sad

Mandal et al. (1999) 12 SZ with predominant

positive symptoms, 12 SZ

with negative symptoms

(inpatient)

Negative symptom group impaired on all emotions.

Positive symptom group impaired on sad faces only.

Same-Or-Different Emotion Discrimination Tasks

PANSS Facial affect matching task

(adapted from Feinberg et al., 1986)

Addington & Addington (1998) 40 SZ (inpatient, then 3

months later)

(-) correlation with Negative Symptoms (inpatient

phase only).

Fakra et al. (2015) 30 SZ (unmedicated)* (-) correlation with Negative Symptoms.

SAPS/SANS Facial affect matching task

(adapted from Feinberg et al., 1986)

Edwards et al. (2001) 29 SZ, 23 affective psychoses,

28 other psychosis (inc. SZA

and schizophreniform)

(first episode, inpatient).

(-) correlation with Negative Symptoms (but

inconsistent across groups).

Facial Emotion Discrimination Task

(Kerr & Neale, 1993)

Silver & Shlomo 2001) 36 chronic SZ (inpatient) N. S.

Leitman et al. (2005) 43 SZ* N. S.

Facial Affect Recognition/

Discrimination (Bediou, Franck et

al., 2005)

Bediou, Franck et al. (2005) 30 SZ* N. S.

143

Same-or-different emotion matching

task (Martin et al., 2005)

Martin et al. (2005) 20 SZ (inpatient) (+) correlation with Delusions (SAPS).

3-choice emotion matching task

(Doop & Park, 2009)

Doop & Park (2009) 16 SZ/SZA* (-) correlation with Negative Symptoms.

BPRS Facial Emotion Discrimination Task

(Kerr & Neale, 1993)

Mueser et al. (1996) 28 SZ (inpatient) N. S.

Salem et al. (1996) 23 SZ (inpatient) N. S.

Leitman et al. (2005) 43 SZ* (-) correlation with Positive Symptoms and Conceptual

Disorganisation.

3-choice emotion matching task

(Doop & Park, 2009)

Doop & Park (2009) 16 SZ/SZA* (-) correlation with BPRS scores.

RPMIP Facial affect matching task

(adapted from Feinberg et al., 1986)

Edwards et al. (2001) 29 SZ, 23 affective psychoses,

28 other psychosis (inc. SZA

and schizophreniform)

(first episode, inpatient).

(-) correlation with Bleulerian factor (negative,

disorganised, and catatonic features) for sadness/fear

recognition sum scores.

Note: scores summed across multiple affect tasks.

N/A – compared

deficit syndrome and

non-deficit groups.

Facial affect matching task

(adapted from Feinberg et al., 1986)

Strauss et al. (2010) 15 deficit-syndrome SZ, 15

non-deficit syndrome SZ.

Deficit syndrome (high negative symptoms) group less

accurate than non-deficit (low negative symptoms)

group.

Judgements of Emotional Intensity

PANSS 2-choice morphed emotion

discrimination (Norton et al., 2009)

Which face looks more afraid/happy?

Norton et al. (2009) 32 SZ/ SZA (inpatient) (-) correlation with Negative Symptoms and General

Psychopathology for fearful faces.

(-) correlation with Negative Symptoms for happy

faces.

SAPS/SANS EMODIFF (Erwin et al., 1992) Silver et al. (2002) 24 chronic SZ (inpatient) (-) correlation with Negative Symptoms and Anhedonia.

Sachs et al. (2004) 40 SZ (inpatient) N. S.

144

Which face in pair is more

sad/happy?

Gur et al. (2006) 162 SZ (inpatient and

outpatient)

Affect scores (SANS) predicted accuracy.

Group of patients with Flat Affect were less accurate

than group without.

Note:scores were summed across PEAT and

EMODIFF.

BPRS EMODIFF (Erwin et al., 1992)

Which face in pair is more

sad/happy?

Sachs et al. (2004) 40 SZ (inpatient) N. S.

Tasks Using Digitally Generated/Modified Faces

PANSS

Computer-generated morphed

emotion labelling (Sprengelmeyer et

al, 1996)

Hall et al. (2004) 20 SZ* (-) correlation with Anxiety.

Group with positive symptoms less accurate than group

without.

Degraded, morphed emotion

labelling task (Frigero et al., 2002)

4-choice

Van’t Wout et al. (2007) 37 SZ/ SZA /

Schizophreniform (inpatient

and outpatient)

(-) correlation with Negative Symptoms for fearful

faces only.

Fett et al. (2013) 1032 non-affective

psychosis*

(-) correlation with Disorganised, Negative, and

Positive Symptoms.

Note: 5 factor PANSS model used (Van der Gaag et al.,

2006)

Tasks Using Dynamic Faces

PANSS Static and Dynamic Emotion Task

(Johnston et al., 2010)

Fear or surprise?

Johnston et al. (2010) 19 SZ/SZA (outpatient) (-) correlation with Negative Symptoms for static faces.

(-) correlation with Positive Symptoms for dynamic

faces.

EEMT (Blaire et al., 2004) Mendoza et al. (2011) 93 SZ* (-) correlations with both Positive and Negative

symptoms.

145

SAPS/SANS The Videotape Affect Perception

Test (Bellack et al. 1996)

6-choice, label emotion shown in

movie clip (not limited to face).

Bellack et al. (1996) 35 SZ/SZA (inpatient) N. S.

TRENDS (Behere, 2008) Behere et al., (2011) 63 SZ (unmedicated,

outpatient)

N. S.

BPRS The Videotape Affect Perception

Test (Bellack et al. 1996)

6-choice, label emotion shown in

movie clip (not limited to face).

Bellack et al. (1996) 35 SZ/SZA (inpatient) N. S.

Kee et al. (2003) 81 SZ/ SZA

/schizophreniform (outpatient)

(-) correlation with Conceptual Disorganisation

Note: Used summed scores combining face, voice, and

video tasks.

N/A – compared

paranoid and non-

paranoid patients.

Dynamic emotion labelling task

Davis & Gibson, 2000)

6-choice

Davis & Gibson (2000) 10 paranoid SZ, 10 non-

paranoid SZ

(inpatient)

Paranoid SZ group distinguished genuine negative

emotions better than non-paranoid group, and

equivalent to HCs.

Groups equally impaired on posed emotions.

Abbreviations: PANSS = Positive and Negative Syndrome Scale (Kay, Fizbein & Opler, 1987); SAPS = Scale for the Assessment of Positive Symptoms (Andreasen, 1984); SANS = Scale for

the Assessment of Negative Symptoms (Andreasen, 1984); BPRS = The Brief Psychiatric Rating Scale (Overall & Gorham, 1962); K-MS =. Krawiecka–Manchester Scale (Krawiecka,

Goldberg, & Vaughan, 1977); RPMIP = Royal Park Multi-Diagnostic Instrument for Psychoses (McGorry et al., 1989); FEIT = Facial Emotion Identification Test (Kerr & Neale, 1993); PEAT

= Penn Emotion Acuity Test (Erwin et al., 1992); SZ = schizophrenia; SZA = schizoaffective disorder.

* = did not specify whether sample was inpatient or outpatient.

146

Table 11.2. Studies reporting symptom correlates with reduced performance on non-emotion face tasks in schizophrenia spectrum disorders.

Task Design Symptom Measure Author (year) Participants Significant Results

Identity Recognition Tasks

Benton Test of Facial

Recognition

(Benton et al., 1978)

PANSS Addington & Addington (1998) 40 SZ (inpatient, then 3

months later)

(-) correlation with Negative Symptoms (outpatient

phase only).

Hall et al., (2004) 20 SZ* N. S.

BPRS Mueser et al. (1996) 28 SZ (long-term inpatient) (-) correlation with Anergia

Salem et al. (1996) 23 SZ (inpatient) N. S.

Sachse et al. (2014) 19 SZ (paranoid)* N. S.

K-MS Evangeli & Broks (2000) 12 SZ (inpatient) N. S.

Morphed face identity matching

task

(Match target to 1 of 2 choices)

PANSS Chen et al. (2009) 29 SZ /SZA* (-) correlation with Negative Symptoms and Positive

Symptoms.

Chen et al. (2015) 25 SZ/SZA* (-) correlation with Negative Symptoms.

Chen & Ekstrom (2016) 35 SZ/SZA (outpatient) (-) correlation with Negative Symptoms and Positive

Symptoms.

Norton et al. (2009) 32 SZ/ SZA (inpatient) (-) correlation with Negative Symptoms

Identity recognition task

(Is this Person A or B?)

SAPS/SANS

Baudouin et al. (2002) 12 SZ (inpatient) No correlations with task performance, but interference

from Identity on an Emotion task correlated (+) with

Negative Symptoms.

Identity discrimination

(same/different judgment of

serially presented faces)

PANSS Fakra et al. (2015) 30 unmedicated SZ N. S.

SAPS/SANS Martin et al. (2005) 20 SZ (inpatient) (-) correlation with Negative Symptoms (SANS).

Interference from identity on Emotion task correlated (+)

with Negative Symptoms.

Single-Feature Recognition Tasks

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Morphed sex-labelling task PANSS Bediou et al. (2005) 29 SZ* N. S.

Bediou et al. (2007) 40 drug-naïve first-episode

psychosis (inpatient)

N. S.

Sex-labelling task PANSS Van’t Wout et al. (2007) 37 SZ/ SZA/

schizophreniform (inpatient

and outpatient)

N. S.

Age discrimination task

(over or under 30 years?)

N/A – compared

paranoid and non-

paranoid patients.

Pinkham et al. (2008) 12 paranoid SZ

12 non-paranoid SZ/SZA

N. S.

Age discrimination task

(Erwin et al., 1992)

(indicate decade:

1=teens…7=70s)

SAPS/SANS Schneider et al. (1995) 40 SZ (mostly outpatient) (-) correlations with Alogia subscale and the

SANS/SAPS global Factor 2 (Thought disorder, bizarre

behaviour and attention; Gur et al., 1991).

Kohler et al. (2000) 35 SZ* N. S.

BPRS Schneider et al. (1995) 40 SZ (mostly outpatient) N. S.

Self-Other Discrimination Tasks

Morphed self-other

discrimination task

(self or other?)

PANSS Bortolon et al. (2016) 24 SZ (inpatient and

outpatient)

N. S.

Self-other discrimination task

(self, relative or unknown face?)

PANSS Kircher et al. (2007) 20 SZ (inpatient) (-) correlation with Positive Symptoms, Negative

Symptoms, Delusions (item) and Hallucinations (item)

for self-face recognition.

(-) correlation with Positive Symptoms and PANSS sum

scores for unknown-face recognition.

Self-other discrimination task

(self or famous face?)

PANSS Yun et al. (2014) 8 SZ* (-) correlation with General Psychopathology for self-

face recognition.

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Tests of Face Memory

Delayed face matching task

(3 and 10 s delay conditions)

PANSS Chen et al. (2009) 29 SZ/ SZA* (-) correlation with Positive Symptoms for 3 s delay

condition only.

The Warrington Recognition

Memory Test–Faces subtest

(Warrington,1984).

K-MS Evangeli & Broks (2000) 12 SZ (inpatient) N. S.

Penn Face Memory Test

(Erwin et al., 1992).

SANS Sachs et al. (2004) 40 SZ (inpatient) (-) correlation with Avolition-Apathy subscale

BPRS Sachs et al. (2004) 40 SZ (inpatient) N. S.

Famous faces task

(Calder et al., 1996).

K-MS Evangeli & Broks (2000) 12 SZ (inpatient) N. S.

Abbreviations: PANSS = Positive and Negative Syndrome Scale (Kay, Fizbein & Opler, 1987); SAPS = Scale for the Assessment of Positive Symptoms (Andreasen, 1984); SANS = Scale for

the Assessment of Negative Symptoms (Andreasen, 1984); BPRS = The Brief Psychiatric Rating Scale (Overall & Gorham, 1962); K-MS =. Krawiecka–Manchester Scale (Krawiecka,

Goldberg, & Vaughan, 1977); SZ = schizophrenia; SZA = schizoaffective disorder.

* = did not specify whether sample was inpatient or outpatient.

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Associations between symptom ratings and identity processing in

schizophrenia

Table 11.2 summarises the results from 23 symptom correlation experiments published since

1991. This section will review these findings according to the type of task used to measure static

facial identity processing. All associations mentioned refer to negative correlations unless otherwise

specified.

Identity recognition tasks

‘True’ identity recognition tasks are those which require participants to attend to the identity

of a face shown (typically with non-face features such as hair removed) and either match it to, or

distinguish it from, another face. Of the 12 experiments reviewed here, five reported no significant

associations with symptoms. One study using the Benton Test of Facial Recognition found a

correlation with negative symptoms on the PANSS (Addington & Addington, 1998) , while another

reported a correlation with Anergia from the BPRS only (Mueser et al., 1996). Two studies using a

more subtle morphed-face matching task reported correlations with both positive and negative

symptoms on the PANSS (Chen & Ekstrom, 2016; Chen et al., 2009), while two found correlations

with negative symptoms only (Y. Chen et al., 2015; Norton et al., 2009). A single study using a

learning phase followed a recognition task (Is this face person A or person B?) found a correlation

between negative symptoms on the SANS and interference from identity information on an emotion

labelling task (Baudouin et al., 2002). This finding suggests that patients with more negative

symptoms were less able to ignore identity information during an unrelated emotion task. Finally, a

study using a same-or-different identity task found an association between performance and negative

symptoms on the SANS, as well as between negative symptoms and interference from identity

information on an emotion task (Martin et al., 2005). In sum, the majority of studies reported

correlations with negative symptoms regardless of task. However, two studies using morphed faces

(argued to be better for detecting subtle impairments in identity processing), also reported correlations

with positive symptoms.

Single-feature recognition tasks

In contrast to ‘true’ identity recognition, single-feature tasks only require participants to

attend to one aspect of identity, such as the perceived age or sex of a face. Of the six studies

identified, only one reported a significant association. Schneider and colleagues (1995) found an

association between performance on an age discrimination task and Alogia on the SANS. They also

reported a correlation with a factor derived from the SAPS and SANS which included a combination

of thought disorder, bizarre behaviour, and poor attention. The fact that most studies reported null

results likely reflects the finding that patients with schizophrenia tend to be unimpaired on simple

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single-feature recognition tasks (e.g.: Bediou et al., 2005, 2007; Pinkham et al., 2008; with the notable

exception of Schneider et al., 1995, who reported reduced performance in SZ patients compared to

HCs).

Self-other discrimination tasks

Self-other discrimination tasks require the participant to judge whether each face shows their

own face or that of another person, which may be either familiar (e.g.: a relative) or unknown. Several

studies suggest that patients with schizophrenia and those at ultra-high risk of psychosis are impaired

on such tasks compared to healthy controls (Irani et al., 2006; Jia, Yang, Zhu, Liu, & Barnaby, 2015),

although others reported that this impairment was not specific to self-recognition (Heinisch et al.,

2013; Lee et al., 2007). Three studies were identified which compared associations between

symptoms and performance. Kircher and colleagues (2007) found correlations between self-face

recognition and both positive and negative symptoms on the PANSS, while unknown-face recognition

correlated with positive symptoms and overall PANSS scores only. In contrast, Yun and colleagues

(2014) reported a correlation between self-recognition and general psychopathology on the PANSS,

while Bortolon and colleagues (2016) found no significant correlations with PANSS scores. Clearly

further evidence is required to elucidate any potential relationship between self-recognition and

symptomatology in schizophrenia.

Tests of face memory

Unlike the other tasks described here, face memory tests require participants to draw on long-

term memory to make judgements about identity. This includes old/new paradigms, matching tasks

with a substantial delay between stimulus exposures, and famous faces tasks. The use of these tasks to

evaluate identity processing in schizophrenia has attracted criticism because they may confound face

perception ability with more general memory impairments. Only three studies were identified which

examined associations between face memory performance and symptom ratings in schizophrenia.

Chen and colleagues (2009) found a correlation with positive symptoms on the PANSS for a 3-second

delay condition, but not with a 10-second delay. Sachs and colleagues (2004) found a correlation with

the Avolition-Apathy subscale on the SANS, but no significant correlations with scores on the SAPS

or BPRS. Finally, Evangeli and Broks (2000) used two different face memory paradigms and found

no significant correlations with K-MS scores, although this is unsurprising given their small sample

size (n=12).

Conclusions and summary

While the number of correlational studies investigating identity processing are far fewer than

those reviewed in the emotion processing section, some tentative conclusions may be drawn. First, the

most frequently reported correlate of deficits seen on ‘true’ identity tasks was negative symptoms.

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However, two studies using morphed stimuli also reported correlations with positive symptoms,

suggesting that this may be a smaller effect size requiring more sensitive techniques for assessing

deficits. Tasks that assess only one aspect of identity, such as age or sex, did not tend to correlate with

symptoms. However, this is probably because single-feature tasks are rarely able to detect impairment

in schizophrenia. Although patients with schizophrenia typically show impairment on other types of

facial identity tasks – such as self-other discrimination and tests of face memory – only a handful of

studies have examined correlations with symptoms, and findings are inconsistent.

The current study

The goal of this Results section is to clarify the relationship between face processing deficits

– both emotion and facial identity – and clinical symptoms in a large patient population (n=86).

Patients from a range of diagnostic categories other than schizophrenia were included in order to

obtain a broader spread of symptoms independent of diagnosis. The research question is therefore: do

specific symptoms correlate with either facial emotion or identity performance? This will be

addressed by examining correlations between PANSS symptom ratings and performance on a range of

dynamic face tasks. This section will also describe the results of a cluster analysis conducted to

identify possible subgroups of patients based on task performance.

Results: Symptom correlates of face-processing performance

Correlations between PANSS scores and task performance

Table 11.3 shows Pearson product-moment correlations between PANSS subscale scores and

task performance. Bootstrapping was used to calculate bias corrected and accelerated (BCa)

confidence intervals using 1000 resamples (DiCiccio & Efron, 1996). Positive Symptoms were

negatively correlated with all tasks except Sex Labelling (rs= -.31-.41). General Psychopathology

scores correlated negatively with Car Discrimination only (r=.37). No correlations with Negative

Symptoms approached significance for any of the tasks.

Correlations between individual PANSS items and task performance

Spearman-rank correlations between individual items on the PANSS and task performance

are shown in Table 11.4. BCa confidence intervals were calculated using 1000 bootstrapping samples.

When considered overall, it appears that classically positive symptoms - such as Delusions,

Grandiosity, Suspiciousness, and Unusual Thought Content – correlated negatively with performance

on the two Emotion tasks, and also with Car Discrimination. In contrast, no positive symptoms

correlated with the two Identity tasks (with the exception of Unusual Thought Content, which was

positively correlated with Sex Labelling). Note that the association between PANSS Positive subscale

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scores and Identity Discrimination described in the previous section appears to have been driven

primarily by Cognitive Disorganisation, which the PANSS classifies as a positive item, whereas

proponents of the prevailing three-factor model would categorise this item separately as a

disorganised symptom (e.g.: Tandon et al., 2009) .

The other trend to note is that performance on all tasks (except Sex Labelling) tended to

correlate negatively with cognitive symptoms, such as Conceptual Disorganisation, Difficulty in

Abstract Thinking, Stereotyped Thinking, Poor Attention, Disorientation, and Lack of Judgement and

Insight. These items also appear to drive the association reported between General Psychopathology

subscale scores and Car Discrimination performance. Interestingly, almost no correlations were found

with affective symptoms, such as Depression, Emotional withdrawal, and Blunted Affect. The only

exception was that higher Depression and Guilt Feelings was associated with better performance on

Emotion Discrimination, but not any other tasks.

Hierarchical clustering

An exploratory hierarchical cluster analysis was conducted using Ward’s method (Ward,

1963) to establish clusters based on performance (d prime) across the five tasks. A four cluster

solution was selected based on the ‘elbow’ method (Zambelli, 2016). Characteristics of the four

clusters are shown in Table 11.5.

Demographics

One-way ANOVAs revealed that the clusters differed significantly in duration of illness,

F(3,80)=2.99, p=.04, and daily benzodiazepine dose, F(3,12)=4.02, p=.03. A difference in Age also

trended towards significance, F(3,80)=2.62, p=.06. The clusters did not differ significantly in years of

education, F(3, 80)=.82, p=.49, estimated IQ, F(3, 77)=1.31, p=.28, or average daily antipsychotic

dose, F(3, 59)=.46, p=.72. Post-hoc Bonferroni-corrected comparisons revealed that Cluster 2 had a

significantly shorter average duration of illness than Cluster 4 (Mean difference= 9.06 years, p=.048).

Cluster 2 was also significantly younger compared to Cluster 3 (Mean difference = 10.57 years,

p=.04). No other comparisons approached significance.

PANSS symptom scores

One-way ANOVAs showed significant differences between clusters for Positive Symptoms

on the PANSS, F(3, 80)=4.86, p=.004. Differences between clusters for General Psychopathology,

F(3, 80)=2.44, p=.07, were not significant, however differences for Negative Symptoms, F(3,

80)=2.70, p=.051, approached significance. Bonferroni-corrected post-hoc comparisons revealed that

Cluster 2 had significantly fewer positive symptoms than Cluster 1 (Mean difference = 5.79, p=.02)

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and Cluster 4 (Mean difference = 7.19, p=.01). Cluster 3 had significantly fewer negative symptoms

than Cluster 4 (Mean difference = 3.30, p=.04). No other comparisons approached significance.

Task performance

Mean performance (d’) across the five dynamic tasks are shown in Figure 11. A series of one-

way ANOVAs were conducted to compare performances of the four clusters for each task. Significant

group effects were found for all tasks (p=.003 to .001). Bonferroni-corrected post-hoc comparisons

were conducted for each task.

Emotion discrimination. Cluster 2 significantly outperformed all other groups (p

values<.001), while Clusters 1 and 3 both outperformed Cluster 4 (p values=.01 to <.001).

Emotion labelling. Cluster 2 significantly outperformed all other clusters (p values<.001).

Cluster 3 outperformed Clusters 1 and 4 (p values<.001), which did not differ from one another.

Identity discrimination. Clusters 2 and 3 performed significantly better than Clusters 1 (p

values=.002 to <.001) and 4 (p values<.001). Cluster 1 also outperformed Cluster 4 (p=.02).

Sex labelling. Cluster 1 and 3 outperformed Cluster 4 (p values=.01-.003). No other

comparisons were significant.

Car discrimination. Cluster 2 performed significantly better than Cluster 1 (p=.02) and

Cluster 4 (p<.001). Clusters 1 and 3 also outperformed Cluster 4 (p values<.001) but were not

different from one another.

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Table 11.3. Pearson correlations between PANSS scores and performance (d’) on the five dynamic tasks.

Emotion Discrimination Emotion Labelling Identity Discrimination Sex Labelling Car Discrimination

r [95% CI] r [95% CI] r [95% CI] r [95% CI] r [95% CI]

Positive Symptoms -.41* [-.55, -.24] -.33* [-.50, -.13] -.31* [-.50, -.08] .06 [-.10, .22] -.35* [.55, .11]

Negative Symptoms -.06 [-.33, .21] -.20 [-.39, .03] -.21 [-.41, .00] -.09 [-.31, .13] -.24 [-.48, .03]

General Psychopathology -.11 [-.34, .14] -.16 [-.35, .05] -.22 [-.44, .05] -.12 [-.33, .09] -.37* [-.53, -.17]

Note: * indicates a significant correlation, where the 95% confidence interval for r does not include 0.

Table 11.4. Spearman rank correlations between individual PANSS items and performance (d’) on the five dynamic tasks.

Emotion Discrimination Emotion Labelling Identity Discrimination Sex Labelling Car Discrimination

rs [95% CI] rs [95% CI] rs [95% CI] rs [95% CI] rs [95% CI]

Delusions -.42* [-.60, -.23] -.24* [-.45. -.01] -.18 [-.39, .06] .09 [-.14, .30] -.27* [-.47, -.07]

Conceptual disorganisation -.50* [-.65, -.32] -.29* [-.49, -.06] -.26* [-.46, -.03] .15 [-.09, .37] -.34* [-.55, -.10]

Hallucinations -.08 [-.27, .12] -.09 [-.30, .13] -.15 [-.36., .07] -.07 [-.30, .17] -.12 [-.32, .09]

Excitement -.16 [-.38, .07] -.20 [-.41, .03] -.22 [-.42, .04] .11 [-.13, .34] -.31* [-.51, -.06]

Grandiosity -.32* [-.52, -.09] -.29* [-.49, .08] -.17 [-.38, .07] .23* [.02, .45] -.24* [-.43, -.02]

Suspiciousness -.28* [-.48, -.06] -.25* [-.46, -.03] -.14 [-.36, .09] .02 [-.20, .22] -.25* [-.46, -.02]

Hostility -.06 [-.28, .15] -.10 [-.30, .10] -.12 [-.33, .12] -.06 [-.28, .19] -.11 [-.33, .11]

Blunted affect .02 [-.20. .25] -.10 [-.31, .12] -.20 [-.40, .02] -.05 [-.26, .17] -.14 [-.36, .13]

Emotional withdrawal .00 [-.27, .27] -.02 [-.23, .20] -.03 [-.22, .18] .02 [-.14, .16] .001 [-.17, .16]

Poor rapport .07 [-.16, .30] .04 [-.17, .23] -.03 [-.24, .19] -.08 [-.27, .12] -.05 [-.29, .19]

Passive apathetic withdrawal .21 [-.03, .43] .09 [-.12, .30] .11 [-.09, .30] -.06 [-.28, .15] .12 [-.08, .31]

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Difficulty in abstract thinking -.24* [-.44, -.04] -.31* [-.47, .13] -.32* [-.50, -.12] -.04 [-.26, .18] -.26* [-.45, -.05]

Lack of spontaneity .05 [-.18, .30] .06 [-.18, .27] -.03 [-.24, .18] -.22 [-.40, .002] -.13 [-.36, .09]

Stereotyped thinking -.29* [-.49, -.06] -.34* [-.51, -.12] -.29* [-.47, -.07] .03 [-.21, .27] -.28* [-.47, -.04]

Somatic concern .17 [-.07, .36] .17 [-.04, .37] .18 [-.05, .40] .01 [-.23, .23] .01 [-.22, .20]

Anxiety .18 [-.02, .38] .18 [-.03, .38] .04 [-.18, .24] -.09 [-.31, .13] .03 [-.20, .26]

Guilt feelings .22* [.03, .40] .12 [-.09, .31] .05 [-.16, .27] -.10 [-.31, .12] .18 [-.04, .39]

Tension .004 [-.21, .22] .05 [-.17, .26] -.01 [-.24, .24] .004 [-.24, .26] -.16 [-.38, .07]

Mannerisms and posturing .002 [-.20, .22] .04 [-.19, .27] .07 [-.12, .25] .04 [-.19, .28] -.04 [-.20, .13]

Depression .34* [.15, .52] .13 [-.08, .32] .10 [-.12, .29] -.13 [-.35, .09] .11 [-.12, .34]

Motor retardation .09 [.-.13, .29] -.02 [-.24, .17] -.03 [-.21, .15] -.16 [-.34, .04] -.11 [-.29, .12]

Uncooperativeness -.17 [-.36, .04] -.10 [-.30, .11] -.10 [-.34, .16] -.14 [-.35, .10] -.17 [-.40, .05]

Unusual thought content -.46* [-.65, -.23] -.33* [-.52, -.12] -.15 [-.38, .09] .28* [.06, .48] -.27* [-.48, -.02]

Disorientation .02 [-.16, .20] -.12 [-.36, .13] -.26* [-.41, -.08] -.17 [-.38, .07] -.24* [-.42, -.03]

Poor attention -.28* [-.47, -.07] -.29* [-.47, -.06] -.39* [-.57, -.14] -.23 [-.44, .04] -.49* [-.63, .29]

Lack of judgement and insight -.39* [-.57, -.18] -.40* [-.57, -.19] -.19 [-.40, .04] .04 [-.20, .28] -.25* [-.48, -.01]

Disturbance of volition -.02 [-.22, .18] -.08 [-.24, .07] -.29* [-.45, -.10] -.16 [-.39, .12] -.14 [-.32, .06]

Poor impulse control -.07 [-.27, .15] -.08 [-.28, .13] -.11 [-.31, .13] -.11 [-.36, .15] -.17 [-.39, .05]

Preoccupation -.14 [-.33, .07] -.06 [-.28, .17] -.14 [-.35, .07] .09 [-.12, .31] -.30* [-.47, -.10]

Active social avoidance -.15 [-.34, .06] -.14 [-.36, .07] -.14 [-.36, .10] -.03 [-.24, .18] -.22 [-.44, .02]

Note: * indicates a significant correlation, where the 95% confidence interval for rs does not include 0.

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Table 11.5. Characteristics of four clusters based on patient performance.

Cluster 1 Cluster 2 Cluster 3 Cluster 4

n=27 n=11 n=31 n=15

M (SD) M (SD) M (SD) M (SD)

Age 35.19 (11.70) 26.36# (9.06) 36.94# (10.62) 35.13 (10.99)

Years of

Education

11.19 (1.86) 12.18 (2.480 11.55 (3.23) 10.67 (2.29)

IQ estimate 103.80 (7.34) 102.27 (8.17) 105.35 (8.29) 100.55 (6.84)

Illness duration* 6.75 (8.33) 4.35# (4.60) 7.61 (7.96) 13.41# (11.09)

Antipsychotic

daily dose

341.15

n=24

(216.90) 238.89

n=3

(315.49) 299.09

n=22

(221.83) 379.31

n=14

(312.83)

Benzodiazepine

daily dose*

20.00

n=4

(14.72)

7.50

n=1

- 24.00

n=5

(17.10) 58.33

n=6

(27.14)

Diagnostic group

(n)

Schizophrenia 11 (41%) 0 (0%) 13 (42%) 10 (67%)

Bipolar I disorder 7 (26%) 1 (9%) 4 (13%) 3 (20%)

Other psychosis 6 (22%) 3 (27%) 7 (23%) 1 (7%)

Non-psychosis 3 (11%) 7 (64%) 7 (23%) 1 (7%)

Mean task

performance (d’)

Emotion

discrimination

1.22 (.35) 2.24 (.30) 1.43 (.59) .71 (.53)

Emotion labelling .69 (.45) 3.01 (.70) 1.77 (.49) .56 (.54)

Identity

discrimination

2.49 (.37) 3.04 (.53) 2.94 (.34) 2.08 (.50)

Sex labelling 2.12 (.47) 2.07 (.33) 2.04 (.68) 1.46 (.57)

Car discrimination 2.37 (.52) 2.88 (.63) 2.49 (.35) 1.19 (.52)

PANSS

Positive total* 16.33# (5.93) 10.55#⸸ (3.83) 13.94 (5.18) 17.73⸸ (5.37)

Negative totala 10.70 (3.18) 10.82 (4.73) 10.03# (2.99) 13.33# (5.09)

General total 30.89 (4.96) 28.64 (4.18) 31.81 (5.75) 34.60 (7.95)

*p<.05; ap=.051; #,⸸Significant difference between clusters.

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Figure 11. Mean performance across five dynamic tasks for the four patient clusters. Error bars indicate 95% confidence intervals. *p>.05; **p>.001.

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Discussion

The primary aim of this section was to determine whether clinical symptoms are associated

with face processing deficits in patients with a range of psychiatric disorders. It was found that both

positive symptoms and symptoms of cognitive disorganisation correlated with emotion-processing

impairments, and also with performance on a non-face control task. In contrast, mostly cognitive

symptoms – as well as cognitive disorganisation – correlated with performance on identity-processing

tasks. An exploratory hierarchical cluster analysis was also conducted to cluster patients based on task

performance. The solution produced hints at four possible subgroups with differing patterns of

psychopathology.

Associations with positive and negative symptoms

The results of this experiment suggest that performance on two dynamic emotion-processing

tasks is associated with the positive symptom items on the PANSS. This implies that regardless of

diagnosis, patients with positive symptoms such as delusions, suspiciousness, and unusual beliefs are

most likely to show emotion processing difficulties compared to those with other symptoms. This

result is at odds with the majority of past research using the PANSS and other similar scales, which

predominantly report associations with negative symptoms. These include studies using same-or-

different emotion discrimination tasks (Addington & Addington, 1998; Doop & Park, 2009; Edwards

et al., 2001; Fakra et al., 2015) as well as restricted-choice emotion labelling tasks (Baudouin, Martin,

Tiberghien, Verlut, & Franck, 2002; Gur et al., 2006; Kohler et al., 2000; Sachs, Steger-Wuchse,

Kryspin-Exner, Gur, & Katschnig, 2004; Schneider, Gur, Gur, & Shtasel, 1995; Turetsky et al., 2007),

although a smaller number of studies also reported correlations with positive symptoms (Kohler et al.,

2000; Leitman et al., 2005; Loughland et al., 2002b; Silver et al., 2002). One possible explanation for

this discrepancy is that the current study used dynamic stimuli rather than static. Johnston and

colleagues (2010) found that positive symptoms correlated with dynamic emotion processing, while

negative symptoms correlated with static emotion processing. Given that dynamic faces are shown to

elicit different patterns of brain activation compared to static faces, the authors posited that positive

and negative symptoms may have differential effects on these brain networks. However, it is worth

noting that subsequent studies using dynamic emotion tasks have not replicated this dissociation

between positive and negative symptoms (Behere et al., 2011; Mendoza et al., 2011).

Interestingly, the current study found that performance on two dynamic identity-processing

tasks was not associated with positive symptoms or negative symptoms. As no previous study has

employed dynamic stimuli to examine identity processing in patients, this is a novel finding.

However, this is inconsistent with previous studies using static stimuli, which typically did report

159

associations with negative symptoms (Martin et al., 2005; Norton et al., 2009), or with both positive

and negative symptoms (Chen & Ekstrom, 2016; Chen et al., 2009).

On the surface, the finding that positive symptoms were associated with impaired processing

of expressions, but not facial identity, fits with the idea that emotional information is selectively

impaired in patients exhibiting the positive symptoms of psychosis. However, the current study also

found that positive symptoms correlated with performance on a non-face control task. Therefore, it is

unlikely that the association with positive symptoms can be meaningfully interpreted as evidence of

any kind of emotion-selective process. Furthermore, as the four clusters differed significantly in mean

benzodiazepine dose, it is possible that performance differences were at least partly affected the

sedating side-effects of this medication.

Associations with ‘cognitive’ symptoms

The results of the current experiment showed that patient performance was associated with a

range of cognitive symptoms such as cognitive disorganisation, difficulty in abstract thinking,

stereotyped thinking, and poor attention. This pattern was seen across tasks of emotion-processing,

identity-processing, and the non-face control task. The only exception was the Sex Labelling task,

which is unsurprising given that a) this task did not distinguish between patients and healthy controls

in previous analyses (see Chapter 8), and b) previous studies using static versions of this task reported

no correlations with symptoms in schizophrenia (Bediou et al., 2005, 2007; Pinkham et al., 2008).

The observation that cognitive-related symptoms correlated negatively with task performance

suggests that patients showing more generalised cognitive difficulties tended to be less accurate

overall, regardless of stimulus type. This finding is consistent with previous studies indicating

associations between cognitive factors and emotion processing in schizophrenia (Bozikas, Kosmidis,

et al., 2006; Sachs et al., 2004; Silver et al., 2002). This finding is not unexpected, particularly given

the predominance of attentional difficulties in psychiatric disorders such as schizophrenia (Keefe &

Harvey, 2012). However, it does highlight the pervasive impact of generalised cognitive deficits, even

on tasks that are intended to tap into specialised areas of perceptual deficit, such as emotion

processing. Although some authors argue that emotion-processing and generalised cognitive

impairment are two overlapping but separable areas of deficit in schizophrenia (Barkhof, de

Sonneville, Meijer, & de Haan, 2015; Megreya, 2016), the current results align with the opposing

view that emotion-processing deficits may be fully accounted for by more general cognitive

disturbance (Pomarol-Clotet et al., 2010).

Exploratory cluster analysis

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The present study employed hierarchical clustering analysis to identify four potential

subgroups of patients based on their performance across the five tasks. Cluster 2 tended to be

younger, have shorter illness duration, fewer positive symptoms, and had the highest accuracy across

all tasks. This group was primarily composed of patients with non-psychotic disorders, or ‘other’

forms of psychosis, such as drug-induced psychosis. In contrast, Cluster 4 was the lowest performing

across all tasks, tended to be older, have a longer duration of illness, more positive and negative

symptoms, and was primarily comprised of patients with a formal schizophrenia spectrum disorder.

The two largest groups, Clusters 1 and 3, were similar in composition, with intermediate task

performance, moderate age and illness duration, and both comprised a mix of diagnoses including

bipolar I disorder, schizophrenia spectrum, and non-psychotic and other-psychotic disorders. The only

notable difference was that, when compared to the least-impaired group (Cluster 2), Cluster 3 showed

selective impairments on emotion tasks, while Cluster 1 showed impairments across tasks.

Overall, the exploratory cluster analysis only partly approximated diagnostic groups. Patients

with schizophrenia spectrum disorders were overrepresented in the most impaired cluster, while non-

psychosis patients predominantly made up the least-impaired cluster. However, the majority of all

patients, irrespective of diagnosis, fell into one of two intermediate clusters: one with

disproportionately poor emotion-processing, and one with generally impaired performance. Therefore,

there is no evidence to suggest that performance on these tasks could reliably distinguish between

different patient groups.

Limitations

Several important caveats must be made when interpreting these results. The first is that these

analyses focused on the interpretation of individual symptom items, rather than aggregated subscale

scores, which are more statistically robust (Kay et al., 1987). While previous studies typically report

subscale scores such as ‘Positive Symptoms’ on the PANSS, these were considered less interpretable

because they do not directly correspond to the prevailing three-factor model of positive, negative and

disorganised symptoms (Mortimer, 2007). The findings cited in previous studies may be further

complicated by the fact some symptom scales conflate generalised cognitive symptoms with non-

cognitive positive and negative symptoms. For example, the PANSS includes ‘Cognitive

Disorganisation’ in the calculation of positive symptom scores, and ‘Difficulty in Abstract Thinking’

and ‘Stereotyped Thinking’ in the calculation of overall negative symptoms. A further limitation is

that the cluster analysis described was only exploratory in nature, lacked theoretical context, and

produced small, uneven-sized groups. Therefore, these results should be interpreted with appropriate

caution.

Conclusions

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This section explored associations between clinical symptom ratings and performance on a

series of tasks evaluating emotion-processing, identity-processing, and non-face discrimination tasks.

It was found that non-cognitive positive symptoms – such as delusions and suspiciousness – were

negatively correlated with both emotion-processing and non-face discrimination, but not identity-

processing ability. Although inconsistent with the majority of previous literature using static tasks,

this finding agrees with one study using dynamic stimuli (Johnston et al., 2010). The finding that

positive symptoms correlated with performance on a non-face task suggests that these symptoms are

unlikely to indicate an emotion-specific processing impairment. Furthermore, more severe cognitive

symptoms were associated with generally reduced performance across tasks. It is suggested that

previous research using aggregate scores from instruments such as the PANSS may conflate the

presence of cognitive and non-cognitive symptoms in schizophrenia. Therefore, future studies using

these instruments should consider individual items, or alternative groupings of items, to evaluate the

role of specific symptom clusters.

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Chapter 12: General Discussion

The overarching aim of this thesis was to better characterise the nature of face-processing

deficits in schizophrenia and other psychiatric disorders. This involved addressing the following

research questions:

1. Defining the deficit: Is the emotion-processing deficit specific to emotion, or is it better

explained by broader deficits in processing faces, or a generalised cognitive impairment?

2. Are these deficits specific to schizophrenia, or shared across similar disorders?

3. What symptoms correlate with these deficits?

While emotional face-processing in schizophrenia has attracted a substantial body of research,

inconsistent findings across studies mean that the questions above remain unanswered. This

inconsistency is likely due to differences in patient populations studied, small sample sizes,

differences in methods used to assess face-processing, and a tendency to not include control tasks. A

major point raised in Chapter 5 of this thesis is that the majority of past studies used only static

images to assess face-processing, which lack the ecological validity and sensitivity of dynamic

(video-based) stimuli. In recognition of this issue, I created a novel set of dynamic morphed stimuli

that were designed to assess not only facial emotion-processing, but also non-emotional face-

processing (i.e.: facial identity) and non-face processing (i.e.: cars). To the best of my knowledge, this

is the first stimulus set to combine morphing with dynamic video to test non-emotional and non-face

processing ability. These stimuli were used in a large study of healthy controls (n=82) and two studies

of psychiatric inpatients (n=106 and n=45), and are available to download from

http://go.unimelb.edu.au/e3t6.

The healthy control study revealed that, as predicted, the newly-developed set of dynamic

stimuli were recognised more accurately than static versions of the stimuli. Furthermore, correlations

with schizotypy scores showed that healthy controls who were high in schizophrenia-like traits were

generally less accurate at labelling dynamic emotions compared to individuals who were low in

schizotypy. This suggests that these new dynamic tasks are sensitive to the subtle emotion-processing

deficits typically seen in individuals with schizophrenia-like traits.

The main inpatient study revealed that, as expected, patients with schizophrenia showed

impairments in both labelling and discriminating between emotions compared to healthy controls.

However, performance on a non-face task was also impaired, and performance on a non-emotional

(identity) face task was marginally impaired. An additional inpatient study designed to assess

visuospatial attention revealed that although patients with schizophrenia showed marked impairment

in attending to the ‘global’ level of a stimulus, this deficit does not appear to be associated with

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emotion-processing ability. Overall, these findings suggest that the “emotion-processing deficit” seen

in schizophrenia likely extends to non-face and non-emotional tasks as well. However, these deficits

cannot be explained by broader impairments in the allocation of visual attention.

Considering all psychiatric disorders, the main inpatient study revealed that patients with

schizophrenia and those with bipolar disorder both showed comparable deficits in emotion processing,

accompanied by intact non-emotional face-processing (identity processing) compared to age-matched

healthy controls. These groups also showed similar deficits in discriminating non-face objects (cars).

Patients with non-schizophrenia psychosis showed mild deficits in emotion-processing, but no other

significant deficits. In contrast, patients with non-psychotic disorders performed no differently to

healthy controls. Correlational analyses showed that deficits in emotion-processing and non-face

processing were both associated with positive symptoms of psychosis as well as cognitive

disorganisation. In contrast, non-emotional face-processing was correlated with cognitive symptoms

only.

This chapter will begin by discussing the findings of these studies with reference to the

various models proposed to explain emotion-processing deficits in schizophrenia. It will then discuss

these findings in the context of the continuum model of psychosis, with particular attention to the role

of symptomatology. Finally, this chapter will discuss the broader implications of these studies,

limitations, and directions for future research.

Defining the deficit: Different models to explain emotion-processing

deficits in schizophrenia

The first aim of this thesis was to better characterise emotion-processing deficits in

schizophrenia. Specifically, to establish whether the facial emotion processing impairments

demonstrated in previous studies represent a true deficit in emotion-processing, or whether they are

better explained by other, overlapping areas of cognitive or visuoperceptual impairment. This section

will recap the different models proposed to explain emotion-processing in schizophrenia, then discuss

which of these models are best supported by the results of the current study.

A generalised cognitive deficit

The idea that a specific deficit in schizophrenia may actually be due to a more general deficit

is nothing new. A recurring controversy in schizophrenia research is the “generalised deficit

problem”: the concern that evidence of specific impairment on a certain task (such as face

recognition) may be better accounted for by a generalised cognitive deficit (such as poor attention;

Green, Horan & Sugar, 2013) . Many studies claim to capture specific deficits by demonstrating

impaired performance on one task (such as facial emotion recognition) but intact performance on

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another task. However, it has been argued that the differential performance across two different tasks

may be the result of task parameters (such as difficulty or perceptual constraints) rather than a true

differential deficit (Green et al., 2013). In their 2013 commentary, Green and colleagues argue that the

literature does not support the presence of a generalised cognitive deficit in schizophrenia. Rather,

there is consistent evidence for sparing of certain cognitive domains such as speed of attentional

shifting, forms of implicit learning, emotional experience, and aspects of basic perceptual processing

(Gold, Hahn, Strauss, & Waltz, 2009; Horan, Foti, Hajcak, Wynn, & Green, 2012; Lee et al., 2013).

Furthermore, they point out that a truly generalised deficit would assume high levels of shared

variance across tasks sensitive to deficits, whereas many studies report negligible correlations

between deficits in different domains in schizophrenia (Gold et al., 2012). An opposing view is

presented by Gold and Dickinson (2013) who argue that, while there is some evidence of differential

deficits in schizophrenia, these studies are vastly outweighed by studies that report consistent, broad

deficit across all domains in schizophrenia (Green & Harvey, 2014; Mesholam-Gately, Giuliano,

Goff, Faraone, & Seidman, 2009). That is, while there is reason to believe that there are certain areas

of relatively spared performance in schizophrenia – such as selective attention in suppressing

irrelevant items on a working memory task (Gold et al., 2009) – this does not negate the finding that

patients consistently show broad, overall performance deficits compared to healthy controls (Gold &

Dickinson, 2013). A more appropriate approach is to examine whether emotion processing

impairments in schizophrenia can be entirely accounted for by the generalised performance deficit,

and in this respect the evidence is inconsistent. Two recent studies indicate that, although patients

with schizophrenia show reduced performance across tasks (indicating some degree of generalised

deficit) they nevertheless show disproportionately poor performance on facial emotion tasks

compared to matched tasks using non-face stimuli such as abstract patterns and line drawings

(Barkhof et al., 2015; Megreya, 2016). In contrast, other studies have reported comparable deficits on

both facial emotion tasks and tasks using non-face stimuli (Chen et al., 2009; Laprevote, Oliva,

Delerue, Thomas, & Boucart, 2010; Laprevote et al., 2013; Soria Bauser et al., 2012), supporting the

idea that emotion impairments in schizophrenia may be due to more general cognitive (or perceptual)

impairments.

A deficit in processing information from faces (a face -specific deficit)

Another possible explanation is that emotion-processing deficits in schizophrenia are actually

due to a more general perceptual impairment in processing faces that is unrelated to emotional

content. Chapter 3 presented a more in-depth review of studies demonstrating impairments in non-

emotional face processing in schizophrenia. To briefly recap, individuals with schizophrenia show

impairments in different aspects of basic face processing including face detection (McBain, Norton, &

Chen, 2010), extracting configural information from faces (Kim et al., 2010), discriminating age and

sex (Delerue et al., 2010; Schneider et al., 2006), and recognising the identity of a face (Bortolon,

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Capdevielle, & Raffard, 2015). Many researchers investigating emotion-processing assume that non-

emotional face processing is unimpaired in schizophrenia, and therefore either exclude non-emotional

face tasks altogether, or include tasks that are poorly matched to the emotion task of interest (eg.: the

Benton Test of Facial Recognition). Of the small number of studies that have directly compared facial

identity processing and emotion processing in schizophrenia, results were predictably mixed. Several

studies demonstrated a disproportionate deficit in distinguishing facial emotions compared to

distinguishing facial identity (Kosmidis et al., 2007; Kucharska-Pietura et al., 2005; Penn et al., 2000;

Poole, Tobias, & Vinogradov, 2000; Schneider et al., 2006), while others showed that patients were

equally impaired on both domains (Addington & Addington, 1998; Pomarol-Clotet et al., 2010; Sachs

et al., 2004). One meta-analysis examined 28 studies comparing performance on an emotion task with

a non-emotional face control task in schizophrenia patients (Chan et al., 2010). It was found that,

while patients were significantly impaired on the control tasks with an overall effect size of -.70 (a

moderate-to-large effect), a greater impairment was seen on the facial emotion tasks with an effect

size of -.85 (a large effect). The authors concluded that, while there is evidence of a generalised

impairment in face processing, the processing of facial emotions is disproportionately affected in

schizophrenia. However, whether this indicates specific impairment in the perceptual or cognitive

processes required for recognising emotion, or simply reflects differences in task parameters (such as

greater difficulty) remains unclear.

A multi-modal deficit in processing emotional information (an emotion -specific

deficit)

Finally, a third explanation for facial emotion deficits in schizophrenia is that they represent a

multi-modal deficit in emotion processing which is not specific to faces. Although not as thoroughly

studied as the face literature, researchers have found evidence for impairments in recognising emotion

in other domains, such as affective prosody (i.e. emotion conveyed in the melody of speech).

Compared to healthy controls, individuals with schizophrenia were found to have greater difficulty

identifying the emotion portrayed in vocal recordings, regardless of the emotion being expressed

(Bozikas, Kosmidis, et al., 2006; Edwards et al., 2002; Edwards et al., 2001; Kucharska-Pietura et al.,

2005; Leitman et al., 2005; Matsumoto et al., 2006). Functional MRI investigations also revealed

abnormal activations in schizophrenia when listening to emotional prosody, including reduced

activations in the insula, superior temporal gyrus, and inferior frontal gyrus (Leitman et al., 2011;

Mitchell, Elliott, Barry, Cruttenden, & Woodruff, 2004). Similarly, two ERP studies have shown

atypical responses of the P50, N100 and P200 and waveforms in patients with schizophrenia during a

vocal emotion discrimination task, which were congruent with reduced accuracy (Pinheiro et al.,

2013; Pinheiro et al., 2014). Given that schizophrenia is associated with anatomical and functional

abnormalities of structures of the limbic system (see Chapter 2), it is possible that the

disproportionately large impairment in facial emotion recognition reported in the literature is driven

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(at least in part) by dysfunction in higher-order emotion processing, rather than visuoperceptual

mechanisms such as face perception.

Results of the current study

Results from the inpatient studies (presented in Chapters 8 and 9) indicated that patients with

schizophrenia showed prominent impairment in both labelling and discriminating between dynamic

emotions but showed no (or negligible) impairment on two non-emotional face tasks which were

matched on task demands. Therefore, the results are consistent with previous studies which reported

that emotion-processing is disproportionately impaired compared to non-emotional face processing

(e.g.: Kosmidis et al., 2007; Kucharska-Pietura, David, Masiak, & Phillips, 2005; Penn et al., 2000;

Poole, Tobias, & Vinogradov, 2000; Schneider et al., 2006). However, inpatients with schizophrenia

also showed substantial deficits on a car discrimination task. This suggests that, while non-emotional

face processing may be (mostly) spared, there is clear evidence that the very task of discriminating

between two dynamic objects is reduced compared to healthy controls. Again, the finding that non-

face object recognition is just as impaired as emotion-processing is consistent with several previous

studies (e.g.: Laprevote et al., 2013; Soria Bauser et al., 2012), but to my knowledge this is the first

time this has been investigated using dynamic stimuli.

Taken together, these results are consistent with the idea that emotion-processing deficits are

simply the result of a general cognitive impairment, such as inattention. Performance on the non-

emotional face tasks may have been spared due to the fact that non-emotional face processing is

simply not equivalent to other visual tasks. Structural or non-emotional face information is extracted

from the environment through ‘holistic’ face processing (McKone & Robbins, 2011). Holistic face-

processing refers to a rapid, involuntary face-specific perceptual process that integrates information

across the face as a whole. It includes such information as the shapes of individual features, the

relative distances between them, and the contour of the cheeks and jaw (Maurer et al., 2002; McKone

& Yovel, 2009). Holistic processing operates for upright faces only, and is absent for objects and

inverted faces (Rossion, 2008). Importantly, this process is specific to invariant information, and is

therefore critical for perceiving identity (McKone & Robbins, 2011; also see Chapter 2). In contrast,

recognition of emotions depends on changeable face information (i.e.: transient facial movements)

which is not extracted through holistic processing. Studies suggest that holistic face processing is

intact in schizophrenia (Butler et al., 2008; Chambon et al., 2006; Schwartz et al., 2002), therefore it

is entirely possible that performance on the two non-emotional face tasks in the current studies was

facilitated through this rapid, specialised process. In contrast, performance on the two emotion tasks

and the non-face (car) task likely relied on more directed forms of voluntary attention, and as a result

may be more vulnerable to generalised cognitive impairment. Further research is necessary to explore

this hypothesis. Nevertheless, it can be concluded from the experiments described in this thesis that

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the emotion-processing deficits typically associated with schizophrenia cannot be accounted for by

face-specific perceptual impairments. Rather, the evidence supports a generalised cognitive

impairment which is unrelated to either emotions or faces.

Results from the symptom correlate analyses also fit with the idea that impaired performance

on the emotion tasks and car task were due to a shared deficit. Both positive symptoms of psychosis

(e.g.: delusions) and cognitive symptoms (e.g.: poor attention) were associated with deficits in both

emotion-processing and non-face discrimination, as shown by the respective dynamic tasks. In

contrast, performance on the two non-emotional face tasks were unimpaired in the schizophrenia

group, and performance on these tasks was associated only with cognitive symptoms. Therefore, it

can be tentatively concluded that the “emotion-processing deficits” reported in this thesis, and in

previous literature, actually represent a generalised cognitive impairment which is particularly

associated with positive symptoms of psychosis.

Are these deficits specific to schizophrenia? Implications for

transdiagnostic models of psychiatric illness

The second aim of this thesis was to determine whether face-processing impairments are

specific to schizophrenia, and to what extent they are shared by other psychiatric disorders. The third

aim of this thesis was to examine associations between these deficits and specific symptoms in

inpatients with a range of psychiatric diagnoses. By characterising patterns of deficits across

psychiatric disorders, researchers can draw inferences about the nosological boundaries – or lack

thereof – between these disorders. This section will briefly review spectrum, dimensional, or

‘transdiagnostic’ theories of psychosis. It will then discuss the findings of the current studies with

relation to these theories.

Why should we consider dimensional models of illness?

Traditional conceptualisations of psychopathology, such as that found in the DSM-V, focuses

on defining distinct categories of illness in order to facilitate effective diagnosis, treatment planning,

and estimates of prognosis (Lawrie et al., 2010). Although this categorical approach dominates in both

clinical and research settings, evidence suggests that psychopathology does not naturally manifest in

such clearly defined categories. For instance, it has been shown that individual symptoms, such as

depression, mania, and psychosis, are better explained as dimensional phenomena that occur

frequently across different diagnoses as well as in subclinical forms (Wigman, de Vos, Wichers, van

Os, & Bartels-Velthuis, 2017). Even seemingly rare symptoms, such as the presence of delusions, are

commonly found across a wide range of disorders such as major depression (estimated 20% of

patients), bipolar disorder (up to 50%), borderline personality disorder (33%), obsessive-compulsive

disorder (14%) and even Alzheimer's disease (30%) (Bebbington & Freeman, 2017). As a result of

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this heterogeneity, comorbidity rates in psychiatric disorders are understandably high, with almost

half of patients meeting criteria for two or more different disorders (Kessler et al., 2005). Overall, the

evidence suggests that conventional categories are not adequately capturing the expression of

different disorders. Could a dimensional approach to diagnosis overcome these problems? Some

authors argue that dimensional models do not add clinical utility (e.g. see Lawrie et al., 2010). Others

argue, however, that improving identification and treatment of comorbid symptoms could improve

patient outcomes (Kelleher & Cannon, 2016). For example, the presence of psychotic symptoms in

individuals with primary non-psychotic disorders is associated with greater suicidality and poorer

social, cognitive, and occupational outcomes (Kelleher et al., 2013; Kelleher et al., 2014; Wigman et

al., 2012). By the same token, specific treatment of non-psychotic symptoms in psychotic disorders

has been shown to improve patient outcome (de Bont et al., 2016), although more research is needed

to establish evidence-based treatment guidelines for common comorbidities, such as depression

(Upthegrove, Marwaha, & Birchwood, 2017). Consideration of spectrum models of symptoms may

well be the approach needed to develop transdiagnostic methods of treatment that focus on symptoms

in concert, rather than the symptoms of the primary diagnosis alone.

A spectrum of psychosis in the general population

As discussed in Chapter 6, psychosis-like experiences are reported not only in psychiatric

patients, but also in otherwise healthy individuals. Termed ‘schizotypy’, these experiences include a

range of milder or subclinical versions of symptoms such as delusions, hallucinations and social

anhedonia (Mason & Claridge, 2006). These psychosis-like experiences are shown to vary in intensity

across the general population, affecting an estimated 7% of adults (van Os, Linscott, Myin-Germeys,

Delespaul, & Krabbendam, 2009). It has been argued, therefore, that psychosis is not a discrete

phenomenon, but a continuum with formal schizophrenia representing the most extreme pole of a

spectrum of normal experience (Claridge, 1997; DeRosse & Karlsgodt, 2015). While approximately

8% of high-schizotypy individuals will convert to a formal psychotic diagnosis in the following years,

the majority of individuals will not go on to meet diagnostic criteria (Hanssen et al., 2005). Therefore,

although schizotypy serves as a prodrome to formal diagnosis for some, this is not accurate in the

majority of cases (Kelleher & Cannon, 2011). Importantly, high levels of schizotypy are associated

with other areas of deficit commonly seen in schizophrenia, such as mildly impaired emotion-

processing, neurophysiological abnormalities, and cognitive deficit (Batty et al., 2014; Germine &

Hooker, 2011; Korponay, Nitzburg, Malhotra, & DeRosse, 2014). This suggests that formal psychotic

disorders and trait schizotypy may share a common biological basis, further supporting the continuum

model.

The experiment described in Chapter 6 found that schizotypy scores in healthy controls were

negatively correlated with the ability to correctly label emotions on a dynamic task. Interestingly, the

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‘Unusual Experiences’ subscale was associated with impaired performance, but not the other

subscales (‘Cognitive Disorganisation’, ‘Introvertive Anhedonia’ and ‘Impulsive Nonconformity’).

This finding is in line with previous research indicating associations between emotion-processing

deficits and ‘positive’ schizotypy (Abbott & Byrne, 2013; Kerns, 2005; Shean et al., 2007; van 't

Wout et al., 2004). However, to my knowledge this is the first study to report this result using

dynamic tasks. These findings support the notion that the same pattern of deficits seen in clinical

psychosis are also expressed to a milder degree in schizotypy, suggesting that they may represent

different severities of the same construct. Broadly speaking, these findings are consistent with the idea

that psychosis is best represented as a continuum of experience.

The bipolar-schizophrenia spectrum model

Traditionally, schizophrenia spectrum disorders and bipolar disorder have been

conceptualised as discrete nosological entities; a distinction that is sometimes referred to as the

‘Kraepelinian dichotomy’ (Craddock & Owen, 2010). However, many patients present with a

combination of prominent affective and psychotic symptoms, making it difficult to differentiate

between schizophrenia and bipolar disorder in practice (Dacquino et al., 2015; Malaspina et al., 2013).

These disorders overlap not only in the expression of symptoms, but aetiologically as well. For

instance, a substantial body of evidence point towards shared genetic risk factors for both bipolar

disorder and schizophrenia (Cardno, Rijsdijk, Sham, Murray, & McGuffin, 2002; Houenou et al.,

2017; Lichtenstein et al., 2009; The International Schizophrenia Consortium, 2009). Both disorders

respond similarly to antipsychotic treatments (Hill et al., 2013), and share environmental risk factors

such as urbanicity, childhood adversity, substance abuse, and prenatal insult (Clarke, Harley, &

Cannon, 2006; Heinz, Deserno, & Reininghaus, 2013; Lichtenstein et al., 2009; Matheson, Shepherd,

Pinchbeck, Laurens, & Carr, 2013).

A large study by the Bipolar and Schizophrenia Network for Intermediate Phenotypes

(BSNIP) consortium were conducted to identify phenotypic markers in bipolar disorder and

schizophrenia. This included over 2000 patients, their first-degree relatives, and healthy controls who

completed a battery of tests investigating social functioning, cognition, brain volumetry, and a variety

of EEG and fMRI assessments. Surprisingly, almost every one of the biomarkers tested overlapped so

strongly that they were unable to discriminate between bipolar and schizophrenia groups (Tamminga

et al., 2014). The only biomarker that differed substantially between diagnoses was grey matter

volume. While schizophrenia and schizoaffective patients showed widespread grey matter reduction

across the cortices, bipolar patients showed only minor reductions in the prefrontal and limbic

regions. Taken together, the biomarker evidence fits with genetic and phenomenological studies

which indicate that schizophrenia and bipolar disorder overlap substantially and may even fall upon a

spectrum of disease without clearly delineated boundaries.

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Studies examining cognitive deficits across different diagnoses also appear to support a

spectrum model. Cognitive deficits in schizophrenia are present at or prior to the first episode of

psychosis, are largely unaffected by treatment, and are predictive of poorer functional outcome (Hill

et al., 2013; Keefe & Harvey, 2012). Although bipolar disorder is typically associated with less

profound cognitive deficits, these deficits are seemingly stable over time and are worse in patients

who have experienced symptoms of psychosis (Bora, Yucel, & Pantelis, 2010; Burdick, Goldberg,

Harrow, Faull, & Malhotra, 2006; Glahn et al., 2007; Hill et al., 2009). Schizoaffective disorder,

which some consider an intermediary diagnosis between bipolar disorder and schizophrenia, is

similarly associated with cognitive deficits which appear to fall in between these two groups (Hill et

al., 2013; Hooper et al., 2010). One large study investigating cognition across a range of psychotic

disorders (n>650) found that deficits were largest in the schizophrenia group, intermediate in

schizoaffective disorder, and smallest (but still significant) in psychotic bipolar disorder (Hill et al.,

2013). These findings fit with the idea that bipolar disorder and schizophrenia may be conceptualised

as opposing poles on a spectrum, with schizoaffective disorder in the middle. Rather than distinct

entities, these disorders vary along two factors: severity of affective symptoms, and severity of

psychotic symptoms.

The schizophrenia-bipolar spectrum model is supported by evidence indicating substantial

overlap in symptomatology, genetic and environmental risks, biomarkers and neurocognitive deficits

between these disorders. However, this model does not take into account the substantial overlap

between psychotic disorders and other types of disorders, such as unipolar depression, anxiety, autism

and even intellectual disability (van Os & Reininghaus, 2016). For instance, evidence suggests that

symptoms of psychosis or psychosis-like experiences are not randomly distributed among the general

population, but are twice as likely to occur in individuals with a diagnosis of anxiety or depression

(Wigman et al., 2012). In fact, symptoms of psychosis are predicted not only by psychotic diagnoses,

but also by all mood and anxiety disorders, eating disorders, ADHD, and alcohol abuse (McGrath et

al., 2016). There is also evidence of similar biomarkers in schizophrenia and depression, including

white matter changes, circulating inflammatory markers, and limbic system abnormalities

(Upthegrove et al., 2017). In recognition of these shared factors, Reininghaus and colleagues (2016)

have suggested a bi-factor model consisting of a general psychosis dimension, which underlies both

affective and non-affective psychotic symptoms across disorders, and five specific psychosis

dimensions (see Figure 12.1). The specific factors include positive symptoms, negative symptoms,

disorganisation, mania and depression. Unlike the simpler schizophrenia-bipolar spectrum, this six-

dimensional model accounts for both shared and specific features of different disorders.

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Figure 12.1. Pentagonal bifactor model of psychosis. Standardised mean factor scores on each

dimension are shown for four different diagnostic categories: bipolar disorder with mania, bipolar

disorder with hypomania, schizoaffective disorder and schizophrenia. Standardised factor scores have

a mean of 0 and standard deviation of 1. Image from Reininghaus and colleagues (2016;

supplementary materials).

The results of the experiments outlined in this thesis provide further support for the idea that

schizophrenia and bipolar disorder may exist on a spectrum. The schizophrenia and bipolar groups

performed similarly across all tasks, and were significantly different from controls on two emotion-

processing tasks and a car discrimination task. This suggests that, in line with previous studies

investigating face processing using static stimuli (Bellack et al., 1996; Derntl et al., 2012; Edwards et

al., 2001), both schizophrenia and bipolar patients have similar deficits in both emotion-processing

and non-face discrimination. Correlational analyses also indicated that performance on these tasks

was positively associated with positive symptoms and cognitive symptoms. As discussed in the

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previous section, the performance on these tasks may represent a generalised cognitive deficit which

is most severe in patients with psychosis but negligible in patients with non-psychotic disorders.

Where does substance-induced psychosis fit in?

The previous sections have explored the concept of psychosis as a dimensional phenomenon

that varies in intensity and frequency across the general population and transcends diagnostic

categories. However, our understanding of psychosis is further complicated by a comparatively under-

studied form of psychotic disorder: substance-induced psychosis. The incidence rate of substance-

induced psychosis is estimated at 5 per 100,000 people per year (Chen, Hsieh, Chang, Hung, & Chan,

2015). A percentage of these individuals will progress to a formal diagnosis of schizophrenia in the

years following, however this number varies according to the substance implicated. For instance, 5-

11% of patients with alcohol-induced psychosis will develop schizophrenia within 8 years, compared

to approximately 20-30% of patients with amphetamine-induced psychosis and 46% of patients

cannabis-induced psychosis (W. L. Chen et al., 2015; Niemi-Pynttari et al., 2013; Starzer et al., 2018).

This raises the question: is substance-induced psychosis a discrete disorder, or merely a prodrome to

schizophrenia?

The relationship between drug use and psychosis is complex. Studies suggest that individuals

with psychotic disorders are at a greatly increased risk of developing a substance use disorder, with a

lifetime prevalence of 48-60% (Cantor-Graae, Nordstrom, & McNeil, 2001; Swartz et al., 2006), and

comorbid substance abuse is associated with poorer remission rates, more severe symptoms and

treatment non-compliance (Lambert et al., 2008; Malla et al., 2008; Miller, 2008). Animal and human

studies suggest that schizophrenia and substance use disorders share a neurobiological basis, possibly

relating to abnormalities in striatal dopamine levels (Chambers, Sentir, & Engleman, 2010; Thompson

et al., 2013).

Clinically, the acute presentation of substance-induced psychosis is indistinguishable from

primary psychosis (Bramness et al., 2012). Generally speaking, substance-induced psychosis is

thought to involve similar neurochemical pathways to schizophrenia (Paparelli et al., 2011), however

the exact mechanism is unclear. For example, repeated amphetamine abuse may precipitate psychosis

through acute effects on CNS dopamine levels, but indirect effects such as sleep deprivation may also

play a role (Murray, Paparelli, Morrison, Marconi, & Di Forti, 2013). Additionally, the long-term

neurotoxic effects of amphetamines may reduce the threshold for developing psychosis over time

(Bramness et al., 2012). Cannabis-induced psychosis may also precipitate psychosis by altering

dopamine levels. Studies suggest that cannabis use affects striatal dopamine, particularly in

individuals with a familial history of schizophrenia, likely indicating a shared genetic vulnerability to

substance-induced psychosis (Kuepper et al., 2013).

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Should substance-induced psychosis be considered a separate diagnostic entity to

schizophrenia? Despite high rates of transformation to schizophrenia later in life, the majority of

patients with substance-induced psychosis will never meet criteria for a primary psychosis (W. L.

Chen et al., 2015; Niemi-Pynttari et al., 2013). Clearly there are other factors involved that determine

whether an individual converts to primary psychosis or remits altogether. Bramness and colleagues

(2012) argue that substance-induced psychosis and schizophrenia can be reconciled using the

traditional stress-diathesis model (See Figure 12.2). In this model, the amount of exposure to a

substance (such as amphetamines) sufficient to precipitate psychosis depends on an individual’s level

of genetic vulnerability. That is, an individual with low vulnerability may use large amounts of

amphetamines without becoming psychotic (substance use disorder without psychosis), while

someone with high vulnerability develops primary psychosis without any exposure to drugs

(schizophrenia). Those who develop substance-induced psychosis therefore have a moderate genetic

vulnerability to psychosis which is precipitated by drug use, and whether they transition to

schizophrenia may depend on both genetic vulnerability and severity of repeated amphetamine use.

This model of schizophrenia is also consistent with the ‘continuum model of psychosis’ discussed in a

previous section, which posits that formal psychotic disorders represent the most extreme end of a

continuum of psychotic-like traits distributed throughout the general population (Mason & Claridge,

2006).

Figure 12.2. A stress-diathesis model demonstrating the interaction between exposure and genetic

vulnerability to psychosis. From Bramness and colleagues (2012, p.4).

The results of the experiments presented in this thesis also provide some indirect support for

the idea that substance-induced psychosis sits in an intermediate position between psychotic disorders

and unaffected individuals. Although substance-induced psychosis was not examined in isolation, the

174

‘non-schizophrenia psychosis’ group was comprised of 53% substance-induced psychosis patients,

with the remainder mostly depression with psychosis. Results showed that performance on the

emotion tasks was mildly impaired for this group, with accuracy falling in between the healthy

controls and the more substantially impaired schizophrenia and bipolar groups. This suggests that this

group of non-schizophrenia psychotic disorders may experience a less severe form of emotion-

processing deficit which is positively associated with the severity of psychotic symptoms.

Methodological and practical implications of this research

Teasing apart the role of face and emotion-processing deficits in schizophrenia may seem, to

some, to be an exercise in academic pedantry. However, this research has important practical

implications. Deficits in facial emotion-processing have real-world consequences, as they are strongly

associated with poorer social, occupational, and community functioning in patients with schizophrenia

(Irani et al., 2012; Meyer & Kurtz, 2009; Tsunoda et al., 2012). Furthermore, these deficits may

predict conversion to schizophrenia in healthy individuals who are at clinically-high risk of psychotic

illness (Corcoran et al., 2015). Tasks that effectively assess these deficits, therefore, may have clinical

utility in identifying patients who are at greater risk of poorer functioning, and may even play a role in

predicting who will develop schizophrenia. Tools that can increase timely diagnosis of psychosis are

especially useful, as the duration of untreated psychosis prior to diagnosis is associated with poorer

outcome, and more severe cognitive and negative symptoms following treatment (Chang et al., 2013).

Another way in which emotion-processing tasks may benefit patients is through remediation.

Several studies have trialled programs designed to improve emotion recognition ability in patients

with schizophrenia. Although some authors reported no improvement with training (Bechi et al.,

2012) other studies have demonstrated emotion-processing improvements following training which

are accompanied by increased activation in brain regions relevant to recognising emotions (Habel et

al., 2010; Hooker et al., 2013; Hooker et al., 2012; Ramsay & MacDonald, 2015). Whether these

improvements translate into improved real-life functioning, however, remains to be seen.

If emotion-processing tasks are to be useful for either assessment or remediation, it is

important that they be sensitive to the deficit of interest. This thesis uncovered several methodological

issues that may have implications for future research. First, it was found that dynamic stimuli are

more accurately recognised and are also more sensitive to positive schizotypy (psychosis-like

experiences) in healthy participants compared to traditional static stimuli. This finding adds further

weight to a growing body of evidence that dynamic emotion tasks are more ecologically valid, less

prone to ceiling effects, and more sensitive to subtle impairments than static tasks (Davis & Gibson,

2000; Harms et al., 2010). Taken together, there are clear incentives for researchers to choose to

175

employ dynamic tasks over traditional static measures, especially considering the increasing

availability of video-based emotion stimuli online (e.g.: https://mmifacedb.eu/).

An additional finding was that the lesser-utilised emotion discrimination paradigm (i.e.:same-

or-different?) may in fact be a better measure of motion-sensitive emotional information than the

more popular emotion labelling paradigm. Although this incidental discovery has not yet been

replicated, it would be interesting to explore whether these paradigms also produce differing patterns

of neural activity, and whether this has further implications for the use of one paradigm over another.

A third finding from this thesis was that sex-labelling tasks are likely not an appropriate

measure of facial identity processing. Deficits in identity-processing are less well-studied than

emotion-processing deficits in schizophrenia, however many authors assume that non-emotional face

processing is intact in this disorder (see Chapter 3, however, for a review of studies that suggest the

opposite). One problem with this area of research is that the varying paradigms used to assess

identity-processing deficits may not be measuring the same construct. This thesis revealed that a

dynamic sex-labelling task (i.e., is this morphed person male or female?) was entirely insensitive to

patient symptomatology, whereas a dynamic identity discrimination task (same-or-different?) was

associated with a variety of patient symptoms. This finding is in line with three previous studies

which reported that sex-labelling tasks were not correlated with deficits in patients with schizophrenia

(Bediou et al., 2007; Bediou, Krolak-Salmon, et al., 2005; van 't Wout et al., 2007). It is possible that

making decisions about the sex of a face does not involve the same neural processes as recognising

the entire identity of a face. Alternatively, the same-or-different discrimination paradigm may simply

be more sensitive to cognitive deficits in schizophrenia. Regardless, it is clear that sex-labelling tasks

are not appropriate for investigating perceptual deficits in schizophrenia, regardless of whether stimuli

are dynamic or static.

Study limitations

As was touched upon in previous chapters, the research presented in this thesis has several

limitations. First, despite attempts to match participant demographics in the main inpatient study, the

schizophrenia group had significantly fewer years of education, and lower IQ estimates compared to

healthy controls. Moreover, almost all patients were receiving psychotropic medication, which may

have impacted on their performance. Although our analyses (see Chapter 7) suggest that these factors

alone cannot account for the group differences reported in this study, their influence cannot be

definitively excluded.

An additional limitation is group size. Due to time constraint and the challenges surrounding

the recruitment of acutely unwell psychiatric inpatients, group sizes were uneven and relatively small

176

(n<20 for the three non-schizophrenia patient groups). This reduced the power to detect significant

differences in performance between these comparison groups. Given that significant differences were

still found, however, this limitation is unlikely to have had a major impact on the interpretation of

these results.

Furthermore, while our results support the idea of a generalised cognitive deficit, the inpatient

studies did not include any direct measures of cognition. These were excluded from the dynamic

battery in order to keep testing sessions within a 2-hour time frame. However, future studies would

benefit from including standardised cognitive measures, such as tests of basic attention or processing

speed, to evaluate whether specific domains of cognition correlate with performance on these

dynamic tasks.

The inpatient experiments reaffirmed the reports of deficits in face processing previously

demonstrated using static images using more ecologically-valid dynamic stimuli (Johnston et al.,

2010). However, it is important to acknowledge that these stimuli are still a distant approximation of

everyday social interactions. In future, tasks with even greater ecological validity, such as immersive

virtual environments, may be an advance in examining these mechanisms further.

Conclusions and future directions

The findings of this thesis suggest that, contrary to widespread belief, there is no evidence for

an emotion-specific face processing impairment in schizophrenia. Rather, the results point towards a

generalised cognitive deficit that is found across tasks assessing both emotion-processing and non-

face object processing. Correlational analyses revealed that both positive symptoms (such as

hallucinations and delusions) and cognitive symptoms (such as inattention) were associated with

deficits on these tasks. Moreover, these impairments were not specific to schizophrenia patients, but

also found in bipolar patients and, to a lesser degree, in patients with non-schizophrenia psychosis.

Patients with non-psychotic disorders, in contrast, were unimpaired. These findings support disease

models which conceptualise psychosis as a transdiagnostic spectrum rather than a series of categorical

disorders. Further support for dimensional models comes from a study of healthy controls, which

found that the positive dimension of schizotypy (e.g.: unusual perceptual experiences) was associated

with poorer performance on emotion-processing tasks. This suggest that the deficits associated with

clinical psychosis are present in an attenuated form in healthy individuals who report psychosis-like

experiences.

This was the first time that emotion-processing, identity-processing, and non-face processing

were examined in inpatients using a newly developed set of matched dynamic tasks. These studies

added to literature demonstrating the increased effectiveness of using dynamic stimuli over traditional

177

static measures. However, future research is needed to explore the relationship between cognitive

deficits – such as those seen in schizophrenia – and impairments in processing dynamic emotional and

non-emotional stimuli. It would also be useful to explore the neural correlates of these deficits in

patients, particularly as the correlates of dynamic emotion-processing are shown to differ from those

shown during static emotion-processing (Arsalidou et al., 2011; Sato et al., 2004). This thesis also

highlights the need for researchers to focus on populations with psychotic symptoms outside of

schizophrenia, such as patients with substance-induced psychosis or depression with psychotic

features. This would shed additional light on the specific cognitive domains and brain pathways

affected by psychosis, allowing researchers to separate the impact of psychotic features from the more

severe, global sequelae seen in those diagnosed with schizophrenia. Furthermore, characterisation of

these patterns of deficits, including the identification of accompanying genetic and neuroimaging

markers, is necessary for creating a comprehensive model of psychotic disorders. This could

eventually inform the development of better interventions to ameliorate the devastating functional

disability experienced by sufferers of these disorders.

178

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Appendices

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Appendix A: Participant information and consent form (Healthy control)

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Appendix B: Questionnaire for healthy controls

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Appendix C: NART word card and scoring form

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Appendix D: The Oxford-Liverpool Inventory of Feelings and Experiences

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Appendix E: Participant information and consent form (Inpatient)

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Appendix F: Patient demographic questionnaire

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Appendix G: Edinburgh handedness inventory

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Appendix H: The SCI-PANSS interview and rating form

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Minerva Access is the Institutional Repository of The University of Melbourne

Author/s:

Darke, Hayley

Title:

Face processing impairments in schizophrenia and other psychiatric disorders

Date:

2018

Persistent Link:

http://hdl.handle.net/11343/217206

File Description:

Face Processing Impairments in Schizophrenia and Other Psychiatric Disorders - Amended

thesis

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