mnemonic influences on perception as revealed by visual...

Mnemonic Influences on Perception as Revealed by Visual Aftereffects

Jae-Jin Ryu

Department of Psychology

McGill University

Montreal, Quebec, Canada

April 2009

A thesis submitted to the Faculty of Graduate Studies and Research in

partial fulfillment of the requirements of the degree of Doctor of Philosophy

Jae-Jin Ryu, 2009

Table of Contents

Abstract ............................................................................................................. 2

Résumé.............................................................................................................. 5

Acknowledgments ............................................................................................ 6

ORIGINAL CONTRIBUTIONS TO KNOWLEDGE............................................. 8

CONTRIBUTION OF AUTHORS...................................................................... 10

Chapter 1 General Introduction ..................................................................... 11

Chapter 2 Literature review: Top-down influences on vision ..................... 14 Visual Search........................................................................................................... 15 Top-down influence: perception of ambiguous images ...................................... 16 Top-down influence on perception of simple stimulus features and perceptual learning ................................................................................................. 18

Chapter 3 Representations of familiar and unfamiliar faces as revealed by viewpoint-aftereffects ................................................................ 22

Abstract .................................................................................................................... 22 Introduction.............................................................................................................. 23 Methods.................................................................................................................... 27

Participants............................................................................................................ 27 Apparatus and stimuli ............................................................................................ 27 Procedure .............................................................................................................. 28

Results...................................................................................................................... 30 Experiment 1 ......................................................................................................... 30 Experiment 2 ......................................................................................................... 32

Discussion ............................................................................................................... 33 Conclusions ............................................................................................................. 36 Acknowledgments................................................................................................... 38 Figure Legends........................................................................................................ 38

Chapter 4 Imagine Jane and Identify John: Face Identity Aftereffects Induced by Imagined Faces ...................................................... 43

Abstract .................................................................................................................... 43 Introduction.............................................................................................................. 45 Materials and Methods............................................................................................ 48

Participants............................................................................................................ 48 Apparatus and stimuli ............................................................................................ 48 Procedures ............................................................................................................ 49

Results...................................................................................................................... 52 Aftereffect tasks..................................................................................................... 52 Discrimination Task ............................................................................................... 54

Discussion ............................................................................................................... 54 Conclusions ............................................................................................................. 59 Figure Legends........................................................................................................ 61

Chapter 5 Dynamic motion aftereffects induced by static images previously associated with unidirectional motion ....................................... 68

2

3

Abstract .................................................................................................................... 68 Introduction.............................................................................................................. 69 Methods.................................................................................................................... 72

Participants............................................................................................................ 72 Apparatus and stimuli ............................................................................................ 73 Procedures ............................................................................................................ 74

Results...................................................................................................................... 77 Experiment 1 ......................................................................................................... 77 Experiment 2 ......................................................................................................... 78 Experiment 3 ......................................................................................................... 79

Discussion ............................................................................................................... 80 Figure Legends........................................................................................................ 84

Chapter 6 Concluding Remarks..................................................................... 91 Summary .................................................................................................................. 91 Limitations ............................................................................................................... 93 Possible neural mechanisms mediating mnemonic influence on perception of complex images............................................................................... 95 Mnenmonic influence and perception of simple stimulus features ................... 96 Perception as results of Interactions amongst different visual areas ............... 98

References..................................................................................................... 100

Abstract

Perceiving a visual object often leads to the formation of its

representation in memory. In this case, the role of visual perception in

memory is emphasized, but it is also conceivable that memory plays a

role in the processing of a visual stimulus in a top-down manner. One

way to study whether memory does influence visual perception is to

make use of the selective adaptation method designed to produce

aftereffects. In a typical selective adaptation experiment, a stimulus is

presented for an extended period of time (adapting stimulus) and this

results in a temporary distortion in the perception of subsequent stimuli

(aftereffects). The selective adaptation method has mainly been used to

behaviorally elucidate neural mechanisms involved in the processing of

the adapting stimulus. However, it also is a useful tool to study possible

influences of memory processes on visual perception, because it leads

to the hypothesis that adapting stimuli with different and similar

mnemonic contents should produce different and similar visual

aftereffects, respectively. Results described in the current thesis show

that visual processing of motion and faces, both believed to recruit

specialized areas in the visual cortex are subject to mnemonic

influences.

4

Résumé

La perception visuelle d’un objet mène souvent à la formation de sa

représentation dans la mémoire. Dans ce cas, le rôle de la perception

visuelle est importante, mais il demeure possible que la mémoire est

impliquée dans le traitement d’un stimulus visuel d’une manière

descendante. Une façon d’étudier l’influence de la mémoire sur la

perception visuelle est d’utiliser la méthode d’adaptation sélective

conçue pour produire des effets consécutifs. Lors d’une expérience

d’adaptation sélective typique, un stimulus est présenté pour une

période prolongée (stimulus d’adaptation) et ceci mène à une distorsion

temporaire de la perception des stimuli subséquents (effets consécutifs).

La méthode d’adaptation sélective est surtout utilisée pour élucider au

niveau du comportement les mécanismes neuronnes impliqués dans le

traitement du stimulus d’adaptation. Cependant, c’est aussi un outil utile

pour étudier les influences possibles du procesus de la mémoire sur la

perception visuelle. Ceci mène à l’hypothèse que des stimuli

d’adaptation avec des effets mnémoniques différents et similaires

devraient produire des effets consécutifs différents et similaires,

respectivement. Les résultats de cette thèse montrent que le traitement

visuel de la motion et des visages, qui implique des régions spécialisées

du cortex visuel, est soumis à l’influence mnémonique.

5

Acknowledgments

I am very fortunate to have the following people in my life, who

have helped, inspired, and challenged me throughout the whole

process.

I would first like to thank my supervisor, Prof. Avi Chaudhuri, who

allowed me to pursue my own questions and curiosity in his lab. I am

grateful for the support and encouragements that he has given me,

whether he was in Montreal, or halfway across the globe.

I am thoroughly indebted to my colleagues at CVL who have now

become my friends. They not only helped me to mature as a scientist,

but also made my time at the lab immensely enjoyable. I would

especially like to thank Carmelo Milo for being the best lab manager in

the world, and Reza Farivar for being my scientific inspiration. I would

also like to thank Karen Borrmann, Pascal Lachance, Caitlin Mouri and

Dana Hayward for their invaluable friendship. I will miss you guys very

much.

I am eternally grateful to Jung-Kyong Kim for understanding

everything and making a lizard out of a snake, Lucia Yoon for being so

proud of me, Clara Yoo for just being her, and Sophia Koukoui for

making my life in Montreal twinkle with glamour.

6

I would also like to thank my parents for believing in me, and my

sister and brother for being there. Words cannot describe how much

they mean to me. My newest family member is my husband, Jae-Hun

Kim, who basically made everything possible. I love you very much.

When I was five, my maternal grandparents always talked about

how much they wished me to become a “bak-sa”, which is Korean for

someone with a Ph. D. degree. Therefore, it was probably not a

coincidence that I decided to pursue graduate studies. For that, I thank

them.

7

Original Contribution to Knowledge

This doctoral thesis presents a number of original contributions

about how different mnemonic processes influence visual perception.

Chapter 1 presents the rationale for the present investigations

and explains why the classical adaptation approach is a useful tool for

the purpose of the current thesis.

Chapter 2 briefly reviews previous studies that examined top-

down influences on visual processing.

Chapter 3 presents the results from the investigation that

examined the role of familiarity on the perception of viewpoints of faces.

The psychophysical data suggest that neurons that process viewpoint

information are also involved in the representations of familiar faces,

which are traditionally thought to be view-invariant. This chapter is

based on the following published manuscript.

Ryu, J. J., & Chaudhuri, A. (2006). Representations of familiar

and unfamiliar faces as revealed by viewpoint-aftereffects. Vision

Research, 46(23), 4059- 4063.

8

Chapter 4 describes the study that investigated whether similar

neural networks are involved in the perception and imagery of familiar

face identities. This chapter is based on the following published

manuscript.

Ryu, J.J., Borrmann, K., & Chaudhuri, A. (2008). Imagine Jane

and identify John: face identity aftereffects induced by imagined

faces. PLoS ONE, 3(5), e2195

Finally, Chapter 5 reports that mnemonic processes can influence

the perception of motion, as demonstrated by the dynamic motion

aftereffect induced by static images previously associated with motion.

This chapter is based on the following manuscript.

Ryu, J.J., & Chaudhuri, A. (2009). Top-down influence on motion

perception: Dynamic motion aftereffects induced by static images

previously associated with unidirectional motion.

9

10

C ontribution of Authors

The contribution of the authors to the manuscripts on which this

thesis is based is as follows. Jae-Jin Ryu conceived the research

questions, designed and conducted the experiments, analyzed the data

and wrote the majority of the manuscripts. Karen Borrmann analyzed

the data described in Chapter 4 and wrote parts of the corresponding

manuscript. The supervisor, Dr. Avi Chaudhuri provided guidance

throughout the research process.

Chapter 1 General Introduction

Chapter 1

General Introduction

According to the bottom-up view of mnemonic processes,

perception of a visual input is often the first step in the construction of its

representations in memory (Magnussen, Greenlee, Asplund, & Dyrnes,

1991; Schacter, Norman, & Koutstaal, 1998). In this view, the effect of

visual perception on memory formation is emphasized. However, it is

also possible that once the representation of a visual object is formed in

memory, this representation could, in turn, influence the perception of

subsequent objects in a top-down manner. Compared to the body of

research investigating the bottom-up processes relating perception and

memory (Bentin, Moscovitch, & Nirhod, 1998; Busey & Loftus, 1998;

Craik, 2002; Magnussen et al., 1991; Medendorp, Tweed, & Crawford,

2003; Slotnick & Schacter, 2004; Sperling et al., 2001; Suzuki, Zola-

Morgan, Squire, & Amaral, 1993; Wagner, Koutstaal, & Schacter, 1999),

few have directly examined possible mnemonic influences on the

processing of incoming visual information. Therefore, the present thesis

sought to study ways in which different mnemonic processes affect

visual perception.

11


Processing of visual information is believed to occur in a

hierarchical manner in multiple brain structures (Felleman & Van Essen,

1991; Konen & Kastner, 2008; Nassi & Callaway, 2006). If mnemonic

processes do indeed influence visual processing, one could ask further

questions regarding locations at which this interaction may occur. For

example, do mnemonic processes exclusively influence a selected

visual area or is this effect exerted across multiple regions? In order to

answer these questions, it is necessary to employ a method that is

applicable to various stages of visual processing in a consistent manner.

If mnemonic processes do influence perception, one can expect

that this influence would result in changes in visual perception. One

simple, yet powerful way to reveal transient changes in visual

processing is through the use of classical adaptation method. In a

typical adaptation experiment, an “adapting” stimulus is presented for an

extended period of time, producing a temporary distortion in the

perception of subsequent stimuli. This perceptual distortion, or

aftereffect, is attributed to the overall shift in response profile of neural

networks involved in the processing of the adapting stimulus.

Aftereffects have been found with a wide range of visual stimuli, from

simple lines (Gibson & Radner, 1937), to complex patterns such as

faces (Leopold, O'Toole, Vetter, & Blanz, 2001; Webster, Kaping,

Mizokami, & Duhamel, 2004). Indeed, the method of selective

12


13

adaptation is often referred to as the psychologist’s microelectrode

because it allows researchers to make inferences about the activity of a

specific neural ensemble engaged in the processing of the adapting

stimulus (Frisby, 1980).

The adaptation method is a suitable tool for the purposes of the

current topic for two reasons. First, it can be used with various types of

visual stimuli that recruit different processing areas in the visual stream.

Second, it leads to the hypothesis that adapting stimuli with different and

similar mnemonic contents should produce different and similar visual

aftereffects, respectively. Despite these advantages, this method has

mainly been used to elucidate neural mechanisms mediating visual

perception and rarely has it been applied to investigate possible top-

down influences on visual perception.

In Chapter 2, I will review previous studies that examined other

top-down influences on visual processing. In Chapters 3, 4 and 5, I will

present how different manifestations of mnemonic processes, such as

familiarity, imagery and associative learning can influence perception of

various types of visual stimuli. The present thesis focuses on memory

processes that are mediated by the medial temporal lobe, which result in

conscious, explicit mnemonic representations (Tulving, 1987).

Chapter 2 Top-down Influences on Vision

Chapter 2

Literature review: Top‐down influences on vision

According to the bottom-up processing theory of sensory

information, it is the incoming perceptual input that gives rise to higher

cognitive processes. For example, conscious perception of a complex

image activates a number of brain regions involved in visual processing,

and this often results in the formation of its representation in memory.

However, it is also possible that these cognitive influences affect the

processing of sensory information in a top-down manner. In the current

chapter, different ways in which these top-down processes can have an

effect on visual perception are discussed.

Top-down processes refer to the general mechanisms that modify

or constrain processing of incoming perceptual information (Puce,

Allison, & McCarthy, 1999). In this context, the phrase “top-down

processes” encompasses a wide range of neural and behavioral

phenomena, ranging from mnemonic influences stemming from prior

knowledge and experiences, to introspective, subjective factors

including expectancies and motivation. Existing investigations show that

multiple aspects of visual perception are subject to these top-down

influences.

14


Visual Search

One experimental manipulation that clearly reveals top-down

influences on perception is to limit the amount of time during which

visual stimuli are presented. This is often the case in a visual-search

study, in which participants are required to locate a target among

multiple distractor-items. The presentation duration of these stimuli is

often in the range of 100-200 ms, prompting participants to make a

quick response. The efficiency of visual search is reflected in decreased

response time.

The effect of top-down signals on visual search can be either

short-term or long-term. Response time for a search that was resumed

after a brief interruption is shorter than that of a newly initiated search

(Lleras, Rensink, & Enns, 2005). This response time benefit is attributed

to the retrieval of perceptual information stored in memory during the

initial search. This case of top-down influences on perception can be

considered short term because the top-down representations are

developed in a relatively short period of time (less than a few seconds).

Top-down influences on perception are also observed after long-

term learning, during which the nature of visual search changes from

15


“serial” to “parallel” (Treisman & Gelade, 1980). When visual search is

serial, the reaction time is positively correlated with the number of

distractor-items in the visual display. However, when visual search

becomes parallel after extensive training, the detection of a target

occurs almost instantly, as if the target perceptually “pops-out” amongst

multiple distractor-items. Furthermore, the number of distractor-items

exerts relatively little impact on the reaction time. The representations of

the target item, developed after long-term, extensive prior learning,

enable almost instantaneous detection of the target in visual search

(Wang, Cavanagh, & Green, 1994 1994). The acquired representations

of target items that influence visual search are often perceptual in

nature, which are distinct from those acquired through explicit

associative learning (Korner & Gilchrist, 2008).

Top‐down influence: perception of ambiguous images

The influence of top-down signals also becomes conspicuous

when perception is ambiguous. The effect of prior knowledge and

experience on conscious perception is demonstrated in studies

examining visual identification of degraded or incomprehensible images

(Ramachandran, Ruskin, Cobb, Rogers-Ramachandran, & Tyler, 1994;

Snodgrass & Feenan, 1990; Snodgrass & Hirshman, 1994). These

studies commonly show that the identification of degraded images of an

16


object is facilitated if an intact image of the same object is previously

presented.

Additional evidence showing the effect of top-down influences on

perception comes from studies reporting multi-stable phenomena.

These phenomena occur when multiple percepts are produced from a

single visual stimulus. A well-known visual stimulus producing multi-

stable percept is Boring’s my wife and my mother-in-law figure (Boring,

1930). The percepts produced by these images are highly subjective,

relying heavily on an individual’s expectations and prior knowledge

(Leopold & Logothetis, 1999).

Although both the perception of degraded images and multi-

stable phenomena are subject to top-down influences, the extents to

which the primary visual cortex mediates these processes appear to

differ significantly. The identification of objects in degraded images is

mainly modulated by the activity of high-level visual processing areas.

Neuroimaging investigations have revealed that the activity in medial

parietal cortex and the fusiform gyrus was increased, but there was no

change in the level of activity of the primary visual cortex when

degraded images of objects were presented before and after the

presentation of intact images (Dolan et al., 1997; Eger, Schweinberger,

Dolan, & Henson, 2005). Furthermore, the activity of these high-level

visual processing areas is specific to the type of stimuli shown. For

17


example, when participants were asked to detect faces in progressively

degrading pure-noise images, face-specific fusiform face area (FFA)

showed a significant increase in activation. The activity of FFA was not

significantly different from baseline when non-face stimuli were detected

(Zhang et al., 2008). Top-down influences thus enhance the perception

of degraded images by modulating the activity of high-level visual

processing areas in a stimulus-specific manner.

In contrast to the patterns of activity produced by the perception

of degraded images, the primary visual cortex is actively engaged during

the perception of multi-stable images. Various studies report that the

activities of early visual areas are closely correlated with the switching of

percepts produced by these images (Parkkonen, Andersson,

Hamalainen, & Hari, 2008; Shulman et al., 1997). Similar patterns of

results have also been shown in the cases of binocular rivalry, in which

alternating percepts are produced due to incongruent inputs to the two

eyes (Leopold & Logothetis, 1999; Tong, Meng, & Blake, 2006).

Top‐down influence on perception of simple stimulus features and perceptual learning

Processing of simple stimulus features, such as depth, motion

and orientation is traditionally believed to be stimulus-driven and largely

immune to top-down influences (Pylyshyn, 1999). However, more recent

investigations show that perception of simple stimulus features can be

18


influenced by previous experience. Evidence supporting top-down

influence on perception of simple stimulus features mainly comes from

studies examining perceptual learning. Perceptual learning refers to

improvement in perception of a sensory attribute through repeated

discriminatory trainings. The neural substrates of this type of implicit

learning are found outside of the medial temporal cortex (Gilbert &

Sigman, 2007). Consequently, perceptual learning is distinct from

explicit, conscious learning which requires the involvement of structures

in the medial temporal lobe.

Improved ability to discriminate a visual attribute due to extensive

training has been shown in the perception of orientation (Schoups,

Vogels, Qian, & Orban, 2001), direction of motion (Zohary, Celebrini,

Britten, & Newsome, 1994), and depth (Ramachandran & Braddick,

1973; Westheimer & Truong, 1988). In many cases, perceptual

sensitivity was accompanied by corresponding improvement in neuronal

sensitivity. For example, Zohary and colleagues measured behavioral

and neural responses to dynamic random dot stimuli with varying

coherence levels (Zohary et al., 1994). They report that perceptual

sensitivity to motion improved as a result of extensive training and that

this increase in motion sensitivity was accompanied by increased

sensitivity of motion-selective MT neurons. The increased perceptual

sensitivity accompanied with increased neuronal sensitivity has also

19


been found with the perception of orientation and orientation-selective

V1 neurons (Schoups et al., 2001). These findings show that top-down

influence on visual processing occurs at both behavioral and

neurophysiological levels.

The mechanisms underlying the modified V1 activity due to top-

down influence appear to operate at two different levels. First, top-down

influences can produce intrinsic changes in response properties of

individual neurons. For example, in the case of orientation-selective

neurons, perceptual learning can cause sharpening or narrowing of their

tuning curves (Schoups et al., 2001). In addition, top-down signals can

also alter contextual tuning of V1 neurons, by changing the nature of the

lateral interactions in response to stimuli placed outside of the receptive

field (Crist, Li, & Gilbert, 2001; Li, Piech, & Gilbert, 2004). These

changes result in context-dependent response to the identical visual

stimuli. In V1, top-down signals mediate the intrinsic response properties

of visual neurons, as well as modulating networks that act on individual

neurons.

Results from previous studies show that visual processing is

affected by top-down influences. Out of many cognitive aspects that

comprise top-down influences, the current investigations focus on the

influence of mnemonic processes on perception. Visual perception is

20


21

closely linked to the formation of memory (Baddeley, 1992; Magnussen

et al., 1991; Schacter et al., 1998). Using a selective adaptation method

to measure transient changes in perception, the influence of long-term,

declarative mnemonic representations and related processes on visual

processing is examined.

Chapter 3 Familiarity and Viewpoint Perception

Chapter 3

Representations of familiar and unfamiliar faces as revealed by viewpoint‐aftereffects

A bstract

A viewpoint-dependent aftereffect occurs after prolonged viewing of a

stimulus of a particular orientation, with the result that the test image is

perceived to be facing away from the adapting orientation. Prior

psychophysical work has led to the suggestion that the visual brain

encodes a limited range of viewpoint information with regard to complex

images. In this study, we investigated whether familiar faces were

susceptible to a viewpoint aftereffect. Familiar faces are believed to be

represented in a view-invariant manner, whereas unfamiliar faces are

represented in a viewpoint-dependent manner. Adaptation to both

familiar and unfamiliar faces influenced the perception of viewpoint of

subsequent face images. However, category-specific transfer of a

repulsive viewpoint-dependent aftereffect was observed with unfamiliar

faces. Our results suggest that neural networks that mediate viewpoint

information are also involved in view-invariant representation of familiar

faces.

22


I ntroduction

Multiple encounters with faces rarely occur from identical vantage

points in real-life situations. However, humans are often able to

recognize the face of a familiar person despite significant changes in

viewpoint. The ability to recognize faces from different viewpoints is

limited when the observer is not familiar with the face (Burton, 1999;

Hancock, Bruce, & Burton, 2000; O'Toole, Deffenbacher, Valentin, &

Abdi, 1994). This differential ability to recognize familiar and unfamiliar

faces across different viewpoints has led to the suggestion that they are

represented in qualitatively different ways in the brain. Familiar faces are

believed to be represented in a view-invariant or abstract manner

whereas unfamiliar faces are represented in a viewpoint-dependent

manner (Bruce & Young, 1986; Burton, 1999; Eger et al., 2005; Hill,

Schyns, & Akamatsu, 1997).

It has been postulated that facial familiarity is acquired largely

through two processes – multiple exposures to a face and acquisition of

semantic information about the face (Bruce and Young, 1986; Burton et

al., 1999; Pourtois, Schwartz, Seghier, Lazeyras, & Vuilleumier, 2005).

In experimental settings, familiar faces are often equated with famous

faces whose semantic information can be easily retrieved (e.g., the face

of an actor or a well-known politician). In this context, the representation

23


of a familiar face is believed to be linked to semantic information about

the identity of that face. Therefore, the abstract nature of

representations of familiar faces may be partly due to a strong cognitive

link to semantic information that is separate from visually driven

perceptual information. Representations of unfamiliar faces, on the other

hand, are more dependent on viewpoint because they are reliant upon

images obtained from prior encounters. The viewpoint from which these

encounters occurred may then determine how perceptual

representations of unfamiliar faces are formed.

The abstract nature of familiar face representations is

emphasized in several cognitive models of face processing (Bruce &

Young, 1986; Ellis, 1992; Valentine & Bruce, 1986). One influential

model has been proposed by Bruce and colleagues (Bruce & Young,

1986; Burton, 1999), in which representations of familiar faces are

composed of different units or nodes, with each node being responsible

for processing different types of information, including visual structure of

the face and its identity. Among the nodes is a pool of cognitive units

that is responsible for familiar-face recognition, known as Face

Recognition Units (FRUs). A notable feature of FRU is that they are

view-independent.

Recent findings from neuroimaging studies report distinct

patterns of activation in response to familiar and unfamiliar faces.

24


Familiar faces produced a greater response in several brain areas,

including the left anterior middle temporal gyrus (Gorno-Tempini & Price,

2001), as well as other areas in the left hemisphere (Leube, Erb, Grodd,

Bartels, & Kircher, 2003; Paller et al., 2003). Interestingly, it appears that

areas sensitive to changes in viewpoints are different for familiar and

unfamiliar faces, possibly reflecting the different weights associated with

viewpoint-relevant information in facial representations. Pourtois and

colleagues (2005) conducted a study in which different images of

familiar and unfamiliar faces were shown. They reported that repeated

presentations of unfamiliar faces with varying viewpoints produced

selective repetition decreases in a medial portion of the right fusiform

gyrus, whereas repeated presentations of familiar faces from different

viewpoints produced a similar pattern of responses in the left middle

temporal and interior frontal cortex. These results reinforce behavioral

data as well as current models that suggest distinct encoding of

viewpoint information of familiar and unfamiliar faces.

One way to explore the behavioral relevance of viewpoint-

dependent versus viewpoint-independent representation is through a

classical adaptation approach. Recently, Fang and He (2005) showed

that adaptation to complex images of a particular orientation produced

an aftereffect that altered the perception of viewpoint. Their viewpoint

aftereffects were obtained with objects within the same categories and

25


were greater when the adapting and test images were of the same

object or identity. What is particularly noteworthy is that they obtained

similar results with unfamiliar faces, suggesting that neural assemblies

that encode this information are susceptible to viewpoint-dependent

stimulus adaptation. The question then remains as to whether a similar

phenomenon arises with familiar faces, which has not been previously

examined.

Based on currently accepted theories of abstract representation

of familiar faces, we hypothesized that familiar faces are not susceptible

to a similar viewpoint-dependent aftereffect as was shown to be the

case for unfamiliar faces. If so, then the question arises as to the nature

of the aftereffect with familiar faces and whether it applies across

alternate exemplars within the same category. We show here that use of

a selective adaptation procedure produces view-dependent aftereffects

with familiar faces that are distinctly different than those with unfamiliar

faces, suggesting that neural assemblies that process viewpoint

information are recruited in the representation of familiar faces.

26


Methods

P articipants

Six undergraduate students from McGill University participated in

each experiment (2 males, mean age of 21 for experiment 1; 1 male,

mean age of 21 for experiment 2). Participants were naïve to the

purpose of the experiment. All had normal or corrected-to-normal vision.

The study was reviewed and approved by an institutional ethics board

for human psychophysical studies. Written consent was acquired from

each participant prior to the experimental session.

A pparatus and stimuli

All stimuli were presented on an LG flat-screen monitor with 1024

x 768 resolution and 85 HZ refresh rate. The stimuli were presented on

a uniform grey background of 18.6 cd/m2. The presentation sequence

was programmed in MATLAB software using the Psychophysics

Toolbox extensions (Brainard, 1997). A chinrest was used to stabilize

the head position at a distance of 57 cm from the monitor surface.

Face images were acquired from the Max-Plank face database

(http://faces.kyb.tuebinggen.mpg.de). Adapting and test face stimuli

were created by projecting the 3-D images onto a two-dimensional plane

27

http://faces.kyb.tuebinggen.mpg.de/


with different in-depth orientation angles. Adapting stimuli were face

images oriented 300 to the left or right. The degree of orientation of the

adapting stimuli was chosen based on a previous study on objects and

unfamiliar faces (Fang & He, 2005), which reported the maximum

viewpoint-dependent aftereffect to occur at this orientation value. The

test stimuli included images in frontal view as well as off-frontal

orientation at 30 and 60 to the left and right. The size of all stimuli was 70

x 8.50.

P rocedure

Each participant completed three sessions – familiar, unfamiliar

and baseline. The familiar session began with a learning phase during

which four faces were repeatedly presented along with their fictional

names and occupations. Nine different views of each face were created

(frontal and 300, 450, 600, and 900 rotated to the left or right) and

presented in a sequential manner, twice clockwise and twice counter-

clockwise (Fig.1). Each image was presented for 1s. At the end of the

learning phase, a recognition test was conducted to verify the

participant’s familiarity with the faces. All participants were able to

achieve 100% person recognition before proceeding to the aftereffect

task.

28


The aftereffect task consisted of the following regime.

Participants were first exposed for 5s to an adapting face image chosen

randomly from the four previously learned faces. A central fixation point

appeared at the end of this exposure for 2 s. Test stimuli were randomly

chosen from the five orientations (frontal; 30 and 60 to the left or right)

and presented at one of the four corners (upper left, upper right, lower

left and lower right) of the monitor for 400 ms in order to avoid possible

low-level, location-specific aftereffects. The center of the test image was

located at approximately 10.50 away from the central fixation point, at

one of four following angles = 450, 1350, 2250, or 3150. Participants were

allowed to alter their fixation to the test stimulus and report whether they

perceived it to be oriented to the left or right by way of a key press. An

inter-trial interval of 5s was used.

The adapting and test stimuli during the familiar session

consisted only of the four previously learned faces. In the unfamiliar

session, a battery of 16 novel faces was used. In Experiment 1, the

adapting and test stimuli for both familiar and unfamiliar sessions within

each trial were of the same face identity (e.g., the face of Joe, shown

from different viewpoints). In Experiment 2, the test faces in both

sessions were different from the adapting faces.

In the first experiment, each session consisted of 320 trials as

follows – 80 presentations of each familiar face during the familiar, and

29


20 presentations of each unfamiliar face during the unfamiliar session,

both divided equally between right and left adapting orientations. In the

second experiment, the familiar session consisted of 360 trials – 90

presentations of each familiar face, divided equally between right and

left adapting orientations. As with the first experiment, each unfamiliar

face in the unfamiliar session was presented 20 times for a total of 320

trials. The order of familiar and unfamiliar sessions was counterbalanced

across participants.

The faces used in both sessions were presented without

adaptation during the baseline session. Participants were asked to

decide which direction the test stimuli were facing (left or right). The

baseline session was only administered after the two sessions were

completed.

Results

E xperiment 1

The proportion of trials in which the test stimuli were perceived to

be facing the opposite direction relative to the adapting stimuli is plotted

against orientation angles of test stimuli and shown in Fig. 2. The

logistic function, 1/(1+exp( - *)) was fitted to the data. and are

free parameters that determined the midpoint and the slope of the

30


psychometric function. The orientation angles of the test stimuli were

labeled with respect to those of the adapting stimuli such that they were

the same or opposite to the direction of adapting stimuli. The points of

subject equality were extrapolated at the threshold of 0.5 from the

psychometric function for each experimental session and indicated as U

for the unfamiliar and F for the familiar session.

Baseline scores were calculated based on orientation-

discrimination accuracy. Paired T-tests between accuracy scores

obtained for test stimuli oriented 30 to the left and right, and 60 to the left

and right revealed no significant differences in the baseline perception of

these stimuli.

The bias in perception produced by a viewpoint-dependent

aftereffect has been found to be in the opposite direction to the adapted

viewpoint (Fang & He, 2005). Therefore, the repulsive bias in perception

was more likely to be observed with test stimuli that are in frontal view or

those oriented in the same direction as adapting stimuli. Indeed, with

these test stimuli, a consistent leftward shift from baseline scores was

observed in both experimental sessions.

In order to examine possible differences in viewpoint-dependent

aftereffects observed in familiar and unfamiliar sessions, the respective

differences from baseline at the selected test stimuli (Same 6, Same 3,

0) were submitted to a two-way ANOVA (session x test stimuli). Main

31


effects of session and test stimuli were both significant (F(1,5) = 114.86,

p < 0.001 for session; F(2, 10) = 5.31, p < 0.05 for test stimuli;

Greenhouse-Geisser correction). Interaction between the two factors

was not significant.

Test stimuli oriented in the same direction as the adapting stimuli

were more likely to be perceived to be facing away from the adapting

stimuli. Adapting to both familiar and unfamiliar faces produced

repulsive viewpoint-dependent aftereffects. However, this shift in

perception of viewpoints was shown to be greater following adaptation

to a familiar face. These viewpoint-dependent aftereffects were obtained

when the adapting and test stimuli were of the same face identities.

A notable feature of the viewpoint-dependent aftereffect is that it

transfers across different exemplars within the same category (Fang &

He, 2005). A second experiment was therefore conducted to investigate

whether the viewpoint-dependent aftereffect induced by a familiar face

influences the subsequent perception of a different familiar face.

E xperiment 2

In this experiment, the adapting and test images were of different

identities in both familiar and unfamiliar sessions. The familiar session

consisted of 360 trials, and the unfamiliar, 320 trials. All other aspects

and parameters of the experiment were identical to Experiment 1.

32


The proportion of trials in which test stimuli were perceived to be

oriented in the opposite direction to the adapting stimuli is shown in Fig.

3. The psychometric function for the performance in the unfamiliar

session showed a consistent leftward shift from the baseline

performance across all test stimuli.

Familiar adapting faces, on the other hand, appeared to have

induced a significant disruption in the subsequent perception of

viewpoint, as the performance was near the chance level across all test

stimuli. Indeed, a logistic function was not able to fit the data due to the

relatively constant level of performance across the test stimuli. A

repeated ANOVA on the performance from the familiar session revealed

a non-significant main effect of the test stimuli.

D iscussion

We investigated the effect of familiarity on the viewpoint

aftereffect phenomena by using a selective adaptation approach with

both familiar and unfamiliar faces. When adapting and test stimuli were

of the same identity, adaptation to familiar and unfamiliar faces viewed

from a particular angle produced similar shifts in the subsequent

perception of viewpoint. However, when different faces were used for

adapting and test stimuli, familiar adapting images produced a

33


viewpoint-dependent aftereffect that was qualitatively distinct from that

produced by unfamiliar adapting images, Category-specific transfer of a

systematic viewpoint-dependent aftereffects was observed with

unfamiliar faces. Together, our results suggest that repulsive viewpoint-

dependent aftereffects produced by familiar faces are identity-specific.

Our failure to obtain a systematic within-category transfer of the

viewpoint-dependent aftereffect with familiar faces may be attributed to

the additional processing of the changed, familiar identities. The

presentation of a different, yet familiar, test face after prolonged

exposure to a familiar adapting face may cause activation of semantic

information associated with the newly presented face. This new

activation of information may have interfered with the processing of the

viewpoint information, thus producing the near-chance performance

when the identities of the adapting and test faces were different.

Familiar faces are believed to be represented in a view-invariant

manner (Bruce & Young, 1986). Given the discovery of viewpoint-

dependent aftereffects with complex images (Fang & He, 2005), we

asked whether a similar effect persists with familiar faces and if so,

could the nature of the phenomenon provide further insight into the

neural mechanisms that mediate the abstract nature of familiar faces.

Fang and He (2005) suggested on the basis of their results that neurons

34


mediating the perception of viewpoint are organized in a manner similar

to orientation-selective neurons in earlier cortical areas.

We sought to examine whether the existence of view-invariant

representations of familiar faces are susceptible to viewpoint-dependent

aftereffects. Our finding that selective adaptation of familiar faces

influenced the subsequent perception of viewpoint suggests that the

neural assemblies mediating familiar face perception are functionally

linked to biological processing of viewpoint information. However, the

manner in which these neurons are activated in response to familiar and

unfamiliar faces appears to differ, as suggested by the distinct nature of

the viewpoint-dependent aftereffect induced by these separate images.

A systematic transfer of the aftereffect to other faces was observed with

unfamiliar faces, thus replicating findings from a previous study (Fang &

He, 2005), which suggested category-specificity of a viewpoint-

aftereffect. However, a similar within-category transfer of the aftereffect

was not observed with familiar faces.

The bias in perception induced by adaptation has long been

attributed to decreased sensitivity of neurons selectively recruited during

adaptation (McCollough, 1965; Wenderoth & Johnstone, 1987; Yoshida,

1978). The brief impairment in perception of orientation following

selective adaptation to familiar face images suggests that neurons

processing information about viewpoints were involved in the processing

35


of familiar adaptor images. Significantly reduced orientation-judgment

performance following adaptation to different but familiar face images

provides support for this argument.

Facial familiarity is achieved through the acquisition of semantic

information and multiple exposures to the images under different

viewing conditions (Bruce & Young, 1986). The accumulation of different

images of a familiar face is likely to be crucial in the formation of an

abstract representation of the face. Once the abstract, view-invariant

representation has formed, the overall activation of viewpoint-selective

neurons may provide easier access to semantic information, enabling

identification of the face despite alterations in viewpoint. An important

and unanswered question in face perception research concerns the

transitional nature of the neural representation as unfamiliar faces

become familiar and the corresponding conversion from a view-

dependent to a view-invariant representation.

C onclusions

When adapting and test stimuli were of the same identity,

adaptation to familiar and unfamiliar faces produced similar viewpoint-

dependent aftereffects, suggesting the involvement of viewpoint-

selective neurons in the processing of both types of face stimuli.

36


However, category-specific transfer of a systematic viewpoint aftereffect

was observed only with unfamiliar faces. This may be due to the shift in

attention to the changed, familiar identity, subsequently disrupting the

perception of viewpoint. The present results provide behavioral support

for the notion of differential weights attached to viewpoint information

contained in representations of familiar and unfamiliar faces (Pourtois et

al., 2005).

When an unfamiliar identity becomes familiar, semantic

information concerning that identity (names, occupation) is often

associated with the corresponding visual image. Accordingly, multiple

encounters with the familiar identity eventually lead to the generation of

visual imagery of the face when relevant semantic information about the

identity is presented. Considerable evidence from neurophysiological

investigations shows that biological basis for imagery of familiar faces is

similar to those mediating visual perception (Ishai & Sagi, 1995;

Kosslyn, Thompson, & Alpert, 1997; Kreiman, Koch, & Fried, 2000;

O'Craven & Kanwisher, 2000). The purpose of the following study is to

further probe neural networks underlying perception and imagery of

familiar face identities using the selective adaptation method.

37


A cknowledgments

We wish to thank Dr. Alain Mignault for help in creating the face

stimuli, and Karen Borrmann for helpful discussions. We thank two

anonymous reviewers for their extremely helpful comments on an earlier

version of this paper. This study was funded by operating grants from

the Canadian Institutes of Health Research (CIHR) to A.C.

Figure Legends

Figure 1. Different images of the same face presented during the

learning phase (Familiar session).

Figure 2. The mean psychometric functions for viewpoint judgments

under each viewing condition. Proportion of trials in which test images

were perceived to be facing opposite to the direction of the adapting

stimuli was plotted against different test stimuli. The solid horizontal line

indicates threshold for the point of subjective equality (.5). The point of

subjective equality was extrapolated for each experimental session (U

for unfamiliar, F for familiar). Bars indicate standard errors.

38


Figure 3. Results from Experiment 2 in which adaptor and test images

were of different faces. The mean psychometric functions for viewpoint

judgments for the baseline and unfamiliar conditions. A logistic function

was not able to fit the data from the familiar condition. The solid

horizontal line indicates the threshold for the point of subjective equality

(.5). The point of subjective equality was extrapolated for the unfamiliar

condition only. Bars indicate standard errors.

39


Figure 1

40


Figure 2

41


42

Figure 3

Chapter 4 Face Identity Aftereffects and Imagery

Chapter 4

Imagine Jane and Identify John: Face Identity Aftereffects Induced by Imagined Faces

A bstract

It is not known whether prolonged exposure to perceived and imagined

complex visual images produces similar shifts in subsequent perception

through selective adaptation. This question is important because a

positive finding would suggest that perception and imagery of visual

stimuli are mediated by shared neural networks. In this study, we used a

selective adaptation procedure designed to induce high-level face-

identity aftereffects – a phenomenon in which extended exposure to a

particular face facilitates recognition of subsequent faces with opposite

features while impairing recognition of all other faces. We report here

that adaptation to either real or imagined faces produces a similar shift

in perception and that identity boundaries represented in real and

imagined faces are equivalent. Together, our results show that identity

information contained in imagined and real faces produce similar

behavioral outcomes. Our findings of high-level visual aftereffects

induced by imagined stimuli can be taken as evidence for the

43


44

involvement of shared neural networks that mediate perception and

imagery of complex visual stimuli.


I ntroduction

An encounter with a familiar person’s name often generates the image of

that person in our mind. The process by which an image is created without

actual retinal input is referred to as visual imagery. Although there are reports of

patients with deficits of perception but intact imagery (Behrmann, Moscovitch, &

Winocur, 1994; Michelon & Biederman, 2003), multiple lines of evidence from

behavioral (Farah, 1985; Ishai & Sagi, 1995), and neuroimaging (Ishai,

Ungerleider, & Haxby, 2000; Kosslyn, Thompson, & Alpert, 1997; O'Craven &

Kanwisher, 2000) studies suggest that the properties and neural substrates of

imagery are similar to those of perception. The similarities in neural structures

underling imagery and perception are further corroborated by an

electrophysiological study showing that single neurons in the human medial

temporal lobe respond to both imagery and perception (Kreiman, Koch, & Fried,

2000).

It is possible also to use psychophysical methods to study the neural

mechanisms underlying perception and imagery. One approach is to use

selective adaptation to directly probe the biological basis of cognitive function. In

a selective adaptation experiment, an adapting stimulus is presented for an

extended period of time, resulting in a temporary perceptual distortion, or

aftereffect (Koehler & Wallach, 1944). Aftereffects have been found with a wide

45


range of visual stimuli, from simple lines (Gibson, 1933; Gibson & Radner,

1937), to complex patterns such as faces (Leopold, O'Toole, Vetter, & Blanz,

2001; Webster, Kaping, Mizokami, & Duhamel, 2004). It is believed that

sustained activity of neurons during adaptation causes a shift in their

subsequent response level, leading to a perceptual bias towards opposite (or

complementary) stimulus attributes (Frisby, 1980). The method of selective

adaptation is often referred to as the psychologist’s microelectrode because it

allows researchers to make inferences on the activity of neurons engaged in the

processing of adapting stimuli (Frisby, 1980).

The extant physiological evidence that similar neural structures are

involved in perception and imagery leads to the hypothesis that perceived and

imagined stimuli should produce similar behavioral results in a selective

adaptation study. Despite this expectation, the results from several prior

experiments have been inconsistent, reflecting the difficulty associated with

using imagined visual stimuli in experimental settings (Broerse & Crassini, 1980;

Finke & Schmidt, 1977, 1978; Moradi, Koch, & Shimojo, 2005; Over & Broerse,

1972; Singer & Sheehan, 1965). Some of these studies only examined

aftereffects induced by imagined stimuli with simple visual attributes, such as

color and orientation, which are believed to activate early areas in the visual

processing stream (Hubel & Wiesel, 1968). It is experimentally challenging to

control various visual attributes such as precise hue, orientation and size of

imagined stimuli (Finke & Schmidt, 1978; Singer & Sheehan, 1965).

46


Furthermore, low-level visual aftereffects are sensitive to a variety of

manipulations including changes in the size and position of adapting stimuli

(Koehler & Wallach, 1944). While problems that are inherent in the use of simple

stimuli may partly explain some of the inconsistencies found in the extant

literature, a previous study which did not find significant visual aftereffects

induced by imagined complex stimuli such as faces (Moradi et al., 2005)

warrants further considerations of other factors such as participants’ familiarity of

the experimental tasks and stimuli.

In this study, we sought to directly probe the neural networks that underlie

visual imagery and perception by inducing high-level, face-identity aftereffects

(FIA) through selective adaptation. FIA occurs when adaptation to a particular

face facilitates identification of subsequent faces with opposite features (anti-

faces) while impairing identification of unrelated faces (Leopold et al., 2001).

Unlike aftereffects induced by low-level stimuli, the biological mechanisms

mediating FIA are invariant to changes in stimulus size, position, and orientation

(Leopold et al., 2001; Watson & Clifford, 2003).

For proper comparison of high-level aftereffects induced by imagined and

perceived stimuli, the task requirements in both conditions should be identical. In

order to ensure that the difficulty of imagining complex stimuli would not interfere

with task performance, we used a fixed-order, within-subject design in which the

aftereffect task with perceived stimuli preceded that with imagined stimuli. In

addition, we added a discrimination task to ensure the identity information

47


contained in perceived and imagined faces was equivalent. We hypothesized

that if perception and imagery were indeed mediated by shared neural networks

then adaptation to real and imagined faces would produce a similar bias in

perception of subsequent faces.

Materials and Methods

P articipants

Ten undergraduate students from McGill University participated in the

study (2 males, mean age of 19.1 years). Participants were naïve to the purpose

of the experiment. All had normal or corrected-to-normal vision. The study was

reviewed and approved by an institutional ethics board for human

psychophysical studies. Written consent was acquired from each participant

prior to the experimental session. Data from two participants whose baseline

identification accuracy of test faces with 45% identity strength did not exceed

75% during the aftereffect tasks were removed from analysis.


All stimuli were presented on an LG flat-screen monitor with 1024 x 768

resolution and 85 Hz refresh rate. The stimuli were centrally presented on a

uniform black background. The presentation sequence was programmed in

MATLAB software using the Psychophysics Toolbox extensions (Brainard,

48


1997). A chinrest was used to stabilize head position at a distance of 57 cm from

the monitor surface. All experiments were carried out in a dark testing room. The

size of stimuli did not exceed 7 x 10.5 degrees.

Three face/anti-face pairs were taken from those used in the previous

study reporting FIA (Leopold et al., 2001). According to the face space model,

the anti-face of a face is located on the same identity axis but on the other side

of the mean on the computationally derived, multi-dimensional face space

(Leopold et al., 2001; T. Valentine, 1991). Therefore, the facial features of the

face/anti-face pair are completely opposite to each other. The identity strength of

each face was manipulated by adjusting its distance from the average face on

the face space. Identity strength of the face stimuli ranged from 0 (the average

face) to 45%. Due to the computational processes through which anti-faces are

generated, the maximal identity strength of an anti-face was 45%.

P rocedures

The experiment was divided into real- and imagined- stimulus conditions.

Both conditions began with a training session in which participants were

familiarized with the face stimuli. The identity strength of the faces was 45%.

Each face appeared for five seconds on the monitor along with its fictional name

(6 faces in total). The serial presentation of the face was repeated seven times

and the order of presentation was randomized. The participants’ familiarity with

49


the faces was probed with a verbal identification task in which each face was

presented without its name. The presentation of the faces and identification task

were repeated until 100% accuracy on the identification task was achieved.

In order to maximize the participants’ familiarity with the anti-faces during

the imagined-stimulus condition, all participants completed the real-stimulus

condition before proceeding to the imagined-stimulus condition. In the imagined-

stimulus condition, a discrimination task (described below) was added and was

administered before the aftereffect task. In both real- and imagined-stimulus

conditions, the baseline task, in which participants were required to identify test

faces without adaptation, was completed after the aftereffect task.

Aftereffect tasks

In each trial of the aftereffect task, a five-second presentation of the

adapting face was followed by a brief presentation (400 ms) of a morphed test

face (Figure 1). There was no ISI between the adapting and test images. The

adapting face was one of the three anti-faces. The identity strength of a test face

used in the aftereffect task ranged from 0 to 45%. The identity strength of anti-

faces was 45%. In the real-stimulus condition, the adaptor was a true face

image, whereas in the imagined-stimulus condition a name served as a cue

prompting participants to vividly visualize the corresponding face with their eyes

open. After adaptation, observers were asked to identify the test face among

three shown face names and indicate their answers by pressing the appropriate

50


button on the keyboard. Each adapting anti-face was shown 100 times during

the aftereffect task, for the total of 300 trials in each condition.

The aftereffect tasks were composed of “matching” trials, in which the

features of adapting and test stimuli were opposite to each other (face/anti-face

pair) and “non-matching” trials, in which the two face stimuli were not

perceptually related. An equal number (150) of matching and non-matching trials

was randomly interleaved. To reduce a possible learning effect, we administered

a baseline task after the respective aftereffect tasks. The baseline task required

participants to identify test faces without adaptation. The test faces, as well as

the number of trials presented during the baseline task, were identical to those

shown during the aftereffect task.

Discrimination Task

To assess possible differences in the degree of face identity contained in

real and imagined stimuli, we sought to measure the difference threshold for

identities in real and imaged faces in a discrimination task (Figure 2). In this

task, participants were instructed to compare either the real or the mental

images of the anti-faces (Face 1) with subsequent real face (Face 2).

Participants indicated whether the two face stimuli (Faces 1 & 2) belong to the

same person with a key press. Face 2 matched the identity of Face 1 (i.e., both

faces can be found on the same identity trajectory on the face-space), but the

identity strengths of Face 2 stimuli were less than those of the Face 1 stimuli:

The identity strengths of Face 1 stimuli were 45% whereas the identity strengths

51


of Face 2 stimuli varied from 5 to 40%. Each Face 2 stimulus appeared 5 times

with each of the real and imagined Face 1 stimuli. Participants completed 180

trials in total.

R esults

Data were averaged over 8 participants whose baseline identification

accuracy of test faces with 45% identity strength exceeded 75%. A logistic

function 0.333+0.667*1/(1+exp(-(x-c)/a)) was fitted to the data, in which ‘a’ and

‘c’ are free parameters that determine the slope and midpoint of the

psychometric function.

A ftereffect tasks

At the end of the imagined-stimulus condition, all participants reported to

be able to imagine a corresponding face during the adapting period. The

imagined-stimulus condition was preceded by the real-stimulus condition for all

participants. Therefore, their familiarity with the face stimuli was expected to be

greater in the imagined-stimulus condition. The baseline tasks in both conditions

involved identification of the morphed test faces without adaptation. The

increased familiarity with the stimuli in the imagined-stimulus condition was

reflected by the significantly increased performance on the baseline task

compared to that of the real-stimulus condition (Greenhouse-Giesser correction

for sphericity, F (1,7) = 8.313, p < 0.05). Due to the difference in the baseline

52


performance between the real- and imagined- stimulus conditions, the

differential effects of adaptation to matching (adapting face is the anti-face of the

test face) and non-matching (adapting and test faces do not have opposite

features) faces were analyzed for each stimulus condition.

In the real-stimulus condition, there was a significant increase in

identification performance after adaptation to “matching” faces compared with

the baseline condition. In contrast, identification performance after adaptation to

“non-matching” faces was diminished compared to baseline (Figure 3). The

fractions of trials in which participants correctly identified the test face in

matching and non-matching trials were compared to the baseline performance.

The respective differences in identification performance between the trial types

(matching and non-matching) and baseline were subjected to a repeated-

measure two-way analysis of variance (ANOVA) with trial type and identity

strength as factors. The main effect of trial type (F (1,7) = 61.536, p < 0.001),

and interaction effect (F (2,832, 19.824) = 11.364, p < 0.001) were significant.

The main effect of identity strength was marginally significant (F (4.467, 31,266)

= 3.651, p< 0.05).

The imagined-stimulus condition produced a similar pattern of results to

the real-stimulus condition (Figure 4). An ANOVA revealed a significant main

effect of trial type (F(1,7) = 28.613, p < 0.01) and interaction effect (F (3.981,

27.864) = 4.051, p < 0.001). The main effect of identity strength was not

significant (F(3.607, 25.251) = 1.397, p > 0.05).

53


D iscrimination Task

The proportion of trials in which participants perceived the second face

(Face 2) differently from the anti-face (Face 1) on the discrimination task was

plotted against the difference in the identity strengths of the two faces (Figure 5).

The anti-face was either perceived or imagined. The data was fitted to a logistic

function in order to extrapolate difference thresholds (75% different responses)

for identifying visual and imagined faces. The difference thresholds were

identical at 13%.

D iscussion

We have used selective adaptation methods designed to induce face-

identity aftereffects (FIA) to test psychophysically whether face perception and

imagery are processed by shared neuronal ensembles. We found that

adaptation to physically presented matching anti-face images enhanced the

recognition of test faces, whereas adaptation to anti-face images that were not

matched to the test face resulted in reduced recognition performance compared

to baseline. These effects can be seen from the corresponding shifts of the data

points in Figure 3. Adaptation to imagined matching and non-matching anti-

faces produced similar results – i.e., a significant increase in identification of test

54


faces after adaptation to imagined ‘matching’ anti-faces and reduced

identification performance relative to baseline after adaptation to non-matching

imagined anti- faces (Figure 4).

Compared to the condition in which adapting stimuli were physically

presented, the magnitude of shift from the corresponding baseline was much

smaller when participants were asked to imagine the adapting stimuli (Figures

3a & 4a). The apparent reduction of the aftereffect following adaptation to

imagined stimuli could be explained by the difficulty in visualizing a complex

image in a sustained, coherent manner. This may have led to diminished

activation of neuronal ensembles that otherwise show greater response to visual

stimuli. The decreased activation of these neurons may have produced a

smaller net adaptation leading to a smaller aftereffect.

We also measured the respective difference thresholds for identity

strength in a discrimination task to investigate this possibility and examine

potential differences between the properties of real and mental images of

learned faces. If imagined faces indeed had wider identity boundaries than real

images of those same faces, this should be reflected in larger difference

thresholds for imagined faces. However, we found that difference thresholds for

real and imagined faces were the same, thus showing that the identity

represented in the imagined-stimulus condition was similar to that contained in

the real-stimulus condition.

55


We used a fixed-order, within-subject design to achieve participants’

maximum familiarity with the experimental task and stimuli during adaptation to

imagined stimuli. It is possible that the order in which the stimulus conditions

were presented could have influenced participants’ performance. Indeed, the

increased familiarity with the face stimuli during the imagined-stimulus condition

could have contributed to the reduced magnitude of aftereffect, as the baseline

performance during the imagined-stimulus condition was significantly increased.

However, despite the increased familiarity with the face stimuli, adaptation to

non-matching imagined faces still reduced recognition performance, suggesting

that the adaptation effect of non-matching imagined faces was comparable to

that of real faces.

It is interesting to note that a similar study conducted by Moradi et al.,

(2005) failed to report a significant high-level aftereffect induced by imagined

faces. Due to the top-down nature of imagery, it is absolutely necessary to

minimize possible cognitive and perceptual interferences during adaptation to

imagined stimuli. In our task, the presentation of the name of an adapting face

was brief, merely serving as cue for participants to imagine the corresponding

face. Consequently, no visual stimulus was presented during adaptation. On the

other hand, the name of an adapting face continued to be shown during

adaptation in the study by Moradi et al. It is possible that continuous visual input

during adaptation could have interfered with imagery, resulting in non-significant

aftereffects.

56


This notion that neuronal responses are diminished during adaptation to

an imagined stimulus is reinforced by a neuroimaging study that compared

fusiform face area (FFA) and parahippocampal place area (PPA) activation

during viewing or imagery of faces and places (O'Craven & Kanwisher, 2000).

This study showed that both perception and imagery of faces selectively

activated portions of FFA, whereas viewing and imagery of places produced

greater activation in PPA. Interestingly, within a region responding more strongly

to a given stimulus category, O’Craven and Kanwisher (2000) also reported

stronger levels of activation for real compared to imagined stimuli of that

category. This finding is consistent with our finding of a larger magnitude of FIA

for real as compared to imagined faces.

We have shown that adaptation to a visually presented anti-face and to

an imagined anti-face produces similar perceptual aftereffects. The occurrence

of such a high-level visual aftereffect from a purely mental image reveals that a

close neural interaction exists between visual perception and imagery. The

processing of complex visual stimuli such as faces is believed to be specialized

in the later stages of the occipito-temporal visual processing stream (Ishai,

Ungerleider, Martin, Schouten, & Haxby, 1999; Kanwisher, McDermott, & Chun,

1997). Accordingly, a recent neuroimaging study investigating the neural activity

underlying FIA reported that areas in the anterior temporal lobe are involved in

the mediation of the effect (Furl, van Rijsbergen, Treves, & Dolan, 2007). Given

the similarity in the shift of perception following adaptation to imagined and real

57


faces, these areas are likely to be involved in producing FIA induced by

imagined faces.

Representations of a familiar face not only contain information about its

visual attributes, but also semantic information regarding its identity. It appears

that the two types of information are closely linked together. Support for this idea

comes from a single-neuron study that reported neurons in the human medial

temporal cortex that responded to both the presentation of a familiar face as well

as the proper name associated with the face (Quiroga, Reddy, Kreiman, Koch, &

Fried, 2005). Since our aftereffect tasks involved perception and identification of

familiar faces, some aspects of memory processes may have contributed to our

results. It is likely that both identity and visual information are activated during

adaptation to perceived and imagined familiar faces.

Our results are consistent with neuroimaging studies that have shown

selective activation of stimulus-specific brain regions in extrastriate cortex

following exposure to both real and imagined stimuli. Similar activity following

presentations of real and imagined stimuli have been found in face- (Ishai et al.,

2000; O'Craven & Kanwisher, 2000), object- (Kosslyn et al., 1997), and place-

selective (O'Craven & Kanwisher, 2000) regions. However, our evidence

supporting the commonality between neural structures underlying perception

and imagery appears to be in conflict with previous reports of patients with

dissociable deficits (Bartolomeo et al., 1998; Behrmann et al., 1994; Michelon &

Biederman, 2003). These patients showed severely impaired perception but

58


more intact imagery of complex objects. It is possible, however, that intact

imagery of these patients was mainly due to the retrieval of representations of

objects acquired before the lesion. In that case, imagery tasks are likely to

measure the patient’s ability to remember, rather than to imagine what was just

perceived. Although these studies provide interesting insight into the overall

brain networks supporting perception and imagery, the dissociable deficits found

in patients do not necessarily provide support for separate neural structures

mediating these two experiences.

Our finding of equivalent identity boundaries for visual and mental images

suggests that face imagery activates robust and accurate face representations

that are similar to those produced by visual stimulation. We propose that the

similarity in identity contained in imagined and real faces is produced by

activation of shared neural networks that code for these representations.

C onclusions

Evidence from the present study suggests that overlapping neural

networks mediate perception and imagery of familiar face identities. As

mentioned before, familiarity with a facial identity develops as a result of formed

association between a face image and corresponding semantic information

about the identity (i.e., the name of the face). Therefore, visual imagery of the

familiar face upon the presentation semantic information becomes possible.

59


Associative learning can be considered an essential process responsible

for building mnemonic representations. The current study showed that

association formed between semantic and visual information about face

identities ultimately influences perception of face images. If semantic information

and a visual image can be associated in memory, it may also be possible that

similar associations are formed between two different visual images. Indeed,

various behavioral and neurophysiological studies have provided evidence for

this type of association (Messinger, Squire, Zola, & Albright, 2001; Miyashita,

1988; Miyashita, Kameyama, Hasegawa, & Fukushima, 1998; Schlack &

Albright, 2007). However, what remains unanswered is whether association

between different visual images also exerts similar influence on perception in a

selective adaptation task. The following chapter describes a study that

attempted to provide answers for this question.

60


Figure Legends

Figure 1. Trial sequence in the aftereffect tasks during the real- and imagined-

stimulus conditions. A five-second presentation of the adapting stimulus was

followed by a brief presentation of a test face. After adaptation, observers were

asked to identify the test face among three shown faces.

Figure 2. A trial in the discrimination tasks on real and imagined stimuli.

Participants were asked to judge whether either the real or imagined anti-face

and test face belonged to the same person. The identity strengths of the test

faces were varied to be less than those of the anti-faces.

Figure 3. Sensitivity to face identity in real-stimulus conditions.

The logistic function 0.333+0.667*1/(/(1+exp(-(x-c)/a)) was fitted to the data, in

which ‘a’ and ‘c’ are free parameters that determine the midpoint and the slope

of the psychometric function. The fraction of trials in which participants correctly

identified the test face is plotted in relation to the identity percentage contained

in test faces. Data from baseline with no adaptation (squares), adaptation to

matching anti-face (triangles), and adaptation to non-matching anti-face (circles)

are shown. The recognition threshold for each condition was taken to be the

61


inflection point of the corresponding cure and shown accordingly. (a) Average of

all participants. Standard errors are shown. (b and c) Individual data from two

participants.

Figure 4. Results from imagined-stimulus conditions.

(a) average of all participants, with corresponding threshold for each condition.

Standard error of mean (SEM) are shown. (b & c) Data from two individual

participants. (b & c) Data from two individual participants.

Figure 5. Difference thresholds for real (triangles) and imagined (squares) faces.

The fraction of trials in which participants perceived the test face to be different

from the previously learned face is plotted in relation to identity difference

between the anti- and test faces. Difference thresholds (75% different

responses) for the two psychometric functions for visual and imagined faces are

identical.

62


Figure 1

63


Figure 2

64


Figure 3

65


Figure 4

66


67

Figure 5

Chapter 5 Motion Aftereffects and Stable Images

Chapter 5

Dynamic motion aftereffects induced by static images previously associated with unidirectional motion

A bstract

Current neurophysiological evidence suggests that it is possible to alter stimulus

selectivity of neurons of MT through associative learning, such that they show

increased firing with stable images that were previously paired with motion

(Schlack & Albright, 2007). This finding leads to the question of whether the

neurological association between static and dynamic stimuli has a

corresponding impact on perceived motion. We measured changes in perceived

motion after adaptation to static shapes that were previously associated with

unidirectional motion. We report that a dynamic motion aftereffect was evident

after adaptation to the static images. A delay of 3.5 seconds following adaptation

to the static images or moving dots significantly decreased the magnitude of the

effect. A dynamic MAE was also produced after adaptation to static images of

arrows without any explicit associative learning. Our results show that

associative influence can alter the perception of motion in a top-down manner.

68


I ntroduction

It is well known that the visual area MT is crucial for the processing of

dynamic stimuli. Various neurophysiological studies have shown that this area is

active during the perception of motion (Albright, 1984; Maunsell & Van Essen,

1983; Tootell et al., 1995), and that damages to MT produce a significant

impairment in the perception of dynamic stimuli (Newsome & Pare, 1988;

Newsome, Wurtz, Dursteler, & Mikami, 1985). Area MT neurons display strong

directional selectivity, with stable images normally eliciting a poor response

(Albright, Desimone, & Gross, 1984; Born & Bradley, 2005).

However, recent studies have shown that static stimuli that imply motion

are also capable of activating similar motion-processing mechanisms in humans

(Jellema & Perrett, 2003; Kourtzi & Kanwisher, 2000; Lorteije et al., 2006;

Winawer, Huk, & Boroditsky, 2008). These stimuli are often static images of

natural objects motion (e.g., flying bird, running dog, etc.). Unlike dynamic visual

stimuli that automatically produce perceptual sensation of motion, processing of

the static images of objects in motion may engage additional high-level cognitive

processes that link these stimuli to motion. For example, the activity of the

motion-selective mechanisms in response to a picture of a running athlete may

be modulated by the top-down signals resulting from the activation of the

69


dynamic context (i.e., position of the athlete in moments leading up to, and

following the time point at which the picture of taken) associated with the image.

Interestingly, it appears that the dynamic information associated with a

static image can be acquired through forced associative learning. For example,

a static, abstract image that does not represent motion in natural settings can

come to elicit responses in directionally selective neurons in MT. In a study by

Schlack and Albright (2007), the activity of neurons in visual area MT were

recorded in awake, behaving animals prior to and after a training period during

which they learned to associate moving stimuli with static images of arrows. The

authors showed that after the associative learning trials, area MT neurons

responded to the stable images in a manner that was consistent with the nature

of the learned associations.

This intriguing finding shows that the range of static stimuli that can

potentially activate motion-processing mechanisms is not limited to images of

natural objects in motion. It led us to question whether static abstract images

that are previously associated with motion are capable of influencing perception

of real motion. In the current experiment, we employed the selective adaptation

method designed to induce dynamic motion aftereffects to capture transient

changes in perception of motion. During a selective adaptation experiment, a

stimulus is presented for an extended period of time, resulting in temporary

perceptual distortions (or aftereffects). Dynamic MAE makes use of dynamic

random dot stimuli (Hiris & Blake, 1992; Newsome & Pare, 1988), which are

70


groups of dots moving in different directions over time. The directional

coherence of these stimuli is determined by the percentage of dots moving in

the same direction, which can be varied from 0 (no global direction) to 100%

(total correlated motion). The dynamic MAE is produced after extended

exposure to dots moving in one direction, resulting in heightened perceptual bias

to movement in the opposite direction. It is believed that MT is a key visual area

mediating aftereffects related to motion (Antal et al., 2004; Rees, 2001;

Tikhonov, Handel, Haarmeier, Lutzenberger, & Thier, 2007).

If an abstract, static stimulus previously associated with a particular

direction of motion can activate motion-processing mechanisms, then prolonged

exposure to that stable stimulus alone should be sufficient to influence the

perception of subsequent motion. We show here that adaptation to a static

image associated with unidirectional movement subsequently increased

sensitivity to movement in the direction opposite to the associated direction in

our first two experiments. In our last experiment, we also report that a dynamic

MAE was produced after adaptation to stable arrow images without any recent

associative learning in experimental settings. It is presumed that the meaning of

abstract shapes such as arrows is explicitly achieved through prior associative

experience in humans. In this experiment, there was no learning phase during

which the images of arrows became explicitly associated with motion and

instead relied on an intrinsic or preexisting semantic representation of arrow

shape and movement direction. Together, our results show that adaptation to

71


both static stimuli associated with motion and dynamic stimuli can influence

subsequent perception of motion in similar manners.

Methods

P articipants

A total of ten observers participated in Experiment 1(mean age = 24.6; five

males). Following the completion of Experiment 1, the same group of observers

participated in Experiment 2. Five new subjects participated in Experiment 3

(mean age = 25 yrs; one male). All participants had previously participated in

psychophysical experiments but were naïve to the purpose of the present

experiment. All had normal or corrected-to-normal vision. The study was

reviewed and approved by the McGill institutional ethics board for human

psychophysical studies. Written consent was acquired from each participant

prior to the experimental sessions. The data from two participants who

participated in both Experiments 1 and 2 were excluded from analysis because

their motion discrimination of moving dots at the 30% coherence level during the

control condition did not exceed chance (50% accuracy).

72



All stimuli were presented on an LG flat-screen, CRT monitor with 1024 x 768

resolution and 85 Hz refresh rate. The stimuli were centrally presented on a

uniform black background. The presentation sequence was programmed in

MATLAB software using the Psychophysics Toolbox extension (Brainard, 1997).

A chinrest was used to maintain head position at a distance of 57 cm from the

monitor surface. Gaze direction was monitored by an eye-movement tracking

camera (Arrington Research, AZ) positioned under the right eye to ensure

proper fixation. All experiments were carried out in a dark testing room.

The stable images in Experiment 1 and 2 were made up of four filled

geometric shapes (circle, square, diamond and hexagon) whereas those for

Experiment 3 were arrows pointing in one of four directions (up, down, left and

right). The size of the stable images did not exceed 7 x 7 degrees. The moving

dots appeared within a circular window of 15 degrees diameter. The dot density

was 16.7 dots/deg2 and the size of each dot was approximately 3 x 3 pixels. The

center of the window was marked with a cross. A subset of dots, depending on

the coherence level, was repositioned from the original location in one of the

four directions (left, right, up and down), with the remaining dots being randomly

repositioned in incoherent directions. The speed of dots was 6.0 deg/s. During

73


associative learning and adaptation, dot coherence was 80% and the direction

of the dots was either left or right. The coherence of test dots varied from 0 to

30%.

P rocedures

Experiment 1

Participants completed an associative learning phase before proceeding to the

motion aftereffect tasks. During the associative learning phase, two of the four

geometric shapes (circle, square, diamond or hexagon) were randomly selected

for each subject. Subjects then learned that each shape was paired with one

direction of motion (left or right). These stimuli are illustrated in Figure 1. The

pairing of stable images and directions of motion was randomized across

participants. The presentations of the stimuli within a pair occurred in a

sequential manner. The presentation of the abstract shape always preceded that

of the moving dots. Each stimulus in the pair was presented for 5 seconds. The

two pairs were presented a minimum of 10 times in randomized order

throughout the associative learning phase. Participants were asked to maintain

fixation on the central cross during the presentation of the paired stimuli. At the

end of the presentation period, participants were asked to verbally describe the

direction of motion associated with each shape. The presentation of the pairs

was repeated until 100% accuracy in verbal description was achieved.

74


The motion aftereffect tasks were presented immediately after the

associative learning phase. As shown in Figure 2, there were three adaptation

conditions depending on the type of the adapting stimuli: one of the two abstract

shapes presented during the associative learning phase (stable-image

condition), a group of dots moving in one of the two directions (moving-dots

condition), or one of the two new abstract shapes with no associated direction of

motion (control condition). Each adaptation condition was tested by way of two

blocks of 140 trials and each adapting stimulus appeared for one block of trials.

The adapting stimulus would remain constant within each block (e.g., one

abstract shape or one movement direction per block). The duration of the

adapting stimulus was 10 seconds for the first trial of each block, and 5 seconds

for all subsequent trials. For the stable-image condition, participants were asked

to simultaneously imagine the direction of motion associated with the image. A

total of six blocks, two from each condition, were presented in an intermixed,

pseudo-randomized order.

The adapting stimulus was followed by brief presentation of a central

fixation cross (500 ms) and a 1 second presentation of the test stimulus (Fig. 2).

Test stimuli were a group of moving dots that could have one of seven

coherence levels (0 – 30%) in two different directions (same or opposite to the

direction represented by the adapting stimulus). Participants were made aware

of these two directional possibilities. Their task was to identify the direction of

global motion of the test stimulus with a key press (left or right). Each stimulus

75


condition (control, stable-image and moving –dots) was composed of two

blocks, with each block containing 140 trials. A total of ten presentations were

made at each of the seven coherence levels and two directions (total = 140

trials) within each block.

Participants were asked to fixate on the central cross throughout the task.

For all trials, the participants’ eye movements were monitored to ensure

constant fixation.

Experiment 2

Upon the completion of Experiment 1, the same participants also

participated in Experiment 2. Each participant completed the same associative

learning task described in Experiment 1, as a reminder, before proceeding to the

MAE tasks. There were two adaptation conditions in Experiment 2: stable-image

and moving-dots. The MAE tasks were identical to those in Experiment 1,

except that the inter-stimulus interval (ISI) was increased to 3.5 seconds. A

central fixation cross was presented for the duration of ISI.

Experiment 3

In this experiment, the participants completed the MAE tasks without any

associative learning. As with Experiment 1, there were three adaptation

conditions (stable-image, moving-dots and control). However, the adapting

76


stimuli in the stable-image condition images were composed of arrows pointing

in one of four directions (up, down, left, and right). The adapting stimuli in the

control condition were composed of the four abstract shapes described in

Experiment 1. All participants completed the three conditions in the following

order – stable-image, moving-dots and control. All other parameters were

identical to those of Experiment 1.

Results

E xperiment 1

All participants whose data were included for analysis achieved 100%

accuracy in describing the relationship between stable images and directions of

motion after the first session of the associative learning task (10 presentations of

each pair). Data from eight participants were used for analysis. The performance

data were fitted to a logistic function.

Fig. 3a shows averaged proportions of trials in which participants

perceived the test stimulus to be moving in a direction opposite to that

represented by the adapting stimulus, plotted against the coherence level of the

test stimulus in two directions (same or opposite to adapting). A logistic function

was fitted to the average of the raw data. Compared to the control condition

during which adapting stimuli were stable images with no associated motion,

adaptation to either moving dots or stable images that were previously

77


associated with unidirectional motion caused a leftward shift, suggesting a

perceptual bias towards motion in the opposite direction. We observed a greater

leftward shift with the moving-dots condition compared to the stable-image

condition. The data from two representative individuals in this experiment are

separately presented in Figures. 3b and 3c.

To assess the magnitude of the aftereffect produced in the different

adaptation conditions, the coherence of test dots that produced no perceived

global motion (nulling percentage) was calculated, and compared to that of the

control condition. A paired t-test revealed a significant shift in the nulling

percentage from the control condition in both adaptation conditions (moving-dots

condition [t(7) = 10.859, p = .000, prep =.99 one- tailed], mean shift in nulling

percentage = 18.130, SEM= 1.670; stable-image condition [t(7) = 4.919, p

=0.001, prep =.99 one- tailed], mean shift in nulling percentage = 8.070, SEM =

1.640).

E xperiment 2

In Experiment 2, we increased the inter-stimulus interval between the

adapting and test stimuli to 3.5 seconds in both moving-dots and stable-images

conditions. Data from the same 8 participants of Experiment 1 were included for

analysis. Group averages in moving-dots and stable-images conditions were

compared to those in the respective conditions from Experiment 1. Data from

the control condition from Experiment 1 are shown to provide reference points.

78


Figure 4a shows the response profiles in directional judgments following

adaptation to moving-dots obtained from Experiments 1 and 2. Compared to

those from Experiment 1 (ISI of 0.5 s), the ISI of 3.5s in Experiment 2 caused a

decrease in the magnitude of the aftereffect, as indicated by the decreased

shifts in the nulling percentage (mean shift of 10.159, SEM = 1.935, compared

to the mean shift of 18.129 in Experiment 1).

Similar patterns were observed in performances following adaptation to

stable images previously associated with motion. As shown in Figure 4b, the

increased ISI produced a decrease in the magnitude of the aftereffect (mean

shift of 2.518, SEM = 1.096, compared to the mean shift of 8.070 in Experiment

1).

E xperiment 3

In this experiment, the adapting stimulus in the stable-image condition

consisted of a set of arrows randomly presented in one of the four cardinal

directions. There was no associative learning involved. The moving-dots

condition was the same as in Experiment 1. Fig. 5a shows that the pattern of

results based on the performance of five participants was similar to that found in

Experiment 1. Adaptation to arrow images and unidirectional moving dots

produced a leftward shift from the control condition. The shift in nulling

79


percentages caused by adaptation to the arrow images was statistically

significant [t(4) = 4.102, p = 0.003, prep =.97, one- tailed].

D iscussion

We have used dynamic random dot displays with varying coherence

levels to measure transient changes in perceived motion after exposure to static

adapting stimuli composed of various geometric shapes. Subjects first

completed a learning phase in which they associated a given shape with a

particular direction of motion. We discovered that subsequent adaptation to a

given static image produced a bias in perceived motion opposite in direction to

that associated with the object. This motion aftereffect, which was generated by

a static object, was significantly distinguishable from control (no associative

learning) but not as strong as the MAE produced by actual moving dots. In a

subsequent experiment, we discovered that the magnitude of the effect is

significantly decreased after a delay of 3.5 seconds, suggesting that a common

mechanism mediates the MAE following adaptation to unidirectional motion and

stable images previously associated with motion.

Previous studies have shown that static images of objects in motion (e.g.,

flying bird, running dog, etc.) activate motion-processing mechanisms (Kourtzi &

Kanwisher, 2000; Lorteije et al., 2006; Winawer, Huk, & Boroditsky, 2008). In

our third experiment, we further explored whether abstract shapes implying a

80


particular motion direction can also generate an MAE in the absence of recent

associative learning. Unlike images of natural objects in motion, the meaning of

abstract shapes such as arrows is achieved through prior associative

experience. In this experiment, there was no learning phase during which the

images of arrows became explicitly associated with motion and instead relied on

an intrinsic or preexisting semantic representation of arrow shape and

movement direction. Adaptation to stable images of arrows produced an MAE,

suggesting that the phenomenon of implied motion can be extended to include

abstract images indicating directions of motion.

The results of our study extend the findings of Schlack and Albright

(2007) into the human behavioral domain. We have shown that adaptation to

static images previously associated with movement direction produces a

perceptual bias that is similar in nature to that observed at the biological level.

Compared to adaptation to moving dots, the magnitude of the perceptual MAE

as measured by shifts in nulling percentages was smaller when the adapting

stimulus was a stable image. This result is also consistent with previous reports

of reduced activity of motion-sensitive neurons when stimulated by images

associated with motion (Schlack & Albright, 2008). It appears that the response

selectivity of a neuron can be altered through associative learning, but the level

of response may not be equivalent to that elicited by its inherently preferred

stimulus.

81


One interesting finding in our study concerns the MAEs induced by arrow

images, which occurred in the absence of any recent associative learning. It

appears that the presentation of an arrow produced automatic activation of its

semantic representation that in turn may have primed motion-selective neurons

during adaptation. In contrast, Schlack and Albright (2007) found that area MT

neurons do not show a response bias to arrow presentation prior to associative

learning. This is to be expected given that monkeys are not likely to have pre-

existing knowledge of arrow connotations whereas prior human association with

such stimuli likely accounts for the effects we observed.

The magnitude of the aftereffect following adaptation to moving dots and

stable images with associated motion was significantly decreased when the ISI

between adapting and test stimuli was increased. As pointed out by Winawer

and colleagues (2008) whose study reported motion aftereffects from

photographs depicting motion, a short delay between the presentations of

adapting and test stimuli would not influence the size of the aftereffect, if the

effect were based purely on cognitive processes. The observed decrease in the

aftereffect magnitude caused by the delay suggests that the bias following

adaptation to stable images previously associated with motion has a perceptual

basis.

While viewing stable images previously associated with motion during

adaptation, participants were asked to mentally recall the direction of associated

motion. Therefore, it is possible that the imagery of moving dots may have

82


contributed to the effect. Indeed, imagery of motion has been shown to activate

brain areas involved in processing of real motion (Grossman & Blake, 2001).

Nonetheless, continuous, strong visual inputs have been suggested to interrupt

imagery (Pearson, Clifford, & Tong, 2008; Ryu, Borrmann, & Chaudhuri, 2008).

Since stable images remained present during the adaptation period, imagery

alone cannot account for the subsequent perceptual bias reported here.

Our results lead to the question of what brain structures may be

responsible for the neural basis of object–motion associations. Motion

aftereffects induced by stable images suggest the involvement of neural

mechanisms that specialize in the processing of both motion and simple shapes.

One possible region, at least in humans, where such integration may occur can

be found in the posterior middle temporal gyrus. Neuroimaging studies have

revealed increased activity in this area in response to stable images implying

motion (Kourtzi & Kanwisher, 2000; Krekelberg, Vatakis, & Kourtzi, 2005),

mental imagery of moving objects (Grossman & Blake, 2001), and words that

represent motion (Martin, Haxby, Lalonde, Wiggs, & Ungerleider, 1995;

(Wallentin, Lund, Ostergaard, Ostergaard, & Roepstorff, 2005). Located anterior

to the human MT area, this area may also be involved in integrating static cues

and motion, and possibly modulates the activity of area MT neurons in a top-

down manner.

83


Figure Legends

Figure 1. a. Basic geometric shapes used in stable-images and control

conditions. Two geometric shapes were randomly chosen from the four basic

shapes for each participant. Participants then underwent the learning phase

prior to the motion aftereffect task, during which they were asked to associate

each geometric shape with a direction of motion (shown in b).

Figure 2. Trial sequence during the motion aftereffect task in Experiment 1. The

adapting stimulus was a geometric shape previously associated with

unidirectional motion in the stable-images condition, a group of dots moving in

one direction (left or right) in the moving-dots condition, or a geometric shape

with no associated motion in the control condition. The adapting stimulus was

followed by brief presentation of a central fixation cross (500 ms) and a 1

second presentation of the test stimulus. Test stimuli were a group of moving

dots that could have one of seven coherence levels (0 – 30%) in two different

directions (same or opposite to the direction represented by the adapting

stimulus). Participants were asked to indicate the overall direction of the test

stimulus.

Figure 3. Performance on the motion aftereffect tasks in Experiment 1.

Proportions of trials in which participants perceived the test stimulus to be

84


moving in a direction opposite to that represented by the adapting stimulus

plotted against coherence level of the test stimulus in two directions (same or

opposite to adapting). A logistic function [1/(1+exp(-(x-c)/a))] was fitted to the

data, in which ‘a’ and ‘c’ are free parameters that determine the midpoint and

slope of the psychometric function. Data from control (no associative learning),

stable-image and moving-dots conditions are shown. a. Averaged data from

eight participants. Standard errors of mean are indicated. b & c. Data from two

individuals.

Figure 4. Performance on the motion aftereffect tasks in Experiment 2 in which

the ISI between adapting and test stimuli was increased to 3.5 seconds (Delay).

Participants from Experiment 1 completed the tasks in Experiment 2. Data from

the control and corresponding conditions from Experiment 1 are shown to

provide reference points. Standard errors of the mean are shown. a. Group data

from the moving-dots conditions from Experiments 1 and 2. b. Group data from

the stable-images conditions from Experiments 1 and 2.

Figure 5. Performance on the motion aftereffect tasks in Experiment 3. During

the stable-images condition, static images of arrows were presented as adapting

stimuli. a. Averaged data from five participants. b & c. Data from two individuals.

85


Figure 1

86


Figure 2

87


Figure 3

88


Figure 4

89


90

Figure 5

Chapter 6 Concluding Remarks

Chapter 6

Concluding Remarks

Summary

The present thesis describes experiments that attempted to examine how

mnemonic processes influence perception of different types of visual stimuli.

Selective adaptation methods designed to produce visual aftereffects were used

to test the hypothesis that adaptation to stimuli with different mnemonic contents

should produce different types of aftereffects. Indeed, transient changes in

perception produced by mnemonic representations associated with adapting

stimuli were revealed by various visual aftereffects. The present results suggest

that processing of motion and faces, each believed to engage specialized visual

processing areas, is subject to top-down mnemonic influences.

In the first study described in Chapter 3, the principle underling viewpoint-

dependent aftereffects was applied to discover the effects of familiarity on the

perception of facial viewpoints. Adaptation to slightly rotated images of familiar

and unfamiliar faces produced temporary distortion in the perception of

viewpoint of subsequent images of faces. This result suggests that neural

networks mediating viewpoint information are involved in the representation of

these faces. However, category-specific transfer of the viewpoint-dependent

aftereffect occurred only with unfamiliar faces, providing evidence for the idea

91


that differential weights are attached to viewpoint information of the

representations of familiar and unfamiliar faces.

Representations of familiar facial identities are further explored in the

subsequent study, in which face-identity aftereffect was used to probe neural

networks underlying perception and imagery of familiar faces. Previous works

reporting overlapping neural mechanisms underlying perception and imagery

lead to the hypothesis that adaptation to both perceived and imagined familiar

faces should produce similar biases in the perception of subsequent face

identities. Indeed, face-identity aftereffect was produced after adaption to both

perceived and imagined familiar faces. Additional experiments examining the

identity information contained in these faces showed that identity boundaries of

perceived and imagined faces were equivalent. Together, these findings show

that identity information contained in imagined and perceived faces produce

similar bias in face perception in a selective adaptation study.

The influence of mnemonic processes on motion perception was also

evident, as shown in dynamic motion aftereffect produced by adaptation to static

images previously associated with unidirectional motion. This pattern of

behavioral outcome is consistent with the altered stimulus-selectivity of MT

neurons produced by associative learning reported in the previous

neurophysiological investigation (Schlack & Albright, 2007). Similar bias in

motion perception was produced after adaptation to static images of arrows

without any explicit associative learning in the experimental context, suggesting

92


that pre-existing connotations about the arrow images could also prime

mechanisms mediating motion perception. The dynamic motion aftereffects

produced in the absence of real dynamic stimuli show that top-down influences

resulting from associative learning affect the perception of motion.

L imitations

In the study investigating the effects of facial familiarity on the perception

of viewpoints, it was concluded that the stimulus-specific transfer of the

aftereffect occurred only with unfamiliar faces, because the viewpoint perception

was near chance when the adapting and test faces were of different identities.

However, it is entirely possible that this disruption in viewpoint perception is

limited to the orientation shown in the test faces. Presentation of test faces with

additional degree of orientation may help to uncover the true extent of viewpoint

perception in that experimental condition.

The decreased magnitude of the aftereffect produced after adaptation to

the imagined faces described in Chapter 3 was attributed to the difficulty

associated with imagining a stimulus in a coherent, consistent manner, which

may have led to weaker neural activations during the adaptation period. The

equivalent difference thresholds for both real and imagined faces found in the

subsequent experiment ruled out the possibility that the decreased magnitude of

93


the aftereffect was due to the difference in the identity information contained in

these faces. However, this is only one of many alternative explanations that may

exist. For example, the time course of neural activity elicited by imagined and

perceived faces may be completely different, which may result in qualitative

differences in perception of subsequent faces if the presentation duration of

adapting face was varied.

While viewing static images previously associated with motion during

adaptation in the experiment described in the previous chapter, participants

were asked to mentally recall the direction of associated motion. Therefore, it is

possible that the imagery of moving dots may have contributed to the effect.

Nonetheless, continuous, strong visual inputs have been suggested to interrupt

imagery (Pearson & Brascamp, 2008; Ryu, Borrmann, & Chaudhuri, 2008).

During the adaptation period, a stable image remained present and participants

were asked to fixate on the center of the image. Since stable images remained

present during the adaptation period, imagery alone cannot account for the

subsequent perceptual bias reported here. Therefore, it is highly unlikely that the

reported perceptual bias was solely driven from motion imagery. The only

instances in which mental imagery may have contributed to the performance

were the delayed conditions in Experiment 2, in which a blank screen with a

small fixation cross appeared during the delay period. However, the magnitude

of the motion aftereffect was decreased, not increased, after the delay.

94


Possible neural mechanisms mediating mnemonic influence on perception of complex images

The results reported in Chapters 2 and 3 suggest close interaction

between visual processing and mnemonic representation of faces. One question

that arises from the present finding is whether this type of interaction shown at

the behavioral level can also be found in the neural structures mediating

perception and memory. It is well known that face-selective cells are located in

the high-level visual processing areas in the inferior temporal cortex. The type of

learning that allows the formation of conscious representations of faces is

mediated by structures in the medial temporal cortex. While there are reports

suggesting that the influence of mnemonic processes on perception is achieved

through interactive signals amongst putative structures mediating perceptual

and mnemonic functions (Miyashita, Kameyama, Hasegawa, & Fukushima,

1998; Sugiura, Shah, Zilles, & Fink, 2005), other studies have revealed that

there may be additional brain regions that act as a mediator between the

memory-related and perception-related structures (Bussey & Saksida, 2007;

Murray, Bussey, & Saksida, 2007; Suzuki & Amaral, 1994; Suzuki, Zola-Morgan,

Squire, & Amaral, 1993).

The perirhinal cortex, located at the junction of medial temporal cortex

and the inferior temporal cortex, has been suggested to be the structure that

relays mnemonic signals to vision-related areas. This area displays extensive

bilateral connections to inferior temporal and medial temporal areas, allowing

95


easy exchange of signals from both perception-related and memory-related

areas (Suzuki & Amaral, 1994). Furthermore, it has been shown that damage to

this area caused impairments in tasks requiring memory and visual

discrimination of complex images (Bussey & Saksida, 2002, 2005). In fact,

according to the Perceptual-Mnemonic/Feature Conjunction (PMFC) model, the

perirhinal cortex is crucial in binding and discrimination of complex visual

features, which require both perceptual and mnemonic functions (Bussey &

Saksida, 2005; Lee et al., 2005). Therefore, the perirhinal cortex may be

involved in mediating mnemonic influences on the activity of high-level visual

areas that selectively process complex images.

Mnemonic influences and perception of simple stimulus features

Dynamic motion aftereffects produced by static images previously

associated with motion showed that mnemonic processes exert influences on

the processing of motion. This leads one to question whether the observed

mnemonic influence can be extended to the processing of other simple stimulus

features. Indeed, Bulthoff and colleagues showed that internal representations

of familiar objects could alter the perception of 3-D structures that are incoherent

to the objects (Bulthoff, Bulthoff, & Sinha, 1998). They also showed that

anomalous stereo-depth cues did not interfere with object recognition,

96


suggesting that top-down influences stemming from representations of a familiar

object can completely override the incoming depth information of that object.

The finding that familiarity could actually supersede incoming depth

information in producing conscious percepts is rather intriguing because it raises

further questions on the extent to which mnemonic processes could influence

visual processing of different stimulus features. For example, the strength of the

mnemonic influence exerted on the perception of motion appears to be weaker,

because the magnitude of the dynamic motion aftereffect produced by static

images was never greater than that produced by real motion. Whether this

discrepancy is due to the increased task demand of imagining, as opposed to

merely perceiving visual stimuli, or due to differential extents to which

processing of different visual features is amenable to top-down influence is a

subject of further investigation.

Processing of stimulus orientation is believed to mainly occur in the

primary visual cortex (V1). While several studies show that the activity of V1

neurons are subject to top-down influences of attention (Crist et al., 2001; Li &

Gilbert, 2002; Li et al., 2004), it is yet to be investigated whether memory-related

signals can also affect the activity of these neurons. This investigation may be of

particular interest, because perception resulting from the activity of the primary

cortex is often categorized as “early vision”, which is believed to be

encapsulated from other cognitive influences beside attention (Pylyshyn, 1999).

In the context of the current research, a further experiment reporting biased

97


perception of orientation following adaptation to complex images previously

associated with line segments of various orientations would provide evidence for

mnemonic influences on early vision.

Perception as results of Interactions amongst different visual areas

Cortical visual processing starts in the primary visual cortex and is

believed to occur in a hierarchical manner. This hierarchy is reflected in the

extensive feed-forward, or ascending connections found in the pathways

responsible for bottom-up processing of visual stimuli (Callaway, 1998; Reid,

2001). However, just as extensive are the feedback, or descending connections

projecting from high-level to low-level visual areas. For example, V1 receives

direct projections from IT as well as projections from V2, V4 and MT (Angelucci

et al., 2002; Hupe et al., 1998; Shmuel et al., 2005). Indeed, the intensity of the

feedback connections found in different visual areas have led some researchers

to question the utility of the traditional “low” and “high” labeling of different visual

areas (Gilbert & Sigman, 2007).

Just as the feed-forward connections provide circuitry for bottom-up

processing of visual stimuli, the feedback connections may provide circuitry for

top-down processing of visual information. Furthermore, these extensive

connections suggest that perception can be influenced by dynamic interactions

98


99

between different visual areas. Indeed, the evidence of perception due to the

dynamic interaction between different visual area has been shown in the study

reported by Kovacs and colleagues (Kovacs, Papathomas, Yang, & Feher,

1996). In that study, two complex images of objects that were divided into

patches and different, complementary patches of the images were presented to

each eye to induce binocular rivalry. They found that the alternating percept is

object-based, rather than eye-based. Given that the binocular rivalry is mediated

by the activity of the primary visual cortex, the alternating percepts are likely due

to the interaction between primary visual cortex and areas in the inferior

temporal cortex that process complex images.

Research conducted in the past few decades has revealed that different

visual areas are responsible for processing different aspects of visual stimuli.

However, most studies have focused on the activity of a visual area in isolation,

with little regard to its interaction with other areas. Recent findings, including the

ones described in the current thesis, show that perception is a rather dynamic

process, mediated by the activity of multiple brain areas. Therefore, it is

necessary to further study the nature of interactions amongst different structures

within the visual system, as well as how the visual system as a whole interacts

with other cognitive mechanisms in the brain.

R eferences

Albright, T. D., Desimone, R., & Gross, C. G. (1984). Columnar organization of

directionally selective cells in visual area MT of the macaque. J

Neurophysiol, 51(1), 16-31.

Angelucci, A., Levitt, J. B., Walton, E. J., Hupe, J. M., Bullier, J., & Lund, J. S.

(2002). Circuits for local and global signal integration in primary visual

cortex. J Neurosci, 22(19), 8633-8646.

Antal, A., Varga, E. T., Nitsche, M. A., Chadaide, Z., Paulus, W., Kovacs, G., et

al. (2004). Direct current stimulation over MT+/V5 modulates motion

aftereffect in humans. Neuroreport, 15(16), 2491-2494.

Baddeley, A. (1992). Working memory. Science, 255(5044), 556-559.

Bartolomeo, P., Bachoud-Levi, A. C., De Gelder, B., Denes, G., Dalla Barba, G.,

Brugieres, P., et al. (1998). Multiple-domain dissociation between

100

impaired visual perception and preserved mental imagery in a patient with

bilateral extrastriate lesions. Neuropsychologia, 36(3), 239-249.

Behrmann, M., Moscovitch, M., & Winocur, G. (1994). Intact visual imagery and

impaired visual perception in a patient with visual agnosia. J Exp Psychol

Hum Percept Perform, 20(5), 1068-1087.

Bentin, S., Moscovitch, M., & Nirhod, O. (1998). Levels of processing and

selective attention effects on encoding in memory. Acta Psychol (Amst),

98(2-3), 311-341.

Boring, E. G. (1930). A new ambiguous figure. Americal Journal of Psychology,

42, 444-445.

Born, R. T., & Bradley, D. C. (2005). Structure and function of visual area MT.

Annu Rev Neurosci, 28, 157-189.

Brainard, D. H. (1997). The Psychophysics Toolbox. Spat Vis, 10(4), 433-436.

Broerse, J., & Crassini, B. (1980). The influence of imagery ability on color

aftereffects produced by physically present and imagined induction

stimuli. Percept Psychophys, 28(6), 560-568.

Bruce, V., & Young, A. (1986). Understanding face recognition. Br J Psychol, 77

( Pt 3), 305-327.

Bulthoff, I., Bulthoff, H., & Sinha, P. (1998). Top-down influences on

stereoscopic depth-perception. Nat Neurosci, 1(3), 254-257.

Burton, A. M., Bruce, V., & Hancock, P. J. B. (1999). From pixels to people: a

model of familiar face recognition. Cognitive Science, 23, 1-31.

101

Busey, T. A., & Loftus, G. R. (1998). Binocular information acquisition and visual

memory. J Exp Psychol Hum Percept Perform, 24(4), 1188-1214.

Bussey, T. J., & Saksida, L. M. (2002). The organization of visual object

representations: a connectionist model of effects of lesions in perirhinal

cortex. Eur J Neurosci, 15(2), 355-364.

Bussey, T. J., & Saksida, L. M. (2005). Object memory and perception in the

medial temporal lobe: an alternative approach. Curr Opin Neurobiol,

15(6), 730-737.

Bussey, T. J., & Saksida, L. M. (2007). Memory, perception, and the ventral

visual-perirhinal-hippocampal stream: thinking outside of the boxes.

Hippocampus, 17(9), 898-908.

Callaway, E. M. (1998). Local circuits in primary visual cortex of the macaque

monkey. Annu Rev Neurosci, 21, 47-74.

Craik, F. I. (2002). Levels of processing: past, present. and future? Memory,

10(5-6), 305-318.

Crist, R. E., Li, W., & Gilbert, C. D. (2001). Learning to see: experience and

attention in primary visual cortex. Nat Neurosci, 4(5), 519-525.

Dolan, R. J., Fink, G. R., Rolls, E., Booth, M., Holmes, A., Frackowiak, R. S., et

al. (1997). How the brain learns to see objects and faces in an

impoverished context. Nature, 389(6651), 596-599.

Eger, E., Schweinberger, S. R., Dolan, R. J., & Henson, R. N. (2005). Familiarity

enhances invariance of face representations in human ventral visual

cortex: fMRI evidence. Neuroimage, 26(4), 1128-1139.

102

Ellis, A. W. (1992). Cognitive mechanisms of face processing. Philos Trans R

Soc Lond B Biol Sci, 335(1273), 113-119.

Fang, F., & He, S. (2005). Viewer-centered object representation in the human

visual system revealed by viewpoint aftereffects. Neuron, 45(5), 793-800.

Farah, M. J. (1985). Psychophysical evidence for a shared representational

medium for mental images and percepts. J Exp Psychol Gen, 114(1), 91-

103.

Felleman, D. J., & Van Essen, D. C. (1991). Distributed hierarchical processing

in the primate cerebral cortex. Cereb Cortex, 1(1), 1-47.

Finke, R. A., & Schmidt, M. J. (1977). Orientation-specific color after-effects

following imagination. Journal of Experimental Psychology: Human

Perception and Performance, 3, 599-606.

Finke, R. A., & Schmidt, M. J. (1978). The quantitative measure of pattern

representation in images using orientation-specific color aftereffects.

Percept Psychophys, 23(6), 515-520.

Frisby, J. (1980). Seeing: Illusion, brain and the mind. Oxford: Oxford United

Press.

Furl, N., van Rijsbergen, N. J., Treves, A., & Dolan, R. J. (2007). Face

adaptation aftereffects reveal anterior medial temporal cortex role in high

level category representation. Neuroimage, 37(1), 300-310.

Gibson, J. J. (1933). Adaptation, after-effect and contrast in the perception of

cuved lines. Journal of Experimental Psychology, 20, 453-467.

103

Gibson, J. J., & Radner, M. (1937). Adaptation, after-effect and contrast in the

perception of tilted lines. Journal of Experimental Psychology, 20, 453-

467.

Gilbert, C. D., & Sigman, M. (2007). Brain states: top-down influences in sensory

processing. Neuron, 54(5), 677-696.

Gorno-Tempini, M. L., & Price, C. J. (2001). Identification of famous faces and

buildings: a functional neuroimaging study of semantically unique items.

Brain, 124(Pt 10), 2087-2097.

Grossman, E. D., & Blake, R. (2001). Brain activity evoked by inverted and

imagined biological motion. Vision Res, 41(10-11), 1475-1482.

Hancock, P. J., Bruce, V. V., & Burton, A. M. (2000). Recognition of unfamiliar

faces. Trends Cogn Sci, 4(9), 330-337.

Hebb, D. O. (1949). The Organization of Behavior; A neuropsychological

Theory. New York: Wiley.

Hill, H., Schyns, P. G., & Akamatsu, S. (1997). Information and viewpoint

dependence in face recognition. Cognition, 62(2), 201-222.

Hiris, E., & Blake, R. (1992). Another perspective on the visual motion

aftereffect. Proc Natl Acad Sci U S A, 89(19), 9025-9028.

Hubel, D. H., & Wiesel, T. N. (1968). Receptive fields and functional architecture

of monkey striate cortex. J Physiol, 195(1), 215-243.

104

Hupe, J. M., James, A. C., Payne, B. R., Lomber, S. G., Girard, P., & Bullier, J.

(1998). Cortical feedback improves discrimination between figure and

background by V1, V2 and V3 neurons. Nature, 394(6695), 784-787.

Ishai, A., & Sagi, D. (1995). Common mechanisms of visual imagery and

perception. Science, 268(5218), 1772-1774.

Ishai, A., Ungerleider, L. G., & Haxby, J. V. (2000). Distributed neural systems

for the generation of visual images. Neuron, 28(3), 979-990.

Ishai, A., Ungerleider, L. G., Martin, A., Schouten, J. L., & Haxby, J. V. (1999).

Distributed representation of objects in the human ventral visual pathway.

Proc Natl Acad Sci U S A, 96(16), 9379-9384.

Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: a

module in human extrastriate cortex specialized for face perception. J

Neurosci, 17(11), 4302-4311.

Koehler, W., & Wallach, H. (1944). Figural afterefects. An investigation of visual

processes. Proceedings of Americal Philosophical Society, 88, 269-260.

Konen, C. S., & Kastner, S. (2008). Two hierarchically organized neural systems

for object information in human visual cortex. Nat Neurosci, 11(2), 224-

231.

Korner, C., & Gilchrist, I. D. (2008). Memory processes in multiple-target visual

search. Psychol Res, 72(1), 99-105.

Kosslyn, S. M., Thompson, W. L., & Alpert, N. M. (1997). Neural systems shared

by visual imagery and visual perception: a positron emission tomography

study. Neuroimage, 6(4), 320-334.

105

Kourtzi, Z., & Kanwisher, N. (2000). Activation in human MT/MST by static

images with implied motion. J Cogn Neurosci, 12(1), 48-55.

Kovacs, I., Papathomas, T. V., Yang, M., & Feher, A. (1996). When the brain

changes its mind: interocular grouping during binocular rivalry. Proc Natl

Acad Sci U S A, 93(26), 15508-15511.

Kreiman, G., Koch, C., & Fried, I. (2000). Imagery neurons in the human brain.

Nature, 408(6810), 357-361.

Krekelberg, B., Vatakis, A., & Kourtzi, Z. (2005). Implied motion from form in the

human visual cortex. J Neurophysiol, 94(6), 4373-4386.

Lee, A. C., Bussey, T. J., Murray, E. A., Saksida, L. M., Epstein, R. A., Kapur,

N., et al. (2005). Perceptual deficits in amnesia: challenging the medial

temporal lobe 'mnemonic' view. Neuropsychologia, 43(1), 1-11.

Leopold, D. A., & Logothetis, N. K. (1999). Multistable phenomena: changing

views in perception. Trends Cogn Sci, 3(7), 254-264.

Leopold, D. A., O'Toole, A. J., Vetter, T., & Blanz, V. (2001). Prototype-

referenced shape encoding revealed by high-level aftereffects. Nat

Neurosci, 4(1), 89-94.

Leube, D. T., Erb, M., Grodd, W., Bartels, M., & Kircher, T. T. (2003). Successful

episodic memory retrieval of newly learned faces activates a left fronto-

parietal network. Brain Res Cogn Brain Res, 18(1), 97-101.

106

Li, W., & Gilbert, C. D. (2002). Global contour saliency and local colinear

interactions. J Neurophysiol, 88(5), 2846-2856.

Li, W., Piech, V., & Gilbert, C. D. (2004). Perceptual learning and top-down

influences in primary visual cortex. Nat Neurosci, 7(6), 651-657.

Lleras, A., Rensink, R. A., & Enns, J. T. (2005). Rapid resumption of interrupted

visual search. New insights on the interaction between vision and

memory. Psychol Sci, 16(9), 684-688.

Lorteije, J. A., Kenemans, J. L., Jellema, T., van der Lubbe, R. H., de Heer, F., &

van Wezel, R. J. (2006). Delayed response to animate implied motion in

human motion processing areas. J Cogn Neurosci, 18(2), 158-168.

Magnussen, S., Greenlee, M. W., Asplund, R., & Dyrnes, S. (1991). Stimulus-

specific mechanisms of visual short-term memory. Vision Res, 31(7-8),

1213-1219.

Martin, A., Haxby, J. V., Lalonde, F. M., Wiggs, C. L., & Ungerleider, L. G.

(1995). Discrete cortical regions associated with knowledge of color and

knowledge of action. Science, 270(5233), 102-105.

McCollough, C. (1965). The Conditioning of Color-Perception. Am J Psychol, 78,

362-378.

Medendorp, W. P., Tweed, D. B., & Crawford, J. D. (2003). Motion parallax is

computed in the updating of human spatial memory. J Neurosci, 23(22),

8135-8142.

107

Messinger, A., Squire, L. R., Zola, S. M., & Albright, T. D. (2001). Neuronal

representations of stimulus associations develop in the temporal lobe

during learning. Proc Natl Acad Sci U S A, 98(21), 12239-12244.

Michelon, P., & Biederman, I. (2003). Less impairment in face imagery than face

perception in early prosopagnosia. Neuropsychologia, 41(4), 421-441.

Miyashita, Y. (1988). Neuronal correlate of visual associative long-term memory

in the primate temporal cortex. Nature, 335(6193), 817-820.

Miyashita, Y., Kameyama, M., Hasegawa, I., & Fukushima, T. (1998).

Consolidation of visual associative long-term memory in the temporal

cortex of primates. Neurobiol Learn Mem, 70(1-2), 197-211.

Moradi, F., Koch, C., & Shimojo, S. (2005). Face adaptation depends on seeing

the face. Neuron, 45(1), 169-175.

Murray, E. A., Bussey, T. J., & Saksida, L. M. (2007). Visual perception and

memory: a new view of medial temporal lobe function in primates and

rodents. Annu Rev Neurosci, 30, 99-122.

Nassi, J. J., & Callaway, E. M. (2006). Multiple circuits relaying primate parallel

visual pathways to the middle temporal area. J Neurosci, 26(49), 12789-

12798.

Newsome, W. T., & Pare, E. B. (1988). A selective impairment of motion

perception following lesions of the middle temporal visual area (MT). J

Neurosci, 8(6), 2201-2211.

108

O'Craven, K. M., & Kanwisher, N. (2000). Mental imagery of faces and places

activates corresponding stiimulus-specific brain regions. J Cogn

Neurosci, 12(6), 1013-1023.

O'Toole, A. J., Deffenbacher, K. A., Valentin, D., & Abdi, H. (1994). Structural

aspects of face recognition and the other-race effect. Mem Cognit, 22(2),

208-224.

Over, R., & Broerse, J. (1972). Imagined lines fail to induce contour masking.

Psychonomic Science, 29, 203-204.

Paller, K. A., Ranganath, C., Gonsalves, B., LaBar, K. S., Parrish, T. B.,

Gitelman, D. R., et al. (2003). Neural correlates of person recognition.

Learn Mem, 10(4), 253-260.

Parkkonen, L., Andersson, J., Hamalainen, M., & Hari, R. (2008). Early visual

brain areas reflect the percept of an ambiguous scene. Proc Natl Acad

Sci U S A, 105(51), 20500-20504.

Pearson, J., Clifford, C. W., & Tong, F. (2008). The functional impact of mental

imagery on conscious perception. Curr Biol, 18(13), 982-986.

Pourtois, G., Schwartz, S., Seghier, M. L., Lazeyras, F., & Vuilleumier, P.

(2005). View-independent coding of face identity in frontal and temporal

cortices is modulated by familiarity: an event-related fMRI study.

Neuroimage, 24(4), 1214-1224.

Puce, A., Allison, T., & McCarthy, G. (1999). Electrophysiological studies of

human face perception. III: Effects of top-down processing on face-

specific potentials. Cereb Cortex, 9(5), 445-458.

109

Pylyshyn, Z. (1999). Is vision continuous with cognition? The case for cognitive

impenetrability of visual perception. Behav Brain Sci, 22(3), 341-365;

discussion 366-423.

Quiroga, R. Q., Reddy, L., Kreiman, G., Koch, C., & Fried, I. (2005). Invariant

visual representation by single neurons in the human brain. Nature,

435(7045), 1102-1107.

Ramachandran, V. S., & Braddick, O. (1973). Orientation-specific learning in

stereopsis. Perception, 2(3), 371-376.

Ramachandran, V. S., Ruskin, D., Cobb, S., Rogers-Ramachandran, D., &

Tyler, C. W. (1994). On the perception of illusory contours. Vision Res,

34(23), 3145-3152.

Rees, G. (2001). Attention, adaptation, and the motion aftereffect. Neuron,

32(1), 6-8.

Reid, R. C. (2001). Divergence and reconvergence: multielectrode analysis of

feedforward connections in the visual system. Prog Brain Res, 130, 141-

154.

Ryu, J. J., Borrmann, K., & Chaudhuri, A. (2008). Imagine Jane and identify

John: face identity aftereffects induced by imagined faces. PLoS ONE,

3(5), e2195.

Schacter, D. L., Norman, K. A., & Koutstaal, W. (1998). The cognitive

neuroscience of constructive memory. Annu Rev Psychol, 49, 289-318.

110

Schlack, A., & Albright, T. D. (2007). Remembering visual motion: neural

correlates of associative plasticity and motion recall in cortical area MT.

Neuron, 53(6), 881-890.

Schoups, A., Vogels, R., Qian, N., & Orban, G. (2001). Practising orientation

identification improves orientation coding in V1 neurons. Nature,

412(6846), 549-553.

Shmuel, A., Korman, M., Sterkin, A., Harel, M., Ullman, S., Malach, R., et al.

(2005). Retinotopic axis specificity and selective clustering of feedback

projections from V2 to V1 in the owl monkey. J Neurosci, 25(8), 2117-

2131.

Shulman, G. L., Corbetta, M., Buckner, R. L., Raichle, M. E., Fiez, J. A., Miezin,

F. M., et al. (1997). Top-down modulation of early sensory cortex. Cereb

Cortex, 7(3), 193-206.

Singer, G., & Sheehan, P. W. (1965). The effect of demand characteristics on

the figural aftereffect with real and imaged inducing figures. Am J

Psychol, 78, 96-101.

Slotnick, S. D., & Schacter, D. L. (2004). A sensory signature that distinguishes

true from false memories. Nat Neurosci, 7(6), 664-672.

Snodgrass, J. G., & Feenan, K. (1990). Priming effects in picture fragment

completion: support for the perceptual closure hypothesis. J Exp Psychol

Gen, 119(3), 276-296.

111

Snodgrass, J. G., & Hirshman, E. (1994). Dissociations among implicit and

explicit memory tasks: the role of stimulus similarity. J Exp Psychol Learn

Mem Cogn, 20(1), 150-160.

Sperling, R. A., Bates, J. F., Cocchiarella, A. J., Schacter, D. L., Rosen, B. R., &

Albert, M. S. (2001). Encoding novel face-name associations: a functional

MRI study. Hum Brain Mapp, 14(3), 129-139.

Sugiura, M., Shah, N. J., Zilles, K., & Fink, G. R. (2005). Cortical representations

of personally familiar objects and places: functional organization of the

human posterior cingulate cortex. J Cogn Neurosci, 17(2), 183-198.

Suzuki, W. A., & Amaral, D. G. (1994). Topographic organization of the

reciprocal connections between the monkey entorhinal cortex and the

perirhinal and parahippocampal cortices. J Neurosci, 14(3 Pt 2), 1856-

1877.

Suzuki, W. A., Zola-Morgan, S., Squire, L. R., & Amaral, D. G. (1993). Lesions

of the perirhinal and parahippocampal cortices in the monkey produce

long-lasting memory impairment in the visual and tactual modalities. J

Neurosci, 13(6), 2430-2451.

Tikhonov, A., Handel, B., Haarmeier, T., Lutzenberger, W., & Thier, P. (2007).

Gamma oscillations underlying the visual motion aftereffect. Neuroimage,

38(4), 708-719.

Tong, F., Meng, M., & Blake, R. (2006). Neural bases of binocular rivalry.

Trends Cogn Sci, 10(11), 502-511.

112

Treisman, A. M., & Gelade, G. (1980). A feature-integration theory of attention.

Cogn Psychol, 12(1), 97-136.

Tulving, E. (1987). Multiple memory systems and consciousness. Hum

Neurobiol, 6(2), 67-80.

Valentine, T. (1991). A unified account of the effects of distinctiveness,

inversion, and race in face recognition. Q J Exp Psychol A, 43(2), 161-

204.

Valentine, T., and Bruce, V. (1986). Recognising familiar faces: The role of

distinctiveness and familiarity. Canadian Journal of Psychology, 40, 300-

305.

Wagner, A. D., Koutstaal, W., & Schacter, D. L. (1999). When encoding yields

remembering: insights from event-related neuroimaging. Philos Trans R

Soc Lond B Biol Sci, 354(1387), 1307-1324.

Wallentin, M., Lund, T. E., Ostergaard, S., Ostergaard, L., & Roepstorff, A.

(2005). Motion verb sentences activate left posterior middle temporal

cortex despite static context. Neuroreport, 16(6), 649-652.

Wang, Q., Cavanagh, P., & Green, M. (1994). Familiarity and pop-out in visual

search. Percept Psychophys, 56(5), 495-500.

Watson, T. L., & Clifford, C. W. (2003). Pulling faces: an investigation of the

face-distortion aftereffect. Perception, 32(9), 1109-1116.

Webster, M. A., Kaping, D., Mizokami, Y., & Duhamel, P. (2004). Adaptation to

natural facial categories. Nature, 428(6982), 557-561.

113

114

Wenderoth, P., & Johnstone, S. (1987). Possible neural substrates for

orientation analysis and perception. Perception, 16(6), 693-709.

Westheimer, G., & Truong, T. T. (1988). Target crowding in foveal and

peripheral stereoacuity. Am J Optom Physiol Opt, 65(5), 395-399.

Winawer, J., Huk, A. C., & Boroditsky, L. (2008). A motion aftereffect from still

photographs depicting motion. Psychol Sci, 19(3), 276-283.

Yoshida, T. (1978). Orientation detectors in the human visual system and figural

aftereffects. Perception, 7(6), 717-723.

Zhang, H., Liu, J., Huber, D. E., Rieth, C. A., Tian, J., & Lee, K. (2008).

Detecting faces in pure noise images: a functional MRI study on top-down

perception. Neuroreport, 19(2), 229-233.

Zohary, E., Celebrini, S., Britten, K. H., & Newsome, W. T. (1994). Neuronal

plasticity that underlies improvement in perceptual performance. Science,

263(5151), 1289-1292.

mnemonic influences on perception as revealed by visual...

Documents