Project DyAdd: Visual attention in adult dyslexia and ADHD

Marja Laasonen a,b,, Jonna Salomaa a, Denis Cousineau c, Sami Leppmki d, Pekka Tani d,Laura Hokkanen a, Matthew Dye e

a Institute of Behavioural Sciences, Division of Cognitive and Neuropsychology, University of Helsinki, Helsinki, FinlandbDepartment of Phoniatrics, Helsinki University Central Hospital, Helsinki, Finlandccole de psychologie, Universit dOttawa, CanadadDepartment of Psychiatry, Clinic for Neuropsychiatry, Helsinki University Central Hospital, Helsinki, FinlandeDepartment of Speech & Hearing Science, University of Illinois at Urbana-Champaign, USA

a r t i c l e i n f o

Article history:Accepted 9 August 2012

Available online 6 October 2012

Keywords:ADHD

Attentional blink

Dyslexia

Multiple object tracking

Spatial attention

a b s t r a c t

In this study of the project DyAdd, three aspects of visual attention were investigated in adults (18

55 years) with dyslexia (n = 35) or attention deficit/hyperactivity disorder (ADHD, n = 22), and in healthycontrols (n = 35). Temporal characteristics of visual attention were assessed with Attentional Blink (AB),capacity of visual attention with Multiple Object Tracking (MOT), and spatial aspects of visual attention

with Useful Field of View (UFOV) task. Results showed that adults with dyslexia had difficulties perform-

ing the AB and UFOV tasks, which were explained by an impaired ability to process dual targets, longer

AB recovery time, and deficits in processing rapidly changing visual displays. The ADHD group did not

have difficulties in any of the tasks. Further, performance in the visual attention tasks predicted variation

in measures of phonological processing and reading when all of the participants were considered

together. Thus, difficulties in tasks of visual attention were related to dyslexia and variation of visual

attention had a role in the reading ability of the general population.

2012 Elsevier Inc. All rights reserved.

1. Introduction

Dyslexia and attention deficithyperactivity disorder (ADHD)

are two of themost common developmental disabilities (Polanczyk,

de Lima, Horta, Biederman, & Rohde, 2007; Snowling & Maughan,

2006), both of which affect at least 5% of a population (Faraone,

Sergeant, Gillberg, & Biederman, 2003; Katusic, Colligan, Barbaresi,

Schaid, & Jacobsen, 2001). The conditions also often co-occur; up

to 45% of those with ADHD or dyslexia fulfill the diagnostic criteria

of the other disability (Carroll, Maughan, Goodman, & Meltzer,

2005; Dykman & Ackerman, 1991; Willcutt & Pennington, 2000).

Accordingly, it has been suggested that the disabilities may be

related at some level of analysis.

At the biological level of analysis, dyslexia and ADHD have beenshown, for example, to share genetic influences (Gayn et al., 2005;

Gilger, Pennington, & DeFries, 1992; Willcutt, Pennington, &

DeFries, 2000; Willcutt et al., 2002) and fatty acid status character-

istics (Horrobin, 1998; Horrobin & Bennett, 1999; Horrobin, Glen, &

Hudson, 1995; Laasonen, Hokkanen, Leppmki, Tani, & Erkkil,

2009a, 2009b). At the clinical neuropsychological level, individualsoften display symptoms of both disabilities even without a double

diagnosis. Impaired phonological processing (Bradley & Bryant,

1978, 1983) and poor word identification or reading (Critchley,

1970; Orton Dyslexia Society, 1994), which characterize develop-

mental dyslexia, have been found to be affected also in ADHD

(Laasonen, Lehtinen, Leppmki, Tani, & Hokkanen, 2010). On the

other hand, the behavioral symptoms of ADHD, that is, hyperactiv-

ity, impulsivity, and inattention (American Psychiatric Association,

1994; World Health Organization, 1998), are often elevated in indi-

viduals diagnosed with dyslexia (Carroll et al., 2005). Finally at the

cognitive level of analysis, those with dyslexia have been shown tosuffer from impairments in, for example, temporal processing or

acuity (Laasonen, Service, & Virsu, 2001, 2002; Tallal, 1980), atten-

tion (Hari & Renvall, 2001; Hari, Renvall, & Tanskanen, 2001),

short-term and working memory (Siegel, 1994), and learning

(Nicolson, Daum, Schugens, Fawcett, & Schulz, 2002; Vicari,

Marotta, Menghini, Molinari, & Petrosini, 2003). In ADHD research,

cognitive deficits have been suggested, for example, in executive

functions (Barkley, 1997; Castellanos & Tannock, 2002; Pennington

& Ozonoff, 1996; Schachar, Mota, Logan, Tannock, & Klim, 2000),

delay aversion (Sonuga-Barke, 2003), regulation of arousal and

activation (Sergeant, 2000), and temporal processing (Barkley,

Murphy, & Bush, 2001; Toplak, Rucklidge, Hetherington, John, &

Tannock, 2003).

Thus, there seems to be similarities between the two disabilities

at several levels of analysis, but the shared and differentiating

0278-2626/$ - see front matter 2012 Elsevier Inc. All rights reserved.

http://dx.doi.org/10.1016/j.bandc.2012.08.002

Corresponding author. Address: Institute of Behavioural Sciences, P.O. Box 9

(Siltavuorenpenger 1), FIN-00014 University of Helsinki, Finland. Fax: +358 9

19129443.

E-mail address: Marja.Laasonen@helsinki.fi (M. Laasonen).

Brain and Cognition 80 (2012) 311327

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characteristics are yet to be determined. The general aim of the

DyAdd project (Adult Dyslexia and Attention Deficit Disorder in

Finland) is to find such differentiating and shared characteristics

at each of these levels of analysis, using biological and clinical neu-

ropsychological methods, and experimental studies of cognition

(Laasonen, Hokkanen, et al., 2009a, 2009b; Laasonen, Lehtinen,

et al., 2010; Laasonen, Leppmki, Tani, & Hokkanen, 2009). The fo-

cus of the study reported here is on investigations of visual atten-

tion skills in adults with dyslexia and ADHD.

1.1. Visual attention, dyslexia, and reading impairment

Developmental dyslexia is a learning disability presumably

neurological in origin that is characterized by deficits in accurate,

fluent word recognition, and weaknesses in spelling and print

decoding (Sawyer, 2006), with diagnosis often based upon a signif-

icant discrepancy between observed reading skills and those ex-

pected on the basis of IQ, age, and level of education (Shovman &

Ahissar, 2006). Dyslexia often persists into adulthood and can pres-

ent secondary problems such as poor reading comprehension, dis-

ordered handwriting, clumsiness, forgetfulness, distractibility, and

weak phonological processing. Furthermore, there is a significant

co-morbidity between dyslexia and other learning disabilities such

as ADHD (Shovman & Ahissar, 2006).

The most widely accepted theory of the proximal cause of dys-

lexia is the phonological deficit theory, which posits that peoplewith dyslexia cannot encode phonemes as well as typical readers

can, resulting in reading difficulties (Shovman & Ahissar, 2006).

However, some authors have suggested that dyslexia could be

caused by deficits or differences in visual attention processes

(e.g., Bosse, Tainturier, & Valdois, 2007; Facoetti, Lorusso, Cattaneo,

Galli, & Molteni, 2005; Facoetti et al., 2010; Hari & Renvall, 2001;

Hari et al., 2001; Valdois, Bosse, & Tainturier, 2004). Visual deficittheories propose that reading is a demanding task for the visualsystem, requiring fine spatial discrimination and rapid temporal

processing. Further, it suggests that some individuals with dyslexia

may have a visual processing deficit, making the task of reading

more difficult (Shovman & Ahissar, 2006). The magnocellular deficittheory expands upon this further by suggesting that this deficit oc-curs along the magnocellular visual pathway, which is more sensi-

tive to direction of movement, direction of gaze, visuospatial

attention, eye movements, and peripheral vision (Stein & Walsh,

1997). Deficiencies in this system can be detected when testing vi-

sual motion sensitivity at low contrast and light levels, and is usu-

ally found to be mildly affected in dyslexia, if at all. However,

according to some research, this mild deficit in magnocellular

function or organization multiplies up to greater deficits in the

posterior parietal cortex, which is dominated by magnocellular

properties (Stein & Walsh, 1997). Further evidence for the impor-

tance of the posterior parietal cortex in reading ability can be

found in cases of acquired reading disorders that resulted from in-

jury to the posterior parietal cortex (Stein & Walsh, 1997). While

some studies have failed to find evidence of a magnocellular defi-

cit, it has been suggested that there are multiple subtypes of dys-

lexics, some with more phonological deficits (dysphonetic

dyslexia), some with more visual deficits (dyseidetic dyslexia),

and some with both (dysphoneidetic dyslexia) (Stein & Walsh,

1997).

In recent years, two research groups have taken novel ap-

proaches in developing a visual deficit theory that takes into ac-

count the reported magnocellular deficits. Sylviane Valdois and

colleagues have suggested that dyseidetic dyslexia stems from

reading problems due to limitations in the number of distinct visual

elements that can be processed in parallel from a multi-element

array. Valdois refers to this as a limitation in the size of the visual

attention span in dyseidetic dyslexics (Bosse et al., 2007). This visual

attention span deficit hypothesis proposes that there is a deficit inthe distribution of visual attention across a string of letters of

symbols, which limits the number of letters that can be processed

during reading. In support of this hypothesis, they found that as a

group, dyslexic children perform worse on visual-attention span

tasks than non-dyslexic children. Furthermore, they found that

the dyslexic groups performance on visual attention tasks was a

significant predictor of their performance on the reading accuracy

tests (Bosse et al., 2007). Similarly, Facoetti and colleagues have

proposed that dyseidetic dyslexics have a visual attention deficit,

but stemming from a graduated change in how visual attention

is distributed across words (Facoetti & Molteni, 2001). Their

studies have reported an abnormal and asymmetric distribution

of attention across words in dyslexic children and adults, suggest-

ing inattention to letters left of fixation and an over-emphasis on

letters to the right of fixation (Facoetti & Molteni, 2001). Unlike

Valdois, who focuses only on the number of items in a letter string,

Facoetti perceives the spatial distance between the fixation point

and the target letters to be important.

In addition to this research implicating compromised spatial vi-

sual attention in individuals with dyslexia, there have also been

suggestions that temporal aspects of visual attention are impaired.

One task that has been used widely in dyslexia research, with

conflicting results, is the Attentional Blink (AB) task (Raymond,

Shapiro, & Arnell, 1992). Children and adults with dyslexia have

often been interpreted to differ from their controls in attentional

blink (see however, Badcock, Hogben, & Fletcher, 2008; Buchholz

& Davies, 2007; Facoetti, Ruffino, Peru, Paganoni, & Chelazzi,

2008; Hari, Valta, & Uutela, 1999; Lacroix et al., 2005; Lallier,

Donnadieu, & Valdois, 2010; McLean, Castles, Coltheart, & Stuart,

2010; Visser, Boden, & Giaschi, 2004). Most often, this has been

suggested to result from a prolonged blink (Buchholz & Davies,

2007; Facoetti et al., 2008; Hari et al., 1999; Visser et al., 2004).

However, as recently reviewed by McLean and colleagues (2010),

most of the evidence is for poorer dual-target task performance

in those with dyslexia and not for a specific attentional blink

deficit. McLean and colleagues (2010) concluded that previous

research does not support a prolonged attentional blink since the

detect-if-identified performance has recovered by 600 ms. They

suggest further, that there is no evidence for a deeper attentional

blink either, since previous research has resulted only in significant

main effects of group, not group by lag interactions, for the T2

detection accuracy in the dual target condition. This is true with

two exceptions. First, Lacroix and colleagues (2005) found a

significant group lag interaction but in their sample dyslexic

adolescents tended to perform better than their controls. Second,

Lallier and colleagues (2010) found in small groups of dyslexic

and fluently reading children a significant main effect of group

together with a significant interaction of group lag and a poorer

attentional blink minimum in those with dyslexia (Cousineau,

Charbonneau, & Jolicoeur, 2006). Large age variation within the

dyslexic group and various exclusion criteria make interpretation

of the results of the latter study difficult. Previous research has also

suggested that the AB task correlates with phonological processing,especially Rapid Automatized Naming (RAN), in combined samples

of dyslexic children or adults and their healthy controls (Badcock

et al., 2008; Lallier et al., 2010; McLean et al., 2010) and within

samples of healthy developing readers or adults (Arnell, Joanisse,

Klein, Busseri, & Tannock, 2009; McLean, Stuart, Visser, & Castles,

2009). Further, AB task performance has been found to correlate

and predict reading ability in combined samples of dyslexic chil-dren and their healthy controls or within healthy developing read-

ers (Facoetti et al., 2008; Lallier et al., 2010; McLean et al., 2009).

Thus, there is some evidence for deficits in spatial and temporal

aspects of visual attention that may contribute to some of the

reading difficulties experienced by individuals with dyslexia.

312 M. Laasonen et al. / Brain and Cognition 80 (2012) 311327

Specifically, dyslexic individuals appear to differ from typical read-

ers in how they allocate their visual attention spatially across text,

and in how successfully they can attend to multiple targets in ra-

pid, transient visual inputs.

1.2. Visual attention, ADHD, and inattention

ADHD is a behavioral disorder with onset usually occurring in

childhood. It is characterized by lack of persistence, impulsivity,

and excessive activity (American Psychiatric Association, 1994;

World Health Organization, 1998). Behavioral difficulties related

to inattention are one of the bases of ADHD diagnosis (American

Psychiatric Association, 1994; World Health Organization, 1998),

although it has been suggested that perhaps not all people with

ADHD suffer from such neuropsychological or cognitive deficits

(Nigg, Willcutt, Doyle, & Sonuga-Barke, 2005). Studies that have

examined visual attention in children with ADHD have reported

possible differences in visuospatial orienting of attention (Nigg,

Swanson, & Hinshaw, 1997; Swanson et al., 1991), accompanied

by inattention to the left visual field (Jones, Craver-Lemley, &

Barrett, 2008; Nigg et al., 1997). However, there is some evidence

that deficits on visuospatial attention tasks in those with ADHD

could be attributed to an inability to sustain attention to the task

(Dobler et al., 2005; George, Dobler, Nicholls, & Manly, 2005). In

the temporal domain, previous research with both children and

adults has suggested a prolonged attentional blink associated with

ADHD (cf. significant group lag interactions, Armstrong &

Munoz, 2003; Hollingsworth, McAuliffe, & Knowlton, 2001; Li,

Lin, Chang, & Hung, 2004). However, there is a corresponding num-

ber of studies suggesting that the attentional blink is not elongated

in those with ADHD compared to the controls (Carr, Henderson, &

Nigg, 2010; Carr, Nigg, & Henderson, 2006; Mason, Humphreys, &

Kent, 2005). Further, the control groups in the studies suggesting

an impairment have been poorly characterized those with ADHD

have had comorbid conditions, or those with ADHD have had

difficulties with the baseline task or in the T1 identification compo-

nent of the dual task (Armstrong & Munoz, 2003; Hollingsworth

et al., 2001; Li et al., 2004).

1.3. Characterizing visual attention deficits in dyslexia and ADHD

The current study focuses on three separate aspects of visual

attention, looking at group differences between adults with dys-

lexia or ADHD, and the relationships between visual attention task

performance and various clinical neuropsychological measures in

these populations. Tasks were selected to provide measures of vis-

uospatial attention (Useful Field of View or UFOV; Ball, Beard,

Roenker, Miller, & Griggs, 1988), temporal attention (Attentional

Blink or AB; Raymond et al., 1992) and visuospatial attentional

capacity (Multiple Object Tracking or MOT; Pylyshyn & Storm,

1988). Previous research with children and adults has suggested

that these three tasks measure separable, relatively independent

aspects of attention in the visual modality (Dye & Bavelier, 2010).

The UFOV task consisted of two experimental conditions. In the

first, each participant made a two alternative forced choice deci-

sion requiring discrimination of a central stimulus, while simulta-

neously localizing a peripheral stimulus. In the second condition,

the peripheral targets was embedded in a field of distractors. Ball

and colleagues (Ball et al., 1988; Okonkwo, Wadley, Ball, Vance,

& Crowe, 2008) have defined these conditions as reflecting dividedattention (the condition without distractors) and selective attention(the condition with distractors). Work by Bavelier, Dye, and col-

leagues (Dye & Bavelier, 2010; Dye, Hauser, & Bavelier, 2009;

Green & Bavelier, 2006a) has further characterized how the UFOV

task provides an index of how visual selective attention is distrib-

uted across a spatial scene when attention has to be divided or

allocated across central and peripheral locations.

The AB task was conducted using two conditions, and with let-

ters as stimuli. In the first, baseline, condition, each participant had

to detect the presence or absence of a black target letter X within a

stream of rapidly changing black letters. In the second dual-task

condition, the participant had to both identify a white letter and

then to detect the presence or absence of the black target. The

attentional blink measures the capacity (or lack of) to switch atten-

tion rapidly to a second object while keeping the first in working

memory. Some models of AB suggest that the second target is per-

ceived but is not processed as long as the processing of the first tar-

get is incomplete (Jolicoeur, 1999).

Finally, in the MOT task, participants were required to track a

variable number of blue dots. They had to simultaneously track

1, 3, 5, or 7 blue dots within a larger set of yellow dots. Various

models of MOT have been suggested (for reviews, see Cavanagh

& Alvarez, 2005; Oksama & Hyn, 2004; Scholl, 2009) that try to

explain how the tracking of multiple objects is enabled. One of

the main differences between the models is whether there are a

single or multiple foci of attention. For example, the targets could

be mentally grouped together and followed with a single focus of

attention, a single focus of attention could be rapidly moved from

one target to another, or each target could attract an index that

are serially followed by a single focus of attention. Alternatively,

each target could attract one focus of a multifocal attention or

there could be object files that track and process the moving tar-

gets with multiple foci. It is also possible that both parallel and se-

rial processes are required in the process of tracking (Oksama &

Hyn, 2004, 2008). Previous research has suggested that MOT

performance is related to various aspects of visuospatial memory

(e.g., short-term memory (STM): Oksama & Hyn, 2004, 2008;

WM, Zhang, Xuan, Fu, & Pylyshyn, 2010) and attention (attention

switching, Oksama & Hyn, 2004, 2008; inhibition, Pylyshyn,

2006; e.g., Scholl, 2009). While this task has not been used in dys-

lexia or ADHD research before, it provides one measure of how

well visual attention can be allocated to multiple objects. However,

the visual attention span deficit theory of dyslexia (e.g., Bosse et al.,2007) predicts that some individuals with dyslexia have problems

encoding multiple letters in visual working memory during the

reading process. To the extent that MOT performance provides

an index of this ability, it is possible that dyslexic individuals will

perform more poorly.

1.4. Aims and hypotheses

The first aim of the current study was to compare the perfor-

mance of dyslexic, ADHD, and healthy control adults on these three

aspects of visual attention. We aimed to determine whether the

participants with dyslexia or ADHD suffer from difficulties com-

pared to the healthy controls, and to clarify whether the possible

difficulties were shared between or specific to the clinical groups.

Based upon the literature reviewed above, we predicted that indi-

viduals with dyslexia would perform worse than healthy controls

on all measures of visual attention (UFOV, AB, and MOT). We also

expected broad deficits stemming from general inattention in indi-

viduals with ADHD to manifest as an impairment relative to

healthy controls on all three visual attention tasks. Of interest is

whether the observed patterns of deficits serve to differentiate

individuals with dyslexia from those with ADHD.

The second aim was to investigate relationships between these

different aspects of visual attention and performance on clinical

neuropsychological measures that are typically used to character-

ize dyslexia or ADHD. We predicted that performance in

attentional tasks that are impaired in individuals with dyslexia

would be related to phonological processing, reading, spelling,

M. Laasonen et al. / Brain and Cognition 80 (2012) 311327 313

and arithmetic (difficulties often comorbid with dyslexia, Landerl &

Moll, 2010), and performance in attentional tasks that are impaired

in individuals with ADHD would be related to executive functions

and attention.

2. General material and methods

A full description of the methods used in the project DyAdd can

be found in a previous article (Laasonen, Leppmki, et al., 2009).

2.1. Participants

All participants were volunteers and provided their informed

consent. The appropriate ethical committee of Helsinki University

Central Hospital approved the project.

2.1.1. DyslexiaParticipants (n = 35) in the dyslexia group were required to

have a prior diagnosis of dyslexia as an inclusion criterion. Their

diagnoses were based on achievement criteria that varied slightly

across recruitment sites. Therefore, the current phonological pro-

cessing and reading status of each participant in this group was

checked against the age-corrected values of our previous (Laaso-

nen, 2002) and current control data. Participants in the dyslexia

group performed more than 1 standard deviation below average

in phonological processing and reading as assessed with phonolog-

ical naming (rapid alternate stimulus naming (RAS) speed/accu-

racy, Wolf, 1986), phonological awareness (phonological

synthesis accuracy, Laasonen et al., 2002), phonological memory

(WAIS digit span forward length, Wechsler, 2005), and reading

(oral reading speed/accuracy, task details in, Laasonen et al.,

2002). See Appendix A for the values. One participant with diag-

nosed dyslexia and a history of reading difficulties was impaired

only in phonological processing. We chose to include this partici-

pant in the dyslexia group, since it has been suggested that child-

hood dyslexia could manifest itself only in phonological difficulties

in adulthood (Daryn, 2000; Felton, Naylor, & Wood, 1990). Thus, in

this paper the label dyslexia refers to the common form reading

difficulty that combines with phonological difficulties, not, for

example, to attentional dyslexia, letter position dyslexia, or neglect

dyslexia. Diagnosis of ADHD and/or a history of ADHD-related dif-

ficulties were exclusion criteria for the dyslexia group.

2.1.2. ADHDParticipants (n = 22) in the ADHD group were required to have a

prior diagnosis of ADHD as an inclusion criterion. They were all

diagnosed according to DSM-IV criteria (American Psychiatric

Association, 1994) using CAADID (Epstein, Johnson, & Conners,

2001) by a medical doctor specialized in neuropsychiatry (author

SL or PT in most cases). Confounding psychiatric disorders were ex-

cluded by SCID-I (First, Spitzer, Gibbon, & Williams, 1996) and

SCID-II interviews (First, Gibbon, Spitzer, Williams, & Benjamin,

1997). Thus, hyperactivity was not a required characteristic, and

also those with only inattention were included. Therefore, in this

paper the label ADHD refers both to those with attention deficit

disorder (ADD) and those with ADHD. Diagnosis of dyslexia and/

or a history of reading difficulties were exclusion criteria for the

ADHD group.

2.1.3. ComorbidThe current sample included also eight comorbid participants,

that is, they had both dyslexia and ADHD diagnoses. This group

was included only in the regression analyses relating visual atten-

tion task performance to clinical neuropsychological measures, due

to its small size.

2.1.4. ControlDiagnosis of ADHD, diagnosis of dyslexia, a history of reading

difficulties, or history of ADHD-related difficulties were exclusion

criteria for the control group (n = 35).

2.1.5. General inclusion and exclusion criteriaFinnish as a native language and age 1855 years were inclu-

sion criteria for all the groups. General exclusion criteria were

brain injury, a somatic or psychiatric condition affecting cognitive

functions (including major depression), psychotropic drugs affect-

ing cognitive functions, and substance abuse. Blood samples were

collected to rule out endocrinopathies (e.g., dysfunction of the thy-

roid gland), diabetes, renal dysfunction, abuse of alcohol, and sim-

ilar somatic states which might compromise cognitive functions.

Laboratory tests included hemoglobin, RBC, WBC, platelet count,

thyroid stimulating hormone, serum creatinine, alanine amino-

transferase, gamma-glutamyltransferase, and fasting blood glu-

cose. Patients with ADHD participated in the project

unmedicated. If they were currently using methylphenidate, a

wash-out period of at least 1 week was required before and during

the study appointments. ADHD participants with medication with

a longer half-life than methylphenidate were excluded from the

project. WASI full intelligence quotient (FIQ) (Wechsler Abbrevi-

ated Scale of Intelligence, Wechsler, 2005) was required to be at

least 70 (that is, within 2 standard deviations from the average)

due to the ICD-10 criteria for specific reading disorder (World

Health Organization, 1998).

Demographic characteristics of the participants are presented in

Table 1. The groups did not differ statistically in terms of age,

F(3,96) = 1.367, p = .257, gender, v2(3) = 4.112, p = .250, educa-tional level, v2(6) = 9.383, p = .153, or handedness, v2(3) = 2.641,p = .450. This was achieved by screening the participants into bal-anced cohorts according to the first three characteristics. The

groups differed in their FIQ, F(3,96) = 3.043, p = .033, with theADHD group having statistically significantly lower FIQs than the

controls (p = .028). FIQ was used as a covariate in analyses whencomparing differences between ADHD and control groups, but in

no case did it have an effect on statistical significance of other fac-

tors or interactions.

2.2. General apparatus and procedures in tasks of visual attention

Stimuli were presented with a computer (Power Mac G4,

256 MB; Mac OS 9) that was attached to an LCD touchscreen (1900

Elo Touchsystems 1925L; refresh rate 75 Hz, resolution

1280 1024). Tasks were administered with Psychtoolbox version

2.55 (Brainard, 1997; Pelli, 1997) run by Matlab version 5.2.1. Re-

sponses were accepted via a standard keyboard and a chin rest was

used to control both viewing distance (36 cm) and vertical position

(eyes and point of fixation lined). Participants were tested individ-

ually and the experimenter was blind to the participants group

(control, ADHD, dyslexic, or comorbid). The order of the three vi-

sual attention tasks was counterbalanced, and each was adminis-

tered as the first, second, or third task the same amount of times

within each group.

2.3. Neuropsychological tasks

The neuropsychological tasks were part of a larger neuropsy-

chological battery, which is described in more detail in previous

studies (Laasonen, Hokkanen, et al., 2009a; Laasonen, Lehtinen,

et al., 2010). The current study focused on domains that character-

ize dyslexia or ADHD, that is, phonological processing, technical

reading, reading comprehension, spelling, arithmetic, executive

functions, and attention. The variables are described below and

in Table 2. A more detailed description of the tasks and group com-

314 M. Laasonen et al. / Brain and Cognition 80 (2012) 311327

parisons in them can be found in previous studies (Laasonen,

Hokkanen, et al., 2009a; Laasonen, Lehtinen, et al., 2010).

2.4. Approach to statistical analyses

2.4.1. Group differences in visual attention task performanceThe overall group differences were tested with mixed ANOVAs

and ANCOVAs (with FIQ as a covariate). Separate ANOVAs and AN-

COVAs (with FIQ) were conducted depending on the interactions

and main effects. The alpha level was set at p = .05. Post hoc testswere conducted with Bonferroni corrected t-tests, or Tamhanes Twhen the homogeneity of variance could not be confirmed. Many

of the variables had distributions that departed from normality.

These were also analyzed with nonparametric methods (Krusk-

allWallis ANOVA and MannWhitney/Wilcoxons rank-sum test

with p-values corrected for the number of multiple comparisons).The results of the ANCOVAs are reported if the significance of the

group difference between those with ADHD and controls changed

with FIQ as a covariate. The results of the nonparametric analyses

are reported if their level of significance differed from those of the

original parametric analyses. Due to response bias in both the AB

and MOT tasks, which resulted from differing amounts of true po-

sitive compared to true negative, and false positive compared to

false negative responses, the nonparametric measure of sensitivity

A0 was calculated when possible (Snodgrass & Corwin, 1988). The

results based on A0 are presented only if they differed from the ori-ginal analyses with percent correct variables.

2.4.2. Clinical neuropsychological composite variablesFor the sake of simplicity and to reduce the error variance re-

lated to individual task scores, the neuropsychological variables

were analyzed as composite variables that were averages of indi-

vidual task scores (see Table 2). The composite variables were

the following: phonological processing (average of (1) phonologicalawareness accuracy, (2) phonological memory accuracy, and (3)

phonological naming speed); technical reading (average of (4) tech-nical reading speed and (5) accuracy); reading comprehension(average of (6) reading comprehension speed and (7) accuracy);

spelling accuracy; arithmetic accuracy; executive functions (averageof (8) set shifting, (9) inhibition, and (10) planning); and attention(average of (11) sustained and (12) divided aspects).

The scores of all participants were transformed based on the

age-corrected performance of the control group. This was achieved

by using age as an independent variable and a given neuropsycho-

logical task score as a dependent variable in a linear regression

analysis within the control group. The age corrected values, that

is residuals, were z-score standardized within the control groupand then converted to consistently indicate better performance

with larger positive values. After this, the variables were trans-

formed to have a mean of 10 and a standard deviation of 3 and

Table 1

Demographic characteristics of the participants.

Group

Control ADHD Dyslexia Comorbid

n 35 22 35 8

Age (years) Mean (SD) 37.51 (11.14) 32.09 (8.71) 36.11 (10.64) 33.38 (10.70)

FIQ Mean (SD) 109.91 (8.56) 102.55 (10.48) 106.31 (8.78) 104.00 (13.14)

Gender Female n (%) 19 (54%) 8 (36%) 16 (46%) 6 (75%)

Handedness Right n (%) 30 (86%) 19 (86%) 33 (94%) 8 (100%)Left n (%) 5 (14%) 3 (14%) 2 (6%) 0 (0%)Ambidextrous n (%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)

Educational level Basic n (%) 11 (31%) 14 (67%) 18 (51%) 5 (63%)Middle n (%) 11 (31%) 2 (10%) 10 (29%) 2 (25%)High n (%) 13 (37%) 5 (24%) 7 (20%) 1 (13%)

Table 2

Neuropsychological domains used in the analyses, together with composite variables reflecting their sub-components (numbered), individual tasks, and variables (in

parentheses).

Phonological processing, average of1. Awareness, accuracy (synthesis (correct), Laasonen, 2002; Pig Latin (correct), Nevala et al., 2006)

2. Memory, accuracy (pseudoword span length (correct), Service, Maury, & Luotoniemi, 2007; WMS-III digit span forward length (correct), Wechsler, 2008)

3. Naming, speed (Stroop color naming (speed), Lezak, Howieson, Loring, Hannay, & Fischer, 2004; RAS (speeds for two trials), Wolf, 1986)

Technical reading, average of4. Speed (narrative text (speed), Laasonen, 2002; word list and pseudoword list reading (speed), Nevala et al., 2006)

5. Accuracy (segregating word chains (correct) and searching for misspellings (correct), Holopainen et al., 2004; narrative text (correct), Laasonen, 2002; word list

and pseudoword list reading (correct), Nevala et al., 2006)

Reading comprehension, average of6. Speed (searching for incorrect words within a story (speed), Holopainen et al., 2004; forced choice task (speed), Nevala et al., 2006)

7. Accuracy (searching for incorrect words within a story (correct), Holopainen et al., 2004; forced choice task (correct), Nevala et al., 2006)

Spelling, accuracy (pseudoword writing (correct), Holopainen et al., 2004)

Arithmetic, accuracy (RMAT (correct), Rsnen, 2004; WAIS-III Arithmetic (correct), Wechsler, 2005)

Executive functions, average of8. Set shifting (CANTAB Intra-extra dimensional set shifting (stages completed, total errors adjusted), Cambridge Neuropsychological Test Automated Battery, 2004)

9. Inhibition (Color Trails Test (difference score), DElia, Satz, Uchiyama, & White, 1996; Stroop (inhibition errors, difference score), Lezak et al., 2004)

10. Planning (CANTAB Stockings of Cambridge (mean initial thinking time 5 moves, problems solved in minimum moves), Cambridge Neuropsychological Test

Automated Battery, 2004)

Attention, average of11. Sustained (Color Trails Test (speed for first trial), DElia et al., 1996; Dual task (sustained attention for dots, sustained attention for numbers), Lezak et al., 2004)

12. Divided (Color Trails Test (speed for second trial), DElia et al., 1996; Dual task (divided attention for dots, divided attention for numbers), Lezak et al., 2004)

M. Laasonen et al. / Brain and Cognition 80 (2012) 311327 315

averaged into the composite variables. The raw scores of other

groups were transformed based on the values of the control group.

Thus, in every variable, 10 indicates the age-corrected control

mean, 13 indicates performance that is one standard deviation bet-

ter than the control mean, and 7 indicates performance that is one

standard deviation poorer than the control mean (a scale similar to

the WAIS subtests, Wechsler, 2005). The grouping of the variables

was based on that presented in the manuals of the standardized

batteries (Holopainen, Kairaluoma, Nevala, Ahonen, & Aro, 2004;

Nevala, Kairaluoma, Ahonen, Aro, & Holopainen, 2006). For other

tasks, the grouping was based on theoretical grounds. The specific

characteristics of the neuropsychological composite variables are

presented in an Appendix B.

2.4.3. Relationships between visual attention measures and compositeneuropsychological variables

Sequential regression analyses were conducted in order to

investigate the relationships between the measures of visual atten-

tion task performance and the composite neuropsychological do-

mains detailed in Table 2. These analyses were conducted using

the total sample of participants that also included those with a

comorbid diagnosis (n = 8). A regression analysis was computedfor each combination of composite neuropsychological variable

and visual attention task variable. The neuropsychologicial com-

posite score was the dependent variable (phonological processing,

technical reading, reading comprehension, spelling, arithmetic,

executive functions, or attention), and a performance measure that

Useful Field of View (UFOV)

(B3) Distractor condition

(B1) Control condition(A) Fixation

(B2) No distractors condition

(D) Answer

OR

(C) Noise

Fig. 1. Structure of the Useful Field of View (UFOV) task. For details, see Section 3.1.

316 M. Laasonen et al. / Brain and Cognition 80 (2012) 311327

best differentiated between the groups from a visual attention task

(UFOV AB, or MOT) was entered at the first step as an independentvariable. Group was converted into two dummy variables (pres-

ence or absence of ADHD or dyslexia) and entered at the second

step. The first focus was on whether the given visual attention task

performance measure alone resulted in a significant R2 at step 1(alpha = .01, two-tailed, due to the large number of comparisons).

Then, the changes in the significance levels (overall R2 and betasof the visual attention variables) were examined when the partic-

ipant group covariate was added at step 2. The additional signifi-

cant findings with individual neuropsychological task variables as

dependent variables are presented in an Appendix B.

3. Spatial characteristics of visual attention: Useful Field of View

(UFOV)

3.1. Material and methods

3.1.1. StimuliThe background of the screen was black (RGB 0, 0, 0, luminance

2.5 cd/m2) with a gray filled circle in the middle of the screen (RGB

128, 128, 128, luminance 56 cd/m2, diameter 79.8). A black fixa-

tion dot with a white outline was located in the middle of the black

screen/gray circle (diameter 0.3). There were two central smiley

face target stimuli (yellow background; R 250, G 250, B 1, lumi-

nance 180 cd/m2, diameter 2; with black outlined eyes, mouth,

and hair). One had longer and the other shorter hair, with only

one of these stimuli appearing on any one trial. The peripheral tar-

get stimulus was a white circle (outline width 0.3, diameter 2)

with a gray background (RGB 128, 128, 128, luminance 56 cd/m2)

and a five-pointed filled white star within it (RGB 0, 0, 0, luminance

182 cd/m2). When present, the peripheral distractor stimuli were

white squares (outline width 0.3, diameter 2) with a gray back-

ground (RGB 128, 128, 128, luminance 56 cd/m2).

3.1.2. ProcedureThe participant was asked to fixate on the fixation dot and in-

structed, with a written sentence below the dot, to begin the trial

by pressing any key (height 1.8, font: Tunga). After this, the first

stimulus appeared with a random 2001000 ms delay. There were

three conditions (see Fig. 1). In the first, control condition, partici-pants had to discriminate whether the central smiley face targethad long or short hair. The response was given by pressing a key-

board button (S for short or X for long hair) with the index fin-

ger of the left hand. The buttons were marked with a smiley face

with short or long hair, respectively. After the response, another

target stimulus appeared when the participant pressed any key.

In the second condition, the experimental condition without dis-tractors, participants had to perform the smiley face discriminationtask and simultaneously localize a peripheral star target that ap-peared along one of eight possible invisible axes (tilt angles: 0/

360, 45, 90, 135, 180, 225, 270, and 315). In the first part

of the experiment, the eccentricity of the peripheral target was

7, and in the second part, the eccentricity was 21 from the central

fixation dot. The order of administration of the eccentricity manip-

ulation was counterbalanced across subjects. The central and

peripheral targets appeared and disappeared simultaneously.

Then, the screen was filled with a black-and-white noise mask

for 26 ms. After this, the eight possible axes were represented as

visible white lines (outline width 0.3) and the participant had to

touch the axis that the peripheral target had appeared on with

their right index finger. The discrimination task was answered as

described above and the order of the discrimination and localiza-

tion responses was not restricted.

The third condition, the experimental condition with distractors,was similar to the experimental condition without distractors with

the following exception: distractor squares were presented along

the eight axes at peripheral eccentricities of 7, 14, and 21 from

the central fixation dot. That is, there was one peripheral star tar-

get and 23 square distractors. Again, in the first part the distance of

the peripheral target was 7 and in the second part 21 from the

central fixation dot. The order of the tasks was counterbalanced

across subjects.

The presentation duration of the stimuli was controlled with a

1:3 adaptive algorithm that resulted in a 79.3% correct threshold

estimation (Wetherill & Levitt, 1965). At the beginning of each con-

dition, the stimuli were presented for 146.3 ms. The refresh rate of

the screen (75 Hz) resulted in 13.3 ms steps. Participants had to

correctly answer both the discrimination and localization tasks

for three consecutive times in order to shorten the presentation

duration. One incorrect answer to either task resulted in a longer

presentation duration. The task was terminated after 12 reversals,

10 consecutive correct responses at the shortest possible presenta-

tion duration, or 72 trials. The 79.3% accuracy threshold was esti-

mated by averaging the presentation durations of the last ten

correct trials. Before each condition, the participant rehearsed with

very easy trials (stimulus presentation duration 399 ms).

3.2. Results

3.2.1. Group comparisonsThe average thresholds in milliseconds for correct performance

in the different conditions for the participants are presented in

Fig. 2.

First, we analyzed the center task with group (control, ADHD,

dyslexic) as a between subjects factor and accuracy threshold

as the dependent variable. The main effect of group was not

significant (F(2,89) = 1.796, p = .172, partial g2 = .039, observedpower = .367).

Second, the experimental conditions without distractors were

analyzed with a 2 3 mixed ANOVA with eccentricity of periphe-

ral target (7/21) as within subjects factor, group (control, ADHD,

dyslexic) as a between subjects factor, and accuracy threshold as

50

0

150

Thre

shold

in m

s (

mean +

/- S

EM

)

Useful Field of View (UFOV)

Task

Control 7

No distractors

21

Distractors

7 21

Control

ADHD

Dyslexia

100

Fig. 2. Threshold in milliseconds for correct performance of the control (black),

ADHD (gray), and dyslexia (white) groups in the Useful Field of View (UFOV) task

assessing the spatial aspects of visual attention. The bars indicate the group mean

with 1 SEMs in the control task and in the experimental tasks without and with

distractors, with separate conditions with close (at 7) and distant targets (at 21).

M. Laasonen et al. / Brain and Cognition 80 (2012) 311327 317

the dependent variable. This resulted in a non-significant main ef-

fect of eccentricity (F(1,86) = .196, p = .659, partial g2 = .002, ob-served power = .072). The main effect of group was almost

significant (F(2,86) = 3.013, p = .054, partial g2 = .065, observedpower = .570). Post hoc tests indicated that dyslexia group was

poorer than the controls (Tamhane T, p = .032). The interaction be-tween group and eccentricity was not significant (F(2,86) = .741,p = .480, partial g2 = .017, observed power = .172). Thus, the groupsdiffered from each other but, overall, localization time did not dif-

fer between distant compared to close peripheral targets.

Last, the experimental conditions with distractors were ana-

lyzed with a 2 3 mixed ANOVA with eccentricity of peripheral

target (7/21) as within subjects factor, group (control, ADHD,

dyslexic) as a between subjects factor, and accuracy threshold as

the dependent variable. This resulted in a significant main effect

of eccentricity (F(1,86) = 80.684, p < .0001, partial g2 = .484, ob-served power = 1.000) with more distant targets being more diffi-

cult to locate. The main effect of group was not significant

(F(2,86) = 1.920, p = .153, partial g2 = .043, observed power = .389),nor was the interaction between group and eccentricity

(F(2,86) = 1.071, p = .347, partial g2 = .024, observed power = .232).Thus, the groups did not differ from each other and, overall, all

groups required longer presentation time to localize distant com-

pared to close peripheral targets.

3.2.2. Relations between the variablesThe UFOV measure that best differentiated between the groups

was used in the regression analysis (main effect of group in one-

way ANOVA, F(2,60) = 3.269, p = .045). UFOV variable 21 withoutdistractors was not significantly related to any of the neuropsycho-

logical domains with the criterion of p < .01 (phonological process-ing, technical reading, reading comprehension, spelling, arithmetic,

executive functions, or attention; see Table 2). However, the accu-

racy threshold data for the UFOV task 21 without distractors en-

tered at the first step in a sequential regression analysis almost

significantly predicted variation in technical reading (R2 = .057,F(1,95) = 5.709, p = .019). When the group was entered as a covar-iate at step 2, R was not significantly different from zero anymore(R2 = .079, F(3,93) = 2.658, p = .053) and the R2 change was not sig-nificant (R2 change = .022, Finc(2,93) = 1.125, p = .329), that is, add-ing the group membership did not improve the prediction beyond

that provided by the UFOV variables. Thus, performance in the

UFOV task tended to predict that in the dyslexia-related domain

of technical reading.

3.3. Discussion

Dyslexic readers were slow in the experimental tasks without

distractors. That is, they had difficulties with processing rapidly

presented material in their central vision. However, the selective

attention component of the task did not differentiate between

the groups as indicated by the nonsignificant group differences.

Further, closer targets accompanied by distractors were easier

(i.e., faster) to locate than the more distant targets with distractors.

These results are in line with the study by Ball and colleagues who

showed that the ability to localize a peripheral target decreases

with eccentricity, distraction, and when the center task is made

more difficult (Ball et al., 1988).

To our knowledge, there are no previous UFOV studies on ADHD

and only two with participants with dyslexia. In children, a meet-

ing abstract by Edwards and colleagues suggests UFOV difficulties

in those with dyslexia: they processed information more slowly,

were more affected by distractors, and made more errors of local-

ization (Edwards & Ball, 1995). However, university students with

mainly compensated (n = 21) but some with a persistent form ofdyslexia (n = 7) were not impaired in an UFOV task (Edwards,

Walley, & Ball, 2003). The authors concluded that the UFOV

impairment of those with dyslexia may have improved with devel-

opment, reflecting a developmental lag. However, Edwards and

colleagues (2003) conducted only tasks with distractors and, thus,

could not assess the possible difficulties in the easier conditions of

center task and experimental task without distractors.

The regressions between UFOV and various neuropsychological

domains suggested that UFOV performance is possibly related to

the dyslexia-related domain of technical reading. UFOV perfor-

mance has been investigated mainly in the elderly and in various

clinical populations and has been shown, for example, to predict

various driving outcomes in older adults (Clay et al., 2005). Corre-

lations between UFOV performance and sensation/perception have

usually been lower than those between UFOV and various cogni-

tive tasks, for example, overall cognitive ability (Fiorentino, 2008;

Okonkwo et al., 2008), processing speed (Edwards et al., 2006),

and visual search (Edwards et al., 2006). Thus, there remains some

controversy as to whether the UFOV should be considered to be

more than just a task of visual attention, for example, a task reflect-

ing processing speed (Lunsman et al., 2008; Okonkwo et al., 2008).

Facoetti and collleagues have suggested a multisensory spatial

attention deficit hypothesis for dyslexia (Facoetti et al., 2010),

which suggests that sluggish attentional shifting affects sublexical

mechanisms that are essential for reading. The results of the cur-

rent study do not contradict this suggestion, since those with dys-

lexia were disproportionally slow in the experimental task without

distractors and spatial attention was related to reading.

Taken together, adults with dyslexia experienced difficulties

with the temporal requirements of the UFOV task. However, the

dyslexic group was not observed to differ from the ADHD and con-

trol groups in terms of their peripheral selective visuospatial atten-

tion. Variation in UFOV predicted performance in the dyslexia-

related domain of technical reading.

4. Temporal characteristics of visual attention: Attentional

Blink (AB)

4.1. Material and methods

4.1.1. StimuliThe background of the screen was gray (RGB 50, 50, 50, lumi-

nance 5.1 cd/m2) with a black fixation cross in the center (1.4,

RGB 0, 0, 0, luminance 2.5 cd/m2). The stimuli were upper case let-

ters A, B, C, D, E, F, G, H, J, K, L, M, N, P, Q, R, S, T, U, V, W, X, Y, and Z

(height 1.8, font: Tunga). One of the letters was white (RGB 255,

255, 255, luminance 81 cd/m2) and the remainder were black

(RGB 0, 0, 0, luminance 2.5 cd/m2).

4.1.2. ProcedureThe attentional blink procedure was similar to that employed

by Green and Bavelier (2003, see Fig. 3). The participant was in-

structed, with a written sentence below the fixation cross (height

0.5, font: Tunga, RGB 255, 255, 255, luminance 81 cd/m2), to begin

the trial by pressing the space key. Immediately after this, 715

black letters appeared randomly at the same location as the fixa-

tion cross, before the white target letter to be identified was pre-sented (henceforth, target 1 or T1). Then, another 0, 1, 2, 3, 4, 6,

8, or 10 black letters were presented (referred to as the T1T2

lag), before the black target letter X to be detected (henceforth, tar-get 2 or T2) was presented. T2 was present in 50% of the trials. The

trial ended by presenting all the black letters still available, so that

all the letters were presented once within the trial. Thus, a trial

consisted of 1624 letters in which each letter was presented no

more than once. The presentation length of the letter stimuli was

318 M. Laasonen et al. / Brain and Cognition 80 (2012) 311327

26.7 ms (two frames) with an ISI of 106.7 ms (eight frames) for an

SOA of 133.3 ms.

First of all, a baseline task was conducted where the participant

had to detect the presence or absence of T2, but was given no

instruction with respect to the white letter (T1), which was to be

ignored. After each of 32 trials, the participant was asked, with a

written sentence presented on the screen (height .5, font: Tunga,

RGB 0, 0, 0, luminance 2.5 cd/m2), whether they had seen the letter

X or not. The response was given by pressing a keyboard button

marked yes or no with the index finger of the right hand. After

the response, the fixation cross appeared again.

After this, in the dual task, the participant had to both identify

T1 and to detect the presence/absence of T2. After each of the 32

trials, the participant was asked with a written sentence presented

at the screen (height .5, font: Tunga, RGB 0, 0, 0, luminance 2.5 cd/

m2), first, to indicate the identity of the white letter (T1) and, then,

to indicate whether or not they saw the letter X. The response was

given by pressing, first, the keyboard letter corresponding to T1,

and then a keyboard button marked yes or no with index finger

of the right hand to indicate whether or not T2 was detected. After

the responses, the fixation cross appeared again. Before the base-

line and dual conditions, the tasks were rehearsed with very easy

trials (933.3 ms SOA).

4.2. Results

4.2.1. Group comparisonsPercent correct performance as a function of the T1T2 lag in

the baseline task (detect only) and dual task (identify and detect ifidentified) is illustrated in Fig. 4.

4.2.1.1. Attentional blink. The attentional blink is characterized by achange in T2 detection accuracy as a function of T1T2 lag when T1

must be identified (dual task), relative to T2 detection accuracy

when T1 is to be ignored (baseline task).

For the dual task, T2 detection accuracy was determined based

solely upon trials where T1 was correctly identified. A mixed ANO-

VA was conducted with group (control, ADHD, dyslexia) as a be-

tween subjects factor, task (baseline, dual) and T1T2 lag (133,

267, 400, 533, 667, 933, 1200, or 1466 ms after the first target)

as within subjects factors, and T2 detection accuracy as the depen-

dent variable. This resulted in a trend for a main effect of group

(F(2,87) = 2.549, p = .084, partial g2 = .055, observed power = .497).Post hoc comparisons were nonsignificant. There was a significant

main effect of task (F(1,87) = 102.924, p < .0001, partial g2 = .542,observed power = 1.000) and T1T2 lag (F(7,609) = 23.667, p