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Brain and Cognition
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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: [email protected] (M. Laasonen).
Brain and Cognition 80 (2012) 311327
Contents lists available at SciVerse ScienceDirect
Brain and Cognition
journal homepage: www.elsevier .com/ locate /b&c
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