Relation ASD- ADHD

ORIGINAL PAPER

The Specificity of Inhibitory Impairments in Autismand Their Relation to ADHD-Type Symptoms

Charlotte Sanderson Melissa L. Allen

Published online: 9 October 2012

Springer Science+Business Media, LLC 2012

Abstract Findings on inhibitory control in autism have

been inconsistent. This is perhaps a reflection of the different

tasks that have been used. Children with autism (CWA) and

typically developing controls, matched for verbal and non-

verbal mental age, completed three tasks of inhibition, each

representing different inhibitory subcomponents: Go/No-Go

(delay inhibition), Dog-Pig Stroop (conflict inhibition), and

a Flanker task (resistance to distractor inhibition). Behav-

ioural ratings of inattention and hyperactivity/impulsivity

were also obtained, as a possible source of heterogeneity in

inhibitory ability. CWA were only impaired on the conflict

inhibition task, suggesting that inhibitory difficulty is not a

core executive deficit in autism. Symptoms of inattention

were related to conflict task performance, and thus may be an

important predictor of inhibitory heterogeneity.

Keywords Autism Inhibition ADHD Executive function

Introduction

A dominant cognitive theory of autism links the social and

non-social features of the disorder to deficits in executive

function (EF) (Hill 2004a; Russell 1997). This umbrella

term refers to a range of higher-order cognitive abilities

that together permit an individual to plan and carry out

non-automatic, flexible and goal-directed actions (Welsh

and Pennington 1988). However, executive impairments

have been linked to various developmental disorders,

including attention-deficit/hyperactivity disorder (ADHD)

(Ozonoff and Jensen 1999) and Tourette syndrome

(Leckman et al. 1987). It is thus difficult for an executive

account, in its simplest form, to explain the differential

clinical outcomes in each disorder (Pennington 1997).

Some have argued that autism may thus be distinguish-

able by a unique executive profile or pattern of strength and

weakness across various subcomponents of EF (e.g. working

memory, planning, inhibitory control) (Pennington 1997;

Happe et al. 2006; Hill 2004a). A specific suggestion is that

inhibitory control may be intact in children with autism

(CWA), which would in particular help to discriminate the

disorder from ADHDwhere inhibition is often considered

a core cognitive deficit (e.g. Happe et al. 2006; Sinzig et al.

2008; Bramham et al. 2009; Lijffijt et al. 2005). However,

the evidence for intact inhibitory control in autism remains

equivocal (Christ et al. 2007; Adams and Jarrold 2009; Hill

2004a, b). Though many studies have failed to identify any

deficit relative to controls, a notable number have reported

impairments in CWA on both verbal and non-verbal inhi-

bition tasks (see Table 1).

Inhibitory Control

Inhibitory control is defined as the ability to suppress

information or a response that may interfere with the

attainment of a cognitive or behavioural goal (Nigg 2000;

Dagenbach and Carr 1994). However, like EF, it is argu-

ably not a unitary construct (e.g. Friedman and Miyake

C. Sanderson M. L. AllenDepartment of Psychology, Lancaster University,

Lancashire, UK

Present Address:C. Sanderson (&)Behavioural and Brain Sciences Unit, University College

London (UCL) Institute of Child Health, 30 Guilford Street,

London WC1N 1EH, UK

e-mail: [email protected];

[email protected]

123

J Autism Dev Disord (2013) 43:10651079

DOI 10.1007/s10803-012-1650-5

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1066 J Autism Dev Disord (2013) 43:10651079

123

2004), and there may be fundamental distinctions between

different tasks in terms of the cognitive processes they

draw upon. It may thus be that some types of inhibition

task are more problematic for CWA than others, which

could fuel inconsistency between studies. In this study,

three types of inhibitory paradigm that have been used are

considered, which we refer to as delay, conflict and resis-

tance to distractor inhibition.

Delay paradigms require children to delay, temper, or

altogether suppress an impulsive response when a task calls

for it (Carlson and Moses 2001, p. 103). Examples

include the Go/No-Go task (e.g. Christ et al. 2007; Happe

et al. 2006; Noterdaeme et al. 2001; Ozonoff et al. 1994;

and Sinzig et al. 2008), the stop-Signal task (Ozonoff and

Strayer 1997; Lemon et al. 2011) and the TEA-Ch Walk-

Dont Walk subtest (Manly et al. 1999). In the widely used

Go/No-Go task, participants are typically presented with a

sequence of visual stimuli (e.g. shapes or letters), and they

must make a speeded motor response to the majority of

stimuli (Go) but withhold responding to one or more

pre-specified shapes/letters. These No-Go stimuli appear

only on a minority (e.g. 25 %) of trials, thus generating a

prepotent response tendency. Inhibitory performance is

gauged by the number of false positive responses made.

The core cognitive process in these tasks is the active

suppression of a habitual response, and thus they are often

construed as simple tests of the supervisory attentional

system (SAS) (Norman and Shallice 1986). Indeed, per-

formance declines with concurrent cognitive load (e.g.

Mitchell et al. 2002) and with age (e.g. Butler et al. 1999)

indicating a limited-capacity executive process. Neuroim-

aging points towards widespread activation across the

frontal cortex with delay inhibition, particularly in right

inferior (ventrolateral) regions (Aron and Poldrack 2005),

and patients with right inferior frontal damage show dis-

rupted performance (Aron et al. 2003).

Conflict inhibition paradigms share functional similari-

ties with delay tasks, in that they too test individuals

ability to hold in mind task rules/goals and actively sup-

press prepotent responses (Carlson and Moses 2001).

However, importantly, conflict tasks also require partici-

pants to replace that suppressed response with an opposing

onewhich may explain why the two task-types load onto

distinct factors in factor analysis (Carlson and Moses

2001). The classic conflict task is the colour-word Stroop

task (Stroop 1935). In this, participants are shown colour-

words printed in different coloured ink (e.g. BLUE printed

in red ink) and are asked to name the ink-colour instead of

the written word. Yet, although widely used, it is thought

that this task may systematically overestimate inhibitory

abilities in ASD populations (Adams and Jarrold 2009).

This is because as the to-be-inhibited information is the

semantic meaning of written words (MacLeod 1991)andTa

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les

J Autism Dev Disord (2013) 43:10651079 1067

123

CWA appear to access the semantic meaning of written

words less automatically (Nation 2006).

Other conflict inhibition tasks avoid this confound

however, and are thus more suitable for ASD samplesfor

instance, the Windows (Russell et al. 1991), detour

reaching (Russell et al. 1999), TEA-Ch Opposite World

(Manly, et al. 1999), Day/Night (Gerstadt et al. 1994) and

Dog/Pig Stroop (Ames and Jarrold 2007) tasks. Like the

colour-word Stroop, each of these requires the suppression

of a prepotent response and the replacement of that

response with a conflicting and less salient one. This

additional process undoubtedly augments both working

memory and conflict resolution demands, which may

explain why conflict tasks better predict working memory

(and Theory of Mind) ability than delay tasks (Carlson and

Moses 2001; Carlson et al. 2002, 2004; Hala et al. 2003).

Nonetheless, the executive processes involved are still

thought to be largely frontal lobe mediated, with lateral

prefrontal lesions again known to be detrimental to task

accuracy (Vendrell et al. 1995; Perret 1974).

Resistance to distractor paradigms require participants to

select targets presented alongside irrelevant distractors, or

more broadly, resist interference from information in the

external environment that is irrelevant to the task (Fried-

man and Miyake 2004, p. 104). One such task is the com-

puterised Eriksen flanker task (Eriksen and Eriksen 1974).

Here, participants identify a target (e.g. letter/shape/arrow)

that is presented either on its own, or flanked by response-

incompatible stimuli. Performance is thought to be associ-

ated with the ability to actively suppress distracting infor-

mation (e.g. Tipper 1985) and/or capacities for focussed and

selective attention. These tasks arguably therefore involve

earlier (i.e. perceptual) stages of information processing and

preattentive sensory gating. But again, aging (Earles et al.

1997) and frontal lesions (Stuss et al. 1999) are both detri-

mental to performance, indicating the recruitment of

domain-general executive processes. Neuroimaging studies

suggest particularly important roles for the dorsolateral PFC,

ventrolateral PFC and ACC (Wager and Smith 2003).

Inhibitory Control in Autism

Existing research indicates that some inhibition task-types

might be more problematic for CWA than others (Christ

et al. 2007). For delay inhibition, the majority of evidence

has come from the Go/No-Go task, and these have typically

found no evidence of impairment. Indeed, the one study

that did report impaired Go/No-Go performance (Ozonoff

et al. 1994) has since been contested due to a set-shifting

confound. Corroborating findings from the Go/No-Go,

Ozonoff and Strayer (1997) found equivalent error-rates

and stop-signal reaction times (SSRTs) in CWA and TD

controls on the Stop-task. Although another study (Lemon

et al. 2011) did find significantly slower SSRTs in CWA

(females only), mean full-scale IQ was considerably lower

than that of the controls, probably only failing to reach

significance due to low power. Yerys et al. (2009) found no

evidence of impairment amongst CWA on the TEA-Ch

Walk-Dont Walk task. Together these indicate that simple

delay tasks of behavioural inhibition are not problematic

for CWA.

Regarding conflict inhibition, the most dominant task in

ASD research has been the classic colour-word Stroopand

these typically suggest no evidence of impairment (e.g.

Ozonoff and Jensen 1999). However, due to the earlier

discussed confound, findings using this particular paradigm

should be interpreted with caution in CWA. This is partic-

ularly true given the less favourable picture painted by other

conflict paradigms. Russell and colleagues (1991; Hughes

and Russell 1993) reported that CWA persistently failed on

both the Windows and Detour reaching tasks, making more

errors than TD/MLD children of matched verbal mental age.

Similarly, CWA have been shown to make more inhibitory

errors than controls on the Opposite World task (Bishop and

Norbury 2005) and the Dog/Pig Stroop task (Ames and

Jarrold 2007). Russell et al. (1999) found no evidence of

impairment on the Day/Night task, despite structural simi-

larities to the Dog/Pig Stroopbut this study notably only

matched groups on non-verbal mental age (Jarrold and

Table 2 Participant background/matching measures

Autism group (N = 31) TD group (N = 28) F value p value Effect-size (g2p)

M SD M SD

CARS autism rating 32.60 5.97 15.66 1.12 218.22*** \0.001 0.793CA (months) 164.03 24.80 106.71 16.24 117.56*** \0.001 0.673BPVS age (months; max. 160) 91.87 20.36 93.79 12.38 0.186 0.668 0.003

Ravens CPM score (max. 36) 28.10a 4.98 29.46a 3.89 1.362 0.248 0.023

Inattentiveness (VADTRS total score) 14.29 4.89 2.86 3.62 102.463*** \0.001 0.643Hyperactivity/Impulsivity (VADTRS total score) 11.26 5.92 2.29 3.91 46.123*** \0.001 0.447

* p \ 0.05; ** p \ 0.01, *** p \ 0.001a Ravens CPM score of 2830 is equivalent to a standardised score (UK) of 9.510 years (Raven et al. 1990)


123

Brock 2004). This can leave CWA at a disadvantage, as

verbal ability is often poorer than non-verbal ability in this

population (Joseph et al. 2002).

Finally, findings on resistance to distractor inhibition have

been both scarce and inconsistent. Whereas one study

reported clear inhibitory impairments in CWA (Christ et al.

2007), two others found no such evidence (Dichter and

Belger 2007; Henderson et al. 2006). This again may relate to

a non-inhibitory confound. The flanker task in Christ et al.

required children to maintain online numerous arbitrary

response mappings, which arguably augments working

memory demands compared to the simpler designs used

elsewhere (i.e. arrows flanker tasks). Nonetheless, it is

notable that both Dichter and Belger (2007) and Henderson

et al. (2006) used adult samples. It is therefore conceivable

that delays in resistance to distraction development are

observable amongst CWA, but that typical functioning is

reached by adulthood. No study has implemented a simple,

arrows flanker task with a child ASD sample to address this

possibility.

Overall, existing research suggests that conflict tasks of

inhibition may be most problematic for CWA, perhaps

owing to greater working memory and conflict demands

although evidence on resistance to distractor evidence is

limited and difficult to decipher. However, without

administering all three paradigm types to the same sample

of CWA, it is hard to rule out the possibility that disparate

findings are cohort, rather than task related (Christ et al.

2007)especially given the known heterogeneity in the

autism population. Therefore the first aim of this study was

to administer three different tasks to CWA and a control

group matched for verbal and non-verbal mental age: one

delay, one conflict, and one resistance to distractor. These

tasks, respectively, were a Go/No-Go task, the Dog/Pig

Stroop task and an arrows Flanker task. These are all well

established in the inhibitory literature, have been applied

successfully in ASD research and avoid various known

confounds. We expected that impairment was most likely

to emerge on the Dog-Pig Stroop (conflict) task.

ASD, Inhibitory Control and the Role of ADHD-Type

Symptoms

A further aim of our investigation was to address a poten-

tially important source of heterogeneity in inhibitory ability

in this population. At least 30 % of CWA meet diagnostic

criteria for ADHD, and many more show elevated symptoms

of inattention and hyperactivity (Leyfer et al. 2006; see also

Sinzig et al. 2009). Associations between autism and

attentional disorders have been suggested at a genetic (e.g.

Rommelse et al. 2010), functional (e.g. Brieber et al. 2007)

and behavioural level (Sinzig et al. 2008). This overlap is of

particular relevance to studies of inhibition in autism

because inhibitory control is often considered the core def-

icit in ADHD, with children with the disorder almost

invariably exhibiting broad-ranging inhibitory impairments

(Barkley 1997; Nigg 2001; Aron and Poldrack 2005; Will-

cutt et al. 2005). Given symptomatic overlaps, it may be that

covarying ADHD-type symptoms are more important pre-

dictors of inhibitory ability in CWA than core autistic

symptoms themselves. Indeed, ADHD-type symptoms may

be a significant driver of group differences (Castellanos and

Tannock 2002).

In high-functioning groups, the presence and severity of

both inattentive and hyperactive traits have already been

shown to predict performance on a Go/No-Go task (Sinzig

et al. 2008) and change task (a conflict variant of the

Stop-task) (Verte et al. 2006). Notably, in both studies this

was despite overall group differences between CWA and

TD controls failing to reach significance. Bishop and

Norbury (2005) also found symptoms of inattention and

hyperactivity/impulsivity to be moderately predictive of

inhibitory impairment on the Opposite World and Walk/

Dont Walk tasks in CWA (high-functioning), pragmatic

language impairment (PLI) and specific language impair-

ment (SLI). It appears therefore that ADHD symptom-

presence may be an important predictor of individual dif-

ferences in inhibitory ability amongst CWA.

Thus, for each child taking part, an objective measure of

inattention and hyperactivity/impulsivity was obtained to

gauge the importance of ADHD-type symptoms in any

observed group differences. The Vanderbilt ADHD Diag-

nostic Teacher Rating Scale (VADTRS; Wolraich et al.

1998) was used as it provides separate estimates for

symptoms of inattention and hyperactivity/impulsivity

based upon DSM-IV (American Psychiatric Association

[APA] 1994) criteria. Given that associations between both

inattention (e.g. Bishop and Norbury 2005) and overac-

tivity (e.g. Ames and White 2010; Verte et al. 2006) and

inhibitory performance in CWA have been reported across

various inhibition tasks, we expected that both ADHD-

symptom types might predict inhibitory deficits, and no

task specific predictions were made.

Together, the two overarching predictions of this

research have various implications for our understanding of

inhibition in autism. First, uneven patterns of performance

across different tasks would help to explain a record of

inconsistency in the field. Impairment on the Dog/Pig

Stroop only would suggest that inhibitory control is not a

core or pervasive deficit in CWA, but also that generalist

claims of intact inhibition are misguided. Crucially, this

notion would help to differentiate the executive profile of

autism from that of other developmental disorders with

known executive dysfunction (particularly ADHD). Sec-

ond, if symptoms of inattention and/or hyperactivity help

predict which children demonstrate inhibitory difficulties,


123

it would highlight the importance of considering overlap-

ping symptoms between developmental disorders.

Method

Participants

Written informed consent was obtained from a parent/

guardian of all children taking part. Children also provided

verbal consent prior to commencing sessions.

ASD Group

All CWA who participated were enrolled in a specialist

school for autism and had formal diagnoses of an ASD made

by a qualified clinical or educational psychologist using

DSM criteria (e.g. ADOS/ADI). A teacher/teaching assis-

tant completed the Childhood Autism Rating Scale (CARS)

to confirm that clear ASD symptomatology was still present.

The CARS was selected as it provides a cut-off for symptom

severity. Thirty-one participants (5 female, 26 male) with

CARS scores of \27 points (Mesibov et al. 1989) and whocompleted all tasks successfully were included in the final

sample (Mean Age = 163 months, SD = 22.8 months).

Control Group

Twenty-eight typically developing (TD) children, aged

611 years, were recruited from three local primary

schools. No child had any clinical diagnosis or was on the

school register for special educational needs. On the

CARS, all TD children scored between 15 (the minimum

score) and 19 points. Thus, all twenty-eight children (17

female, 11 male) were included in the final sample (Mean

Age = 106 months, SD = 16.2 months).

ASD and TD groups were matched on estimated verbal

and non-verbal mental age (MA), according to perfor-

mance on the BPVS (Dunn et al. 1997) and the Ravens

Coloured Progressive Matrices (Ravens CPM) (Raven

et al. 1990). For all children, a teacher completed the first

19-items of the 35-item Vanderbilt AD/HD Diagnostic

Teacher Rating Scale (VADTRS). The VADTRS assesses

symptoms of attention-deficit/hyperactivity disorder based

upon DSM-IV (1994) criteria, with 19-items rated on a

four-point frequency scale (0 = Never; 1 = Occasionally;

2 = Often; 3 = Very Often). A child is indicated as above

the DSM-IV threshold for AD/HD Inattentive-Type if they

receive a rating of 2 or 3 on six or more from Items 19.

AD/HD-Hyperactive/Impulsive Type is indicated by six or

more ratings of 2 or 3 on Items 1019. AD/HD Combined

type is indicated by six or more ratings of 2 or 3 on both

dimensions. For normative data, see Wolraich et al. (1998).

Procedure and Task Design(s)

Participants were tested individually in a well-lit, quiet, and

distraction-free room. Each completed the three inhibitory

control tasks (Go/No-Go; Dog-Pig Stroop; Flanker) and

two standardised measures (RCPM; BPVS) in counterbal-

anced order. Sessions lasted 4060 min. Inhibition tasks

were written in Psyscript, and run on a computer using an

OS X 10.6 operating system.

Go/No-Go Task

Task Design

On each trial, a shape (O, D, h, or e) appeared centrallyon the computer screen. Children were instructed to

respond to three of the shapes by pressing a large external

star button (i.e. Go stimuli), but to resist responding

to a fourth shape (i.e. the No-Go stimulus). The shape

designated as the No-Go stimulus was counterbalanced

between participants. To generate a prepotent response,

75 % of trials were Go trials requiring a button press,

and 25 % of trials were No-Go trials where the response

should be withheld.

The maximum inter-stimulus interval (ISI) was

2,500 ms. At the start of each trial, a fixation-cross

appeared at the centre of the screen for 200 ms. The

stimulus then appeared for 200 ms. After stimulus offset,

participants had a further 1,000 ms to respond before the

trial automatically terminated. There was then a 1,100 ms

pause before the next trial commenced. An error tone

(bleep) was played immediately if the child made an

omission error (i.e. failed to respond on a Go trial), or a

false positive (i.e. pressed the star button on a No-Go

trial). A positive feedback-noise (ping) was played for

correct responses.

Procedure

A warm-up session was initially conducted to familiarize

participants with the stimuli. Training was terminated only

when the child could correctly identify the required

response for each shape (i.e. Go vs. No-Go). Children then

completed a short practice block of eight trials containing

all four stimuli presented in a fixed but superficially ran-

dom order. 144 experimental trials then followed, split into

three 48-trial blocks separated by short breaks. Stimulus

presentation was randomised throughout each half block.

Four measures of task performance were obtained: False

positive rate (No-Go trials on which the button was

pressed); hit rate (Go trials on which the child respon-

ded); hit trial reaction time (RT); and task sensitivity. The

latter differentiates participants who make fewer false


123

positives despite a good hit rate (good sensitivity) from

those who make fewer false positives but fewer hits as well

(poor sensitivity) (see Grier 1971).

Dog-Pig Stroop Task

Task Design

Stimuli were two simple line drawings of a dog and a pig,

which were presented centrally on a computer screen. Two

experimental conditions, each containing 32 trials were

administered. In the control (baseline) condition, children

were instructed to say dog when they see the dog image

and pig when they see a pig, as quickly and accurately as

possible. In the Stroop (i.e. inhibition) condition, children

were instructed to say dog to pig images, and pig to

dog-images.

Childrens responses were recorded online during the

task and audiotaped for subsequent checking. If a child

made a mistake on a trial but then corrected him/herself,

the initial response was recorded. To estimate response

latency on each trial, the experimenter pressed a large

external button in time with the childs initial response.

Although this measure of reaction time is relatively crude,

many of the children would not have been testable with

throat microphones that measure voice-onset due to inter-

ference from task irrelevant movements and vocalisations.

Notably, any additional error introduced via the reaction-

time estimate was constant across groups.

On each trial, the stimulus remained centrally on-screen

until a response had been registered. After 3,000 ms had

elapsed without a response, the trial automatically termi-

nated and the message Too Slow was presented for

500 ms. Stimulus presentation was followed by a 2,000 ms

pause before the next trial commenced. The maximum ISI

was 5,500 ms.

Procedure

All children completed the control condition first to obtain

baseline picture naming speed and accuracy, followed by

Stroop training slides. After completing four Stroop prac-

tice trials successfully, children commenced the 32-trial

Stroop condition block.

Flanker Task

Task Design

Children were presented with two large arrow-shaped

buttonsone pointing left and one pointing right. There

were three conditions in this task. In all conditions, chil-

dren were asked to press the button pointing the same way

as a white target arrow that would appear (centrally) on-

screen. On baseline trials, the target arrow was presented

alone. On congruent trials, the target arrow was flanked by

four red distractor arrows (two either side) pointing the

same way as the target (e.g. ). On incongruent trials,

the target arrow was flanked by distractor arrows facing in

the opposite direction to the target (e.g. ).

The maximum ISI was 2,900 ms. On each trial, a fixa-

tion cross appeared centrally on-screen for 200 ms. This

was replaced by the stimulus (neutral, congruent or

incongruent), which remained on-screen until a button-

press was registered. If no response had been registered

after 1,200 ms, the trial automatically terminated and a

Too-Slow message was briefly displayed accompanied

by an error tone (bleep). The error-tone also sounded if

the participant responded incorrectly. A positive feedback-

noise was played (ping) for correct responses. There was

a 1,100 ms pause (inter-trial interval) between trials.

Procedure

Familiarisation trials were followed by three blocks of 30

trials, each separated by a short break (90 trials in total).

Each block contained ten baseline, ten congruent and ten

distractor trials, which were distributed randomly. Error-

rates and mean RTs were recorded for all trials.

Planned Comparisons

A MANOVA with group as the between-group factor was

used to assess group differences in chronological age (CA),

BPVS, Ravens CPM, CARS and VADTRS scores. Dif-

ference in gender-distribution between groups was esti-

mated using a Chi-square test. Regarding task

performance, a series of Pearson correlations were imple-

mented between the three inhibitory measures (all partici-

pants included). To permit these correlations, single-figure

estimates of inhibitory performance had to be calculated

for the Dog-Pig Stroop and Flanker task by subtracting

participants baseline RT and error-rates from their Stroop/

incongruent trial RT and error-rates. False positive rate was

used to represent inhibitory performance on the Go/No-Go.

Between task correlations (N = 59) could detect large

effect sizes (r = 0.5) at a = 0.05 (2-tailed) at 99 % power,and medium effect sizes (r = 0.3) at approximately 66 %

power. Simple bivariate correlations were also performed

to highlight any associations between CA, MA (BPVS/

RCPM) and CARS rating and inhibitory outcome measures

(see Table 3). As these were carried out separately for each

group (N = *30), we only had sufficient power (80 %) todetect large effect sizes (r = 0.5) at a = 0.05 (2-tailed).

Although CA was not matched between groups, CA was

not used as a covariate for group comparisons of inhibitory


123

performance. This is because covariates have been ill

advised for the control of non-trivial groups differences

(Miller and Chapman 2001), as it can cause observed

means to be adjusted for spurious reasons (Jarrold and

Brock 2004). Pearsons correlations were used, however, to

estimate any influence CA may have had on inhibitory

performance and possible implications for group differ-

ences. However, again, there was only sufficient power to

detect large (i.e. r = 0.5) effect sizes at a = 0.05.

Group differences in Go/No-Go task performance were

assessed via a one-way MANOVA. Group was the

between-participants factor, and false alarms, hits, A (task

sensitivity), Go-trial RT and No-Go-trial RT were the

dependent variables. The main measure of inhibition was

false alarm rate. Group differences in Dog-Pig Stroop task

performance were investigated using a 2 9 2 mixed MA-

NOVA. Trial-type was the within-participants factor, group

was the between-participants factor, and error-rate and RT

were the dependent variables. Errors were classified as any

trial on which the child either said the wrong animal name

(i.e. Incorrect Response Errors) or failed to make a

response before the trial terminated (i.e. Too Slow Errors).

Group differences on the Flanker task were assessed using

a 3 9 2 mixed MANOVA, with trial-type as a within-

participants factor, group as a between-participants factor,

and error-rate and RT as the dependent variables. Post hoc

pair-wise comparisons (using Bonferroni correction) clar-

ified group differences across conditions (no-distractor,

congruent, incongruent).

Associations between inhibitory performance and

ADHD-like symptoms were assessed in the ASD group

only as TD children scored very low (mostly zero) on both

dimensions. For this, subgroups of high and low scoring

CWA were identified for both inattentiveness and hyper-

activity/impulsivity scales. High Scorers were children

scoring six or more ratings of 2 (often) or 3 (very often) on

relevant items. This is cut-off flags children who may meet

the diagnostic criteria for ADHD-Inattentive type (n = 9),

or ADHD-Hyperactive/Impulsive type (n = 7) (APA

1994). Low scorers received no more than one rating of

2 (often) or 3 (very often) on items for ADHD-Inattentive

type (n = 11) and ADHD-Hyperactive/Impulsive type

(n = 12). Performance of high/low scoring groups was

compared via one-way multiple ANOVAs with the key

inhibitory performance measures as dependent variables.

This subgroup approach was used to maintain the thresh-

old-based scoring method on which the VADTRS was

validated.

For all comparisons, an alpha level of 0.05 was used and

exact p values and effect sizes are provided where appro-

priate to make results as transparent and easily comparable

to previous findings as possible. Although a number of

statistical tests were performed, Howell (2002) argues that

correction for multiple comparisons is not warranted where

a priori predictions are made. However, we caution readers

to interpret non-corrected results conservatively, particu-

larly where strong a priori predictions were not made (e.g.

ADHD-symptom effects).

Results

As shown in Table 2, CARS ratings were higher amongst

CWA than TD controls, as were VADTRS inattention and

hyperactivity scores. VADTRS inattention ratings and

CARS-rating were positively correlated in CWA,

r(29) = 0.384, p = 0.033 (2-tailed). The two groups were

satisfactorily matched on BPVS and Ravens score.

Although the ASD group was significantly older than the

TD group, Pearsons correlations indicated few associations

between CA and inhibitory performance: in the ASD group

only, higher CA was predictive of faster inhibitory RTs on

the flanker task (p = 0.016) and the Dog-Pig Stroop

(p = 0.007). In terms of MA, higher RCPM scores were

predictive of better inhibitory performance (faster RT)

amongst TD children in the Dog-Pig Stroop (p = 0.033).

Amongst CWA, higher Ravens scores predicted more

inhibitory errors on the Flanker task (p = 0.014) and more

false-positives (p = 0.010) on the Go/No-Go. Higher BPVS

Table 3 Pearson (r) correlations between CA, BPVS, Ravens CPMand CARS (ASD only) and inhibitory performance

CA BPVS Ravens

TD group (N = 28)

Flanker

Inhibitory RT -0.174 -0.072 0.019

Inhibitory error -0.311 0.039 0.010

Dog Pig Stroop

Inhibitory errors -0.139 -0.179 -0.136

Inhibitory RT -0.095 -0.334 -0.405*

Go/No-Go

False positives -0.031 -0.033 -0.083

CA BPVS Ravens CARS

Autism group (N = 31)

Flanker

Inhibitory RT -0.429* -0.394* 0.042 0.012

Inhibitory error -0.096 -0.175 -0.435* 0.012

Dog Pig Stroop

Inhibitory errors 0.037 -0.374* -0.299 0.156

Inhibitory RT -0.482** -0.127 -0.207 -0.357

Go/No-Go

False positives 0.060 -0.161 -0.456** 0.315

* p \ 0.05; ** p \ 0.01 (2-tailed)


123

scores were associated with slower inhibitory RTs on the

Flanker Task, and more inhibitory errors on the Dog-Pig

Stroop. Notably, there were no significant correlations

between inhibitory performance on any task and CARS

score (all r \ 0.357, p [ 0.05).

Inhibitory Control Measures

There were no significant correlations between any of the

main inhibitory measures between tasks, although the

association between inhibitory error-rates on the Flanker

and Dog-Pig Stroop tasks did approach significance,

r(56) = 0.248, p = 0.061 (2-tailed) (see Table 4).

On the Go/No-Go task, CWA made significantly fewer

false positives than TD children (p = 0.01) (see Table 5).

Logistic regression showed that false alarm rate still helped

to differentiate between CWA and TD participants after the

higher hit-rate in the ASD group has been taken into

account (v2(1, N = 59) = 5.078, p = 0.024). CWA alsoshowed better task sensitivity (A0) than TD children(p \ 0.05). Together these indicate better overall accuracyon the task, rather than just a tendency to press the button

less across the board.

On the Dog-Pig Stroop Task, there was a significant

within-participants effect of trial-type on error-rate,

F(1,56) = 58.970, p \ 0.001, g2p = 0.513, and on RT,F(1,56) = 147.544, p \ 0.001, g2p = 0.725, with childrenmaking more errors and responding slower on Stroop trials

than on baseline trials. This indicates that Stroop trials did

impose additional inhibitory demands.

There was a marginally significant effect of group upon

error-rate, F(1,56) = 3.264, p = 0.076, g2p = 0.055, withCWA making more total errors (M = 3.3; SE = 0.445)

than TD children (M = 2.1; SE = 0.461) across both

baseline and Stroop trials. The main effect of group upon

RT was also significant, F(1,56) = 4.576, p = 0.037,

g2p = 0.076, with CWA tending to respond significantlyslower (M = 0.881 s; SE = 0.036) than TD children

(M = 0.772; SE = 0.037).

Figure 1 illustrates a significant trial-type h group

interaction for error-rate, F(1,56) = 7.425, p = 0.009,

g2p = 0.117. Post hoc exploration indicated that this wasdue to CWA making more errors on Stroop trials than con-

trols, t(59) = 2.277, p = 0.027, despite an equivalent error-

rate on baseline trials t(59) = -0.287, p = 0.775. However,

there was no significant trial-typeh group interaction for RT

on this task, F(1,56) = 0.396, p = 0.532, g2p = 0.007.For the Flanker Task, the main effect of trial-type was

significant for both error-rate, F(1.60,116) = 41.763,

p \ 0.001, g2p = 0.419, and RT, F(1.54,116) = 218.595,p \ 0.001, g2p = 0.790. Post hoc exploration showed thatthis was because children made significantly more errors on

incongruent distractor trials (M = 4.44, SD = 2.334) than

on baseline (M = 2.00, SD = 2.147) or congruent distractor

trials (M = 1.90, SD = 2.147) (both, p \ 0.001). Childrenalso responded significantly slower on incongruent distrac-

tor trials (M = 0.675 s, SD = 0.128 s) than on both base-

line (M = 0.560 s, SD = 0.111 s) and congruent distractor

trials (M = 0.584 s, SD = 0.117 s) (both, p \ 0.001).Children were still significantly slower on congruent than

Table 4 Pearson correlations (N) between inhibitory performance on the three inhibitory control tasks

Flanker (errors) Dog-Pig Stroop (errors) Flanker (RT) Dog-Pig Stroop (RT)

Go/No Go (false alarms) 0.193 (59) -0.030 (58) -0.075 (59) 0.169 (58)

Flanker (errors) 0.248 (58) 0.043 (59) 0.032 (58)

Dog-Pig Stroop (errors) 0.152 (58) 0.262 (58)

Flanker (RT) 0.149 (58)

* p \ 0.05; ** p \ 0.01 (2-tailed)

Table 5 Group effects (ASD/TD) for Go/No-Go task measures

ASD group (N = 31) TD group (N = 28) F value p value Effect-size (g2p)

M SD M SD

False alarms (/36 No-Go trials) 17.74 8.05 22.71 6.35 6.830 0.011* 0.107

Hits(/108 Go-trials) 104.4 4.43 106.3 2.00 4.629 0.036* 0.075

Task sensitivity (A0) (0.5 = random;1.0 = perfect sensitivity)

0.623 0.118 0.561 0.061 6.275 0.015* 0.099

Go-trial RT (ms) 0.422 s 0.112 s 0.410 s 0.064 s 0.262 0.611 0.005

No-Go-trial RT (ms) 0.341 s 0.081 s 0.343 s 0.043 s 0.004 0.950 0.000


123

baseline trials though (p \ 0.001), suggesting that even non-conflicting peripheral stimuli cause some interference.

There was no main effect of group on error-rate,

F(1,57) = 0.780, p = 0.381, g2p = 0.014, but there was asignificant group effect on RT, F(1,57) = 5.316,

p = 0.018, g2p = 0.085, with CWA tending to respondsignificantly faster (M = 0.575; SE = 0.020) than TD

children (M = 0.642; SE = 0.021). However, the trial-type

h group interaction was insignificant for error-rate,

F(1.605, 114) = 0.882, p = 0.417, g2p = 0.015, and RT,F(1.534,114) = 0.049, p = 0.914, g2p = 0.001, implyingno inhibitory advantage in CWA (see Fig. 2).

Symptoms of Inattention and Hyperactivity/Impulsivity

There were no significant differences between the high and

low hyperactivity/impulsivity subgroups of CWA on any

inhibitory measure (all p [ 0.3). The high inattention sub-group did, however, show significantly poorer inhibitory

performance on the Dog-Pig Stroop task than the low inat-

tention subgroup, F(1,18) = 4.273, p = 0.05, g2p = 0.192(see Fig. 3). No differences between inattention subgroups

were observed on the Go/No-Go and Flanker (all p [ 0.1).Two further one-way ANOVAs were performed to

gauge whether overall group-differences in Dog-Pig Stroop

error-rate were driven by poor performance in the high-

inattention ASD subgroup. These demonstrated that the

high inattention CWA made significantly more inhibitory

errors than a random subgroup of TD children (N = 10),

F(1,17) = 6.322, p = 0.022, g2p = 0.659, but that the lowinattentive subgroup did not, F(1,17) = 0.383, p = 0.543,

g2p = 0.091.

Discussion

Overall, the present research suggests that children do not

perform equivalently on different tasks of inhibitory con-

trol, and that some tasks may be more likely to reveal

impairments in CWA than others. It is possible to make this

statement with confidence because this study administered

multiple measures of inhibitory control to the same cohort of

Fig. 2 Mean and SE of a errorrate and b reaction time on theFlanker task for no distractor,

congruent and incongruent

distractor trials

0

1

2

3

4

5

6

7

8

High Low

Num

ber

of I

nhib

ition

Err

ors

(Dog

-Pig

Str

oop)

Symptoms of Inattention

Fig. 3 Mean and SE of error rate for the high and low inattentionsubgroups of CWA on the Dog-Pig Stroop

Fig. 1 Mean and SE of a errorrate and b reaction time on theDog-Pig Stroop task


123

children (Christ et al. 2007). Impairments in inhibitory

control were seen on the Dog/Pig Stroop task, but not on two

other tasks thought to tap into inhibitory function. We

propose that this selective impairment might relate to dis-

parities in the cognitive and executive demands imposed by

the tasks. In particular, conflict tasks of inhibition might be

more problematic for CWA because of the considerable (and

simultaneous) working memory demands they exert.

On the Go/No-Go task, our measure of delay inhibition,

CWA actually demonstrated better inhibitory performance

than TD controls. They made fewer false positives and

showed better task sensitivity, which indicates a genuine

inhibitory advantage (i.e. CWA did not just response less

across both Go and No-Go stimuli, which would more

indicate sustained attentional difficulties). Although

intact inhibitory performance amongst CWA has been

documented on numerous occasions on this task (e.g.

Christ et al. 2007; Geurts et al. 2004; Noterdaeme et al.

2001; Sinzig et al. 2008), superior performance has not. It

is thus conceivable the CA difference between the two

groups was influential. Although no significant correlation

between inhibitory performance and CA was observed in

either group on this task, voluntary inhibitory functions are

known to develop and improve throughout childhood

(Diamond and Taylor 1996; Levin et al. 1991) and into

adolescence (Ridderinkhof et al. 1999).

For this reason, the observed superiority of CWA on our

Go/No-Go task should be interpreted with some caution.

However, this result is still relatively strong evidence that

autism is not associated with Go/No-Go task impairments,

adding to a growing body of research from tasks tapping

simple delayed response inhibition like the Go/No-Go task,

Stop-task (Ozonoff and Strayer 1997) and TEA-Ch Walk-

Dont Walk task (Yerys et al. 2009; although see Bishop

and Norbury 2005). Particularly notable here is that these

children showed spared performance on the Go/No-Go

despite showing an inhibitory deficit on another taskthe

Dog/Pig Stroop (Gerstadt et al. 1994). This is a powerful

indication that something about the tasks themselves is

driving differential inhibitory performance.

The Dog-Pig Stroop task has been used once previously

to measure inhibitory capacities in CWA (Ames and Jarrold

2007), and similar inhibitory impairments were observed.

However, the single-trial computerised version used here is

arguably more sensitive to group-based differences in per-

formance, providing separate trial-by-trial measures of

accuracy and response time (Perlstein et al. 1998). These

results corroborate a number of previous studies that have

observed difficulties amongst CWA on other tasks of con-

flict inhibition, including the Windows (Hughes and Russell

1993; Russell et al. 2003), detour reaching (Biro and Russell

2001), and the TEA-Ch Opposite Worlds tasks (Bishop and

Norbury 2005, although see Geurts et al. 2004).

The CWAs performance on the conflict inhibition task

suggests that inhibition is not completely spared in autism

(cf. Pennington 1997). However, it is perhaps more inter-

esting to ask why CWA might have more difficulty with this

paradigm compared to others. One argument is that these

tasks impose considerable working memory as well as

inhibitory demandsby necessitating the suppression of an

inappropriate (yet prepotent) response, whilst also simulta-

neously activating and implementing a conflicting and novel

response (Carlson and Moses 2001; Carlson et al. 2002).

This additional requirement is absent in the Go/No-Go task

and Flanker task, in which children need only suppress either

a prepotent response or distracting information. Supporting

this conjecture, Carlson et al. demonstrated that perfor-

mance on conflict inhibition tasks was significantly corre-

lated with working memory abilities in young TD children,

but performance on delay inhibition tasks was not. Thus it

may be this simultaneous strain on two core executive

components that it problematic for CWA.

Our measure of resistance to distractor inhibition, the

arrows Flanker task, measured participants ability to

suppress interference from distracting information in the

external environment that is irrelevant to the task at hand.

Both CWA and TD children had difficulty suppressing

irrelevant distractors. Further, although incongruent dis-

tractors were most problematic, children in both groups

experienced some interference even from non-conflicting

distractors, illustrating the sensitivity of the task to low-

level attentional processes. However, CWA showed no

greater inhibitory difficulty than controls, corroborating

two previous studies that administered a similar arrows

Flanker task to adults with autism (Henderson et al. 2006;

Dichter and Belger 2007). The results do conflict with

another study, however, that reported clearly impaired

performance amongst CWA using a slightly different

Flanker task (Christ et al. 2007)which may point towards

subtle non-inhibitory differences between the two task-

versions (see Friedman and Miyake 2004). For instance, in

Christ et al., children had to hold in mind four different

arbitrary response mappings throughout the task, whereas

an arrows paradigm only requires participants to hold in

mind two logical stimulusresponse mappings. Thus, it

may be that elevated working memory demands make

Christ et al.s task more problematic for CWA, as opposed

to the central inhibitory components. Given this, we would

argue that our task is the purer measure. However, before

generalising to claims of intact resistance to distractor

inhibition, it is important to undertake further research with

different task variants.

The lack of any strong correlation between tasks and the

disparate group-differences across paradigms arguably

point towards separate inhibitory functions that are in turn

differentially affected in autism. However, it is critical to


123

recognise other factors may have influenced task perfor-

mance differences. One possibility is that the strength or

potency of the to be inhibited information or impulse

varies between tasks. That is, the Dog-Pig Stroop may

simply be more sensitive to group differences because of

its inhibitory difficulty, and performance on the three tasks

may not correlate well simply because the prepotent-

response strengths are not equivalent. Indeed, through

computational modelling using various task parameters,

Davelaar and Cooper (2010) showed that the strongest (and

in fact only) mediator of correlation between two inhibitory

tasks (a Stop-task and a Stroop task) was the prepotent

response potency channel. Although more extensive com-

putational modelling using a numerous delay, resistance to

distractor and conflict tasks would be required to

strengthen this argument, it is important to remember that

shared/non-shared executive processes are not the only

possible mediators of group differences and between-task

correlations. Regardless of the interpretation of group dif-

ferences, however, the current findings are still a relatively

strong indication that inhibitory function is not a core

deficit in ASD (e.g. Yerys et al. 2009, although see

Simpson and Riggs 2005). This message is particularly

clear if one considers that children with AD/HD almost

invariably show inhibitory impairments irrespective of the

task choice (e.g. Happe et al. 2006).

Our second objective was to consider the role of symp-

toms of inattention and/or hyperactivity/impulsivity in pre-

dicting inhibitory impairments in CWA. As expected, these

symptoms were far more common in CWA than TD children

(see Lee and Ousley 2006)with approximately one-third

of CWA scoring above the clinical cut-off for AD/HD-

inattentive type, and one quarter above cut-off for hyper-

active/impulsive type on the VADTRS. This corroborates a

recent large-scale study that estimated that 31 % of indi-

viduals with autism meet diagnostic criteria for AD/HD

(Leyfer et al. 2006). In contrast, no TD child came close to

either cut-off and the majority scored extremely low on both

scales.

We found no link between symptoms of hyperactivity/

impulsivity and inhibitory performance in CWA (unlikely

due to low power because the observed effect sizes were

relatively small). This is perhaps somewhat surprising

given that correlations between inhibitory performance on

various different tasks and these symptoms in CWA have

been reported (e.g. Ames and White 2010; Bishop and

Norbury 2005; Sinzig et al. 2008; Verte et al. 2006). One

plausible explanation is that VADTRS is a less sensitive

measure of hyperactive/impulsive symptoms as those used

in previous studies, because although based upon DSM-IV

criteria, the VADTRS is a relatively new scale backed by

relatively limited validity and normative data. However, a

review of the psychometric properties of various ADHD

rating scales, Collett et al. (2003) did conclude that the

VADTRS is psychometrically strong despite its relative

youth (p. 1027). Therefore, it seems unlikely that insensi-

tivity of the VADTRS is responsible for the null hyper-

activity/impulsivity findings.

In contrast, CWA who scored over the diagnostic cut-off

for AD/HD inattentive-type made more inhibitory errors on

the Dog/Pig Stroop than those with low inattentiveness

ratings. Furthermore, whereas the high-inattention sub-

group of CWA performed more poorly on the task than a

randomly selected TD subgroup, the low-inattention sub-

group of CWA did not. One possible implication here is

that the ASD group impairment on the Dog/Pig Stroop may

have been driven primarily by the poor performance of this

highly inattentive subgroup of CWA.

Although this finding corroborates others studies linking

symptoms of inattention to inhibitory control in autism

(Bishop and Norbury 2005; Chhabildas et al. 2001), why

would attentional-deficits in CWA predict inhibitory

impairments on some, but not all, tasks of inhibitory con-

trol? One possibility, again, is the fact that conflict tasks

draw heavily (and simultaneously) upon both inhibitory

control abilities and working memory (Carlson and Moses

2001; Carlson et al. 2002): the two domains of executive

function that have most consistently been shown to be

impaired in attentional disorders (Castellanos and Tannock

2002; Gioia et al. 2002; Semrud-Clikeman et al. 2010).

Indeed, in a meta-analysis of 83 studies, Willcutt et al.

(2005) reported that the most robust and consistent exec-

utive impairments amongst children with ADHD are

response inhibition and working memory (verbal and spa-

tial), as well as vigilance and planning. The Dog/Pig Stroop

task may thus be particularly sensitive to attentional

impairments because it draws upon not one, but two, of the

key areas of executive dysfunction associated with atten-

tional difficulties. That is, in these children, the need to

actively maintain rule-bound processes may divert (lim-

ited) attentional resources away from the inhibitory ele-

ment of the task, leading to poorer inhibitory performance.

Explicit measures of working memory capacity would be

needed to explore this hypothesis further.

Again, the fact that inattention only predicted poorer

performance on the Dog-Pig Stroop is particularly poignant

if we consider that inhibitory impairments in AD/HD

populations are far less dependent on the task measurement

used (Barkley 1997). Indeed, the specificity of inhibitory

difficulties in these CWA compared to AD/HD groups

shows some support for executive accounts of autism,

because it suggests that different profiles of executive

ability may indeed be observable in different disorders

(Pennington 1997). It is perhaps rather unrealistic to expect

disorders to be characterised by impaired subcomponents

of EF alongside completely spared ones, as subcomponents


123

are inherently intertwined (Welsh and Pennington 1988).

The results we obtained here, however, support the notion

that disorders differ in terms of where their core or most

pervasive executive difficulties lie.

Although speculative, our findings also point to a more

fundamental issue. When differences are found between

individuals with autism and control groups, there is often

an assumption that they relate to the core symptoms of the

autistic disorder. For instance, authors readily link

observed impairments in inhibitory function to restricted or

repetitive behaviours (e.g. Turner 1997; Lopez et al. 2005).

However, it might be that group differences are more

closely associated with a covariate of the disorder or

underlying endophenotypes (e.g. symptoms of inattention),

rather than core autistic symptoms themselves. It is thus

important that studies of autism take into account symp-

toms of ADHD in their sample (and vice versa for ADHD-

related studies)particularly when investigating impair-

ments, like executive dysfunction, that have been impli-

cated in both disorders.

Several methodological limitations of the study should be

noted. First, whilst ASD and TD groups were matched for

non-verbal and verbal mental age, they were not matched for

chronological age. Although a CA difference is expected

when comparing TD and ASD groups, future studies would

benefit from ensuring sufficient age overlap between com-

parison groups. This would permit the use of CA as a

covariate and additional CA-matched subgroup analyses

(Jarrold and Brock 2004). Correlational analyses indicated

that between-group differences were if anything more con-

servative as a result of the CA disparity, as older CWA

showed faster inhibitory reaction times on the Flanker and

Stroop task than younger CWA. The fact that this relation-

ship was observed in the ASD group only may have reflected

the wide age range of the group. We also note that we only

had sufficient power (80 %) to identify relatively large effect

sizes. Secondly, it is notable that the two groups had non-

equivalent gender distributions. Although we confirmed no

sex-related differences in inhibitory performance in either

group on any task, gender differences have been observed in

brain activation on inhibition tasks (Garavan et al. 2006). It

should be stressed, however, that studies have typically

found no evidence of sex differences in behavioural per-

formance on inhibition tasks including the Stop task

(Williams et al. 1999), the Go/No-Go (Garavan et al. 2006)

and the Stroop task (MacLeod 1991). Finally, although all

children in the ASD group had received a formal diagnosis of

an ASD using DSM criteria and their current symptoms were

clearly suggested by the CARS, confirmation of current

symptomology using the ADOS-G/ADI-R would have per-

mitted more detailed analysis of the autistic symptom-profile

of the ASD group, and would describe current symptomol-

ogy according to DSM-IV most appropriately.

Acknowledgments Many thanks to the staff and students of Pe-terhouse School, Presfield School, Larkfield Primary School, Trinity

St. Peters C of E Primary School and Holy Trinity, Southport.

Conflict of interest The authors declare that they have no conflictof interest.

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