PS YC HOLOGICA L SC IENCE
Research Article
Selective Attention and
Perceptual Load in Autism
Spectrum Disorder
Anna Remington, John Swettenham, Ruth Campbell, and Mike Coleman
Department of Developmental Science, University College London
ABSTRACT—It has been suggested that the locus of selective
attention (early vs. late in processing) is dependent on the
perceptual load of the task. When perceptual load is low,
irrelevant distractors are processed (late selection),
whereas when perceptual load is high, distractor interference disappears (early selection). Attentional abnormalities have long been reported within autism spectrum
disorder (ASD), and this study is the first to examine the
effect of perceptual load on selective attention in this
population. Fourteen adults with ASD and 23 adults
without ASD performed a selective attention task with
varying perceptual loads. Compared with the non-ASD
group, the ASD group required higher levels of perceptual
load to successfully ignore irrelevant distractors; moreover, the ASD group did not show any general reduction in
performance speed or accuracy. These results suggest enhanced perceptual capacity in the ASD group and are
consistent with previous observations regarding superior
visual search abilities among individuals with ASD.
The ability to focus on particular aspects of the environment while
ignoring others is crucial given that the brain has limited sensory and
information-processing systems, which are constantly bombarded
with an excess of information (Broadbent, 1958). ‘‘Without selective
interest, experience is an utter chaos’’ (James, 1890, p. 402).
Anecdotally, this ‘‘chaos’’ is often observed in autistic spectrum disorder (ASD), in which individuals appear to fixate inappropriately on seemingly irrelevant information in the
environment (Bryson, Wainwright-Sharp, & Smith, 1990). Although ASD is currently defined by impairments in social in-
Address correspondence to Anna Remington or John Swettenham,
Department of Developmental Science, University College London,
Chandler House, 2 Wakefield St., London WC1N 1PF, e-mail:
a.remington@ucl.ac.uk or j.swettenham@ucl.ac.uk.
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teraction, communication, and repetitive behaviors, the original
definition of autism also made reference to attentional deficits
associated with the condition (Kanner, 1943). Building on recent advances in the field of attention research, this study further investigated selective attention in ASD.
SELECTIVE ATTENTION
Over the past decades, conflicting reports about the locus of
selection (early vs. late) have been put forward. Theories of early
selection assert that perception requires selective attention in
order to proceed; therefore, selection occurs after only basic
physical properties of stimuli have been processed (Broadbent,
1958). Conversely, the argument for late selection states that
selection takes place only after stimuli have been perceived
fully in an automatic, parallel fashion (Deutsch & Deutsch,
1963). Experimental evidence in favor of each of these apparently conflicting theories has been presented (see Driver, 2001).
Lavie (1995) put forward a proposal that seems to resolve the
dispute. She suggested that the locus of selection is dependent
on the perceptual load (amount of potentially task-relevant information) of the task in question. When the perceptual load of a
task is low, and does not exceed perceptual capacity, distractors
are processed (late selection). However, when the perceptual
load of a task is high, irrelevant distractors are not processed
(early selection). It should be noted that perceptual load is not
synonymous with task difficulty. For example, reducing target
visibility may slow down performance, but it does not reduce
distractor interference (Lavie & de Fockert, 2003).
Evidence for Lavie’s load theory has come from a number of
behavioral studies in which participants detected a target in the
presence of distractors or in which researchers measured priming
effects in response to irrelevant distractors (for a review, see Lavie, 2005). Neuroimaging studies have demonstrated that neural
activity related to irrelevant distractors is modulated by perceptual-load levels (Rees, Frith, & Lavie, 1997; Rees, Russell, Frith,
Copyright r 2009 Association for Psychological Science
1
Selective Attention in Autism
& Driver, 1999), and that visual cortex excitability to magnetic
stimulation also appears to be affected by perceptual load
(Muggleton, Lamb, Walsh, & Lavie, 2008). The perceptual-load
effect has also been investigated within subsets of the population.
Children and the elderly demonstrate early selection at lower set
sizes than young adults, which suggests that children and the
elderly have reduced perceptual capacity (Huang-Pollock, Carr,
& Nigg, 2002; Maylor & Lavie, 1998). If this perceptual-load
paradigm can be used to investigate selective attention and perceptual capacity, then it may have a valuable application within
research into atypical populations, such as individuals with ASD,
whose altered attentional abilities have often been highlighted
(Burack, Enns, Stauder, Mottron, & Randolph, 1997).
AUTISM AND ATTENTION
The results of research concerning autism and attention have been
diverse and contradictory. Dawson and Lewy (1989) suggested that
a reduced ability to filter out irrelevant information could cause
individuals with ASD to be bombarded with information, leading to
overarousal. Indeed, Ciesielski, Courchesne, and Elmasian (1990)
showed that participants with autism had more difficulty filtering
out distracting stimuli than a control group did. Ciesielski et al. also
showed that there was an altered pattern of event-related potentials
(ERPs) in the autism group during selective attention tasks in both
visual and auditory modalities. Selective attention ERPs that indicate interchannel- (Nd and N270) and intrachannel- (Nc and
P3b) stimulus selection were observed in the control group. In
contrast, the Nd, N270, and Nc ERPs were not seen in the autism
group, and the P3b was significantly reduced. This pattern was
seen even in individuals with autism whose task performance was
as good as, or better than, the control participants’ performance, as
measured by reaction time (RT) and accuracy data.
Similarly, Burack (1994) noted that there was an increased
effect of distractors on the performance of participants with ASD
compared with the performance of control participants, and
concluded that individuals with ASD appeared to have an inefficient, overly broad attentional lens that failed to contract
appropriately when attending to stimuli.
It is important to reexamine selective attention among individuals with ASD within the new frame of reference provided by
the perceptual-load paradigm (Lavie, 1995). We designed the
current study to address this need. We predicted that, compared
with control participants, individuals with ASD would require a
higher level of perceptual load to eliminate distractor processing. According to load theory, such a finding would indicate
increased perceptual capacity in individuals with ASD.
METHOD
Participants
Participants in this study were 15 adults with ASD and 25 adults
without ASD (control group). One individual with ASD and 2
2
control participants were removed from the sample because their
error rates were more than 2.5 standard deviations above their
group mean. All participants in the ASD group had received a
clinical diagnosis of autism (n 5 3) or Asperger’s syndrome (n 5
11) from a trained, independent clinician who used the criteria
listed in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (American Psychiatric Association, 1994).
Diagnosis was then confirmed by assessment with Module 4 of the
Autism Diagnostic Observational Schedule (Lord, Rutter,
DiLavore, & Risi, 2002). None of the participants had any other
mental or neurological disorder. Groups were matched for nonverbal IQ using a subscale from the Wechsler Abbreviated Scale
for Intelligence (Wechsler, 1999; see Table 1).
Stimuli and Procedure
Stimuli were created using Microsoft Visual Basic (version 6),
were run on a custom-built desktop computer, and were displayed on a ProLite 15-in. flat LCD screen (resolution of 1280 1024 pixels, 2-ms response rate). Viewing distance was 60 cm.
The task was a response-competition paradigm based on a task
developed by Eriksen and Eriksen (1974) and adapted by
Maylor and Lavie (1998) in which participants were required to
respond to a relevant target while ignoring irrelevant distractors.
Stimuli were presented in light gray against a black background. Following the presentation of a fixation cross in the center
of the screen for 1,000 ms, a target letter (X or N) was presented in
any of six positions that were located around the circumference of
an imaginary circle with a radius of 2.11 of visual angle from the
fixation point. The other positions in the ring were occupied by
nontarget letters (Z, H, K, Y, or V) or by a combination of nontarget
letters and small dots, so that all positions were filled. The number
of letters and dots varied by condition. Target and nontarget elements measured 0.41 0.61. A slightly larger distractor letter
(0.51 0.91) was presented on the right or left of the ring of
letters, 4.31 from the center (see Fig. 1).
Participants were told that on each trial, they would see a ring
made up of letters or of letters and dots in the center of the screen
and that one letter would be either an X or an N. They were to
TABLE 1
Descriptive Statistics for Each Group
ASD group
Measure
Age (years:months)
WASI score
Vocabulary subtest
Matrix Reasoning
Full-scale IQ
Mean SD
Range
Control group
Mean SD
23:6 4:4 18:10–30:8 26:7
64.1 7.0
52.9 8.4
115 12.3
Range
2:2 21:5–30:0
58–79 65.7 6.4 52–74
39–67 56.9 7.6 41–67
97–136 119 11.1 101–138
Note. ASD 5 autism spectrum disorder; WASI 5 Wechsler Abbreviated Scale
for Intelligence (Wechsler, 1999). The ASD group (n 5 14) comprised 7 males
and 7 females. The control group (n 5 23) comprised 13 males and 10 females.
Full-scale IQ comprises the Vocabulary and Matrix Reasoning subtests.
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A. Remington et al.
Z
K
X
.
H
N
Y
V
.
.
X
.
L
.
Fig. 1. Examples of the experimental stimuli. The example on the left
illustrates a trial with a high perceptual load and an incompatible distractor; the set size is 6, and the target is X. The example on the right
illustrates a trial with a low perceptual load and a neutral distractor; the
set size is 1, and the target is X.
press the ‘‘X’’ key if an X was present and the ‘‘N’’ key if an N was
present. It was emphasized that they should ignore the letters
that were presented outside the ring. The accuracy and RT for
each trial was recorded by the computer program.
The perceptual load of the task was manipulated by changing
the number of nontarget letters in the ring to create trials with set
sizes of 1 (only the target letter in the ring), 2 (target plus one
nontarget), 4 (target plus three nontargets), and 6 (target plus five
nontargets). The irrelevant distractors were either neutral (unrelated to the target letter, either T or L) or incompatible (an X
distractor when the target was N, and vice versa). The experimental display was presented for only 100 ms in order to preclude voluntary eye movement.
Six blocks of 72 trials were created; within each block, each
set size and distractor condition appeared equally often. Target
and nontarget letters were equally likely to appear in each of the
six ring positions, and the proximity of the target to the distractor
letter was counterbalanced. For set sizes 2 and 4, the position of
the group of letters and the position of the target within the group
(edge vs. middle) was also counterbalanced.
RESULTS
Reaction Times
RTs above 3,000 ms were discarded. Median RTs for correct
responses and error rates were calculated for each distractor
condition at each set size (see Table 2). All trials with incorrect
responses were excluded from further analyses. Median RTs
were examined in an analysis of variance (ANOVA) with group
(ASD or control) as a between-subjects factor, and distractor
condition (neutral or incompatible) and set size (1, 2, 4, or 6) as
within-subjects factors.
There was a significant main effect of set size, F(3, 105) 5
93.095, p < .001, Zp 2 5 .747, indicating that participants were
slower to respond to trials with higher set sizes. There was also a
main effect of distractor condition, F(1, 35) 5 18.092, p < .001,
Zp 2 5 .341, reflecting the fact that RTs were faster for neutral
trials than for incompatible trials. These two main effects suggest that perceptual load was effectively manipulated and that
participants engaged in the task in the way that was intended.
There was no main effect of group, F(1, 26) 5 0.405, p 5 .539,
Zp 2 5 .095; in other words, RTs were not significantly slower in
one group than in the other. This indicates that any differences
in RTs between the groups were not due to an overall reduction
in processing speed in individuals with ASD.
The most important findings were a significant interaction
between set size and distractor condition, F(3, 105) 5 4.285,
p 5 .007, Zp 2 5 .109, and a significant three-way interaction of
group, set size, and distractor condition, F(3, 105) 5 2.785,
p 5 .044, Zp 2 5 .074. The two-way interaction implies that
the effect of distractor condition varied with set size. The threeway interaction suggests that the two groups showed different
patterns of distractor-condition effects across the set sizes.
These patterns can be seen in Figure 2.
Using t tests to explore the interactions revealed that for the
control group, there was a significant effect of distractor condition at set size 1, t(22) 5 3.470, p 5 .002, and set size 2,
t(22) 5 3.925, p 5 .001, but no significant effect at set size 4,
t(22) 5 1.191, p 5 .246, or set size 6, t(22) 5 0.308, p 5 .761
(Fig. 3a). For the ASD group, there was a significant effect of
distractor condition at set size 1, t(22) 5 3.355, p 5 .005; set
size 2, t(22) 5 4.619, p < .001; and set size 4, t(22) 5 2.751,
p 5 .017; there was no effect at set size 6, t(22) 5 0.499, p 5
.626 (Fig. 3b). Thus, lingering interference by distractors was
found at set size 4 in the ASD group, but the interference effect
was eliminated at set size 4 in the control group. The lack of a
TABLE 2
Mean Reaction Times for Each Set Size and Distractor Condition
ASD group
Control group
Set size
Neutral
distractors
Incompatible
distractors
Neutral
distractors
Incompatible
distractors
1
2
4
6
561 (106)
608 (125)
651 (129)
733 (158)
582 (116)
642 (123)
679 (144)
726 (173)
538 (98)
584 (97)
651 (122)
701 (129)
563 (96)
611 (104)
636 (94)
704 (131)
Note. Reaction times are given in milliseconds. Standard deviations are given in parentheses.
ASD 5 autism spectrum disorder.
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3
a
ASD Group
Control Group
45
35
750
Neutral Trials
25
Incompatible Trials
700
15
5
–5
1
2
4
RT (ms)
Incompatible – Neutral RT (ms)
Selective Attention in Autism
6
–15
650
600
–25
550
–35
Set Size
500
Fig. 2. Difference between reaction times (RTs) on incompatible and
neutral trials as a function of set size and group. Error bars indicate
standard errors of the mean. ASD 5 autism spectrum disorder.
b
significant effect of distractor condition at set size 4 in the
control group appears to be the result of a reduction in RTs for
trials with incompatible distractors (see Fig. 3a).
1
800
2
Set Size
4
6
4
6
Neutral Trials
Incompatible Trials
750
700
RT (ms)
Accuracy Data
An ANOVA on the number of errors (see Table 3 for accuracy
rates) revealed main effects of set size, F(3, 105) 5 151.824,
p < .001, Zp 2 5 .813, and distractor condition, F(1, 35) 5 8.287,
p 5 .007, Zp 2 5 .191; more errors were made on trials with
higher set sizes, and more errors were made on trials within
compatible, rather than neutral, distractors. Overall, there was
no significant difference between the number of errors made by
the two groups, F(1, 35) 5 0.478, p 5 .494, Zp 2 5 .013. There
were no significant interactions among distractor condition, set
size, and group.
650
600
550
500
1
2
Set Size
Fig. 3. Mean reaction times (RTs) on neutral and incompatible trials as a
function of set size for (a) the control group and (b) the autism spectrum
disorder group. Error bars indicate standard errors of the mean.
DISCUSSION
As predicted, individuals with ASD seemed to process distractors at higher levels of perceptual load than the control
adults did. Given that there was no significant difference between the two groups in their RTs and error rates, it is not the
case that the findings are a result of a general selective attention
deficit in ASD. A general selective attention deficit would prevent individuals from effectively assigning their attention to the
TABLE 3
Accuracy for Each Set Size and Distractor Condition
ASD group
Control group
Set size
Neutral
distractors
Incompatible
distractors
Neutral
distractors
Incompatible
distractors
1
2
4
6
.944 (.048)
.852 (.087)
.815 (.078)
.665 (.057)
.889 (.091)
.907 (.104)
.815 (.104)
.694 (.103)
.944 (.048)
.944 (.059)
.852 (.062)
.741 (.091)
.977 (.098)
.870 (.120)
.778 (.120)
.704 (.127)
Note. Accuracy was calculated as the proportion of trials with correct responses. Standard
deviations are given in parentheses. ASD 5 autism spectrum disorder.
4
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A. Remington et al.
central task, regardless of perceptual load, and would be reflected in an overall drop in accuracy or speed.
The results presented here seem to suggest that the control
and ASD groups had different perceptual capacities. According
to Lavie (1995), if the central task does not exhaust perceptual
capacity, then additional information within the visual field is
also processed. If individuals with ASD have a larger perceptual
capacity than individuals without ASD, this would result in the
former engaging in more distractor processing at high levels of
perceptual load, but without a reduction in task performance.
This increased distractibility could be conceptualized as a reflection of an increased ability, rather than a deficit, even though
distractibility has potentially detrimental effects in real-life
situations.
The performance of the control group in this study differed
slightly from the performance of control groups in previous
studies. Maylor and Lavie (1998), Huang-Pollock et al. (2002),
and Lavie and de Fockert (2003) reported that adults without
ASD exhibited a reduction in interference effects between
set sizes 4 and 6. The control group in the current study showed
an earlier reduction in interference (between set sizes 2 and 4).
The reason for this difference in performance is unclear given
that similar procedures were used, and that participants in the
various studies had similar ages and IQs. There may have been
some differences in stimulus presentation (e.g., exact values for
screen contrast, room lighting levels, and the exact color of the
stimuli and background were not reported in previous studies),
although it is unclear whether these factors could influence the
perceptual-load effect (Lavie & de Fockert, 2003). It is important to note, however, that all participants in this study (both
ASD and control individuals) performed the tasks under the
same experimental conditions, and therefore the difference
between the two groups is meaningful despite the shift in the
absolute values seen in the control group.
These findings regarding increased perceptual capacity may
also explain the superior visual search abilities that have consistently been found among individuals with ASD (O’Riordan &
Plaisted, 2001; O’Riordan, Plaisted, Driver, & Baron-Cohen,
2001; Plaisted, O’Riordan, & Baron-Cohen, 1998). In both
feature- and conjunctive-search tasks, participants with ASD
are more efficient than participants without ASD at detecting the
location of a target within an array of nontarget elements.
Increased perceptual capacity would allow more elements to be
processed in a parallel fashion and would facilitate faster target
location, and therefore may underlie these findings. In our study,
inspection of the visual search slopes in the neutral distractor
condition (the increase in RT resulting from the addition of extra
elements to the central search task) indicated that the individuals with ASD were no more efficient at searching for the
target (36.5 ms per item) than the control individuals (34.8 ms
per item; see Fig. 3).
Last, we should emphasize that this study was performed with
an adult population. It is currently not known whether the same
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pattern of increased distractor processing is seen in children with
ASD or whether this develops later in life as a result of experience of a detail-focused cognitive style (Happe & Frith, 2006).
Acknowledgments—This research was supported by a departmental studentship awarded to Anna Remington. We thank Nilli
Lavie for her invaluable comments, and we gratefully acknowledge the support of all the people who took part in the study.
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