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  Autism 16(1) 59–73 Attentional status of faces for _Ó

  The Author(s) 2012 Reprints and permissions: people with autism spectrum sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1362361311409257 disorder aut.sagepub.com

  Anna Remington Developmental Science, University College London, London, UK Ruth Campbell Developmental Science, University College London, London, UK John Swettenham Developmental Science, University College London, London, UK

  Abstract In recent years there has been a growing interest in the role of attention in the processing of social stimuli in individuals with autism spectrum disorders (ASD). Research has demonstrated that, for typical adults, faces have a special status in attention and are processed in an automatic and mandatory fashion even when participants attempt to ignore them. Under conditions of high load in a selective attention task, when irrelevant stimuli are usually not processed, typical adults continue to process distractor faces. Although there is evidence of a lack of attentional bias towards faces in ASD, there has been no direct test of whether faces are processed automatically using the distractor- face paradigm.

  In the present study 16 typical adults and 16 adults with ASD performed selective attention tasks with face and musical instrument distractors. The results indicated that even when the load of the central task was high, typical adults continued to be distracted by irrelevant face stimuli, whereas individuals with ASD were able to ignore them. In the equivalent non-social task, distractors had no effect at high load for either group. The results suggest that faces are processed in an automatic and mandatory fashion in typical adults but not in adults with ASD.

  Keywords face processing, selective attention, perception Autism spectrum disorder (ASD) is a congenital and lifelong condition characterized by behavioural rigidities, deficits in communication and problems with social interaction

  Corresponding authors: Anna Remington and John Swettenham, University College London, 26 Bedford Way, London WC1H 0AP, UK Email: a.remington@ucl.ac.uk, j.swettenham@ucl.ac.uk

  Autism 16(1) (American Psychiatric Association, 1994). One impairment that has been documented is the lack of expertise for face processing – a fundamental component of social interaction. Although such deficits may not be specific to faces, may involve more general perceptual impairments and may not be universal the impairments that have been demonstrated may offer a useful insight into the condition. The typical brain has both cortical (e.g. fusiform face area, FFA) and subcortical networks (e.g. amygdala) that specialize in face processing yet most of the behavioural and neuroimaging studies that have been performed with individuals with ASD have highlighted reduced or absent activation in the FFA (Critchley et al., 2000; Hubl et al., 2003; Pierce et al., 2001; Schultz et al., 2000).

  It has been suggested that the origin of this deficit is a reduced level of orienting towards faces. Typical infants are born with a tendency to look towards the faces of others (Johnson et al., 1991; Valenza et al., 1996) – possibly underpinned by subcortical structures such as the amygdala, superior colliculus and pulvinar, which then facilitate cortical specialization In ASD, however, this attentional bias does not seem to be present and a lack of facial orienting is one of the symptoms most often reported in young children with the condition. Analysis of home videos from first birthday parties have demonstrated that infants who were later diagnosed with ASD looked less at other people than typically developing infants or those later diagnosed with general learning difficulties (Osterling and Dawson, 1994; Osterling et al., 2002). It was also shown that 20-month-old infants looked less at others’ faces than typical children or children with developmental delay (Swettenham et al., 1998) and when watching film of people interacting, eye tracking data has revealed that children with ASD spend more time looking at body parts or objects than at faces

  Change blindness paradigms have also been used to assess attention to faces in ASD by examining the speed taken to spot a face change versus an object change in image pairs. The results demonstrate that while typically developing children were faster at detecting face changes than object changes, the ASD group showed no difference between the conditions, again reinforcing the idea that the children with ASD lack the attentional bias towards faces

  In a functional MRI (fMRI) study, showed that there was a lack of attentional modulation of the neural responses to face stimuli in ASD. On each trial, participants were shown four pictures arranged in a cross formation: one pair of face stimuli and one pair of house stimuli. They were asked to make a same/different judgement about one of the pairs (i.e. vertical or horizontal) and the neural responses to the face and house stimuli when in the attended and unattended positions were compared. It was seen that for typical individuals, attention to the face stimuli resulted in enhanced FFA activation levels. For the ASD group, however, no such modulation was observed. With house stimuli, attention modulated the neural response in house-selective areas for both participant groups. The authors hypothesized that the absence of attentional modulation to social stimuli was due to weaker connectivity between V1 and extrastriate areas in ASD.

  This body of research clearly indicates that in ASD there is a lack of attentional bias towards faces under conditions where faces and other stimuli compete for attention and a lack of attentional modulation for faces when they are required to pay attention to them. However, further distinct attentional effects, specific to face stimuli, have been demonstrated in typical individuals but have never been examined in ASD. In a task where participants are specifically instructed to ignore irrelevant distractor faces, typical adults continue to process Remington et al. the faces even when processing interferes with task performance – faces are extremely difficult to ignore. This finding has led to the suggestion that faces have a special status in attention, being processed in an automatic and mandatory fashion (Farah et al., 1995; Kanwisher et al., 1997). One way in which this has been demonstrated experimentally is by using a selective attention task in which perceptual load is manipulated (Lavie et al., 2003).

  According to perceptual load theory the processing of irrelevant stimuli in the visual field is dependent on the perceptual load (amount of potentially task- relevant information) of the central task in question. If the perceptual load of a task is low, and does not exceed perceptual capacity, then distractor elements are processed. However, if the perceptual load is high, such that processing capacity is exhausted, then irrelevant distractors are not processed. neatly demonstrated this effect using a task in which participants were presented with a ring of letters and asked to identify a target (X or N) within the ring. The number of letters in the ring was varied in order to manipulate the perceptual load of the task. Participants were explicitly told to ignore distractor letters that were presented slightly offset from the central display. Distractor letters were either incompatible (X when the target was N, or N when the target was X) or neutral (e.g. T or L) and therefore unrelated to the target letters. If the distractor letters were processed then an incompatible distractor would lead to confusion over which target was present and slow down the target identification response time. The incongruent trials would therefore be slower than the neutral trials. However, if distractor letters were not processed then the time taken to respond in both conditions, incompatible and neutral, would be equivalent. Comparing the response times to neutral and incompatible trials at each level of load revealed that distractor letters appear to be processed at low levels of perceptual load but are no longer processed at high levels of perceptual load.

  Interestingly though, the relations between perceptual load and distractor processing is not seen when the distractor elements are faces. Lavie et al. (2003) presented participants with a famous name hidden among a list of non-words and asked them to classify the name as a pop star or politician. Flanking the list were distractor faces that were either the same category as the name (congruent) or the opposite category (incongruent). Participants were explicitly instructed to ignore the faces, and the time taken to classify the name was recorded. The perceptual load of the central task was adjusted by varying the number of non-words in the list. In this task participants were consistently slower to respond to incongruent trials versus congruent trials regardless of perceptual load, that is, distractor faces were processed even when the central task was at the highest level of perceptual load. It was argued that this effect demonstrated that faces have a special status in attention, being processed in an automatic and mandatory fashion.

  In the current study we examined whether adults with ASD would process faces regardless of perceptual load in a selective attention task. We used a task similar in structure to Lavie et al. (2003). However, given the potential lack of world knowledge in the clinical group, the task was modified to include anonymous male and female face distractors and participants were asked to classify the written target name according to gender. Congruity therefore depended on whether the face and target name were the same or different gender.

  Given the previous evidence suggesting a lack of attentional bias and weaker attentional modulation for faces in ASD, our prediction was that at the highest level of perceptual load, typical adults would continue to show evidence of distractor interference for faces whereas adults with ASD would show no signs of distractor interference. A non-face control task,

  Autism 16(1) using photos and names of musical instruments, was also included and participants were asked to classify the target named instrument according to instrument category (string or wind) whilst ignoring photos of instrument flankers. It was predicted that for both the ASD and typical comparison group there would be no evidence of distractor interference using these non-social stimuli at the highest level of perceptual load.

  Experiment 1 Methods

  A total of 16 adults with ASD and 16 comparison adults took part in the Participants. study, and all participants gave their informed consent before being included in the study. Participants in the ASD group had received a clinical diagnosis of ASD 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 et al., 2002). None of the participants had any other mental or neurological disorder. Groups were matched for non-verbal IQ using a subscale from the Wechsler Abbreviated Scale for Intelligence (WASI, Wechsler, 1999) and reading ability was verified using the National Adult Reading Test (NART, (Table 1).

  Independent samples t-tests showed that the WASI and NART scores of the two groups were not significantly different (all p values > .05).

  

Stimuli. Stimuli were created using Microsoft Visual Basic (version 6), run on a custom-built

  desktop computer, and displayed on a ProLite 15 inch flat LCD screen (resolution of 1280 1024 pixels, 2 ms response rate). Viewing distance was 60 cm. Stimuli were presented in black against a grey background. Following the presentation of a fixation cross in the centre of the screen for 500 ms, a target name was presented in centre of the screen. In order to manipulate the perceptual load of the task, the name was presented alone (set size 1) or among a list of one, three or five non-words (set sizes 2, 4 and 6, respectively) (see Figure 1). On each trial, a distractor face (from a set of 12 faces: 6 male, 6 female) measuring 4.1 3.3 visual angles was presented at the side of the list of words, 5 from

  Table 1 Descriptive statistics for each group in experiment 1 Age WASI WASI WASI (years: vocabulary matrix full scale IQ

Group Statistic months) subtest reasoning (2 subtests) NART

  Autism spectrum disorder group Mean 23:8

63.1

52.1 113

  18.7 (n ¼ 16) 10 males SD 4:0

  

5.7

  9.1

  12.0

  5.8

6 females Range 18:8–33:7 54–72 37–67 95–136 10–28

Comparison group Mean 26:8

  

64.6

57.9 120

  15.7 (n ¼ 16) 11 males SD 2:5

  

7.4

  6.6

  11.6

  6.4

5 females Range 21:5–29:9 52–74 41–67 101–138 7–27 NART: National Adult Reading Test, WAIS: Weschler Adult Intelligence Scale. Remington et al.

  Figure 1 Examples of stimuli used in experiment 1: (a) example of an incompatible trial with a high perceptual load; (b) example of a compatible trial with a low perceptual load.

  fixation. These distractor faces were either congruent (same sex as the target name) or incongruent (opposite sex as target name). Four blocks of 192 trials were created with each set size and distractor condition appearing equally. Condition presentation order and target position within the list of words was also counterbalanced. A set of six male (John, James, David, Henry, Thomas, Michael) and six female forenames (Lucy, Mary, Sarah, Jane, Katie, Sophie) were used as the target words and a set of 12 non-words were used as the non-target elements.

  

Procedure. Participants were told to indicate with a button press whether the name hidden

  among the non-words was male or female. They were explicitly told to ignore the face stimuli and to perform the task as quickly as possible. For each trial, the key-press, response time

  Autism 16(1)

  

Table 2 Overall mean, median reaction times (RTs, ms), accuracy rates (proportion correct) and standard

deviations (SD) for the two groups under congruent (cong.) and incongruent (incong.) distractor conditions

at each set size for experiment 1 Set size 1 Set size 2 Set size 4 Set size 6 cong incong. cong incong. cong incong. cong incong. Autism spectrum disorder group

RT 702 749 803 852 1086 1096 1356 1317

  (231) (200) (235) (322) (303) (362) (345) (377)

Accuracy 0.948 0.943 0.948 0.945 0.979 0.956 0.956 0.971

(0.047) (0.052) (0.054) (0.069) (0.043) (0.058) (0.044) (0.039) Comparison group

  

RT 574 607 651 676 848 885 1088 1126

(68.7) (95.2) (110) (108) (167) (138) (172) (155)

Accuracy 0.927 0.945 0.956 0.919 0.948 0.943 0.961 0.948

(0.076) (0.045) (0.041) (0.086) (0.047) (0.037) (0.057) (0.202)

  (milliseconds from stimulus onset time) and accuracy were automatically recorded. All stimuli and procedures were approved by the University College London ethics committee and were therefore performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki.

  Results

  The median reaction time for correct responses and error rates for each Data reduction. distractor condition at set size 1, 2, 4 and 6 were calculated. All incorrect trials were excluded from reaction time analyses. For each participant, the median reaction times for correct responses to congruent and incongruent trials at each set size were calculated (Table 2). Where group/condition averages are stated, these are the mean of the median reaction times of each participant in that group or condition.

  Analysis of variance (ANOVA) was performed on the number of errors for Accuracy data. each group across each condition. There were no significant main effects or interactions between the error rates for both groups under each condition (all p values > .05). The error rates are consistently low across all conditions for both participants with ASD and comparison participants.

  An ANOVA was performed on the mean median correct reaction Reaction time data. times with group (ASD vs. control) as the between-subjects factor and distractor congruency (congruent vs. incongruent) and set size (1, 2, 4 and 6) as within-subject factors.

  2 p ¼ 0.93) and of distractor

  There was a main effect of set size (F(3,90) ¼ 369.69, p < .001, Z

  2 p ¼ 0.29). Inspection of the means revealed that

  congruency (F(1,30) ¼ 11.94, p ¼ .002, Z reaction times increased with set size (as the task became more difficult) and were longer for incongruent trials. The main effect of congruency did not interact with set size

  2

  2

  (F(3,90) ¼ 2.03, p ¼ .116, Z p ¼ 0.06) or group (F(3,90) ¼ 1.27, p ¼ .268, Z p ¼ 0.04). There

  Remington et al.

  Figure 2 Congruency effects of each group at each set size. RT: reaction time.

  was also no significant interaction between set size and group (F(3,90) ¼ 3.09, p ¼ .063,

2 Z p ¼ 0.09).

  2 p ¼ 0.27) due to generally

  There was a main effect of group (F (1,30) ¼ 10.88, p ¼ .003, Z slower reaction times of the ASD participants. The three-way interaction between group, set size and distractor congruency was also

  2 p

  significant (F (3,90) ¼ 3.22, p ¼ .039, Z ¼ 0.10) indicating that the two groups were showing different congruency effects at the various set sizes (Figure 2).

  Post hoc t-tests (with Bonferroni–Holm adjustments to the significance level) were used to further investigate the significant three-way interaction. In the comparison group, there was a significant effect of congruency at all set sizes (set size 1, t(15) ¼ 3.06 p ¼ .008; set size2, t(15) ¼ 4.32, p ¼ .001; set size 4, t(15) ¼ 2.51, p ¼ .024; set size 6, t(15) ¼ 2.27, p ¼ .039) indicating that typical adults continue to be distracted by faces at all levels of perceptual load.

  In the ASD group, there was significant effect of congruency at set size 1(t(15) ¼ 3.97, p ¼ .001) and 2 (t(15) ¼ 3.26, p ¼ .005) but not for set size 4 (t(15) ¼ 0.68, p ¼ .509) or 6 (t(15) ¼ –0.84, p ¼ .414) demonstrating that, unlike typical adults, ASD individuals only process faces at low levels of load; when the perceptual load of the central task was increased no congruency effect was observed.

  Experiment 2

  Our first experiment’s results showed that the typical adults, but not the individuals with ASD, continued to process distractor faces even at high levels of perceptual load. In order to determine whether the effects were specific to faces, we ran a second experiment using meaningful non-face distractors, following This task differed from experiment 1 in that it used musical instruments instead of faces. Participants were asked to classify the name of an instrument (string or wind) while ignoring pictures of the instruments that flanked the words. By using photographs of musical instruments, the distractors are meaningful, of similar visual complexity to faces and require subordinate category

  Autism 16(1)

  Table 3 Descriptive statistics for each participant group in experiment 2 Age WASI WASI WASI (years: vocabulary matrix full scale IQ

Group Statistic months) subtest reasoning (2 subtests) NART

  ASD Mean 24:8 64.4 54.4 117

  16.6 (n ¼ 14) 11 males SD 4:9

  6.9

  10.5

  14.9

  5.5

3 females Range 19:8–33:7 54–76 37–67 95–138 8–25

Comparison Mean 26:1

  65.0 52.1 116

  15.7 (n ¼ 14) 10 males SD 2:9

  6.7

  8.2

  11.3

  6.9

4 females Range 21:5–31:2 59–71 41–65 101–133 7–27

NART: National Adult Reading Test, WAIS: Weschler Adult Intelligence Scale.

  discrimination. By running this control task, the specific impact of face stimuli on selective attention in ASD can be assessed.

  Methods

Participants. A total of 14 young adults with ASD and 14 comparison adults took part in

  the study. Diagnostic and matching procedures were identical to those of experiment 1 (Table 3). A new set of participants were recruited here in order to avoid practice effects that would arise if the individuals who had previously performed experiment 1 took part in this subsequent task. These participants were matched closely on age, sex and non-verbal IQ with the corresponding group from experiment 1.

  Independent samples t-tests verified that there was no difference between the groups on these measures (all p values > .4).

  The experimental paradigm was similar to that of experiment 1, but Stimuli and procedure. the male and female names and face distractors were replaced by the names of wind or stringed musical instruments and corresponding pictures. Stimuli were presented in black against a grey background. Following the presentation of a fixation cross in the centre of the screen for 500 ms, then name of a target instrument was presented in centre of the screen.

  The target could be one of six wind instruments (clarinet, horn, saxophone, trombone, trumpet, tuba) or one of six stringed instruments (banjo, bass, cello, guitar, harp, violin). Participants were told to indicate with a button press whether the name of the instrument hidden among the non-words was either a string or a wind instrument. In order to manipulate the perceptual load of the task, the name would be presented alone (set size 1) or among a list of one, three or five non-words (set sizes 2, 4 and 6, respectively) (Figure 3). A set of 12 non-words were used as the non-target elements. The names and non-words were matched in length. On each trial, a distractor instrument measuring 4.1 3.3 visual angles (identical size to face distractor from experiment 1) was presented at the side of the list of words, 5 from fixation. These distractor instruments were either congruent (same instrument class as the target name) or incongruent (opposite instrument class to the target name). Four blocks of 192 trials were created with each set size and distractor condition appearing equally. For set sizes 2 and 4, the position of the target within the

  Remington et al.

  

Figure 3 Example of stimuli used in experiment 2: (a) example of a congruent trial with a high perceptual

load; (b) example of an incongruent trial with a low perceptual load.

  list was counterbalanced. Following the completion of the experimental trials, the pictures of the instruments were presented to each participant and they were asked to both name the instrument and indicate which group (string or wind) it belonged to. This was to ensure that all participants had the relevant knowledge to meaningfully complete the task.

  Results

  The median correct reaction time and error rates for each distractor Data reduction. condition at set sizes 1, 2, 4 and 6 were calculated. All incorrect trials were excluded from

  Autism 16(1)

  

Table 4 Overall mean median reaction times (RTs, ms), accuracy rates (proportion correct) and standard

deviations (SD) for the two groups under congruent (cong.) and incongruent (incong.) distractor conditions

at each set size for experiment 2 Set size 1 Set size 2 Set size 4 Set size 6 cong incong. cong incong. cong incong. cong incong.

(SD) (SD) (SD) (SD)

ASD

  

RT 730 827 897 924 1154 1151 1420 1436

(103) (107) (211) (159) (272) (196) (273) (253)

Accuracy 0.976 0.920 0.917 0.935 0.964 0.961 0.979 0.958

(0.035) (0.101) (0.126) (0.084) (0.065) (0.060) (0.036) (0.057) Comparison

RT 731 841 822 856 1060 1109 1344 1382

  (143) (206) (139) (139) (170) (198) (205) (244)

Accuracy 0.973 0.884 0.958 0.949 0.967 0.949 0.967 0.961

(0.031) (0.066) (0.046) (0.047) (0.041) (0.052) (0.059) (0.042)

  further analyses. For each participant, the median reaction times for correct responses to congruent and incongruent trials at each set size were calculated (Table 4).

  

Accuracy data. An ANOVA performed on the number of errors for each group across each

  2 p

  condition revealed a main effect of set size (F(3,78) ¼ 5.83, p ¼ .001, Z ¼ 0.18) and a main

  2 p

  effect of distractor congruency (F(1,26) ¼ 9.43, p ¼ .005, Z ¼ 0.27); the error rates were higher for the lower set sizes and for incongruent trials. The latter observation is logical, while the idea of more errors being made at lower set sizes is a little counterintuitive. This may be a speed-accuracy trade-off, however, given the incongruent distractors were being processed at the lower set sizes, it is more likely that this was negatively influencing the responses (i.e. increasing error rates) at low levels of load. There was a significant interaction

  2 p ¼ 0.23). In line with the

  between congruency and set size (F(3,78) ¼ 7.90, p < .001, Z reasoning regarding the main effect of set size, this reflects a greater number of errors in the incongruent trials versus congruent trials at the lower set sizes. There was no significant

  2 p ¼ 0.10) or between

  interaction between set size and group (F (3,78) ¼ 2.85, p ¼ .055, Z

  2 p ¼ 0.04). There was no main effect of

  congruency and group (F (1,26) ¼ 0.98, p ¼ .331, Z

  2

  p

  < group (F(1,26) < 0.01, p > .999, Z 0.01).

  The three-way interaction between group, set size and distractor congruency was not

  2 p ¼ 0.03) indicating that the two groups were not

  significant (F(3,78) ¼ 0.76, p ¼ .520, Z showing a different pattern of error rates across the various conditions.

  An ANOVA was performed on the median correct reaction times with Reaction time data. group (ASD vs. control) as the between-subjects factor and distractor congruency (congruent vs. incongruent) and set size (1, 2, 4 and 6) as within-subject factors.

  2 p ¼ 0.92) and of distractor

  There was a main effect of set size (F(3,84) ¼ 322.50, p < .001, Z

  2 p ¼ 0.53). These significant effects reflect the fact

  congruency (F(1,28) ¼ 31.88, p < .001, Z that reaction times increased with set size (as the task became more difficult) and were longer for incongruent trials. The main effect of congruency did not interact with group

  Remington et al.

  Figure 4 Congruency effects for each group at each set size. RT: reaction time.

  2 p ¼ 0.01). There was also no significant interaction between set

  (F(3,84) ¼ 0.18, p ¼ .671, Z

  

2

p ¼ 0.03). There was, however, a significant

  size and group (F(3,84) ¼ 0.94, p ¼ .427, Z

  2 p ¼ 0.10). This is a

  interaction between set size and congruency (F(3,84) ¼ 3.11, p ¼ .031, Z reflection of the fact that there was a congruency effect at low set sizes (when the perceptual load of the task is low) but not at higher set sizes.

  2 p ¼ 0.03); overall, both

  There was no main effect of group (F(1,28) ¼ 0.76, p ¼ .392, Z groups were performing the task at a similar speed. The three-way interaction between group, set size and distractor congruency was not significant (F(3,84) ¼ 0.05, p ¼ .984,

  2

  < 0.01) indicating that the two groups were showing the same pattern of congruency

  Z p effects at the various set sizes (Figure 4). It appears that both groups displayed an effect of distractor type at the lower set sizes, which then disappeared as the perceptual load of the task increased.

  Post hoc t-tests (with Bonferroni–Holm adjustment to the significance level) confirmed that in both the comparison and ASD groups, there was only a significant effect of congruency at set size 1 (p < .01), indicating that participants were only processing the distractors at the low level of perceptual load.

  Discussion

  The findings from these experiments highlight how typical adults and high-functioning young adults with ASD differ in their attentional response to social distractor stimuli. The results for the typical adults were similar to those reported by the congruency effect of non-social distractors was eliminated at high levels of perceptual load whereas face distractors were processed at all set sizes. In contrast, the individuals with ASD showed the same pattern of results with both the face and non-social distractors. Congruency effects were evident for both faces and instruments at low set sizes but when perceptual load increased there was no evidence of distractor interference for either type of distractor.

  This finding provides evidence that faces do not capture attention in adults with ASD in the same manner as for typical adults. In mainstream attention research, the inability to ignore distractor faces at high levels of load is used in support of the idea that face processing

  Autism 16(1) is an automatic and mandatory process and is not subject to general capacity limits. The data presented here question whether faces play a special role in attention for individuals with ASD.

  The distractor faces were, however, not always ignored by the individuals with ASD; congruency effects in the ASD group at lower levels of perceptual load demonstrated that gender was processed from the distractor faces and that this influenced the time taken to classify the gender of the written name. We cannot completely rule out the possibility that the group difference was due to a tendency by the typical adults to make overt gaze shifts and fixations on the to-be-ignored faces in the high perceptual load conditions, as we did not collect eye tracking data. However, the task requirements to classify the written name as quickly as possible and to ignore the flanker stimuli meant participants were discouraged from shifting gaze from the central task toward the face. In addition, looks toward the face would be likely to afford a large reaction time cost and one would predict slower reaction times for performance on the central task in the typical adults if they were looking at the faces. In fact, inspection of the mean reaction times and the main effect of group in experiment 1 indicate that the ASD group had a slower reaction time than the typical adults at all set sizes. It would be interesting in future studies to use eye tracking data to examine directly whether there were gaze shifts toward the distractor faces in either group. It is unclear why reaction time was slower in the ASD group as the groups were matched for reading ability and IQ. One possibility is that the element of social processing involved in the central task, classifying the written name in terms of gender, meant the task was more difficult for participants with ASD. Even if this was the case, it would not explain the similarities and differences in the congruency effects at different set sizes compared with typical adults as the social demands of the central task did not change with set size.

  When we compared group performance on experiment 2 (non-social distractor stimuli), we found no difference between the groups in the size of the congruency effect at different levels of perceptual load. The congruency effect disappeared at similar (high) levels of perceptual load in both groups. What we have not shown here is evidence of increased perceptual capacity in adults with ASD when non-social stimuli are used as distractors, as might be predicted from our earlier study using a different methodology and by a more recent replication demonstrating increased perceptual capacity and conscious awareness of non-social distractors in adults with ASD (Remington et al., manuscript submitted).

  In these studies adults with ASD continued to process distractor letters and detect an expected critical stimulus (Remington et al., manuscript submitted) at moderately high levels of perceptual load (four items) when a comparison group of typical adults showed no distractor interference. This finding was interpreted as demonstrating that individuals with ASD have a higher perceptual capacity. The study also showed that at the highest level of perceptual load (six items), both clinical and comparison groups showed no distractor interference, suggesting that although perceptual capacity is higher in individuals with ASD there is still a limit at which capacity is exhausted and distractors are not processed. Although the current study uses a slightly different methodology it is interesting to compare the results presented here with those from the earlier studies and consider why, on the non-social control task, no difference in distractor processing at higher levels of perceptual load was evident between the two groups. One possibility is that the current methodology does not re- create the appropriate level of perceptual load to differentiate group performance; that perceptual load increases from low to high too rapidly to reveal the increased capacity in Remington et al. ASD. If this is the case it is even more impressive that the typical adults continued to process distractor faces in experiment 1, regardless of perceptual load.

  In typical adults, the finding that face distractors are processed irrespective of perceptual load has been interpreted as evidence for the existence of a separate processing capacity for faces Further support for this idea is provided by a study showing that only the addition of other face stimuli, and not additional non-face distractors, can dilute the congruency effect However, as these authors point out, it is possible that the separate capacity in typical adults is not innately assigned for faces but becomes face-specific because of the strong interest in faces in typical development and the resultant expertise. That is, the separate processing store may be reserved for any specialized interest, which in typical adults is social stimuli, but in ASD may be a non-social object category such as types of trains or buildings. Given that individuals with ASD often have an unusually strong, if not obsessive interest in a specific category of non-social objects, it would be interesting to examine whether a separate processing capacity exists for exemplars of a category of interest, with distractor processing continuing at high levels of perceptual load. This would be one way to test whether the lack of special status for faces in attention in individuals with ASD is a result of the lack of experience attending to faces, something which is clear from early in development and throughout childhood (e.g.

  Why is there no separate capacity for faces in adults with ASD? We speculate here that the special status in attention for faces in typical adults could be the result of an innate tendency to preferentially orient to others (and especially faces) and to build up experience and expertise with social stimuli. There is now growing evidence that infants and young children with ASD do not preferentially orient toward social stimuli (e.g. and do not therefore gather as much experience with faces. The existence of a separate capacity for an alternative category of expertise in individuals with ASD would provide some support for this view.

  Acknowledgements

  

This research was supported by a departmental studentship awarded to Anna Remington from the

Department of Developmental Science, University College London. We thank Elizabeth An˜ez for her

invaluable advice regarding the statistics and we gratefully acknowledge the support of all those who

took part in the study.

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