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Reduced Reliance on Optimal FacialInformation for Identity Recognition inAutism Spectrum DisorderHayley C. Leonard a , Dagmara Annaz b , Annette Karmiloff-Smith a

& Mark H. Johnson aa Birkbeck, University of London, United Kingdomb Birkbeck, Middlesex University, United KingdomAccepted author version posted online: 23 Apr 2012.Version ofrecord first published: 18 Mar 2013.

To cite this article: Hayley C. Leonard , Dagmara Annaz , Annette Karmiloff-Smith & Mark H. Johnson(2012): Reduced Reliance on Optimal Facial Information for Identity Recognition in Autism SpectrumDisorder, Journal of Cognition and Development, DOI:10.1080/15248372.2012.664592

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ARTICLE

Reduced Reliance on Optimal Facial Information forIdentity Recognition in Autism Spectrum Disorder

Hayley C. Leonard

Birkbeck, University of London, United Kingdom

Dagmara Annaz

Birkbeck, Middlesex University, United Kingdom

Annette Karmiloff-Smith and Mark H. Johnson

Birkbeck, University of London, United Kingdom

Previous research into face processing in autism spectrum disorder (ASD) has revealed atypical

biases toward particular facial information during identity recognition. Specifically, a focus on fea-

tures (or high spatial frequencies [HSFs]) has been reported for both face and nonface processing in

ASD. The current study investigated the development of spatial frequency biases in face recognition

in children and adolescents with and without ASD, using nonverbal mental age to assess changes in

biases over developmental time. Using this measure, the control group showed a gradual specializa-

tion over time toward middle spatial frequencies (MSFs), which are thought to provide the optimal

information for face recognition in adults. By contrast, individuals with ASD did not show a bias to

one spatial frequency band at any stage of development. These data suggest that the ‘‘midband bias’’

emerges through increasing face-specific experience and that atypical face recognition performance

may be related to reduced specialization toward optimal spatial frequencies in ASD.

INTRODUCTION

Face Processing in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a pervasive neurodevelopmental disorder diagnosed on the

basis of impaired development of social interaction and communication, as well as markedly

Correspondence should be sent to Hayley C. Leonard, Center for Brain and Cognitive Development, Birkbeck,

University of London, Malet Street, London, WC1E 7HX, United Kingdom. E-mail: h.leonard@gold.ac.uk

JOURNAL OF COGNITION AND DEVELOPMENT, 0(0):1–13

Copyright # 2013 Taylor & Francis Group, LLC

ISSN: 1524-8372 print=1532-7647 online

DOI: 10.1080/15248372.2012.664592

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restricted activities and interests (Diagnostic and Statistical Manual of Mental Disorders-Fourth

Edition, Text Revision [DSM-IV-TR]; American Psychiatric Association, 2000). Due to the cru-

cial role of faces in the social contexts in which those with ASD are particularly impaired

(Joseph & Tager-Flusberg, 2009), face processing has received a great deal of attention in the

study of this neurodevelopmental disorder. Withdrawal from social situations was highlighted

in the first reports of childhood autism by Kanner (1943), and a lack of interest in faces and shar-

ing information can be traced back to early infancy through retrospective reports and videotapes,

as well as prospective studies of children at risk for developing ASD (Johnson, Frith, Siddons, &

Morton, 1992; see also Elsabbagh & Johnson, 2007, for a review).

In terms of the processing of face identity, individuals with ASD often fall below standar-

dized norms on tests of face recognition (Klin et al., 1999), possibly due to unusual strategies

for processing face stimuli. Indeed, atypical patterns of attention during face processing have

been reported in behavioral studies (e.g., Annaz, Karmiloff-Smith, Johnson, & Thomas, 2009;

Joseph & Tanaka, 2003; Langdell, 1978; Riby, Doherty-Sneddon, & Bruce, 2008a, 2008b)

and during eye tracking (e.g., Falck-Ytter, 2008; Klin, Jones, Schultz, Volkmar, & Cohen,

2002; Pelphrey et al., 2002; van der Geest, Kemner, Verbaten, & van Engeland, 2002). Faces

do not capture the attention of individuals with ASD in the same way as in typically developing

individuals (Riby & Hancock, 2009), and ASD has been characterized by reduced looking times

to people in general, and to faces in particular, in both static and dynamic social scenes (e.g.,

Klin et al., 2002; Riby & Hancock, 2008; Speer, Cook, McMahon, & Clark, 2007). Finally,

some, but not all, brain imaging studies have found slower and less specific neural responses

to faces in ASD compared with controls (Grice, Spratling, Karmiloff-Smith, Halit, Csibra, de

Haan, & Johnson, 2001; Humphreys, Hasson, Avidan, Minshew, & Behrmann, 2008;

McPartland, Dawson, Webb, Panagiotides, & Carver, 2004; Webb, Dawson, Bernier, &

Panagiotides, 2006).

Although the research reviewed here indicates atypical face processing in ASD, the reasons

underlying this atypicality remain unclear, and even less is known about emergence throughout

development. To shed light on these issues, in the current study we investigated the use of

different spatial frequency bands in face recognition in individuals with ASD and controls.

Spatial Frequency Biases in Face Recognition

It is only recently that spatial frequency biases in face recognition have been investigated in

ASD (e.g., Boeschoten, Kenemans, van Engeland, & Kemner, 2007; Deruelle, Rondan, Gepner,

& Tardif, 2004; Deruelle, Rondan, Salle-Collemiche, Bastard-Rosset, & Da Fonseca, 2008;

Leonard, Annaz, Karmiloff-Smith, & Johnson, 2011; Vlamings, Jonkmann, van Daalen,

van der Gaag, & Kemner, 2010). Different spatial frequencies correspond to varying levels of

detail in the visual environment, with low spatial frequencies (LSFs) generally thought to convey

information about the global shape and overall contours of visual stimuli, while HSFs carry

information about the more detailed features (Goldstein, 2009). In comparing spatial frequency

use for face recognition in ASD and controls, several studies have now reported a greater

reliance in ASD on HSFs as compared with an LSF bias in typically developing individuals

(e.g., Boeschoten et al., 2007; Deruelle et al., 2004, 2008; Vlamings et al., 2010). These results

are consistent with previous findings of more featural, detailed processing of faces and other

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visual stimuli in ASD (e.g., Frith, 2003). However, two methodological issues need to be

considered when interpreting the above research. First, the developmental dimension is missing:

No study has directly compared children and adults using the same experimental procedure,

making it unclear how spatial frequency biases might emerge during development. Second,

all of the studies included only LSFs and HSFs of the face stimuli presented, making it difficult

to assess any other spatial frequency bias that participants might have. This second issue turns

out to be important, because a large body of literature now suggests that although LSFs and

HSFs are useful and sometimes sufficient for face recognition (e.g., Fiorentini, Maffei, &

Sandini, 1983; Halit, de Haan, Schyns, & Johnson, 2006) in adults, the optimal band for face

recognition consists of MSFs (between 8 and 24 cycles per face; Costen, Parker, & Craw,

1994; Hayes, Morrone, & Burr, 1986; Leonard, Karmiloff-Smith, & Johnson, 2010; see

Ruiz-Soler & Beltran, 2006, for a review). Furthermore, Leonard et al. (2010) found that in typi-

cal development, this ‘‘midband bias’’ was actually rather late to develop, with 7- and 8-year-old

children still relying more on HSFs for face recognition than older children and adults. It is there-

fore possible that previous accounts of an HSF bias in ASD depend both on the age at which the

individuals were tested and on the lack of stimuli testing the midband bias. It is therefore critical

to investigate the use of MSFs for face recognition in ASD within a developmental context.

This point was addressed by Leonard et al. (2011), who found a surprisingly similar pattern of

developing biases for face recognition over chronological age in a developmental comparison of

individuals with ASD and typically developing controls. However, chronological age may not

accurately reflect the level of functioning of an individual with ASD, as they can be develop-

mentally delayed even in the relatively stronger domain of visuospatial processing (e.g., Joseph,

Tager-Flusberg, & Lord, 2002). For this reason, the current study tracked spatial frequency

biases for face recognition in relation to nonverbal mental age, with the implication that those

with lower nonverbal mental ages will have reduced face-specific experience because they are

younger (as in the controls), or because they are lower-functioning children with ASD, who

show reduced looking time to faces compared with their relatively high-functioning counterparts

(Riby & Hancock, 2009). The analyses presented in this article thus assess how variance in men-

tal age affected spatial frequency biases for face recognition, rather than controlling for this vari-

ance through chronological age matching. Data from inverted faces were also analyzed and

provided a stimulus with which neither group would have much experience (see Leonard

et al., 2010). In line with previous findings, it was predicted that the control group would show

a gradual decrease in the reliance on HSFs, resulting in an MSF bias by adolescence. If the

development of the midband bias for face recognition relies on increased experience with faces

in typically developing children (Leonard et al., 2010), the ASD group should not show a bias

toward MSFs for upright face recognition at any stage. In addition, based on previous work, it

was predicted that neither group should be biased toward MSFs for inverted faces.

METHODS

Participants

Thirty-two males (age range¼ 7;2–15;5) participated in the study in two separate groups.

Previous piloting in typically developing children indicated that testing participants younger than

FACE IDENTITY RECOGNITION IN ASD 3

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7 years of age resulted in a drastically increased dropout rate, which would likely be even greater

in children with autism. The control group consisted of 17 participants (mean chronological

age¼ 11;5; SD¼ 29 months; mean nonverbal mental age¼ 10;3; SD¼ 34 months), who had

no reported learning difficulties or clinical diagnoses. The remaining 15 participants (mean

age¼ 10;4; SD¼ 30 months; mean nonverbal mental age¼ 9;7; SD¼ 35 months) were in the

ASD group. All had a UK statement of special needs, with a primary diagnosis of ASD from

a trained psychiatrist or pediatrician, using established criteria from the DSM-IV-TR. Recent

research has yielded a high level of agreement between clinical and research diagnoses

(Mazefsky & Oswald, 2006). In line with other recently published studies (e.g., Franklin,

Sowden, Burley, Notman, & Alder, 2008; Williams & Jarrold, 2010), therefore, the official diag-

nosis from an experienced, trained clinician and the nonverbal and face recognition data col-

lected here were considered sufficient background information for the current report.

The two groups did not differ from each other on either chronological age, t(30)¼�1.64,

p¼ .11, or nonverbal mental age, t(30)¼�0.65, p¼ .52 (see the ‘‘Materials’’ section for expla-

nation of the measure of nonverbal mental age). As expected from previous research (e.g., Annaz

et al., 2009), the groups differed on the Benton Test of Facial Recognition (see the ‘‘Materials’’

section for details), with a significantly lower mean score in the ASD group (M¼ 18.33;

SD¼ 3.22) than in the control group (M¼ 20.82; SD¼ 2.72), t(30)¼�2.37, p¼ .02.

Materials

Each child was tested on a series of standardized and experimental tasks from Leonard et al.

(2011), including Raven’s Standard Progressive Matrices (Raven, Raven, & Court, 2000), which

was used as a measure of nonverbal mental age (NVMA) for both groups, and the Benton Test

of Facial Recognition (Benton, Hamsher, Varney, & Spreen, 1983). Raven’s Matrices are often

used for matching purposes in the literature (Mottron, 2004) and are appropriate for a wide age

range (Riby et al., 2008a). The Benton Test has also been widely used in children with and with-

out neurodevelopmental disorders in previous studies utilizing the developmental trajectory

approach (e.g., Annaz et al., 2009; Karmiloff-Smith et al., 2004; Thomas et al., 2009).

Both upright and inverted face stimuli were viewed by participants (see Figure 1 for exam-

ples). Only two face identities were presented to keep memory demands to a minimum for the

youngest children and for those with ASD. The upright face stimuli were adopted from a set pro-

duced by Nasanen (1999) and included the original unmasked face images and three masked

faces, in which a narrow band of spatial frequencies was masked by noise. One noise mask cover-

ing each of 8, 16, or 32 cycles per image was chosen, corresponding to 1.1, 2.2, and 4.4 cycles per

degree during presentation and representing LSF, MSF, and HSF masks, respectively. A further

‘‘training stimulus’’ was produced for the computerized task, with black bars (subtending 0.1� ofvisual angle) added to the face image using the Windows Paint program. Inverted face stimuli

were produced by rotation of the above face images by 180� in Adobe Photoshop. All face stimuli

subtended 7� � 7� of visual angle at the viewing distance of approximately 53 cm.

Procedure

Participants followed the ‘‘child procedure’’ outlined in Leonard et al. (2010), completing a

familiarization=training period with the face identities through a number of games before

4 LEONARD ET AL.

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beginning the computerized task. These games included both naming and memory tasks, for

which the child earned points for each correct answer. Once the main task began, trials were

blocked so that upright trials always preceded inverted trials. In both sets of trials, a test face

(either masked or unmasked) was presented, followed by the two original unmasked faces. Part-

icipants had to decide which of the two face identities had been presented on the test trial and

demonstrated their choice by pointing to the face on the screen. The positions of the ‘‘choice stim-

uli’’ (e.g., left or right) were counterbalanced, with the two face identities appearing equally often

on both sides of the screen. The duration of stimulus presentation depended on the age and group

membership of the participant: The ASD group and younger control children saw the target face

for 2 seconds, while control children older than 10 years of age saw the target face for 0.5 seconds.

Extensive piloting with control children revealed these to be the optimal exposure durations for

recognition of the target faces. The different durations did not affect the pattern of spatial fre-

quency biases found in previous testing of a group of 10-year-olds (see Leonard et al., 2010).

Piloting with children with ASD revealed that the very quick exposure was demotivating for them

as they found it too difficult and that the 2-second exposure ensured that they received at least an

equal amount of exposure to the face as the control group. In addition, when assessed by chrono-

logical age, individuals with autism presented a very similar pattern of results to the control group

using these different exposure durations (Leonard et al., 2011), suggesting that differences in spa-

tial frequency biases between the two groups in the current study are due to levels of functioning

or nonverbal mental age and are not due to the differences in target duration.

During the test trials, each of the spatial frequency masks was presented a total of 16 times

(8 times in upright trials, 8 times in inverted trials), with 16 unmasked faces randomly presented

throughout these trials to provide the baseline measure in each face orientation (producing a total

of 64 trials). Trials were initiated by the experimenter and began when participants were judged

to be attending to the fixation point. The experimenter recorded the participant’s answer by

pressing the appropriate button on a mouse attached to the computer. Upon finishing the com-

puterized task, participants completed the Raven’s Matrices and the short form of the Benton

Test. All participants were rewarded with a choice of stickers or school merit awards throughout

the procedure and received a certificate when all tasks were completed.

FIGURE 1 Examples of upright and inverted noise-masked face stimuli presented during the task in a) upright trials,

b) inverted trials, and c) choice trials. Note that the images illustrated are not the same size as the actual experimental

stimuli, and so the relative importance of the spatial frequency bands during the task may be different from the printed

stimuli presented here.

FACE IDENTITY RECOGNITION IN ASD 5

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RESULTS AND DISCUSSION

The mean and standard deviations of spatial frequency used for both groups are presented in

Table 1. Lower accuracy on the task represents greater use of that particular band for face rec-

ognition (i.e., participants relying on LSFs would find it more difficult when the LSFs were

masked and would therefore show lower accuracy for those stimuli). The use of each spatial fre-

quency can therefore be calculated by subtracting task accuracy from 100% (achieving 100%accuracy for the LSF mask would demonstrate that the LSF band was not being used in the task,

resulting in a ‘‘use’’ score of 0%). The data suggest that the mean use of each spatial frequency

differed more in upright than in inverted trials between groups. However, a mixed analysis of

variance with spatial frequency (LSF, MSF, HSF) mask and stimulus orientation (upright,

inverted) as within-subjects factors and group (ASD, control) as the between-subjects factor

revealed only a significant main effect of spatial frequency mask, F(2,60)¼ 18.74, p< .001,

g2p ¼ :4 (Greenhouse-Geisser corrected statistic reported). Post-hoc pairwise comparisons with

Bonferroni corrections revealed that this effect was due to significantly lower use of LSFs

(M¼ 8.32) compared with MSFs (M¼ 21.01) or HSFs (M¼ 27.44), p< .001. No other main

effects or interactions were significant (Fs< 3.80, ps> .06).

Although no significant differences between group means were found using this standard

approach, it is important to consider the effect of development on these spatial frequency biases

across the wide range of nonverbal mental ages studied. Cross-sectional developmental trajec-

tories are therefore presented throughout the rest of this section, using NVMA as a covariate

(see Thomas et al., 2009, for a more detailed explanation of this approach). A 3 (spatial fre-

quency: LSF, MSF, HSF)� 2 (orientation: upright, inverted)� 2 (group: ASD, control) mixed

analysis of covariance (ANCOVA) was first conducted on the upright and inverted data from

the two groups, with NVMA as covariate. The within-subjects effects are independent of the

covariate and will be reported from analyses excluding NVMA as a factor. Degrees of freedom

may therefore differ between main effects and interactions and between within- and

between-subjects factors (see Annaz et al., 2009, for an explanation). Greenhouse-Geisser

corrected statistics are reported where necessary due to violations of sphericity.

Analyses revealed that identity recognition was affected differently by the three spatial fre-

quency masks, F(2,60)¼ 18.74, p< .001, g2p ¼ :4, and by NVMA, F(1,28)¼ 4.80, p¼ .04,

g2p ¼ :1, but not by group membership, F(1,28)¼ 1.14, p¼ .29, g2p ¼ :04, or by orientation,

F(1,30)¼ 1.77, p¼ .19, g2p ¼ :1. The effect of spatial frequency mask differed with changing

NVMA, F(2,56)¼ 4.38, p¼ .02, g2p ¼ :1, and with group membership, F(2,56)¼ 4.54,

TABLE 1

Mean Percentage Use of Each Spatial Frequency Mask (and Standard Deviations) in Upright and Inverted

Conditions for Control and ASD Groups

Group

Upright Inverted

LSF MSF HSF LSF MSF HSF

Control 8.09 (10.77) 25.00 (20.25) 30.15 (18.78) 11.03 (18.16) 19.85 (20.28) 27.94 (19.02)

ASD 2.50 (7.01) 17.50 (13.19) 20.83 (19.29) 11.67 (13.75) 21.67 (22.39) 30.83 (22.09)

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p¼ .02, g2p ¼ :1, but not with orientation, F(2,60)¼ 0.82, p¼ .42, g2p ¼ :3. There was a signifi-cant three-way interaction between spatial frequency mask, NVMA, and group, F(2,56)¼ 4.77,

p¼ .01, g2p ¼ :1, suggesting that the use of particular spatial frequencies for identity recognition

changed with nonverbal mental age differently in the ASD and control groups. No significant

interaction was found between group and NVMA, F(1,28)¼ 2.07, p¼ .16, g2p ¼ :1.Although no main effect of orientation was found, there were significant interactions between

orientation and group, F(1,28)¼ 5.84, p¼ .02, g2p ¼ :1, and between orientation, spatial fre-

quency mask, and group, F(2,56)¼ 3.15, p¼ .05, g2p ¼ :1. There was a nonsignificant trend

between orientation, group, and NVMA, F(1,28)¼ 3.70, p¼ .07, g2p ¼ :1. No significant inter-

actions were found between orientation and NVMA, F(1,28)¼ 0.19, p¼ .67, g2p ¼ :01, or

between orientation, spatial frequency mask, and NVMA, F(2,56)¼ 0.13, p¼ .88, g2p ¼ :01,but there was a marginally significant interaction between all four factors, F(2,56)¼ 3.00,

p¼ .06, g2p ¼ :1. Examination of Figure 2 confirms the suggestion from these analyses that spa-

tial frequency masks affected identity recognition differently in the two groups for upright and

inverted faces and that these differences were further affected by nonverbal mental age. In

addition, inspection of within-subjects contrasts revealed a significant linear interaction between

FIGURE 2 The use of LSFs, MSFs and HSFs for upright and inverted face recognition in the control and ASD groups.

a) Upright trials and b) inverted trials in the control group. c) Upright trials and d) inverted trials in the ASD group. R2

values indicate the proportion of variance explained by each trajectory.

FACE IDENTITY RECOGNITION IN ASD 7

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the four factors, F(1,28)¼ 5.02, p¼ .03, g2p ¼ :2, suggesting that the significant four-way

interaction may have been masked in the initial analyses by increased variability in one or more

of the factors between the two groups (e.g., Annaz et al., 2009; Thomas et al., 2009). For both

these reasons, it was decided to conduct follow-up analyses within each group to clarify the

different patterns of spatial frequency biases suggested by these initial results.

Control Group

A 3 (spatial frequency mask: LSF, MSF, HSF)� 2 (orientation: upright, inverted)repeated-measures ANCOVA was conducted on the data from the control group, with NVMA

included as covariate. These analyses revealed a significant main effect of spatial frequency

mask, F(2,32)¼ 9.42, p¼ .001, g2p ¼ :4, but no other main effects reached significance

(Fs< 0.6, ps> .6). Although there was a significant interaction between spatial frequency mask

and NVMA, F(2,30)¼ 7.52, p< .01, g2p ¼ :3, the interaction between orientation and NVMA

and between all three factors did not reach significance (Fs< 2.3, ps> .1). However, inspection

of Figure 2 (a and b) suggests that the changing use of HSFs with nonverbal mental age did

differ between upright and inverted faces in this group. In particular, those with a higher NVMA

relied much less on HSFs for identity recognition than did those with a lower NVMA for upright

faces; this difference was not seen for inverted faces. Indeed, parameter estimates from

these analyses show that NVMA significantly predicted the use of HSFs (b¼�.39, SE¼ .10,

t¼�3.95, p¼ .001) and of LSFs (b¼�.17, SE¼ .07, t¼�2.54, p¼ .02) for upright faces,

but it was not a significant predictor of the use of MSFs (b¼ .22, SE¼ .14, t¼ 1.64, p¼ .12)

or for any spatial frequency in inverted trials (ps> .1). Linear regression analyses conducted

on these data, with NVMA as the independent variable and either LSF use or HSF use as the

dependent variable, revealed that nonverbal mental age accounted for a quarter of the variance

in the use of LSFs (adjusted R2¼ .25) and almost half the variance in the use of HSFs (adjusted

R2¼ .48).

The pattern of results in the control group therefore supported the hypothesis that a reliance

on HSFs would decrease over developmental time, resulting in the midband bias found pre-

viously among older children and adolescents (e.g., Leonard et al., 2010, 2011). Although some

effects may not have reached significance, possibly due to the small sample size, the pattern of

results is highly similar to that seen for the much larger sample presented in Leonard et al.

(2011). A greater bias toward the use of HSFs compared with either LSFs or MSFs was found

in children with a lower nonverbal mental age, and the use of MSFs did not change significantly.

Importantly, this suggests that younger children are just as capable of using the MSFs as are

older children and adults, but they are not biased toward this ‘‘optimal band’’ until later in devel-

opment (e.g., Leonard et al., 2010, 2011). While the use of LSFs also decreased significantly

with nonverbal mental age, a smaller proportion of the variance in LSF use was explained by

NVMA than in HSF use, and a bias toward LSFs for upright faces was not found at any point

in the developmental trajectory.

In addition, the use of HSFs did not decrease with increasing nonverbal mental age for

inverted faces, suggesting that HSFs, or more featural information, may be equally important

for the recognition of inverted faces throughout development. As there are fewer opportunities

to be exposed to inverted faces outside the laboratory, this finding lends support to the idea that

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the mid-band bias is specific to upright faces and may be the result of increasing experience with

faces in the environment. Individuals who spend less time looking at faces, therefore, are

unlikely to follow the same trajectory, with the development of the midband bias being delayed

or even absent. This hypothesis was tested with regard to ASD, given all the reports of reduced

focus on faces throughout development in this group.

ASD Group

The same analyses were conducted for the ASD data, yielding significant main effects of spatial

frequency mask, F(2,28)¼ 9.61, p¼ .001, g2p ¼ :4, orientation, F(1,14)¼ 7.19, p¼ .02, g2p ¼ :3,and NVMA, F(1,13)¼ 6.57, p¼ .02, g2p ¼ :3. None of the interactions between any of the fac-

tors was significant (Fs< 2.0, ps> .2). Once again, inspection of data in Figure 2 (c and d), and

of the parameter estimates from the analyses, suggests that the ASD group did show a different

pattern of changing biases for upright and inverted faces. In particular, only the use of HSFs

in the inverted trials changed significantly with nonverbal mental age (b¼�.42, SE¼ .13,

t¼�3.22, p¼ .01; adjusted R2¼ .40). While the relatively small sample size may once again

have limited the power of some of the effects presented here, these very different patterns of

changing biases across the two groups support the marginally significant result found in the

between-group comparisons presented earlier.

The pattern of findings for the ASD group therefore lends support to the hypothesis that the

development of the midband bias may be related to the amount of face-specific experience an

individual has, as a midband bias is not found in a group that spends less time looking at faces

than controls of the same age (e.g., Klin et al., 2002; Riby & Hancock, 2008; Speer et al., 2007).

Indeed, it seems that a reduced interest in faces could prevent individuals with ASD from spe-

cializing in any one spatial frequency band. As presented in Figure 2 (c and d), individuals with

a lower mental age in the ASD group were not biased to one specific spatial frequency band for

the upright faces, finding the MSF and HSF masks equally difficult, but they relied more on the

HSFs for recognizing the inverted faces. The performance of those with a higher mental age in

the ASD group, on the other hand, was close to ceiling for both upright and inverted trials. This

suggests that they were not reliant on the MSF band in the same way as the older control chil-

dren, as they could use any of the spatial frequency information remaining in the stimulus to

recognize the face. Individuals in the ASD group did not, therefore, demonstrate a bias toward

using the HSFs as suggested by previous research (e.g., Boeschoten et al., 2007; Deruelle et al.,

2004, 2008; Vlamings et al., 2010) or as might have been expected from the more featural pro-

cessing described in both face and object processing (e.g., Frith, 2003). However, the reduced

attention to faces present in ASD may explain the reduced specialization toward one spatial

frequency band (i.e., the midband) observed in the current experiment.

Further support for the role of face-specific experience in the development of the midband

bias is the fact that previous studies have found no differences in the contrast sensitivity func-

tions between control and ASD groups (e.g., Behrmann et al., 2006; de Jonge et al., 2007). This

suggests that the different biases in the current task may be related to the visual cognitive proces-

sing of faces, rather than to general spatial frequency processing differences in the ASD group.

However, a recent article reported reduced functional segregation between visual channels

responsible for processing HSFs and MSFs in adults with ASD compared with controls (Jemel,

FACE IDENTITY RECOGNITION IN ASD 9

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Mimeault, Saint-Amour, Hosein, & Mottron, 2010). This could potentially account for the lack

of a bias for upright faces in the current data for individuals with ASD, as the use of MSFs and

HSFs is very similar in upright trials.

We note, however, that reduced functional segregation between visual channels does not

explain the data from inverted faces in the current experiment. Specifically, compared with con-

trols, the changing use of MSFs and HSFs is very different in the ASD group for inverted faces,

with only the use of HSFs changing significantly with increasing mental age. In the current task,

the processing channels devoted to MSFs and HSFs thus do not appear to function as one unit,

despite the fact that the spatial frequency masks should trigger channels that are even closer

together than those in the study by Jemel and colleagues (2010; i.e., 2.2 and 4.4 cycles per

degree in the current experiment compared with 2.8 and 8 cycles per degree in the Jemel et al.

study). At least the low-functioning children with ASD, therefore, seem to have functionally seg-

regated MSF and HSF processing channels, and so a different explanation of the lack of spatial

frequency biases in this group is called for. It is possible that individuals with a low mental age

could be recruiting additional spatial frequency channels to process upright faces because they

find them more difficult compared with inverted faces, possibly due to additional demands in

coding and understanding facial expressions in this group (e.g., Adolphs, Sears, & Piven,

2001). In addition, individuals with a higher mental age may have failed to specialize toward

one spatial frequency band for upright faces because faces are not given priority status over other

stimuli in the environment (e.g., Klin et al., 2002; Riby & Hancock, 2009). A more direct way of

addressing this issue in the future would be to assess contrast sensitivity and spatial frequency

biases for face and nonface processing in a within-subject design, using a range of spatial

frequency masks for a more continuous measure of these biases.

To clarify the role of face perception and social information processing on the pattern of

results in the ASD group, it might also be useful in the future to include experimental measures

of looking time to faces (as in Riby & Hancock, 2009), as well as more standardized measures of

social behavior or ASD symptom severity that could better delineate the heterogeneity in the

ASD sample, such as the Social Communication Questionnaire (Rutter, Bailey, & Lord,

2003), in conjunction with the current measures. This will increase the generalizability of the

findings to the wider population of individuals with autism. It is important to note, however, that

the independent test of face recognition in the current study, the Benton Test of Face Recog-

nition (Benton et al., 1983), did reveal significantly poorer performance by the ASD group com-

pared with controls, and this difference was evident over both chronological and mental age. To

test whether this poorer performance was caused by atypical use of spatial frequency infor-

mation, or whether reduced looking time toward faces and poorer face identity discrimination

affected the spatial frequencies used for the task, it will be necessary to test younger children,

ideally using a longitudinal approach.

SUMMARY AND CONCLUSIONS

The current experiment examined the effects of nonverbal mental age on the development of

spatial frequency biases for face identity recognition in children and adolescents with and with-

out ASD. The results demonstrate very different patterns of developing biases for both upright

and inverted face recognition in controls and individuals with ASD. Typically developing

10 LEONARD ET AL.

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controls showed a decreasing use of HSFs for upright but not inverted face recognition,

eventually resulting in a midband bias for upright faces only, and thus supporting the view that

MSFs are optimal for expert face recognition (e.g., Costen et al., 1994; Hayes et al., 1986;

Leonard et al., 2010). Individuals with ASD, however, did not show a midband bias for either

upright or inverted faces at any age point. This result differs from the preliminary analyses pre-

sented in Leonard et al. (2011), in which the ASD group seemed to follow a similar developmen-

tal trajectory to the one shown by the control group with increasing chronological age. Although

this is correct, the current analyses using mental age reveal the critical importance of accounting

for differences between chronological age and mental age over cross-sectional developmental

trajectories. Our new result can be explained by the fact that in the ASD group, chronological

age turned out not to be a good predictor of scores on the Raven’s Standard Progressive Matrices

(Raven et al., 2000). The use of nonverbal mental age as a covariate in the current analyses thus

provides a much clearer picture of task performance over time in ASD and demonstrates the

importance of including a measure of development in the study of developmental disorders

(Karmiloff-Smith, 1998), as a standard group-matching approach did not reveal the subtle differ-

ences between groups in the current study. In addition, it seems that the different patterns

between groups is not due to reduced attention to the task or face memory difficulties in the

ASD group; all individuals with ASD passed the baseline level of recognition required to be

included in the final sample and performed close to ceiling level for at least one of the spatial

frequency bands. The contrasting patterns of development, therefore, are likely to be related

to differences in face recognition per se, rather than any task-specific difficulties.

To conclude, the current experiment has provided insight into the emergence of different

spatial frequency biases for face identity recognition and the role of face-specific experience

in the development of these biases by comparing two groups who show differing amounts of

attention to faces. Taken together, the results support previous work suggesting that the midband

bias develops because this range of spatial frequencies conveys the most diagnostic information

for recognizing faces and suggesting that pressure to achieve this end state may drive changes in

spatial frequency biases over developmental time. Individuals with reduced face-specific experi-

ence, however, do not develop a bias toward one particular spatial frequency band, thereby

reducing the typical processing advantages for faces over other objects in the environment.

ACKNOWLEDGMENTS

This research was supported by UK Medical Research Council Grant G0701484 (ID# 85031) to

the last author and by a UK Medical Research Council studentship to the first author.

We are grateful to Risto Nasanen and Topi Tanskanen for providing the stimuli and to

Michael Thomas for his advice and support. Our thanks go to the charities and schools that

helped with recruitment and all the parents and children who participated in the study.

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