Running Head: SPATIAL FREQUENCY BIASES FOR …epubs.surrey.ac.uk/811905/1/Leonard et al 2012_JCD_final... · Web viewRaven’s Matrices are often used for matching purposes in the

FACE IDENTITY RECOGNITION IN ASD 1

Running Head: FACE IDENTITY RECOGNITION IN ASD

Reduced reliance on optimal facial information for identity recognition in Autism

Spectrum Disorder

Word count: 5,198


Abstract

Previous research into face processing in Autism Spectrum Disorder (ASD) has

revealed atypical biases towards particular facial information during identity

recognition. Specifically, a focus on features (or high spatial frequencies) has been

reported for both face and non-face 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 non-verbal mental age to assess changes in

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

gradual specialisation over time towards middle spatial frequencies, 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 ‘mid-band bias' emerges through

increasing face-specific experience, and that atypical face recognition performance

may be related to reduced specialisation towards optimal spatial frequencies in ASD.

KEYWORDS: Face processing; spatial frequency; autism; development; experience


1. Introduction

1.1 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 restricted activities and interests (DSM IV-TR,

American Psychiatric Association, 2000). Due to the crucial 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 sharing information can be traced back to early infancy through

retrospective reports and videotapes, as well as prospective studies of children at-risk

of 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 standardised norms on tests of face recognition (Klin, Sparrow, de Bildt,

Cicchetti, Cohen & Volkmar, 1999), possibly due to unusual strategies for processing

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

reported in behavioural studies (e.g., Annaz, Karmiloff-Smith, Johnson, & Thomas,

2009; Joseph & Tanaka, 2003; Langdell, 1978; Riby, Doherty-Sneddon, & Bruce,

2008a, 2008b), and during eyetracking (e.g., Falck-Ytter, 2008; Klin, Jones, Schultz,

Volkmar, & Cohen, 2002; Pelphrey, Sasson, Reznick, Paul, Goldman & Piven, 2002;

van der Geest, Kemner, Verbatern, & van Engeland, 2002). Faces do not capture the

attention of individuals with ASD in the same way as in typically-developing controls


(Riby & Hancock, 2009), and ASD has been characterised 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 to 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 above indicates atypical face processing in

ASD, the reasons underlying this atypicality remain unclear, and even less is known

about emergence over development. To shed light on these issues, in the current

study we investigated the use of different spatial frequency bands in face recognition

in ASD and controls.

1.2 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, Salle-Collemiche, Bastard-Rosset, & Da Fonséca, 2008; Deruelle,

Rondan, Gepner, & Tardif, 2004; Leonard, Annaz, Karmiloff-Smith, & Johnson,

2011; Vlamings, Marthe, van Daalen, van der Gaag, Jan, & 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 high spatial frequencies

(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 to an LSF

bias in typically-developing controls (e.g., Boeschoten et al., 2007; Deruelle et al.,

2008, 2004; Vlamings et al., 2010). These results are consistent with previous

findings of more featural, detailed processing of faces and other 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 middle spatial frequencies (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, 2007, for a review).

Furthermore, Leonard et al. (2010) found that in typical development, this ‘mid-band

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 therefore

possible that previous accounts of a high spatial frequency bias in ASD depend both

on the age at which the individuals were tested and on the lack of stimuli testing the

mid-band bias. It is therefore critical to investigate the use of middle spatial

frequencies for face recognition in ASD within a developmental context.


The above 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 developmentally

delayed even in the relatively stronger domain of visuo-spatial processing (e.g.,

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

spatial frequency biases for face recognition in relation to non-verbal mental age, with

the implication that those with lower non-verbal 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 than

their relatively high-functioning counterparts (Riby & Hancock, 2009). The analyses

presented in this paper thus assess how variance in mental age affected spatial

frequency biases for face recognition, rather than controlling for this variance through

chronological age matching. Data from inverted faces were also analysed, providing 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 mid-band 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.

2. Methods


2.1 Participants

Thirty-two males (age range: 7 years 2 months – 15 years 5 months)

participated in the study in two separate groups. Previous piloting in typically-

developing children found that testing participants below 7 years resulted in a

drastically increased drop-out rate, which would likely be even greater in children

with autism. The control group consisted of seventeen participants (mean

chronological age: 11 years 5 months, SD: 2 years 5 months; mean non-verbal mental

age: 10 years 3 months, SD: 2 years 10 months), who had no reported learning

difficulties or clinical diagnoses. The remaining fifteen participants (mean age: 10

years 4 months, SD: 2 years 6 months; mean non-verbal mental age: 9 years 7

months, SD: 2 years 11 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 diagnosis from an experienced, trained clinician and the non-verbal and face

recognition data collected 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) = -1.64, p = .11, or non-verbal mental age, t(30) = -.65, p = .52 (see Materials

for explanation of the measure of non-verbal mental age). As expected from previous

research (e.g., Annaz et al., 2009), the groups differed on the Benton Test of Facial

Recognition (see Materials for details), with a significantly lower mean score in the


ASD group (M = 18.33; SD = 3.22) than in the controls (M = 20.82; SD = 2.72), t(30)

= -2.37, p = .02.

2.2 Materials

Each child was tested on a series of standardised and experimental tasks from

Leonard et al. (2011), including Raven’s Standard Progressive Matrices (Raven et al.,

2000), which was used as a measure of NVMA for both groups, and the Benton Test

of Facial Recognition (Benton, Sivan, 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 without neurodevelopmental disorders in

previous studies utilising the developmental trajectory approach (e.g., Annaz et al.,

2009; Karmiloff-Smith et al., 2004; Thomas, Annaz, Ansari, Scerif, Jarrold, &

Karmiloff-Smith, 2009).

Both upright and inverted face stimuli were viewed by participants (see

Figure 1 for examples). Only two face identities were presented in order 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 produced by Näsänen (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 covering

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 computerised task,

with black bars (subtending 0.1 degree of visual 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 x 7

degrees of visual angle at the viewing distance of approximately 53 cm.

[place Figure 1 about here]

2.3 Procedure

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

completing a familiarisation/training period with the face identities through a number

of games before beginning the computerised 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. Participants had to decide

which of the two face identities had been presented on the test trial, demonstrating

their choice by pointing to the face on the screen. The positions of the ‘choice stimuli’

(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 over

the age of ten 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 frequency

biases found in previous testing of a group of ten-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 two-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 chronological 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

spatial frequency biases between the two groups in the current study are due to levels

of functioning or non-verbal mental age and not due to the differences in target

duration.

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

(eight in upright trials, eight in inverted trials), with sixteen 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 computerised 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 with a certificate when all tasks were completed.

3. Results and Discussion

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

are presented in Table 1. Scores were calculated by subtracting task accuracy from

100% (i.e., 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


inverted trials between groups. However, a mixed analysis of variance (ANOVA)

with spatial frequency (SF: 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 SF mask, F(2,60) = 18.74, p < .001,

ηp2 = .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 (M = 8.32) than MSFs (M = 21.01) or HSFs (M =

27.44), p < .001. No other main effects or interactions were significant (Fs < 3.80, ps

> .06).

[place Table about here]

While 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 non-verbal mental ages studied.

Cross-sectional developmental trajectories 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(SF: LSF, MSF, HSF) x 2(Orientation:

upright, inverted) x 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 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 SF masks, F(2,60) = 18.74, p < .001, ηp2 = .4, and by NVMA, F(1,28) = 4.80, p

= .04, ηp2 = .1, but not by group membership, F(1,28) = 1.14, p = .29, ηp

2 = .04, or by

orientation, F(1,30) = 1.77, p = .19, ηp2 = .1. The effect of SF mask differed with

changing NVMA, F(2,56) = 4.38, p = .02, ηp2 = .1, and with group membership,

F(2,56) = 4.54, p = .02, ηp2 = .1, but not with orientation, F(2,60) = .82, p = .42, ηp

2

= .3. There was a significant three-way interaction between SF mask, NVMA and

group, F(2,56) = 4.77, p = .01, ηp2 = .1, suggesting that the use of particular SFs for

identity recognition changed with non-verbal mental age differently in the ASD and

control groups. No significant interaction was found between group and NVMA,

F(1,28) = 2.07, p = .16, ηp2 = .1.

Although no main effect of orientation was found, there were significant

interactions between orientation and group, F(1,28) = 5.84, p = .02, ηp2 = .1, and

between orientation, SF mask and group, F(2,56) = 3.15, p = .05, ηp2 = .1. There was a

non-significant trend between orientation, group and NVMA, F(1,28) = 3.70, p = .07,

ηp2 = .1. No significant interactions were found between orientation and NVMA,

F(1,28) = .19, p = .67, ηp2 = .01, or between orientation, SF mask and NVMA, F(2,56)

= .13, p = .88, ηp2 = .01, but there was a marginally significant interaction between all

four factors, F(2,56) = 3.00, p = .06, ηp2 = .1. Examination of Figure 2 confirms the

suggestion from these analyses that SF masks affected identity recognition differently

in the two groups for upright and inverted faces, and that these differences were

further affected by non-verbal mental age. In addition, inspection of within-subjects

contrasts revealed a significant linear interaction between the four factors, F(1,28) =

5.02, p = .03, ηp2 = .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 in

order to clarify the different patterns of spatial frequency biases suggested by these

initial results.

[place Figure 2 about here]

3.1 Control group

A 3(SF mask: LSF, MSF, HSF) x 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 SF mask,

F(2,32) = 9.42, p = .001, ηp2 = .4, but no other main effects reached significance (Fs <

.6, ps > .6). Although there was a significant interaction between SF mask and

NVMA, F(2,30) = 7.52, p < .01, ηp2 = .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 non-verbal 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 (b = -.39, SE = .10, t = -3.95, p

= .001), and of LSFs (b = -.17, SE = .07, t = -2.54, p = .02) for upright faces, but was

not a significant predictor of the use of MSFs (b = .22, SE = .14, t = 1.64, p = .12) or

for any SF 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 non-verbal mental age accounted for a quarter of the

variance in the use of LSFs (Adj. R2 = .25) and almost half the variance in the use of

HSFs (Adj. R2 = .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 mid-

band bias found previously in older children and adolescents (e.g., Leonard et al.,

2011; Leonard et al., 2010). 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 to either LSFs or MSFs was found in

children with a lower non-verbal 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 are not biased towards this

‘optimal band’ until later in development (e.g., Leonard et al., 2011; Leonard et al.,

2010). While the use of LSFs also decreased significantly with non-verbal mental age,

a smaller proportion of the variance in LSF use was explained by NVMA than in HSF

use, and a bias towards 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 non-verbal

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 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 mid-band 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.

3.2 ASD group

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

effects of SF mask, F(2,28) = 9.61, p = .001, ηp2 = .4, orientation, F(1,14) = 7.19, p

= .02, ηp2 = .3, and NVMA, F(1,13) = 6.57, p = .02, ηp

2 = .3. None of the interactions

between any of the factors 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,

suggest 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 non-verbal mental age, b = -.42, SE = .13, t = -3.22, p = .01; Adj. R2

= .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 mid-band bias may be related to the amount of

face-specific experience an individual has, as a mid-band bias is not found in a group

that spend 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 specialising to 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 SF band for the upright


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

for recognising 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 children, as they could use any of the SF information

remaining in the stimulus to recognise 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., 2008, 2004; Vlamings et al.,

2010), or as might have been expected from the more featural processing described in

both face and object processing (e.g., Frith, 2003). However, the reduced attention to

faces present in ASD may explain the reduced specialisation toward one SF band (i.e.,

the mid-band) observed in the current experiment.

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

the mid-band bias is the fact that previous studies have found no differences in the

contrast sensitivity functions between control and ASD groups (e.g., Behrmann,

Avidan, et al., 2006; De Jonge, Kemner, de Haan, Coppens, van den Berg, & van

Engeland, 2007). This suggests that the different biases in the current task may be

related to the visual cognitive processing 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 to controls (Jemel,

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 to controls 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 (i.e., 2.2 and 4.4 cycles per degree in

the current experiment, compared to 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-segregated middle and high spatial frequency processing channels, and

so a different explanation of the lack of SF biases in this group is called for. It is

possible that individuals with a low mental age could be recruiting additional spatial

frequency channels in order to process upright faces because they find them more

difficult than 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 specialise

towards one SF 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 non-face processing in a within-

subject design, using a range of spatial frequency masks for a more continuous

measure of these biases.

In order 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 future to include


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

as more standardised 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 and Lord, 2003) in conjunction with

the current measures. This will increase the generalisability 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

Recognition (Benton et al, 1983) did reveal significantly poorer performance by the

ASD group than controls, and this difference was evident over both chronological and

mental age. In order to test whether this poorer performance was caused by atypical

use of spatial frequency information, or whether reduced looking time towards 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.

4. Summary and Conclusions

The current experiment examined the effects of non-verbal mental age on the

development of spatial frequency biases for face identity recognition in children and

adolescents with and without ASD. The results demonstrate very different patterns of

developing biases for both upright and inverted face recognition in controls and ASD.

Typically-developing controls showed a decreasing use of high spatial frequencies for

upright but not inverted face recognition, eventually resulting in a mid-band bias for

upright faces only, and thus supporting the view that middle spatial frequencies 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 mid-band bias


for either upright or inverted faces at any age point. This result differs from the

preliminary analyses presented in Leonard et al. (2011), in which the ASD group

seemed to follow a similar developmental trajectory to the one shown by the control

group with increasing chronological age. While 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 non-verbal mental age as a covariate in the

current analyses thus provides a much clearer picture of task performance over time in

Autism Spectrum Disorder, and demonstrates the importance of including a measure

of development into the study of developmental disorders (Karmiloff-Smith, 1998), as

a standard group matching approach did not reveal the subtle differences 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 mid-band bias develops because this range of


spatial frequencies conveys the most diagnostic information for recognising faces, and

that pressure to achieve this end state may drive changes in spatial frequency biases

over developmental time. Individuals with reduced face-specific experience, however,

do not develop a bias towards one particular spatial frequency band, thereby reducing

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

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Table 1


Mean percentage use of each spatial frequency mask (and standard deviations) in

upright and inverted conditions for control and ASD groups

GroupUpright Inverted

LSF MSF HSF LSF MSF HSF

Control8.09

(10.77)

25.00

(20.25)

30.15

(18.78)

11.03

(18.16)

19.85

(20.28)

27.94

(19.02)

ASD2.50

(7.01)

17.50

(13.19)

20.83

(19.29)

11.67

(13.75)

21.67

(22.39)

30.83

(22.09)

Figure Captions


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

to the printed stimuli presented here.

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.


Fig. 1


Fig.2


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