Psychiatry Research 189 (2011) 292–298

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Psychiatry Research

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Visual scanpath abnormalities in 22q11.2 deletion syndrome: Is this a facespecific deficit?

Kathryn McCabe a,b, Dominique Rich a,b, Carmel Maree Loughland a,b,Ulrich Schall a,b,c, Linda Elisabet Campbell a,b,c,⁎a Schizophrenia Research Institute, 405 Liverpool St Darlinghurst NSW 2010, Australiab Centre for Brain and Mental Health Research, University of Newcastle, McAuley entre, Edith Street, Waratah, NSW 2298, Australiac Hunter Medical Research Institute, Locked Bag 1, Hunter Region Mail Centre NSW 2310, Australia

⁎ Corresponding author at: CBMHR, University of New2300, Australia. Tel.: +61 2 40335735; fax: +61 2 403

E-mail addresses: Kathryn.mccabe@newcastle.edu.auDominique.rich@newcastle.edu.au (D. Rich), Carmel.lou(C.M. Loughland), Linda.e.campbell@newcastle.edu.au (

0165-1781/$ – see front matter © 2011 Elsevier Irelanddoi:10.1016/j.psychres.2011.06.012

a b s t r a c t

a r t i c l e i n f o

Article history:Received 1 September 2010Received in revised form 27 May 2011Accepted 15 June 2011

Keywords:Velo-cardio-facial syndrome (VCFS)Face processingVisual information processing

People with 22q11.2 deletion syndrome (22q11DS) have deficits in face emotion recognition. However, it isnot known whether this is a deficit specific to faces, or represents maladaptive information processingstrategies to complex stimuli in general. This study examined the specificity of face emotion processingdeficits in 22q11DS by exploring recognition accuracy and visual scanpath performance to a Faces taskcompared to a Weather Scene task. Seventeen adolescents with 22q11DS (11=females, age=17.4) and 18healthy controls (11=females, age=17.7) participated in the study. People with 22q11DS displayed anoverall impoverished scanning strategy to face and weather stimuli alike, resulting in poorer accuracy acrossall stimuli for the 22q11DS participants compared to controls. While the control subjects altered theirinformation processing in response to faces, a similar change was not present in the 22q11DS group indicatingdifferent visual scanpath strategies to identify category within each of the tasks, of which faces appear torepresent a particularly difficult subcategory. To conclude, while this study indicates that people with22q11DS have a general visual processing deficit, the lack of strategic change between tasks suggest that the22q11DS group did not adapt to the change in stimuli content as well as the controls, indicative of cognitiveinflexibility rather than a face specific deficit.

castle, Po Box 833, Newcastle35692.(K. McCabe),

ghland@newcastle.edu.auL.E. Campbell).

Ltd. All rights reserved.

© 2011 Elsevier Ireland Ltd. All rights reserved.

1. Introduction

Velocardiofacial syndrome (VCFS), also widely known as 22q11.2deletion syndrome (22q11DS), is associated with a deletion on thelong arm of chromosome 22 and is the most common microdeletiondisorder with an estimated prevalence of 1 in 4,000 live births(Oscarsdottir et al., 2004). 22q11DS is characterised by cardiac andpalatal anomalies, facial dysmorphology, intellectual disabilities andcognitive deficits in multiple domains including, but not limited to,memory, attention and areas related to social cognition (Gerdes et al.,1999; Scherer et al., 1999, 2001; Glaser et al., 2002). Moreover,22q11DS appear to be a risk factor for psychiatric disorders such asautism spectrum disorders (Vorstman et al., 2006), attention deficithyperactivity disorder (Gothelf et al., 2007), affective disorders(Green et al., 2009) and schizophrenia-like psychotic disorders(Murphy et al., 1999).

Over the last decade there has been an increase in research focusedon the cognitive profiles of people with 22q11DS. Specifically, there hasbeen a growing interest in their perceptual and social cognition abilitiesdue to the poor social functioning experienced by these individuals. Onesuch area is face processing, and while disturbances are commonlyobserved in other developmental disorders including autism (Philip etal., 2010) and schizophrenia (Loughland et al., 2004) face processinghave only recently been investigated in 22q11DS. In a studybyCampbelland colleagues, children with 22q11DS were observed to perform notonly more poorly than healthy controls (Campbell et al., 2011) but theyalso performed poorer than developmentally matched children withWilliamssyndrome(Campbell et al., 2009) on faceprocessing tasks. Thiswas observed across three face matching-tasks involving identity,emotional expression and gaze direction. In addition, we have recentlyshown that children with 22q11.2 have more difficulties with visualmemory retention of faces compared to dots and to verbal memory(Campbell et al., 2010). More recently, our group has utilised visualscanpath technology to record the eye movements of individuals with22q11DS while they view face stimuli so as to further investigate theneurocognitive strategies these individuals employ while viewing andinterpreting facial information. This research showed that participantswith 22q11DS, compared with healthy controls, display atypical visualscanpath patterns, characterized by fewer fixations, shorter raw

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scanpath length (a ‘staring’ pattern), and more fixations focused on themouth region or to irrelevant (i.e., ears, hair) areas of the face, ratherthan salient facial regions (eyes). Abnormal visual scanning in the22q11DS group was associated with poorer emotion recognitionaccuracy relative to the control group (Campbell et al., 2010), suggestingthat more impoverished scanning is associated with greater faceprocessing deficits. Similar patterns of visual scanning strategies haverecently been reported in a related study investigating featural andconfigural facial processing in a sample of adolescents with 22q11DS(Glaser et al., 2010). The authors report that whilst accuracy inrecognising emotions did not differ significantly between the clinicaland typically developing group, the 22q11DS group spent more timefixating on the mouth region and less time fixating at the eyes. Further,evidence from functional neuroimaging studies revealed atypical neuralactivation to faces relative to pictures (i.e., houses) in people with22q11DS (van Amelsvoort et al., 2006; Andersson et al., 2008).

Cumulatively, these findings suggest a face-specific, rather than ageneralised deficit in visual information processing (Andersson et al.,2008). However, face processing is a complex higher-order skill thatmay be mediated by disturbances in other cognitive domains that mayin turn be affected by the deletion of chromosome22q11.2. For instance,research findings reveal that children with 22q11DS perform consis-tently poorer on visual memory tasks, in particular visual-spatialmemory, compared with verbal memory (Wang et al., 2000; Beardenet al., 2001;Woodin et al., 2001; Campbell et al., 2010),with earlierworkby Swillen and colleagues (Swillen et al., 1997) showing that childrenwith22q11DShaveweaker, but not deficient, visual-perceptual skills. Inaddition, it has been reported that children (Campbell, 2006) and adults(Henry et al., 2002) with 22q11DS display deficits in visual discrimi-nation of objects, especially in tasks that require the recognition ofobjects from unusual views. Hence, it is important to explore thepossibility that the reported deficits in facial processing are not simplydue to a general visual processing dysfunction.

When investigating the specificity of face emotion processingdysfunction in other groups (e.g. schizophrenia), performanceaccuracy on tasks using facial stimuli have been compared toperformance on various non-face control stimuli. Holmes (Holmeset al., 2005) and Shin (Shin et al., 2008) used chair discrimination as acontrol task for emotion discrimination; while Clark et al.(2008)examined face emotion recognition abilities relative to a non-emotional landscape categorization test. Cars, scrambled cars,butterflies and hands have also been used as control stimuli toinvestigate the specificity of face emotion dysfunction (Allison et al.,1994a,b; Johnston et al., 2008). However, whilst emotion recognitionability and information processing strategies (indexed using scan-paths) associated with facial processing in these groups have beenwell documented there remains a lack of consensus regarding thespecificity of these deficits. One reason for this is the lack ofappropriate control tasks, since they rarely include stimuli of similarcomplexity and dimensionality that also share the social relevanceinherent to facial stimuli (Morrison et al., 1988; Mandal et al., 1998;Edwards et al., 2002; Marwick & Hall, 2008).

Given that efficient visual processing skills are critical for cognitionand learning, and that these deficits have far reaching academic andsocial consequences, visual processing deficits in 22q11DS warrantsfurther systematic investigation. The aim of the current study is toexamine the specificity of face processing deficits in 22q11DS. To do thiswe recorded eye movements (visual scanpaths) while participantsviewed complex visual stimuli, both faces and other socially relevant,non-face stimuli. For this purpose, aWeather Scene taskwas constructedof images depicting seven weather scenarios with three levels of taskdifficulty (i.e., easy, moderate and hard) (see methodology section for adescription). This research will extend previous efforts by investigatingfor the first time the specificity of previously identified face processinganomalies in people with 22q11DS while recording concurrently theinformation processing strategies used to do this (using scanpaths).

Based on previous findings in other developmental disorders withsocial cognition impairments (i.e., schizophrenia), we expect that the22q11DS group will display face-specific deficits, as demonstrated bydifferential group performance on the Faces andWeather Scene Taskscompared with typically developing controls. We predict that thisdifferential performance will be reflected both in terms of thescanpath strategies employed (i.e., more impoverished visual scan-path performance) and in reduced recognition accuracy performanceto face verse weather scene stimuli.

2. Materials and Methods

2.1. Participants

The 22q11DS group consisted of 18 adolescents (7 males, 11 females) withgenetically confirmed 22q11DS, recruited through the Velo-Cardio-Facial SyndromeFoundation of New South Wales and local Hunter Area Health services. 22q11DSparticipants were aged between 12 and 21 years, with a mean age of 17.44 years(SD=3.09) and a mean full scale IQ of 72.11 (SD=12.99). The control group consistedof 17 typically developing controls that were age (mean age=17.65 years; SD=2.99)and gender-matched (7 males, 11 females) to the 22q11DS group. Controls wererecruited from the community (n=12) and healthy siblings (n=6) of the 22q11DSparticipants. The mean full scale IQ of the control group was 107.94 (SD=18.86).

Control participants with a history of past or present neurological or Axis 1psychiatric disorders were excluded from the study. Further, control subjects who hadeither a family or personal history of a psychotic illness, or a history of mentalretardation/ developmental delay were excluded from the study. Exclusion criteria forthe 22q11DS and control group included uncorrected visual impairments, the presenceof anymedical disorder known to affect brain structure (e.g. epilepsy), a history of headinjury or stroke and any other ocular or neurological problems. Seven individuals with22q11DSwere identified as having (or having had) psychiatric diagnoses such as ADHD(n=3), oppositional defiant disorder (n=1), generalized anxiety disorder (n=2),obsessive compulsive disorder (n=3), trichotillomania (n=1). Four individuals werecurrently on medications including antipsychotics such as risperidone (n=2), moodstabilizers such as valproate (n=2), methylphenidate (n=1), and SSRIs (n=2).

Approval for the study was granted by the local ethics committee. Threeparticipants with 22q11DS were excluded due to scanning problems caused by hoodedupper eyelids and one participant was excluded due to an inability to sit still during thescanning. One control participant was also excluded due to the presence of severemental health problems. Participants over 18 gave written informed consent toparticipate and, for those participants younger than 18 years, parental consent wasobtained.

2.2. Experimental tasks

2.2.1. Assessment of intellectual functioningWechsler Abbreviated Scale of Intelligence (WASI; (Wechsler, 1999) was used to

assess general intellectual functioning of the participants. Performance IQ, Verbal IQand full scale IQ were calculated.

2.2.2. Visual scanning

2.2.2.1. Apparatus. Visual scanpath technology was used to record each individual's eyemovements whilst viewing the experimental stimuli. The optical assembly consisted ofa low-level infrared light and camera that recorded (1000 Hz sampling frame) theretinal and corneal reflections produced by the infrared light (Eyelink 1000). Thescanpath data collected in this study included temporal scanpath measures (fixationcount and average fixation duration) and spatial scanpath measures (raw scanpathlength and average distance between fixations).

2.2.2.2. Faces task. Stimuli for the Faces Task were obtained from the NimStim faceemotion database (Tottenham et al., 2009). The stimuli consisted of 28 colour images(506×624 pixels) of male and female actors expressing seven emotions (happy, sad,surprised, disgusted, afraid, angry and neutral, see Fig. 1 and for colour images, seeonline supplementary material 1). The stimuli were selected on the basis of the highestinter-rater reliability of all the stimuli in the database.

2.2.2.3. Weather scene task. Stimuli for the Weather Scene Task were obtained fromthe Weather Condition Stimuli Database (McCabe, 2011, unpublished thesis). Thestimuli used consisted of 35 colour images (506×624 pixels) of both city and ruralscenes depicting seven different weather conditions (sunny, cloudy, stormy, rainy,foggy, snowy and windy; see Fig. 2 and for colour images, see online supplementarymaterial 2). The Weather Scene Task was designed to be comparable to the FacesTask with regards to complexity (within category variation of three levels ofdifficulty: easy, moderate and hard, see Fig. 3 and for colour images, see onlinesupplementary material 3), dimensionality (face and weather task contained 7response options) and social relevance. While the unique spatial arrangement offaces makes it impossible to fully replicated in control tasks such as the Weather

Fig. 1. Examples of facial emotional stimuli from the NIMSTIM battery. From left; angry, disgust, fear, happy, neutral, sad, surprise (Tottenham et al., 2009).

Fig. 2. Examples of weather stimuli. From left; rainy, windy, stormy, foggy, snowy, cloudy, sunny.

Fig. 3. Examples of weather stimuli organised by level of difficulty from hard–moderate–easy.

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Scene Task, both face and weather scene stimuli were matched on number offeatural components. The Weather Scene stimuli were also selected on the basis ofthe highest inter-rater reliability within each category. (For demonstration of thestimuli, see online supplementary material 4.)

2.3. Procedure

Tasks were counterbalanced to control for order effects. Emotion recognitionaccuracy and scanpath performance were measured concurrently for the Faces task andrecognition accuracy and scanpath performance for the weather scenes was measuredconcurrently for the Weather Scene task. Participants were seated 60 cm from thecomputer display screen, which was linked to an auxiliary PC from which theexperimenter ran the task and monitored the participant's eye movements. Theparticipant's head was positioned in a soft head rest to minimize movement. Prior toeach task, calibration and validation tasks were conducted using a 9-point matrix to

ensure a point of fixation error rate of less than 0.5°. For the two tasks stimuli werepresented for 6 seconds each, followed by the list of response options that werepresented in the same order horizontally across the screen after each image. The nextimage was cued when participants fixated on a centrally located dot (3 cm diameter)presented on the computer screen for more than 1000 ms. Recognition accuracy wasthe number of correctly identified emotions or weather conditions from the 35 stimuliused in each task.

2.4. Analysis

Independent samples t-tests were conducted to examine demographic andintellectual functioning (as measured by the WASI) between the groups. Emotionrecognition accuracy data and scanpath performance data were examined using themixed between-within subjects ANOVAs to determine if there were significant group-by-task interactions. Bonferroni adjustments were made to account for multiple

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comparisons. Threshold for significance was set as pb0.01 for the major analyses.Correlational analyses were conducted to explore potential relationships betweenpoorer intellectual functioning and performance accuracy on the recognition tasks.Scanpath parameters consisted of both spatial (raw scanpath length, and averagedistance between fixations) and temporal indices (fixation count, average fixationduration).

Full scale IQ score (reported WASI scores) was omitted from analysis as a covariatein the repeated measures design as reported IQ differences were greater than twostandard deviations between the groups andwas therefore considered a group definingcharacteristic.

3. Results

3.1. Demographic data

The data analysis was conducted on 18 participants with 22q11DSand 17 control participants. Means and standard deviations of thedemographic data are presented in Table 1. Independent-samples t-testsand chi square compared groups on age, gender and intellectualfunctioning with no difference on age (t (33)=−0.191, p=0.85) orgender (p=1.0). The groups differed on the measure of intellectualfunctioning (F(1,32)=42.12, pb0.001, eta squared=0.57) with the22q11DS group performing approximately 2 standard deviationsbelow mean.

3.2. Weather scene task by difficulty level (easy–moderate–hard)

3.2.1. Recognition accuracyThe means and standard deviations for the recognition accuracy

scores on the Weather Scene Task by level of difficulty are presented

Table 2Means and standard deviations of the scanpath parameters and recognition accuracyscores for the weather task and the Faces task for both groups.

Weather task Faces task

22q11DS Control 22q11DS Control

Mean (SD) Mean (SD) Mean (SD) Mean (SD)

ScanpathparametersFixation count 16.81(2.55) 18.21(2.52) 16.42(2.71) 19.95(1.92)Average fixationduration (ms)

322.86(78.25)

308.76(56.63)

337.67(80.19)

283.60(39.85)

Raw scanpathlength (mm)

867.74(157.92)

964.47(207.62)

652.22(148.03)

775.36(147.39)

Average distancebetween fixations(mm)

55.11(8.78) 55.64(10.79) 41.39(6.16) 40.44(6.38)

Recognitionaccuracy score

25.67(3.94) 29.76(1.99) 22.33(3.36) 27.59(3.08)

Easy level 6.4 (1.2) 7.4 (1.2)Medium level 6.2 (1.1) 7.6 (0.9)Difficult levels 13.0 (2.6) 14.8 (1.0)

Table 1Means and standard deviations (SD) of demographic data (age, IQ, gender) for 22q11DSand control groups.

Condition Mean SD

Age 22q11DS 17.44 3.09Control 17.65 3.18

IQ⁎⁎ 22q11DS 72.11 12.99Control 108.44 19.36

Females Males

Gender 22q11DS 11 7Control 10 7

⁎⁎Significant at pb0.001.

in Table 2. There was a statistically significant main effect of accuracy,F(2,32)=29.18, pb0.0005, Wilks' Lambda=0.35, partial etasquared=0.65, and group, F(1,33)=16.15, pb0.0005, partial etasquared=0.33, with lower accuracy on harder items and with the22q11DS group having lower accuracy across all levels of difficultycompared with controls. No Group-By-Stimulus difficulty interactionwas identified (pN0.05).

3.2.2. Scanpath parameters

3.2.2.1. Temporal measures. Fixation count: There was a trend leveleffect for stimulus difficulty (p=0.062) but no main effect of group orGroup-By-Stimulus difficulty interaction present (pN0.05).

Fixation duration: There was no significant main effect of stimulusdifficulty or group (pN0.05), but therewas aGroup-By-Stimulusdifficultyinteractionpresent, F(2,32)=4.32,pb0.02,Wilks' Lambda=0 .79, partialeta squared=0.21, with the 22q11DS participants spending more timefixating on more difficult items.

3.2.2.2. Spatial measures. Raw scanpath length and average distancebetween fixations: There were no significant main effects or interac-tion present.

3.3. Faces task

3.3.1. Recognition accuracyBriefly, a significant main effect of emotion (F(6,27)=59.97,

pb0.0001, Wilks' Lambda=0.07, partial eta squared=0.93) andgroup (F(1,32)=25.95, pb0.0005, partial eta squared=0.45) foraccuracy of emotion recognition across all emotional stimuliwere reported, with the comparison group performing better thanthe 22q11DS group. There was also a group×emotion interaction(F(6,27)=3.52, p=0.01, Wilks' Lambda=0.56, partial etasquared=0.44) and subsequent follow-up ANOVAs found thatthe 22q11DS group had significantly more problems correctlyidentifying the emotions of anger (F(1,32)=16.9, p=0.0005,partial eta squared=0.35), disgust (F(1,32)=11.35, p=0.002,partial eta squared=0.26), fear (F(1,32)=12.14, p=0.001, partial etasquared=0.28) and neutral (F(1,32)=7.74, p=0.009, partial etasquared=0.2). Meanwhile, they performed similarly to controls onfacial emotions such as happy, surprise and sad (pN0.05). The meansand standard deviations are presented in Table 2.

3.3.2. Scanpath parametersGroup comparisons for fixations and raw scanpath length revealed

significant group differences.

3.3.2.1. Temporal measures. Fixation count: Number of fixations wassignificantly different between groups (F(1,32)=8.61, p=0.006,partial eta squared=0.21), with the 22q11DS participants makingsignificantly fewer fixations to face stimuli compared with healthycontrols. No main effect for emotion category or interaction withgroup (pN0.05) was observed.

Fixation duration: The duration of fixations was not affected bygroup or emotion category (pN0.05).

3.3.2.2. Spatial measures. Raw scanpath length and average distancebetween fixations: In terms of raw scanpath length, statistically non-significant differences were observed for emotion category (F(6,27)=2.21, p=0.07, Wilks' Lambda=0.67, partial eta squared=0.33) andgroup (F(1,32)=3.28, p=0.08, partial eta squared=0.09), with the22q11DS participants tending to produce shorter raw scanpathlengths compared with the control group. No interaction wasobserved between emotion category×group.

The average distance between fixations also differed for emotioncategory (F(6,27)=4.5, p=0.003, Wilks' Lambda=0.5, partial eta

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squared=0.5) but not group. Sad and happy stimuli were observed tohave longer average distance between fixations.

3.4. Weather scene task compared with faces task

3.4.1. Recognition accuracyThe means and standard deviations for the recognition accuracy

scores are presented in Table 2. For recognition accuracy, a statisticallysignificant main effect of group was observed (F(1,33)=28.75,pb0.001, partial eta squared=0.47) and task (F(1,33)=18.86,pb0.001; Wilks' Lambda=0.64; partial eta squared=0.36) with the22q11DS group performing more poorly than the control group onboth tasks and both groups performing poorer on the Faces Taskcompared to theWeather Scene Task. There was no significant Group-By-Task interaction (pN0.05). Significant positive correlations be-tween IQ and accuracy (Weather task r=0.60, pb0.001, Faces Taskr=0.64, pb0.001) were also identified.

3.4.2. Scanpath parametersThe means and standard deviations for the scanpath parameters

are presented in Table 2.

3.4.2.1. Temporal measures. Fixation count: The data revealed asignificant main effect of group for average fixation count (F(1,33)=10.92, p=0.002, partial eta squared=0.25) with the 2211.2 groupmaking fewer fixations on both tasks. No significantmain effect of taskwas observed (pN0.05). However, there was a significant Group-by-Task interaction (F(1,33)=8.71, p=0.006, Wilks' Lambda=0.79,partial eta squared=0.21), with the magnitude of the differencesbetween groups greater on the Faces Task than on the Weather SceneTask with the control group having significantly more fixations on theFaces Task compared to the Weather Scene Task while the 22q11DSgroup had a similar number of fixations on both tasks.

Fixation duration: There were no significant main effects of groupor task (pN0.05) for average fixation duration, however a significantGroup-by-Task interaction was reported (F(1,33)=7.70, p=0.009,Wilks' Lambda=0.81, partial eta squared=0.19) demonstrating thatthe magnitude of the differences between groups was not the samefor the two tasks, with the control group having significantly shorterfixation durations in the Faces Task comparedwith theWeather SceneTask while the 22q11DS group had similar lengths of fixationdurations on both tasks.

3.4.2.2. Spatial measures. Raw scanpath length: A trend level effect ofgroup was observed with the 22q11DS group producing shorter rawscanpath lengths compared to controls (F(1,33)=4.20, p=0.05,partial eta squared=0.11). A main effect of task was identified withboth groups producing significantly shorter raw scanpath on the FacesTask compared with the Weather Scene Task (F(1,33)=137.34,pb0.001, Wilks' Lambda=0.19, partial eta squared=0.81). Therewas no Group-by-Task interaction (pN0.05).

Average distance between fixations: There was no statisticallysignificant main effect of group for the average distance betweenfixations (pN0.05). However, there was a statistically significant maineffect of task, F(1,33)=165.42, pb0.001, Wilks' Lambda=0.17,partial eta squared=0.83, with both groups having a shorter distancebetween fixations for the Faces Task than for the Weather Scene Task.There was no statistically significant Group-by-Task interactionobserved (pN0.05).

3.5. The relationship of the level of difficulty in the weather scene taskand performance on the faces task

When the more difficult Weather Scene items were compared inisolation to the Faces task, there was no change in the outcomesoutlined above. Moreover, IQ was not associated with any of the

scanpath parameters for either the Emotion Task or theWeather Task,nor was it associated with the three levels of difficulty within theWeather Task.

4. Discussion

This study is the first to investigate the specificity of faceprocessing deficits compared with non-face stimuli in adolescentswith 22q11DS using visual scanpath technology. Scanpaths weremeasured concurrently with emotion and non-face (weather scenes)recognition accuracy. Data analysis revealed that people with22q11DS display atypical visual scanpath patterns of performance toboth face emotion and non-face (weather scenes) stimuli comparedto a typically developing control group.

Across scanpath measures of performance, the 22q11DS groupproduced a consistently impoverished scanpath strategy withsignificantly fewer fixations of longer duration. The results alsosuggest that the 22q11DS group had shorter raw scanpath lengths,although this did not reach statistical significance. These results arereminiscent of visual scanpath strategies of people with schizophre-nia, among whom findings of ‘restricted’ scanpaths to faces are wellestablished. In addition, the impoverished scanning strategy to faceand weather stimuli alike resulted in poorer accuracy across allstimuli for the 22q11DS group compared to controls, suggesting thatpoor information processing impacts negatively on higher orderfunctioning.

With regards to performance on the Faces Task compared with theWeather Scene task, the results suggests that the 22q11DS and controlparticipants may have utilised different strategies to identify categorywithin each of the discrimination tasks. In particular, the controlgroup had an increased number of fixations and fixation duration onthe Faces Task compared to the Weather Scene Task. This pattern ofperformance was not present in the 22q11DS group, who had asimilar number of fixations of a comparable length for face andweather stimuli. While a differential performance on the face andweather task can be explained in part by the differences in the spatialproperties of the two tasks, the significant between-group differencesin temporal measures (i.e., number and duration of fixations) suggestthat the two groups used different strategies.

The control group scanned the face stimuli more broadly than the22q11DS group, possibly in an attempt to meet the increaseddemands of a more difficult task. Indeed, both groups displayed areduced level of accuracy for the Face Task compared to the WeatherScene Task, suggesting that the faces are harder to interpret than theweather scenes in this study. Therefore, the findings are consistentwith a more impoverished visual scanpath strategy for faces amongpeople with 22q11DS, consistent with observations from other groupsof neurodevelopmental origins (i.e., psychosis: Loughland et al.,2004). However, this differential performance was not reflected inaccuracy with group differences of a similar magnitude in both tasks.

There may be several reasons for the poorer performance on thefacial stimuli, for instance, the stimuli may be more socially complexcompared to the weather scenes and/or the information in theweather scenes may have been easier to decode, so that moreinformation was available to the participant for accurate interpreta-tion. In this study the magnitude of difference observed between thetwo tasks is of particular interest. While poorer performance on thefaces task was reported for both groups, a corresponding alteration toscanpath parameters was observed for the control group alone. Thislack of effect could indicate cognitive inflexibility of the 22q11DSgroup in their abilities to meet the increasing task demands of theFaces Task, and a failure to make subsequent changes to theirinformation processing strategy to meet these demands. Evidence forcognitive inflexibility has been reported previously in this populationwith 22q11DS participants performing poorer than typically devel-oping controls on modified versions of theWisconsin card sorting test

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(MCST)(Rockers et al., 2009) and a intradimensional/extradimen-sional set-shifting task (Campbell et al., 2010). In the latter study,children and adolescents with 22q11DS completed fewer stages andhad more errors after the extradimensional shift than age matchedsibling controls indicating more problems with changing theirresponse strategy according to the context.

Further, the failure for these alterations in scanning strategy toyield downstream performance gains (lack of group×task interactionon accuracy) suggests that categorisation or discrimination taskelements not captured by scanpath indices may explain performance.So while decreased accuracy on the faces task by both groups suggeststhat it was more difficult, and the differential scanpath performancebetween these tasks suggests that the controls changed theirinformation processing strategy to meet task demands, this adapta-tion did not yield a change in the magnitude of difference on accuracybetween groups on the two tasks.

This is the first study to explore the specificity of face emotionprocessing deficits and the visual scanpath performance of peoplewith 22q11DS. However, there are several limitations associated withthis research. The first concerns the issue of sample representative-ness. This study recruited 22q11DS participants from a local supportgroup and newspaper advertisements. Despite efforts to ensure abroad sampling strategy, the present sample is a convenience sampleand quite small in size. The second relates to the absence of anintellectually matched comparison group and the inherent difficultyassociated with controlling for the effects of IQ on quite complexcognitive tasks. Further, some participants in the 22q11DS groupreported clinically significant mental health problems which mayhave impacted performance on these tasks. However, these were toofew in number to warrant statistical control. Future studies of a largersample may investigate the effects of comorbid mental healthconditions in this group.

These findings provide preliminary evidence for a generalisedprocessing deficit in people with 22q11DS to complex visual stimuli,however the results are also indicative of maladaptive atypical visuo-cognitive strategies for the processing of facial information. Indeed,the differential scanpath performance to face and weather scenestimuli may provide further evidence of the unique complexity offaces, rather than a particularly difficult group for object discrimina-tion. For future studies, it will be important to study the influence ofboth low-order oculomotor abilities and high-order featural versesconfigural processing strategies on visual information processing inpeople with 22q11DS, as well as taking into account the cognitivecomponents required for these tasks not accounted for using scanpathmeasures.

Supplementarymaterials related to this article can be found onlineat doi:10.1016/j.psychres.2011.06.012.

Acknowledgements

Wewould like to thank Gavin Cooper for all his assistance with the development ofthe experimental paradigm used in the current study. We would also like to thank DrKathryn Leadbeater for her assistance in recruitment for the current study.

Funding for the current study was obtained from the Hunter Medical ResearchInstitute in the form of a Port-Waratah Coal Services post-doctoral fellowship and fromthe NMMRC in the form of an Australian Training Fellowship (455624). Infra structuresupport was also obtained from the Schizophrenia Research Institute.

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