Priming Facial Gender and Emotional Valence: The Influence of Spatial Frequency on Face Perception in ASD

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1 J Autism Dev Disord (2017) 47: DOI /s ORIGINAL PAPER Priming Facial Gender and Emotional Valence: The Influence of Spatial Frequency on Face Perception in ASD Steven Vanmarcke 1,2,3 Johan Wagemans 1,2 Published online: 9 January 2017 Springer Science+Business Media New York 2017 Abstract Adolescents with and without autism spectrum disorder (ASD) performed two priming experiments in which they implicitly processed a prime stimulus, containing high and/or low spatial frequency information, and then explicitly categorized a target face either as male/female (gender task) or as positive/negative (Valence task). Adolescents with ASD made more categorization errors than typically developing adolescents. They also showed an agedependent improvement in categorization speed and had more difficulties with categorizing facial expressions than gender. However, in neither of the categorization tasks, we found group differences in the processing of coarse versus fine prime information. This contradicted our expectations, and indicated that the perceptual differences between adolescents with and without ASD critically depended on the processing time available for the primes. Keywords Autism spectrum disorder Face perception Vision research Spatial frequency Electronic supplementary material The online version of this article (doi: /s ) contains supplementary material, which is available to authorized users. * Steven Vanmarcke steven.vanmarcke@kuleuven.be Brain and Cognition, KU Leuven, Leuven, Belgium Leuven Autism Research (LAuRes), KU Leuven, Leuven, Belgium Brain and Cognition, Department of Brain & Cognition, University of Leuven (KU Leuven), 3000 Leuven, Belgium Introduction Successful social interactions critically depend on the rapid identification and categorization of meaningful others based on, for instance, the rapid processing of face information. Despite the high intra-class stimulus similarity, such face perception seems to occur without effort in typically developing (TD) children, thanks to a network of subcortical and cortical brain regions critically responsive to face-like object information (Johnson et al. 2015; Kanwisher and Yovel 2006; Pyles et al. 2013). The subcortical pathway encompasses the superior colliculus, the pulvinar complex and the amygdala complex with projections to the dorsal visual cortex (Kaas and Lyon 2007). Previous research indicated that these subcortical brain areas direct TD newborns to preferentially attend to facelike patterns containing the basic spatial configuration of face areas (Johnson 2005). This initial orientation to facelike stimuli is considered to be a reflexive, genetically determined (innate) and experience-expectant mechanism (Klin et al. 2015). It is argued to operate on low spatial frequency information (LSF) of the face and to spontaneously direct attention to the eyes of the observed person (Senju and Johnson 2009a). As a consequence, this innate face preference allows for cortical brain specialization and the acquisition (or learning) of key human functions such as social cognition and communication (Klin et al. 2015). This cortical specialization for face processing (shifting from subcortical to cortical control during conscious face perception) is hypothesized to develop gradually, as a result of accumulating experience, with an adult-like organization of face-sensitive cortical areas only emerging in children older than 10 years (Kadosh et al. 2013; Johnson 2011). Interestingly, this neural system seems to be centered on the activity of a multi-focal neural network in the inferior Vol.:( )

2 928 J Autism Dev Disord (2017) 47: fronto-temporal cortex (Campatelli et al. 2013). In particular, it was found that, when a TD adolescent or adult is consciously perceiving a face, the fusiform face area (FFA) is accountable for processing the invariant facial aspects, such as identity and gender, and the posterior superior temporal sulcus (psts) processes the changeable facial aspects, such as facial expression and eye gaze (Haxby et al. 2000; Gobbini and Haxby 2007). Previous behavioral research thereby suggested a more low-level processing dissociation of facial information (Deruelle and Fagot 2005), with the low spatial frequency (LSF) components of the face (e.g., global information about the shape, such as general orientation and proportions) being critical for gender and/or identity categorization (e.g., Deruelle and Fagot 2005; Goffaux et al. 2003) and the high spatial frequency (HSF) information (e.g., spatial changes in the facial shape, such as edges, generally corresponding to featural information and fine detail) being associated with the processing of facial expressions (e.g., Deruelle and Fagot 2005; Vuilleumier et al. 2003). Furthermore, the overall importance of HSF for correctly categorizing facial expressions seems to be modulated by the type of emotion being processed: While the identification of sad expressions is mainly dependent on fine (HSF) facial components, happy expressions are more guided by coarse (LSF) facial information (Kumar and Srinivasan 2011; Srinivasan and Gupta 2011). Consistent with a coarse-to-fine account of visual perception in TD participants (Bar 2003; Kauffmann et al. 2015), happy emotions are generally found to be identified faster than sad expressions (Hitenan and Leppanen 2004; Srivastava and Srinivasan 2010). Finally, these behavioral findings might not solely reflect the activation of cortical processing areas during face perception (Campatelli et al. 2013). The rapid, implicit, processing of holistic (or coarse) facial information by older children was found to be strongly influenced by the subcortical brain pathway (Johnson et al. 2015; Tomalski et al. 2009). This suggested that the function of these subcortical brain areas for face detection goes beyond the mere innate detection of socially relevant stimuli and becomes engaged in selective attention on the basis of motivational factors for the purpose of executing adequate social actions. Face Processing in Autism Spectrum Disorder Autism spectrum disorders (ASD) are characterized by difficulties in social and emotional information processing (Dawson et al. 2004). For example, children and adults with ASD show diminished face recognition and face memory skills, they are typically poor at establishing and maintaining eye contact with others, and in interpreting social signals conveyed by gaze shifts (Dawson et al. 2004; Senju and Johnson 2009b). Over the past decades, research indicated that these deficits in social perception and cognition resulted from poor orienting and attention to important social stimuli, such as faces, during early infancy (Johnson 2014). Recent findings suggested that infants, later diagnosed with ASD, did not inhibit the reflexive, subcortically-mediated orientation towards face-like stimuli by the cortically-mediated emergence of an experience-dependent and conscious processing of face information (Klerk et al. 2014; Klin et al. 2015). This argued for a developmentally delayed, yet still present, cortical brain specialization for face processing in ASD, which might relate to the behaviorally observed differences in preferential viewing (fixation) patterns in people with and without ASD, with less attention towards the eye region (Papagiannopoulou et al. 2014) and a stronger focus on the mouth region (Falck-Ytter and von Hofsten 2010). Furthermore, frequent reports of atypical FFA (cortical) and/or amygdala (subcortical) responses to face information in ASD could indicate that processing differences are also related to the differential visual frequency information conveyed by parvocellular (HSF) or magnocellular (LSF) pathways (Corradi-Dell Acqua et al. 2014). The latter pathway, for processing coarse spatial information, might be especially problematic in ASD, given the functional abnormalities in this subcortical face processing system (mainly activated by LSF information) observed in supraliminal face detection and automatic emotional face processing (Kleinhans et al. 2011; Rutishauser et al. 2013). Furthermore, although one behavioral study reported adults with ASD to process LSF expressions in a comparable way to TD adults (Rondan and Deruelle 2004), other studies also argued that ASD mainly affected the rapid processing of coarse facial information (Kikuchi et al. 2013; Vlamings et al. 2010) or suggested a decreased sensitivity to fine information when processing these global facial features (Corradi-Dell Acque et al. 2014). This could be explained by a difficulty in ASD to integrate multiple sources of local information, such as during active gender discrimination. However, individuals with and without ASD were found to perform similarly on tasks requiring the explicit categorization of gender or the identification of a basic emotional facial expression (Deruelle et al. 2008; Evers et al. 2014). Local/Global Processing Style During Face Processing in ASD This inefficient processing of LSF face information in ASD, possibly due to an atypical functioning of the attentional orienting initiated by the subcortical processing pathway (Johnson et al. 2015), could be related to the detail-focused processing style in ASD. More precisely, recent research indicated that the attention to global and local aspects of a hierarchical display (such as a face) is

3 J Autism Dev Disord (2017) 47: mediated by the selective attention to coarse and fine SF, respectively (for review, see Flevaris and Robertson 2016). This argued for the existence of a dynamic relationship between low-level processing in multiple SF channels and high-level attentional selection of these channels, enabling our brain to structure the incoming information into different hierarchical levels. Consequently, these findings explicitly link the coarse-to-fine processing of visual SF information with research on local/global processing in people with and without ASD. In this research, people with ASD are generally found to focus more on details and local information of visual stimuli instead of attending more globally, to the stimulus as a whole (e.g., Happé and Frith 2006; Mottron et al. 2006). While individuals with ASD often perform better than TD individuals in (non-social) local visual search tasks, where an item surrounded by similar distracters has to be identified (e.g., identifying the letter u among o), they often have difficulties with global visual tasks such as the identification of the motion direction of multiple dots moving more or less coherently (e.g., identifying the circular motion of random dots) (for review, see Simmons et al. 2009). Furthermore, the atypical processing of global/ local information in ASD might also affect the detection/ categorization of human faces and emotional expressions (for review, see Campatelli et al. 2013; Weigelt et al. 2012). TD individuals often rely on holistic (or global) processing where face features (eyes, nose, mouth) are perceived as a coherent whole, while expression and gaze processing also rely on biological motion mechanisms due to their dynamic real-world representation (Nomi and Uddin 2015). Therefore, low-level global processing deficits in ASD may hinder higher-order global face perception, while low-level motion processing deficits may hinder higher-order gaze and expression biological motion processing (Behrmann et al. 2006). Nonetheless, previous findings on global face processing in ASD are mixed: There is no strong evidence for either a reduced global or an enhanced local face processing style in ASD (Weigelt et al. 2012). These findings might be related to the explicit task demands in most of these studies. More precisely, people with ASD might show an intact processing of explicitly cued global information, but fail when more implicit global processing, less prone to cognitively-mediated compensatory mechanisms, is required (Koldewyn et al. 2013; Vanmarcke et al. 2016). Recent results thereby specifically argued for an atypical, less globally oriented face processing strategy in ASD (Evers et al. 2016). This would also be in line with the idea of an atypically functioning subcortical face processing system, mainly relying on coarse (LSF) stimulus information (Johnson et al. 2015). Previous findings indicated that specific task instructions might influence the spatial scales preferentially used and perceived for the rapid recognition of both facial (Schyns 929 and Oliva 1999) and scene (Oliva and Schyns 1997) information. To explain this flexibility in spatial scale selection, the authors emphasized that visual recognition is most efficient when attention is tuned towards the most diagnostically informative spatial frequencies in the scene. These findings were obtained using hybrid stimuli, combining two overlaid images of real-world scenes or faces consisting of different stimulus properties (Oliva 2013). Interestingly, a recent study on the rapid scene categorization of TD participants (Vanmarcke and Wagemans 2016) was able to replicate the behavioral findings on the coarse-to-fine processing of SF information in non-social visual scenes, using a priming paradigm similar to the pioneering experiments conducted by Oliva and Schyns (1997). This priming paradigm allowed the authors to test the influence of an implicitly processed prime stimulus on the explicit categorization of a target stimulus. Furthermore, by systematically manipulating the duration and spatial frequency content of this prime stimulus, this study could investigate the processing time needed to extract coarse and/or fine spatial frequency prime information. More precisely, the results indicated that coarse spatial information was more effective with shorter prime presentation times (PT) and fine spatial information required a longer prime presentation to influence participant performance. Interestingly, and as an extension of the research of Aude Oliva and Philippe Schyns, higher scores on the autism-spectrum quotient (AQ) questionnaire modified the impact of HSF prime information on target identification and influenced performance when ambiguous hybrid prime information preceded the categorization task in TD adults. More precisely, people with higher ASDrelated traits had a lower accuracy on scene categorization than people with lower ASD-related traits. This performance was not specific to LSF and/or HSF prime-target category congruent information but was argued to reflect a difficulty in people with high ASD-related traits to rapidly make use of complex stimulus information. Current Study In the current set of experiments, we have used a similar priming paradigm, but now using socially relevant facial stimuli (an overview of the different priming conditions, showing stimulus examples, is provided briefly in Fig. 1 and more elaborately in the Supplementary materials). In the first experiment, we presented faces either as an unfiltered picture or solely defined by LSF or HSF information. More precisely, there were three different prime conditions, in which the presented image was either (1) an unfiltered face, (2) a low-pass filtered face (containing only LSF, below 2 cycles/degree of visual angle) or (3) a high-pass filtered face (containing only HSF, above 6 cycles/degree of visual angle). In the second

4 930 J Autism Dev Disord (2017) 47: Fig. 1 a Provides a graphical overview of the experimental design. Participants were first shown a fixation cross for 500 ms. Afterwards, a prime stimulus appeared for 83 ms, directly followed by a perceptual mask (83 ms). Finally, an unfiltered target stimulus was shown (83 ms). The inter-trial interval (ITI) between trials varied, in each task, between 1000 and 1500 ms. b Provides an overview of the type of images used in both contextual priming experiments, for both experimental tasks (Valence and Gender) separately. The complete picture set is available online on resources/supplementary-material experiment, we also used hybrid images, consisting of overlaid LSF and HSF information from these faces, as prime stimuli, to investigate which of these SF modulated the detection of an unfiltered target picture most. These hybrid stimulus were computed by combining the LSF information (below 2 cycles/degree of visual angle) from one unfiltered face with the HSF information (above 6 cycles/degree of visual angle) from another face. Participants always had to categorize the target stimulus as rapidly and correctly as possible as being either (A) male or female (Gender task) or (B) expressing a positive or negative emotion (Valence task). The target stimulus could either belong to the same or a different category as the prime stimulus (congruent versus incongruent, resp.). We formulated the following hypotheses. Hypothesis 1 Previous research in the TD population, indicated (1) an overall bias for LSF information for facial gender discrimination and a bias for HSF information for emotion discrimination (Deruelle and Fagot 2005), (2) a differential modulation of the spatial scale processing of LSF and HSF face information based on changes in task demands (e.g., gender or familiarity categorization) (Goffaux et al. 2003) and (3) a stronger dependency on LSF information

5 J Autism Dev Disord (2017) 47: for the identification of happy expressions coinciding with a stronger dependency on HSF information for the identification of sad expressions (Kumar and Srinivasan 2011). We expected to replicate these findings in the TD participants in the current priming categorization experiments. We thereby expected to observe an overall stronger LSF priming effect, and smaller HSF priming effect, in the Gender compared to the Valence task. Simultaneously, positive prime and/or target emotions were expected to be identified faster than negative emotions in the Valence task and HSF priming information was expected to influence performance most when negative faces were presented to the participants. Due to the absence of specific prime PT manipulations in the current study (fixed at ~83 ms), we did not predict time-dependent differences in the strength of the priming effect when presenting either coarse or fine prime-target category congruent information to the TD participants (as reported in a previous study (Vanmarcke and Wagemans 2016)). Hypothesis 2 In contrast to the literature on TD participants, and more interesting for the present study, previous findings indicated that (1) people with ASD, compared to TD participants, generally show an atypical processing of coarse (global) and fine (local) visual information (Koldewyn et al. 2013; Vlamings et al. 2010) and (2) the attention to global and local aspects of a hierarchical display (such as a face) is mediated by the selective attention to coarse and fine SF, respectively (Flevaris and Robertson 2016). We therefore predicted that participants with ASD would be less affected by the coarse (LSF) priming information than their TD counterparts in both the Valence and Gender task. 931 This could be due to the observed functional abnormalities in the subcortical face processing system (mainly activated by LSF information) in ASD, hampering automatic face detection and emotional face processing (Kleinhans et al. 2011; Rutishauser et al. 2013). Furthermore, we also expected that the adolescents with ASD would make more categorization errors than the TD adolescents. This prediction would be in line with recent findings using non-social priming cues in TD participants (Vanmarcke and Wagemans 2016), in which participants with higher ASD-related traits made more categorization errors compared to the TD participants with lower ASD-related traits. It would also be in line with the observed atypical (innate) allocation of spatial attention to face-like information in ASD (e.g., attending more to the mouth than to the eyes compared to TD participants), leading to a delayed cortical brain specialization and acquisition (or learning) of face processing strategies (Klin et al. 2015). Materials and Methods Participants In both Experiments 1 and 2, a group of 26 adolescents (22 men, 4 women) with ASD (mean age = 14.00; SD = 1.44; [min max] years old = [11 17]; Inter Quartile Range (IQR) = 2) and a TD control group (mean age = 14.35; SD = 1.23; [min max] years old = [12 17] ; Inter Quartile Range (IQR) = 2), which were individually matched on age, gender and IQ, participated in this study (see Table 1 for participant characteristics). IQ was estimated using an abbreviated four-subtest (vocabulary, similarities, picture completion and block design) version of the WISC-III Table 1 Overview of the average group-level performance (SD between brackets), for participants with ASD (n = 26) and TD participants (n = 26) Variable TD adolescents ASD adolescents TD versus ASD Effect size? Age (1.23) (1.44) F 1,50 = 0.87; p =.36 η 2 = 0.02 Full-scale IQ (8.78) (10.76) F 1,50 = 1.78; p =.19 η 2 = 0.03 Verbal IQ (7.06) (15.79) F 1,50 = 3.21; p =.08 η 2 = 0.06 Performance IQ (12.97) (15.76) F 1,50 = 0.11; p =.75 η 2 < 0.01 SRS (overall) (8.85) (13.05) F 1,50 = 96.31; p <.001 η 2 = 0.66 SRS (social consciousness) (7.31) (9.65) F 1,50 = 74.21; p <.001 η 2 = 0.60 SRS (social cognition) (8.38) (11.67) F 1,50 = ; p <.001 η 2 = 0.72 SRS (social communication) (8.83) (15.18) F 1,50 = 70.69; p <.001 η 2 = 0.59 SRS (motivation) (11.32) (17.68) F 1,50 = 28.64; p <.001 η 2 = 0.36 SRS (preoccupation) (9.46) (13.48) F 1,50 = 52.08; p <.001 η 2 = 0.51 Visual acuity (LogMAR) 0.22 (0.07) 0.24 (0.08) F 1,50 = 1.57; p =.22 η 2 = 0.03 On each of the descriptive tests separately, a one-way ANOVA with ASD as between-participants factor was conducted. When required, a Bonferroni correction for multiple comparisons was applied to avoid false positive results Overview of the used abbreviations: logmar logarithm of minimum angle of resolution, WISC-III Wechsler intelligence scale for children-iii, SRS social responsiveness scale, TD typically developing

6 932 J Autism Dev Disord (2017) 47: (Sattler 2001; Wechsler 1997). All participants also completed the Dutch Social Responsiveness Scale (SRS) questionnaire (Roeyers et al. 2011) to get an overall estimation of individual and/or group-level differences in ASD traits. Participants from the ASD group were previously diagnosed with a pervasive developmental disorder (Autistic disorder, Asperger syndrome or PPD-NOS), according to DSM-IV-TR criteria (American Psychiatric Association 2000), by a multidisciplinary team. Recruitment was exclusively set up via the Autism Expertise Centre of the University Hospital in Leuven. Furthermore, a trained clinical psychologist administered the Dutch version of the Autism Diagnostic Observation Schedule 2 (ADOS-2) module 3 (Gotham et al. 2006; Dutch version:; de Bildt et al. 2009) from all participants with a clinical diagnosis. ASD diagnoses were re-confirmed in 24 of the 26 adolescents, with the new ADOS Algorithm for DSM-IV/ICD-10 (ADOS-2). Since the analyses did not differ depending on whether we in- or excluded the participants scoring below the ADOS-2 cut-off score, we followed the clinical diagnosis of the participants and reported the results of the full ASD group. All participants had normal or corrected-to-normal vision. Furthermore, we also tested the visual acuity of our participants using the Landolt C task of the Freiburg Vision Test (FrACT3), version (Bach 1996). This was important given that refractive errors can affect the processing of SF information, particularly for HSF (e.g., Collins and Carney 1990). Importantly, no group-level differences in visual acuity were found in the current study (Table 1). The study was approved by the Medical Ethics Commission of KU Leuven and both the participants themselves and their parents provided written informed consent before onset of the experiment. Stimuli This section provides an overview of the stimuli used in the different computer tasks and the preceding practice trials. Some examples of the used stimuli in the tasks are shown in Fig. 1. All images can be found on be/en/resources/supplementary-material. A total set of 260 grey-level, non-expressive or neutral, faces ( pixels) were selected out of the Amsterdam Dynamic Facial Expression Set (ADFES) database to form a new gender stimulus set (Van der Schalk et al. 2011). We hereby matched 130 males with 130 females of corresponding age and race. A total set of 260 grey-level, emotional faces ( pixels) were selected out of the Warsaw set of emotional facial expression pictures (30 pairs of stimuli; Olszanowski et al. 2015), the Radboud faces database (67 pairs of stimuli; Langner et al. 2010) and the Karolinska Directed Emotional Faces (33 pairs of stimuli; Lundqvist et al. 1998) to form a new emotional expression stimulus set. We hereby selected 130 people, of which we always selected both a happy and a sad emotional expression (e.g., resulting in a total of 260 images). All of these were selected by unanimous consensus between several lab members including the first author. Computer Tasks All participants were tested on two separate priming experiments in which they first implicitly processed a prime stimulus, containing high and/or low spatial frequency information, and then explicitly categorized a target face either as male/female (Gender task) or as positive/negative (Valence task) within a blocked experimental trial design. The first experiment used faces, either as an unfiltered picture or solely defined by LSF or HSF information, as prime stimuli. The second experiment relied on hybrid images, consisting of overlaid LSF and HSF information from these face stimuli, to prime participant categorization responses. Participants were asked to take a comfortable position in front of the computer screen (at about 57 cm from the computer display) and placed both hands on the keyboard (spacebar) in front of the computer monitor (resolution: ; refresh rate: 60 Hz; type: DellP2211H). Before starting each task, participants completed a brief practice session with visual trial-by-trial feedback (a green or red fixation cross after each correct or incorrect response, respectively) to familiarize them with the design (5 stimuli per test block; 50% targets). During the actual experiment no feedback was provided and the test order of the tasks was counterbalanced across participants. Experiment 1: HSF, LSF or Unfiltered Primes In the first experiment (Fig. 1) participants were asked to respond to the brief presentation of a meaningful grey-level picture (apparent size: ). A fixation cross (apparent size: 1 1 of visual angle) appeared for 500 ms, directly followed by a briefly flashed (83 ms) prime. This PT was similar to previous studies (e.g., Corradi-Dell Acqua et al. 2014; Vanmarcke and Wagemans 2015) and based on rigorous pilot testing, indicating that TD participants could correctly categorize the gender and valence of a face at this PT. Spatial frequency is a measure of how often sinusoidal components (as determined by the Fourier transform) of the structure repeat per unit of distance (e.g., degree of visual angle). More precisely, in accordance with the International System of Units (SI), the SI unit of spatial frequency in visual perception research is the number of oscillations (cycles) per degree. In Experiment 1, there were three different prime conditions, in which the presented image was either (1) an unfiltered face, (2) a low-pass filtered face (containing only LSF, below 2 cycles/degree of

7 J Autism Dev Disord (2017) 47: visual angle) or (3) a high-pass filtered face (containing only HSF, above 6 cycles/degree of visual angle). Mathwork Matlab (R2015a) was used to create these images and the detailed procedure was adapted from the one used in previous studies (Schyns and Oliva 1999; Winston et al. 2003). After the prime presentation a perceptual mask was shown (83 ms). In all conditions, this mask was computed by dividing the images into pixel-squares (2 by 2 pixels per square) that were subsequently randomized in the spatial domain, in accordance with the three prime conditions (unfiltered, LSF and HSF information). After mask offset, a briefly flashed (83 ms) target face was presented. In half of the trials there was prime-target category congruency (e.g., both male faces in the Gender task or both positive facial expressions in the Valence task); in the other half the two stimuli were category-incongruent (e.g., male versus female faces in the Gender task or positive versus negative facial expressions in the Valence task). Participants then got a 2000 ms response window to indicate (1) in the Gender task whether the presented target stimulus was male or female by pressing either m or f or (2) in the Valence task whether the presented target stimulus displayed a positive or negative emotion by pressing, respectively, p or n on the keyboard. The ITI was randomized within a range of ms and a total of 48 stimuli per prime condition, per task (Gender or Valence), were shown to each of the participants in a randomized order. Extra information on the different prime conditions (with some stimulus examples) is provided in the Supplementary materials. With respect to the research hypothesis, we expected the following results in Experiment 1: Hypothesis 1 We expected the TD participants to show a stronger priming effect in the LSF, compared to the HSF, priming condition of the Gender task. This prediction was based on previous research in which facial gender discrimination was found to be more dependent of coarse (LSF) compared to fine (HSF) facial information (Deruelle and Fagot 2005). Simultaneously, and in line with studies indicating that happy emotions are generally found to be identified faster than sad expressions (Hitenan and Leppanen 2004; Srivastava and Srinivasan 2010), positive emotions were expected to be identified faster than negative emotions in the Valence task. Finally, HSF priming information was expected to influence performance most when negative faces were presented to the participants. This would be in line with findings indicating a stronger dependency on HSF information for the identification of sad, compared to happy, facial expressions (Kumar and Srinivasan 2011). Hypothesis 2 Furthermore, as the main focus of the current study, we expected the participants with ASD to show a smaller priming effect in the LSF priming condition. This would be in line with previous findings indicating an inefficient processing of coarse (global) visual information in 933 ASD (Koldewyn et al. 2013; Vlamings et al. 2010). We also expected people with ASD to perform worse in all three conditions of both the Valence and Gender task than their TD counterparts. This prediction would be in line with recent findings using non-social priming cues (in a similar priming paradigm as the current study), in which TD participants with higher ASD-related traits made more categorization errors compared to participants with lower ASD-related traits (Vanmarcke and Wagemans 2016). Experiment 2: Consistent and Inconsistent Hybrid Primes In the second experiment (Fig. 1) participants were asked to respond to the brief presentation of a meaningful grey-level picture (apparent size: ). A fixation cross (apparent size: 1 1 of visual angle) appeared for 500 ms, directly followed by a briefly flashed (83 ms) prime. This prime was always a hybrid stimulus, computed by combining the LSF information (below 2 cycles/ degree of visual angle) from one unfiltered face with the HSF information (above 6 cycles/degree of visual angle) from another face. Nonetheless, there were two different priming conditions: in the category-consistent hybrid primes the spatial frequencies were computed from two images of the same stimulus category (e.g., either male or female faces in the Gender task and either positive or negative facial expressions in the Valence task), while in the category-inconsistent hybrid primes these were computed from two images of the opposite stimulus category (e.g., a male LSF face combined with a female HSF face in the Gender task or a positive LSF facial expression combined with a negative HSF facial expression in the Valence task). Mathwork Matlab (R2015a) was used to create these images. After the prime presentation, a perceptual mask was shown (66 ms). In both conditions, this mask was computed by combining the scrambled lowpass (below 2 cycles/degree of visual angle) and highpass (above 6 cycles/degree of visual angle) information of the original face images from which the priming stimulus was made. Thereafter, a briefly flashed (83 ms) target face was presented and participants got a 2000 ms response window to window to indicate (1) in the Gender task whether the presented target stimulus was male or female by pressing either m or f or (2) in the Valence task whether the presented target stimulus displayed a positive or negative emotion by pressing, respectively, p or n on the keyboard. As a result, we can therefore differentiate between four separate priming conditions within the experimental framework: (1) the HSF and LSF prime-target category congruent (H+ L+) condition, (2) the HSF prime-target category congruent and LSF incongruent (H+ L ) condition, (3) the LSF prime-target category congruent and HSF incongruent (H L+) condition,

8 934 J Autism Dev Disord (2017) 47: and (4) the LSF and HSF prime-target category incongruent (H L ) condition. Furthermore, in conditions (1) and (4) consistent primes were presented (e.g., both overlaid HSF and LSF elements of the hybrid prime were either male or female faces in the Gender task and either positive or negative facial expressions in the Valence task) and in conditions (2) and (3) inconsistent primes (e.g., one of the overlaid HSF and LSF elements of the hybrid prime in the Gender task was male (female), while the other one was female (male). In the Valence task, one of the overlaid elements would be a positive (negative) and the other a negative (positive) facial expression). A total of 48 stimuli per condition, per task (gender or valence), were shown to each of the participants in a randomized order and the ITI was randomized within a range of ms. Extra information on the different prime conditions (with some stimulus examples) is provided in the Supplementary materials. With respect to the research hypothesis, we expected the following results in Experiment 2: Hypothesis 1 We expected that the performance of the TD participants was most strongly influenced by LSF, compared to HSF, prime-target category congruency in the Gender, but not in the Valence, task. This prediction was, similar to Experiment 1, based on previous research in which facial gender discrimination was found to be more dependent of coarse (LSF) compared to fine (HSF) facial information (Deruelle and Fagot 2005). Simultaneously, and in line with studies indicating that happy emotions are generally found to be identified faster than sad expressions (Hitenan and Leppanen 2004; Srivastava and Srinivasan 2010), positive emotions were expected to be identified faster than negative emotions in the Valence task. Finally, HSF priming information was expected to influence participant performance most when negative faces were presented to the participants. This would be in line with findings indicating a stronger dependency on HSF information for the identification of sad, compared to happy, facial expressions (Kumar and Srinivasan 2011). Hypothesis 2 Furthermore, as the main focus of the current study, we also expected that the performance of participants with ASD, compared to their TD counterparts, would be mostly affected by the presence of HSF, but not LSF, prime-target category congruency. This would be in line with previous findings indicating an inefficient processing of coarse (global) and an enhanced processing of fine (local) visual information in ASD (Koldewyn et al. 2013; Vlamings et al. 2010). We also expected that the performance of people with ASD would be worse in all conditions of both the Valence and Gender task. Such a finding would be in line with a recent study using non-social priming cues (in a similar priming paradigm as the current study), in which TD participants with higher ASD-related traits made more categorization errors compared to participants with lower ASD-related traits (Vanmarcke and Wagemans 2016). Analyses All outcomes were obtained by using the statistical software program R (R core team 2013) and IBM SPSS (version 22). For all relevant parameters, included in the mixed ANOVA analysis of both experiments, the assumptions of normality and homogeneity were checked by means of a visual inspection of the histogram, a qq-plot, as well as a Shapiro Wilk and Kolmogorov Smirnov test. Furthermore, post hoc power analysis were conducted utilizing G*Power (Erdfelder et al. 1996), with an alpha level of 0.05 and a total sample size of 52 participants (reported for all significant findings in the results section). Experiment 1: HSF, LSF or Unfiltered Primes We calculated the individual median RT on the correct (hit) trials and mean accuracy performance for each of the participants for both prime-target category congruent and incongruent trials. We then conducted a mixed ANOVA, both for RT and accuracy, with Group (adolescents with versus without ASD) as a between-subjects factor and Prime-target congruency (congruent versus incongruent trials), Condition (HSF, LSF or unfiltered primes) and Task (Gender versus Valence) as within-subjects factors. Furthermore, given the presence of a significant Task * Condition and Task * Group interaction, we also conducted a mixed ANOVA per task, with Group as between-subjects factor and Prime-target congruency and Condition as within-subjects factors. Finally, in the Valence task, we also added Prime valence (Positive versus Negative prime emotion) as a within-subjects factor to the mixed ANOVA. When calculating the pairwise contrasts between conditions, we always used a Bonferroni correction for multiple comparisons. Participants were regarded as a random factor. Experiment 2: Consistent and Inconsistent Hybrid Primes We calculated the individual median RT on the correct (hit) trials and mean accuracy performance for each of the participants in each of the prime conditions: (1) the HSF and LSF prime-target category congruent (H+ L+) condition, (2) the HSF prime-target category congruent and LSF incongruent (H+ L ) condition, (3) the LSF primetarget category congruent and HSF incongruent (H L+) condition, and (4) the LSF and HSF prime-target category incongruent (H L ) condition. We used these variables, median RT and mean accuracy, as dependent variables in a

9 J Autism Dev Disord (2017) 47: mixed ANOVA with Group (adolescents with versus without ASD) as a between-subjects factor and Task (Valence or Gender) and Condition (H+ L+, H+ L, H L+ and H L ) as within-subjects factors. Furthermore, given the presence of a significant Task * Condition interaction, we also conducted a mixed ANOVA per task, with Group as between-subjects factor and Prime-target congruency and Condition as within-subjects factors. Finally, in the Valence task, we also added Prime valence (Positive versus Negative prime emotion) as a within-subjects factor to the mixed ANOVA. When calculating the pairwise contrasts between conditions, we always used a Bonferroni correction for multiple comparisons. Participants were regarded as a random factor. Correlating Participant Characteristics with Performance Finally, we calculated the bilateral Pearson correlation coefficients between the median RT and mean accuracy performance per participant, for both groups (TD and ASD) and tasks (Gender and Valence task) separately, and the different participant characteristics: (1) age (in years), (2) Full-scale IQ, (3) verbal IQ, (4) performance IQ, (5) overall SRS and (6) visual acuity (LogMAR) scores. This was done for both experiments separately. To conclude, we also added age (in years) as a median-split (young versus old) between-subjects factor to the mixed ANOVA analysis of the median RT performance in Experiment 1 and 2. Results Experiment 1 (Fig. 2) Group-level differences more categorization errors in ASD, but no differences between TD and ASD in coarse-to-fine processing. The mixed ANOVA of the data indicated, for accuracy, a main effect of Group (Accuracy:F 1,50 = 7.39; p <.01; η 2 = 0.13; power =.99), and a significant Task * Group interaction effect (Accuracy:F 1,50 = 5.64; p =.02; η 2 = 0.10; power =.90). Although people with ASD were found to be generally worse than TD participants on both the Valence and the Gender Task, people with ASD had more difficulties with correctly categorizing positive/negative facial expressions than to correctly determine male/ female gender. Interestingly, this difference in categorization performance between people with and without ASD did not significantly interact with the SF content of the prime (Condition). This indicated that there were no differences between adolescents with and without ASD in the processing of coarse (LSF) versus fine (HSF) prime information in Experiment Priming effect in both tasks for all participants, except for the HSF condition of the Gender task. We found that prime-target category congruency was a significant predictor of RT performance (RT:F 1,50 = 20.84; p <.001; η 2 = 0.29; power =.98). More precisely, when the prime stimulus was category-congruent with the target, participants were faster than when there was a prime-target category incongruence. For accuracy, we observed a significant Task * prime-target category congruency interaction (Accuracy:F 1,50 = 4.06; p =.05; η 2 = 0.08; power =.85), together with a significant Condition * prime-target category congruency (Accuracy:F 2,100 = 6.36; p <.01; η 2 = 0.11; power =.98) and a significant Condition * Task * primetarget category congruency (Accuracy:F 2,100 = 6.64; p <.01; η 2 = 0.12; power =.99) interaction. These findings urged us to focus specifically on the separate ANOVA analysis of the Valence and Gender task. This made it clear that, while there was an overall main effect of prime-target category congruency in the Valence task (RT:F 1,50 = 12.43; p =.001; η 2 = 0.20; power =.93 Accuracy:F 1,50 = 6.26; p =.02; η 2 = 0.11; power =.88), not all conditions in the Gender task showed an equal prime-target congruency effect (as exemplified by the significant Condition * primetarget category congruency interaction (RT:F 2,100 = 6.13; p <.01; η 2 = 0.11; power =.98 Accuracy:F 2,100 = 12.20; p <.001; η 2 = 0.20; power =.99)). Further analysis revealed that only the HSF prime condition of the Gender task did not show a prime-target category congruency effect, indicating that our participants performed equally well on both the prime-target category congruent and incongruent trials in this condition (Fig. 2B, D). We therefore concluded that we found a clear priming effect across both experiments, except for the HSF prime condition of the Gender task. Absent priming effect in the HSF condition of the Gender task for all participants. We also observed (1) a main effect of Task (RT:F 1,50 = 9.96; p <.01; η 2 = 0.17; power =.88), (2) a main effect of Condition (RT:F 2,100 = 7.93; p <.01; η 2 = 0.14; power =.95 Accuracy:F 2,100 = 8.51; p <.001; η 2 = 0.15; power =.97), and (3) an interaction effect between Task * Condition (RT:F 2,100 = 3.66; p =.03; η 2 = 0.07; power =.92 Accuracy:F 2,100 = 3.00; p =.05; η 2 = 0.06; power =.87). By conducting the pairwise between-condition contrasts in both the Valence and Gender task, these findings could be attributed to a significantly faster and better performance in the HSF prime condition of the Gender task, compared to the LSF and the neutral prime conditions (Fig. 2b, d). This observation could be explained by the absence of a priming effect in the HSF prime condition of the Gender task. All participants responded faster to positive versus negative facial expressions. When, finally, adding Prime valence to the analysis of the Valence task, we found that participants responded significantly faster in trials in which

10 936 J Autism Dev Disord (2017) 47: Fig. 2 Overview of reaction time (a, b) and accuracy (c, d) data in Experiment 1 (HSF, LSF and unfiltered primes). The data are represented as the mean performance across participants, with error bars depicting the standard error of the mean (SEM). For accuracy, mean and SEM were calculated based on the logistic transformation of the values and then retransformed into percentage correct (%) data. Panels (a) and (c) provide the data for the Valence task, while panels (b) and (d) illustrate the outcomes for the Gender task. In all panels, the prime condition is indicated on the abscissa. The prime-target category congruent stimuli are depicted in light blue for the TD adolescents and in light green for the adolescents with ASD, while the prime-target category incongruent stimuli are depicted in dark blue for the TD adolescents and in dark green for the adolescents with ASD. (Color figure online) the prime stimuli contained a positive, compared to a negative, facial expression (RT:F 1,50 = 7.61; p <.01; η 2 = 0.13; power =.83; Fig. 3). Experiment 2: Consistent and Inconsistent Hybrid Primes (Fig. 4) Group-level differences: more categorization errors in ASD, but no differences between TD and ASD in coarseto-fine processing. The mixed ANOVA of the data indicated that there was a main effect of Group for accuracy (Accuracy:F 1,50 = 7.96; p <.01; η 2 = 0.14; power =.99). More precisely, we found that the adolescents with ASD performed worse than their TD counterparts on both the Valence and the Gender Task. Interestingly, we also found that all participants were faster to correctly categorize the target stimuli in the Gender task, compared to the Valence task (F 1,50 = 5.58; p =.02; η 2 = 0.10; power =.81). Furthermore, this difference in categorization performance between people with and without ASD did not significantly interact with the SF content of the prime (Condition). This indicated that there were no differences between adolescents with and without ASD in the processing of coarse (LSF) versus fine (HSF) prime information in experiment 2. Priming effect in both tasks for all participants, but no differences in processing positive versus negative facial expressions. We also observed a significant main effect of Condition (RT:F 3,150 = 3.45; p =.02; η 2 = 0.07; power =.80 Accuracy:F 3,150 = 11.85; p <.001; η 2 = 0.19; power =.99) as well as a significant Task * Condition interaction (Accuracy:F 3,150 = 2.84; p =.04; η 2 = 0.05; power =.89).

11 J Autism Dev Disord (2017) 47: Fig. 3 Overview of reaction time (a) and accuracy (b) data in the Valence task of Experiment 1 (HSF, LSF and unfiltered primes). The data are represented as the mean performance across participants, with error bars depicting the SEM. For accuracy, mean and SEM were calculated based on the logistic transformation of the values and then retransformed into percentage correct (%) data. In all panels, the prime condition is indicated on the abscissa. The prime-target category congruent stimuli are depicted in light brown for the negative prime expressions and in pink for the positive prime expressions, while the prime-target category incongruent stimuli are depicted in dark brown for the negative prime expressions and in dark red for the positive prime expressions. (Color figure online) More specifically, as indicated by the pairwise contrasts between the four different conditions, in both the Valence and the Gender task participants performed better in the H+ L+ condition than in the H L condition, but this effect was more pronounced in the Gender task compared to the Valence task. Finally, no behavioral differences were observed between the processing of positive and negative expressions of the priming stimuli. Correlating participant characteristics with performance (Tables 2, 3; Fig. 5) Significant age-related improvement of RT performance for participants with, but not without, ASD. Our results indicated a significant correlation between age and median RT performance for the adolescents with ASD (Gender task (Experiment 1): r =.50; p <.002 Valence task (Experiment 1): r =.50; p <.002 Gender task (Experiment 2): r =.43; p =.03 Valence task (Experiment 2): r =.51; p <.002), but not the TD adolescents (Gender task (Experiment 1): r =.21; p =.32 Valence task (Experiment 1): r =.28; p =.17 Gender task (Experiment 2): r =.20; p =.34 Valence task (Experiment 2): r =.24; p =.24), in both prime categorization experiments. More precisely, younger adolescents with ASD were slower than their older counterparts (Fig. 5). To verify the strength of this finding further, we have also added Age as a mediansplit (young versus old), between-subjects variable to the mixed ANOVA analysis of the median RT performance of Experiment 1 and 2. Consistent with the observed correlations between the median RT performance of participants with ASD and age, we observed a significant Age * Group interaction effect for Experiment 1 (F 1,48 = 5.34; p =.03; η 2 = 0.10; power =.97) and a marginally significant Age * Group interaction for Experiment 2 (F 1,48 = 2.99; p =.09; η 2 = 0.06; power =.85). Influence of full-scale IQ on categorization performance. Finally, the correlation analysis also revealed a significant correlation between the full-scale IQ and the mean accuracy of the ASD participants in the Gender task of the first experiment (r =.50; p <.002). This indicated that the participants with ASD with a higher overall IQ performed better at the Gender task than ASD participants with lower IQ scores. No other significant correlations were found. Discussion In this study, adolescents with and without Autism Spectrum Disorder (ASD) performed two separate priming experiments in which they implicitly processed a prime stimulus, containing high and/or low spatial frequency information, and explicitly categorized a target face either as male/female (Gender task) or as positive/negative (Valence task) within a blocked experimental trial design. The first experiment used faces, either as an unfiltered picture or solely defined by LSF or HSF information, as prime stimuli. The second experiment relied on hybrid images, consisting of overlaid LSF and HSF information from these face stimuli, to prime participant categorization responses. Our results indicated that all participants were able to effectively complete both experiments. Importantly, a clear priming effect was found for both the Gender and the Valence task.

12 938 J Autism Dev Disord (2017) 47: Fig. 4 Overview of reaction time (a, b) and accuracy (c, d) data in Experiment 2 (consistent and inconsistent primes). The data are represented as the mean performance across participants, with error bars depicting the SEM. For accuracy, mean and SEM were calculated based on the logistic transformation of the values and then retransformed into percentage correct (%) data. Panels (a) and (c) provide the data for the Valence task, while panels (b) and (d) illustrate the outcomes for the Gender task. In all panels, the four different prime conditions are indicated on the abscissa: (1) H+ L+, (2) H+ L, (3) H L+ and (4) H L condition. Furthermore, TD adolescents are always depicted in blue and adolescents with ASD in green. (Color figure online) Hypothesis 1: Validation of expected findings in TD adolescents Our results were generally in line with our first hypothesis, indicating that (1) there was no priming effect for HSF prime information in the Gender task, (2) the gender categorization was conducted faster than the valence categorization task, and (3) positive emotions were identified faster than negative emotions. However, we did not find evidence to support the claim that HSF priming information had a stronger effect when categorizing negative, compared to positive, emotional expressions. Overall, we believe that these results validate previous findings on the coarse-tofine processing account of face perception in TD participants (Goffaux et al. 2003; Kumar and Srinivasan 2011; Srivastava and Srinivasan 2010). More precisely, our findings indicated that fine facial components were more critical for the processing of facial expressions (e.g., no priming effect of HSF in the Gender task and in the Valence task) and coarse facial information had a stronger impact on gender categorization (e.g., only a priming effect for LSF and unfiltered primes in the Gender task). In line with previous results indicating that greater distance and poorer lighting has a stronger influence on the correct categorization of facial expression than on the categorization of facial gender (Gao and Maurer 2011), this could mean that more facial details are needed to recognize facial expression than to recognize facial gender. Furthermore, we did not manipulate prime PT (fixed at ~83 ms) in the current study. We therefore did not expect, nor find, the time-dependent

13 J Autism Dev Disord (2017) 47: Table 2 Overview of the correlation matrix (with the corresponding p-value of each correlation between brackets) between the median RT and mean accuracy performance per participant in Experiment 1, for both groups (TD and ASD) and tasks (gender and valence task) separately, and the different participant characteristics: (1) age (in years), (2) Full-scale IQ, (3) verbal IQ, (4) performance IQ, (5) overall SRS and (6) visual acuity (LogMAR) scores Median RT Age Full-scale IQ Verbal IQ Performance IQ SRS (overall) Visual acuity (LogMAR) TD (gender task) 0.21 (p =.32) TD (valence task) 0.28 (p =.17) ASD (gender task) 0.50* (p <.002) ASD (valence task) 0.50* (p <.002) Mean accuracy 0.05 (p =.83) 0.01 (p =.98) 0.35 (p =.08) 0.16 (p =.44) 0.03 (p =.90) 0.09 (p =.68) 0.24 (p =.24) 0.12 (p =.55) 0.08 (p =.72) 0.04 (p =.85) 0.36 (p =.07) 0.16 (p =.43) 0.03 (p =.90) 0.09 (p =.67) 0.08 (p =.69) 0.07 (p =.72) 0.35 (p =.08) 0.34 (p =.09) 0.08 (p =.69) 0.07 (p =.75) Age Full-scale IQ Verbal IQ Performance IQ SRS (overall) Visual acuity (LogMAR) TD (gender task) 0.48 (p =.01) TD (valence task) 0.29 (p =.15) ASD (gender task) 0.12 (p =.55) ASD (valence task) 0.05 (p =.81) 0.15 (p =.47) 0.04 (p =.86) 0.50* (p <.002) 0.40 (p =.04) 0.33 (p =.10) 0.01 (p =.98) 0.33 (p =.10) 0.37 (p =.06) 0.02 (p =.92) 0.05 (p =.82) 0.42 (p =.04) 0.25 (p =.22) 1.00 * 10 3 (p =.99) 0.05 (p =.80) 0.03 (p =.88) 0.01 (p =.96) 0.16 (p =.44) 0.39 (p =.05) 0.09 (p =.67) 0.14 (p =.49) *Bonferonni corrected level: p <.002 Overview of the used abbreviations: IQ intelligence quotient, logmar logarithm of minimum angle of resolution, SRS social responsiveness scale, TD typically developing differences in the strength of the prime-target congruency effect of coarse and/or fine prime information that were observed in previous studies using a similar priming paradigm (Vanmarcke and Wagemans 2016; Oliva and Schyns 1997; Schyns and Oliva 1994). Interestingly, we also did not observe a significant correlation between participant age and task performance, although it should be noted that we tested adolescents (~14 years old) in the current study. Previous studies indicated that children s ability to recognize facial information continues to develop during adolescence (e.g., Gao and Maurer 2009; Mondloch et al. 2002). It was suggested that this developmental maturation may not be face-specific, but arises from general differences in extracting signals from noise, selective attention, and/or high-level visual abilities (Gao and Maurer 2011). Thus, although the innate capacity to organize our environment can already be observed in early infancy, the capacity to comprehend and structure more abstract/complex input information continues to improve throughout childhood (Gutheil and Gelman 1997). This could indicate that problems with sustained attention and inhibition generally results in a worse and/or slower categorization performance in adolescence, but not in adulthood (Jonkman et al. 2003). Furthermore, the current study used static image information, instead of more realistic dynamic facial displays (for an overview, see Jack and Schyns 2015). Previous affective priming paradigms, using positive or negative facial expressions, did not always yield strong and robust effects using static image displays (Sato et al. 2014). Only with more dynamic manipulations of facial expressions (e.g., smile or frown formation), stronger behavioral reactions were observed. This would indicate that the current findings on gender and/or expression categorization could become more pronounced when using more ecologically valid stimuli. Finally, the overall variability of our sample of TD participants (SD = 1.23; [min max] years old = [12 17] ; IQR = 2) was rather small. In previous studies, in which a cortical face processing maturation effect was found, this effect was mainly driven by the observed differences in cortical activation between the younger age group (e.g.,<10 years old) and the adult group (e.g.,>18 years old) (e.g., Kadosh et al. 2013; Golarai et al. 2007; Karayanidis et al. 2009). The TD adolescent group (e.g., years old) generally show no differences in behavioral performance with the adult participants and only mild differences in the cortical response patterns of face-selective

14 940 J Autism Dev Disord (2017) 47: Table 3 Overview of the correlation matrix (with the corresponding p-value of each correlation between brackets) between the median RT and mean accuracy performance per participant in Experiment 2, for both groups (TD and ASD) and tasks (gender and valence task) separately, and the different participant characteristics: (1) age (in years), (2) Full-scale IQ, (3) verbal IQ, (4) performance IQ, (5) overall SRS and (6) visual acuity (LogMAR) scores Median RT Age Full-scale IQ Verbal IQ Performance IQ SRS (overall) Visual acuity (LogMAR) TD (gender task) 0.20 (p =.34) 0.02 (p =.92) 0.07 (p =.73) 0.07 (p =.75) 0.10 (p =.64) 0.34 (p =.10) TD (valence task) 0.24 (p =.24) 0.02 (p =.91) 0.06 (p =.78) 1.00 * 10 3 (p =.99) 0.16 (p =.42) 0.37 (p =.06) ASD (gender task) 0.43 (p =.03) 0.32 (p =.11) 0.20 (p =.33) 0.34 (p =.09) 0.12 (p =.56) 0.02 (p =.94) ASD (valence task) 0.51* (p <.002) 0.17 (p =.41) 0.22 (p =.29) 0.15 (p =.48) 0.11 (p =.61) 0.03 (p =.90) Mean accuracy Age Full-scale IQ Verbal IQ Performance IQ SRS (overall) Visual acuity (LogMAR) TD (gender task) 0.38 (p =.06) 0.22 (p =.29) 0.29 (p =.16) 0.14 (p =.51) 0.09 (p =.65) 2.00 * 10 3 (p =.99) TD (valence task) 0.30 (p =.13) 0.05 (p =.82) 0.16 (p =.42) 0.02 (p =.91) 0.02 (p =.93) 0.12 (p =.57) ASD (gender task) 0.17 (p =.41) 0.45 (p =.02) 0.32 (p =.12) 0.40 (p =.04) 0.06 (p =.79) 0.09 (p =.66) ASD (valence task) 0.01 (p =.98) 0.26 (p =.20) 0.39 (p =.05) 0.08 (p =.71) 0.04 (p =.83) 0.21 (p =.31) *Bonferonni corrected level: p <.002 Overview of the used abbreviations: IQ intelligence quotient, logmar logarithm of minimum angle of resolution, SRS social responsiveness scale, TD typically developing Fig. 5 Visualization of the data for Experiment 1 (a) and Experiment 2 (b), with the linear (per group) regression line indicating the strength of the correlation between the median RT per participant (averaged across both the gender and valence task per experiment) on the ordinate axis and age (in years) on the abscissa. TD adolescents are depicted in blue and adolescents with ASD in green. (Color figure online)