Comprehensive Social Trait Judgments From Faces in Autism Spectrum Disorder.

Processing social information from faces is difficult for individuals with autism spectrum disorder (ASD). However, it remains unclear whether individuals with ASD make high-level social trait judgments from faces in the same way as neurotypical individuals. Here, we comprehensively addressed this question using naturalistic face images and representatively sampled traits. Despite similar underlying dimensional structures across traits, online adult participants with self-reported ASD showed different judgments and reduced specificity within each trait compared with neurotypical individuals. Deep neural networks revealed that these group differences were driven by specific types of faces and differential utilization of features within a face. Our results were replicated in well-characterized in-lab participants and partially generalized to more controlled face images (a preregistered study). By investigating social trait judgments in a broader population, including individuals with neurodevelopmental variations, we found important theoretical implications for the fundamental dimensions, variations, and potential behavioral consequences of social cognition.

Reduced Pupil Oscillation During Facial Emotion Judgment in People with Autism Spectrum Disorder.

People with autism spectrum disorder (ASD) show abnormal face perception and emotion recognition. However, it remains largely unknown whether these differences are associated with abnormal physiological responses when viewing faces. In this study, we employed a sensitive emotion judgment task and conducted a detailed investigation of pupil dilation/constriction and oscillation in high-functioning adult participants with ASD and matched controls. We found that participants with ASD showed normal pupil constriction to faces; however, they demonstrated reduced pupil oscillation, which was independent of stimulus properties and participants' perception of the emotion. Together, our results have revealed an abnormal physiological response to faces in people with ASD, which may in turn be associated with impaired face perception previously found in many studies.

Review: Posed vs. Genuine Facial Emotion Recognition and Expression in Autism and Implications for Intervention.

Different styles of social interaction are one of the core characteristics of autism spectrum disorder (ASD). Social differences among individuals with ASD often include difficulty in discerning the emotions of neurotypical people based on their facial expressions. This review first covers the rich body of literature studying differences in facial emotion recognition (FER) in those with ASD, including behavioral studies and neurological findings. In particular, we highlight subtle emotion recognition and various factors related to inconsistent findings in behavioral studies of FER in ASD. Then, we discuss the dual problem of FER - namely facial emotion expression (FEE) or the production of facial expressions of emotion. Despite being less studied, social interaction involves both the ability to recognize emotions and to produce appropriate facial expressions. How others perceive facial expressions of emotion in those with ASD has remained an under-researched area. Finally, we propose a method for teaching FER [FER teaching hierarchy (FERTH)] based on recent research investigating FER in ASD, considering the use of posed vs. genuine emotions and static vs. dynamic stimuli. We also propose two possible teaching approaches: (1) a standard method of teaching progressively from simple drawings and cartoon characters to more complex audio-visual video clips of genuine human expressions of emotion with context clues or (2) teaching in a field of images that includes posed and genuine emotions to improve generalizability before progressing to more complex audio-visual stimuli. Lastly, we advocate for autism interventionists to use FER stimuli developed primarily for research purposes to facilitate the incorporation of well-controlled stimuli to teach FER and bridge the gap between intervention and research in this area.

Deep Neural Network Reveals the World of Autism From a First-Person Perspective.

People with autism spectrum disorder (ASD) show atypical attention to social stimuli and aberrant gaze when viewing images of the physical world. However, it is unknown how they perceive the world from a first-person perspective. In this study, we used machine learning to classify photos taken in three different categories (people, indoors, and outdoors) as either having been taken by individuals with ASD or by peers without ASD. Our classifier effectively discriminated photos from all three categories, but was particularly successful at classifying photos of people with >80% accuracy. Importantly, visualization of our model revealed critical features that led to successful discrimination and showed that our model adopted a strategy similar to that of ASD experts. Furthermore, for the first time we showed that photos taken by individuals with ASD contained less salient objects, especially in the central visual field. Notably, our model outperformed classification of these photos by ASD experts. Together, we demonstrate an effective and novel method that is capable of discerning photos taken by individuals with ASD and revealing aberrant visual attention in ASD from a unique first-person perspective. Our method may in turn provide an objective measure for evaluations of individuals with ASD. LAY SUMMARY: People with autism spectrum disorder (ASD) demonstrate atypical visual attention to social stimuli. However, it remains largely unclear how they perceive the world from a first-person perspective. In this study, we employed a deep learning approach to analyze a unique dataset of photos taken by people with and without ASD. Our computer modeling was not only able to discern which photos were taken by individuals with ASD, outperforming ASD experts, but importantly, it revealed critical features that led to successful discrimination, revealing aspects of atypical visual attention in ASD from their first-person perspective.

Meta-Analyses Support a Taxonomic Model for Representations of Different Categories of Audio-Visual Interaction Events in the Human Brain.

Our ability to perceive meaningful action events involving objects, people, and other animate agents is characterized in part by an interplay of visual and auditory sensory processing and their cross-modal interactions. However, this multisensory ability can be altered or dysfunctional in some hearing and sighted individuals, and in some clinical populations. The present meta-analysis sought to test current hypotheses regarding neurobiological architectures that may mediate audio-visual multisensory processing. Reported coordinates from 82 neuroimaging studies (137 experiments) that revealed some form of audio-visual interaction in discrete brain regions were compiled, converted to a common coordinate space, and then organized along specific categorical dimensions to generate activation likelihood estimate (ALE) brain maps and various contrasts of those derived maps. The results revealed brain regions (cortical "hubs") preferentially involved in multisensory processing along different stimulus category dimensions, including 1) living versus nonliving audio-visual events, 2) audio-visual events involving vocalizations versus actions by living sources, 3) emotionally valent events, and 4) dynamic-visual versus static-visual audio-visual stimuli. These meta-analysis results are discussed in the context of neurocomputational theories of semantic knowledge representations and perception, and the brain volumes of interest are available for download to facilitate data interpretation for future neuroimaging studies.

Detection of Genuine and Posed Facial Expressions of Emotion: Databases and Methods.

Facial expressions of emotion play an important role in human social interactions. However, posed expressions of emotion are not always the same as genuine feelings. Recent research has found that facial expressions are increasingly used as a tool for understanding social interactions instead of personal emotions. Therefore, the credibility assessment of facial expressions, namely, the discrimination of genuine (spontaneous) expressions from posed (deliberate/volitional/deceptive) ones, is a crucial yet challenging task in facial expression understanding. With recent advances in computer vision and machine learning techniques, rapid progress has been made in recent years for automatic detection of genuine and posed facial expressions. This paper presents a general review of the relevant research, including several spontaneous vs. posed (SVP) facial expression databases and various computer vision based detection methods. In addition, a variety of factors that will influence the performance of SVP detection methods are discussed along with open issues and technical challenges in this nascent field.

Processing of Real-World, Dynamic Natural Stimuli in Autism is Linked to Corticobasal Function.

Many individuals with autism spectrum disorder (ASD) have been shown to perceive everyday sensory information differently compared to peers without autism. Research examining these sensory differences has primarily utilized nonnatural stimuli or natural stimuli using static photos with few having utilized dynamic, real-world nonverbal stimuli. Therefore, in this study, we used functional magnetic resonance imaging to characterize brain activation of individuals with high-functioning autism when viewing and listening to a video of a real-world scene (a person bouncing a ball) and anticipating the bounce. We investigated both multisensory and unisensory processing and hypothesized that individuals with ASD would show differential activation in (a) primary auditory and visual sensory cortical and association areas, and in (b) cortical and subcortical regions where auditory and visual information is integrated (e.g. temporal-parietal junction, pulvinar, superior colliculus). Contrary to our hypotheses, the whole-brain analysis revealed similar activation between the groups in these brain regions. However, compared to controls the ASD group showed significant hypoactivation in the left intraparietal sulcus and left putamen/globus pallidus. We theorize that this hypoactivation reflected underconnectivity for mediating spatiotemporal processing of the visual biological motion stimuli with the task demands of anticipating the timing of the bounce event. The paradigm thus may have tapped into a specific left-lateralized aberrant corticobasal circuit or loop involved in initiating or inhibiting motor responses. This was consistent with a dual "when versus where" psychophysical model of corticobasal function, which may reflect core differences in sensory processing of real-world, nonverbal natural stimuli in ASD. Autism Res 2020, 13: 539-549. © 2020 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: To understand how individuals with autism perceive the real-world, using magnetic resonance imaging we examined brain activation in individuals with autism while watching a video of someone bouncing a basketball. Those with autism had similar activation to controls in auditory and visual sensory brain regions, but less activation in an area that processes information about body movements and in a region involved in modulating movements. These areas are important for understanding the actions of others and developing social skills.

Divergent Human Cortical Regions for Processing Distinct Acoustic-Semantic Categories of Natural Sounds: Animal Action Sounds vs. Vocalizations.

A major gap in our understanding of natural sound processing is knowledge of where or how in a cortical hierarchy differential processing leads to categorical perception at a semantic level. Here, using functional magnetic resonance imaging (fMRI) we sought to determine if and where cortical pathways in humans might diverge for processing action sounds vs. vocalizations as distinct acoustic-semantic categories of real-world sound when matched for duration and intensity. This was tested by using relatively less semantically complex natural sounds produced by non-conspecific animals rather than humans. Our results revealed a striking double-dissociation of activated networks bilaterally. This included a previously well described pathway preferential for processing vocalization signals directed laterally from functionally defined primary auditory cortices to the anterior superior temporal gyri, and a less well-described pathway preferential for processing animal action sounds directed medially to the posterior insulae. We additionally found that some of these regions and associated cortical networks showed parametric sensitivity to high-order quantifiable acoustic signal attributes and/or to perceptual features of the natural stimuli, such as the degree of perceived recognition or intentional understanding. Overall, these results supported a neurobiological theoretical framework for how the mammalian brain may be fundamentally organized to process acoustically and acoustic-semantically distinct categories of ethologically valid, real-world sounds.