The P1 and N170 components, two event-related potentials sensitive to face processing, were examined in response to faces and vehicles for children with autism and typical development. P1 amplitude decreased, P1 latency decreased, and N170 amplitude became more negative with age. Children with typical development had larger P1 amplitudes for inverted faces than upright faces, but children with autism did not show this pattern. Children with autism had longer N170 latencies than children with typical development. Smaller P1 amplitudes and more negative N170 amplitudes for upright faces were associated with better social skills for children with typical development.

Although prior studies have demonstrated reduced resting state EEG coherence in adults with autism spectrum disorder (ASD), no studies have explored the nature of EEG coherence during joint attention. We examined the EEG coherence of the joint attention network in adolescents with and without ASD during congruent and incongruent joint attention perception and an eyes-open resting condition. Across conditions, adolescents with ASD showed reduced right hemisphere temporal–central alpha coherence compared to typically developing adolescents. Greater right temporal–central alpha coherence during joint attention was positively associated with social cognitive performance in typical development but not in ASD. These results suggest that, in addition to a resting state, EEG coherence during joint attention perception is reduced in ASD.

Higher-functioning participants with and without autism spectrum disorder (ASD) viewed a series of face stimuli, made decisions regarding the affect of each face, and indicated their confidence in each decision. Confidence significantly predicted accuracy across all participants, but this relation was stronger for participants with typical development than participants with ASD. In the hierarchical linear modeling analysis, there were no differences in face processing accuracy between participants with and without ASD, but participants with ASD were more confident in their decisions. These results suggest that individuals with ASD have metacognitive impairments and are overconfident in face processing. Additionally, greater metacognitive awareness was predictive of better face processing accuracy, suggesting that metacognition may be a pivotal skill to teach in interventions.

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Gaze following allows individuals to detect the locus of attention of both conspecifics and other species. However, little is known about how this ability develops. We explored the emergence of bobwhite quail hatchlings' ability to track human gaze by assessing their avoidance behavior in an open arena under five testing conditions: (1) a Direct Gaze condition, in which an experimenter looking down was positioned above one of two approach areas; (2) a Gaze Follow condition in which an experimenter, positioned equidistant between two approach areas, directed his/her gaze towards one of the areas; (3) a Masked Gaze Follow condition, in which the experimenter wore a mask during the Gaze Follow test; (4) a Deprived Face Experience condition, in which hatchlings were deprived of experience with human faces prior to the Gaze Follow test; and (5) a Control condition in which no experimenter was present during testing. Results revealed that hatchlings from the Direct Gaze condition preferred the non-gazed approach area at all ages tested. Hatchlings from the Gaze Follow condition preferred the non-gazed approach area at 48 and 72 h, but not at 24 h of age. In contrast, hatchlings from the Masked Gaze Follow, Deprived Face and Control conditions did not prefer either approach area at any age tested. These results indicate that experience with human faces plays a key role in the rapid emergence of gaze following behavior in bobwhite quail hatchlings.

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Spectrum Disorder (ASD) is a neurodevelopmental condition which affects a persons cognition and behaviour. It is a lifelong condition which cannot be cured completely using any intervention to date. However, early diagnosis and follow-up treatments have a major impact on autistic people. Unfortunately, the current diagnostic practices, which are subjective and behaviour dependent, delay the diagnosis at an early age and makes it harder to distinguish autism from other developmental disorders. Several works of literature explore the possible behaviour-independent measures to diagnose ASD. Abnormalities in EEG can be used as reliable biomarkers to diagnose ASD. This work presents a low-cost and straightforward diagnostic approach to classify ASD based on EEG signal processing and learning models. Possibilities to use a minimum number of EEG channels have been explored. Statistical features are extracted from noise filtered EEG data before and after Discrete Wavelet Transform. Relevant features and EEG channels were selected using correlation-based feature selection. Several learning models and feature vectors have been studied and possibilities to use the minimum number of EEG channels have also been explored. Using Random Forest and Correlation-based Feature Selection, an accuracy level of 93% was obtained.