Autism spectrum disorder is characterized by impaired social communication and interaction. As a neurodevelopmental disorder typically diagnosed during childhood, diagnosis in adulthood is preceded by a resource-heavy clinical assessment period. The ongoing developments in digital phenotyping give rise to novel opportunities within the screening and diagnostic process. In this study, we investigated videos of naturalistic social interaction between autistic and non-autistic adults on their predictiveness for autistic behaviors. Non-autistic control participants were either paired with each other or an autistic participant to engage in two conversational tasks. We used existing computer vision algorithms to extract information based on the synchrony of movement and facial expression. These were subsequently used as features in a support vector machine learning model to predict interaction dyad membership. Results showed high predictive accuracy of synchrony in facial movements, underlining the distinctive nature of non-verbal behavior in autism and its feasibility for digitalized assessment.

Background: Figure-disembedding is one of the most discussed visuo-cognitive functions, in which individuals with Autism Spectrum Disorder (ASD) have been reported to outperform non-autistic individuals. A local processing bias has been assumed to underlie such superior performance patterns. The aim of the current study is to investigate whether processing preferences can be modified by procedural priming. Method: The current study used a procedural priming task (Navon figures) to induce more local or global processing in 25 autistic and 21 typically-developing (TD) control participants, using hierarchical figures preceding the figure-disembedding task. Results: Participants with ASD outperformed non-autistic individuals in the unprimed baseline task version. The performance was not modulated by priming in either direction (towards a local or global processing style) in both groups. However, the performance of TD control participants was improved by training to the same level as that observed in the ASD group. Conclusions: Figure-disembedding performance in ASD is superior to that in TD control participants and robust against procedural priming or training. In contrast, performance in the TD control group can be improved up to the level of the ASD group. Any studies reporting superiority in individuals with ASD in figure-disembedding should consider training effects when evaluating group differences.

Individuals with Autism Spectrum Disorder (ASD) are thought to under-rely on prior knowledge in perceptual decision-making. This study examined whether this applies to decisions of attention allocation, of relevance for ‘predictive-coding’ accounts of ASD. In a visual search task, a salient but task-irrelevant distractor appeared with higher probability in one display half. Individuals with ASD learned to avoid ‘attentional capture’ by distractors in the probable region as effectively as control participants—indicating typical priors for deploying attention. However, capture by a ‘surprising’ distractor at an unlikely location led to greatly slowed identification of a subsequent target at that location—indicating that individuals with ASD attempt to control surprise (unexpected attentional capture) by over-regulating parameters in post-selective decision-making.