Altered semantic prediction error processing with increasing schizotypal and autistic traits

Predictive processing has been proposed as a framework for symptom development in autism (ASD) and schizophrenia (SSD) spectrum disorders, with ASD being associated with an overweighting of (low-level) sensory evidence whereas SSD is characterized by an overweighting of (high-level) prior beliefs, both resulting in altered prediction error processing. This study investigated these hypotheses and their electrophysiological correlates in subclinical expressions of ASD and SSD during language processing. Participants completed an auditory comprehension task that manipulated the precision of high-level semantic prior beliefs and low-level sensory evidence. Hierarchical Bayesian belief-updating modeling and electroencephalography were used to examine whether imbalances in the weighting of prior beliefs and sensory evidence were reflected in altered processing of semantic prediction errors, characterized by mean N400 amplitudes. Computational modeling revealed that increasing schizotypal traits were associated with significant overweighting of prior beliefs, while autistic traits did not show a significant shift. Linear mixed-effect models of mean N400 amplitudes indicated that schizotypy-related overweighting of semantic prior beliefs was reflected in a reduced semantic prediction error signal, indexed by smaller N400 differences between high predictability sentences and both low predictability and high predictability mismatch sentences. A similar but weaker and less distinct pattern emerged for increasing autistic traits. These findings provide converging computational and electrophysiological support for overweighting of semantic prior beliefs with increasing subclinical schizotypy. In contrast, increasing autistic traits were not associated with overweighting of sensory evidence, with electrophysiological results instead suggesting subtle alterations in the weighting of semantic prior beliefs.