the neural based of predictive styles along the autism-schizophrenia continuum

The present thesis aimed to push the boundaries of understanding on the pivotal role played by neural oscillations in modulating Bayesian inference and decision-making processes. Additionally, it explored how inter-individual differences in Autistic and Schizotypal traits shape the neuro-behavioral mechanisms underpinning perceptual inference. A primary contribution of this work is the development of a novel bio-behavioral model of the Autism Spectrum Disorder (ASD) - Schizophrenia Spectrum Disorder (SSD) continuum. This model conceptualizes both conditions as disorders of predictive abilities, stemming from disruptions in brain oscillatory patterns. Building on foundational insights, the thesis employs advanced computational models and state-of-art EEG methodologies to elucidate the distinct oscillatory signatures of predictive processing and understand the rhythmic underpinnings of maladaptive predictive inference in ASD and SSD. Specifically, the research investigated 1) the brain connectivity patterns related to ASD-SSD continuum, 2) the behavioral and electrophysiological mechanisms of prior knowledge integration in decision-making, and 3) how dispositional factors associated with ASD and SSD traits direct individual predictive strategies in both laboratory settings and real-world scenarios. These studies provided a comprehensive understanding of cognitive styles and brain oscillatory codes governing perception, decision-making, and health-related attitudes, contributing significantly to our knowledge of how these mechanisms underlie manifestations observable in the ASD-SSD continuum.