Artificial neural networks have recently achieved many successes in solving sequential
processing and planning tasks. Their success is often ascribed to the emergence of the …

Linguistic repetitions in children are conceptualized as negative in children with autism–
echolalia, without communicative purpose–and positive in typically develo** (TD) children …

Autism spectrum disorders have been proposed to arise from impairments in the
probabilistic integration of prior knowledge with sensory inputs. Circular inference is one …

Aberrant motor-sensory predictive functions have been linked to symptoms of psychosis,
particularly reduced attenuation of self-generated sensations and misattribution of self …

When making risky choices in social contexts, humans typically combine social information
with individual preferences about the options at stake. It remains unknown how such …

The circular inference (CI) computational model assumes a corruption of sensory data by
prior information and vice versa, leading at the extremes to'see what we expect'(through …

Background Due to the high heterogeneity of schizophrenia, the factors influencing social
cognitive impairment are controversial. The purpose of this study was to investigate the …

Sociopolitical crises causing uncertainty have accumulated in recent years, providing fertile
ground for the emergence of conspiracy ideations. Computational models constitute …

IntroductionMachine learning (ML) approaches are a promising venue for identifying vocal
markers of neuropsychiatric disorders, such as schizophrenia. While recent studies have …

Circular Inference is a Bayesian model of psychiatric disorders, previously designed to
account for clinical manifestations of schizophrenia and psychosis. Circular Inference relies …