Impaired formation and updating of internal predictive models in a rat model of Fragile X Syndrome

Abstract

Predictive coding frameworks propose that perception emerges from a continuous comparison of incoming sensory signals with internally generated predictions, with mismatches between the two computed as prediction errors. Disruptions to the balance between these top-down predictions and bottom-up sensory signals are theorized to contribute to sensory abnormalities in neuropsychiatric conditions like autism spectrum disorders. However, disambiguating bottom-up from top-down contributions to sensory perception remains a difficult challenge, particularly in animal models. Here we develop a probabilistic oddball detection task in which rats must track local statistics within a trial to detect a deviant stimulus, as well as global statistics across trials to anticipate when a deviant will occur. This design enables formation of experimentally specified internal models of deviant expectation that can be quantitatively derived from behavior and manipulated independently of local stimulus statistics. We used this task to characterize sensory predictive behavior in a Fmr1 KO rat model of Fragile X Syndrome, the most common monogenic cause of autism. Male Fmr1 KO rats detected deviant stimuli at wildtype levels but exhibited reduced anticipation of deviant occurrence based on cross-trial statistics and failed to adapt their behavior when these statistics changed. Computational modeling revealed that these behavioral deficits reflected imprecise and unstable internal predictive models skewed towards sensory immediacy. These findings provide evidence for disrupted predictive processing in Fragile X Syndrome, consistent with active inference accounts of autism, and highlight the utility of this probabilistic oddball task design for interrogating predictive coding and perceptual impairments in neuropsychiatric conditions.