Dynamical circular inference in the general population and the psychosis spectrum : insights from perceptual decision making ; Inférence circulaire dynamique en population générale et dans le spectre psychotique : apports de la prise de décision perceptive

We live in an uncertain world, yet our survival depends on how quickly and accurately we can make decisions and act upon them. To address this problem, modern neuroscience reconceptualised perception as an inference process, in which the brain combines sensory inputs and prior expectations to reconstruct a plausible image of the world. In addition to that, influential theories in the emerging field of computational psychiatry suggest that various psychiatric disorders, including schizophrenia, could be the outcome of impaired predictive processing. Among those theories, the circular inference framework suggests that an unconstrained propagation of information in the cortex, underlain by an excitatory to inhibitory imbalance, can generate false percepts and beliefs, similar to those exhibited by schizophrenia patients. In the present thesis, we probed the role of circular inference from normal to pathological brain functioning, gaining insights from perceptual decision making in the presence of high ambiguity. In the first part of the thesis, we focused on the role of circularity in bistable perception in the general population. Bistability occurs when two mutually exclusive interpretations compete and switch as dominant percepts every few seconds. In a 1st article, we manipulated sensory evidence and priors in a Necker cube task, asking how the brain combines low-level and high-level information to form perceptual interpretations. We found a significant effect of each manipulation but also an interaction between the two, a finding incompatible with Bayes optimal integration. Bayesian model comparison further supported this observation, showing that a circular inference model outperformed purely Bayesian models. Having established a link between circular inference and bistable perception, we then put forward a functional theory of bistability, based on circularity (2nd article). In particular, we derived the dynamics of a dynamical circular inference model, showing that descending loops (i.e. a form of ...