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Dynamical circular inference in the general population and the psychosis spectrum : insights from perceptual decision making

Abstract : 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 circularity resulting in aberrant amplification of the priors) transform what is normally a leaky integration of noisy evidence into a bistable attractor with two highly trusted stable states. Importantly, this model can explain both the existence and the phenomenological properties of bistable perception, making a number of testable predictions. Finally, in a 3rd article, we tested one of the model’s predictions, namely the perceptual behaviour when the stimulus is presented discontinuously. We ran two Necker cube experiments using a novel intermittent-presentation methodology, and we calculated the stabilisation curves (i.e. persistence as a function of blank durations). We found that participants’ behaviour was compatible with the model’s prediction for a system with descending loops, suggesting that circularity constitutes a general mechanism that shapes the way healthy individuals perceive the world. In the second part, we studied circular inference in pathological conditions related to psychosis. We notably focused on two varieties of the psychotic experience, namely schizophrenia-related psychosis and drug-induced psychosis. After discussing the links between behaviour, aberrant message-passing and the corresponding neural networks (4th article), we used bistable perception to probe the computational mechanisms underlying schizophrenia in a 5th article. We compared patients with prominent positive symptoms with matched healthy controls in two bistable perception tasks. Our results suggest an enhanced amplification of sensory inputs in patients, combined with an overestimation of the environmental volatility. In the last article (6th), we delineated a multiscale account of psychedelics, ultimately linking the macroscale (i.e. phenomenological considerations such as the crossmodal character of the psychedelics experience), the mesoscale (i.e. loops) and the microscale (i.e. neuromodulators and canonical microcircuits).
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Submitted on : Thursday, May 16, 2019 - 7:44:29 PM
Last modification on : Thursday, October 22, 2020 - 10:44:09 AM


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  • HAL Id : tel-02132179, version 1



Pantelis Leptourgos. Dynamical circular inference in the general population and the psychosis spectrum : insights from perceptual decision making. Neuroscience. Université Paris sciences et lettres, 2018. English. ⟨NNT : 2018PSLEE032⟩. ⟨tel-02132179⟩



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