Abstract

A state of pathological uncertainty about environmental regularities might represent a key step in the pathway to psychotic illness. Early psychosis can be investigated in healthy volunteers under ketamine, an NMDA receptor antagonist. Here, we explored the effects of ketamine on contingency learning using a placebo-controlled, double-blind, crossover design. During functional magnetic resonance imaging, participants performed an instrumental learning task, in which cue-outcome contingencies were probabilistic and reversed between blocks. Bayesian model comparison indicated that in such an unstable environment, reinforcement learning parameters are downregulated depending on confidence level, an adaptive mechanism that was specifically disrupted by ketamine administration. Drug effects were underpinned by altered neural activity in a fronto-parietal network, which reflected the confidence-based shift to exploitation of learned contingencies. Our findings suggest that an early characteristic of psychosis lies in a persistent doubt that undermines the stabilization of behavioral policy resulting in a failure to exploit regularities in the environment.

Highlights

  • One of the big challenges facing psychiatry is to develop an understanding of psychotic symptoms that goes beyond clinical description to uncover underlying computational and neurobiological mechanisms

  • Probabilistic learning tasks have been widely studied in schizophrenia, providing evidence for a complex pattern of deficit depending on the precise nature of the task as well as of the profile of recruited patients

  • Paired t-tests indicated that ketamine caused a significant increase in positive psychotic symptoms as measured by the Rating Scale for Psychotic Symptoms (t(20) = 5.43, P o0.001) and the Brief Psychiatric Rating Scale (t(20) = 2.8, P = 0.011), as well as in dissociative symptoms as measured by the Clinician Administered Dissociative States Scale (t(20) = 3.72, P = 0.0013)

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Summary

Introduction

One of the big challenges facing psychiatry is to develop an understanding of psychotic symptoms that goes beyond clinical description to uncover underlying computational and neurobiological mechanisms. Insights derived from reinforcement learning models have already proven useful in developing theoretical accounts of how psychotic experiences may arise and how they may relate to disrupted brain processes. It has been proposed that the core impairment in schizophrenia might not affect learning ability per se, but rather the flexible control required to perform complex tasks and/or the capacity to optimize behavior in order to maintain a high level of performance.[11] In line with such proposals, our hypothesis is that a key feature of early psychosis is a disruption in how confidence is updated and used to drive behavior in a dynamic environment

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