Perceptual multistability: a window for a multi-facet understanding of psychiatric disorders

TL;DR

This paper explores how perceptual multistability can serve as a valuable, non-invasive window into understanding cognitive functions and dysfunctions in psychiatric disorders, bridging computational models and neural mechanisms.

Contribution

It synthesizes approaches from Bayesian modeling and active inference to connect perceptual multistability with clinical insights and translational research.

Findings

Multistability reveals differences in perception dynamics between clinical and non-clinical populations.

Computational models link perceptual switches to neural mechanisms and behavior.

Perceptual multistability offers a promising tool for clinical and translational research.

Abstract

Perceptual multistability, observed across species and sensory modalities, offers valuable insights into numerous cognitive functions and dysfunctions. For instance, differences in temporal dynamics and information integration during percept formation often distinguish clinical from non-clinical populations. Computational psychiatry can elucidate these variations, through two primary approaches: (i) Bayesian modeling, which treats perception as an unconscious inference, and (ii) an active, information-seeking perspective (e.g., reinforcement learning) framing perceptual switches as internal actions. Our synthesis aims to leverage multistability to bridge these computational psychiatry subfields, linking human and animal studies as well as connecting behavior to underlying neural mechanisms. Perceptual multistability emerges as a promising non-invasive tool for clinical applications, facilitating translational research and enhancing our mechanistic understanding of cognitive processes and their impairments.