Researchers are using two computational lenses to make sense of these signatures. One treats perception as an internal, probabilistic guesswork process that updates with incoming evidence. The other treats perceptual changes as purposeful moves the brain makes to reduce uncertainty or gain information. Together, these perspectives help connect observations in human subjects with findings from animal models and with circuit-level explanations drawn from neuroscience.
For clinicians and researchers interested in human potential, multistability offers a practical window into disorders where perception, decision-making, or information integration go awry. Tests based on these perceptual flips could become noninvasive probes of cognitive function and of how treatments reshape underlying computations. Follow the link to explore how this approach could reshape translational research and expand our tools for understanding cognitive diversity and impairment.
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 nonclinical 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), which frames 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 noninvasive tool for clinical applications, facilitating translational research and enhancing our mechanistic understanding of cognitive processes and their impairments.
