Online Now: Perceptual multistability: a multifaceted window into brain dysfunctions
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.
Thrilled to see the first @cp-trendscognsci.bsky.social paper of the lab out 🤩 Wanna understand one of the most complex cognitive dysfunctions using one of the most basic adaptive functions, perception?
Paper: linkinghub.elsevier.com/retrieve/pii/S136466132600032X
Thread: bsky.app/profile/neur...
Trends in Cognitive Sciences
joss.theoj.org
CMC lab
1/11 Happy to share our TICS paper on using the flexibility of one of the most basic cognitive functions, perception, to understand one of the most complex cognitive dysfunctions, psychiatric conditions (also my first formal work in computational psychiatry 🎉)
📄: www.cell.com/trends/cogni...
🧵 : 👇
Shervin Safavi
Perceptual multistability, observed across species and sensory modalities, offers
valuable insights into numerous cognitive functions and dysfunctions. For instance,
differences in temporal dynamics a...
Online Now: Perceptual multistability: a multifaceted window into brain dysfunctions
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.
