Perceptual multistability: a window for a multi-facet understanding of psychiatric disorders
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909656544182272 |
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| author | Safavi, Shervin Rolland, Danaé Sterzer, Philipp Jardri, Renaud Leptourgos, Pantelis |
| author_facet | Safavi, Shervin Rolland, Danaé Sterzer, Philipp Jardri, Renaud Leptourgos, Pantelis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18176 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Perceptual multistability: a window for a multi-facet understanding of psychiatric disorders Safavi, Shervin Rolland, Danaé Sterzer, Philipp Jardri, Renaud Leptourgos, Pantelis Neurons and Cognition 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. |
| title | Perceptual multistability: a window for a multi-facet understanding of psychiatric disorders |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2506.18176 |