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Visual motion perception as online hierarchical inference
Identifying the structure of motion relations in the environment is critical for navigation, tracking, prediction, and pursuit. Yet, little is known about the mental and neural computations that allow the visual system to infer this structure online from a volatile stream of visual information. We p...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9715570/ https://www.ncbi.nlm.nih.gov/pubmed/36456546 http://dx.doi.org/10.1038/s41467-022-34805-5 |
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author | Bill, Johannes Gershman, Samuel J. Drugowitsch, Jan |
author_facet | Bill, Johannes Gershman, Samuel J. Drugowitsch, Jan |
author_sort | Bill, Johannes |
collection | PubMed |
description | Identifying the structure of motion relations in the environment is critical for navigation, tracking, prediction, and pursuit. Yet, little is known about the mental and neural computations that allow the visual system to infer this structure online from a volatile stream of visual information. We propose online hierarchical Bayesian inference as a principled solution for how the brain might solve this complex perceptual task. We derive an online Expectation-Maximization algorithm that explains human percepts qualitatively and quantitatively for a diverse set of stimuli, covering classical psychophysics experiments, ambiguous motion scenes, and illusory motion displays. We thereby identify normative explanations for the origin of human motion structure perception and make testable predictions for future psychophysics experiments. The proposed online hierarchical inference model furthermore affords a neural network implementation which shares properties with motion-sensitive cortical areas and motivates targeted experiments to reveal the neural representations of latent structure. |
format | Online Article Text |
id | pubmed-9715570 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-97155702022-12-03 Visual motion perception as online hierarchical inference Bill, Johannes Gershman, Samuel J. Drugowitsch, Jan Nat Commun Article Identifying the structure of motion relations in the environment is critical for navigation, tracking, prediction, and pursuit. Yet, little is known about the mental and neural computations that allow the visual system to infer this structure online from a volatile stream of visual information. We propose online hierarchical Bayesian inference as a principled solution for how the brain might solve this complex perceptual task. We derive an online Expectation-Maximization algorithm that explains human percepts qualitatively and quantitatively for a diverse set of stimuli, covering classical psychophysics experiments, ambiguous motion scenes, and illusory motion displays. We thereby identify normative explanations for the origin of human motion structure perception and make testable predictions for future psychophysics experiments. The proposed online hierarchical inference model furthermore affords a neural network implementation which shares properties with motion-sensitive cortical areas and motivates targeted experiments to reveal the neural representations of latent structure. Nature Publishing Group UK 2022-12-01 /pmc/articles/PMC9715570/ /pubmed/36456546 http://dx.doi.org/10.1038/s41467-022-34805-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Bill, Johannes Gershman, Samuel J. Drugowitsch, Jan Visual motion perception as online hierarchical inference |
title | Visual motion perception as online hierarchical inference |
title_full | Visual motion perception as online hierarchical inference |
title_fullStr | Visual motion perception as online hierarchical inference |
title_full_unstemmed | Visual motion perception as online hierarchical inference |
title_short | Visual motion perception as online hierarchical inference |
title_sort | visual motion perception as online hierarchical inference |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9715570/ https://www.ncbi.nlm.nih.gov/pubmed/36456546 http://dx.doi.org/10.1038/s41467-022-34805-5 |
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