Optimal estimation of local motion-in-depth with naturalistic stimuli
Estimating the motion of objects in depth is important for behavior and is strongly supported by binocular visual cues. To understand both how the brain should estimate motion in depth and how natural constraints shape and limit performance in two local 3D motion tasks, we develop image-computable i...
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| Format: | article |
| Language: | English |
| Published: |
2025
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| Online Access: | https://hdl.handle.net/20.500.12008/54084 |
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| _version_ | 1868890149046190080 |
|---|---|
| author | Herrera-Esposito, Daniel |
| author2 | Burge, Johannes |
| author2_role | author |
| author_browse | Burge, Johannes Herrera-Esposito, Daniel |
| author_facet | Herrera-Esposito, Daniel Burge, Johannes |
| author_role | author |
| collection | COLIBRI |
| dc.contributor.none.fl_str_mv | Herrera-Esposito Daniel, Universidad de la República (Uruguay). Facultad de Ciencias. Instituto de Biología. Burge Johannes |
| dc.creator.none.fl_str_mv | Herrera-Esposito, Daniel Burge, Johannes |
| dc.date.none.fl_str_mv | 2025 2026-03-25T13:21:34Z 2026-03-25T13:21:34Z |
| dc.format.none.fl_str_mv | 17 h application/pdf |
| dc.identifier.none.fl_str_mv | Herrera-Esposito, D y Burge, J. "Optimal estimation of local motion-in-depth with naturalistic stimuli". The Journal of Neuroscience. [en línea] 2025, 45(8): e0490242024. 17 h. DOI: 10.1523/JNEUROSCI.0490-24.2024 1529-2401 https://hdl.handle.net/20.500.12008/54084 10.1523/JNEUROSCI.0490-24.2024 |
| dc.language.none.fl_str_mv | en eng |
| dc.publisher.none.fl_str_mv | Society for Neuroscience |
| dc.relation.none.fl_str_mv | The Journal of Neuroscience, 2025, 45(8): e0490242024. |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess Licencia Creative Commons Atribución (CC - By 4.0) |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.none.fl_str_mv | Bayesian perception Binocular vision Ideal observer Motion in depth Natural scene statistics |
| dc.title.none.fl_str_mv | Optimal estimation of local motion-in-depth with naturalistic stimuli |
| dc.type.none.fl_str_mv | Artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| description | Estimating the motion of objects in depth is important for behavior and is strongly supported by binocular visual cues. To understand both how the brain should estimate motion in depth and how natural constraints shape and limit performance in two local 3D motion tasks, we develop image-computable ideal observers from a large number of binocular video clips created from a dataset of natural images. The observers spatiotemporally filter the videos and nonlinearly decode 3D motion from the filter responses. The optimal filters and decoder are dictated by the task-relevant image statistics and are specific to each task. Multiple findings emerge. First, two distinct filter subpopulations are spontaneously learned for each task. For 3D speed estimation, filters emerge for processing either changing disparities over time or interocular velocity differences, cues that are used by humans. For 3D direction estimation, filters emerge for discriminating either left–right or toward–away motion. Second, the filter responses, conditioned on the latent variable, are well-described as jointly Gaussian, and the covariance of the filter responses carries the information about the task-relevant latent variable. Quadratic combination is thus necessary for optimal decoding, which can be implemented by biologically plausible neural computations. Finally, the ideal observer yields nonobvious—and in some cases counterintuitive—patterns of performance like those exhibited by humans. Important characteristics of human 3D motion processing and estimation may therefore result from optimal information processing in the early visual system. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | anni_a00a7e68236c15f6b3d59b340ee84fb8 |
| identifier_str_mv | Herrera-Esposito, D y Burge, J. "Optimal estimation of local motion-in-depth with naturalistic stimuli". The Journal of Neuroscience. [en línea] 2025, 45(8): e0490242024. 17 h. DOI: 10.1523/JNEUROSCI.0490-24.2024 1529-2401 10.1523/JNEUROSCI.0490-24.2024 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | anni |
| network_name_str | oai-lr-anni |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/54084 |
| publishDate | 2025 |
| publishDateSort | 2025 |
| publisher.none.fl_str_mv | Society for Neuroscience |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Licencia Creative Commons Atribución (CC - By 4.0) |
| spelling | Optimal estimation of local motion-in-depth with naturalistic stimuliHerrera-Esposito, DanielBurge, JohannesBayesian perceptionBinocular visionIdeal observerMotion in depthNatural scene statisticsEstimating the motion of objects in depth is important for behavior and is strongly supported by binocular visual cues. To understand both how the brain should estimate motion in depth and how natural constraints shape and limit performance in two local 3D motion tasks, we develop image-computable ideal observers from a large number of binocular video clips created from a dataset of natural images. The observers spatiotemporally filter the videos and nonlinearly decode 3D motion from the filter responses. The optimal filters and decoder are dictated by the task-relevant image statistics and are specific to each task. Multiple findings emerge. First, two distinct filter subpopulations are spontaneously learned for each task. For 3D speed estimation, filters emerge for processing either changing disparities over time or interocular velocity differences, cues that are used by humans. For 3D direction estimation, filters emerge for discriminating either left–right or toward–away motion. Second, the filter responses, conditioned on the latent variable, are well-described as jointly Gaussian, and the covariance of the filter responses carries the information about the task-relevant latent variable. Quadratic combination is thus necessary for optimal decoding, which can be implemented by biologically plausible neural computations. Finally, the ideal observer yields nonobvious—and in some cases counterintuitive—patterns of performance like those exhibited by humans. Important characteristics of human 3D motion processing and estimation may therefore result from optimal information processing in the early visual system.Society for NeuroscienceHerrera-Esposito Daniel, Universidad de la República (Uruguay). Facultad de Ciencias. Instituto de Biología.Burge Johannes2026-03-25T13:21:34Z2026-03-25T13:21:34Z2025Artículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion17 happlication/pdfHerrera-Esposito, D y Burge, J. "Optimal estimation of local motion-in-depth with naturalistic stimuli". The Journal of Neuroscience. [en línea] 2025, 45(8): e0490242024. 17 h. DOI: 10.1523/JNEUROSCI.0490-24.20241529-2401https://hdl.handle.net/20.500.12008/5408410.1523/JNEUROSCI.0490-24.2024reponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaenengThe Journal of Neuroscience, 2025, 45(8): e0490242024.Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución (CC - By 4.0)oai:colibri.udelar.edu.uy:20.500.12008/540842026-04-14T10:11:01Z |
| spellingShingle | Optimal estimation of local motion-in-depth with naturalistic stimuli Herrera-Esposito, Daniel Bayesian perception Binocular vision Ideal observer Motion in depth Natural scene statistics |
| status_str | publishedVersion |
| title | Optimal estimation of local motion-in-depth with naturalistic stimuli |
| title_full | Optimal estimation of local motion-in-depth with naturalistic stimuli |
| title_fullStr | Optimal estimation of local motion-in-depth with naturalistic stimuli |
| title_full_unstemmed | Optimal estimation of local motion-in-depth with naturalistic stimuli |
| title_short | Optimal estimation of local motion-in-depth with naturalistic stimuli |
| title_sort | Optimal estimation of local motion-in-depth with naturalistic stimuli |
| topic | Bayesian perception Binocular vision Ideal observer Motion in depth Natural scene statistics |
| url | https://hdl.handle.net/20.500.12008/54084 |