Speed adaptation as Kalman filtering
- Autores
- Barraza, Jose Fernando; Grzywacz, Norberto M.
- Año de publicación
- 2008
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- If the purpose of adaptation is to fit sensory systems to different environments, it may implement an optimization of the system. What the optimum is depends on the statistics of these environments. Therefore, the system should update its parameters as the environment changes. A Kalman-filtering strategy performs such an update optimally by combining current estimations of the environment with those from the past. We investigate whether the visual system uses such a strategy for speed adaptation. We performed a matching-speed experiment to evaluate the time course of adaptation to an abrupt velocity change. Experimental results are in agreement with Kalman-modeling predictions for speed adaptation. When subjects adapt to a low speed and it suddenly increases, the time course of adaptation presents two phases, namely, a rapid decrease of perceived speed followed by a slower phase. In contrast, when speed changes from fast to slow, adaptation presents a single phase. In the Kalman-model simulations, this asymmetry is due to the prevalence of low speeds in natural images. However, this asymmetry disappears both experimentally and in simulations when the adapting stimulus is noisy. In both transitions, adaptation now occurs in a single phase. Finally, the model also predicts the change in sensitivity to speed discrimination produced by the adaptation.
Fil: Barraza, Jose Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Investigación en Luz, Ambiente y Visión. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Instituto de Investigación en Luz, Ambiente y Visión; Argentina
Fil: Grzywacz, Norberto M.. University of Southern California; Estados Unidos - Materia
-
VISUAL MOTION
MOTION ADAPTATION
KALMAN FILTERING
SPEED PERCEPTION
SPEED DISCRIMINATION
BAYESIAN MODEL - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/105752
Ver los metadatos del registro completo
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Speed adaptation as Kalman filteringBarraza, Jose FernandoGrzywacz, Norberto M.VISUAL MOTIONMOTION ADAPTATIONKALMAN FILTERINGSPEED PERCEPTIONSPEED DISCRIMINATIONBAYESIAN MODELhttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2If the purpose of adaptation is to fit sensory systems to different environments, it may implement an optimization of the system. What the optimum is depends on the statistics of these environments. Therefore, the system should update its parameters as the environment changes. A Kalman-filtering strategy performs such an update optimally by combining current estimations of the environment with those from the past. We investigate whether the visual system uses such a strategy for speed adaptation. We performed a matching-speed experiment to evaluate the time course of adaptation to an abrupt velocity change. Experimental results are in agreement with Kalman-modeling predictions for speed adaptation. When subjects adapt to a low speed and it suddenly increases, the time course of adaptation presents two phases, namely, a rapid decrease of perceived speed followed by a slower phase. In contrast, when speed changes from fast to slow, adaptation presents a single phase. In the Kalman-model simulations, this asymmetry is due to the prevalence of low speeds in natural images. However, this asymmetry disappears both experimentally and in simulations when the adapting stimulus is noisy. In both transitions, adaptation now occurs in a single phase. Finally, the model also predicts the change in sensitivity to speed discrimination produced by the adaptation.Fil: Barraza, Jose Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Investigación en Luz, Ambiente y Visión. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Instituto de Investigación en Luz, Ambiente y Visión; ArgentinaFil: Grzywacz, Norberto M.. University of Southern California; Estados UnidosPergamon-Elsevier Science Ltd2008-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/105752Barraza, Jose Fernando; Grzywacz, Norberto M.; Speed adaptation as Kalman filtering; Pergamon-Elsevier Science Ltd; Vision Research; 48; 23-24; 10-2008; 2485-24910042-69891878-5646CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.visres.2008.08.011info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0042698908004173info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:46:00Zoai:ri.conicet.gov.ar:11336/105752instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:46:01.314CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Speed adaptation as Kalman filtering |
title |
Speed adaptation as Kalman filtering |
spellingShingle |
Speed adaptation as Kalman filtering Barraza, Jose Fernando VISUAL MOTION MOTION ADAPTATION KALMAN FILTERING SPEED PERCEPTION SPEED DISCRIMINATION BAYESIAN MODEL |
title_short |
Speed adaptation as Kalman filtering |
title_full |
Speed adaptation as Kalman filtering |
title_fullStr |
Speed adaptation as Kalman filtering |
title_full_unstemmed |
Speed adaptation as Kalman filtering |
title_sort |
Speed adaptation as Kalman filtering |
dc.creator.none.fl_str_mv |
Barraza, Jose Fernando Grzywacz, Norberto M. |
author |
Barraza, Jose Fernando |
author_facet |
Barraza, Jose Fernando Grzywacz, Norberto M. |
author_role |
author |
author2 |
Grzywacz, Norberto M. |
author2_role |
author |
dc.subject.none.fl_str_mv |
VISUAL MOTION MOTION ADAPTATION KALMAN FILTERING SPEED PERCEPTION SPEED DISCRIMINATION BAYESIAN MODEL |
topic |
VISUAL MOTION MOTION ADAPTATION KALMAN FILTERING SPEED PERCEPTION SPEED DISCRIMINATION BAYESIAN MODEL |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/2.11 https://purl.org/becyt/ford/2 |
dc.description.none.fl_txt_mv |
If the purpose of adaptation is to fit sensory systems to different environments, it may implement an optimization of the system. What the optimum is depends on the statistics of these environments. Therefore, the system should update its parameters as the environment changes. A Kalman-filtering strategy performs such an update optimally by combining current estimations of the environment with those from the past. We investigate whether the visual system uses such a strategy for speed adaptation. We performed a matching-speed experiment to evaluate the time course of adaptation to an abrupt velocity change. Experimental results are in agreement with Kalman-modeling predictions for speed adaptation. When subjects adapt to a low speed and it suddenly increases, the time course of adaptation presents two phases, namely, a rapid decrease of perceived speed followed by a slower phase. In contrast, when speed changes from fast to slow, adaptation presents a single phase. In the Kalman-model simulations, this asymmetry is due to the prevalence of low speeds in natural images. However, this asymmetry disappears both experimentally and in simulations when the adapting stimulus is noisy. In both transitions, adaptation now occurs in a single phase. Finally, the model also predicts the change in sensitivity to speed discrimination produced by the adaptation. Fil: Barraza, Jose Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Investigación en Luz, Ambiente y Visión. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología. Instituto de Investigación en Luz, Ambiente y Visión; Argentina Fil: Grzywacz, Norberto M.. University of Southern California; Estados Unidos |
description |
If the purpose of adaptation is to fit sensory systems to different environments, it may implement an optimization of the system. What the optimum is depends on the statistics of these environments. Therefore, the system should update its parameters as the environment changes. A Kalman-filtering strategy performs such an update optimally by combining current estimations of the environment with those from the past. We investigate whether the visual system uses such a strategy for speed adaptation. We performed a matching-speed experiment to evaluate the time course of adaptation to an abrupt velocity change. Experimental results are in agreement with Kalman-modeling predictions for speed adaptation. When subjects adapt to a low speed and it suddenly increases, the time course of adaptation presents two phases, namely, a rapid decrease of perceived speed followed by a slower phase. In contrast, when speed changes from fast to slow, adaptation presents a single phase. In the Kalman-model simulations, this asymmetry is due to the prevalence of low speeds in natural images. However, this asymmetry disappears both experimentally and in simulations when the adapting stimulus is noisy. In both transitions, adaptation now occurs in a single phase. Finally, the model also predicts the change in sensitivity to speed discrimination produced by the adaptation. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-10 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/105752 Barraza, Jose Fernando; Grzywacz, Norberto M.; Speed adaptation as Kalman filtering; Pergamon-Elsevier Science Ltd; Vision Research; 48; 23-24; 10-2008; 2485-2491 0042-6989 1878-5646 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/105752 |
identifier_str_mv |
Barraza, Jose Fernando; Grzywacz, Norberto M.; Speed adaptation as Kalman filtering; Pergamon-Elsevier Science Ltd; Vision Research; 48; 23-24; 10-2008; 2485-2491 0042-6989 1878-5646 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.visres.2008.08.011 info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0042698908004173 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-nd/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Pergamon-Elsevier Science Ltd |
publisher.none.fl_str_mv |
Pergamon-Elsevier Science Ltd |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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