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
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/105752

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spelling 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
repository.name.fl_str_mv 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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