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Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures

hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorVacher, Jonathan
hal.structure.identifierInstitut de Neurosciences de la Timone [INT]
dc.contributor.authorMeso, Andrew
hal.structure.identifierInstitut de Neurosciences de la Timone [INT]
dc.contributor.authorPerrinet, Laurent U.
HAL ID: 3934
ORCID: 0000-0002-9536-010X
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorPeyré, Gabriel
HAL ID: 1211
dc.date.accessioned2019-01-11T13:22:36Z
dc.date.available2019-01-11T13:22:36Z
dc.date.issued2018
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/18367
dc.language.isoenen
dc.subjectPsychophysicsen
dc.subjectStochastic Partial Differential Equationsen
dc.subjectDynamic texturesen
dc.subjectMotion perceptionen
dc.subjectBayesian Modellingen
dc.subject.ddc621.3en
dc.titleBayesian Modeling of Motion Perception using Dynamical Stochastic Texturesen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenA common practice to account for psychophysical biases in vision is to frame them as consequences of a dynamic process relying on optimal inference with respect to a generative model. The present study details the complete formulation of such a gener-ative model intended to probe visual motion perception with a dynamic texture model. It is first derived in a set of axiomatic steps constrained by biological plausibility. We extend previous contributions by detailing three equivalent formulations of this texture model. First, the composite dynamic textures are constructed by the random ag-gregation of warped patterns, which can be viewed as 3D Gaussian fields. Secondly, these textures are cast as solutions to a stochastic partial differential equation (sPDE). This essential step enables real time, on-the-fly texture synthesis using time-discretized auto-regressive processes. It also allows for the derivation of a local motion-energy model, which corresponds to the log-likelihood of the probability density. The log-likelihoods are essential for the construction of a Bayesian inference framework. We use the dynamic texture model to psychophysically probe speed perception in humans using zoom-like changes in the spatial frequency content of the stimulus. The human data replicates previous findings showing perceived speed to be positively biased by spatial frequency increments. A Bayesian observer who combines a Gaussian likelihood centered at the true speed and a spatial frequency dependent width with a slow speed prior" successfully accounts for the perceptual bias. More precisely, the bias arises from a decrease in the observer's likelihood width estimated from the experiments as the spatial frequency increases. Such a trend is compatible with the trend of the dynamic texture likelihood width."en
dc.relation.isversionofjnlnameNeural Computation
dc.relation.isversionofjnlvol130en
dc.relation.isversionofjnlissue12en
dc.relation.isversionofjnldate2018-12
dc.relation.isversionofjnlpages3355-3392en
dc.relation.isversionofdoi10.1162/neco_a_01142en
dc.relation.isversionofjnlpublisherMassachusetts Institute of Technologyen
dc.subject.ddclabelTraitement du signalen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2019-01-11T13:13:31Z
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