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Bayesian modeling of dynamic motion integration

Abstract : The quality of the representation of an object's motion is limited by the noise in the sensory input as well as by an intrinsic ambiguity due to the spatial limitation of the visual motion analyzers (aperture problern). Perceptual and oculomotor data demonstrate that motion processing of extended objects is initially dominated by the local ID motion cues, related to the object's edges and orthogonal to them, whereas 2D information, related to terminators (or edge-endings), takes progressively over and leads to the final correct representation of global motion. A Bayesian framework accounting for the sensory noise and general expectancies for object velocities has proven successful in explaining several experimental findings concerning early motion processing [Weiss, Y., Adelson, E., 1998. Slow and smooth: a Bayesian theory for the combination of local motion signals in human vision. MIT Technical report, A.I. Memo 1624]. In particular, these models provide a qualitative account for the initial bias induced by the ID motion cue. However, a complete functional model, encompassing the dynamical evolution of object motion perception, including the integration of different motion cues, is still lacking. Here we outline several experimental observations concerning human smooth pursuit of moving objects and more particularly the time course of its initiation phase, which reflects the ongoing motion integration process. In addition, we propose a recursive extension of the Bayesian model, motivated and constrained by our oculomotor data, to describe the dynamical integration of ID and 2D motion information. We compare the model predictions for object motion tracking with human oculomotor recordings. (C) 2007 Elsevier Ltd. All rights reserved.
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https://hal.archives-ouvertes.fr/hal-01440619
Contributor : Jean-Baptiste Melmi Connect in order to contact the contributor
Submitted on : Thursday, January 19, 2017 - 1:35:56 PM
Last modification on : Wednesday, October 27, 2021 - 3:50:45 PM

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Anna Montagnini, Pascal Mamassian, Laurent Perrinet, Eric Castet, Guillaume S. Masson. Bayesian modeling of dynamic motion integration. Journal of Physiology - Paris, Elsevier, 2007, 101 (1-3), pp.64-77. ⟨10.1016/j.jphysparis.2007.10.013⟩. ⟨hal-01440619⟩

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