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Communication Dans Un Congrès Année : 2011

Maximum entropy perception-action space: a Bayesian model of eye movement selection

Résumé

In this article, we investigate the issue of the selection of eye movements in a free-eye Multiple Object Tracking task. We propose a Bayesian model of retinotopic maps with a complex logarithmic mapping. This model is structured in two parts: a representation of the visual scene, and a decision model based on the representation. We compare different decision models based on different features of the representation and we show that taking into account uncertainty helps predict the eye movements of subjects recorded in a psychophysics experiment. Finally, based on experimental data, we postulate that the complex logarithmic mapping has a functional relevance, as the density of objects in this space in more uniform than expected. This may indicate that the representation space and control strategies are such that the object density is of maximum entropy.
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Dates et versions

hal-01142599 , version 1 (15-04-2015)

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Francis Colas, Pierre Bessière, Benoît Girard. Maximum entropy perception-action space: a Bayesian model of eye movement selection. 30th Conf. on Bayesian Methods and Maximum Entropy in Science and Engineering (MaxEnt2010), Jul 2010, Chamonix, France. ⟨10.1063/1.3573660⟩. ⟨hal-01142599⟩
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