Maximum entropy perception-action space: a Bayesian model of eye movement selectionReportar como inadecuado




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1 ASL - Autonomous Systems Lab 2 LPPA - Laboratoire de Physiologie de la Perception et de l-Action 3 LIG - Laboratoire d-Informatique de Grenoble 4 ISIR - Institut des Systèmes Intelligents et de Robotique

Abstract : 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.

Keywords : retinotopic maps Bayesian modelling eye movements





Autor: Francis Colas - Pierre Bessière - Benoît Girard -

Fuente: https://hal.archives-ouvertes.fr/



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