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Communication Dans Un Congrès Année : 2010

Bayesian Action-Perception loop modeling: Application to trajectory generation and recognition using internal motor simulation

Résumé

This paper is about modeling perception-action loops and, more precisely, the study of the influence of motor knowledge during perception tasks. We use the Bayesian Action-Perception (BAP) model, which deals with the sensorimotor loop involved in reading and writing cursive isolated letters and includes an internal simulation of movement loop. By using this probabilistic model we simulate letter recognition, both with and without internal motor simulation. Comparison of their performance yields an experimental prediction, which we set forth.

Domaines

Informatique
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Dates et versions

hal-00961128 , version 1 (19-03-2014)

Identifiants

  • HAL Id : hal-00961128 , version 1

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Estelle Gilet, Julien Diard, Richard Palluel-Germain, Pierre Bessière. Bayesian Action-Perception loop modeling: Application to trajectory generation and recognition using internal motor simulation. Thirtieth International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (Maxent 2010), Jul 2010, Chamonix, France. pp.59--66. ⟨hal-00961128⟩
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