Bayesian Action–Perception Computational Model: Interaction of Production and Recognition of Cursive Letters
Identifieur interne : 000380 ( Main/Exploration ); précédent : 000379; suivant : 000381Bayesian Action–Perception Computational Model: Interaction of Production and Recognition of Cursive Letters
Auteurs : Estelle Gilet [France] ; Julien Diard [France] ; Pierre Bessière [France]Source :
- PLoS ONE [ 1932-6203 ] ; 2011.
Abstract
In this paper, we study the collaboration of perception and action representations involved in cursive letter recognition and production. We propose a mathematical formulation for the whole perception–action loop, based on probabilistic modeling and Bayesian inference, which we call the Bayesian Action–Perception (BAP) model. Being a model of both perception and action processes, the purpose of this model is to study the interaction of these processes. More precisely, the model includes a feedback loop from motor production, which implements an internal simulation of movement. Motor knowledge can therefore be involved during perception tasks. In this paper, we formally define the BAP model and show how it solves the following six varied cognitive tasks using Bayesian inference: i) letter recognition (purely sensory), ii) writer recognition, iii) letter production (with different effectors), iv) copying of trajectories, v) copying of letters, and vi) letter recognition (with internal simulation of movements). We present computer simulations of each of these cognitive tasks, and discuss experimental predictions and theoretical developments.
Url:
DOI: 10.1371/journal.pone.0020387
PubMed: 21674043
PubMed Central: 3106017
Affiliations:
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We propose a mathematical formulation for the whole perception–action loop, based on probabilistic modeling and Bayesian inference, which we call the Bayesian Action–Perception (BAP) model. Being a model of both perception and action processes, the purpose of this model is to study the interaction of these processes. More precisely, the model includes a feedback loop from motor production, which implements an internal simulation of movement. Motor knowledge can therefore be involved during perception tasks. In this paper, we formally define the BAP model and show how it solves the following six varied cognitive tasks using Bayesian inference: i) letter recognition (purely sensory), ii) writer recognition, iii) letter production (with different effectors), iv) copying of trajectories, v) copying of letters, and vi) letter recognition (with internal simulation of movements). 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Bessière</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Marr, D" uniqKey="Marr D">D Marr</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Jones, M" uniqKey="Jones M">M Jones</name></author><author><name sortKey="Love, B" uniqKey="Love B">B Love</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Colas, F" uniqKey="Colas F">F Colas</name></author><author><name sortKey="Droulez, J" uniqKey="Droulez J">J Droulez</name></author><author><name sortKey="Wexler, M" uniqKey="Wexler M">M Wexler</name></author><author><name sortKey="Bessiere, P" uniqKey="Bessiere P">P Bessière</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Daunizeau, J" uniqKey="Daunizeau J">J Daunizeau</name></author><author><name sortKey="Den Ouden, Hem" uniqKey="Den Ouden H">HEM den Ouden</name></author><author><name sortKey="Pessiglione, M" uniqKey="Pessiglione M">M Pessiglione</name></author><author><name sortKey="Kiebel, Sj" uniqKey="Kiebel S">SJ Kiebel</name></author><author><name sortKey="Friston, Kj" uniqKey="Friston K">KJ Friston</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kandel, S" uniqKey="Kandel S">S Kandel</name></author><author><name sortKey="Orliaguet, Jp" uniqKey="Orliaguet J">JP Orliaguet</name></author><author><name sortKey="Boe, Lj" uniqKey="Boe L">LJ Boë</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Diard, J" uniqKey="Diard J">J Diard</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Witt, Jk" uniqKey="Witt J">JK Witt</name></author><author><name sortKey="Profftt, Dr" uniqKey="Profftt D">DR Profftt</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>France</li></country><region><li>Auvergne-Rhône-Alpes</li><li>Rhône-Alpes</li></region><settlement><li>Grenoble</li><li>Paris</li></settlement><orgName><li>Université Pierre-Mendès-France</li></orgName></list><tree><country name="France"><noRegion><name sortKey="Gilet, Estelle" sort="Gilet, Estelle" uniqKey="Gilet E" first="Estelle" last="Gilet">Estelle Gilet</name></noRegion><name sortKey="Bessiere, Pierre" sort="Bessiere, Pierre" uniqKey="Bessiere P" first="Pierre" last="Bessière">Pierre Bessière</name><name sortKey="Bessiere, Pierre" sort="Bessiere, Pierre" uniqKey="Bessiere P" first="Pierre" last="Bessière">Pierre Bessière</name><name sortKey="Diard, Julien" sort="Diard, Julien" uniqKey="Diard J" first="Julien" last="Diard">Julien Diard</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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