Dynamics of reward based decision making a computational study

Bhargav Teja Nallapu 1, 2 Nicolas P. Rougier 1, 3
1 Mnemosyne - Mnemonic Synergy
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, IMN - Institut des Maladies Neurodégénératives [Bordeaux]
2 IIIT-H - International Institute of Information Technology, Hyderabad [Hyderabad]
3 IMN - Institut des Maladies Neurodégénératives [Bordeaux]
Abstract : We consider a biologically plausible model of the basal gan-glia that is able to learn a probabilistic two armed bandit task using reinforcement learning. This model is able to choose the best option and to reach optimal performances after only a few trials. However, we show in this study that the influence of exogenous factors such as stimuli salience and/or timing seems to prevail over optimal decision making, hence questioning the very definition of action-selection. What are the ecological conditions for optimal action selection ?
Keywords : Decision making neural dynamics basal ganglia optimal behavior
Type de document :
Communication dans un congrès
ICANN 2016 , Sep 2016, Barcelona, France. 2016, ICANN 2016 - The 25th International Conference on Artificial Neural Networks. 〈http://icann2016.org〉
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Dernière modification le : jeudi 11 janvier 2018 - 06:25:42
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Bhargav Teja Nallapu, Nicolas P. Rougier. Dynamics of reward based decision making a computational study. ICANN 2016 , Sep 2016, Barcelona, France. 2016, ICANN 2016 - The 25th International Conference on Artificial Neural Networks. 〈http://icann2016.org〉. 〈hal-01333210〉

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