Neural Model for Learning-to-Learn of Novel Task Sets in the Motor Domain
| Autor: | Raphael Braud, Philippe Gaussier, Sylvain Mahé, Mathias Quoy, Alexandre Pitti |
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| Přispěvatelé: | Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), CY Cergy Paris Université (CY)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA), Neurocybernétique, CY Cergy Paris Université (CY)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-CY Cergy Paris Université (CY)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA) |
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Computer science
lcsh:BF1-990 decision making Task (project management) 03 medical and health sciences 0302 clinical medicine Component (UML) Cognitive development Intrinsic motivation Psychology [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] Original Research Article tool-use Set (psychology) intrinsic motivation General Psychology 030304 developmental biology Cognitive science Structure (mathematical logic) task sets incremental learning 0303 health sciences business.industry Mechanism (biology) cortical plasticity Cognition lcsh:Psychology Artificial intelligence business fronto-parietal system 030217 neurology & neurosurgery error-reward processing gain-field mechanism |
| Zdroj: | Frontiers in Psychology Frontiers in Psychology, Frontiers, 2013, pp.771. ⟨10.3389/fpsyg.2013.00771⟩ Frontiers in Psychology, Vol 4 (2013) |
| ISSN: | 1664-1078 |
| DOI: | 10.3389/fpsyg.2013.00771⟩ |
| Popis: | International audience; During development, infants learn to differentiate their motor behaviors relative to various contexts by exploring and identifying the correct structures of causes and effects that they can perform; these structures of actions are called task sets or internal models. The ability to detect the structure of new actions, to learn them and to select on the fly the proper one given the current task set is one great leap in infants cognition. This behavior is an important component of the child's ability of learning-to-learn, a mechanism akin to the one of intrinsic motivation that is argued to drive cognitive development. Accordingly, we propose to model a dual system based on (1) the learning of new task sets and on (2) their evaluation relative to their uncertainty and prediction error. The architecture is designed as a two-level-based neural system for context-dependent behavior (the first system) and task exploration and exploitation (the second system). In our model, the task sets are learned separately by reinforcement learning in the first network after their evaluation and selection in the second one. We perform two different experimental setups to show the sensorimotor mapping and switching between tasks, a first one in a neural simulation for modeling cognitive tasks and a second one with an arm-robot for motor task learning and switching. We show that the interplay of several intrinsic mechanisms drive the rapid formation of the neural populations with respect to novel task sets. |
| Databáze: | OpenAIRE |
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