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UID:20251121T190659EST-5841gGAcsh@132.216.98.100
DTSTAMP:20251122T000659Z
DESCRIPTION:Two well-established literatures have provided elegant computat
 ional models of sensorimotor learning and decision making\, with relative 
 little connection between the two.  I will discuss ways in which we can br
 ing these two worlds together.   In the first part of the talk\, I will di
 scuss work that has brought into focus the relevance of multiple learning 
 mechanisms for sensorimotor learning\, examining how strategic processes i
 nteract with a cerebellar error-based learning system in sensorimotor adap
 tation.  The results here highlight a distinction between error signals th
 at improve action execution or action selection.   In the second part\, I 
 will turn to learning in decision making tasks\, asking how competence in 
 motor execution might be incorporated in models of reinforcement learning.
    Our work here addresses the question of how an agent determines if the 
 absence of reward reflects a property of the environment or an error in mo
 tor execution.  We suggest that sensorimotor errors provide a “gating” sig
 nal to regulate reinforcement learning\, providing a simple solution to th
 is fundamental credit assignment problem.\n
DTSTART:20160401T193000Z
DTEND:20160401T203000Z
LOCATION:Room 501\, CA\, QC\, Montreal\, H3A 1A3\, Rosalind & Morris Goodma
 n Cancer Research Centre\, 1160\, avenue des Pins
SUMMARY:Embodied Decision Making:  System interactions in movement executio
 n and action selection
URL:/channels-contribute/channels/event/embodied-decis
 ion-making-system-interactions-movement-execution-and-action-selection-259
 763
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