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Yul Kang (CBL)
Tuesday 26 June 2018, 16:00-17:00
Computational Neuroscience Journal Club
- š¤ Speaker: Yul Kang (CBL)
- š Date & Time: Tuesday 26 June 2018, 16:00 - 17:00
- š Venue: Cambridge University Engineering Department, CBL, BE4-38 (http://learning.eng.cam.ac.uk/Public/Directions)
Abstract
Yul Kang will cover:
ā¢ The successor representation in human reinforcement learning
ā¢ I. Momennejad, E. M. Russek, J. H. Cheong, M. M. Botvinick, N. D. Daw & S. J. Gershman
ā¢ Nature Human Behaviour (2017)
ā¢ https://www.nature.com/articles/s41562-017-0180-8
Abstract: Theories of reward learning in neuroscience have focused on two families of algorithms thought to capture deliberative versus habitual choice. āModel-basedā algorithms compute the value of candidate actions from scratch, whereas āmodel-freeā algorithms make choice more efficient but less flexible by storing pre-computed action values. We examine an intermediate algorithmic family, the successor representation, which balances flexibility and efficiency by storing partially computed action values: predictions about future events. These pre-computation strategies differ in how they update their choices following changes in a task. The successor representationās reliance on stored predictions about future states predicts a unique signature of insensitivity to changes in the taskās sequence of events, but flexible adjustment following changes to rewards. We provide evidence for such differential sensitivity in two behavioural studies with humans. These results suggest that the successor representation is a computational substrate for semi-flexible choice in humans, introducing a subtler, more cognitive notion of habit.
Series This talk is part of the Computational Neuroscience series.
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