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Model-based predictions for dopamine
Phasic dopamine responses are thought to encode a prediction-error signal consistent with model-free reinforcement learning theories. However, a number of recent findings highlight the influence of model-based computations on dopamine responses, and suggest that dopamine prediction errors reflect more dimensions of an expected outcome than scalar reward value. In this talk I will focus on these challenges to the scalar prediction-error theory of dopamine, and to the strict dichotomy between model-based and model-free learning, suggesting that these may better be viewed as a set of intertwined computations rather than two alternative systems. Alas, phasic dopamine signals, until recently a beacon of computationally-interpretable brain activity, may not be as simple as we once hoped they were.
Date:
9 April 2020, 13:00
Venue:
Venue to be announced
Speaker:
Dr Yael Niv (Princeton University)
Organising department:
Nuffield Department of Clinical Neurosciences
Organiser:
Nancy Rawlings (University of Oxford)
Part of:
OxCIN Seminar Series
Booking required?:
Not required
Audience:
Members of the University only
Editor:
Nancy Rawlings
