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 (Thursday, -2nd week, Trinity 2020)
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: WIN Wednesdays Seminar Series
Booking required?: Not required
Audience: Members of the University only
Editor: Nancy Rawlings