Скачать с ютуб Yael Niv - Model-based predictions for dopamine - ViDA 2020 в хорошем качестве

Yael Niv - Model-based predictions for dopamine - ViDA 2020

Yael Niv Princeton University 05-21-2020 Model-based predictions for dopamine ​Moderator: Howard Fields (UCSF) Co-Moderator: Lauren Burgeno (University of Oxford) 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.