• Monday, August 13th 2018 at 15:00 - 16:00 UTC (Other timezones)
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As discussed in previous TCPW presentations, ideas from the computational field of reinforcement learning are extremely relevant to understanding aberrant decision making in mental illness. Much emphasis has been put on parameters of the learning process: exploration rate, learning rate, etc. However, the first ingredient in any reinforcement learning algorithm is a representation of the task as a sequence of “states”. This representation is critical for correct and efficient learning and decision making. In this talk I will first argue, and demonstrate using behavioral experiments, that animals and humans learn a state representation that corresponds to the latent (hidden) structure of a task using two processes — selective attention to learn to ignore stimuli that are irrelevant to the task, and Bayesian inference (or an approximation thereof) to partition events into meaningful states. I will then discuss some very preliminary ideas for how alterations in these processes can be related to mood disorders and schizophrenia. I look forward to discussing how task representations relate to mental illness, and how these theoretical ideas can be pursued within computational psychiatry.

 

 

 

 

 

 

Yael Niv
Associate Professor
Princeton Neuroscience Institute
and
Psychology Department
Princeton Neuroscience Institute room 143
Princeton University

Yael Niv – How we learn task representations, and what this suggests for computational psychiatry