- Monday, August 13th 2018 at 15:00 - 16:00 UK (Other timezones)
- General participation info | Participate online | + Phone in United States (Toll Free): 1 877 309 2073 United States: +1 (571) 317-3129 Australia (Toll Free): 1 800 193 385 Australia: +61 2 8355 1020 Austria (Toll Free): 0 800 202148 Belgium (Toll Free): 0 800 78884 Canada (Toll Free): 1 888 455 1389 Denmark (Toll Free): 8090 1924 France (Toll Free): 0 805 541 047 Germany (Toll Free): 0 800 184 4222 Greece (Toll Free): 00 800 4414 3838 Hungary (Toll Free): (06) 80 986 255 Iceland (Toll Free): 800 9869 Ireland (Toll Free): 1 800 946 538 Israel (Toll Free): 1 809 454 830 Italy (Toll Free): 800 793887 Japan (Toll Free): 0 120 663 800 Luxembourg (Toll Free): 800 22104 Netherlands (Toll Free): 0 800 020 0182 New Zealand (Toll Free): 0 800 47 0011 Norway (Toll Free): 800 69 046 Poland (Toll Free): 00 800 1213979 Portugal (Toll Free): 800 819 575 Spain (Toll Free): 800 900 582 Sweden (Toll Free): 0 200 330 905 Switzerland (Toll Free): 0 800 740 393 United Kingdom (Toll Free): 0 800 169 0432 Access Code: 731-636-357
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