発表

SL-002

How the Brain Learns from the Past to Make Good Decisions for the Future: The Neural Basis of Human Reinforcement-learning

[講演者] John P. O'Doherty:1
[司会者] 片平 健太郎#:2
1:California Institute of Technology, 2:名古屋大学

In order to make good decisions, we need to learn from the actions we took in our past. Here I will describe how an approach called computational fMRI in which formal computational models are combined with fMRI data can be used to investigate the means by which the brain carries out value-based learning and action selection. In particular I will illustrate how a family of computational models called reinforcement-learning provide a good account of the patterns of neural activity across a network of brain regions during learning and decision-making. Our findings point to the existence of multiple mechanisms within the brain that may compete and co-operate in order to control behavior. Finally, I will speculate on the relevance of this approach as a means of providing insight not only into how healthy brains make decisions, but also as to how various psychiatric disorders may potentially arise from impairments in these basic computations.
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