Natural RLDM: Optimal and Subptimal Control in Brain and Behavior

author: Nathaniel Daw, Center for Neural Science, New York University (NYU)
published: July 28, 2015,   recorded: June 2015,   views: 4755

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Approaches to reinforcement learning and statistical decision theory from artificial intelligence offer appealing frameworks for understanding how biological brains solve decision problems in the natural world. In particular, these engineering approaches typically begin with a clear, normative analysis of the optimal solution to the problem. However, rather than stopping there, they focus on realizing it, often approximately, with a step-by-step algorithmic solution, which lends itself naturally to process-level accounts of the psychological and neural mechanisms underlying behavior and its suboptimalities. In this tutorial I will review research into biological decision making and reinforcement learning from psychology, ethology, behavioral economics, and neuroscience. I will focus on how the brain may implement different approximations to the ideal observer, how this may help to explain notions of modularity or multiplicity of decision systems across several domains, how these approximations might be understood as boundedly rational when taking into account the costs and benefits of computation, and how these mechanisms might be implicated in self control and psychiatric disorders.

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Reviews and comments:

Comment1 souvik ghosh, July 31, 2015 at 2:30 p.m.:

I will audit research into organic choice making and fortification gaining from brain research, ethology, behavioral financial matters, and neuroscience. I will concentrate on how the cerebrum may actualize distinctive rough guesses to the perfect eyewitness, how this may help to clarify thoughts of measured quality or variety of choice frameworks over a few spaces, how these estimates may be seen as boundedly balanced when considering the expenses and advantages of calculation, and how these components may be embroiled in poise and psychiatric issue.

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