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Neurocomputational mechanisms of rei...

Cohen, Michael Steven.

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Neurocomputational mechanisms of reinforcement learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Neurocomputational mechanisms of reinforcement learning./
Author:	Cohen, Michael Steven.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2007,
Description:	108 p.
Notes:	Source: Dissertations Abstracts International, Volume: 69-07, Section: B.
Contained By:	Dissertations Abstracts International69-07B.
Subject:	Cognitive therapy. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3282958
ISBN:	9780549253808

Neurocomputational mechanisms of reinforcement learning.
Cohen, Michael Steven.

Neurocomputational mechanisms of reinforcement learning. - Ann Arbor : ProQuest Dissertations & Theses, 2007 - 108 p.

Source: Dissertations Abstracts International, Volume: 69-07, Section: B.

Thesis (Ph.D.)--University of California, Davis, 2007.

Our world is filled with uncertainty and limited resources. Nearly every decision we face is clouded in uncertainty-uncertainty about the consequences of our decisions, uncertainty about whether others will obtain resources before we do, uncertainty about how different individuals will respond in similar contexts. Fortunately, we are often faced with the same or similar decision problems repeatedly, providing the opportunity to learn from our previous decisions and outcomes, and dynamically adapt decision-making strategies in the service of maximizing good outcomes and minimizing bad outcomes. Theories and computational models of reinforcement learning provide a biologically and mathematically grounded framework within which to characterize, predict, and understand the behavioral, cognitive, and neural bases of reward-guided decision-making. Neuroscience research is making strides in mapping out the neurocomputational mechanisms of reinforcement learning, at approaches spanning synaptic, neurochemical, systems, and large neural network. Much of this nascent research has focused on elucidating the neural systems and computations that underlie instantaneous reinforcement processing-that is, how we are able to identify whether environmental stimuli or consequences of our actions are relatively good or bad. In contrast, however, little research has focused on the mechanisms by which reinforcements might be used to guide future decision-making. I have attempted to help bridge this gap by designing research studies and analysis approaches to better characterize how humans use reward information to guide and optimize their decision-making. The research detailed here represents my first attempts to characterize the neurocomputational mechanisms involved in reinforcement learning, and therefore my efforts to help move the field forward, from studying the mechanisms of reinforcement processing to the mechanisms of reinforcement learning.

ISBN: 9780549253808Subjects--Topical Terms:

524357
Cognitive therapy.

Neurocomputational mechanisms of reinforcement learning.
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