자료유형  

 학위논문

Control Number  

0017160999

International Standard Book Number  

9798384447351

Dewey Decimal Classification Number  

150

Main Entry-Personal Name  

Rmus, Milena.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of California, Berkeley., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

146 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

General Note  

Advisor: Collins, Anne G. E.

Dissertation Note  

Thesis (Ph.D.)--University of California, Berkeley, 2024.

Summary, Etc.  

요약The thesis investigates applications and extensions of reinforcement learning (RL) algorithms to modeling human cognition, and focuses on development of new tools for fitting cognitive models to behavioral data. The first part of the thesis examines the effect of choice abstraction on recruitment of RL mechanisms. This work challenges the basic RL assumption that action space is always finite and defined, and tests the variability in processes that best describe the data when the appropriate choice features are ambiguous (e.g. abstract). Results indicate that when choices of multiple levels of abstraction are plausible, less abstract choices (e.g. simple motor actions) interfere with more abstract choices (e.g. goal selection). Further cognitive modeling and experimental tests showed that working memory (WM) contribution to more abstract choice process was reduced relative to that of RL, potentially due to the use of WM resources for defining the appropriate choice features in the abstract condition. Second project explored the effect of subgoals, the intermediate learning milestones, on learning in the context of hierarchical reinforcement learning (HRL) framework. In this project we operationalized subgoals in a way that removes the features commonly associated with subgoals (novelty, reward associations, frequency) and sought to test whether subgoals contribute to learning hierarchically organized policies and generalization through a pseudoreinforcing effect independent of these features. The results revealed that participants solved the hierarchical task, with data patterns implying the effect of subgoals on behavior; generalization tests showed that generalization of subgoals, under the constraint of our sub-goal definition, was possible but predicated on explicit recognition of subgoal features. The third project focused on development of new cognitive model-fitting tool leveraging artificial neural networks (ANN). The results demonstrating ANN efficacy in fitting parameters and identifying models with tractable and intractable likelihoods, with comparable (or better) performance relative to standard methods where standard methods were applicable.

Subject Added Entry-Topical Term  

Psychology.

Subject Added Entry-Topical Term  

Neurosciences.

Subject Added Entry-Topical Term  

Cognitive psychology.

Index Term-Uncontrolled  

Artificial neural networks

Index Term-Uncontrolled  

Computational cognitive models

Index Term-Uncontrolled  

Hierarchy

Index Term-Uncontrolled  

Reinforcement learning

Added Entry-Corporate Name  

University of California, Berkeley Psychology

Host Item Entry  

Dissertations Abstracts International. 86-03B.

Electronic Location and Access  

로그인을 한후 보실 수 있는 자료입니다.

Control Number  

joongbu:654717