Date of Award

Spring 1-1-2015

Document Type

Thesis

Degree Name

Master of Arts (MA)

Department

Psychology & Neuroscience

First Advisor

Matt Jones

Second Advisor

Albert Kim

Third Advisor

Randall C. O'Reilly

Fourth Advisor

Tor Wager

Abstract

How do people learn new abstract concepts? The approach taken in this work is to develop a theoretical and computational framework for how such concepts are learned and applied. The framework integrates established principles of cognition (analogy and reinforcement learning) and explores their computational power and empirical validity. The first section of this thesis presents computational models and simulation results in the domain of tic-tac-toe. The goal of the computational model is to demonstrate a how a synthesis of established principles of cognition provides a framework for constructing abstract relational concepts and evaluating their usefulness. The second section describes experiments with humans that qualitatively test some of the model predictions. The goal of the experiments is to explore how feedback (in particular, reward) and frequency affect the reinforcement of relational concepts. The final section discusses implications of the modeling and empirical results and situates this work within relevant prior research.

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