Dissertation/Thesis Abstract

Decision-making and motor control: computational models of human sensorimotor processing
by Huang, He, Ph.D., University of California, San Diego, 2014, 147; 3673994
Abstract (Summary)

To survive and effectively interact with the environment, human sensorimotor control system collects sensory information and acts based on the state of the world. Human behavior can be considered and studied at discrete time or continuous time. For the former, human makes discrete categorical decisions when presented with different alternative choices (e.g. choose Left or Right at an intersection). For the later, humans plan and execute continuous movements when instructed to perform a motor task (e.g. drive to a destination). In this dissertation we examine human behavior at both levels. Part I focuses on understanding decision-making at discrete time using Bayesian Models. We start by investigating the influence of environmental statistics in a saccadic visual search ask, in which we use a dynamic belief model to describe subjects' learning process of the environment statistics cross-trials. Then we look at a special effect of decision- making, the sequential effect, and apply the dynamic belief model to explain subjects' cross-trial learning and a drift diffusion model to explain their within-trial decision- making process. Part II focuses on examining motor control at continuous time using Optimal Control Theory. We start by investigating the objective functions in oculomotor control (saccadic eye movement, smooth pursuit, and applications in eye-hand coordination) with an infomax model. Then we apply inverse optimal control model to study impaired motor behavior in depressed individuals. In particular, we present a framework based on optimal control theory, which can distinguish the effects of sensorimotor speed, goal setting and motivational factors in goal-directed motor tasks. Finally, we propose to use facial expression as another measure of the emotional state in depressed individuals, which can be used to provide further understanding of the behavior and model parameters estimated from the proposed inverse framework.

Indexing (document details)
Advisor: Movellan, Javier, Paulus, Martin P.
Commitee: Bartlett, Marian, Chiba, Andrea, Creel, Sarah, Krichmar, Jeffery, Movellan, Javier, Nitz, Douglas, Paulus, Martin
School: University of California, San Diego
Department: Cognitive Science
School Location: United States -- California
Source: DAI-B 76/06(E), Dissertation Abstracts International
Subjects: Mental health, Cognitive psychology
Keywords: Bayesian models, Decision making, Depression, Motor control, Optimal control theory
Publication Number: 3673994
ISBN: 978-1-321-51674-6
Copyright © 2020 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy