Discovering optical control strategies : a data-mining approach

Authors
Weber, Romann M.
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Other Contributors
Fajen, Brett R.
Gray, Wayne D., 1950-
Si, Mei
Changizi, Mark A.
Issue Date
2013-05
Keywords
Cognitive science
Degree
PhD
Terms of Use
Attribution-NonCommercial-NoDerivs 3.0 United States
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
A major focus of research into visually guided action (VGA) is the identification of control strategies that map optical information to actions. The traditional approach to this research has been to test the behavioral predictions of a few hypothesized strategies against subject performance in environments in which various manipulations to available information are made. While important and compelling results have been achieved with this standard approach, they are potentially limited by small sets of hypotheses and the methods used to test them. In this dissertation, I introduce a novel application of data-mining and machine-learning techniques in a comparatively "assumption-lite" analysis of experimental data that is able to both describe and model human behavior. This method identifies the signature of optical control in the information-action patterns that are embedded in subject data. I also introduce a modeling philosophy that conceptualizes continuous control as an effort to realize preferred trajectories through an optical state space. In applying this philosophy, I show how simple models of the evolution of these trajectories can be mined from subject data. I demonstrate the effectiveness of this approach in the analysis and modeling of data from a collision-avoidance task and in the development of a new model of visually guided, speed-controlled steering.
Description
May 2013
School of Humanities, Arts, and Social Sciences
Department
Dept. of Cognitive Science
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
Access
CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.