Decision-tree analysis of control strategies
A major focus of research on visually guided action is the identification of control strategies that map optical information to actions. The traditional approach has been to test the behavioral predictions of a few hypothesized strategies against subject behavior in environments in which various manipulations of available information have been made. While important and compelling results have been achieved with these methods, they are potentially limited by small sets of hypotheses and the methods used to test them. In this study, we introduce a novel application of data-mining techniques in an analysis of experimental data that is able to both describe and model human behavior. This method permits the rapid testing of a wide range of possible control strategies using arbitrarily complex combinations of optical variables. Through the use of decision-tree techniques, subject data can be transformed into an easily interpretable, algorithmic form. This output can then be immediately incorporated into a working model of subject behavior. We tested the effectiveness of this method in identifying the optical information used by human subjects in a collision-avoidance task. Our results comport with published research on collision-avoidance control strategies while also providing additional insight not possible with traditional methods. Further, the modeling component of our method produces behavior that closely resembles that of the subjects upon whose data the models were based. Taken together, the findings demonstrate that data-mining techniques provide powerful new tools for analyzing human data and building models that can be applied to a wide range of perception-action tasks, even outside the visual-control setting we describe.
© 2014 Psychonomic Society, Inc. Published online: 15 October 2014. Work on this project was supported by a Rensselaer Humanities, Arts and Social Sciences Graduate Fellowship awarded to the first author. The first author would like to thank his coauthor and Wayne Gray, Mei Si, and Mark Changizi for their guidance during the development of this work. Both authors would like to thank this journal's editorial staff and an anonymous reviewer for their helpful comments.