Decision-tree analysis of control strategies
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  • 作者:Romann M. Weber ; Brett R. Fajen
  • 关键词:Math modeling and model evaluation ; Computational modeling ; Perception and action ; Perceptual attunement
  • 刊名:Psychonomic Bulletin & Review
  • 出版年:2015
  • 出版时间:June 2015
  • 年:2015
  • 卷:22
  • 期:3
  • 页码:653-672
  • 全文大小:1,952 KB
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  • 作者单位:Romann M. Weber (1)
    Brett R. Fajen (2)

    1. California Institute of Technology, MC 228-77, Pasadena, CA, 91125, USA
    2. Rensselaer Polytechnic Institute, Troy, NY, USA
  • 刊物主题:Cognitive Psychology;
  • 出版者:Springer US
  • ISSN:1531-5320
文摘
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.
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