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Decision-tree analysis of control strategies.
Romann M Weber1, Brett R Fajen
1California Institute of Technology, MC 228-77, Pasadena, CA, 91125, USA, rweber@caltech.edu.
Psychonomic Bulletin & Review
|October 16, 2014
Summary
This study introduces data-mining techniques to analyze visually guided actions, enabling rapid testing of numerous control strategies for human behavior modeling. The method effectively models collision avoidance, offering insights beyond traditional approaches.
Area of Science:
- Cognitive Science
- Human-Computer Interaction
- Robotics
Background:
- Visually guided action research traditionally identifies control strategies by testing limited hypotheses against behavioral data.
- Existing methods, while effective, face limitations due to small hypothesis sets and testing methodologies.
Purpose of the Study:
- To introduce and evaluate a novel data-mining approach for analyzing experimental data in visually guided action research.
- To enable rapid testing of a broad range of control strategies and model human behavior effectively.
Main Methods:
- Application of data-mining techniques, specifically decision-tree methods, to analyze experimental data.
- Transformation of subject data into interpretable algorithmic forms for direct incorporation into behavioral models.
Main Results:
- Effectively identified optical information used by human subjects in a collision-avoidance task.
- Results align with existing collision-avoidance research while providing novel insights.
- Generated behavioral models closely mimicking subject performance.
Conclusions:
- Data-mining techniques offer powerful tools for analyzing human data and building predictive models.
- This approach facilitates the study of perception-action tasks across various domains.
- The method enhances our understanding of how optical information guides actions.
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