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Mouse tracking reveals structure knowledge in the absence of model-based choice
Arkady Konovalov1,2, Ian Krajbich3,4
1Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Blümlisalpstrasse, 10 8006, Zurich, Switzerland.
Nature Communications
|April 22, 2020
Summary
Mouse tracking reveals hidden structure learning in complex environments. This technique uncovers model-based learning insights beyond traditional behavioral measures, enhancing our understanding of human decision-making.
Area of Science:
- Cognitive Science
- Neuroscience
- Behavioral Economics
Background:
- Humans use model-free and model-based learning strategies in complex environments.
- Model-based learning, which uses environmental structure, is thought to be less common unless highly rewarded.
Purpose of the Study:
- To investigate model-based learning using mouse tracking in varying environments.
- To determine if mouse movements can reveal underlying structure learning not captured by traditional methods.
Main Methods:
- Utilized mouse tracking to analyze learning in stochastic and deterministic environments.
- Compared mouse movement data with standard behavior-based learning estimates.
Main Results:
- Mouse movements indicated structure learning in both stochastic and deterministic tasks.
- Standard behavioral measures failed to detect learning in the stochastic environment.
Conclusions:
- Mouse tracking is a sensitive method for detecting implicit structure knowledge.
- Structure knowledge, revealed by mouse tracking, is a prerequisite for model-based choice.
