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Domain-specific contexts promote model-based decision making for basketball players
Liu Yang1, Chenglin Zhou2, Biao Sang3
1School of Psychology, Shanghai University of Sport, No.399, Changhai Road, Yangpu District, Shanghai, China.
Scientific Reports
|June 18, 2026
Summary
Basketball expertise enhances model-based (MB) decision-making by integrating task structure with feedback. Sport-specific training may improve player performance without explicit instruction.
Area of Science:
- Cognitive Psychology
- Neuroscience
- Sports Science
Background:
- Decision-making is crucial in sports, particularly basketball.
- Reinforcement Learning (RL) models explain how agents learn from feedback.
- Model-based (MB) learning utilizes environmental understanding, contrasting with model-free (MF) approaches.
Purpose of the Study:
- To investigate if domain-specific contexts, like basketball tactics, promote MB decision strategies.
- To compare decision-making strategies between expert basketball players and novices.
Main Methods:
- A two-stage task was used with abstract symbols and basketball tactical diagrams.
- Participants included 35 national basketball players and 27 novices.
- Hierarchical logistic regression and ANOVAs analyzed decision coefficients and reaction times.
Main Results:
- Basketball players consistently used MB strategies, unlike novices who showed more MF tendencies.
- Expertise facilitated integrating task structure with outcome feedback.
- Longer reaction times correlated with MB strategies in players but not novices.
Conclusions:
- Expertise enhances MB decision-making by improving the integration of environmental structure and feedback.
- Sport-specific visual aids in training can potentially boost decision-making performance.
- Understanding cognitive strategies can inform effective sports training methodologies.
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