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Reinforcement learning in professional basketball players.
Tal Neiman1, Yonatan Loewenstein
1Department of Neurobiology, The Interdisciplinary Center for Neural Computation and Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel. tal.neiman@mail.huji.ac.il
Nature Communications
|December 8, 2011
Summary
Professional basketball players overgeneralize reinforcement learning from recent actions, impacting performance. Despite expertise, their shot selection is negatively correlated, indicating suboptimal decision-making in complex environments.
Area of Science:
- Behavioral economics
- Cognitive science
- Sports analytics
Background:
- Reinforcement learning (RL) in complex environments requires generalizing action outcomes across states.
- Human expert generalization capabilities in RL are not well understood.
- Understanding expert decision-making can inform RL model development.
Purpose of the Study:
- To investigate generalization in human experts within a naturalistic RL task.
- To analyze professional basketball players' decision-making in response to field goal outcomes.
- To determine if expert players overgeneralize, impacting performance.
Main Methods:
- Analysis of field goal attempt sequences from professional basketball players.
- Statistical examination of the relationship between shot outcomes and subsequent shot selection (e.g., 3-point attempts).
- Correlation analysis of successive field goal attempt outcomes.
Main Results:
- Single field goal outcomes significantly influenced subsequent 3-point shot attempt rates, aligning with RL models.
- Behavioral changes were associated with negative correlations between successive shot outcomes.
- Professional players demonstrated overgeneralization from recent experiences, leading to performance decrements.
Conclusions:
- Human experts, even highly motivated ones, may overgeneralize in complex RL tasks.
- Overgeneralization from recent outcomes can lead to suboptimal performance in expert decision-making.
- Findings suggest a need for more robust generalization strategies in artificial RL agents and potentially in human training.
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