Reinforcement learning in professional basketball players
AbstractReinforcement learning in complex natural environments is a challenging task because the agent should generalize from the outcomes of actions taken in one state of the world to future actions in different states of the world. The extent to which human experts find the proper level of generalization is unclear. Here we show, using the sequences of field goal attempts made by professional basketball players, that the outcome of even a single field goal attempt has a considerable effect on the rate of subsequent 3 point shot attempts, in line with standard models of reinforcement learning. However, this change in behaviour is associated with negative correlations between the outcomes of successive field goal attempts. These results indicate that despite years of experience and high motivation, professional players overgeneralize from the outcomes of their most recent actions, which leads to decreased performance.
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Bibliographic InfoPaper provided by The Center for the Study of Rationality, Hebrew University, Jerusalem in its series Discussion Paper Series with number dp593.
Length: 16 pages
Date of creation: Dec 2011
Date of revision:
Publication status: Published in Nature Communications 2:569.
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-01-18 (All new papers)
- NEP-CBE-2012-01-18 (Cognitive & Behavioural Economics)
- NEP-SPO-2012-01-18 (Sports & Economics)
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