Domain-specific contexts promote model-based decision making for basketball players
Liu Yang, Chenglin Zhou, Biao Sang · 2026
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Description
Liu Yang, Chenglin Zhou, and Biao Sang ask whether basketball expertise turns decision making more planful when the task wears basketball clothing, in a 2026 psychology experiment. Reinforcement learning theory separates model-based choices, which use a mental map of how actions lead to outcomes, from model-free habits that simply repeat rewarded moves. Thirty-five national-level players and twenty-seven novices played a two-stage decision game in two skins: abstract symbols and basketball tactical diagrams.
Players leaned model-based in both versions and reported understanding the task better, while novices leaned habitual. Players using the planful style also answered more slowly, though slow answers in novices did not signal planning. The authors conclude expertise helps people fuse feedback with task structure, and they suggest training with sport-specific displays can sharpen decisions without lectures on strategy.
Catalog record
| CREATOR | Liu Yang, Chenglin Zhou, Biao Sang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 35759 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |