The decision-making process in basketball is inherently dynamic, involving the continuous integration of context and experience. While this process aligns with the core tenets of reinforcement learning (RL), classical RL models have been limited in their ability to capture the dynamic characteristics of multi-alternative, continuous decision-making. To address these limitations, the present study employed a modified version of the intrinsically enhanced model to investigate the effects of context and sports experience on the decision-making of basketball players. Moreover, the modified model was compared with several classic models, including one-trial back logistic regression, model-free model, and model-based model, to evaluate its performance in explaining and predicting decision behavior in the two-stage decision task.
This study aimed to examine how internal reward signals derived from process goals and external reward outcomes are integrated during decision-making in basketball. Specifically, we aimed to assess whether the incorporation of internal reward signals, which represent process-related achievements (e.g., tactical execution), could improve the predictive power of a modified intrinsically enhanced model. Furthermore, we explored how contextual factors modulate the decision strategies and reaction times, and whether these effects differ between experienced basketball players and novices.
A 2 (stimulus type: abstract symbols vs. basketball tactical diagrams) × 2 (group: novices vs. basketball players) mixed experimental design was employed. A total of 56 participants were recruited, including 29 basketball players with competitive experience and 27 novices with no basketball experience. Participants performed a two-stage decision task developed in MATLAB R2021b using Psychtoolbox (v3.0.19). At the outset of each trial, a fixation point was displayed for one second. In Stage 1 (S1), participants were presented with two options, representing either tactical initiation phases (in the basketball condition) or abstract alternatives (in the abstract condition). Their choice probabilistically determined the subsequent Stage 2 (S2) state, with common transitions occurring with a 70% probability and rare transitions with a 30% probability. During S2, participants made a binary decision and received immediate feedback (reward or no reward) with each option's reward probability fixed at 50%, thereby controlling for variability in external outcomes.
Behavioral responses and reaction times were recorded for both stages. Hierarchical Bayesian models were employed to analyze the data, with parameter estimation conducted via Bayesian methods using Stan and PyStan (v3.10) within a Python 3.12 environment. Model performance was evaluated using the widely applicable information criterion (WAIC), specifically the expected log pointwise predictive density (ELPD_WAIC), which simultaneously accounts for model fit and complexity. Key parameters of interest included the learning rate (α), which captures the effect of immediate reward feedback; the inverse temperature (β), which reflects the balance between exploitation and exploration; and the internal reward weight (θ), which indicates the degree to which process goals influence decision-making.
Model comparison revealed that the modified internally enhanced model outperformed all alternative models across all experimental conditions, as evidenced by consistently higher ELPD_WAIC values. This finding supports the enhanced model's ability to effectively integrate both internal reward signals and external outcomes in explaining decision behavior.
Analysis of the model parameters showed that, the α parameter, which reflects the immediate influence of reward feedback, remained stable across conditions. This finding implies that the underlying reward processing mechanism is robust and relatively unaffected by differences in experience or stimulus type. Although both basketball players and novices predominantly made outcome-driven decisions, basketball players exhibited a significantly higher sensitivity to process goals. Specifically, the internal reward weight (θ) for basketball players was approximately 0.33, indicating a relatively greater, but still sub-dominant, influence of process-related internal rewards compared to external outcomes. Furthermore, the β parameter was significantly lower in the basketball tactical diagram condition compared to the abstract condition, suggesting that participants were more inclined to employ an exploration strategy when exposed to contextually rich stimuli. Interestingly, the increased complexity inherent in the basketball tactical diagrams also led to prolonged reaction times. This finding indicates that additional cognitive load imposed by complex information requires individuals to invest more cognitive resources to process and integrate information, which tends to promote exploration strategies and prolong reaction times.
In summary, our study demonstrates that the modified intrinsically enhanced model provides a superior framework for capturing the dynamic decision-making processes of basketball players. While both experienced basketball players and novices primarily exhibit outcome-driven decision-making, basketball players display a higher sensitivity to process goals, reflecting the influence of extensive training and experience. Moreover, the increased information complexity associated with basketball tactical stimuli significantly prolongs reaction times and facilitates exploration strategies due to heightened cognitive load. These findings underscore the necessity of integrating both internal and external reward mechanisms to comprehensively model decision behavior in complex, real-world settings.
Key words
reinforcement learning /
intrinsically enhanced model /
basketball /
two-stage task /
decision making
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