摘要：

对运动决策的研究是认知运动心理学的一个重要领域。运动预期被认为是运动决策的核心, 受到运动学和非运动学信息的影响。其中, 对运动预期研究的一个关键问题是探讨不同信息源对运动结果预期的贡献以及两者之间的相互作用。研究者运用贝叶斯决策理论解释运动预期中不同信息的整合过程, 分析运动员在复杂的竞赛情景中如何做出最佳决策, 尤其是对该理论在网球和足球领域的潜在应用进行分析。在不确定的情况下, 竞技体育中并非所有的选择、结果或概率都是已知的, 故有研究者认为概率论和经典的决策理论不能有效解决此类问题。然而新近提出的启发式近似, 为运动员在贝叶斯框架下如何快速做出选择提供了理论依据：首先, 在复杂和有时间压力的竞赛情景中, 启发式近似假设运动员依据竞赛中不同信息源的不确定程度, 很可能选择在运动学信息和情境先验之间进行切换启发式, 提高运动预期的效率。其次, 判断效用通过卷积效应影响两种信息源的整合, 降低情境先验的影响程度。

Abstract:

The study of sports decision making is a significant field of cognitive psychology in sports. Sports decision making is characterized by less available information, greater time pressure and uncertainty of the outcome. While athletes are under great time pressure and have to make decisions quickly, they tend to use intuitive decision making. The core of sport decision making is action anticipation, which is usually thought to be influenced by kinematic and non-kinematic factors. Considering that athletes use and rely on two kind of information simultaneously in sports decision making, one of the key issues in the study of action anticipation is to explore the contribution of diverse information sources to the expectation of action outcome and the interaction between them. Therefore, some researchers try to use Bayesian decision theory to explain the integration of different information and analyze how athletes make the optimal choice in complex competition environments in sports.

Due to athletes have time pressure in complex competition environments, Bayesian sport decision theory provides a basic framework for how to better combine opponent's (visual) kinematic information in real-time with non-kinematic (contextual) information. On the one hand, Bayesian decision theory is composed of Bayesian statistics and decision theory, which can be used to explain the two psychological processes of sport decision making, that is, action anticipation and action selection response, respectively. When integrating multiple information sources, the uncertainty of information sources is used to weigh the influence of different ones, so as to generate an ideal decision. On the other hand, athletes may use heuristics approximation to make decision quickly. Heuristics approximation assumes that athletes may choose to switch between kinematic and non-kinematic information according to the degree of uncertainty of diverse information sources in competition under time pressure, thus improving the efficiency of sport decision making. Then judgement utility disrupts the integration of contextual priors and kinematic information, which results in decreased impact of explicit contextual priors during action anticipation. Put in Bayesian terms, the weighted average of the reliability conveyed by contextual prior and current sources of information is convolved with the utility values assigned to possible judgements. Therefore, a fundamental aspect of Bayesian theory is that our ultimate judgements are affected by both the reliability of available information and the potential costs and rewards associated with inaccurate and accurate judgements.

At first, this review discusses the diverse information sources affected the effect of action anticipation in detail, and then illustrates the potential application of Bayesian decision theory in the field of tennis and soccer field in which inspired by Bayesian decision model research, respectively. This review also discusses the basic framework of Bayesian decision theory from the perspectives of Bayesian decision theory and heuristic approximation, and tries to construct Bayesian sports decision theory to explain complex decision making in sports. As a result, more empirical studies are needed to improve the decision making in sports under the Bayesian framework in the future, which may be beneficial to improve the ability of decision making in athletes in competition.

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