Promotion of Deviation Decisions in Table Tennis Players by Internal ModelA Quantitative Analysis Based on the HDDM
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摘要:目的
基于视觉的知觉-动作双系统理论，选取乒乓球发球动作作为研究对象，采用漂移扩散模型（DDM）探讨运动经验如何影响动作预期偏差的监控能力，旨在深化对于运动员认知加工机制的理解并提供理解运动控制和决策过程中细微差异的有效量化方法。
方法通过DDM中的起始点和漂移率参数，分别量化被试基于内部模型的有效偏差感知以及动作控制过程中偏差证据的累积速度，实现对感知与动作决策过程的定量分离。
结果运动员表现出更高的偏差感知敏感性，其起始点受偏差程度的影响，而新手不受影响。此外，运动员组在偏差程度变化时的漂移率调整更为精细。
结论运动经验通过优化内部模型和形成特定认知策略的方式对动作预判和偏差监控过程产生影响，揭示了在高水平运动员运动表现中认知加工的多层次作用。
Abstract:ObjectBased on the perception-action double-system theory, this study selects the table tennis serve as the research object and uses the Drift Diffusion Model (DDM) to explore how motor experience affects the ability to monitor action anticipation deviations.
MethodsBy quantifying the starting point and drift rate parameters in DDM, the study measures the effective deviation perception based on the internal model and the accumulation speed of deviation evidence during the action control process, achieving a quantitative separation of perception and action decision processes.
ResultsPlayers exhibited higher sensitivity to deviation perception, with their starting points being influenced by the degree of deviation, while novices were not affected. Moreover, the players' group showed more refined adjustments in the drift rate with changes in deviation degree.
ConclusionMotor experience produces the effect on action anticipation and deviation monitoring processes by optimizing the internal model and forming specific cognitive strategies, revealing the multi-level role of cognitive processing in high-level elite players.
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Keywords:
- deviation decision /
- motor experience /
- internal model /
- graded mapping /
- drift-diffusion model
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体育场馆是重要的赛会遗产，其赛后维护和可持续利用一直是世界性难题。近年来，“白象”这一概念被越来越多的大型体育赛事主办国家或城市所熟知，它通常用来比喻珍贵却无用的事物。一些因赛而建的体育场馆赛后利用率极低，维护成本远超其使用价值，极易成为“白象”。如何避免“白象”效应的产生是学界、业界长期探讨的话题。大阪成蹊大学的Isao Okada和哈佛商学院的Stephen A. Greyser在2018年的研究报告《狂欢之后：增进奥运遗产保护并防止奥运遗址成为“白象”的关键因素》中，对避免奥运场馆赛后闲置的因素和对策进行了讨论，梳理了避免“白象”效应出现的八大关键因素：赛后特定设施（如跑道）的拆除、赛后部分看台拆除、赛后进行持续有效投资、公共交通的可达性、周边不存在同类场馆竞争、无债务负担、场馆设计独特并获得全球知名度、周边区域成功再开发所带来的奥运遗产。研究从有效再利用（文体赛事活动、展会、观光旅游的上座率与使用天数）、可持续性（赛后改造、赛后持续投资、公共交通、现有大型场馆的竞争、债务负担）和遗产价值（场馆的独特设计和全球认可、周边地区的成功再开发）三方面构建了 OkadaGreyser 模型，旨在评估奥运场馆成为“白象”的风险值。对历届奥运场馆的实证分析发现，2004年雅典奥运会主体育场的“白象”风险相对最高，而2000年悉尼奥运会主体育场的风险相对偏低。研究还对场馆是否存在固定租户、地区人口或经济规模、场馆所有权属性等传统意义上被认为可以避免“白象”效应的因素进行了批判性讨论，认为影响结果并不一致，需结合不同国家或城市的实际情况分类讨论。
该研究创新性地将“白象”效应量化，为我国场馆赛后利用的理论研究与实践探索提供了参考。在后续研究及应用中，应重视场馆“白象”风险评估，提前谋划赛后利用；同时，重视我国本土经验的总结与提炼。在北京2022年冬奥会上，科技创新成为一大亮点，零碳场馆、 “水冰转换”等技术应用以及兼顾赛时需要和赛后利用的灵活设计为赛后反复、综合、持久利用创造了条件，向世界展现了可持续发展的中国智慧和中国方案。结合我国实际情况，建议对Okada-Greyser模型进行适当修订，将科技创新、低碳使用、综合利用等要素融入“白象”效应的诊断标准之中，为更多发展中国家承办大型体育赛事、投资规划大型体育场馆提供参考。
（浙江师范大学 方雪默，华中师范大学 陈元欣）
作者贡献声明：陆颖之：提出论文选题，撰写论文，指导修改论文；作者贡献声明：陈奕衡：采集、预处理数据；作者贡献声明：王吟月：采集数据；作者贡献声明：栾梦恺：分析数据，撰写论文。 -
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图 1 偏差决策刺激材料的分区与呈现
注：图（a）为实际球飞行轨迹与观察到的球飞行轨迹之间的偏差程度，7种颜色代表7种偏差刺激材料；图（b）为连续图片示例，每个刺激材料包含球拍和球接触前的18帧、球拍和球接触时的1帧以及球拍和球接触后的17帧。
Figure 1. Partitioning and presentation of deviation decision stimulus materials
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图 2 运动员组和对照组在各偏差程度条件下的反应时（a）和判定偏差比例（b）
Figure 2. Reaction time (a) and proportion of classified discrepancy (b) across discrepancy conditions and groups
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图 4 运动员组和对照组在各偏差程度条件下的漂移率（a）和起始点（b）
注：对照组的最优模型中起始点不随偏差程度变化而变化。
Figure 4. Drift rate (a) and starting point (b) across discrepancy conditions and groups
表 1 刺激材料的变量设置及对应的具体落点
Table 1 The variable settings of stimulus materials and their corresponding landing zones
偏差程度 发球动作的落点区域（剪辑呈现的落点区域） 偏差0
（无偏差）1（1）；2（2）；3（3）；4（4）；5（5）；6（6）；7（7） 偏差1 1（2）；2（3）；3（4）；4（5）； 5（6）；6（7）；2（1）；3（2）；4（3）；5（4）；
6（5）；7（6）偏差2 1（3）；2（4）；3（5）；4（6）；5（7）；3（1）；4（2）；5（3）；6（4）；7（5） 偏差3 1（4）；2（5）；3（6）；4（7）；4（1）；5（2）；6（3）； 7（4） 偏差4 1（5）；2（6）；3（7）；5（1）；6（2）；7（3） 偏差5 1（6）；2（7）；6（1）；7（2） 偏差6 1（7）；7（1） 注：以球落在第1区的发球视频为例，将其运动学信息图片和原始球飞行轨迹信息图片（即落点在第1区）的组合设定为偏差程度为0的刺激材料，依此类推，将其运动学信息图片和落点在第7区球飞行轨迹信息图片的组合设定为偏差程度为6的刺激材料 （图1）。偏差程度数值越大表示实际球飞行轨迹与观察到的球飞行轨迹之间的偏差越大。 
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表 2 各竞争模型的偏差信息准则（DIC）
Table 2 The deviance information criterion (DIC) value for each of the competing models
模型 模型参数 运动员组DIC 对照组DIC 模型1 none 9899.26 9599.44 模型2 z～contrast 9636.22 9553.92 模型3 v～contrast 9303.57 9449.14 模型4 a～contrast 9917.42 9603.52 模型5 z～contrast, v～contrast 9296.60 9459.34 模型6 v～contrast, a～contrast 9311.44 9450.28 模型7 z～contrast, a～contrast 9633.26 9560.47 模型8 z～contrast, v～contrast, a～contrast 9308.82 9465.69
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[1] WILLIAMS A M,JACKSON R C. Anticipation in sport:Fifty years on,what have we learned and what research still needs to be undertaken?[J]. Psychology of Sport and Exercise,2019,42:16-24 doi: 10.1016/j.psychsport.2018.11.014
[2] TOMEO E,CESARI P,AGLIOTI S M,et al. Fooling the kickers but not the goalkeepers:Behavioral and neurophysiological correlates of fake action detection in soccer[J]. Cerebral Cortex,2013,23(11):2765-2778 doi: 10.1093/cercor/bhs279
[3] GÜLDENPENNING I,KUNDE W,WEIGELT M. How to trick your opponent:A review article on deceptive actions in interactive sports[J]. Frontiers in Psychology,2017,8:917 doi: 10.3389/fpsyg.2017.00917
[4] ZHAO Q,LU Y Z,JAQUESS K J,et al. Utilization of cues in action anticipation in table tennis players[J]. Journal of Sports Sciences,2018,36(23):2699-2705 doi: 10.1080/02640414.2018.1462545
[5] LU Y Z,YANG T T,HATFIELD B D,et al. Influence of cognitive-motor expertise on brain dynamics of anticipatory-based outcome processing[J]. Psychophysiology,2020,57(2):e13477 doi: 10.1111/psyp.13477
[6] NORMAN J. Two visual systems and two theories of perception:An attempt to reconcile the constructivist and ecological approaches[J]. Behavioral and Brain Sciences,2002,25(1):73-96 doi: 10.1017/S0140525X0200002X
[7] PEZZULO G,RIGOLI F,FRISTON K. Active inference,homeostatic regulation and adaptive behavioural control[J]. [J]. Progress in Neurobiology,2015,134:17-35 doi: 10.1016/j.pneurobio.2015.09.001
[8] 张航,傅小兰. 视觉的知觉-动作双系统理论中的论争[J]. 心理科学进展,2005,13(1):1-9 doi: 10.3969/j.issn.1671-3710.2005.01.001 [9] SPINELLI G,TIERI G,PAVONE E F,et al. Wronger than wrong:Graded mapping of the errors of an avatar in the performance monitoring system of the onlooker[J]. NeuroImage,2018,167:1-10 doi: 10.1016/j.neuroimage.2017.11.019
[10] HARRIS D J,ARTHUR T,BROADBENT D P,et al. An active inference account of skilled anticipation in sport:Using computational models to formalise theory and generate new hypotheses[J]. Sports Medicine,2022,52(9):2023-2038 doi: 10.1007/s40279-022-01689-w
[11] AGLIOTI S M,CESARI P,ROMANI M,et al. Action anticipation and motor resonance in elite basketball players[J]. Nature Neuroscience,2008,11(9):1109-1116 doi: 10.1038/nn.2182
[12] MAKRIS S,URGESI C. Neural underpinnings of superior action prediction abilities in soccer players[J]. Social Cognitive and Affective Neuroscience,2015,10(3):342-351 doi: 10.1093/scan/nsu052
[13] ROWE R,HORSWILL M S,KRONVALL-PARKINSON M,et al. The effect of disguise on novice and expert tennis players' anticipation ability[J]. Journal of Applied Sport Psychology,2009,21(2):178-185 doi: 10.1080/10413200902785811
[14] CAÑAL-BRULAND R,SCHMIDT M. Response bias in judging deceptive movements[J]. Acta Psychologica,2009,130(3):235-240 doi: 10.1016/j.actpsy.2008.12.009
[15] YEUNG N,BOTVINICK M M,COHEN J D. The neural basis of error detection:Conflict monitoring and the error-related negativity[J]. Psychological Review,2004,111(4):931-959 doi: 10.1037/0033-295X.111.4.931
[16] HUNG T M,SPALDING T W,MARIA D L S,et al. Assessment of reactive motor performance with event-related brain potentials:Attention processes in elite table tennis players[J]. Journal of Sport and Exercise Psychology,2004,26(2):317-337 doi: 10.1123/jsep.26.2.317
[17] BOGACZ R,GURNEY K. The basal Ganglia and cortex implement optimal decision making between alternative actions[J]. Neural Computation,2007,19(2):442-477 doi: 10.1162/neco.2007.19.2.442
[18] RATCLIFF R,MCKOON G. The diffusion decision model:Theory and data for two-choice decision tasks[J]. Neural Computation,2008,20(4):873-922 doi: 10.1162/neco.2008.12-06-420
[19] RATCLIFF R,SMITH P L,BROWN S D,et al. Diffusion decision model:Current issues and history[J]. Trends in Cognitive Sciences,2016,20(4):260-281 doi: 10.1016/j.tics.2016.01.007
[20] JOHNSON D J,HOPWOOD C J,CESARIO J,et al. Advancing research on cognitive processes in social and personality psychology[J]. Social Psychological and Personality Science,2017,8(4):413-423 doi: 10.1177/1948550617703174
[21] MACMILLAN N A,KAPLAN H L. Detection theory analysis of group data:Estimating sensitivity from average hit and false-alarm rates[J]. Psychological Bulletin,1985,98(1):185-199 doi: 10.1037/0033-2909.98.1.185
[22] WIECKI T V,SOFER I,FRANK M J. HDDM:Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python[J]. Frontiers in Neuroinformatics,2013,7:14
[23] RATCLIFF R. A diffusion model account of response time and accuracy in a brightness discrimination task:Fitting real data and failing to fit fake but plausible data[J]. Psychonomic Bulletin & Review,2002,9(2):278-291
[24] ULRICHSEN K M,ALNAES D,KOLSKÅR K K,et al. Dissecting the cognitive phenotype of post-stroke fatigue using computerized assessment and computational modeling of sustained attention[J]. European Journal of Neuroscience,2020,52(7):3828-3845 doi: 10.1111/ejn.14861
[25] SHAPIRO Z,HUANG-POLLOCK C. A diffusion-model analysis of timing deficits among children with ADHD[J]. Neuropsychology,2019,33(6):883-892 doi: 10.1037/neu0000562
[26] KRUSCHKE J. Doing Bayesian data analysis:A tutorial with R,JAGS,and Stan [M]. 2nd ed. New York:Academic Press/Elsevier,2014:143-189
[27] GELMAN A,RUBIN D B. Inference from iterative simulation using multiple sequences[J]. Statistical Science,1992,7(4):457-472
[28] AMORUSO L,SEDEÑO L,HUEPE D,et al. Time to Tango:Expertise and contextual anticipation during action observation[J]. NeuroImage,2014,98:366-385 doi: 10.1016/j.neuroimage.2014.05.005
[29] AMORUSO L,PUSIL S,GARCÍA A M,et al. Decoding motor expertise from fine-tuned oscillatory network organization[J]. Human Brain Mapping,2022,43(9):2817-2832 doi: 10.1002/hbm.25818
[30] CHEN J C,LI Y N,ZHANG G H,et al. Enhanced inhibitory control during re-engagement processing in badminton athletes:An event-related potential study[J]. Journal of Sport and Health Science,2019,8(6):585-594 doi: 10.1016/j.jshs.2019.05.005
[31] BALSER N,LOREY B,PILGRAMM S,et al. The influence of expertise on brain activation of the action observation network during anticipation of tennis and volleyball serves[J]. Frontiers in Human Neuroscience,2014,8:568
[32] CALLAN D E,NAITO E. Neural processes distinguishing elite from expert and novice athletes[J]. Cognitive and Behavioral Neurology,2014,27(4):183-188 doi: 10.1097/WNN.0000000000000043
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