目录
摘要
ABSTRACT
1 前言
1.1 选题依据
1.2 选题意义
1.2.1 理论意义
1.2.2 现实意义
1.3 文献综述
1.3.1 运动决策的概念及其相关理论
1.3.2 运动决策的研究现状
1.4 研究任务
2 研究对象和研究方法
2.1 研究对象
2.2 研究方法
2.2.1 文献资料法
2.2.2 实验法
2.3 数据的记录与分析
3 研究结果
3.1 行为学数据结果
3.1.1 反应时
3.1.2 正确率
3.2 不同水平篮球运动员在判断任务上的眼动特征差异
3.2.1 注视点数目
3.2.2 平均注视时间
3.2.3 兴趣区内注视点数目
3.2.4 兴趣区内注视时间
3.3 事件相关电位数据结果
3.3.1 专家与新手在 N1 成分的总平均波形比较
3.3.2 专家与新手在图片呈现 150ms 下 N1 成分的峰值比较
3.3.3 专家与新手在图片呈现 150ms 下 N1 成分的潜伏期比较
3.3.4 专家与新手在图片呈现 600ms 下 N1 成分的峰值比较
3.3.5 专家与新手在图片呈现 600ms 下 N1 成分的潜伏期比较
4 讨论
4.1 行为学数据分析与讨论
4.2 眼动数据分析与讨论
4.3 事件相关电位数据分析与讨论
4.4 本研究存在的不足
5 结论
参考文献
致谢
摘要
运动情境中的决策研究一直是运动认知领域的焦点问题，对于那些开放性的、同场对抗类的集体竞技项目来说，运动决策的水平直接影响着运动员运动能力和技战术水平的发挥。本研究试图通过对被试在篮球比赛场景搜索任务中眼动及脑电的测试与分析，探索篮球专项运动员信息加工方式特点及其神经机制。
本研究随机选取我校篮球队的 20 名二级运动员，平均年龄 20 岁，和 22 名运动训练专业学生，平均年龄 21 岁。采用改良的 oddball 范式：在屏幕中央呈现十字，而后快速呈现篮球比赛场景的图片，要求被试准确且快速地判断图片上是否有篮球，按键反应。图片包括原始图片（标准刺激）160 张和修改图片（偏差刺激，PS 图片）40 张随机呈现，图片呈现时间为 150ms 和 600ms 两种。实验程序通过 E-prime2.0 编制，记录反应时和正确率作为行为学指标。眼动数据通过 iView X 软件进行采集，对注视次数，平均注视时间，兴趣区内注视点和注视时间眼动指标进行分析。使用NeuroScan Nuamps40 导系统采集脑电信号，用 Curry 软件对 ERPs 的相关成分的峰值和峰值潜伏期进行离线分析。
研究结果的行为学数据显示，专家较新手在标准刺激下的反应速度和准确性表现出一定的优势，但并达到显着水平。专家组在 PS 图上的正确率显着性低于新手组，说明非篮球专项运动员在信息加工时更依赖具体信息。眼动数据显示，图片呈现600ms 时，在 PS 图片上，新手组观察图片的注视点数目和平均注视时间显着性多于专家组，而在兴趣区内的注视点和注视时间不存在组别差异，说明篮球专项运动员具有更高效的视觉搜索策略，依赖整体线索自上而下加工。事件相关电位数据显示，呈现时间 150ms 时，专家组对 PS 图在枕区诱发的 N1 成分的潜伏期显着早于新手组；呈现 600ms 时，专家组在枕区诱发的 N1 成分的潜伏期显着性早于新手组，表明篮球专项运动员有更加快速的视觉搜索和信息加工的能力。
结论：非篮球专项运动员更依赖观察到的具体信息从而进行决策。而篮球专项运动员在对情景任务进行信息加工和决策时更依赖整体线索，相比非篮球专项运动员，更不易受与真实情境有冲突的信息的影响，具有更加快速的视觉搜索和信息加工的能力。
关键词：篮球运动员，信息加工，视觉搜索，N1
ABSTRACT
Decision-making in sports context has always been the focus in the field of sportscongition. For those open-ended, cometiing against class collective sports,decision-makiing level directly affects the players' athletic ability and the technical andtactical level of pslay. Through the game of basketball scenne search task, this studyattempts to explore information processing characteristics and the underlying neuralmechanisms of basketball players.
In this study, I randomly selected 20 second grade sportsman from school basketballteam, aveage aged 20 years old, and 22 students majoring in sport training, with an averageage of 21 years old. A modified oddball paradigm is used: A cross appears in the middle ofthe screen, then according to flashing basketball game scene pictures, participants arerequired to judge accurately and quickly whether there is a basketball in the picture andpress the corresponding buttoon. Pictures are divided into the 160 orginal pictures （stanardstimulus） and 40 modified pictures （deviant stimulus），randomly presented. And thepictures are presented for 150ms and then 600ms. Experimental program is edited byE-prime 2.0. The response time and accuracy are recorded as behavioral indicators. Weanalyse eye-movement data, collected through iView X software, which involve fixationnumber, average fixation duration, AOI fixation and AOI fixation duration. EEG is acquredby NeuroScan Nuamps40 system. And related ERPs components'peak and peak latencyare analysed by Curry for offline.
Results of behavioral data show that experts to novices demonstrate some advantageson response time and accuracy in the stanard stimulus, but not significantly. The accuracyof expert group on PS is significantly lower than the novice group, which indicates thatlow level basketball player depending on the specific information in informationprocessing. Eye-movement data shows that when the pictures display for 600ms, of the PSpictures, the number of fixation and average fixation duration of the novice gruoupssignificantly more than expert groups, however, there is no group difference in the numberof fixation and fixation duration in AOI. That indicates high level basketball players arewith a more efficient visual search strategies and top-down processing. Event-relatedpotentials data show that in 150ms presentation time, the N1 occipital-induced latency onPS images of expert group significantly earlier than the novice group; for 600ms, N1latency evoked in the occipital area of the expert groups significantly earlier than thenovice groups, which demonstrates that high level basketball players are with faster visualsearch and information processing capabilities.
Conclusions: high level basketball players more depend on global clue than low levelbasketball player in information processing and decision-making tasks and they are lesssusceptible to conflicting information with the real-life situation. However, low levelbasketball athletes rely more on observation of specific information for decision-making.
Therefore, high level basketball players have more rapid visual searching and informationprocessing capabilities.
Key words: basketball athlete, information processing, visual search, N1
