劉有德Yeou-Teh Liu謝宗諭Tsung Yu Hsieh2019-09-052007-7-102019-09-052007http://etds.lib.ntnu.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=id=%22GN0693300641%22.&%22.id.&amp;http://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/105621以間斷性劃線動作檢驗常態分佈 日期：民國九十六年六月二十七日 研究生：謝宗諭 指導教授：劉有德 老師 摘要 變異性這樣的現象常常在運動行為領域被探討，又由於各種限制的因素，綜合這些種種因素自然的使人體在相同情況之下企圖產生同樣的動作型態會有所不同，人體運動的特性是能夠依據工作目標的不同來控制不同的自由度(degree of freedom)，人體產生的運動型態可從知覺系統中根據所需要的目的不同或環境的限制之下來控制多個不同大小的自由度而產生出來不同的協調動作，在人體運動的協調與控制之中所要使用的自由度是非常大量的。運動行為的領域裡，許多文獻是利用描述統計中的標準差來檢驗人體運動能力的結果(e.g., schimdt et al., 1979)。這一種方式逐漸越來越多出現在行為的研究中，但是單一利用標準差的方式來檢驗似乎不足來描述人類行為的變異，原因是利用此一方法是假設人類動作行為的結果是為常態分配。Hancock and Newell (1985)建議到除了標準差外我們還必須考慮到偏態(skewness)與峰值(kurtosis)另外再加上平均數與標準差才算是能足夠的來觀察人類行為結果的情形。綜合以上，本研究的目的為檢驗根據有系統化的工作限制來觀察人類運動表現為何，利用間斷性劃線動作的方式，加上不同比重的回饋方程式。12位實驗參與者每天隨機分配在不同情境，一天一種情境，共五天(快、中快、又快又準、準確、非常準確)，每一位受試者需做300次試做，試做的動作時間、誤差與分數為分析資料。結果顯示利用單一樣本t考驗檢驗五組的偏態有顯著差異t11=3.550(第一情境)，t11=4.047(第二情境)p<.05；峰值也有顯著差異t11=3.182(第二情境)，t11=2.432(第四情境), p<.05，在利用訊息熵(information entropy)來檢驗，訊息熵是利用原始資料與假設為常態的資料，利用相同的標準差來做分佈，其結果利用成對的單一樣本t考驗來檢驗兩種不同分佈的訊息熵值有顯著差異t11=-5.580(第一情境)，t11=-4.775(第二情境)，t11=-3.535(第三情境)，t11=-3.414(第五情境)p<.05，這樣的結果顯示出在這五個情境中在動作時間裡無任何的常態分配。雖然常態分配常常被假設來檢驗動作表現的資料，以目前研究的基礎，還需要利用更完整的方法來檢驗資料的分佈才足夠。Abstract Graduate student: Tsung Yu Hsieh Advisor: Yeou-Teh Liu Motor control literatures often used the standard deviation to describe the variability of human movement outcomes (e.g., schimdt et al., 1979). This method gradually became popular in motor behavior research, but using standard deviation alone does not seem to be enough to describe the variability of human behavior because it is implicitly assumed that human behavior outcomes are distributed normally. Hancock and Newell (1985) suggested that we still have to consider skewness and kurtosis in addition to mean and standard deviation in order to describe distribution completely. Although there has been direct or indirect evidence suggesting human movement performance may not be distributed normally (Lai et al., 2005), a systematic examination of the distribution under different task constraints are still lacking.. So the purpose of the study is to examine the distributions of movement performance under systematic manipulation of task constraints. There 12 adults ranging in age from 25.167±2.23. There are 300 trials in each condition and there are 5 conditions, every participant has to finish one condition per day and it takes approximately 5 days to complete the experiment. All participants were follow the methods and procedure to finish the experiment. The results show that used repeated measure ANOVA on movement time in different conditions, conditions were significant F(1.892, 20.810)=24.318, p<.05, for spatial error, conditions were significant F(1.308,14.385)=12.226 p<.05, for performance score, conditions were F(2.303,25.330)=38.647, p<.05. Feedback function could guide participant to the specific area by predetermined weights of time and error. According to performance score, extreme conditions were more difficult, especially on accuracy condition. Entropy derived from the normal distribution was larger than the data entropy in all conditions when SD and bin size were controlled.The peak skewness value of the movement trajectory was systematically influenced by the task constraints. The conclusion was the feedback function can be applied on tasks with two movement goals to guide the movement performance and Information entropy provides a good way to examine the deviation of performance distribution under different task constraints變異性訊息熵分佈回饋方程式feedback functioninformation entropydistribution