Using Probability Distribution to Quantify Dynamics in Motor Learning

• Main Authors: Lin, Bo-Cheng, 林柏成
• Other Authors: Liu, Yeou-Teh
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/82073787295791142395

碩士 === 國立臺灣師範大學 === 運動競技學系 === 104 === The learning process can be classified as a scale learning and learning a new coordination pattern. In coordination learning, qualitative changes of coordination patterns in the dynamic processes may occur. In this case, using the assumption of normality concept of statistic (e.g. mean and standard deviation) to represent dataset may not be appropriate and incomplete. Here we investigated the gamma probability density functions as another candidate approach to qualify and quantify the learning process even though the data distribution deviated from normality. The gamma function with different combinations of the parameters (alpha and beta) may form different shapes to capture qualitative changes of performance outcome through learning process, especially in coordination learning. The purpose of this study was to investigate different distribution models (normal, logarithmic normal, exponential and uniform distributions) to fit the data distribution of scale learning and coordination learning in different learning phases from throwing task (50 trials a day for 3 days) and the rollerball task (50 trials a day for 5 days), respectively. Two factors repeated measure ANOVAs were used to compare the coefficient of determination between distribution models and learning phase. There was a significant difference among distribution models in scale learning, the gamma and lognormal distribution had greater coefficients of determination than the others. In coordination learning, both three and two phase groups had interaction between distribution models and learning phases. The post hoc analyses showed that the coefficient of determination of the gamma and lognormal distribution were both significantly greater than the normal, exponential and uniform distributions at the first and transition phases, the gamma, lognormal and normal distribution were significantly greater than the exponential and uniform distributions at the last phase. In conclusion, the gamma function showed superior descriptive power among the models over the learning phase for both types of learning that have a comparative advantage in the results of curve fitting.