中国科学院心理研究所机构知识库

为定量估计与提高基于时间分割的手写输入系统用户绩效 ,推导了用户绩效的静态与动态模型。实验一检验与修正了这两个模型 ,并获得整合模型。实验二证明该整合模型能对不同训练水平用户的绩效有较高的解释率。应用该整合模型发现 :当识别正确率RA、输入字数N为定值时 ,修改时间T、单字手写时间WT、分割时间ST、识别时间R这四个因素对任务完成时间D的影响大小次序为WT =ST =R >T ; 当这四个因素为定值时 ,RA每提高 1% ,D至少节省 10 0 0ms。

To predict and improve human performance in temporary segmentation Chinese handwriting recognizer , static and dynamic performance models were set up. Experiment 1 verified and modified these two models , and an integrated model(D = N (WT + ST + R + 439) + N[ (1/ RA) - 1 ] [ (0. 816WT - 51. 94 - 439) + WT + ST + R + 439 ] (N/ 24) 4973)was obtained. Experiment 2 further indicated that the integrated model could also account for the variance of the human performance data in another handwriting recognizer satisfactorily ( R square > 0. 7) . The application of the integrated model suggested that : (1) When the recognition accuracy (RA) and the number of characters inputted (N) were constants , the order of influence of the four factors ( repairing time ( T) , average writing time for each character (WT) , segmentation time (ST) , recognition time (R) ) was WT = ST = R > T ; (2) When these four factors were constants , 1 %improvement of recognition accuracy will save the task completion time at least 1000 ms.