Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A New Approach to Modeling the Prediction of Movement Time

Version 1 : Received: 22 April 2021 / Approved: 23 April 2021 / Online: 23 April 2021 (13:02:07 CEST)

How to cite: Lin, C.J.; Cheng, C. A New Approach to Modeling the Prediction of Movement Time. Preprints 2021, 2021040640 (doi: 10.20944/preprints202104.0640.v1). Lin, C.J.; Cheng, C. A New Approach to Modeling the Prediction of Movement Time. Preprints 2021, 2021040640 (doi: 10.20944/preprints202104.0640.v1).

Abstract

Fitts' law predicts the human movement response time for a specific task by a simple linear formulation, in which the intercept and the slope are estimated from the task's empirical data. This research was motivated by our pilot study, which found that the linear regression's essential assumptions are not satisfied in the literature. Furthermore, the keystone hypothesis in Fitts' law, that the movement time per response will be directly proportional to the minimum average amount of information per response demanded by the particular amplitude and target width, has never been formally tested. Therefore, this study developed an optional formulation derived from fusing the findings in psychology, physics, and physiology for fulfilling the statistical assumptions. An experiment was designed to test the hypothesis in Fitts' law and validate the proposed model. To conclude, our results indicated that movement time could be related to the index of difficulty underlying the same constant amplitude. The optional formulation accompanies the index of difficulty in Shannon form robustly performs the prediction better than the traditional model across studies. Finally, a new approach to modeling movement time prediction is deduced from our research results

Subject Areas

Fitts' law; information theory; index of difficulty; SQRT_MT model

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