Abstract

The present research featured the regularities, according to which the accuracy of human movements is associated with the length of these movements and time. The author considered the speed–accuracy tradeoff problem by analyzing the procedural aspect of cognitive performance. The experiment included more than a thousand participants and was performed on a portable touch screen device that tested the subject's attitude to solving problems in terms of speed or accuracy. The research objective was to identify significantly different ways of solving the speed–accuracy tradeoff dilemma. At the fine motor level, the participants failed to accomplish a one-to-one correspondence between target area and target time. This ambiguity was a manifestation of various cognitive strategies for performing a speed–accuracy tradeoff task. The Fitts law violations were determined using a wide range of statistical methods and manifested themselves at the level of criteria analysis for the normality of data distribution, types of variance analysis, and multivariate data analysis. The cluster analysis could register various strategies for performing the speed–accuracy tradeoff task. Additional variables, e.g. professional status of the subjects, made it possible to interpret the differences according to specific skills in solving cognitive tasks and to clarify the nature of these skills.

Highlights

  • The present research featured the regularities, according to which the accuracy of human movements is associated with the length of these movements and time

  • At the fine motor level, the participants failed to accomplish a one-to-one correspondence between target area and target time

  • This ambiguity was a manifestation of various cognitive strategies for performing a speed–accuracy tradeoff task

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Summary

Introduction

U. Mutual benefits: Combining reinforcement learning with sequential sampling models // Neuropsychologia. H. Gvlma: Global validation of linear models assumptions. J. Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models.

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