Ganho de automaticidade e aprendizagem motora via biofeedback do esforço mental

Abstract

The gain in automaticity occurs naturally as the learner advances through the acquisition of motor skills, and its main characteristic comprises the reduction of cognitive effort. However, studies have shown that it is possible to optimize this automaticity by manipulating resources in the task, for example, directing the learner's attention to an external focus. In the present study, we verified the possibility of a biofeedback system, in which individuals learn to generate more automatic mental states with less cognitive effort. Pupillary dynamics appears as a potential candidate for biofeedback measurement since it comprises a measure of cognitive effort and has a high discriminative capacity in tasks with different requirements and levels of difficulty. To test this hypothesis, an integrated system of four cameras was used to read the pupil dynamics and golf putting performance. The threshold of cognitive effort was estimated from the practice of a control group, which fed a machine learning algorithm that drives a series of decisions to maximize the interpretation of data without human intervention. The algorithm was selected due to the inherent difficulties in estimating the ideal pupil diameter in its contextual complexities. As a way of guaranteeing the maintenance of the optimal state, the experimental group was conditioned to reach the calculated mental state, based on an auditory stimulus, so that they could start each trial. The results demonstrate that the active reduction of the cognitive effort favors not only the practice but also motor learning. The reduction of cognitive effort leads to automaticity, favoring the reduction of factors that constrain motor action. The biofeedback system via oculomotor behavior developed in this study, proved to be sensitive enough to promote changes in the cognitive effort, opening an unprecedented field of possibilities and applications of this technology.