Evaluating transfer prediction using machine learning for skill acquisition study under various practice conditions.

ABSTRACT: Recent research highlighted the interest in 1) investigating the effect of variable practice on the dynamics of learning and 2) modeling the dynamics of motor skill learning to enhance understanding of individual pathways learners. Such modeling has not been suitable for predicting future performance, both in terms of retention and transfer to new tasks. The present study attempted to quantify, by means of a machine learning algorithm, the prediction of skill transfer for three practice conditions in a climbing task: constant practice (without any modifications applied during learning), imposed variable practice (with graded contextual modifications, i.e., the variants of the climbing route), and self-controlled variable practice (participants were given some control over their variant pra

SUBMITTER: Aniszewska-Stepien A 

PROVIDER: S-EPMC9937057 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature