通过漂移马尔可夫模型揭示视觉运动技能的持续动态性

IF 0.6 4区 心理学 Q4 PSYCHOLOGY, MATHEMATICAL Nonlinear Dynamics Psychology and Life Sciences Pub Date : 2024-10-01
Emmanouil-Nektarios Kalligeris, Vlad Stefan Barbu, Guillaume Hacques, Ludovic Seifert, Nicolas Vergne
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引用次数: 0

摘要

本研究利用漂移马尔可夫模型研究了长时间学习的攀登动态。攀爬是一项复杂的决策任务,需要有效的视觉运动协调和对环境的探索。漂移马尔可夫模型是一类受约束的异质马尔可夫过程,可以对表现出异质的数据进行建模。通过将后一种模型应用于真实世界的视觉运动技能数据,我们旨在揭示攀爬学习的持续动态。为此,我们在一项实验的基础上进行了实际案例研究,其结果(a)有助于理解在体力要求较高的环境中技能的习得;(b)为探索和视觉运动协调在学习中的作用提供了见解。
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Unveiling the Persistent Dynamics of Visual-Motor Skill via Drifting Markov Modeling.

This study investigates the climbing dynamics of learning on a long-time scale, by using Drifting Markov models. Climbing constitutes a complex decision-making task that requires effective visual-motor coordination and exploration of the environment. Drifting Markov models, is a class of constrained heterogeneous Markov processes that allow the modeling of data that exhibit heterogeneity. By applying the later models on real-world visual motor skill data, we aim to uncover the persistent dynamics of learning in climbing. To that end a real case study is conducted based on an experiment, with results that (a) help in the understanding of skill acquisition in physically demanding environments; and (b) provide insights into the role of exploration and visual-motor coordination in learning.

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来源期刊
CiteScore
1.40
自引率
11.10%
发文量
26
期刊最新文献
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