A0280
Title: Drifting Markov models in learning of climbing
Authors: Emmanouil-Nektarios Kalligeris - University of Sheffield (United Kingdom)
Vlad Stefan Barbu - University of Rouen-Normandy (France)
Guillaume Hacques - University of Rouen Normandie (France)
Ludovic Seifert - University of Rouen Normandie (France)
Nicolas Vergne - University of Rouen Normandy (France) [presenting]
Abstract: The climbing dynamics of learning are investigated 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 to real-world visual motor skill data, the aim is 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 (i) Help in the understanding of skill acquisition in physically demanding environments; and (ii) Provide insights into the role of exploration and visual-motor coordination in learning.
