title: Motor entropy in response to task demands and environmental information.
abstract: This experiment tested the hypothesis that human motor adaptation can be represented as the conservation of entropy across the task, organism, and environment. Healthy young individuals generated a submaximal isometric force with the index finger of their dominant hand. Subjects performed this task under different task demands (error tolerance) and environmental information (feedback frequency) conditions. In order to extend previous findings, we employ the use of approximate entropy (ApEn) to capture the temporal aspects of the variability in the isometric force and to create links to other studies of time-series in human behavior. We showed that ApEn of the force time-series, made conditional upon satisfying the task demands, decreased as the task demands were increased and the environmental information reduced. There was a compensatory interaction between task and environment on the force dynamics that could be represented by a quadratic surface, capturing 92% of the total variance. Our results show that when faced with a reduced likelihood of achieving the task goal (increased task entropy) and an environment that provides little information (increased environmental entropy), the subjects employed similar force production strategies over time, resulting in a more regular pattern.
authors.affiliation: Department of Kinesiology, Louisiana State University, Baton Rouge, Louisiana 70803-6501, USA. shong1@lsu.edu
authors.forename: S Lee
authors.initials: SL
authors.lastname: Hong
authors.forename: Karl M
authors.initials: KM
authors.lastname: Newell
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology; D003198:Computer Simulation; D019277:Entropy; D005385:Fingers / Q000502:physiology*; D006801:Humans; D007537:Isometric Contraction / Q000502:physiology*; D008954:Models, Biological; D009048:Motor Skills / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D017711:Nonlinear Dynamics; D013647:Task Performance and Analysis