A quantitative dynamical systems approach to differential learning: self-organization principle and order parameter equations

• Published in: Biological cybernetics Vol. 98; no. 1; pp. 19 - 31
• Main Authors: Frank, T. D., Michelbrink, M., Beckmann, H., Schöllhorn, W. I.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.01.2008; Springer Nature B.V
• Subjects: Animals; Bioinformatics; Biomedical and Life Sciences; Biomedicine; Biophysics; Complex Systems; Computer Appl. in Life Sciences; Cybernetics; Dynamical systems; Evaluation Studies as Topic; Humans; Learning - physiology; Models, Psychological; Models, Statistical; Motor ability; Motor Skills - physiology; Neurobiology; Neurosciences; Original Paper; Training; Differential Learning; Dynamical System Perspective; Motor Control System; Dynamical System Approach; Learning Rate
• ISSN: 0340-1200; 1432-0770; 1432-0770
• DOI: 10.1007/s00422-007-0193-x

Differential learning is a learning concept that assists subjects to find individual optimal performance patterns for given complex motor skills. To this end, training is provided in terms of noisy training sessions that feature a large variety of between-exercises differences. In several previous experimental studies it has been shown that performance improvement due to differential learning is higher than due to traditional learning and performance improvement due to differential learning occurs even during post-training periods. In this study we develop a quantitative dynamical systems approach to differential learning. Accordingly, differential learning is regarded as a self-organized process that results in the emergence of subject- and context-dependent attractors. These attractors emerge due to noise-induced bifurcations involving order parameters in terms of learning rates. In contrast, traditional learning is regarded as an externally driven process that results in the emergence of environmentally specified attractors. Performance improvement during post-training periods is explained as an hysteresis effect. An order parameter equation for differential learning involving a fourth-order polynomial potential is discussed explicitly. New predictions concerning the relationship between traditional and differential learning are derived.