InterJournal Complex Systems, 2160
Status: Accepted
Manuscript Number: [2160]
Submission Date: 20080226
Multiple Time Scale Model of Self Organized Criticality in Human Motor Learning
Author(s): Gottfried Mayer-Kress ,Yeou-Teh Liu ,Karl Newell

Subject(s): CX.41

Category: Article


Self organized criticality (SOC) has been studied as a universal property of complex adaptive systems. In a series of experiments we could show that principles of SOC also apply to a complex class of motor learning tasks. These tasks ("Roller ball") are characterized by the fact that they do not only exhibit a simple improvement of score values ("Learning Curves") but also involve a sharp transition from a failure state (not being able to solve the task) to success (being able to perform the task). The system is controlled by two critical parameters, "skill level" and "task difficulty" that can induce the transition from failure to success. The skill parameter thereby plays the role of the sand dropped slowly on the sand pile in the classic demonstration model of SOC. The connection between the two processes in given by the generally accepted assumption/axiom that skill level increases with practice time. In the class of experiments that we try to model the learner has control over a continuous parameter that quantifies the difficulty of the task. Experimental results could confirm a conceptual prediction from the psychology of the “flow” phenomenon, namely that learners tend to a condition where skill level matches task difficulty. Here it is quantitatively interpreted as difficulty levels for which the expected success rate is close to 50%. In our discrete, stochastic, piece-wise linear map model we further explore conditions for parameters that make this connection between motor learning and models of SOC more explicit and quantitative.

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