Beyond curve fitting: A dynamical systems account of exponential learning in a discrete timing task

Yeou Teh Liu, Gottfried Mayer-Kress, Karl M. Newell

Research output: Contribution to journalArticle

26 Citations (Scopus)

Abstract

The authors examined the function for learning a discrete timing task from a dynamical systems perspective rather than solely the traditional curve-fitting viewpoint. Adult participants (N = 8) practiced a single-limb angular movement task of 125 ms over 20° for 200 trials. There was no significant difference in percentage of variance accounted for in 3 parameter exponential and power-law nonlinear fits to the individual and averaged data. The percentage of variance increased in both exponential and power-law equations when the data were averaged over participants and trials. Drawing on a dynamical systems approach to time scales in motor learning and on analysis of the distinctive features of exponential and power-law functions, however, the authors conclude that the exponential is the learning function for that task and that level of practice.

Original languageEnglish
Pages (from-to)197-207
Number of pages11
JournalJournal of Motor Behavior
Volume35
Issue number2
DOIs
Publication statusPublished - 2003 Jun

Keywords

  • Dynamical systems theory
  • Learning curve
  • Motor learning

ASJC Scopus subject areas

  • Biophysics
  • Orthopedics and Sports Medicine
  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience

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