The Application of Information Theory in Studying Motor Control

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Abstract

Information theory, originally developed to characterize communication systems, offers powerful tools for quantifying the structure and variability of biological movement. This chapter provides an accessible introduction to core information-theoretic concepts — including Shannon entropy, mutual information, and channel capacity — and illustrates their application to motor control research. We review how entropy-based measures capture movement regularity and predictability across tasks and skill levels, and discuss how these measures relate to classical models of motor control (e.g., optimality, redundancy, noise). Case studies illustrate the use of information theory to index motor learning, age-related dedifferentiation, and task complexity. The chapter concludes by situating information theory within a broader computational neuroscience framework for understanding the brain’s role in planning and executing skilled actions.

Citation

Tien, H.-P., & Chang, E. C. (2025). The application of information theory in studying motor control. In Cognitive and Neural Foundations of Chinese Reading (Chinese Language Learning Sciences, pp. 199–211). Springer.

@incollection{Tien2025,
  author    = {Tien, Hsin-Ping and Chang, Erik Chihhung},
  year      = {2025},
  title     = {The Application of Information Theory in Studying Motor Control},
  booktitle = {Cognitive and Neural Foundations of Chinese Reading},
  series    = {Chinese Language Learning Sciences},
  pages     = {199--211},
  publisher = {Springer},
  doi       = {10.1007/978-981-96-6669-0_12}
}