Sloppy, But Acceptable, Control of Biological Movement: Algorithm-Based Stabilization of Subspaces in Abundant Spaces

Vladimir M. Akulin, Frederic Carlier, Stanislaw Solnik, Mark L. Latash

Research output: Contribution to journalArticle

2 Scopus citations


In this paper, we develop an algorithm-based approach to the problem of stability of salient performance variables during motor actions. This problem is reformulated as stabilizing subspaces within high-dimensional spaces of elemental variables. Our main idea is that the central nervous system does not solve such problems precisely, but uses simple rules that achieve success with sufficiently high probability. Such rules can be applied even if the central nervous system has no knowledge of the mapping between small changes in elemental variables and changes in performance. We start with a rule "Act on the most nimble" (the AMN-rule), when changes in the local feedback-based loops occur for the most unstable variable first. This rule is implemented in a task-specific coordinate system that facilitates local control. Further, we develop and supplement the AMN-rule to improve the success rate. Predictions of implementation of such algorithms are compared with the results of experiments performed on the human hand with both visual and mechanical perturbations. We conclude that physical, including neural, processes associated with everyday motor actions can be adequately represented with a set of simple algorithms leading to sloppy, but satisfactory, solutions. Finally, we discuss implications of this scheme for motor learning and motor disorders.

Original languageEnglish (US)
Pages (from-to)49-72
Number of pages24
JournalJournal of Human Kinetics
Issue number1
StatePublished - Jan 1 2019


All Science Journal Classification (ASJC) codes

  • Physical Therapy, Sports Therapy and Rehabilitation
  • Physiology (medical)

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