Noise, information transmission, and force variability.

Andrew B. Slifkin, Karl Maxim Newell·1999·Journal of Experimental Psychology Human Perception & Performance

Abstract

This study was designed to test the hypothesis derived from information theory that increases in the variability of motor responses result from increases in perceptual-motor noise. Young adults maintained isometric force for extended periods at different levels of their maximum voluntary contraction. Force variability (SD) increased exponentially as a function of force level. However, the signal-to-noise ratio (M/SD), an index of information transmission, as well as measures of noise in both the time (approximate entropy) and frequency (power spectrum) domains, changed according to an inverted U-shaped function over the range of force levels. These findings indicate that force variability is not directly related to noise but that force output noisiness is positively correlated with the amount of information transmitted.

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  2. The Role of Execution Noise in Movement Variability
    van Beers, Robert J., Haggard, Patrick, Wolpert, Daniel M. · 2004 · Journal of Neurophysiology
  3. Variability and Determinism in Motor Behavior
    Riley, Michael A., Turvey, M. T. · 2002 · Journal of Motor Behavior
  4. Training adaptations in the behavior of human motor units
    Duchateau, Jacques, Semmler, John G., Enoka, Roger M. · 2006 · Journal of Applied Physiology
  5. Structure of motor variability in marginally redundant multifinger force production tasks
    Latash, Mark, Scholz, John, Danion, Frederic, Schöner, Gregor · 2001 · Experimental Brain Research

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How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Noise, information transmission, and force variability. https://app.cochat.ai/papers/10.1037//0096-1523.25.3.837
The paper
Slifkin, A. B., & Newell, K. M. (1999). Noise, information transmission, and force variability. Journal of Experimental Psychology Human Perception & Performance. https://doi.org/10.1037//0096-1523.25.3.837

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.