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Perceived Cost and Intrinsic Motor Variability Modulate the Speed-Accuracy Trade-Off

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  • Matteo Bertucco
  • Nasir H Bhanpuri
  • Terence D Sanger

Abstract

Fitts’ Law describes the speed-accuracy trade-off of human movements, and it is an elegant strategy that compensates for random and uncontrollable noise in the motor system. The control strategy during targeted movements may also take into account the rewards or costs of any outcomes that may occur. The aim of this study was to test the hypothesis that movement time in Fitts’ Law emerges not only from the accuracy constraints of the task, but also depends on the perceived cost of error for missing the targets. Subjects were asked to touch targets on an iPad® screen with different costs for missed targets. We manipulated the probability of error by comparing children with dystonia (who are characterized by increased intrinsic motor variability) to typically developing children. The results show a strong effect of the cost of error on the Fitts’ Law relationship characterized by an increase in movement time as cost increased. In addition, we observed a greater sensitivity to increased cost for children with dystonia, and this behavior appears to minimize the average cost. The findings support a proposed mathematical model that explains how movement time in a Fitts-like task is related to perceived risk.

Suggested Citation

  • Matteo Bertucco & Nasir H Bhanpuri & Terence D Sanger, 2015. "Perceived Cost and Intrinsic Motor Variability Modulate the Speed-Accuracy Trade-Off," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-18, October.
    • Handle: RePEc:plo:pone00:0139988
    DOI: 10.1371/journal.pone.0139988
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    References listed on IDEAS

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    1. Christopher M. Harris & Daniel M. Wolpert, 1998. "Signal-dependent noise determines motor planning," Nature, Nature, vol. 394(6695), pages 780-784, August.
    2. Amber Dunning & Atiyeh Ghoreyshi & Matteo Bertucco & Terence D Sanger, 2015. "The Tuning of Human Motor Response to Risk in a Dynamic Environment Task," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-12, April.
    3. Konrad P. Körding & Daniel M. Wolpert, 2004. "Bayesian integration in sensorimotor learning," Nature, Nature, vol. 427(6971), pages 244-247, January.
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