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Optimal Compensation for Temporal Uncertainty in Movement Planning

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  • Todd E Hudson
  • Laurence T Maloney
  • Michael S Landy

Abstract

Motor control requires the generation of a precise temporal sequence of control signals sent to the skeletal musculature. We describe an experiment that, for good performance, requires human subjects to plan movements taking into account uncertainty in their movement duration and the increase in that uncertainty with increasing movement duration. We do this by rewarding movements performed within a specified time window, and penalizing slower movements in some conditions and faster movements in others. Our results indicate that subjects compensated for their natural duration-dependent temporal uncertainty as well as an overall increase in temporal uncertainty that was imposed experimentally. Their compensation for temporal uncertainty, both the natural duration-dependent and imposed overall components, was nearly optimal in the sense of maximizing expected gain in the task. The motor system is able to model its temporal uncertainty and compensate for that uncertainty so as to optimize the consequences of movement.Author Summary: Many recent models of motor planning are based on the idea that the CNS plans movements to minimize “costs” intrinsic to motor performance. A minimum variance model would predict that the motor system plans movements that minimize motor error (as measured by the variance in movement) subject to the constraint that the movement be completed within a specified time limit. A complementary model would predict that the motor system minimizes movement time subject to the constraint that movement variance not exceed a certain fixed threshold. But neither of these models is adequate to predict performance in everyday tasks that include external costs imposed by the environment where good performance requires that the motor system select a tradeoff between speed and accuracy. In driving to the airport to catch a plane, for example, there are very real costs associated with driving too fast and also with being just a bit too late. But the “optimal” tradeoff depends on road conditions and also on how important it is to catch the plane. We examine motor performance in analogous experimental tasks where we impose arbitrary monetary costs on movements that are “late” or “early” and show that humans systematically trade off risk and reward so as to maximize their expected monetary gain.

Suggested Citation

  • Todd E Hudson & Laurence T Maloney & Michael S Landy, 2008. "Optimal Compensation for Temporal Uncertainty in Movement Planning," PLOS Computational Biology, Public Library of Science, vol. 4(7), pages 1-9, July.
  • Handle: RePEc:plo:pcbi00:1000130
    DOI: 10.1371/journal.pcbi.1000130
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    References listed on IDEAS

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    1. Paul L. Gribble & Stephen H. Scott, 2002. "Overlap of internal models in motor cortex for mechanical loads during reaching," Nature, Nature, vol. 417(6892), pages 938-941, June.
    2. Christopher M. Harris & Daniel M. Wolpert, 1998. "Signal-dependent noise determines motor planning," Nature, Nature, vol. 394(6695), pages 780-784, August.
    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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    Cited by:

    1. Shih-Wei Wu & Maria F Dal Martello & Laurence T Maloney, 2009. "Sub-Optimal Allocation of Time in Sequential Movements," PLOS ONE, Public Library of Science, vol. 4(12), pages 1-13, December.
    2. Hang Zhang & Nathaniel D Daw & Laurence T Maloney, 2013. "Testing Whether Humans Have an Accurate Model of Their Own Motor Uncertainty in a Speeded Reaching Task," PLOS Computational Biology, Public Library of Science, vol. 9(5), pages 1-11, May.
    3. Todd E Hudson & Hadley Tassinari & Michael S Landy, 2010. "Compensation for Changing Motor Uncertainty," PLOS Computational Biology, Public Library of Science, vol. 6(11), pages 1-14, November.
    4. Todd E Hudson & Uta Wolfe & Laurence T Maloney, 2012. "Speeded Reaching Movements around Invisible Obstacles," PLOS Computational Biology, Public Library of Science, vol. 8(9), pages 1-9, September.
    5. Joseph Snider & Dongpyo Lee & Howard Poizner & Sergei Gepshtein, 2015. "Prospective Optimization with Limited Resources," PLOS Computational Biology, Public Library of Science, vol. 11(9), pages 1-28, September.
    6. Hang Zhang & Camille Morvan & Louis-Alexandre Etezad-Heydari & Laurence T Maloney, 2012. "Very Slow Search and Reach: Failure to Maximize Expected Gain in an Eye-Hand Coordination Task," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-12, October.
    7. Luigi Acerbi & Daniel M Wolpert & Sethu Vijayakumar, 2012. "Internal Representations of Temporal Statistics and Feedback Calibrate Motor-Sensory Interval Timing," PLOS Computational Biology, Public Library of Science, vol. 8(11), pages 1-19, November.

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