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Do Cost Functions for Tracking Error Generalize across Tasks with Different Noise Levels?

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  • Jonathon Sensinger
  • Adrian Aleman-Zapata
  • Kevin Englehart

Abstract

Control of human-machine interfaces are well modeled by computational control models, which take into account the behavioral decisions people make in estimating task dynamics and state for a given control law. This control law is optimized according to a cost function, which for the sake of mathematical tractability is typically represented as a series of quadratic terms. Recent studies have found that people actually use cost functions for reaching tasks that are slightly different than a quadratic function, but it is unclear which of several cost functions best explain human behavior and if these cost functions generalize across tasks of similar nature but different scale. In this study, we used an inverse-decision-theory technique to reconstruct the cost function from empirical data collected on 24 able-bodied subjects controlling a myoelectric interface. Compared with previous studies, this experimental paradigm involved a different control source (myoelectric control, which has inherently large multiplicative noise), a different control interface (control signal was mapped to cursor velocity), and a different task (the tracking position dynamically moved on the screen throughout each trial). Several cost functions, including a linear-quadratic; an inverted Gaussian, and a power function, accurately described the behavior of subjects throughout this experiment better than a quadratic cost function or other explored candidate cost functions (p

Suggested Citation

  • Jonathon Sensinger & Adrian Aleman-Zapata & Kevin Englehart, 2015. "Do Cost Functions for Tracking Error Generalize across Tasks with Different Noise Levels?," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-13, August.
    • Handle: RePEc:plo:pone00:0136251
    DOI: 10.1371/journal.pone.0136251
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    References listed on IDEAS

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    1. Luigi Acerbi & Sethu Vijayakumar & Daniel M Wolpert, 2014. "On the Origins of Suboptimality in Human Probabilistic Inference," PLOS Computational Biology, Public Library of Science, vol. 10(6), pages 1-23, June.
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    1. Joshua G A Cashaback & Heather R McGregor & Ayman Mohatarem & Paul L Gribble, 2017. "Dissociating error-based and reinforcement-based loss functions during sensorimotor learning," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-28, July.

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