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Dissociating error-based and reinforcement-based loss functions during sensorimotor learning

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  • Joshua G A Cashaback
  • Heather R McGregor
  • Ayman Mohatarem
  • Paul L Gribble

Abstract

It has been proposed that the sensorimotor system uses a loss (cost) function to evaluate potential movements in the presence of random noise. Here we test this idea in the context of both error-based and reinforcement-based learning. In a reaching task, we laterally shifted a cursor relative to true hand position using a skewed probability distribution. This skewed probability distribution had its mean and mode separated, allowing us to dissociate the optimal predictions of an error-based loss function (corresponding to the mean of the lateral shifts) and a reinforcement-based loss function (corresponding to the mode). We then examined how the sensorimotor system uses error feedback and reinforcement feedback, in isolation and combination, when deciding where to aim the hand during a reach. We found that participants compensated differently to the same skewed lateral shift distribution depending on the form of feedback they received. When provided with error feedback, participants compensated based on the mean of the skewed noise. When provided with reinforcement feedback, participants compensated based on the mode. Participants receiving both error and reinforcement feedback continued to compensate based on the mean while repeatedly missing the target, despite receiving auditory, visual and monetary reinforcement feedback that rewarded hitting the target. Our work shows that reinforcement-based and error-based learning are separable and can occur independently. Further, when error and reinforcement feedback are in conflict, the sensorimotor system heavily weights error feedback over reinforcement feedback.Author Summary: Whether serving a tennis ball on a gusty day or walking over an unpredictable surface, the human nervous system has a remarkable ability to account for uncertainty when performing goal-directed actions. Here we address how different types of feedback, error and reinforcement, are used to guide such behavior during sensorimotor learning. Using a task that dissociates the optimal predictions of error-based and reinforcement-based learning, we show that the human sensorimotor system uses two distinct loss functions when deciding where to aim the hand during a reach—one that minimizes error and another that maximizes success. Interestingly, when both of these forms of feedback are available our nervous system heavily weights error feedback over reinforcement feedback.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pcbi00:1005623
    DOI: 10.1371/journal.pcbi.1005623
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Kurt A. Thoroughman & Reza Shadmehr, 2000. "Learning of action through adaptive combination of motor primitives," Nature, Nature, vol. 407(6805), pages 742-747, October.
    4. Christopher M. Harris & Daniel M. Wolpert, 1998. "Signal-dependent noise determines motor planning," Nature, Nature, vol. 394(6695), pages 780-784, August.
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    1. Pierre Vassiliadis & Elena Beanato & Traian Popa & Fabienne Windel & Takuya Morishita & Esra Neufeld & Julie Duque & Gerard Derosiere & Maximilian J. Wessel & Friedhelm C. Hummel, 2024. "Non-invasive stimulation of the human striatum disrupts reinforcement learning of motor skills," Nature Human Behaviour, Nature, vol. 8(8), pages 1581-1598, August.
    2. Joshua G A Cashaback & Christopher K Lao & Dimitrios J Palidis & Susan K Coltman & Heather R McGregor & Paul L Gribble, 2019. "The gradient of the reinforcement landscape influences sensorimotor learning," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-27, March.

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