Perturbation Variability Does Not Influence Implicit Sensorimotor Adaptation

Tianhe Wang, Guy Avraham, Jonathan S Tsay, Sabrina J Abram and Richard B Ivry

PLOS Computational Biology, 2024, vol. 20, issue 4, 1-19

Abstract: Implicit adaptation has been regarded as a rigid process that automatically operates in response to movement errors to keep the sensorimotor system precisely calibrated. This hypothesis has been challenged by recent evidence suggesting flexibility in this learning process. One compelling line of evidence comes from work suggesting that this form of learning is context-dependent, with the rate of learning modulated by error history. Specifically, learning was attenuated in the presence of perturbations exhibiting high variance compared to when the perturbation is fixed. However, these findings are confounded by the fact that the adaptation system corrects for errors of different magnitudes in a non-linear manner, with the adaptive response increasing in a proportional manner to small errors and saturating to large errors. Through simulations, we show that this non-linear motor correction function is sufficient to explain the effect of perturbation variance without referring to an experience-dependent change in error sensitivity. Moreover, by controlling the distribution of errors experienced during training, we provide empirical evidence showing that there is no measurable effect of perturbation variance on implicit adaptation. As such, we argue that the evidence to date remains consistent with the rigidity assumption.Author summary: An ideal learner should be sensitive to the statistics of the environment and adjust their behavior accordingly. For example, the rate of learning in response to a change in the environment is faster when the environment is relatively predictable (low uncertainty) compared to when it is unpredictable (high uncertainty). Here we look at this phenomenon in the context of sensorimotor adaptation, the automatic and implicit process that keeps the sensorimotor system precisely calibrated. The results show that this system is surprisingly rigid, with no modulation of the learning rate in response to changes in the variability of the environment. We propose that, whereas processes associated with the action selection system are flexible and modified based on context, the adaptation system is engineered to ensure that the selected action is properly executed, regardless of the optimality of that action. This insensitivity to context may serve to minimize interference with the more flexible processes involved in action selection.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1011951

DOI: 10.1371/journal.pcbi.1011951

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