Citation

Tsay JS, Avraham G, Kim HE, Parvin DE, Wang Z, Ivry RB. J. Neurophysiol. 2020; ePub(ePub): ePub.

Copyright

(Copyright © 2020, American Physiological Society)

DOI

10.1152/jn.00493.2020

PMID

33236937

Abstract

Sensorimotor adaptation is driven by sensory prediction errors, the difference between the predicted and actual feedback. When the position of the feedback is made uncertain, motor adaptation is attenuated. This effect, in the context of optimal sensory integration models, has been attributed to the motor system discounting noisy feedback, and thus reducing the learning rate. In its simplest form, optimal integration predicts that uncertainty would result in reduced learning for all error sizes. However, these predictions remain untested since manipulations of error size in standard visuomotor tasks introduce confounds in the degree to which performance is influenced by other learning processes such as strategy use. Here, we used a novel visuomotor task that isolates the contribution of implicit adaptation, independent of error size. In two experiments, we varied feedback uncertainty and error size in a factorial manner. At odds with the basic predictions derived from the optimal integration theory, the results show that uncertainty attenuated learning only when the error size was small but had no effect when the error size was large. We discuss possible mechanisms that may account for this interaction, considering how uncertainty may interact with the relevance assigned to the error signal, or how the output of the adaptation system in terms of recalibrating the sensorimotor map may be modified by uncertainty.

Language: en

Keywords

Cerebellum; Error-based learning; Motor Learning; Sensorimotor Adaptation; Sensory Integration