Contextual effects in sensorimotor adaptation adhere to associative learning rules
Traditional associative learning tasks focus on the formation of associations between salient events and arbitrary stimuli that predict those events. This is exemplified in cerebellar-dependent delay eyeblink conditioning, where arbitrary cues such as a light or tone act as conditioning stimuli (CSs) that predict aversive sensations at the cornea (unconditioned stimulus, US). Here we ask if a similar framework could be applied to another type of cerebellar-dependent sensorimotor learning – sensorimotor adaptation. Models of sensorimotor adaptation posit that the introduction of an environmental perturbation results in an error signal that is used to update an internal model of a sensorimotor map for motor planning. Here we take a step towards an integrative account of these two forms of cerebellar-dependent learning, examining the relevance of core concepts from associative learning for sensorimotor adaptation. Using a visuomotor adaptation reaching task, we paired movement-related feedback (US) with neutral auditory or visual contextual cues that served as conditioning stimuli (CSs). Trial-by-trial changes in feedforward movement kinematics exhibited three key signatures of associative learning: Differential conditioning, sensitivity to the CS-US interval, and compound conditioning. Moreover, after compound conditioning, a robust negative correlation was observed between responses to the two elemental CSs of the compound (i.e., overshadowing), consistent with the additivity principle posited by theories of associative learning. The existence of associative learning effects in sensorimotor adaptation provides a proof-of-concept for linking cerebellar-dependent learning paradigms within a common theoretical framework.
All data generated or analysed during this study are included in the manuscript; Source Data files have been provided for Figures 2B, 2C, 2D, 5B, 6B, 6C, 6D, 7B, 7C, 7D, 8B, 8C, 8E and 8F.All raw data files and codes for data analysis and simulations are available from the GitHub repository: https://github.com/guyavr/AssociativeMotorAdaptation.git
Article and author information
National Institutes of Health (NS084948)
- Jordan A Taylor
National Science Foundation (1838462)
- Jordan A Taylor
National Science Foundation (1827550)
- Jordan A Taylor
Office of Naval Research (N00014-18-1-2873)
- Jordan A Taylor
National Institutes of Health (NS116883)
- Richard B Ivry
National Institutes of Health (DC077091)
- Richard B Ivry
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Human subjects: The study was approved by the Institutional Review Board at the University of California, Berkeley (Protocol 2016-02-8439) and adhered to the principles expressed in the Declaration of Helsinki. All participants provided written informed consent to participate in the study.
- Taraz Lee, University of Michigan, United States
- Preprint posted: September 15, 2020 (view preprint)
- Received: November 24, 2021
- Accepted: October 4, 2022
- Accepted Manuscript published: October 5, 2022 (version 1)
- Version of Record published: November 4, 2022 (version 2)
© 2022, Avraham et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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