Distinct computations underlie movement error parsing during implicit motor adaptation and perturbation detection
- 1University of British Columbia
Abstract
The human sensorimotor system is remarkably effective at automatically parsing total movement error into its constituent parts, the error component due to a perturbation, or externally-generated error (EGE), versus the error component due to motor noise, or internally-generated error (IGE). This implicit error parsing, and its associated perceptual processes, directly contrasts previous work showing that humans will explicitly misattribute motor errors caused by relatively large external perturbations to their own motor variability. While the combined results suggest a dissociation between perception for action---which allows for precise and automatic error parsing---and perception for conscious detection, this must be inferred across studies using different methodologies. Here, we combined a within-subjects study design and computational modeling to shed light on the principles underlying implicit adaptation to a perturbation and explicit perturbation detection. Neuro-typical adults participated in two experiments consisting of pseudo-randomized rotations during reaches to a single target, with one session requiring explicit reports after each reach of whether a perturbation was detected. Participants demonstrated a clear dissociation between implicit responses to a perturbation and explicit detection, with robust adaptation to 1° EGEs, but an inability to reliably report the presence of an EGE until it reached ∼4°. Our modeling results explain this behavioral dissociation as a consequence of the sensorimotor system applying distinct computational strategies to these behaviors. For the adaptation task, the best-fitting model assumes the participant compares proprioceptive and visual cues to detect a perturbation and corrects for a proportion of this error. For signal-detection, the best-fitting model used Bayesian causal inference in which sensory cues are optimally integrated with a prior on their cause. Combined, our study sheds light on the dichotomy between perception for action versus perception for recognition in the context of visuomotor control and adaptation.
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BibTeX
@misc{kim2026distinct,
title = {{Distinct computations underlie movement error parsing during implicit motor adaptation and perturbation detection}},
author = {H. E. Kim and J. O. Darley and M. S. Landy and R. Chua and D. J. Fox},
howpublished = {bioRxiv},
year = {2026},
month = jun,
doi = {10.64898/2026.06.25.734533},
url = {https://www.biorxiv.org/content/10.64898/2026.06.25.734533v2},
note = {CC BY},
}
RIS
TY - UNPB
TI - Distinct computations underlie movement error parsing during implicit motor adaptation and perturbation detection
AU - H. E. Kim
AU - J. O. Darley
AU - M. S. Landy
AU - R. Chua
AU - D. J. Fox
PY - 2026
DA - 2026/06/25
PB - bioRxiv
DO - 10.64898/2026.06.25.734533
UR - https://www.biorxiv.org/content/10.64898/2026.06.25.734533v2
AB - The human sensorimotor system is remarkably effective at automatically parsing total movement error into its constituent parts, the error component due to a perturbation, or externally-generated error (EGE), versus the error component due to motor noise, or internally-generated error (IGE). This implicit error parsing, and its associated perceptual processes, directly contrasts previous work showing that humans will explicitly misattribute motor errors caused by relatively large external perturbations to their own motor variability. While the combined results suggest a dissociation between perception for action---which allows for precise and automatic error parsing---and perception for conscious detection, this must be inferred across studies using different methodologies. Here, we combined a within-subjects study design and computational modeling to shed light on the principles underlying implicit adaptation to a perturbation and explicit perturbation detection. Neuro-typical adults participated in two experiments consisting of pseudo-randomized rotations during reaches to a single target, with one session requiring explicit reports after each reach of whether a perturbation was detected. Participants demonstrated a clear dissociation between implicit responses to a perturbation and explicit detection, with robust adaptation to 1° EGEs, but an inability to reliably report the presence of an EGE until it reached ∼4°. Our modeling results explain this behavioral dissociation as a consequence of the sensorimotor system applying distinct computational strategies to these behaviors. For the adaptation task, the best-fitting model assumes the participant compares proprioceptive and visual cues to detect a perturbation and corrects for a proportion of this error. For signal-detection, the best-fitting model used Bayesian causal inference in which sensory cues are optimally integrated with a prior on their cause. Combined, our study sheds light on the dichotomy between perception for action versus perception for recognition in the context of visuomotor control and adaptation.
ER -