Learning to move amid uncertainty
Abstract
We studied how subjects learned to make movements against unpredictable perturbations. Twelve healthy human subjects made goal-directed reaching movements in the horizontal plane while holding the handle of a two-joint robotic manipulator. The robot generated viscous force fields that perturbed the limb perpendicular to the desired direction of movement. The amplitude (but not the direction) of the viscous field varied randomly from trial to trial. Systems identification techniques were employed to characterize how subjects adapted to these random perturbations. Subject performance was quantified primarily using the peak deviation from a straight-line hand path. Subjects adapted their arm movements to the sequence of random force-field amplitudes. This adaptive response compensated for the approximate mean from the random sequence of perturbations and did not depend on the statistical distribution of that sequence. Subjects did not adapt by directly counteracting the mean field strength itself on each trial but rather by using information about perturbations and movement errors from a limited number of previous trials to adjust motor commands on subsequent trials. This strategy permitted subjects to achieve near-optimal performance (defined as minimizing movement errors in a least-squares sense) while maintaining computational efficiency. A simple model using information about movement errors and perturbation amplitudes from a single previous trial predicted subject performance in stochastic environments with a high degree of fidelity and further predicted key performance features observed in nonstochastic environments. This suggests that the neural structures modified during motor adaptation require only short-term memory. Explicit representations regarding movements made more than a few trials in the past are not used in generating optimal motor responses on any given trial.
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The state space model (SSM) fits trial-to-trial perturbation data extremely well.
“Despite its apparent simplicity, the SSM framework fits trial-to-trial perturbation data extremely well (Ethier et al., 2008; Huang and Shadmehr, 2007; Scheidt et al., 2001; Smith et al., 2006; Tanaka et al., 2009).”
From Rethinking motor learning and savings in adaptation paradigms: model-free memory for successful actions combines with internal models (2011) · cites this paper as Scheidt et al., 2001
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- Authors
- Scheidt, R. A., Dingwell, J. B., & Mussa-Ivaldi, F. A.
- Affiliations
- Marquette University (US)
- Shirley Ryan AbilityLab (US)
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 2001-08-01
- Volume
- 86
- Issue
- 2
- Pages
- 971-985
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- OpenAlex
- W1873164297
- Cited by
- 411
- References
- 32
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 10 · 2023: 14 · 2022: 8 · 2021: 20 · 2020: 10 · 2019: 15 · 2018: 14 · 2017: 16 · 2016: 21 · 2015: 12 · 2014: 25 · 2013: 23 · 2012: 16
- FWCI
- 7.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Neurophysiology (publishedVersion)
- PubMed (publishedVersion)
- e-Publications@Marquette (Marquette University) (submittedVersion)
- Funders
- National Institute of Mental Health (P50MH-48185, P50MH048185)
- National Institute of Neurological Disorders and Stroke (NS35673, R01NS035673)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Keywords
- motor adaptation, visuomotor perturbation, reaching movements, short-term memory, stochastic environments
- MeSH terms
- Adaptation, Physiological, Humans, Memory, Models, Biological, Movement, Psychomotor Performance, Regression Analysis, Stochastic Processes, Prefrontal Cortex
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