Flexible Cognitive Strategies during Motor Learning
Open accessAbstract
Visuomotor rotation tasks have proven to be a powerful tool to study adaptation of the motor system. While adaptation in such tasks is seemingly automatic and incremental, participants may gain knowledge of the perturbation and invoke a compensatory strategy. When provided with an explicit strategy to counteract a rotation, participants are initially very accurate, even without on-line feedback. Surprisingly, with further testing, the angle of their reaching movements drifts in the direction of the strategy, producing an increase in endpoint errors. This drift is attributed to the gradual adaptation of an internal model that operates independently from the strategy, even at the cost of task accuracy. Here we identify constraints that influence this process, allowing us to explore models of the interaction between strategic and implicit changes during visuomotor adaptation. When the adaptation phase was extended, participants eventually modified their strategy to offset the rise in endpoint errors. Moreover, when we removed visual markers that provided external landmarks to support a strategy, the degree of drift was sharply attenuated. These effects are accounted for by a setpoint state-space model in which a strategy is flexibly adjusted to offset performance errors arising from the implicit adaptation of an internal model. More generally, these results suggest that strategic processes may operate in many studies of visuomotor adaptation, with participants arriving at a synergy between a strategic plan and the effects of sensorimotor adaptation.
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When adaptation is allowed to progress over a large number of trials, task performance errors plateau and then decrease.
“However, when adaptation is allowed to progress over a large number of trials, task performance errors plateau and then decrease (Taylor and Ivry, 2011; Figure 1C).”
From Cerebellum, Predictions and Errors (2019) · cites this paper as Taylor and Ivry, 2011
The Results (Modeling strategy use during visuomotor adaptation) describe a 320-trial block in which the AT group’s errors eventually fell to near zero, and the Discussion (Two sources of errors) says their initial rise in endpoint error reversed. This supports a peak followed by a decrease, but the paper does not establish that errors generally plateau and decrease with extended adaptation: it reports that the DAT group did not correct its drift. (LLM, conf=0.83)
Experimental — accuracy not yet measuredControlling and learning self-directed motor behaviors involves both an implicit forward model of the effector and an explicit model of the task.
“Recent work has emphasized that controlling and learning self-directed motor behaviors involves both an implicit forward model of the effector and an explicit model of the task (Taylor and Ivry, 2011; Streng et al., 2018b; see Figure 1F).”
From Cerebellum, Predictions and Errors (2019) · cites this paper as Taylor and Ivry, 2011
The Introduction and Discussion describe implicit adaptation of an internal forward model interacting with an explicit aiming strategy during visuomotor learning. They do not establish a separate explicit model of the task, and the findings concern a reaching task rather than self-directed motor behavior generally. The supplied copy gives section labels but no page numbers. (LLM, conf=0.82)
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- Authors
- Taylor, J. A., & Ivry, R. B.
- Affiliations
- University of California, Berkeley (US)
- Corresponding author
- Taylor, Jordan A.
- Ivry, Richard B.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2011-03-03
- Volume
- 7
- Issue
- 3
- Pages
- e1001096
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC3048379
- OpenAlex
- W1989328619
- Cited by
- 352
- References
- 47
- Citations by year
- 2026: 25 · 2025: 28 · 2024: 44 · 2023: 28 · 2022: 26 · 2021: 36 · 2020: 37 · 2019: 25 · 2018: 21 · 2017: 10 · 2016: 10 · 2015: 13 · 2014: 14 · 2013: 14 · 2012: 11
- FWCI
- 7.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation
- National Institute of Neurological Disorders and Stroke (P01NS040813, F32NS064749)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD060306)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Keywords
- visuomotor adaptation, visuomotor rotation, motor learning, compensatory strategies, internal models, state-space model, sensorimotor adaptation, visual landmarks, reaching movements
- MeSH terms
- Algorithms, Cognition, Female, Humans, Learning, Models, Neurological, Motor Skills, Psychomotor Performance, Task Performance and Analysis, Computational Biology, Feedback, Physiological
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