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Cited by 352Motor Control and AdaptationTactile and Sensory InteractionsVisual perception and processing mechanisms

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Flexible Cognitive Strategies during Motor Learning

Open access
Taylor, Jordan A., Ivry, Richard B.·2011·PLoS Computational Biology
DOI 10.1371/journal.pcbi.1001096PMC PMC3048379
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Abstract

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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Figures

Figure 1 Setpoint model simulation. A −45° rotation was introduced on movement 121 and remained present for the next 322 trials. A) Simulated target error for four state space models. Black: Standard state-space model (A = 1, B = 0.02); Green: Setpoint model in which the target error is used to adapt the internal model; Red: Setpoint model with direct-feedthrough in which the aiming error is used to adapt the internal model. Drift is attenuated by either reduced adaptation rate (Cyan: A = 1, B = 0.01) or reducing the availability of the aiming error signal that selectively operates on the strategy (Magneta: K = 0.5). B) Internal model estimation of rotation when a fixed strategy (blue) is combined with either high (red, K = 1) or low (magenta, K = 0.5) certainty of aiming errors. C) Effect of a variable strategy on target error. The strategy was simulated with a low (blue; E = 1 and F = 0.01) or high (orange; F = 0.05) weighting of the target error.
Figure 1 Setpoint model simulation. A −45° rotation was introduced on movement 121 and remained present for the next 322 trials. A) Simulated target error for four state space models. Black: Standard state-space model (A = 1, B = 0.02); Green: Setpoint model in which the target error is used to adapt the internal model; Red: Setpoint model with direct-feedthrough in which the aiming error is used to adapt the internal model. Drift is attenuated by either reduced adaptation rate (Cyan: A = 1, B = 0.01) or reducing the availability of the aiming error signal that selectively operates on the strategy (Magneta: K = 0.5). B) Internal model estimation of rotation when a fixed strategy (blue) is combined with either high (red, K = 1) or low (magenta, K = 0.5) certainty of aiming errors. C) Effect of a variable strategy on target error. The strategy was simulated with a low (blue; E = 1 and F = 0.01) or high (orange; F = 0.05) weighting of the target error.CC BY · PubMed Central
Figure 2 Experimental task design. The experiment workspace consisted of 8 empty blue circles separated by 45° (three locations are shown here). The target was defined when a green circle appeared at one of the locations. The hand was occluded by the apparatus and on feedback trials, a red cursor appeared as soon as the participant crossed a virtual ring, 10-cm from the start location. A) In the baseline block, participants moved towards the cued green target. B) In the strategy-only block, participants moved to the blue circle located 45° in the clockwise direction. Feedback was presented at the veridical hand position. C) For the two rotation probes, participants were instructed to move to the green target, but feedback of hand position was rotated 45° in the counter-clockwise direction. D) In the rotation plus strategy block, participants were instructed to move to the blue circle located 45° clockwise direction from the target. The feedback of hand position was rotated 45° counter-clockwise. E) Two sources of movement error: a target error between the feedback location and target location and an aiming error between the feedback location and aiming location.
Figure 2 Experimental task design. The experiment workspace consisted of 8 empty blue circles separated by 45° (three locations are shown here). The target was defined when a green circle appeared at one of the locations. The hand was occluded by the apparatus and on feedback trials, a red cursor appeared as soon as the participant crossed a virtual ring, 10-cm from the start location. A) In the baseline block, participants moved towards the cued green target. B) In the strategy-only block, participants moved to the blue circle located 45° in the clockwise direction. Feedback was presented at the veridical hand position. C) For the two rotation probes, participants were instructed to move to the green target, but feedback of hand position was rotated 45° in the counter-clockwise direction. D) In the rotation plus strategy block, participants were instructed to move to the blue circle located 45° clockwise direction from the target. The feedback of hand position was rotated 45° counter-clockwise. E) Two sources of movement error: a target error between the feedback location and target location and an aiming error between the feedback location and aiming location.CC BY · PubMed Central
Figure 3 Group averaged endpoint error relative to the target for the three groups. Participants first practiced moving to the cued target without a rotation (black) and while using the strategy without a rotation (orange). The rotation was turned on between movements 121 and 443 (dashed vertical lines). For the first two of these trials, the rotation probes, the participants had not been given the strategy (X's). For the next 320 rotation trials, participants were instructed to use the strategy. Following this, the rotation was turned off and participants were instructed to move towards the cued target, first without endpoint feedback (X's) and then with endpoint feedback (circles). A) Aiming-Target Group (blue). B) Disappearing Aiming-Target Group (magenta). C) No Aiming-Target Group (red). Shading represents the 95% confidence interval of the mean.
Figure 3 Group averaged endpoint error relative to the target for the three groups. Participants first practiced moving to the cued target without a rotation (black) and while using the strategy without a rotation (orange). The rotation was turned on between movements 121 and 443 (dashed vertical lines). For the first two of these trials, the rotation probes, the participants had not been given the strategy (X's). For the next 320 rotation trials, participants were instructed to use the strategy. Following this, the rotation was turned off and participants were instructed to move towards the cued target, first without endpoint feedback (X's) and then with endpoint feedback (circles). A) Aiming-Target Group (blue). B) Disappearing Aiming-Target Group (magenta). C) No Aiming-Target Group (red). Shading represents the 95% confidence interval of the mean.CC BY · PubMed Central
Figure 4 Time course of drift and aftereffect, and the relationship of drift to target location and aftereffect. A) Average endpoint angular error relative to the target for the three groups, binned by averaging over epochs of ten movements (AT group in blue, DAT group in magenta, NoAT group in red). B) Peak drift with respect to the eight target location for the three groups. The empty circles are the target locations. To identify peak drift, 10 bins of four movements were calculated for each direction. C) Angular error after the rotation was turned off and participants were instructed to stop using the strategy. Triangles are average of the first eight post-rotation trials, performed without visual feedback. Squares are washout block with feedback. D) Relationship of drift and aftereffect based on the estimated peak drift for each participant and the first eight post-rotation trials. For B) and D), the means and 95% confidence interval of the mean were estimated through bootstrapping.
Figure 4 Time course of drift and aftereffect, and the relationship of drift to target location and aftereffect. A) Average endpoint angular error relative to the target for the three groups, binned by averaging over epochs of ten movements (AT group in blue, DAT group in magenta, NoAT group in red). B) Peak drift with respect to the eight target location for the three groups. The empty circles are the target locations. To identify peak drift, 10 bins of four movements were calculated for each direction. C) Angular error after the rotation was turned off and participants were instructed to stop using the strategy. Triangles are average of the first eight post-rotation trials, performed without visual feedback. Squares are washout block with feedback. D) Relationship of drift and aftereffect based on the estimated peak drift for each participant and the first eight post-rotation trials. For B) and D), the means and 95% confidence interval of the mean were estimated through bootstrapping.CC BY · PubMed Central
Figure 5 Performance during the rotation block of three participants. A and B are from the AT group; C is from the DAT group. A) Drift followed by large fluctuations in error. B) Drift followed by an abrupt change in target error. C) Continuous drift across training.
Figure 5 Performance during the rotation block of three participants. A and B are from the AT group; C is from the DAT group. A) Drift followed by large fluctuations in error. B) Drift followed by an abrupt change in target error. C) Continuous drift across training.CC BY · PubMed Central
Figure 6 Setpoint model fit. The setpoint model with direct-feedthrough (equations 8–11) was fit to each group's data (from figure 3) via bootstrapping. A–C: Solid functions are averaged model fits for the AT group (blue), DAT group (magenta), and NoAT group (red) in comparison with actual group averaged data (black). D) The target error is the combination of learning within the sensorimotor recalibration process (solid) and the aiming location associated with the strategy (dashed), shown here fro the AT group (blue), DAT group (magenta), and NoAT group (red).
Figure 6 Setpoint model fit. The setpoint model with direct-feedthrough (equations 8–11) was fit to each group's data (from figure 3) via bootstrapping. A–C: Solid functions are averaged model fits for the AT group (blue), DAT group (magenta), and NoAT group (red) in comparison with actual group averaged data (black). D) The target error is the combination of learning within the sensorimotor recalibration process (solid) and the aiming location associated with the strategy (dashed), shown here fro the AT group (blue), DAT group (magenta), and NoAT group (red).CC BY · PubMed Central

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Metadata

Authors
Taylor, J. A., & Ivry, R. B.
Affiliations
University of California, Berkeley (US)
Corresponding author
  • Taylor, Jordan A.
  • Ivry, Richard B.
ORCID
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
DOI
10.1371/journal.pcbi.1001096
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
Licence
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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