Cited by 472Motor Control and AdaptationMemory and Neural MechanismsTactile and Sensory Interactions

The DOI matches its Crossref record; 93 of the 95 references it cites were found in a registry or an index; none of the 63 sources its claims cite is checked for support yet.

Rethinking Motor Learning and Savings in Adaptation Paradigms: Model-Free Memory for Successful Actions Combines with Internal Models

Open access

Abstract

Although motor learning is likely to involve multiple processes, phenomena observed in error-based motor learning paradigms tend to be conceptualized in terms of only a single process – adaptation, which occurs through updating an internal model. Here we argue that fundamental phenomena like movement direction biases, savings (faster relearning) and interference do not relate to adaptation but instead are attributable to two additional learning processes that can be characterized as model-free: use-dependent plasticity and operant reinforcement. Although usually “hidden” behind adaptation, we demonstrate, with modified visuomotor rotation paradigms, that these distinct model-based and model-free processes combine to learn an error-based motor task: (1) Adaptation of an internal model channels movements toward successful error reduction in visual space. (2) Repetition of the newly adapted movement induces directional biases towards the repeated movement. (3) Operant reinforcement through association of the adapted movement with successful error reduction is responsible for savings.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

The paper argues that error-based motor adaptation paradigms conflate three learning processes: model-based adaptation, use-dependent plasticity, and operant reinforcement. Across modified visuomotor rotation experiments, savings depended on prior association of a successful hand-space movement with adaptation, not on adaptation alone.

Key findings

  • Adaptation with movement repetition (Adp+Rep+) induced a directional bias toward the repeated 70° hand-space direction: hand-movement distributions were tighter than adaptation-only (mean SD 4.9±0.4° vs 11.7±0.45°, t(14)=-11.95, p<0.001), with bias slopes of 0.32±0.03 for trained targets and 0.42±0.04 for probe targets.
  • Savings occurred only when adaptation was paired with repetition: Adp+Rep+ relearned faster (0.49±0.08 trial⁻¹) than naïve controls (0.13±0.02 trial⁻¹; t(14)=3.495, p=0.004), whereas adaptation-only (Adp+Rep−, 0.12±0.02 trial⁻¹) and repetition-only (Adp−Rep+) showed no savings. Adp+Rep+ also showed a 35% increase in average error reduced over the first 20 trials versus naïve (p=0.001).
  • Savings transferred to an oppositely signed rotation when the required hand-space solution was the same: SAME-SOLNhand relearned faster (0.23±0.03 trial⁻¹) than initial learning (0.11±0.04 trial⁻¹; p=0.03), while SAME-SOLNvisual showed no savings (0.11±0.02 trial⁻¹; p=0.411).
  • A state-space model fit adaptation-only data well (r²=0.968) but failed for Adp+Rep+ at asymptote (r²=-0.502), supporting an additional model-free process.

Methods

  • Sixty healthy, right-handed subjects (mean age 24.7±4.9, 25 males) performed a visuomotor rotation reaching task with a mirror/air-jet apparatus; subjects were randomly assigned to groups across three experiments.
  • Experiment 1 compared Adp+Rep+ and Adp+Rep− (n=8 each). Experiment 2 compared four groups: Adp+Rep+, Adp+Rep−, Adp−Rep+, Adp−Rep− (n=10, 10, 6, 6). Experiment 3 compared SAME-SOLNhand and SAME-SOLNvisual (n=6 each).
  • Learning and relearning rates were estimated by fitting a single decaying exponential to error time series; trial-to-trial hand directions were simulated with a single-state state-space model; group comparisons used t-tests and repeated-measures ANOVA, with power analysis indicating a minimum of 6 subjects per group.

Limitations

  • Stated in the paper: Experiment 2 had two potential concerns—Adp+Rep+ and Adp+Rep− may have differed slightly in initial adaptation to the 95° target (~6°), and Adp+Rep− trained on 20° but was tested on 25°; the authors addressed these with additional +20° rotation groups at the 90° target, which showed comparable initial adaptation and still greater savings for Adp+Rep+.
  • Stated in the paper: power analysis assumed an effect size from prior savings data and yielded a minimum of 6 subjects per group, so the negative results were argued to be likely true negatives.
  • Evident from the text: group sizes were small (n=6–10), all data came from one laboratory and one reaching paradigm, and the model-free processes were inferred behaviorally rather than formally modeled or neurally dissociated.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1016/j.neuron.2011.04.012

Cites

93 of 93 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

47 Own interpretation · 33 Own result · 32 Not checked yet · 3 Own method · 3 No basis given

Verify all 63 references — free account
  • It may state two separate claims as one. 38
  • Probably not a substantial claim. 20
57 of 57
  • The authors state this as an assumption.

    Asymptotic performance in Adp+Rep− can be completely accounted for by error-based learning of an internal model alone.

    “which supports our assumption that asymptotic performance in Adp+Rep− can be completely accounted for by error-based learning of an internal model alone”
  • The paper relies on this without support.

    The cerebellum is unaffected in Parkinson's disease.

    “a structure unaffected in Parkinson's disease (PD)”
  • The paper relies on this without support.

    Reinforcement is rotation angle invariant; the adaptation-guided direction in hand space is what matters.

    “because reinforcement should be rotation angle invariant as it is the adaptation-guided direction in hand space that matters”
  • The paper relies on this without support.

    Most behavior in error-based motor learning paradigms is well described by adaptation.

    “In spite of the fact that most behavior in error-based motor learning paradigms is well described by adaptation, we argue here that there are phenomena in perturbation paradigms that cannot be explained in terms of adaptation alone.”
  • The paper relies on this without support.

    Any task implicitly carries some notion of success or failure that can be encapsulated mathematically through a cost (reward) function.

    “All motor learning can be conceptualized within this framework; even if there is no explicit reward structure, any task implicitly carries some notion of success or failure that can be encapsulated mathematically through a cost (reward) function.”
  • The authors state this as an assumption.

    In motor control, the reward structure is unambiguous: success is achieved by the cursor reaching the target.

    “In the case of motor control, however, we assume that the reward structure is unambiguous: success is achieved by the cursor reaching the target.”
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Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Statistics recomputed

Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.

  • Consistentt(14) = -11.95reported p < .001 · recomputed p < .001

    “t(14)=-11.95, p < .001”

  • Consistentt(18) = -0.34reported p = .738 · recomputed p = .738

    “t(18)=-0.340, p = .738”

  • Consistentt(14) = 3.495reported p = .004 · recomputed p = .004

    “t(14)=3.495, p = .004”

  • Consistentt(14) = -0.39reported p = .7 · recomputed p = .702

    “t(14)=-0.39, p = .70”

  • Consistentt(18) = 4.62reported p < .001 · recomputed p < .001

    “t(18)=4.62, p < .001”

  • Consistentt(14) = -4.175reported p = .001 · recomputed p < .001

    “t(14)=-4.175, p = .001”

  • Consistentt(14) = -1.047reported p = .31 · recomputed p = .313

    “t(14)=-1.047, p = .31”

  • Consistentt(14) = 3.06reported p = .009 · recomputed p = .008

    “t(14)=3.06, p = .009”

  • Consistentt(10) = 0.594reported p = .565 · recomputed p = .566

    “t(10)=0.594, p = .565”

  • Consistentt(5) = -2.371reported p = .03 · recomputed p = .064

    “t(5)=-2.371, p = .03”

    The paper states a one-tailed test

  • Consistentt(5) = 0.238reported p = .411 · recomputed p = .821

    “t(5)=0.238, p = .411”

    The paper states a one-tailed test

  • Consistentt(5) = 7.56reported p < .001 · recomputed p < .001

    “t(5)=7.56, p < .001”

  • Consistentt(5) = -0.2reported p = .849 · recomputed p = .849

    “t(5)=-0.2, p = .849”

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Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

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Citations

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Metadata

Authors
Huang, V. S., Haith, A., Mazzoni, P., & Krakauer, J. W.
Affiliations
  • Columbia University Irving Medical Center (US)
  • Johns Hopkins University (US)
Corresponding author
  • Huang, Vincent S.
  • Krakauer, John W.
Venue
Neuron
Publisher
Elsevier BV
Published
2011-05
Volume
70
Issue
4
Pages
787-801
ISSN
0896-6273 (ISSN-L)
OpenAlex
W2130609859
Cited by
472
References
95
Citations by year
2026: 19 · 2025: 29 · 2024: 17 · 2023: 23 · 2022: 27 · 2021: 35 · 2020: 28 · 2019: 52 · 2018: 32 · 2017: 39 · 2016: 36 · 2015: 40 · 2014: 35 · 2013: 27 · 2012: 22
FWCI
15.24
Type
journal article
Language
en
Open access
Locations
  • Neuron (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institutes of Health (7r01ns052804-05)
  • National Institute of Neurological Disorders and Stroke (R01NS-052804)
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 learning, visuomotor adaptation, model-based learning, internal models, savings, interference, operant reinforcement, use-dependent plasticity, error-based learning, visuomotor rotation
MeSH terms
Adaptation, Physiological, Adult, Female, Humans, Learning, Male, Memory, Models, Biological, Movement, Photic Stimulation, Psychomotor Performance, Young Adult

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