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Large-scale citizen science reveals predictors of sensorimotor adaptation

Abstract

Sensorimotor adaptation is essential for keeping our movements well calibrated in response to changes in the body and environment. For over a century, researchers have studied sensorimotor adaptation in laboratory settings that typically involve small sample sizes. While this approach has proved useful for characterizing different learning processes, laboratory studies are not well suited for exploring the myriad of factors that may modulate human performance. Here, using a citizen science website, we collected over 2,000 sessions of data on a visuomotor rotation task. This unique dataset has allowed us to replicate, reconcile and challenge classic findings in the learning and memory literature, as well as discover unappreciated demographic constraints associated with implicit and explicit processes that support sensorimotor adaptation. More generally, this study exemplifies how a large-scale exploratory approach can complement traditional hypothesis-driven laboratory research in advancing sensorimotor neuroscience.

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Fig. 1: Web-based sensorimotor adaptation task and behaviour.
Fig. 2: The effects of session and age on sensorimotor adaptation.
Fig. 3: Features of the early adaptation, late adaptation and aftereffect phases.
Fig. 4: Five patterns of data simulated by the competition theory.
Fig. 5: Features corresponding to Pattern 1.
Fig. 6: Features corresponding to Pattern 3.
Fig. 7: Features corresponding to Pattern 4.

Data availability

The data are available at https://osf.io/5n7jf/.

Code availability

The analysis code is available at https://osf.io/5n7jf/.

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Acknowledgements

This project was supported by two NIH grants (no. 1F31NS120448 awarded to J.S.T. and no. R35NS116883-01 awarded to R.B.I.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Author notes

  1. These authors contributed equally: Jonathan S. Tsay, Hrach Asmerian.

Authors and Affiliations

  1. Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA

    Jonathan S. Tsay

  2. Department of Psychology, University of California, Berkeley, Berkeley, CA, USA

    Hrach Asmerian, Richard B. Ivry & Ken Nakayama

  3. Department of Psychiatry, Harvard Medical School, Boston, MA, USA

    Laura T. Germine

  4. Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA

    Laura T. Germine

  5. Department of Psychology, Wellesley College, Wellesley, MA, USA

    Jeremy Wilmer

  6. Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA

    Richard B. Ivry

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Contributions

J.S.T.: conceptualization, resources, data curation, software, formal analysis, funding acquisition, validation, investigation, visualization, methodology, project administration, writing—original draft, writing—review and editing. H.A.: software, formal analysis, validation, investigation, visualization, methodology, writing—review and editing. L.T.G.: data curation, visualization, writing—review and editing. J.W.: data curation, visualization, writing—review and editing. R.B.I.: data curation, writing—review and editing, funding acquisition, validation, investigation, visualization. K.N.: data curation, writing—review and editing, validation, investigation, visualization, project administration.

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Jonathan S. Tsay or Hrach Asmerian.

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R.B.I. is a co-founder with equity in Magnetic Tides, Inc., a biotechnology company created to develop a novel method of non-invasive brain stimulation. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Correlations between different phases of motor adaptation.

Correlation between early and late adaptation (a), aftereffect and early adaptation (b), and aftereffect and late adaptation (c). rp denotes Pearson’s correlation (# of sessions = 1,747); pbf denotes the p-value for a two-tailed t-test (Bonferroni-corrected for three comparisons).

Extended Data Fig. 2 Age distribution.

Blue shading denotes different age groups (that is, rounded to the nearest decade). 107 individuals are closest to age 10, 1068 to age 20, 269 to age 30, 126 to age 40, 61 to age 50, 59 to age 60, 24 to age 70, 28 to age 80, and 5 to age 90. The oldest group was excluded in our aging analyses due to its limited sample size (n = 5).

Extended Data Fig. 3 Correlation matrix.

Color denotes the direction of the Pearson’s correlations (# of sessions = 1,747), and square size denotes correlation magnitude.

Extended Data Fig. 4 Results from our post-hoc Lasso regression were robust to changes in number of folds and percent of data used for training.

a–c, Shading and numbers denote the cross-validated coefficient of determination (({R}_{{cv}}^{2})). The red box denotes the model used in this manuscript (that is, 80% training data split across 10-folds).

Extended Data Fig. 5 Features corresponding to Pattern 3 (continued).

(a, b) Baseline reaction time, (c, d) average amount of sleep every night, and (e, f) computer screen size. Left column: Data are presented as median values ± SEM. Right column: The width of the violin plot represents data density. Vertical black lines represent median values ± 1st/3rd IQR. rs denotes Spearman’s correlation; p value is obtained from a two-tailed t-test. We used data from 1,747 sessions (naïve participants who completed the one-target version of the task).

Extended Data Fig. 6 Features corresponding to Pattern 5.

(a, b) Perturbation direction, (c, d) device used, (e, f) self-reported neurological disease, and (g, h) amount of average computer usage. Left column: Data are presented as median values ± SEM. Right column: The width of the violin plot represents data density. Vertical black lines represent median values ± 1st/3rd IQR. rs denotes Spearman’s correlation; p value is obtained from a two-tailed t-test. We used data from 1,747 sessions (naïve participants who completed the one-target version of the task).

Extended Data Fig. 7 Features corresponding to Pattern 5 (continued).

(a, b) Internet browser used, (c, d) undergraduate major, and (e, f) self-reported ratings of clumsiness. Left column: Data are presented as median values ± SEM. Right column: The width of the violin plot represents data density. Vertical black lines represent median values ± 1st/3rd IQR. We used data from 1,747 sessions (naïve participants who completed the one-target version of the task).

Extended Data Fig. 8 Self-reported neurological disease.

Among the 313 individuals reporting a neurological disease, only 12 described their specific disease, which we categorized into five main categories. AD: Alzheimer′s Disease. CD: Cerebellar Degeneration. MS: Multiple Sclerosis. PD: Parkinson’s Disease. ST: Stroke.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2 and Table 1.

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Tsay, J.S., Asmerian, H., Germine, L.T. et al. Large-scale citizen science reveals predictors of sensorimotor adaptation.
Nat Hum Behav (2024). https://doi.org/10.1038/s41562-023-01798-0

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  • Received: 27 January 2023

  • Accepted: 04 December 2023

  • Published: 30 January 2024

  • DOI: https://doi.org/10.1038/s41562-023-01798-0

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