Large-scale citizen science reveals predictors of sensorimotor adaptation.
Journal
Nature human behaviour
ISSN: 2397-3374
Titre abrégé: Nat Hum Behav
Pays: England
ID NLM: 101697750
Informations de publication
Date de publication:
30 Jan 2024
30 Jan 2024
Historique:
received:
27
01
2023
accepted:
04
12
2023
medline:
31
1
2024
pubmed:
31
1
2024
entrez:
30
1
2024
Statut:
aheadofprint
Résumé
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.
Identifiants
pubmed: 38291127
doi: 10.1038/s41562-023-01798-0
pii: 10.1038/s41562-023-01798-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)
ID : 1F31NS120448
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)
ID : R35NS116883-01
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature Limited.
Références
Krakauer, J., Hadjiosif, A. M., Xu, J., Wong, A. L. & Haith, A. M. Motor learning. Compr. Physiol. 9, 613–663 (2019).
pubmed: 30873583
doi: 10.1002/cphy.c170043
Roemmich, R. T. & Bastian, A. J. Closing the loop: from motor neuroscience to neurorehabilitation. Annu. Rev. Neurosci. 41, 415–429 (2018).
pubmed: 29709206
doi: 10.1146/annurev-neuro-080317-062245
Tsay, J. S. & Winstein, C. J. Five features to look for in early-phase clinical intervention studies. Neurorehabil. Neural Repair 35, 3–9 (2021).
pubmed: 33243083
doi: 10.1177/1545968320975439
Helmholtz, H. L. F. V. Treatise on Physiological Optics (Dover, 1924).
Stratton, G. M. Some preliminary experiments on vision without inversion of the retinal image. Psychol. Rev. 3, 611–617 (1896).
doi: 10.1037/h0072918
Ghilardi, M. et al. Patterns of regional brain activation associated with different forms of motor learning. Brain Res. 871, 127–145 (2000).
pubmed: 10882792
doi: 10.1016/S0006-8993(00)02365-9
Krakauer, J., Pine, Z. M., Ghilardi, M. F. & Ghez, C. Learning of visuomotor transformations for vectorial planning of reaching trajectories. J. Neurosci. 20, 8916–8924 (2000).
pubmed: 11102502
pmcid: 6773094
doi: 10.1523/JNEUROSCI.20-23-08916.2000
Krakauer, J., Ghez, C. & Ghilardi, M. F. Adaptation to visuomotor transformations: consolidation, interference, and forgetting. J. Neurosci. 25, 473–478 (2005).
pubmed: 15647491
pmcid: 6725486
doi: 10.1523/JNEUROSCI.4218-04.2005
Ferrea, E., Franke, J., Morel, P. et al. Statistical determinants of visuomotor adaptation along different dimensions during naturalistic 3D reaches. Sci. Rep. 12, 10198 (2022).
pubmed: 35715529
pmcid: 9205902
doi: 10.1038/s41598-022-13866-y
Kagerer, F. A., Contreras-Vidal, J. L. & Stelmach, G. E. Adaptation to gradual as compared with sudden visuo-motor distortions. Exp. Brain Res. 115, 557–561 (1997).
pubmed: 9262212
doi: 10.1007/PL00005727
Shadmehr, R., Smith, M. A. & Krakauer, J. Error correction, sensory prediction, and adaptation in motor control. Annu. Rev. Neurosci. 33, 89–108 (2010).
pubmed: 20367317
doi: 10.1146/annurev-neuro-060909-153135
Kim, H. E., Avraham, G. & Ivry, R. B. The psychology of reaching: action selection, movement implementation, and sensorimotor learning. Annu. Rev. Psychol. https://doi.org/10.1146/annurev-psych-010419-051053 (2020).
McDougle, S. D., Ivry, R. B. & Taylor, J. A. Taking aim at the cognitive side of learning in sensorimotor adaptation tasks. Trends Cogn. Sci. 20, 535–544 (2016).
pubmed: 27261056
pmcid: 4912867
doi: 10.1016/j.tics.2016.05.002
Hegele, M. & Heuer, H. Implicit and explicit components of dual adaptation to visuomotor rotations. Conscious. Cogn. 19, 906–917 (2010).
pubmed: 20537562
doi: 10.1016/j.concog.2010.05.005
Benson, B. L., Anguera, J. A. & Seidler, R. D. A spatial explicit strategy reduces error but interferes with sensorimotor adaptation. J. Neurophysiol. 105, 2843–2851 (2011).
pubmed: 21451054
pmcid: 3118744
doi: 10.1152/jn.00002.2011
Redding, G. M. & Wallace, B. Adaptive spatial alignment and strategic perceptual–motor control. J. Exp. Psychol. Hum. Percept. Perform. 22, 379–394 (1996).
pubmed: 8934851
doi: 10.1037/0096-1523.22.2.379
Tsay, J. S. et al. Strategic processes in sensorimotor learning: reasoning, refinement, and retrieval. Preprint at PsyArXiv https://doi.org/10.31234/osf.io/x4652 (2023).
Tsay, J. S. et al. The effect of visual uncertainty on implicit motor adaptation. J. Neurophysiol. https://doi.org/10.1152/jn.00493.2020 (2021).
Kim, H. E., Morehead, R., Parvin, D. E., Moazzezi, R. & Ivry, R. B. Invariant errors reveal limitations in motor correction rather than constraints on error sensitivity. Commun. Biol. 1, 19 (2018).
pubmed: 30271906
pmcid: 6123629
doi: 10.1038/s42003-018-0021-y
Herzfeld, D. J., Vaswani, P. A., Marko, M. K. & Shadmehr, R. A memory of errors in sensorimotor learning. Science 345, 1349–1353 (2014).
pubmed: 25123484
pmcid: 4506639
doi: 10.1126/science.1253138
Albert, S. T. et al. An implicit memory of errors limits human sensorimotor adaptation. Nat. Hum. Behav. https://doi.org/10.1038/s41562-020-01036-x (2021).
Redding, G. M. & Wallace, B. Effects on prism adaptation of duration and timing of visual feedback during pointing. J. Mot. Behav. 22, 209–224 (1990).
pubmed: 15111290
doi: 10.1080/00222895.1990.10735511
Held, R., Efstathiou, A. & Greene, M. Adaptation to displaced and delayed visual feedback from the hand. J. Exp. Psychol. 72, 887–891 (1966).
doi: 10.1037/h0023868
Kitazawa, S., Kohno, T. & Uka, T. Effects of delayed visual information on the rate and amount of prism adaptation in the human. J. Neurosci. 15, 7644–7652 (1995).
pubmed: 7472515
pmcid: 6578075
doi: 10.1523/JNEUROSCI.15-11-07644.1995
Brudner, S. N., Kethidi, N., Graeupner, D., Ivry, R. B. & Taylor, J. A. Delayed feedback during sensorimotor learning selectively disrupts adaptation but not strategy use. J. Neurophysiol. 115, 1499–1511 (2016).
pubmed: 26792878
pmcid: 4808111
doi: 10.1152/jn.00066.2015
Tsay, J. S., Irving, C. & Ivry, R. B. Signatures of contextual interference in implicit sensorimotor adaptation. Proc. Biol. Sci. 290, 20222491 (2023).
pubmed: 36787799
pmcid: 9928522
Tsay, J. S., Kim, H., Haith, A. M., & Ivry, R. B. Understanding implicit sensorimotor adaptation as a process of proprioceptive re-alignment. eLife https://doi.org/10.7554/eLife.76639 (2022).
Martin, T. A., Keating, J. G., Goodkin, H. P., Bastian, A. J. & Thach, W. T. Throwing while looking through prisms: I. Focal olivocerebellar lesions impair adaptation. Brain 119, 1183–1198 (1996).
pubmed: 8813282
doi: 10.1093/brain/119.4.1183
Tzvi, E., Loens, S. & Donchin, O. Mini-review: the role of the cerebellum in visuomotor adaptation. Cerebellum https://doi.org/10.1007/s12311-021-01281-4 (2021).
Tsay, J. S., Najafi, T., Schuck, L., Wang, T. & Ivry, R. B. Implicit sensorimotor adaptation is preserved in Parkinson’s disease. Brain Commun. 4, fcac303 (2022).
pubmed: 36531745
pmcid: 9750131
doi: 10.1093/braincomms/fcac303
Tsay, J. S., Schuck, L., & Ivry, R. B. Cerebellar degeneration impairs strategy discovery but not strategy recall. Cerebellum https://doi.org/10.1007/s12311-022-01500-6 (2022).
Mutha, P. K., Sainburg, R. L. & Haaland, K. Y. Left parietal regions are critical for adaptive visuomotor control. J. Neurosci. 31, 6972–6981 (2011).
pubmed: 21562259
pmcid: 3107546
doi: 10.1523/JNEUROSCI.6432-10.2011
Smith, M. A. & Shadmehr, R. Intact ability to learn internal models of arm dynamics in Huntington’s disease but not cerebellar degeneration. J. Neurophysiol. 93, 2809–2821 (2005).
pubmed: 15625094
doi: 10.1152/jn.00943.2004
Henrich, J., Heine, S. J. & Norenzayan, A. The weirdest people in the world? Behav. Brain Sci. 33, 61–83 (2010). Discussion 83–135.
pubmed: 20550733
doi: 10.1017/S0140525X0999152X
Yarkoni, T. & Westfall, J. Choosing prediction over explanation in psychology: lessons from machine learning. Perspect. Psychol. Sci. 12, 1100–1122 (2017).
pubmed: 28841086
pmcid: 6603289
doi: 10.1177/1745691617693393
Wang, X., Abdullah, B. & Samsudin, S. The effect of contextual interference on motor learning among healthy adolescents: a systematic review. J. Posit. Sch. Psychol. 6, 4545–4580 (2022).
Shewokis, P. A. Is the contextual interference effect generalizable to computer games? Percept. Mot. Skills 84, 3–15 (1997).
pubmed: 9132724
doi: 10.2466/pms.1997.84.1.3
Kantner, L. A., Segall, M. H., Campbell, D. T. & Herskovits, M. J. The influence of culture on visual perception. Stud. Art. Educ. 10, 68–71 (1968).
doi: 10.2307/1319670
Pitt, B., Carstensen, A., Boni, I., Piantadosi, S. T. & Gibson, E. Different reference frames on different axes: space and language in indigenous Amazonians. Sci. Adv. 8, eabp9814 (2022).
pubmed: 36427312
pmcid: 9699666
doi: 10.1126/sciadv.abp9814
Anderson, D. I., Lohse, K. R., Lopes, T. C. V. & Williams, A. M. Individual differences in motor skill learning: past, present and future. Hum. Mov. Sci. 78, 102818 (2021).
pubmed: 34049152
doi: 10.1016/j.humov.2021.102818
Seidler, R. D. & Carson, R. G. Sensorimotor learning: neurocognitive mechanisms and individual differences. J. Neuroeng. Rehabil. https://doi.org/10.1186/s12984-017-0279-1 (2017).
Ranganathan, R., Cone, S. & Fox, B. Predicting individual differences in motor learning: a critical review. Neurosci. Biobehav. Rev. 141, 104852 (2022).
pubmed: 36058405
doi: 10.1016/j.neubiorev.2022.104852
Ackerman, P. L. Determinants of individual differences during skill acquisition: cognitive abilities and information processing. J. Exp. Psychol. Gen. 117, 288–318 (1988).
doi: 10.1037/0096-3445.117.3.288
Fleishman, E. A. On the relation between abilities, learning, and human performance. Am. Psychol. 27, 1017–1032 (1972).
doi: 10.1037/h0033881
Tsay, J. S., Lee, A., Ivry, R. B., & Avraham, G. Moving outside the lab: the viability of conducting sensorimotor learning studies online. Neurons Behav. Data Anal. https://doi.org/10.51628/001c.26985 (2021).
Tsay, J. S. et al. OnPoint: a package for online experiments in motor control and motor learning. Preprint at PsyArXiv https://doi.org/10.31234/osf.io/hwmpy (2020).
Germine, L. et al. Is the Web as good as the lab? Comparable performance from Web and lab in cognitive/perceptual experiments. Psychon. Bull. Rev. 19, 847–857 (2012).
pubmed: 22829343
doi: 10.3758/s13423-012-0296-9
Germine, L. T., Duchaine, B. & Nakayama, K. Where cognitive development and aging meet: face learning ability peaks after age 30. Cognition 118, 201–210 (2011).
pubmed: 21130422
doi: 10.1016/j.cognition.2010.11.002
Wilmer, J. B. et al. Capturing specific abilities as a window into human individuality: the example of face recognition. Cogn. Neuropsychol. 29, 360–392 (2012).
pubmed: 23428079
doi: 10.1080/02643294.2012.753433
Kim, H. et al. Multiracial Reading the Mind in the Eyes Test (MRMET): an inclusive version of an influential measure. Preprint at OSF https://doi.org/10.31219/osf.io/y8djm (2022).
Wilmer, J. B. How to use individual differences to isolate functional organization, biology, and utility of visual functions; with illustrative proposals for stereopsis. Spat. Vis. 21, 561–579 (2008).
pubmed: 19017483
pmcid: 2586597
doi: 10.1163/156856808786451408
Bond, K. & Taylor, J. A. Flexible explicit but rigid implicit learning in a visuomotor adaptation task. J. Neurophysiol. 113, 3836–3849 (2015).
pubmed: 25855690
pmcid: 4473515
doi: 10.1152/jn.00009.2015
Shyr, M. C. & Joshi, S. S. A case study of the validity of web-based visuomotor rotation experiments. J. Cogn. Neurosci. 36, 71–94 (2024).
pubmed: 37902584
doi: 10.1162/jocn_a_02080
Kim, O. A., Forrence, A. D. & McDougle, S. D. Motor learning without movement. Proc. Natl Acad. Sci. USA 119, e2204379119 (2022).
pubmed: 35858450
pmcid: 9335319
doi: 10.1073/pnas.2204379119
Taylor, J. A., Krakauer, J. W. & Ivry, R. B. Explicit and implicit contributions to learning in a sensorimotor adaptation task. J. Neurosci. 34, 3023–3032 (2014).
pubmed: 24553942
pmcid: 3931506
doi: 10.1523/JNEUROSCI.3619-13.2014
Anwyl-Irvine, A., Dalmaijer, E.S., Hodges, N. et al. Realistic precision and accuracy of online experiment platforms, web browsers, and devices. Behav. Res. 53, 1407–1425 (2021).
doi: 10.3758/s13428-020-01501-5
Flanagan, J. C. A simplified procedure for determining the reliability of a test by split-halves. J. Educ. Psychol. 28, 99–103 (1937).
Allen, M. J. Introduction to Measurement Theory (Waveland, 1979).
Avraham, G., Morehead, R., Kim, H. E. & Ivry, R. B. Reexposure to a sensorimotor perturbation produces opposite effects on explicit and implicit learning processes. PLoS Biol. 19, e3001147 (2021).
pubmed: 33667219
pmcid: 7968744
doi: 10.1371/journal.pbio.3001147
Tsay, J. S., Kim, H. E., Parvin, D. E., Stover, A. R. & Ivry, R. B. Individual differences in proprioception predict the extent of implicit sensorimotor adaptation. J. Neurophysiol. https://doi.org/10.1152/jn.00585.2020 (2021).
Huberdeau, D. M., Krakauer, J. W. & Haith, A. M. Practice induces a qualitative change in the memory representation for visuomotor learning. J. Neurophysiol. https://doi.org/10.1152/jn.00830.2018 (2019).
Haith, A. M., Huberdeau, D. M. & Krakauer, J. W. The influence of movement preparation time on the expression of visuomotor learning and savings. J. Neurosci. 35, 5109–5117 (2015).
pubmed: 25834038
pmcid: 6705405
doi: 10.1523/JNEUROSCI.3869-14.2015
Morehead, R., Qasim, S. E., Crossley, M. J. & Ivry, R. Savings upon re-aiming in visuomotor adaptation. J. Neurosci. 35, 14386–14396 (2015).
pubmed: 26490874
pmcid: 4683692
doi: 10.1523/JNEUROSCI.1046-15.2015
Schmitz, G. Enhanced cognitive performance after multiple adaptations to visuomotor transformations. PLoS ONE 17, e0274759 (2022).
pubmed: 36129926
pmcid: 9491566
doi: 10.1371/journal.pone.0274759
Tsay, J. S., Irving, C. & Ivry, R. B. Signatures of contextual interference in implicit sensorimotor adaptation. Proc. R. Soc. B 290, 20222491 (2023).
pubmed: 36787799
pmcid: 9928522
doi: 10.1098/rspb.2022.2491
Shea, J. B. & Morgan, R. L. Contextual interference effects on the acquisition, retention, and transfer of a motor skill. J. Exp. Psychol. Hum. Learn. 5, 179–187 (1979).
doi: 10.1037/0278-7393.5.2.179
Hadjiosif, A. M. & Smith, M. A. A double dissociation between savings and long-term memory in motor learning. PLoS Biol. 21, e3001799 (2023).
pubmed: 37104303
pmcid: 10138789
doi: 10.1371/journal.pbio.3001799
Hadjiosif, A. M., Morehead, J. R. & Smith, M. A. A double dissociation between savings and long-term memory in motor learning. PLoS Biol. 21, e3001799 (2023).
pubmed: 37104303
pmcid: 10138789
doi: 10.1371/journal.pbio.3001799
Joiner, W. M. & Smith, M. A. Long-term retention explained by a model of short-term learning in the adaptive control of reaching. J. Neurophysiol. 100, 2948–2955 (2008).
pubmed: 18784273
pmcid: 2585394
doi: 10.1152/jn.90706.2008
Miyamoto, Y. R., Wang, S. & Smith, M. A. Implicit adaptation compensates for erratic explicit strategy in human motor learning. Nat. Neurosci. 23, 443–455 (2020).
pubmed: 32112061
doi: 10.1038/s41593-020-0600-3
Roller, C. A., Cohen, H. S., Kimball, K. T. & Bloomberg, J. J. Effects of normal aging on visuo-motor plasticity. Neurobiol. Aging 23, 117–123 (2002).
pubmed: 11755026
doi: 10.1016/S0197-4580(01)00264-0
Buch, E. R., Young, S. & Contreras-Vidal, J. L. Visuomotor adaptation in normal aging. Learn. Mem. 10, 55–63 (2003).
pubmed: 12551964
pmcid: 196655
doi: 10.1101/lm.50303
Vachon, C. M., Modchalingam, S., ’t Hart, B. M. & Henriques, D. Y. P. The effect of age on visuomotor learning processes. PLoS ONE 15, e0239032 (2020).
pubmed: 32925937
pmcid: 7489529
doi: 10.1371/journal.pone.0239032
Wolpe, N. et al. Age-related reduction in motor adaptation: brain structural correlates and the role of explicit memory. Neurobiol. Aging https://doi.org/10.1016/j.neurobiolaging.2020.02.016 (2020).
Wang, T. S. L., Martinez, M., Festa, E. K., Heindel, W. C. & Song, J.-H. Age-related enhancement in visuomotor learning by a dual-task. Sci. Rep. 12, 5679 (2022).
pubmed: 35383212
pmcid: 8983773
doi: 10.1038/s41598-022-09553-7
Cressman, E. K., Salomonczyk, D. & Henriques, D. Y. P. Visuomotor adaptation and proprioceptive recalibration in older adults. Exp. Brain Res. 205, 533–544 (2010).
pubmed: 20717800
doi: 10.1007/s00221-010-2392-2
Vandevoorde, K. & Orban de Xivry, J.-J. Why is the explicit component of motor adaptation limited in elderly adults? J. Neurophysiol. 124, 152–167 (2020).
pubmed: 32459553
pmcid: 7474453
doi: 10.1152/jn.00659.2019
Wong, A. L., Marvel, C. L., Taylor, J. A. & Krakauer, J. W. Can patients with cerebellar disease switch learning mechanisms to reduce their adaptation deficits? Brain https://doi.org/10.1093/brain/awy334 (2019).
Seidler, R. D. Differential effects of age on sequence learning and sensorimotor adaptation. Brain Res. Bull. 70, 337–346 (2006).
pubmed: 17027769
doi: 10.1016/j.brainresbull.2006.06.008
Ruitenberg, M. F. L., Koppelmans, V., Seidler, R. D. & Schomaker, J. Developmental and age differences in visuomotor adaptation across the lifespan. Psychol. Res. https://doi.org/10.1007/s00426-022-01784-7 (2023).
Vandevoorde, K. & Orban de Xivry, J.-J. Internal model recalibration does not deteriorate with age while motor adaptation does. Neurobiol. Aging 80, 138–153 (2019).
pubmed: 31170534
doi: 10.1016/j.neurobiolaging.2019.03.020
Morehead, R. & de Xivry, J.-J. O. A synthesis of the many errors and learning processes of visuomotor adaptation. Preprint at bioRxiv https://doi.org/10.1101/2021.03.14.435278 (2021).
Verstynen, T. & Kording, K. P. Overfitting to ‘predict’ suicidal ideation. Nat. Hum. Behav. 7, 680–681 (2023).
pubmed: 37024723
doi: 10.1038/s41562-023-01560-6
Albert, S. T. et al. Competition between parallel sensorimotor learning systems. eLife https://doi.org/10.7554/eLife.65361 (2022).
Tottenham, L. S. & Saucier, D. M. Throwing accuracy during prism adaptation: male advantage for throwing accuracy is independent of prism adaptation rate. Percept. Mot. Skills 98, 1449–1455 (2004).
pubmed: 15291237
doi: 10.2466/pms.98.3c.1449-1455
Zar, J. H. Biostatistical Analysis: International Edition 5th edn (Pearson, 2007).
Gajda, K., Sülzenbrück, S. & Heuer, H. Financial incentives enhance adaptation to a sensorimotor transformation. Exp. Brain Res. 234, 2859–2868 (2016).
pubmed: 27271505
doi: 10.1007/s00221-016-4688-3
Tsay, J. S., Tan, S., Chu, M., Ivry, R. B. & Cooper, E. A. Low vision impairs implicit sensorimotor adaptation in response to small errors, but not large errors. J. Cogn. Neurosci. https://doi.org/10.1162/jocn_a_01969 (2023).
Burge, J., Ernst, M. O. & Banks, M. S. The statistical determinants of adaptation rate in human reaching. J. Vis. 8, 20 (2008).
doi: 10.1167/8.4.20
Körding, K. P. & Wolpert, D. M. Bayesian integration in sensorimotor learning. Nature 427, 244–247 (2004).
pubmed: 14724638
doi: 10.1038/nature02169
McDougle, S. D. & Taylor, J. A. Dissociable cognitive strategies for sensorimotor learning. Nat. Commun. 10, 40 (2019).
pubmed: 30604759
pmcid: 6318272
doi: 10.1038/s41467-018-07941-0
Fernandez-Ruiz, J., Wong, W., Armstrong, I. T. & Flanagan, J. R. Relation between reaction time and reach errors during visuomotor adaptation. Behav. Brain Res. 219, 8–14 (2011).
pubmed: 21138745
doi: 10.1016/j.bbr.2010.11.060
Morehead, J. R. & Ivry, R. Intrinsic Biases Systematically Affect Visuomotor Adaptation Experiments (Society for Neural Control of Movement, 2015); http://ivrylab.berkeley.edu/uploads/4/1/1/5/41152143/morehead_ncm2015.pdf
Vindras, P., Desmurget, M., Prablanc, C. & Viviani, P. Pointing errors reflect biases in the perception of the initial hand position. J. Neurophysiol. 79, 3290–3294 (1998).
pubmed: 9636129
doi: 10.1152/jn.1998.79.6.3290
Wilson, E. T., Wong, J. & Gribble, P. L. Mapping proprioception across a 2D horizontal workspace. PLoS ONE 5, e11851 (2010).
pubmed: 20686612
pmcid: 2912297
doi: 10.1371/journal.pone.0011851
McNay, E. C. & Willingham, D. B. Deficit in learning of a motor skill requiring strategy, but not of perceptuomotor recalibration, with aging. Learn. Mem. 4, 411–420 (1998).
pubmed: 10701880
doi: 10.1101/lm.4.5.411
Fernández-Ruiz, J., Hall, C., Vergara, P. & Díiaz, R. Prism adaptation in normal aging: slower adaptation rate and larger aftereffect. Brain Res. Cogn. Brain Res. 9, 223–226 (2000).
pubmed: 10808133
doi: 10.1016/S0926-6410(99)00057-9
Dhawale, A. K., Smith, M. A. & Ölveczky, B. P. The role of variability in motor learning. Annu. Rev. Neurosci. 40, 479–498 (2017).
pubmed: 28489490
pmcid: 6091866
doi: 10.1146/annurev-neuro-072116-031548
He, K. et al. The statistical determinants of the speed of motor learning. PLoS Comput. Biol. 12, e1005023 (2016).
pubmed: 27606808
pmcid: 5015831
doi: 10.1371/journal.pcbi.1005023
Wu, H. G., Miyamoto, Y. R., Castro, L. N. G., Ölveczky, B. P. & Smith, M. A. Temporal structure of motor variability is dynamically regulated and predicts motor learning ability. Nat. Neurosci. 17, 312–321 (2014).
pubmed: 24413700
pmcid: 4442489
doi: 10.1038/nn.3616
Singh, P., Jana, S., Ghosal, A. & Murthy, A. Exploration of joint redundancy but not task space variability facilitates supervised motor learning. Proc. Natl Acad. Sci. USA 113, 14414–14419 (2016).
pubmed: 27911808
pmcid: 5167208
doi: 10.1073/pnas.1613383113
Behrens, T. E. J., Woolrich, M. W., Walton, M. E. & Rushworth, M. F. S. Learning the value of information in an uncertain world. Nat. Neurosci. 10, 1214–1221 (2007).
pubmed: 17676057
doi: 10.1038/nn1954
Tsay, J. S., Kim, H., Haith, A. M. & Ivry, R. B. Understanding implicit sensorimotor adaptation as a process of proprioceptive re-alignment. eLife https://doi.org/10.7554/eLife.76639 (2022).
Bönstrup, M., Iturrate, I., Hebart, M. N., Censor, N. & Cohen, L. G. Mechanisms of offline motor learning at a microscale of seconds in large-scale crowdsourced data. NPJ Sci. Learn. 5, 7 (2020).
pubmed: 32550003
pmcid: 7272649
doi: 10.1038/s41539-020-0066-9
Taylor, J. A. & Ivry, R. B. Flexible cognitive strategies during motor learning. PLoS Comput. Biol. 7, e1001096 (2011).
pubmed: 21390266
pmcid: 3048379
doi: 10.1371/journal.pcbi.1001096
Hebiri, M. & Lederer, J. How correlations influence lasso prediction. IEEE Trans. Inf. Theory 59, 1846–1854 (2013).
doi: 10.1109/TIT.2012.2227680
Burgoyne, A. P., Harris, L. J. & Hambrick, D. Z. Predicting piano skill acquisition in beginners: the role of general intelligence, music aptitude, and mindset. Intelligence 76, 101383 (2019).
doi: 10.1016/j.intell.2019.101383
McGregor, H. R. & Gribble, P. L. Functional connectivity between somatosensory and motor brain areas predicts individual differences in motor learning by observing. J. Neurophysiol. 118, 1235–1243 (2017).
pubmed: 28566463
pmcid: 5547259
doi: 10.1152/jn.00275.2017
Roberts, R. E., Bain, P. G., Day, B. L. & Husain, M. Individual differences in expert motor coordination associated with white matter microstructure in the cerebellum. Cereb. Cortex 23, 2282–2292 (2013).
pubmed: 22892425
doi: 10.1093/cercor/bhs219
Landi, S. M., Baguear, F. & Della-Maggiore, V. One week of motor adaptation induces structural changes in primary motor cortex that predict long-term memory one year later. J. Neurosci. 31, 11808–11813 (2011).
pubmed: 21849541
pmcid: 3180815
doi: 10.1523/JNEUROSCI.2253-11.2011
Koppelmans, V., Bloomberg, J. J., Mulavara, A. P. & Seidler, R. D. Brain structural plasticity with spaceflight. NPJ Microgravity https://doi.org/10.1038/s41526-016-0001-9 (2016).
Pearson-Fuhrhop, K. M., Minton, B., Acevedo, D., Shahbaba, B. & Cramer, S. C. Genetic variation in the human brain dopamine system influences motor learning and its modulation by L-DOPA. PLoS ONE 8, e61197 (2013).
pubmed: 23613810
pmcid: 3629211
doi: 10.1371/journal.pone.0061197
Listman, J. B., Tsay, J. S., Kim, H. E., Mackey, W. E. & Heeger, D. J. Long-term motor learning in the ‘wild’ with high volume video game data. Front. Hum. Neurosci. 15, 777779 (2021).
pubmed: 34987368
pmcid: 8720934
doi: 10.3389/fnhum.2021.777779
Aung, M. et al. Predicting skill learning in a large, longitudinal MOBA dataset. In IEEE Conference on Computational Intelligence and Games (CIG) 1–7 (IEEE, 2018).
Brookes, J., Warburton, M., Alghadier, M., Mon-Williams, M. & Mushtaq, F. Studying human behavior with virtual reality: the Unity Experiment Framework. Behav. Res Methods 52, 455–463 (2020).
pubmed: 31012061
doi: 10.3758/s13428-019-01242-0
Chen, X. et al. Age-dependent Pavlovian biases influence motor decision-making. PLoS Comput. Biol. 14, e1006304 (2018).
pubmed: 29979685
pmcid: 6051643
doi: 10.1371/journal.pcbi.1006304
Donovan, I., Saul, M. A., DeSimone, K., Listman, J. B., Mackey, W. E., & Heeger, D. J. Assessment of human expertise and movement kinematics in first-person shooter games. Front. Hum. Neurosci. https://doi.org/10.3389/fnhum.2022.979293 (2022).
Stafford, T. & Dewar, M. Tracing the trajectory of skill learning with a very large sample of online game players. Psychol. Sci. 25, 511–518 (2014).
pubmed: 24379154
doi: 10.1177/0956797613511466
Stafford, T., & Vaci, N. Maximizing the potential of digital games for understanding skill acquisition. Curr. Dir. Psychol. https://doi.org/10.1177/09637214211057841 (2022).
Balestrucci, P., Wiebusch, D. & Ernst, M. O. ReActLab: a custom framework for sensorimotor experiments ‘in-the-wild’. Front. Psychol. https://doi.org/10.3389/fpsyg.2022.906643 (2022).
Kaur, J. & Balasubramaniam, R. Sequence learning in an online serial reaction time task: the effect of task instructions. J. Mot. Learn. Dev. 1–17 (2022).
Brantley, J. A. & Kording, K. P. Bayesball: Bayesian integration in professional baseball batters. Preprint at bioRxiv https://doi.org/10.1101/2022.10.12.511934 (2022).
Drazan, J. F., Phillips, W. T., Seethapathi, N., Hullfish, T. J. & Baxter, J. R. Moving outside the lab: markerless motion capture accurately quantifies sagittal plane kinematics during the vertical jump. J. Biomech. 125, 110547 (2021).
pubmed: 34175570
pmcid: 8640714
doi: 10.1016/j.jbiomech.2021.110547
Hausmann, S. B., Vargas, A. M., Mathis, A. & Mathis, M. W. Measuring and modeling the motor system with machine learning. Preprint at arXiv https://doi.org/10.48550/arXiv.2103.11775 (2021).
Hooyman, A. & Schaefer, S. Y. Age and sex effects on Super G performance are consistent across internet devices. Int. J. Serious Games 10, 25–36 (2023).
pubmed: 37846217
pmcid: 10578419
doi: 10.17083/ijsg.v10i2.598
Yin, C. & Wei, K. Savings in sensorimotor adaptation without explicit strategy. J. Neurophysiol. https://doi.org/10.1152/jn.00524.2019 (2020).
Coltman, S. K., Cashaback, J. G. A. & Gribble, P. L. Both fast and slow learning processes contribute to savings following sensorimotor adaptation. J. Neurophysiol. 121, 1575–1583 (2019).
pubmed: 30840553
pmcid: 6485725
doi: 10.1152/jn.00794.2018
Morehead, R., Taylor, J. A., Parvin, D. E. & Ivry, R. B. Characteristics of implicit sensorimotor adaptation revealed by task-irrelevant clamped feedback. J. Cogn. Neurosci. 29, 1061–1074 (2017).
pubmed: 28195523
pmcid: 5505262
doi: 10.1162/jocn_a_01108
Maresch, J., Werner, S. & Donchin, O. Methods matter: your measures of explicit and implicit processes in visuomotor adaptation affect your results. Eur. J. Neurosci. https://doi.org/10.1111/ejn.14945 (2020).
Hooyman, A., Huentelman, M. J., De Both, M., Ryan, L. & Schaefer, S. Y. Establishing the validity and reliability of an online motor learning game: applications for Alzheimer’s disease research within MindCrowd. Games Health J. https://doi.org/10.1089/g4h.2022.0042 (2023).
Allen, K. R., Smith, K. A. & Tenenbaum, J. B. Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning. Proc. Natl Acad. Sci. USA 117, 29302–29310 (2020).
pubmed: 33229515
pmcid: 7703630
doi: 10.1073/pnas.1912341117
Tsay, J. S., Schuck, L. & Ivry, R. B. Cerebellar degeneration impairs strategy discovery but not strategy recall. Cerebellum https://doi.org/10.1007/s12311-022-01500-6 (2022).
Saban, W. & Ivry, R. B. PONT: a Protocol for Online Neuropsychological Testing. J. Cogn. Neurosci. 1–13 (2021).
Chakraborty, S. & Wong, S. W. K. BAMBI: An R package for fitting bivariate angular mixture models. J. Stat. Softw. 99, 1–69 (2021).
doi: 10.18637/jss.v099.i11
Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).
pubmed: 20808728
pmcid: 2929880
doi: 10.18637/jss.v033.i01
Choi, Y., Park, R. & Seo, M. Lasso on Categorical Data (CiteSeerX, 2012); https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.278.5439
Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58, 267–288 (1996).
McDougle, S. D., Bond, K. & Taylor, J. A. Implications of plan-based generalization in sensorimotor adaptation. J. Neurophysiol. 118, 383–393 (2017).
pubmed: 28404830
pmcid: 5501918
doi: 10.1152/jn.00974.2016
Day, K. A., Roemmich, R. T., Taylor, J. A. & Bastian, A. J. Visuomotor learning generalizes around the intended movement. eNeuro https://doi.org/10.1523/ENEURO.0005-16.2016 (2016).