Active learning: learning a motor skill without a coach
Open accessAbstract
When we learn a new skill (e.g., golf) without a coach, we are "active learners": we have to choose the specific components of the task on which to train (e.g., iron, driver, putter, etc.). What guides our selection of the training sequence? How do choices that people make compare with choices made by machine learning algorithms that attempt to optimize performance? We asked subjects to learn the novel dynamics of a robotic tool while moving it in four directions. They were instructed to choose their practice directions to maximize their performance in subsequent tests. We found that their choices were strongly influenced by motor errors: subjects tended to immediately repeat an action if that action had produced a large error. This strategy was correlated with better performance on test trials. However, even when participants performed perfectly on a movement, they did not avoid repeating that movement. The probability of repeating an action did not drop below chance even when no errors were observed. This behavior led to suboptimal performance. It also violated a strong prediction of current machine learning algorithms, which solve the active learning problem by choosing a training sequence that will maximally reduce the learner's uncertainty about the task. While we show that these algorithms do not provide an adequate description of human behavior, our results suggest ways to improve human motor learning by helping people choose an optimal training sequence.
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In a previous active learning study, subjects repeated successful movements more frequently than error-based learning would predict.
“We found that subjects repeated successful movements more frequently than error-based learning would predict; from a pure error-based learning perspective, such behavior is sub-optimal as it competes with time that could be spent on practice to target directions still associated with large errors – why revisit targets that you have already solved?”
From Rethinking motor learning and savings in adaptation paradigms: model-free memory for successful actions combines with internal models (2011) · cites this paper as Huang et al., 2008
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Cites
Cortical substrates for exploratory decisions in humans
Daw, N. D., O'Doherty, J. P., Dayan, P., Seymour, B., & Dolan, R. J. (2006) · Nature · doi:10.1038/nature04766 · Cited by 2,224 · see citing works
Active Learning with Statistical Models
Cohn, D. A., Zoubin Ghahramani, A., & Jordan, M. I. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.295 · Cited by 2,054 · see citing works
Bayesian integration in sensorimotor learning
Körding, K. P., & Wolpert, D. M. (2004) · Nature · doi:10.1038/nature02169 · Cited by 2,004 · see citing works
Contextual interference effects on the acquisition, retention, and transfer of a motor skill.
Shea, J. B., & Morgan, R. L. (1979) · Journal of Experimental Psychology: Human Learning and Memory · doi:10.1037/0278-7393.5.2.179 · Cited by 1,227 · see citing works
Neural, mechanical, and geometric factors subserving arm posture in humans
Mussa-Ivaldi, F., Hogan, N., & Bizzi, E. (1985) · Journal of Neuroscience · doi:10.1523/jneurosci.05-10-02732.1985 · Cited by 1,173 · see citing works
Cohen, J. D., McClure, S. M., & Yu, A. J. (2007) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2007.2098 · Cited by 1,121 · see citing works
The central nervous system stabilizes unstable dynamics by learning optimal impedance
Burdet, E., Osu, R., Franklin, D. W., Milner, T. E., & Kawato, M. (2001) · Nature · doi:10.1038/35106566 · Cited by 1,097 · see citing works
Learning of action through adaptive combination of motor primitives
Thoroughman, K. A., & Shadmehr, R. (2000) · Nature · doi:10.1038/35037588 · Cited by 945 · see citing works
Self-citationPrinciples derived from the study of simple skills do not generalize to complex skill learning
Wulf, G., & Shea, C. H. (2002) · Psychonomic Bulletin & Review · doi:10.3758/bf03196276 · Cited by 829 · see citing works
Donchin, O., Francis, J. T., & Shadmehr, R. (2003) · Journal of Neuroscience · doi:10.1523/jneurosci.23-27-09032.2003 · Cited by 442 · see citing works
Self-citationThe dynamics of memory as a consequence of optimal adaptation to a changing body
Kording, K. P., Tenenbaum, J. B., & Shadmehr, R. (2007) · Nature Neuroscience · doi:10.1038/nn1901 · Cited by 393 · see citing works
Self-citationFeedback After Good Trials Enhances Learning
Chiviacowsky, S., & Wulf, G. (2007) · Research Quarterly for Exercise and Sport · doi:10.1080/02701367.2007.10599402 · Cited by 265 · see citing works
A Gain-Field Encoding of Limb Position and Velocity in the Internal Model of Arm Dynamics
Hwang, E. J., Donchin, O., Smith, M. A., & Shadmehr, R. (2003) · PLoS Biology · doi:10.1371/journal.pbio.0000025 · Cited by 118 · see citing works
Self-citationBrydges, R., Carnahan, H., Backstein, D. J., & Dubrowski, A. (2007) · Journal of Motor Behavior · doi:10.3200/jmbr.39.1.40-48 · Cited by 78 · see citing works
Evolution of Motor Memory During the Seconds After Observation of Motor Error
Huang, V. S., & Shadmehr, R. (2007) · Journal of Neurophysiology · doi:10.1152/jn.01281.2006 · Cited by 48 · see citing works
Self-citationLong-Term Effects of Type of Practice on the Learning and Transfer of a Complex Motor Skill
Memmert, D. (2006) · Perceptual and Motor Skills · doi:10.2466/pms.103.3.912-916 · Cited by 47 · see citing works
Cone selectivity derived from the responses of the retinal cone mosaic to natural scenes
Wächtler, T., Doi, E., Lee, T.-W., & B, T. J. S. A. (2007) · Journal of Vision · doi:10.1167/7.8.6 · Cited by 43 · see citing works
A bottom–up model of spatial attention predicts human error patterns in rapid scene recognition
Wolfgang Einhäuser, A., Mundhenk, T. N., Baldi, P., Christof Koch, A., & Itti, L. (2007) · Journal of Vision · doi:10.1167/7.10.6 · Cited by 36 · see citing works
Contextual Interference Effect on Acquisition and Retention of Pistol-Shooting Skills
Keller, G. J., Li, Y., Weiss, L. W., & Relyea, G. E. (2006) · Perceptual and Motor Skills · doi:10.2466/pms.103.1.241-252 · Cited by 32 · see citing works
Three-dimensional spatial skill training in a simulated space station: random vs. blocked designs.
Shebilske, W. L., Tubré, T., Tubrè, A. H., Oman, C. M., & Richards, J. T. (2006) · PubMed · Cited by 11 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Principles of sensorimotor learning
Wolpert, D. M., Diedrichsen, J., & Flanagan, J. R. (2011) · Nature Reviews Neuroscience · doi:10.1038/nrn3112 · Cited by 1,391 · see citing works
Self-citationReview of control strategies for robotic movement training after neurologic injury
Marchal-Crespo, L., & Reinkensmeyer, D. J. (2009) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-6-20 · Cited by 1,182 · see citing works
Inside the brain of an elite athlete: the neural processes that support high achievement in sports
Yarrow, K., Brown, P., & Krakauer, J. W. (2009) · Nature reviews. Neuroscience · doi:10.1038/nrn2672 · Cited by 643 · see citing works
Huang, V. S., Haith, A., Mazzoni, P., & Krakauer, J. W. (2011) · Neuron · doi:10.1016/j.neuron.2011.04.012 · Cited by 472 · see citing works
Self-citationRobotic neurorehabilitation: a computational motor learning perspective
Huang, V. S., & Krakauer, J. W. (2009) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-6-5 · Cited by 426 · see citing works
Self-citationNeuromechanical Principles Underlying Movement Modularity and Their Implications for Rehabilitation
Ting, L. H., Chiel, H. J., Trumbower, R. D., Allen, J. L., McKay, J. L., Hackney, M. E., & Kesar, T. M. (2015) · Neuron · doi:10.1016/j.neuron.2015.02.042 · Cited by 410 · see citing works
Motor learning principles for neurorehabilitation
(2013) · Handbook of Clinical Neurology · doi:10.1016/b978-0-444-52901-5.00008-3 · Cited by 401 · see citing works
Persistence of Motor Memories Reflects Statistics of the Learning Event
Huang, V. S., & Shadmehr, R. (2009) · Journal of Neurophysiology · doi:10.1152/jn.00237.2009 · Cited by 100 · see citing works
Self-citationThe relationship between habits and motor skills in humans
Du, Y., Krakauer, J. W., & Haith, A. M. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.02.002 · Cited by 94 · see citing works
Archer, S. K., Smith, C. H., & Newham, D. J. (2020) · Dysphagia · doi:10.1007/s00455-020-10129-8 · Cited by 85 · see citing works
Face recognition based on convolution neural network
Yan, K., Huang, S., Song, Y., Liu, W., & Fan, N. (2017) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2017.8027997 · Cited by 82 · see citing works
Learning in Closed-Loop Brain–Machine Interfaces: Modeling and Experimental Validation
Héliot, R., Ganguly, K., Jimenez, J., & Carmena, J. M. (2009) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2009.2036931 · Cited by 61 · see citing works
Design and Analysis of a Cable-Driven Articulated Rehabilitation System for Gait Training
Alamdari, A., & Krovi, V. (2015) · Journal of Mechanisms and Robotics · doi:10.1115/1.4032274 · Cited by 59 · see citing works
Carter, M. J., & Ste‐Marie, D. M. (2017) · Psychonomic Bulletin & Review · doi:10.3758/s13423-017-1250-7 · Cited by 52 · see citing works
Augmented Dynamics and Motor Exploration as Training for Stroke
Huang, F. C., & Patton, J. L. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2192116 · Cited by 49 · see citing works
Action Science Emerging: Introduction and Leitmotifs
Herwig, A., Beisert, M., & Prinz, W. (2013) · The MIT Press eBooks · doi:10.7551/mitpress/9780262018555.003.0003 · Cited by 41 · see citing works
Using EEG to study sensorimotor adaptation
Reuter, E., Booms, A., & Leow, L. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.104520 · Cited by 38 · see citing works
Muscle, Biomechanics, and Implications for Neural Control
Ting, L. H., & Chiel, H. J. (2017) · Wiley eBooks · doi:10.1002/9781118873397.ch12 · Cited by 27 · see citing works
On the Role of Error in Motor Learning
Lee, T. D., Eliasz, K. L., Gonzalez, D. A., Alguire, K., Ding, K., & Dhaliwal, C. (2015) · Journal of Motor Behavior · doi:10.1080/00222895.2015.1046545 · Cited by 23 · see citing works
Optimal Schedules in Multitask Motor Learning
Lee, J.-Y., Oh, Y.-M., Kim, S., Scheidt, R. A., & Schweighofer, N. (2016) · Neural Computation · doi:10.1162/neco_a_00823 · Cited by 22 · see citing works
A Face Recognition System Based on Convolution Neural Network
Qiao, S., & Ma, J. (2018) · Chinese Automation Congress · doi:10.1109/cac.2018.8623122 · Cited by 22 · see citing works
Yantha, Z. D., McKay, B., & Ste‐Marie, D. M. (2021) · Journal of Sports Sciences · doi:10.1080/02640414.2021.2015945 · Cited by 20 · see citing works
Selective and compressive sensing for energy-efficient implantable neural decoding
Wang, A., Song, C., Xu, X., Lin, F., Jin, Z., & Xu, W. (2015) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2015.7348375 · Cited by 15 · see citing works
Selective CS: An Energy-Efficient Sensing Architecture for Wireless Implantable Neural Decoding
Song, C., Wang, A., Lin, F., Xiao, J. T., Yao, X., & Xu, W. (2018) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2018.2809906 · Cited by 9 · see citing works
Development of novel devices for upper-extremity rehabilitation
Stienen, A. H. A. (2008) · doi:10.3990/1.9789036527842 · Cited by 9 · see citing works
Changes in Perception Accompany the Development of Music Performance Skills
Hamilton, L. M., & Duke, R. A. (2020) · Journal of Research in Music Education · doi:10.1177/0022429420920567 · Cited by 8 · see citing works
Social Robots as Physical Curiosity Assessment Tools
Epstein, M., & Gordon, G. (2018) · IEEE RO-MAN · doi:10.1109/roman.2018.8525790 · Cited by 7 · see citing works
Backåberg, S., Brunt, D. A., Rask, M., & Gummesson, C. (2019) · European Journal of Physiotherapy · doi:10.1080/21679169.2019.1635639 · Cited by 7 · see citing works
Hodges, N. J., Coppola, T. M., Peters, C. M., & Larssen, B. C. (2021) · Human Movement Science · doi:10.1016/j.humov.2021.102850 · Cited by 6 · see citing works
Kurashige, H., Yamashita, Y., Hanakawa, T., & Honda, M. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00111 · Cited by 8 · see citing works
The relationship between habits and motor skills in humansPreprint
Du, Y., Krakauer, J. W., & Haith, A. M. (2021) · doi:10.31234/osf.io/9qrgd · Cited by 5 · see citing works
A Low-fidelity Simulator for the Development of Vascular Ligation Skills
Pérez‐Rivero, J. J., Barbosa-Callejas, I. M., Delgado-Garduño, L., Rodríguez-Buitrón, L., Lavalle-Avalos, A. E., & Barragán, J. A. H. (2022) · Alternatives to Laboratory Animals · doi:10.1177/02611929221096677 · Cited by 4 · see citing works
Generalization in motor adaptation: A computational perspective on recent developments
Tanaka, H. (2010) · Japanese Psychological Research · doi:10.1111/j.1468-5884.2010.00430.x · Cited by 3 · see citing works
The Effects of Different Motor Teaching Strategies on Learning a Complex Motor Task
Kunavar, T., Jamšek, M., Avila-Mireles, E. J., Rueckert, E., Peternel, L., & Babič, J. (2024) · Sensors · doi:10.3390/s24041231 · Cited by 2 · see citing works
Cognitive effort in contextual interference and implicit motor learning
Rendell, M. A. (2010) · Victoria University Research Repository (Victoria University) · Cited by 2 · see citing works
Marchal–Crespo, L. (2009) · Bern Open Repository and Information System (University of Bern) · doi:10.7892/boris.117120 · Cited by 1 · see citing works
the Learning Event Persistence of Motor Memories Reflects Statistics of
Shadmehr, R., Sağlam, M., Glasauer, S., Lehnen, N., Hussain, S. J., Morton, S. M., de Xivry, J. O., & Lefèvre, P. (2015)
Self-citationSelf-controlled practice and nudging during structural learning of a novel control interface
Lee, M.-H., & Jayasinghe, S. A. L. (2020) · PLoS ONE · doi:10.1371/journal.pone.0223810
Music-induced mood improves retention in visuomotor adaptation
Waclawik, K. (2014) · Scholarship@Western (Western University)
Wang, Z., Narumi, T., & Matsumoto, K. (2026) · Augmented Humans International Conference (AHs) · doi:10.1145/3795011.3795057
Bridging the divide in motor learning research
Grießbach, E., Hernández-Inostroza, A., Cañal‐Bruland, R., & Haith, A. M. (2026) · Nature Human Behaviour · doi:10.1038/s41562-026-02519-z
- Authors
- Huang, V. S., Shadmehr, R., & Diedrichsen, J.
- Affiliations
- Johns Hopkins University (US)
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 2008-08
- Volume
- 100
- Issue
- 2
- Pages
- 879-887
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- PMC
- PMC2525710
- OpenAlex
- W2150169083
- Cited by
- 41
- References
- 20
- Citations by year
- 2026: 2 · 2024: 1 · 2022: 3 · 2021: 3 · 2020: 3 · 2019: 1 · 2018: 4 · 2017: 3 · 2016: 1 · 2015: 5 · 2014: 1 · 2013: 2 · 2012: 1
- FWCI
- 1.89
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Neurophysiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (NS 37422)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Social Sciences › Psychology › Developmental and Educational Psychology › Sport Psychology and Performance
- Keywords
- active learning (machine learning), motor skill learning, motor errors, error-based learning, uncertainty reduction
- MeSH terms
- Choice Behavior, Computer Simulation, Humans, Knowledge of Results, Psychological, Learning, Models, Neurological, Motor Skills, Practice, Psychological, Probability, Task Performance and Analysis, Logistic Models, Problem-Based Learning
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