Abstract
During extended motor adaptation, learning appears to saturate despite persistence of residual errors. This adaptation limit is not fixed but varies with perturbation variance; when variance is high, residual errors become larger. These changes in total adaptation could relate to either implicit or explicit learning systems. Here, we found that when adaptation relied solely on the explicit system, residual errors disappeared and learning was unaltered by perturbation variability. In contrast, when learning depended entirely, or in part, on implicit learning, residual errors reappeared. Total implicit adaptation decreased in the high-variance environment due to changes in error sensitivity, not in forgetting. These observations suggest a model in which the implicit system becomes more sensitive to errors when they occur in a consistent direction. Thus, residual errors in motor adaptation are at least in part caused by an implicit learning system that modulates its error sensitivity in response to the consistency of past errors.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
Data collected in Experiments 1–7 are deposited in OSF and are available at https://osf.io/w3p9d/. Source data are provided with this paper.
Code availability
Code for the state–space memory of errors model is deposited in OSF and is available at https://osf.io/9hzmq/. Additional analysis codes are available on request from the corresponding author.
References
Albert, S. T. & Shadmehr, R. The neural feedback response to error as a teaching signal for the motor learning system. J. Neurosci. 36, 4832–4845 (2016).
Thoroughman, K. A. & Shadmehr, R. Electromyographic correlates of learning an internal model of reaching movements. J. Neurosci. 19, 8573–8588 (1999).
Franklin, D. W. et al. CNS learns stable, accurate, and efficient movements using a simple algorithm. J. Neurosci. 28, 11165–11173 (2008).
Shadmehr, R., Brandt, J. & Corkin, S. Time-dependent motor memory processes in amnesic subjects. J. Neurophysiol. 80, 1590–1597 (1998).
McDougle, S. D., Bond, K. M. & Taylor, J. A. Explicit and implicit processes constitute the fast and slow processes of sensorimotor learning. J. Neurosci. 35, 9568–9579 (2015).
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).
Taylor, J. A. & Ivry, R. B. Flexible cognitive strategies during motor learning. PLoS Comput. Biol. 7, e1001096 (2011).
Donchin, O. et al. Cerebellar regions involved in adaptation to force field and visuomotor perturbation. J. Neurophysiol. 107, 134–147 (2012).
Brashers-Krug, T., Shadmehr, R. & Bizzi, E. Consolidation in human motor memory. Nature 382, 252–255 (1996).
Tseng, Y.-W., Diedrichsen, J., Krakauer, J. W., Shadmehr, R. & Bastian, A. J. Sensory prediction errors drive cerebellum-dependent adaptation of reaching. J. Neurophysiol. 98, 54–62 (2007).
Krakauer, J. W., Pine, Z. M., Ghilardi, M. F. & Ghez, C. Learning of visuomotor transformations for vectorial planning of reaching trajectories. J. Neurosci. 20, 8916–8924 (2000).
Ethier, V., Zee, D. S. & Shadmehr, R. Spontaneous recovery of motor memory during saccade adaptation. J. Neurophysiol. 99, 2577–2583 (2008).
Robinson, F. R., Noto, C. T. & Bevans, S. E. Effect of visual error size on saccade adaptation in monkey. J. Neurophysiol. 90, 1235–1244 (2003).
Malone, L. A., Vasudevan, E. V. L. & Bastian, A. J. Motor adaptation training for faster relearning. J. Neurosci. 31, 15136–15143 (2011).
Fernandes, H. L., Stevenson, I. H. & Kording, K. P. Generalization of stochastic visuomotor rotations. PLoS ONE 7, e43016 (2012).
Therrien, A. S., Wolpert, D. M. & Bastian, A. J. Increasing motor noise impairs reinforcement learning in healthy individuals. eNeuro 5, ENEURO.0050-18.2018 (2018).
Havermann, K. & Lappe, M. The Influence of the consistency of postsaccadic visual errors on saccadic adaptation. J. Neurophysiol. 103, 3302–3310 (2010).
Vaswani, P. A. et al. Persistent residual errors in motor adaptation tasks: reversion to baseline and exploratory escape. J. Neurosci. 35, 6969–6977 (2015).
Hegele, M. & Heuer, H. The impact of augmented information on visuo-motor adaptation in younger and older adults. PLoS ONE 5, e12071–e12071 (2010).
Heuer, H. & Hegele, M. Adaptation to visuomotor rotations in younger and older adults. Psychol. Aging 23, 190–202 (2008).
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).
Neville, K.-M. & Cressman, E. K. The influence of awareness on explicit and implicit contributions to visuomotor adaptation over time. Exp. Brain Res. 236, 2047–2059 (2018).
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).
Langsdorf, L., Maresch, J., Hegele, M., McDougle, S. D. & Schween, R. Prolonged reaction times eliminate residual errors in visuomotor adaptation. Preprint at bioRxiv https://doi.org/10.1101/2019.12.26.888941 (2019).
Morehead, J. 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).
Kim, H. E., Morehead, J. 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).
Held, R., Efstathiou, A. & Greene, M. Adaptation to displaced and delayed visual feedback from the hand. J. Exp. Psychol. 72, 887–891 (1966).
Schween, R. & Hegele, M. Feedback delay attenuates implicit but facilitates explicit adjustments to a visuomotor rotation. Neurobiol. Learn. Mem. 140, 124–133 (2017).
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).
McDougle, S. D. & Taylor, J. A. Dissociable cognitive strategies for sensorimotor learning. Nat. Commun. 10, 40 (2019).
Leow, L.-A., Marinovic, W., de Rugy, A. & Carroll, T. J. Task errors drive memories that improve sensorimotor adaptation. J. Neurosci. 40, 3075–3088 (2020).
Körding, K. P. & Wolpert, D. M. The loss function of sensorimotor learning. Proc. Natl Acad. Sci. USA 101, 9839 LP–9842 LP (2004).
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).
Smith, M. A., Ghazizadeh, A. & Shadmehr, R. Interacting adaptive processes with different timescales underlie short-term motor learning. PLoS Biol. 4, e179 (2006).
Kording, K. P., Tenenbaum, J. B. & Shadmehr, R. The dynamics of memory as a consequence of optimal adaptation to a changing body. Nat. Neurosci. 10, 779–786 (2007).
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).
van der Vliet, R. et al. Individual differences in motor noise and adaptation rate are optimally related. eNeuro 5, ENEURO.0170-18.2018 (2018).
Herzfeld, D. J., Vaswani, P. A., Marko, M. K. & Shadmehr, R. A memory of errors in sensorimotor learning. Science 345, 1349–1353 (2014).
Leow, L.-A., de Rugy, A., Marinovic, W., Riek, S. & Carroll, T. J. Savings for visuomotor adaptation require prior history of error, not prior repetition of successful actions. J. Neurophysiol. 116, 1603–1614 (2016).
Sing, G. C. & Smith, M. A. Reduction in learning rates associated with anterograde interference results from interactions between different timescales in motor adaptation. PLoS Comput. Biol. 6, e1000893 (2010).
Scheidt, R. A., Reinkensmeyer, D. J., Conditt, M., Rymer, W. Z. & Mussa-ivaldi, F. A. Persistence of motor adaptation during constrained, multi-joint, arm movements. J. Neurophysiol. 84, 853–862 (2000).
Mazzoni, P. & Krakauer, J. W. An implicit plan overrides an explicit strategy during visuomotor adaptation. J. Neurosci. 26, 3642–3645 (2006).
Hwang, E. J., Smith, M. A. & Shadmehr, R. Dissociable effects of the implicit and explicit memory systems on learning control of reaching. Exp. Brain Res. 173, 425–437 (2006).
Schween, R., Taube, W., Gollhofer, A. & Leukel, C. Online and post-trial feedback differentially affect implicit adaptation to a visuomotor rotation. Exp. Brain Res. 232, 3007–3013 (2014).
Ekerot, C. F. & Kano, M. Stimulation parameters influencing climbing fibre induced long-term depression of parallel fibre synapses. Neurosci. Res. 6, 264–268 (1989).
Herzfeld, D. J., Kojima, Y., Soetedjo, R. & Shadmehr, R. Encoding of error and learning to correct that error by the Purkinje cells of the cerebellum. Nat. Neurosci. 21, 736–743 (2018).
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).
Leow, L.-A., Gunn, R., Marinovic, W. & Carroll, T. J. Estimating the implicit component of visuomotor rotation learning by constraining movement preparation time. J. Neurophysiol. 118, 666–676 (2017).
Marko, M. K., Haith, A. M., Harran, M. D. & Shadmehr, R. Sensitivity to prediction error in reach adaptation. J. Neurophysiol. 108, 1752–1763 (2012).
Wei, K. & Kording, K. Relevance of error: what drives motor adaptation? J. Neurophysiol. 101, 655–664 (2009).
Yin, C. & Wei, K. Savings in sensorimotor adaptation without an explicit strategy. J. Neurophysiol. 123, 1180–1192 (2020).
Kim, H. E., Parvin, D. E. & Ivry, R. B. The influence of task outcome on implicit motor learning. eLife 8, e39882 (2019).
Wilterson, S. A. & Taylor, J. A. Implicit visuomotor adaptation remains limited after several days of training. Preprint at bioRxiv https://doi.org/10.1101/711598 (2019).
Morehead, J. R., Qasim, S. E., Crossley, M. J. & Ivry, R. Savings upon re-aiming in visuomotor adaptation. J. Neurosci. 35, 14386–14396 (2015).
Bond, K. M. & Taylor, J. A. Flexible explicit but rigid implicit learning in a visuomotor adaptation task. J. Neurophysiol. 113, 3836–3849 (2015).
Avraham, G., Keizman, M. & Shmuelof, L. Environmental consistency modulation of error sensitivity during motor adaptation is explicitly controlled. J. Neurophysiol. 23, 57–69 (2019).
Huberdeau, D. M., Haith, A. M. & Krakauer, J. W. Formation of a long-term memory for visuomotor adaptation following only a few trials of practice. J. Neurophysiol. 114, 969–977 (2015).
Huberdeau, D. M., Krakauer, J. W. & Haith, A. M. Practice induces a qualitative change in the memory representation for visuomotor learning. J. Neurophysiol. 122, 1050–1059 (2019).
Thoroughman, K. & Shadmehr, R. Learning of action through adaptive combination of motor primitives. Nature 407, 742–747 (2000).
van der Kooij, K., Brenner, E., van Beers, R. J. & Smeets, J. B. J. Visuomotor adaptation: how forgetting keeps us conservative. PLoS ONE 10, e0117901 (2015).
Gonzalez Castro, L. N., Hadjiosif, A. M., Hemphill, M. A. & Smith, M. A. Environmental consistency determines the rate of motor adaptation. Curr. Biol. 24, 1050–1061 (2014).
Smith, M. A. & Shadmehr, R. Modulation of the Rate of Error-dependent Learning by Statistical Properties of the Task https://www.seas.harvard.edu/motorlab/Reprints/2004smith.pdf (ACMC, 2004).
Kojima, Y., Iwamoto, Y. & Yoshida, K. Memory of learning facilitates saccadic adaptation in the monkey. J. Neurosci. 24, 7531–7539 (2004).
Mawase, F., Shmuelof, L., Bar-Haim, S. & Karniel, A. Savings in locomotor adaptation explained by changes in learning parameters following initial adaptation. J. Neurophysiol. 111, 1444–1454 (2014).
Zarahn, E., Weston, G. D., Liang, J., Mazzoni, P. & Krakauer, J. W. Explaining savings for visuomotor adaptation: linear time-invariant state-space models are not sufficient. J. Neurophysiol. 100, 2537–2548 (2008).
Kojima, Y. & Soetedjo, R. Change in sensitivity to visual error in superior colliculus during saccade adaptation. Sci. Rep. 7, 9566 (2017).
Kojima, Y. & Soetedjo, R. Elimination of the error signal in the superior colliculus impairs saccade motor learning. Proc. Natl Acad. Sci. USA 115, E8987–E8995 (2018).
Kitago, T., Ryan, S. L., Mazzoni, P., Krakauer, J. W. & Haith, A. M. Unlearning versus savings in visuomotor adaptation: comparing effects of washout, passage of time, and removal of errors on motor memory. Front. Hum. Neurosci. 7, 307 (2013).
Robinson, F. R., Soetedjo, R. & Noto, C. Distinct short-term and long-term adaptation to reduce saccade size in monkey. J. Neurophysiol. 96, 1030–1041 (2006).
Baddeley, R. J., Ingram, H. A. & Miall, R. C. System identification applied to a visuomotor task: near-optimal human performance in a noisy changing task. J. Neurosci. 23, 3066–3075 (2003).
Kalman, R. A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Eng. 82, 35–45 (1960).
Burge, J., Ernst, M. O. & Banks, M. S. The statistical determinants of adaptation rate in human reaching. J. Vis. 8, 1–19 (2008).
van Beers, R. J. How does our motor system determine its learning rate? PLoS ONE 7, e49373 (2012).
Wei, K. & Körding, K. Uncertainty of feedback and state estimation determines the speed of motor adaptation. Front. Comput. Neurosci. 4, 11 (2010).
Xu-Wilson, M., Chen-Harris, H., Zee, D. S. & Shadmehr, R. Cerebellar contributions to adaptive control of saccades in humans. J. Neurosci. 29, 12930–12939 (2009).
Galea, J. M., Vazquez, A., Pasricha, N., Orban De Xivry, J. J. & Celnik, P. Dissociating the roles of the cerebellum and motor cortex during adaptive learning: the motor cortex retains what the cerebellum learns. Cereb. Cortex 21, 1761–1770 (2011).
Herzfeld, D. J. et al. Contributions of the cerebellum and the motor cortex to acquisition and retention of motor memories. Neuroimage 98, 147–158 (2014).
Hanajima, R. et al. Modulation of error-sensitivity during a prism adaptation task in people with cerebellar degeneration. J. Neurophysiol. 114, 2460–2471 (2015).
Kim, S., Ogawa, K., Lv, J., Schweighofer, N. & Imamizu, H. Neural substrates related to motor memory with multiple timescales in sensorimotor adaptation. PLoS Biol. 13, e1002312 (2015).
Herzfeld, D. J., Kojima, Y., Soetedjo, R. & Shadmehr, R. Encoding of action by the Purkinje cells of the cerebellum. Nature 526, 439–442 (2015).
Soetedjo, R., Kojima, Y. & Fuchs, A. F. Complex spike activity in the oculomotor vermis of the cerebellum: a vectorial error signal for saccade motor learning? J. Neurophysiol. 100, 1949–1966 (2008).
Yang, Y. & Lisberger, S. G. Role of plasticity at different sites across the time course of cerebellar motor learning. J. Neurosci. 34, 7077–7090 (2014).
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).
Avraham, G., Morehead, J. R., Kim, H. E. & Ivry, R. B. Re-exposure to a sensorimotor perturbation produces opposite effects on explicit and implicit learning processes. Preprint at bioRxiv https://doi.org/10.1101/2020.07.16.205609 (2020).
Suvrathan, A., Payne, H. L. & Raymond, J. L. Timing rules for synaptic plasticity matched to behavioral function. Neuron 92, 959–967 (2016).
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).
Albert, S. T. & Shadmehr, R. Estimating properties of the fast and slow adaptive processes during sensorimotor adaptation. J. Neurophysiol. 119, 1367–1393 (2018).
Acknowledgements
This work was supported by the National Institutes of Health (grant nos R01NS078311, R01NS096083 and F32NS095706), the National Science Foundation (grant no. CNS-1714623), the Cambridge Trust, the Rutherford Foundation and a travel grant from the Boehringer Ingelheim Fonds. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Additionally, we thank H. Fernandes and K. Kording for so graciously compiling and sharing their data with us. Finally, we recognize the Summer School in Computational Sensory-Motor Neuroscience (CoSMo) and its organizers (G. Blohm, K. Kording and P. Schrater) for giving us the opportunity to learn and develop the original idea for this work.
Author information
Authors and Affiliations
Contributions
All authors contributed to experiment design. S.T.A., J.J., H.R.S., L.T. and K.V. analysed data. S.T.A., J.J. and D.J.H. collected data. S.T.A. performed modelling. S.T.A., J.J., H.R.S., L.T., K.V., D.J.H. and R.S. wrote the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Peer review information Nature Human Behaviour thanks Masaki Abe and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Marike Schiffer.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Variance-dependent changes in error sensitivity are due to learning from error.
We applied our analysis in Fig. 4A to the numerator (a, learning from error) and denominator (b, error) of Eq. (8). For this analysis, we sorted pairs of movements into different bins according to the size of the error on the first movement. For each bin in a, we calculated the total change in reach angle between the trial pairs (discounted by the retention factor a as in Eq. (8)). For each bin in b, we calculated the mean error that occurred on the first trial in each pair. We performed these analyses separately for the zero-variance group (black) and high-variance group (red) in Experiments 1, 4 and 6 (experiments where the retention factor, a, was measured). For a and b, we used a mixed-ANOVA followed by post-hoc Bonferroni-corrected two-sample t-tests. We found a similar statistical pattern in both insets (left: learning from error, mixed-ANOVA, between-subjects effect of variance, F(1,84) = 13.7, P < 0.001, \(\eta _p^2\) = 0.14; post-hoc Bonferroni-corrected two-sample t-tests, t(71) = 3.77, P = 0.0011, d = 0.69, 95% CI = [0.54,2.3] for 5–14°; t(71) = 3.77, P = 0.001, d = 0.76, 95% CI = [0.9,3.38] for 14–22°; t(71) = 1.53, P = 0.45, d = 0.35, 95% CI = [−0.52,2.08] for 22–30°; right: error magnitude, mixed-ANOVA, between-subjects effect of variance, F(1,84) = 19.2, P < 0.001, \(\eta _p^2\) = 0.19; post-hoc Bonferroni-corrected two-sample t-tests, t(71) = 4.65, P < 0.001, d = 0.92, 95% CI = [0.23,0.63] for 5–14°; t(71) = 5.04, P < 0.001, d = 1.15, 95% CI = [0.37,0.81] for 14–22°; t(71) = 0.5, P = 1.0, d = 0.06, 95% CI = [−0.29,0.39] for 22–30°). Error bars are mean ± SEM.
Extended Data Fig. 2 Error sensitivity exhibits trial-by-trial decay.
a, Data were adapted from Robinson and colleagues3. Monkeys were adapted to a gain-down saccade perturbation. The error on each trial was fixed to −1° (top). Middle inset shows saccadic gain on each trial (black points). We fit the ‘decay’ and ‘no decay’ models to behaviour. Decay model is shown in blue. No decay model is shown in magenta. Time course of error sensitivity is shown at bottom. b, Data were adapted from Kojima and colleagues4. Monkeys adapted to a gain-up perturbation, followed by a gain-down perturbation, followed by a re-exposure to the gain-up perturbation. Paradigm is shown at top. Saccadic gain is shown in middle. Black and blue regression lines represent linear fit to first 150 trials during initial and re-exposure to the perturbation. Behaviour predicted by decay-free model shown in solid line at bottom. Dashed line is a copy of model prediction for Exposure 1 (provided for comparison). P1 refers to first gain-up perturbation. P2 refers to second gain-up perturbation. c, Data were adapted from Kojima and colleagues4. Monkeys adapted to a similar perturbation schedule as in a, only now gain-up perturbation periods were separated by a long washout period (top). Saccadic gain is shown in middle. Regression lines indicate the slope of a linear fit to the first 150 trials of initial exposure and re-exposure. The ‘zero-error’ period led to the loss of savings, as indicated by regression line slope. At bottom, we show the behaviour predicted by the ‘no decay’ model (solid magenta line). In addition, we simulated a ‘decay’ model, in which error sensitivity decayed during the zero-error period (shown in blue). d, We quantified the slope of adaptation in c by fitting a line to the behaviour of the ‘decay-free’ and ‘decay’ models over the periods labelled ‘i’, ‘ii’ and ‘iii’. At top, we show the percent change in slope from ‘i’ to ‘ii’ present in the actual data in b. At bottom, we show the percent change in slope from ‘i’ to ‘iii’ present in the actual data, the ‘decay’ model, and the ‘no decay’ model.
Extended Data Fig. 3 Total learning and residual error scale with perturbation magnitude.
Here we consider data adapted from Neville and Cressman9. Participants in an uninstructed condition were placed into 3 groups, each defined by perturbation magnitude. a, At left, we show response of the 20° rotation group. At middle, we show response of the 40° rotation group. At right, we show response of the 60° rotation group. b, We calculated the total amount of learning in each group over the last 10 perturbation epochs (black points). Next, we simulated the total learning predicted by Eq. (6) (Eq. (S1) in Supplementary Information) reproduced at top of inset. Model prediction is shown in blue line. c, We calculated the total residual error (perturbation minus total learning) over the last 10 epochs of the perturbation period (black points). Next, we simulated the total residual error predicted by Eq. (S3), reproduced at top of inset. Model prediction is shown in blue line. Error bars are mean ± SEM.
Extended Data Fig. 4 Error consistency modulates error sensitivity, irrespective of perturbation variance.
Here we report data adapted from Experiment 1 of Herzfeld et al. (2014)2. a, Participants were placed into 3 different groups: low-switch (z = 0.9), medium-switch (z = 0.5), high-switch (z = 0.1). In each group, perturbations followed a Markov chain shown at top. The +1 state indicates a 13 N-s/m force field perturbation. The −1 state indicates a −13 N-s/m force field perturbation. At the end of each 30-trial perturbation mini-block, retention was measured in probe trials (green) and learning from error was measured in a probe-perturbation-probe sequence (purple). b, By design, each group experienced the same set of perturbations irrespective of perturbation statistics. Here we show the standard deviation of the perturbation in each mini-block. c, We considered pairs of trials during the perturbation period. Here we show reach trajectories for example trial pairs. We separated pairs into consistent errors (left, when the direction of error repeated) and inconsistent errors (right, when direction of error switched). d, We calculated the probability of experiencing an inconsistent error in each group (red shows z = 0.9, green shows z = 0.5 and blue shows z = 0.1). Switch probability increased the fraction of inconsistent errors (ANOVA, F(24,2) = 336, P < 0.001, \(\eta _p^2\) = 0.97; post-hoc Bonferroni-corrected two-sample t-tests, t(16) = 20.28, P < 0.001, d = 9.56, 95% CI = [0.39,0.42] for low-medium; t(16) = 21.49, P < 0.001, d = 10.13, 95% CI = [0.61,0.74] for low-high; t(16) = 11.11, P < 0.001, d = 5.24, 95% CI = [0.24,0.35] for medium-high) e, At left, the error sensitivity measured in each group is shown as a function of mini-block (25 mini-blocks in total). At right, the change in error sensitivity from the baseline block to the last 5 three-trial probe sequences is shown. Error bars are mean ± SEM.
Supplementary information
Supplementary Information
Supplementary Results, Supplementary Methods, Supplementary Discussion, Supplementary Tables 1 and 2 and Supplementary References.
Source data
Source Data Fig. 1
We include all data from individual participants for Experiments 1–4, which are reported in Fig. 1.
Source Data Fig. 2
We include all data from individual participants for Experiments 5–7, which are reported in Fig. 2.
Rights and permissions
About this article
Cite this article
Albert, S.T., Jang, J., Sheahan, H.R. et al. An implicit memory of errors limits human sensorimotor adaptation. Nat Hum Behav 5, 920–934 (2021). https://doi.org/10.1038/s41562-020-01036-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41562-020-01036-x
This article is cited by
-
Large-scale citizen science reveals predictors of sensorimotor adaptation
Nature Human Behaviour (2024)
-
Optimism persists when walking in unpredictable environments
Scientific Reports (2023)
-
Error-related brain state analysis using electroencephalography in conjunction with functional near-infrared spectroscopy during a complex surgical motor task
Brain Informatics (2022)
