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Implicit adaptation compensates for erratic explicit strategy in human motor learning

Nature Neuroscience volume 23, pages 443–455 (2020)Cite this article

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Abstract

Sports are replete with strategies, yet coaching lore often emphasizes ‘quieting the mind’, ‘trusting the body’ and ‘avoiding overthinking’ in referring to the importance of relying less on high-level explicit strategies in favor of low-level implicit motor learning. We investigated the interactions between explicit strategy and implicit motor adaptation by designing a sensorimotor learning paradigm that drives adaptive changes in some dimensions but not others. We find that strategy and implicit adaptation synergize in driven dimensions, but effectively cancel each other in undriven dimensions. Independent analyses—based on time lags, the correlational structure in the data and computational modeling—demonstrate that this cancellation occurs because implicit adaptation effectively compensates for noise in explicit strategy rather than the converse, acting to clean up the motor noise resulting from low-fidelity explicit strategy during motor learning. These results provide new insight into why implicit learning increasingly takes over from explicit strategy as skill learning proceeds.

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Fig. 1: Experimental setup.
Fig. 2: Learning curves and frequency space representations of implicit, strategic and combined learning.
Fig. 3: Strategy and implicit learning synergize at perturbation-driven frequencies, but cancel at perturbation-free frequencies.
Fig. 4: SEM analysis of implicit and strategic learning amplitudes shows that implicit learning responds to strategic learning.
Fig. 5: An analysis of temporal lags between implicit and strategic learning shows that implicit learning responds to strategic learning.
Fig. 6: Model of interactions between low-fidelity and high-fidelity learning processes reproduces key experimental results.

Data availability

The data generated and analyzed in the current study are available from the corresponding author upon reasonable request.

Code availability

All analysis code is available from the corresponding author upon reasonable request.

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Acknowledgements

The authors thanks A. Brennan and L. Alhussein for helpful discussions. This work was supported by the National Institutes of Health (NIH) grants R01 AG041878 and R01 NS105839 to M.A.S.

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Authors and Affiliations

  1. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Yohsuke R. Miyamoto & Maurice A. Smith

  2. Department of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China

    Shengxin Wang

  3. Center for Brain Science, Harvard University, Cambridge, MA, USA

    Maurice A. Smith

Authors
  1. Yohsuke R. Miyamoto
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  2. Shengxin Wang
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  3. Maurice A. Smith
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Contributions

Y.R.M., S.W. and M.A.S. designed the experiments. Y.R.M. and M.A.S. analyzed the data and wrote the paper.

Corresponding author

Correspondence to Maurice A. Smith.

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The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Joern Diedrichsen, Ned Jenkinson, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 The presence of time lag between adaptive responses is necessary to suppress the combined learning response at the perturbation-free frequencies in a two-process linear system.

Left panel: Error amplitude resulting from the combination of both processes as a function of the relative noise level between processes. Right panel: Correlation between time series of processes when the processes are shifted from each other by −1, 0, or 1 trial. Black line indicates Lag-0 correlation. Blue line indicates Lag-1 correlation, that is corr(x2(n-1), x1(n)). Red line indicates Lead-1 correlation, that is corr(x2(n + 1), vs x1(n)). Thus the blue line being below the black & red indicates that x2 lags x1.

Extended Data Fig. 2 Simulation of two processes using same noise levels between processes and independently distributed learning rate parameters across participants.

This simulation removes both the difference in noise levels and the across-participant learning rate anti-correlation from the simulation shown in Fig. 6 in the main paper. A_1=0.9, B_1=0.34+x_i, eps_1=1.75, A_2=0.9, B_2=0.34+y_i, eps_2=1.75, where x_i, y_i ~ Unif(−0.16, 0.16). Panels analogous to those in Fig. 6b, c in the manuscript. Upper left: simulated perturbation-driven learning curves for one example, simulated individual. Upper right: Histogram of simulated (n=69) within-participant correlations between perturbation-driven strategic and implicit learning curves. Lower left: analogous to upper left, but for perturbation-free learning curves. Lower right: analogous to upper right, but for perturbation-free learning curves. Please note that Supplementary Fig 1b on the following page presents panels analogous to those in Fig. 6d-f in the manuscript. Fig E1b. Simulation of two processes using same noise levels between processes and independently distributed learning rate parameters across participants (Like Supplementary Fig. 1a). This removes both the difference in noise levels and the across-participant learning rate anti-correlation from the simulation shown in Fig. 6 in the main paper. A_1=0.9, B_1=0.34+x_i, eps_1=1.75, A_2=0.9, B_2=0.34+y_i, eps_2=1.75, where x_i, y_i ~ Unif(−0.16, 0.16). Panels analogous to those in Fig. 6d–f in the manuscript. We performed 100 runs of the simulation, each with n=69. Top 2-by-2 panels: inter-individual relationships among perturbation-driven & perturbation-free strategic and implicit learning from one run of the simulation. Bottom left: log-likelihoods from the SEM analysis across 100 simulations (error bars indicate 95% CI, calculated across 100 simulations). Bottom right: histogram of lags from one run of the simulation. Text on histogram indicates the fraction of the 100 simulation runs for which the mean lag was greater than 0.

Extended Data Fig. 3 Simulation of two processes using same noise levels between processes and anti-correlated learning rate parameters across participants.

This simulation removes the difference in noise levels from the simulation shown in the main paper. A_1 = 0.9, B_1 = 0.34 + x_i, eps_1 = 1.75, A_2 = 0.9, B_2 = 0.34-x_i, eps_2 = 1.75, where x_i ~ Unif(−0.16, 0.16). Panels analogous to those of Supplementary Fig. 1b and Fig. 6d–f in the main paper.

Extended Data Fig. 4 Simulation of two processes using different noise levels between processes and independently distributed learning rate parameters across participants.

This simulation removes the across-participant learning rate anti-correlation from the simulation shown in the main paper. A_1 = 0.9, B_1 = 0.34 + x_i, eps_1 = 1.5, A_2 = 0.9, B_2 = 0.34 + y_i, eps_2 = 2, where x_i, y_i ~ Unif(−0.16, 0.16). Panels analogous to those of Supplementary Fig. 2, 1b, and Fig. 6d–f in the main paper.

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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). https://doi.org/10.1038/s41593-020-0600-3

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