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The dissociable effects of punishment and reward on motor learning

Abstract

A common assumption regarding error-based motor learning (motor adaptation) in humans is that its underlying mechanism is automatic and insensitive to reward- or punishment-based feedback. Contrary to this hypothesis, we show in a double dissociation that the two have independent effects on the learning and retention components of motor adaptation. Negative feedback, whether graded or binary, accelerated learning. While it was not necessary for the negative feedback to be coupled to monetary loss, it had to be clearly related to the actual performance on the preceding movement. Positive feedback did not speed up learning, but it increased retention of the motor memory when performance feedback was withdrawn. These findings reinforce the view that independent mechanisms underpin learning and retention in motor adaptation, reject the assumption that motor adaptation is independent of motivational feedback, and raise new questions regarding the neural basis of negative and positive motivational feedback in motor learning.

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Figure 1: Experimental design.
Figure 2: Punishment led to faster learning, while reward caused greater retention during motor adaptation.
Figure 3: Replication of the double dissociation between reward and punishment using a one-target design.
Figure 4: Direct negative feedback related to poor performance is the critical factor that increases learning rate.

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Acknowledgements

This work was supported by a Birmingham Fellow research fellowship (J.M.G.). We thank P. Celnik for comments on a previous version of the manuscript.

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Contributions

J.M.G. designed experiment 1. J.M.G. and J.D. designed experiment 2. J.M.G. and E.M. performed research. J.M.G., E.M. and J.D. analyzed data. J.M.G., E.M., J.R. and J.D. wrote the paper.

Corresponding author

Correspondence to Joseph M Galea.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Single-rate state-space model (SSM) prediction across experiment 2.

(a) Average angular reach direction (º) in Experiment 2 for random positive (blue), reward (red), punishment (black) groups. Data is first averaged over Epoch (average across 8 trials), then across participants. Dashed/solid vertical lines indicate short rest periods between blocks (<1minute). (b) Predicted angular reach direction from the SSM fitted separately to each experimental phase (separated by solid vertical lines). Although epoch data is shown, the model was estimated using trial-by-trial reach direction data. Note that washout was estimated simply for presentation purposes. Solid lines = mean, shaded areas = SEM.

Supplementary Figure 2 A fixed generalization function leads to a slower rate of learning across groups

The SSM model assumed that learning did not generalise between the 8-target positions used in experiment 2. Yet, previous work has suggested that generalisation can take place between targets that are 45° apart. Therefore, we assumed a fixed generalisation function of: 0° = 1, 45° = 0.44, >90° = 0.2. This generalisation function was then used to estimate the learning rate for each phase/participant as before. As expected, the inclusion of generalisation led to substantially lower estimates of the learning rate. However similarly to the original model, punishment (black) led to significantly faster learning (SSM parameter B) compared to either the reward (p = 0.032; red) or random positive (p = 0.030; blue) group (F(2,41) = 4.65, p = 0.016). Mean ± SEM. *p<0.05.

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Galea, J., Mallia, E., Rothwell, J. et al. The dissociable effects of punishment and reward on motor learning. Nat Neurosci 18, 597–602 (2015). https://doi.org/10.1038/nn.3956

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