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Mixed observation favors motor learning through better estimation of the model’s performance

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Abstract

Observation contributes to motor learning. It was recently demonstrated that the observation of both a novice and an expert model (mixed observation) resulted in better learning of a complex spatio-temporal task than the observation of either a novice or an expert model alone. In the present study, we sought to determine whether the advantage of mixed observation resulted from the development of a better error detection mechanism. The results revealed that mixed observation resulted in a better estimation of the model’s performance than that with other regimens of observation. The results also suggest that observational learning is improved when observation with knowledge of the results (KR) is followed by an observation phase without KR.

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Notes

  1. Specifically, the data were analyzed by an ANOVA contrasting five groups: control group in experiment 1 (n = 16), control group in experiment 2 (n = 16), observers of novice and expert models in experiment 1 (n = 32), CP + E and C − PE groups in experiment 2 (n = 32), and the mixed observation group in experiment 1 (n = 16). The ANOVA revealed a significant main effect of group, F (4, 107) = 29.14, p < .001, η 2p  = .52. The results of the post hoc comparisons can be summarized as follows: control experiment 1 = control experiment 2 (p = 0.95) > C + PE/C − PE (p < 0.001) = O2N/O2E (p = 0.32) > MO (p < 0.05).

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Acknowledgments

This work was supported by a Discovery Grant (L.P.) provided by the Natural Sciences and Engineering Research Council of Canada.

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Correspondence to Luc Proteau.

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Andrieux, M., Proteau, L. Mixed observation favors motor learning through better estimation of the model’s performance. Exp Brain Res 232, 3121–3132 (2014). https://doi.org/10.1007/s00221-014-4000-3

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