阅读文献：
Körding, K. P., & Wolpert, D. M. (2004). Bayesian integration in sensorimotor learning. NATURE, 427(6971), 244-247. doi:10.1038/nature02169
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Abstract
1) According to bayesian theory, an optimal estimate results from combining information about the distribution of velocities—the prior—with evidence from sensory feedback.
2) As uncertainty increases, the system should increasingly rely on prior knowledge.
3) The central nervous system employs probabilistic models during sensorimotor learning.
Result 1:
An examination of the theoretically determined mean squared error for the three models shows that it is minimal for bayesian model (in the middle of Fig.1e), in which subjects could optimally use information about the prior distribution and the uncertainty of the visual feedback to estimate the lateral shift.
Result 2:
1) Slope that increases with increasing uncertainty (Fig. 2b) is compatible with bayesian model (Fig. 2a).
2) The bias and the slope should have a fixed relationship (Fig. 2c) so that subjects do bayesian estimation: they combine prior knowledge of the distribution with sensory evidence to generate appropriate compensatory movements.
3) The true prior (red line) was reliably learned by each subject (Fig. 2d).
Result 3:
From Fig 3, subjects can learn complex distributions (bimodal distribution in this experiment) and represent the bimodal prior, which means they can use bayesian statistics.
Conclusions
1) We show quantitatively that the system performs optimally would require a direct measure of sensory uncertainty before it is integrated with the prior.
2) By imposing experimentally controlled priors we have shown that our results qualitatively match a bayesian integration process, and we expect that such a bayesian process might be fundamental to all aspects of sensorimotor control and learning.