The generalization of prior uncertainty during reaching

Hugo L. Fernandes, Ian H. Stevenson, Iris Vilares, Konrad P. Kording

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


Bayesian statistics defines how new information, given by a likelihood, should be combined with previously acquired information, given by a prior distribution. Many experiments have shown that humans make use of such priors in cognitive, perceptual, and motor tasks, but where do priors come from? As people never experience the same situation twice, they can only construct priors by generalizing from similar past experiences. Here we examine the generalization of priors over stochastic visuomotor perturbations in reaching experiments. In particular, we look into how the first two moments of the prior-the mean and variance (uncertainty)-generalize. We find that uncertainty appears to generalize differently from the mean of the prior, and an interesting asymmetry arises when the mean and the uncertainty are manipulated simultaneously.

Original languageEnglish
Pages (from-to)11470-11484
Number of pages15
JournalJournal Of Neuroscience Methods
Issue number34
Publication statusPublished - 20 Aug 2014


  • Bayesian
  • Generalization
  • Prior
  • Uncertainty
  • Visuomotor rotation


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