Figure 1: Learning and inference in a spatial localization task.

(A) An illustration of a typical trial. Participants estimated the location of a ‘hidden’ target, randomly sampled on each trial from a mixture of two underlying Gaussian distributions. Upon touching a ‘GO’ button, they were presented with uncertain sensory information in the form of a dot cluster, centered on the target location, and subject to one of three levels of variability (low variability shown here; see inset for an illustration of the three levels). Feedback was provided post-touch. (B) An illustration of Bayes-optimal behavior. Considering the example of a ‘hidden’ target drawn from the more variable underlying distribution, the ideal observer would estimate the target location by learning the mean and variance of the prior (the task-relevant underlying distribution) and integrating this knowledge with the likelihood (the sensory information).