Optimality under fire: Dissociating learning from Bayesian integration

In both unisensory and multisensory tasks, human observers have repeatedly been shown to be optimal or near-optimal in their integration of multiple cues (Ernst & Banks, 2002; Körding et al., 2007).
Most of the research on cue integration has assumed that the noise in each cue follows a normal distribution, and thus that (a) the variance of the noise is a perfect indicator of the reliability of the cue, and (b) optimal integration is therefore achieved via a linear combination of the cues. However, little is known about how humans might integrate other noise distributions, e.g., those that may not be symmetric or unimodal, or which may require nonlinear cue combination. Here we ask if human observers are able to learn both lower-order and higher-order statistics (e.g., skewness) of non-normal distributions, and whether and how the acquired statistical features of such distributions affect cue integration.