Human representation of visuo-motor uncertainty as mixtures of orthogonal basis distributions

Individuals must compensate for their motor uncertainty—that is, the discrepancy between intended movement and actual. Here, the authors measured the subjective error representation used in planning reaching movements and found that, while the objective motor error was uni-modal, near-Gaussian, subjective distributions were typically multimodal. This suggests a flexible strategy for computing with uncertainty across many different sorts of problems.