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

• Published in: Nature neuroscience Vol. 18; no. 8; pp. 1152 - 1158
• Main Authors: Zhang, Hang, Daw, Nathaniel D, Maloney, Laurence T
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.08.2015; Nature Publishing Group
• Subjects: 631/378/116/2392; 631/378/1595; 631/378/2632; Adolescent; Adult; Animal Genetics and Genomics; Bayes Theorem; Behavioral Sciences; Biological Techniques; Biomedicine; Brain mapping; Brain research; Choice Behavior - physiology; Decision making; Decision Making - physiology; Design of experiments; Eye-hand coordination; Female; Human mechanics; Human subjects; Humans; Male; Neurobiology; Neurosciences; Normal Distribution; Observations; Probability; Psychomotor Performance - physiology; Statistical Distributions; Uncertainty; Young Adult; Beijing China; New York; United States > US; China
• ISSN: 1097-6256; 1546-1726
• DOI: 10.1038/nn.4055

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. In many laboratory visuo-motor decision tasks, subjects compensate for their own visuo-motor error, earning close to the maximum reward possible. To do so, they must combine information about the distribution of possible error with values associated with different movement outcomes. The optimal solution is a potentially difficult computation that presupposes knowledge of the probability density function (pdf) of visuo-motor error associated with each possible planned movement. It is unclear how the brain represents such pdfs or computes with them. In three experiments, we used a forced-choice method to reveal subjects' internal representations of their spatial visuo-motor error in a speeded reaching movement. Although subjects' objective distributions were unimodal, close to Gaussian, their estimated internal pdfs were typically multimodal and were better described as mixtures of a small number of distributions differing only in location and scale. Mixtures of a small number of uniform distributions outperformed other mixture distributions, including mixtures of Gaussians.