Fast and Accurate Learning When Making Discrete Numerical Estimates.

ABSTRACT: Many everyday estimation tasks have an inherently discrete nature, whether the task is counting objects (e.g., a number of paint buckets) or estimating discretized continuous variables (e.g., the number of paint buckets needed to paint a room). While Bayesian inference is often used for modeling estimates made along continuous scales, discrete numerical estimates have not received as much attention, despite their common everyday occurrence. Using two tasks, a numerosity task and an area estimation task, we invoke Bayesian decision theory to characterize how people learn discrete numerical distributions and make numerical estimates. Across three experiments with novel stimulus distributions we found that participants fell between two common decision functions for converting their uncertain

SUBMITTER: Sanborn AN 

PROVIDER: S-EPMC4829178 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature