Calibrating Uncertainty: Commonalities in the Estimation of Numeric Variability Versus Spatial Prediction

Abstract

To assess whether there may be a common ability related to the understanding and calibration of instance variability and mean behavior, participants performed spatial prediction and numeric estimation tasks. In the first task, participants experienced variability in a set of spatial trajectories whose endpoints they predicted along with a central mean. In the second task, they experienced variability in a set of random numbers whose mean and variability they estimated. For both tasks, estimated variability was compared with the true variability of instances to derive measures of bias (e.g., over-or under-estimation) and precision. Correlations between these estimates across the two experiments revealed mixed evidence for a common ability to estimate variability, but suggested similar performance when estimating mean behavior. Implications for individual differences and interventions are discussed.