Mapping Spatial Uncertainty in Prediction

Abstract

Past research in a range of domains has suggested that individuals tend to underestimate the contribution of random factors in predicting the future and therefore have difficulties understanding variance. This study explored understanding of central tendency versus understanding of underlying variance in a novel spatial task. Participants were required to predict the most likely outcome of an object's somewhat uncertain movement, as well as estimate its probability of it being present at a number of potential locations. Our findings indicate that individuals became adept at predicting the target location, but they were relatively insensitive to the change variability in the underlying distribution and vastly overestimated the likelihood of samples around the mean. Additionally, there appeared to be no strong relationship between performance on prediction of the mean and performance on estimation of the likelihood. These findings highlight the need for interventions to improve human understanding of variability in order to facilitate prediction in real-world settings.