Scaling Uncertainty in Visual Perception and Estimation Tasks

Abstract

Demographic perception—the perception of social quantities of geopolitical scale and social significance—has been extensivelystudied in cognitive and political science (Citrin & Sides, 2008; Gilens, 2001; Herda, 2013). Regular patterns of over- and under-estimation emerge. Americans greatly overestimate, for instance, the proportion of citizens that identify as gay or Muslim, while underestimating those that are Christian. While these errors have been attributed to social factors such as fear of specific minorities (Gallagher, 2003; Wong, 2007), other work has suggested that these patterns result from the psychophysics of the perception of proportions (Landy, Guay & Marghetis 2018). A Bayesian formulation suggests that biases in the estimation of both social proportions and simple visual properties result from a common source: ‘hedging’ uncertain information toward a prior. Here we present a novel lab paradigm and two experiments that manipulate uncertainty in a simple (dot estimation) task, verifying the core assumptions of the Bayesian approach.