Tahra Eissa, Department of Applied Mathematics, University of Colorado Boulder
Normative decision asymmetries with symmetric priors but asymmetric evidence
Decisions based on rare events are challenging because rare events alone can be both informative and unreliable as evidence. How humans should and do overcome this challenge is not well understood. Here we present results from a preregistered study of 200 on-line participants performing a simple inference task in which the evidence was rare and asymmetric but the priors were symmetric. Consistent with a Bayesian ideal observer, most participants exhibited choice asymmetries that reflected a tendency to rationally interpret a rare event as evidence for the alternative likely to produce slightly more events, even when the two alternatives were equally likely a priori. A subset of participants exhibited additional biases based on an under-weighing of rare events. The results provide new quantitative and theoretically grounded insights into rare-event inference, which is relevant to both real-world problems like predicting stock-market crashes and common laboratory tasks like predicting changes in reward contingencies.