Models of human probability judgment errors

Abstract

One of cognitive science’s core challenges is reconciling the success of probabilistic models in explaining human cognition with the observed fallacies in human probability judgments. This tutorial delves into models that address this discrepancy, shedding light on probabilistic fallacies. It encompasses earlier accounts like heuristics and averaging models, as well as contemporary, comprehensive models like quantum probability, the Probability Plus Noise model, and the Bayesian Sampler. The tutorial concludes by introducing the most recent accounts that integrate probability judgments with choice and response time, and highlighting ongoing challenges in the field.