Proceedings of the Annual Meeting of the Cognitive Science Society

Probability judgments appear to violate basic axioms of probability theory, which seems to contradict with the recent successes of Bayesian models of cognition. To explain these violations, we propose the Quantum Sequential Sampler model, which combines quantum probability for explaining conjunction and disjunction fallacies, and a sequential sampling model that maps subjective quantum probabilities into responses. Our model explains probability judgments by a dynamical process, and achieves state-of-the-art performance in the biggest dataset for probability judgments to-date. Comparing with existing Bayesian models, our model predicts both probability judgments and violations of probability identities better.