Proceedings of the Annual Meeting of the Cognitive Science Society

Human probability judgements appear systematically biased, in apparent tension with Bayesian models of cognition. Butperhaps the brain does not represent probabilities explicitly, but approximates probabilistic calculations through a processof sampling, as used in computational probabilistic models in statistics. The Bayesian sampling viewpoint provides asimple rational model of probability judgements, which generates biases such as conservatism. The Bayesian samplerprovides a single framework for explaining phenomena associated with diverse biases and heuristics, including availabilityand representativeness. The approach turns out to provide a rational reinterpretation of noise in an important recent modelof probability judgement, the probability theory plus noise model (Costello & Watts, 2014; 2016; 2017; Costello, Watts,& Fisher, 2018), and captures the empirical data supporting this model.