Bayesian brains without probabilities

Over the past two decades, a wave of Bayesian explanations has swept through cognitive science, explaining behavior in domains from intuitive physics and causal learning, to perception, motor control and language. Yet people produce stunningly incorrect answers in response to even the simplest questions about probabilities. How can a supposedly Bayesian brain paradoxically reason so poorly with probabilities? Perhaps Bayesian brains do not represent or calculate probabilities at all and are, indeed, poorly adapted to do so. Instead the brain could be approximating Bayesian inference through sampling: drawing samples from its distribution of likely hypotheses over time. Only with infinite samples does a Bayesian sampler conform to the laws of probability, and in this talk I show how reasoning with a finite number of samples systematically generates classic probabilistic reasoning errors in individuals, upending the longstanding consensus on these effects. I then discuss how we might determine what kind of sampling algorithm the brain is using, comparing algorithms in their ability to deal with complex probability distributions and to their match with the known characteristics of mental samples