Abstract

Over the past few decades, waves of complex probabilistic explanations have swept through cognitive science, explaining behaviour as tuned to environmental statistics 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 rational brain paradoxically reason so poorly with probabilities? Perhaps our minds 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 using a finite number of samples systematically generates classic probabilistic reasoning errors in individuals, and how an extended model explains estimates, choices, response times, and confidence judgments in a variety of tasks.

Host: Dr Deborah Talmi (dt492@cam.ac.uk)