Friday, 28th March at 11am - Prof. Adam Sanborn

Title: Bayesian brains without probabilities

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, upending the longstanding consensus on these effects, and show how an extended model explains estimates, choices, response times, and confidence judgments.

Bio: Adam Sanborn is a Professor of Psychology at the University of Warwick. He gained his PhD in Psychological & Brain Sciences and Cognitive Science at Indiana University, and did his postdoctoral work at the Gatsby Computational Neuroscience Unit at UCL. Adam is interested in the rationality of human behaviour, which he studies with rational models, approximations to rational models, and behavioural experiments. His research has been published in leading psychology journals such as Psychological Review, Trends in Cognitive Sciences, and Psychological Science, and he has won best paper awards in both psychology and computer science. His work has been funded by the ESRC, ERC, Alan Turing Institute, and NIESR, and he serves as Associate Editor for the Journal of Experimental Psychology: Learning, Memory, and Cognition.