Bayesian learning and reasoning

Everyday learning and reasoning challenges us to make inferences and decisions given limited evidence, time and resources, and under constantly changing environmental conditions. Bayesian inference is a widely used method in cognitive science for modelling learning and reasoning by capturing the probabilistic relations between incoming data and prior knowledge structures and the way they should be combined to generate better predictions. These predictions are increasingly often characterised by proponents of the Bayesian Brain Hypothesis on the subpersonal level of cognition as the ‘brain’s guesses’ about the world. However, this characterisation leaves many open questions to be addressed. For example, how exactly do these guesses interact and integrate with existing knowledge to form novel predictions? In what sense can changes in these predictions over time be considered optimal solutions to everyday learning and reasoning problems? Finally, how does this way of looking at cognition stand to more traditional philosophical and psychological approaches to learning and reasoning? The aim of this 2-day international workshop is to foster a greater understanding of how Bayesian models can support cognitive scientists to form new explanations of the interactions between knowledge, learning, and reasoning under uncertainty. 

Program committee

Ludmila Reimer

Peter Brössel

Matthis Hesse

Bartosz Radomski

Wanja Wiese