Proceedings of the Annual Meeting of the Cognitive Science Society

Throughout our lives, we are faced with a variety of causalreasoning problems. Arguably, the most successful models ofcausal reasoning, Causal Graphical Models (CGMs), performwell in some situations, but there is considerable variation inhow well they are able to account for data, both across scenar-ios and between individuals. We propose a model of causalreasoning based on quantum probability (QP) theory that ac-counts for behavior in situations where CGMs fail. WhetherQP or classical models are appropriate depends on the repre-sentation of events constructed by the reasoner. We describean experiment that suggests the representation of events canchange with experience to become more classical, and that therepresentation constructed can vary between individuals, in away that correlates with a simple measure of cognitive ability,The Cognitive Reflection Task.