%X Causal models are key to flexible and efficient exploitation of the environment. However, learning causal structure is hard, with massive spaces of possible models, hard-to-compute marginals and the need to integrate diverse evidence over many instances. We report on two experiments in which participants learnt about probabilistic causal systems involving three and
four variables from sequences of interventions. Participants were broadly successful, albeit exhibiting sequential dependence and floundering under high background noise. We capture their behavior with a simple model, based on the “Neurath’s ship” metaphor for scientific progress, that neither maintains a probability distribution, nor computes exact likelihoods.
%C Pasadena, California, United States
%L discovery1501877
%J CogSci
%I Cognitive Science Society
%S Cognitive Science Society -  Annual Meeting
%D 2015
%B Proceedings of Annual Meeting of the Cognitive Science Society (CogSci 2015): Mind, Technology, and Society
%A N Bramley
%A P Dayan
%A DA Lagnado
%V 37
%E DC Noelle
%E R Dale
%E AS Warlaumont
%E J Yoshimi
%E T Matlock
%E CD Jennings
%E PP Maglio
%T Staying afloat on Neurath's boat - Heuristics for sequential causal learning