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Staying afloat on Neurath's boat: Heuristics for sequential causal learning

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Bramley, N., Dayan, P., & Lagnado, D. (2015). Staying afloat on Neurath's boat: Heuristics for sequential causal learning. In D. Noelle, R. Dale, A. Warlaumont, J. Yoshimi, T. Matlock, C. Jennings, et al. (Eds.), 37th Annual Meeting of the Cognitive Science Society (CogSci 2015): Mind, Technology and Society (pp. 262-267). Austin, TX, USA: Cognitive Science Society.


Cite as: http://hdl.handle.net/21.11116/0000-0004-BFA3-D
Abstract
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.