RESEARCH ARTICLE


Spontaneous Causal Learning While Controlling A Dynamic System



York Hagmayer*, 1, Bjorn Meder2, Magda Osman3, Stefan Mangold1, David Lagnado4
1 Department of Psychology, University of Göttingen, Göttingen, Germany
2 Max Planck Institute for Human Development, Berlin, Germany
3 School of Biological and Chemical Sciences, Queen Mary University of London, UK
4 Division of Psychology and Language Sciences, University College London, UK


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Creative Commons License
© 2010 Hagmayer et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Psychology, University of Gottingen, Gosslerstrasse 14, 37073 Göttingen, Germany; Tel: +49(0)551-398293; Fax: +49(0)551-393656; E-mail: yhagmay@gwdg.de


Abstract

When dealing with a dynamic causal system people may employ a variety of different strategies. One of these strategies is causal learning, that is, learning about the causal structure and parameters of the system acted upon. In two experiments we examined whether people spontaneously induce a causal model when learning to control the state of an outcome value in a dynamic causal system. After the control task, we modified the causal structure of the environment and assessed decision makers' sensitivity to this manipulation. While purely instrumental knowledge does not support inferences given the new modified structure, causal knowledge does. The results showed that most participants learned the structure of the underlying causal system. However, participants acquired surprisingly little knowledge of the system's parameters when the causal processes that governed the system were not perceptually separated (Experiment 1). Knowledge improved considerably once processes were separated and feedback was made more transparent (Experiment 2). These findings indicate that even without instruction, causal learning is a favored strategy for interacting with and controlling a dynamic causal system.

Keywords: Decision making, dynamic systems, causal models, learning, induction.