Inferences about unobserved causes in human contingency learning

Abstract

Estimates of the causal efficacy of an event need to take into account the possible presence and influence of other unobserved causes that might have contributed to the occurrence of the effect. Current theoretical approaches deal differently with this problem. Associative theories assume that at least one unobserved cause is always present. In contrast, causal Bayes net theories (including Power PC theory) hypothesize that unobserved causes may be present or absent. These theories generally assume independence of different causes of the same event, which greatly simplifies modelling learning and inference. In two experiments participants were requested to learn about the causal relation between a single cause and an effect by observing their co-occurrence (Experiment 1) or by actively intervening in the cause (Experiment 2). Participants' assumptions about the presence of an unobserved cause were assessed either after each learning trial or at the end of the learning phase. The results show an interesting dissociation. Whereas there was a tendency to assume interdependence of the causes in the online judgements during learning, the final judgements tended to be more in the direction of an independence assumption. Possible explanations and implications of these findings are discussed.

Acknowledgments

We thank Tom Beckers, Marc Buehner, and an anonymous reviewer for helpful comments on an earlier draft of this paper. We also thank M. Rappe for his help with planning and running Experiment 2. Portions of this research were presented at the Twenty-fifth Annual Conference of the Cognitive Science Society, Chicago, in 2004.

Notes

¹ We used the following formulae to derive P _gen (a|c) and P _gen (a|∼c) from the observed frequencies with which different patterns of events were predicted: