Proceedings of the Annual Meeting of the Cognitive Science Society

This paper describes and evaluates a computational model of anomalous data integration. This model makes use of three factors: entrenchment of the current theory (the amount of data explained), the relative probability of the contradictory explanations (based on conditional probabilities as part of the domain-knowledge), and the availability of alternative explanations based on learning. In an experimental study we found that the enu-enchment of a theory and the availability and likelihood of an alternative explanation influenced solution speed and the correctness of inferred causal explanations. However, in detail, the single levels of both factors were not cleariy distinguishable and did not follow the predictions. These findings suggest that entrenchment itself is not a major factor in determining the difficulty of a task. Instead, we hypothesize that task difficulty is dominated by a person's ability to construct an alternative explanation of a given situation, a factor that is only indirectly related to entrenchment.