Preferred Inferences in Causal Relational Reasoning: Counting Model Operations

Abstract

Interpreting causal relations plays an important role in every- day life, for example in scientific inquiries and text comprehension. Errors in causal reasoning can be a recipe for disaster. Despite vast literature on the psychology of human causal reasoning, there are few investigations into preferred inferences in relational three-term problems. Based on a previous formal investigation about relevant causal relations we develop a cognitive modeling approach with mental models. The key principle for this approach proves to be the prediction of preferred inferences by model operations and the process of sub model integration. Subsequent experiments test preferred inferences, the number of model operations, and if concrete or generic problems make a difference in causal reasoning performance. Implications of the model are discussed.


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