Rational inference patterns

Abstract

Understanding, formalizing and modelling human reasoning is a core topic of artificial intelligence. In psychology, numerous fallacies and paradoxes have shown that classical logic is not a suitable logical framework for this. In a recent paper, Eichhorn, Kern-Isberner, and Ragni have succeeded in resolving paradoxes and modelling human reasoning consistently in a non-monotonic resp. conditional logic environment with so-called inference patterns. For further studies using inference patterns, however, it is mandatory to understand better how inference patterns are triggered by the characteristics of specific examples used in the empirical tests. The goal of this paper is to categorize empirical tasks by formal inference patterns and then find crucial features of the corresponding reasoning tasks in such a way that they can be used to predict the reasoning of human subjects according to the task. To this end a large amount of psychological studies dealing with human reasoning from the literature were investigated and classified according to the observed inference patterns. From this classification, we learnt a decision tree revealing which features of empirical tasks lead to which inference pattern in most cases. These results provide insights into the reasoning modes of humans which is important for choosing the right formal model, and help setting up proper tasks for testing inference patterns.