# Multinomial Processing Models for Syllogistic Reasoning: A Comparison

- Hannah Dames, Cognitive Computation Lab, Albert-Ludwigs-Universität Freiburg, Freiburg, Baden-Württemberg, Germany
- Jan Ole von Hartz, Cognitive Computation Lab, Albert-Ludwigs-Universität Freiburg, Freiburg, Baden-Württemberg, Germany
- Mario Kantz, Cognitive Computation Lab, Albert-Ludwigs-Universität Freiburg, Freiburg, Baden-Württemberg, Germany
- Nicolas Riesterer, Cognitive Computation Lab, University of Freiburg, Freiburg, BW, Germany
- Marco Ragni, Cognitive Computation Lab, Albert-Ludwigs-Universität Freiburg, Freiburg, BW, Germany

**Abstract**To this day, a great variety of psychological theories of reasoning exist aimed at explaining the underlying cognitive mechanisms. The high number of different theories makes a rigorous comparison of cognitive theories necessary. The present article proposes to use Multinomial Processing Trees to compare two of the most prominent theories of syllogistic reasoning: the Mental Models Theory and the Probability Heuristics Model. For this, we reanalyzed data from a meta-analysis on six studies about syllogistic reasoning. We evaluate both models with respect to their overall fit to the data by means of G^2, AIC, BIC, and FIA, and on a parametric level. Our comparison indicates that a MMT-variant, though having more parameters, is slightly better on all criteria except of the BIC. Yet, none of the two models, realized as MPTs, is clearly superior. We outline the impact of the different theoretical principles and discuss implications for modeling syllogistic reasoning.