Proceedings of the Annual Meeting of the Cognitive Science Society

Multiple cognitive theories make conflicting explainations forhuman reasoning on syllogistic problems. The evaluation andcomparison of these theories can be performed by conceiv-ing them as predictive models. Model evaluation often em-ploys static sets of predictions rather than full implementationsof the theories. However, most theories predict different re-sponses depending on the state of their internal parameters.Disregarding the theories’ capabilities to adapt parameters todifferent reasoners leads to an incomplete picture of their pre-dictive power. This article provides parameterized algorithmicformalizations and implementations of some syllogistic theo-ries regarding the syllogistic single-response task. Evaluationsreveal a substantial improvement for most cognitive theoriesbeing made adaptive over their original static predictions. Thebest performing implementations are PHM, mReasoner andVerbal Models, which almost reach the MFA benchmark. Theresults show that there exist heuristic and model-based theo-ries which are able to capture a large portion of the patterns insyllogistic reasoning data.