AAAI Publications, The Thirty-Third International Flairs Conference

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An Adaptive Model for Cognitive Reasoning
Jonas Bischofberger, Marco Ragni

Last modified: 2020-05-08


How humans reason about syllogistic statements is a problem that currently lacks a comprehensive, universally accepted explanatory theory. The goal of this article is twofold: First, it sheds light on the actual predictive quality of existing theories by providing a standardized implementation of a subset of them. To that end, the theories are algorithmically formalized, including their capabilities for adaptation to an individual reasoner. The implementations are modular with regard to mental operations defined by the cognitive theories. Based on such operations, a novel composite approach is devised, resulting in a prediction model for predicting an individual reasoner before she draws the inference. It uses sequences of operations, selected from possibly different theories, to form its predictions. Among the basic models, our implementations of PHM, mReasoner and Verbal Models make the best predictions. The composite model is able to significantly surpass it by exploiting synergies between different models. Therefore, the composite approach is a promising tool to model and study syllogistic reasoning and possibly other reasoning tasks as well.

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