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Extending TransSet: An Individualized Model for Human Syllogistic Reasoning

Mr. Daniel Brand
University of Freiburg ~ Cognitive Computation Lab
Mr. Nicolas Oliver Riesterer
University of Freiburg ~ Cognitive Computation Lab
Prof. Marco Ragni
University Freiburg ~ University Freiburg

Recently, the TransSet model for human syllogistic reasoning was introduced and shown to outperform the previous state of the art in terms of predictive performance. In this article, we pick up the TransSet model and extend it to allow for capturing individual differences with respect to the conclusion "No Valid Conclusion" indicating that no logically correct conclusion can be derived from a problem's premises. Our evaluation is based on a coverage analysis in which a model's ability to capture individuals in terms of its parameters is assessed. We show that TransSet also outperforms state-of-the-art models on the basis of individuals and provide further evidence for the existence of an NVC aversion bias in human syllogistic reasoning.


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