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Extending TransSet: An Individualized Model for Human Syllogistic Reasoning

Mr. Daniel Brand
Chemnitz University of Technology ~ Predictive Analytics
Mr. Nicolas Riesterer
F. Hoffmann - La Roche
Marco Ragni
TU Chemnitz ~ Behavioral and Social Sciences

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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Cite this as:

Brand, D., Riesterer, N. O., & Ragni, M. (2020, July). Extending TransSet: An Individualized Model for Human Syllogistic Reasoning. Paper presented at Virtual MathPsych/ICCM 2020. Via