Abstract
Cognitive models for human reasoning are often specialized and domain-specific. So the question whether human reasoning across domains shares the same (or at least a similar) mental representation and inference mechanism is still an unexplored territory, as is the endeavor to create cognitive computational models for multiple domains of human reasoning. In this paper, we consider the theory of mental models for conditionals as a test-case and aim to extend it towards syllogistic reasoning using a formal translation. The performance of this new cross-domain theory is comparable to the performance of state-of-the-art domain-specific theories. Potentials and limitations are discussed.
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Notes
- 1.
In the original model by Oberauer [12], the parameter \(1 - r\) describes the probability that an individual will not reason, but guess. In our implementation of the model we do not use guessing.
- 2.
As discussed by Johnson-Laird, no iconic model can show that it represents an entire set, we have no way of knowing whether a model describes the whole set, or just a small number of entities that belong to it [5].
- 3.
From now on we use a compressed version of the models representing sets of the same entity, i.e., one unique entity only, as we do not consider quantifiers like “Most”.
- 4.
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Acknowledgments
This paper was supported by DFG grants RA 1934/3-1, RA 1934/2-1 and RA 1934/4-1 to MR. We are also grateful to Lukas Elflein for helpful comments.
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Todorovikj, S., Friemann, P., Ragni, M. (2019). A Cross-Domain Theory of Mental Models. In: Ohara, K., Bai, Q. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2019. Lecture Notes in Computer Science(), vol 11669. Springer, Cham. https://doi.org/10.1007/978-3-030-30639-7_16
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