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A Computational Logic Approach to the Belief Bias in Human Syllogistic Reasoning

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Modeling and Using Context (CONTEXT 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10257))

Abstract

Psychological experiments on syllogistic reasoning have shown that participants did not always deduce the classical logically valid conclusions. In particular, the results show that they had difficulties to reason with syllogistic statements that contradicted their own beliefs. We consider a syllogistic reasoning task carried out by Evans, Barston and Pollard, who investigated the belief-bias effect with respect to syllogisms. We propose a formalization of the belief-bias effect for human syllogistic reasoning under the Weak Completion Semantics, a logic programming approach that aims at adequately modeling human reasoning.

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Notes

  1. 1.

    This implies that all abnormalities about \( mil \) or \( mil^{\prime } \) are false with respect to \(o_4\).

  2. 2.

    This implies that all abnormalities about \( add \) or \( add^\prime \) are false with respect to \(o_4\).

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Acknowledgements

Many thanks to Steffen Hölldobler and Luís Moniz Pereira for valuable feedback.

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Authors and Affiliations

  1. International Center for Computational Logic, TU Dresden, Dresden, Germany

    Emmanuelle-Anna Dietz

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  1. Emmanuelle-Anna Dietz
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Correspondence to Emmanuelle-Anna Dietz .

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Editors and Affiliations

  1. Pierre and Marie Curie University , Paris, France

    Patrick Brézillon

  2. University of Maine , Orono, Maine, USA

    Roy Turner

  3. University of Genoa , Genoa, Italy

    Carlo Penco

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Dietz, EA. (2017). A Computational Logic Approach to the Belief Bias in Human Syllogistic Reasoning. In: Brézillon, P., Turner, R., Penco, C. (eds) Modeling and Using Context. CONTEXT 2017. Lecture Notes in Computer Science(), vol 10257. Springer, Cham. https://doi.org/10.1007/978-3-319-57837-8_55

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  • DOI: https://doi.org/10.1007/978-3-319-57837-8_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57836-1

  • Online ISBN: 978-3-319-57837-8

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