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Joint German/Austrian Conference on Artificial Intelligence (Künstliche Intelligenz)

KI 2018: KI 2018: Advances in Artificial Intelligence pp 415–421Cite as

The Predictive Power of Heuristic Portfolios in Human Syllogistic Reasoning

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11117))

Abstract

A core method of cognitive science is to investigate cognition by approaching human behavior through model implementations. Recent literature has seen a surge of models which can broadly be classified into detailed theoretical accounts, and fast and frugal heuristics. Being based on simple but general computational principles, these heuristics produce results independent of assumed mental processes.

This paper investigates the potential of heuristic approaches in accounting for behavioral data by adopting a perspective focused on predictive precision. Multiple heuristic accounts are combined to create a portfolio, i.e., a meta-heuristic, capable of achieving state-of-the-art performance in prediction settings. The insights gained from analyzing the portfolio are discussed with respect to the general potential of heuristic approaches.

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Notes

  1. 1.

    https://github.com/nriesterer/syllogistic-portfolios.

  2. 2.

    https://www.mturk.com.

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Acknowledgements

This paper was supported by DFG grants RA 1934/3-1, RA 1934/2-1 and RA 1934/4-1 to MR.

Author information

Authors and Affiliations

  1. Cognitive Computation Lab, University of Freiburg, 79110, Freiburg, Germany

    Nicolas Riesterer & Marco Ragni

  2. Center for Cognitive Science, University of Freiburg, 79104, Freiburg, Germany

    Daniel Brand

Authors
  1. Nicolas Riesterer
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  2. Daniel Brand
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  3. Marco Ragni
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Corresponding author

Correspondence to Nicolas Riesterer .

Editor information

Editors and Affiliations

  1. TU Berlin, Berlin, Germany

    Frank Trollmann

  2. TU Dresden, Dresden, Germany

    Anni-Yasmin Turhan

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Riesterer, N., Brand, D., Ragni, M. (2018). The Predictive Power of Heuristic Portfolios in Human Syllogistic Reasoning. In: Trollmann, F., Turhan, AY. (eds) KI 2018: Advances in Artificial Intelligence. KI 2018. Lecture Notes in Computer Science(), vol 11117. Springer, Cham. https://doi.org/10.1007/978-3-030-00111-7_35

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  • DOI: https://doi.org/10.1007/978-3-030-00111-7_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00110-0

  • Online ISBN: 978-3-030-00111-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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