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Representation and Reality in Humans, Other Living Organisms and Intelligent Machines pp 117–139Cite as

The Information-Theoretic and Algorithmic Approach to Human, Animal, and Artificial Cognition

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Part of the book series: Studies in Applied Philosophy, Epistemology and Rational Ethics ((SAPERE,volume 28))

Abstract

We survey concepts at the frontier of research connecting artificial, animal, and human cognition to computation and information processing—from the Turing test to Searle’s Chinese room argument, from integrated information theory to computational and algorithmic complexity. We start by arguing that passing the Turing test is a trivial computational problem and that its pragmatic difficulty sheds light on the computational nature of the human mind more than it does on the challenge of artificial intelligence. We then review our proposed algorithmic information-theoretic measures for quantifying and characterizing cognition in various forms. These are capable of accounting for known biases in human behavior, thus vindicating a computational algorithmic view of cognition as first suggested by Turing, but this time rooted in the concept of algorithmic probability, which in turn is based on computational universality while being independent of computational model, and which has the virtue of being predictive and testable as a model theory of cognitive behavior.

Nicolas Gauvrit and Hector Zenil authors contributed equally

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Notes

  1. 1.

    See, e.g. http://www.scottaaronson.com/blog/?p=1799, as accessed on December 23, 2015, where Tononi himself provided acceptable, even if not definite, answers.

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Acknowledgements

The authors are indebted to the anonymous referees and to the hard work of the members of the Algorithmic Nature Group, LABORES (http://www.algorithmicnaturelab.org).

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Authors and Affiliations

  1. Human and Artificial Cognition Lab, Université Paris 8 & EPHE, Paris, France

    Nicolas Gauvrit

  2. Unit of Computational Medicine, Department of Medicine Solna, Centre for Molecular Medicine & SciLifeLab, Karolinska Institutet, Stockholm, Sweden

    Hector Zenil & Jesper Tegnér

  3. Department of Computer Science, University of Oxford, Oxford, UK

    Hector Zenil

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  1. Nicolas Gauvrit
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Correspondence to Hector Zenil .

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Editors and Affiliations

  1. Gothenburg, Sweden

    Gordana Dodig-Crnkovic

  2. Faculty of Philosophy, Pontifical Lateran University, Vatican City, Italy

    Raffaela Giovagnoli

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Gauvrit, N., Zenil, H., Tegnér, J. (2017). The Information-Theoretic and Algorithmic Approach to Human, Animal, and Artificial Cognition. In: Dodig-Crnkovic, G., Giovagnoli, R. (eds) Representation and Reality in Humans, Other Living Organisms and Intelligent Machines. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-43784-2_7

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