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International Studies in Phenomenology and Philosophy

Book | Chapter

181757

The information-theoretic and algorithmic approach to human, animal, and artificial cognition

Nicolas Gauvrit Hector Zenil Jesper Tegnér

pp. 117-139

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.

Publication details

Published in:

Dodig Crnkovic Gordana, Giovagnoli Raffaela (2017) Representation and reality in humans, other living organisms and intelligent machines. Dordrecht, Springer.

Pages: 117-139

DOI: 10.1007/978-3-319-43784-2_7

Full citation:

Gauvrit Nicolas, Zenil Hector, Tegnér Jesper (2017) „The information-theoretic and algorithmic approach to human, animal, and artificial cognition“, In: G. Dodig Crnkovic & R. Giovagnoli (eds.), Representation and reality in humans, other living organisms and intelligent machines, Dordrecht, Springer, 117–139.