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

Nicolas Gauvrit

Hector Zenil

Jesper Tegnér

pp. 117-139

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

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.