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

TL;DR

This paper explores the intersection of information theory, computation, and cognition across humans, animals, and AI, proposing algorithmic measures to quantify and understand cognitive processes.

Contribution

It introduces novel algorithmic information-theoretic measures for analyzing cognition, linking computational universality with behavioral biases in a testable framework.

Findings

Turing test is computationally trivial, highlighting its limited diagnostic value.

Proposed measures account for known cognitive biases.

Algorithmic probability offers a universal, predictive model of cognition.

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.