Improved Measures of Integrated Information

• Published in: PLoS computational biology 2016-11, Vol.12 (11), p.e1005123-e1005123
• Main Author: Tegmark, Max
• Format: Article
• Language: English
• Subjects: Animals; Biological research; Biology and Life Sciences; Biology, Experimental; Brain - physiology; Brain Mapping - methods; Cognition - physiology; Computational biology; Computer and Information Sciences; Computer Simulation; Consciousness; Consciousness - physiology; Engineering and Technology; Humans; Information Storage and Retrieval - methods; Laboratories; Machine Learning; Methods; Models, Neurological; Neurosciences; Physical Sciences; Probability; Studies; Systems Integration; Theory
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1005123

Although there is growing interest in measuring integrated information in computational and cognitive systems, current methods for doing so in practice are computationally unfeasible. Existing and novel integration measures are investigated and classified by various desirable properties. A simple taxonomy of Φ-measures is presented where they are each characterized by their choice of factorization method (5 options), choice of probability distributions to compare (3 × 4 options) and choice of measure for comparing probability distributions (7 options). When requiring the Φ-measures to satisfy a minimum of attractive properties, these hundreds of options reduce to a mere handful, some of which turn out to be identical. Useful exact and approximate formulas are derived that can be applied to real-world data from laboratory experiments without posing unreasonable computational demands.