title: Improved Measures of Integrated Information.
abstract: 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.
authors.affiliation: Department of Physics & MIT Kavli Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.|Theiss Research, La Jolla, California, United States of America.
authors.forename: Max
authors.initials: M
authors.lastname: Tegmark
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003071:Cognition / Q000502:physiology*; D003198:Computer Simulation; D003243:Consciousness / Q000502:physiology*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D000069550:Machine Learning; D008959:Models, Neurological; D018511:Systems Integration