Preprint Article Version 1 This version is not peer-reviewed

Evaluating Approximations and Heuristic Measures of Integrated Information

Version 1 : Received: 6 April 2019 / Approved: 8 April 2019 / Online: 8 April 2019 (08:58:29 CEST)

How to cite: Sevenius Nilsen, A.; Juel, B.E.; Marshall, W.; Storm, J.F. Evaluating Approximations and Heuristic Measures of Integrated Information. Preprints 2019, 2019040077 (doi: 10.20944/preprints201904.0077.v1). Sevenius Nilsen, A.; Juel, B.E.; Marshall, W.; Storm, J.F. Evaluating Approximations and Heuristic Measures of Integrated Information. Preprints 2019, 2019040077 (doi: 10.20944/preprints201904.0077.v1).

Abstract

Integrated information theory (IIT) proposes a measure of integrated information (Φ) to capture the level of consciousness for a physical system in a given state. Unfortunately, calculating Φ itself is currently only possible for very small model systems, and far from computable for the kinds of systems typically associated with consciousness (brains). Here, we consider several proposed measures and computational approximations, some of which can be applied to larger systems, and test if they correlate well with Φ. While these measures and approximations capture intuitions underlying IIT and some have had success in practical applications, it has not been shown that they actually quantify the type of integrated information specified by the latest version of IIT. In this study, we evaluated these approximations and heuristic measures, based not on practical or clinical considerations, but rather based on how well they estimate the Φ values of model systems. To do this, we simulated networks consisting of 3–6 binary linear threshold nodes randomly connected with excitatory and inhibitory connections. For each system, we then constructed the system’s state transition probability matrix (TPM), as well as its state transition matrix (STM) over time for all possible initial states. From these matrices, we calculated, approximations to Φ, and measures based on state differentiation, state entropy, state uniqueness, and integrated information. All measures were correlated with Φ in a state dependent and state independent manner. Our findings suggest that Φ can be approximated closely in small binary systems by using one or more of the readily available approximations (r > 0.95), but without major reductions in computational demands. Furthermore, Φ correlated strongly with measures of signal complexity (LZ, rs = 0.722), decoder based integrated information (Φ*, rs = 0.816), and state differentiation (D1, rs = 0.827), on the system level (state independent). These measures could allow for efficient estimation of Φ on a group level, or as accurate predictors of low, but not high, Φ systems. While it’s uncertain whether the results extend to larger systems or systems with other dynamics, we stress the importance that measures aimed at being practical alternatives to Φ are at a minimum rigorously tested in an environment where the ground truth can be established.

Subject Areas

integrated information theory; differentiation; integration; complexity; consciousness; computational; IIT; Phi

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