Measuring Integrated Information from the Decoding Perspective

Accumulating evidence indicates that the capacity to integrate information in the brain is a prerequisite for consciousness. Integrated Information Theory (IIT) of consciousness provides a mathematical approach to quantifying the information integrated in a system, called integrated information, Φ. Integrated information is defined theoretically as the amount of information a system generates as a whole, above and beyond the amount of information its parts independently generate. IIT predicts that the amount of integrated information in the brain should reflect levels of consciousness. Empirical evaluation of this theory requires computing integrated information from neural data acquired from experiments, although difficulties with using the original measure Φ precludes such computations. Although some practical measures have been previously proposed, we found that these measures fail to satisfy the theoretical requirements as a measure of integrated information. Measures of integrated information should satisfy the lower and upper bounds as follows: The lower bound of integrated information should be 0 and is equal to 0 when the system does not generate information (no information) or when the system comprises independent parts (no integration). The upper bound of integrated information is the amount of information generated by the whole system. Here we derive the novel practical measure Φ* by introducing a concept of mismatched decoding developed from information theory. We show that Φ* is properly bounded from below and above, as required, as a measure of integrated information. We derive the analytical expression of Φ* under the Gaussian assumption, which makes it readily applicable to experimental data. Our novel measure Φ* can generally be used as a measure of integrated information in research on consciousness, and also as a tool for network analysis on diverse areas of biology.Author Summary: Integrated Information Theory (IIT) of consciousness attracts scientists who investigate consciousness owing to its explanatory and predictive powers for understanding the neural properties of consciousness. IIT predicts that the levels of consciousness are related to the quantity of information integrated in the brain, which is called integrated information Φ. Integrated information measures excess information generated by a system as a whole above and beyond the amount of information independently generated by its parts. Although IIT predictions are indirectly supported by numerous experiments, validation is required through quantifying integrated information directly from experimental neural data. Practical difficulties account for the absence of direct, quantitative support. To resolve these difficulties, several practical measures of integrated information have been proposed. However, we found that these measures do not satisfy the theoretical requirements of integrated information: First, integrated information should not be below 0; and second, integrated information should not exceed the quantity of information generated by the whole system. Here, we propose a novel practical measure of integrated information, designated as Φ* that satisfies these theoretical requirements by introducing the concept of mismatched decoding developed from information theory. Φ* creates the possibility of empirical and quantitative validations of IIT to gain novel insights into the neural basis of consciousness.