A novel practical measure called Φ* quantifies integrated information in the brain, a property predicted by Integrated Information Theory (IIT) to reflect levels of consciousness. Earlier measures failed to satisfy theoretical lower and upper bounds: zero when no information is generated or when parts are independent, and the total information generated by the whole system. By applying mismatched decoding from information theory, Φ* meets these bounds. Under a Gaussian assumption, Φ* has an analytical expression applicable to experimental neural data. Φ* can serve as a measure of integrated information in consciousness research and as a tool for network analysis in biology.
Integrated Information Theory (IIT) proposes that a brain's capacity to integrate information is essential for consciousness and that a measure called integrated information, Φ, should reflect levels of consciousness. Practical application has been hindered because existing measures fail to satisfy theoretical lower and upper bounds: zero when the system generates no information or consists of independent parts, and equal to the whole system's information when its parts generate no information independently. The authors derive a new practical measure, Φ*, using mismatched decoding from information theory. This measure satisfies the required bounds. They also provide an analytical expression for Φ* under a Gaussian assumption, making it applicable to experimental neural data.