Quantifying Spacetime Integration across a Partition with Synergy

2026-04-18

AI Summary

This study addresses the challenge of quantifying spatiotemporal cross-partition integration in Integrated Information Theory (IIT) by proposing four novel measures grounded in the partial information decomposition framework. Among these, the integration measure centered on synergy aligns more closely with IIT’s requirements for causal structure. The proposed approach not only serves as a general-purpose tool for assessing complexity but also demonstrates superior performance over existing IIT implementations in simple deterministic networks. These findings underscore its theoretical rigor and practical applicability, offering a promising avenue for advancing the formal and empirical foundations of consciousness research within the IIT paradigm.

Technology Category

Cognitive Modeling & Cognitive Systems: Neural Spike CodingMachine Learning: Information TheoryPlanning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsGraph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphsWeb Mining and Content Analysis: Content-based information diffusion

Abstract

In service to the mathematical underpinnings of the Information Integration Theory of Consciousness (IIT), we introduce four measures of integration based on the partial information decomposition framework. We compare our measures to current IIT practice in simple deterministic networks. We find synergy-based measures more suitable for IIT's use-case than current practice. Outside IIT, these measures could also be useful as non-IIT-related measures of complexity within discrete dynamical systems.

Problem

• Information Integration Theory
• spacetime integration
• synergy
• partial information decomposition
• consciousness

Innovation

• synergy
• partial information decomposition
• information integration theory
• discrete dynamical systems
• integration measures