ArticleProceedings of the National Academy of Sciences of the United States of America2016
Unified framework for information integration based on information geometry.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
- Explicit Geodesic Projection Distance on the Statistical Manifold of Multivariate Elliptical Distributions.Entropy (Basel, Switzerland) · 2026Article
- When Wholes Resist Decomposition: A Spectral Measure of Epistemic Emergence.Entropy (Basel, Switzerland) · 2026Article
- EENet-RLA: An Explainable Prediction Learning Framework for Alzheimer's Disease Classification from EEG Signals.Brain topography · 2026Article
- The Geometric Signatures of Brain State Transitions: Recursive Informational Curvature Reveals Hidden Dynamics in Primate Cortex.Neuroscience insights · 2026Article
- Toward a unified taxonomy of information dynamics via Integrated Information Decomposition.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Neural network embedding of functional microconnectome.Network neuroscience (Cambridge, Mass.) · 2025Article
- Decrease and recovery of integrated information Φ during anesthesia and sleep on human functional magnetic resonance imaging.Neuroscience of consciousness · 2025Article
- Meta-representations as representations of processes.Neuroscience of consciousness · 2025Article
- Upper bounds for integrated information.PLoS computational biology · 2024Article
- A synergistic workspace for human consciousness revealed by Integrated Information Decomposition.eLife · 2024Article
- Exploring complex and integrated information during sleep.Neuroscience of consciousness · 2024Article
- Exploring effects of anesthesia on complexity, differentiation, and integrated information in rat EEG.Neuroscience of consciousness · 2024Article
- Stochastic Dynamics of Fusion Low-to-High Confinement Mode (L-H) Transition: Correlation and Causal Analyses Using Information Geometry.Entropy (Basel, Switzerland) · 2023Article
- Information-Theoretic Approaches in EEG Correlates of Auditory Perceptual Awareness under Informational Masking.Biology · 2023Article
- Effects of Stochastic Noises on Limit-Cycle Oscillations and Power Losses in Fusion Plasmas and Information Geometry.Entropy (Basel, Switzerland) · 2023Article
- Bidirectionally connected cores in a mouse connectome: towards extracting the brain subnetworks essential for consciousness.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- The ambiguous feeling between "mine" and "not-mine" measured by integrated information theory during rubber hand illusion.Scientific reports · 2022Article
- Quantifying information of intracellular signaling: progress with machine learning.Reports on progress in physics. Physical Society (Great Britain) · 2022Review
- Thalamic deep brain stimulation paradigm to reduce consciousness: Cortico-striatal dynamics implicated in mechanisms of consciousness.PLoS computational biology · 2022Article
- Embedding entropy: a nonlinear measure of dynamical causality.Journal of the Royal Society, Interface · 2022Article
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3 authors.
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Abstract
Assessment of causal influences is a ubiquitous and important subject across diverse research fields. Drawn from consciousness studies, integrated information is a measure that defines integration as the degree of causal influences among elements. Whereas pairwise causal influences between elements can be quantified with existing methods, quantifying multiple influences among many elements poses two major mathematical difficulties. First, overestimation occurs due to interdependence among influences if each influence is separately quantified in a part-based manner and then simply summed over. Second, it is difficult to isolate causal influences while avoiding noncausal confounding influences. To resolve these difficulties, we propose a theoretical framework based on information geometry for the quantification of multiple causal influences with a holistic approach. We derive a measure of integrated information, which is geometrically interpreted as the divergence between the actual probability distribution of a system and an approximated probability distribution where causal influences among elements are statistically disconnected. This framework provides intuitive geometric interpretations harmonizing various information theoretic measures in a unified manner, including mutual information, transfer entropy, stochastic interaction, and integrated information, each of which is characterized by how causal influences are disconnected. In addition to the mathematical assessment of consciousness, our framework should help to analyze causal relationships in complex systems in a complete and hierarchical manner.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.