Consciousness as Uncommon Self-Knowledge: A Synergistic Information Framework

TL;DR

This paper introduces a formal framework for consciousness based on synergistic self-knowledge, proposing it as a distinctive signature that differentiates conscious processing from other cognitive functions.

Contribution

It presents a novel information-theoretic approach to identify consciousness through synergistic information, offering clear distinctions from metacognition and resolving existing theoretical challenges.

Findings

Synergistic information correlates with conscious processing.

Proposes empirical tests involving timing dissociations in GWT.

Aligns with findings on reduced synergy in anesthesia and Alzheimer's.

Abstract

We propose uncommon self-knowledge (USK) as a candidate criterion for consciousness: synergistic information a system carries about itself that exists only in the joint of its subsystems and is destroyed by decomposition. Drawing on Gottwald's partition-lattice grounding of Partial Information Decomposition (PID), where redundancy corresponds to Aumann's common knowledge and synergy to the gap between separate and joint observation, we propose the synergistic component of self-directed information as a candidate formal signature for conscious processing. If correct, the framework would (1) offer a clean separation between consciousness and metacognition (synergistic vs. redundant self-knowledge), (2) provide principled resolutions to counterexamples that challenge IIT, GWT, and HOT, (3) be operationalizable via Partial Information Rate Decomposition (PIRD) with self-targeting, and (4) generate distinctive empirical predictions, the strongest being a GWT timing dissociation (consciousness correlates with pre-broadcast synergy formation, not broadcast itself) and a specific dissociation between self-report disruption and task-performance disruption under middle-layer perturbation in LLMs. The proposal is consistent with recent empirical findings that both anaesthesia and Alzheimer's disease specifically reduce synergistic information processing while preserving or increasing redundancy.