On the non-uniqueness problem in integrated information theory.
Jake R. Hanson, Sara I. Walker
Neuroscience of Consciousness 2023 DOI: 10.1093/nc/niad014 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Keywords | Computational neuroscience Consciousness Non-uniqueness |
| Key points | Argues that the measure Φ in Integrated Information Theory 3.0 is not well-defined because it yields non-unique values, including both zero and positive values for the same system, rendering consciousness attributions non-decidable. |
Abstract
Integrated Information Theory (IIT) 3.0 is among the leading theories of consciousness in contemporary neuroscience. The core of the theory relies on the calculation of a scalar mathematical measure of consciousness, Φ, which is inspired by the phenomenological axioms of the theory. Here, we show that despite its widespread application, Φ is not a well-defined mathematical concept in the sense that the value it specifies is non-unique. To demonstrate this, we introduce an algorithm that calculates all possible Φ values for a given system in strict accordance with the mathematical definition from the theory. We show that, to date, all published Φ values under consideration are selected arbitrarily from a multitude of equally valid alternatives. Crucially, both [Formula: see text] and [Formula: see text] are often predicted simultaneously, rendering any interpretation of these systems as conscious or not, non-decidable in the current formulation of IIT.