Mind–body problem Neural correlates of consciousness Phenomenology (philosophy) Phenomenology (psychology) Philosophy of mind Qualia Sentience Albantakis, L; Integrated information theory (IIT) proposes a mathematical model for the consciousness of a system. It comprises a framework ultimately intended to explain why some physical systems (such as human brains) are conscious, and to be capable of providing a concrete inference about whether any physical system is conscious, to what degree, and what particular experience it has; why they feel the partic Intrinsicality – experience is intrinsic: it exists for itself Information – experience is specific: it is this… In… IIT is grounded in: Realism – something exists, and persists, independently of one's own experience (a better hypothesis than solipsism) Operational physicalism – what exists is assessed operationally, by observing and manipulating a substrate's units to establish that they can reliably "take and make a difference"; this cause–effect power is the signature of physical existence Operational reductionism ("atomism") – what exists physically should ideally be accounted for in terms of the smallest units that can be observed and manipulated, so that cause–effect power holds "all the way down" to the atomic units === Axioms and postulates === Starting from the zeroth axiom (experience exists), IIT identifies five essential properties of experience: Intrinsicality – experience is intrinsic: it exists for itself Information – experience is specific: it is this one Integration – experience is unitary: it is a whole, irreducible to separate experiences Exclusion – experience is definite: it is this whole Composition – experience is structured: it is composed of distinctions and the relations that bind them together, yielding a phenomenal structure that feels the way it feels Each axiom is formulated as a corresponding physical postulate that the substrate of consciousness must satisfy, expressed in terms of cause–effect power: Intrinsicality – its cause–effect power must be intrinsic: it must take and make a difference within itself Information – its cause–effect power must be specific: it must be in this state and select this cause–effect state, the one with maximal intrinsic information (ii) Integration – its cause–effect power must be unitary: it must specify its cause–effect state as a whole set of units, irreducible to separate subsets of units; irreducibility is measured by integrated information (φs) over the substrate's minimum partition Exclusion – its cause–effect power must be definite: it must specify its cause–effect state as this whole set of units, namely the set that is maximally irreducible (maximum φs, φ*), called a maximal substrate or complex Composition – its cause–effect power must be structured: subsets of its units must specify cause–effect states over subsets of units (distinctions) that can overlap with one another (relations), yielding a cause–effect structure or Φ-structure === Mathematical formalism === A system is described by its transition probability matrix (TPM), denoted T U = p ( u ′ ∣ u ) {\displaystyle T_{U}=p(\mathbf {u} '\mid \mathbf {u} )} , over all its possible states. A significant computational challenge in calculating integrated information is finding the minimum information partition of a neural system, which requires iterating through all possible network partitions. To solve this problem, Daniel Toker and Friedrich T. Sommer have shown that the spectral decomposition of the correlation matrix of a system's dynamics is a quick and robust proxy for the minimum information partition. == Related experimental work == While the algorithm for assessing a system's Φ Max {\displaystyle \Phi ^{\textrm {Max}}} and conceptual structure is relatively straightforward, its high time complexity makes it computationally intractable for many systems of interest. Heuristics and approximations can sometimes be used to provide ballpark estimates of a complex system's integrated information, but precise calculations are often impossible. These computational challenges, combined with the already difficult task of reliably and accurately assessing consciousness under experimental conditions, make testing many of the theory's predictions difficult. Despite these challenges, researchers have attempted to use measures of information integration and differentiation to assess levels of consciousness in a variety of subjects. But he also claims "the parts of IIT that I find less promising are where it claims that integrated information actually is consciousness — that there's an identity between the two.", and has criticized the panpsychist extrapolations of the theory. Philosopher David Chalmers, famous for the idea of the hard problem of consciousness, has expressed some enthusiasm about IIT. According to Chalmers, IIT is a development in the right direction, whether or not it is correct. Max Tegmark has tried to address the problem of the computational complexity behind the calculations. According to Max Tegmark "the integration measure proposed by IIT is computationally infeasible to evaluate for large systems, growing super-exponentially with the system's information content." As a result, Φ can only be approximated in general. However, different ways of approximating Φ provide radically different results. Other works have shown that Φ can be computed in some large mean-field neural network models, although some assumptions of the theory have to be revised to capture phase transitions in these large systems. 10 (5) e1003588. Bibcode:2014PLSCB..10E3588O. doi:10.1371/journal.pcbi.1003588. PMC 4014402. PMID 24811198. S2CID 2578087. Integrated Information Theory: An Updated Account (2012) (First presentation of IIT 3.0) Archived 16 December 2014 at the Wayback Machine Tononi, Giulio (2008). "Consciousness as Integrated Information: A Provisional Manifesto". The Biological Bulletin. 215 (3): 216–242.