Entropy (Basel, Switzerland)
October 11, 2023
Christopher Rourk
Integrated information theory (IIT) can be extended from digital gates to a quantum CNOT logic gate, providing a framework for evaluating consciousness in systems beyond the human brain. A comment on that work describes an adiabatic quantum mechanical energy routing mechanism hypothesized to exist in the human brain, which is part of a hybrid biological computer that provides an action selection mechanism. Predicted evidence for this mechanism has been subsequently observed. The comment aims to motivate further evaluation and extension of IIT to that hypothesized mechanism and to other hybrid biological computers.
Entropy (Basel, Switzerland)
March 3, 2023
Larissa Albantakis, Robert Prentner, Ian T. Durham
Integrated information theory (IIT) was originally developed to characterize the causal information a system specifies about itself as a theory of consciousness, but its compatibility with quantum mechanics has been unclear. This work extends IIT's latest formalism to evaluate mechanism integrated information (φ) for discrete, finite-dimensional quantum systems such as quantum logic gates. The authors translate a measure of intrinsic information into a density matrix formulation and extend conditional independence to accommodate quantum entanglement. The compositional analysis may reveal structure in composite quantum states and operators not accessible through standard information-theoretical methods. The results aim to inform theoretical arguments about the links among consciousness, causation, and physics across classical and quantum domains.
Entropy (Basel, Switzerland)
January 9, 2023
Donald D Hoffman, Chetan Prakash, Robert Prentner
Conscious subjects and experiences are entities beyond spacetime, not within it. Their dynamics are described by Markov chains. Agents can combine into more complex agents, fuse into simpler agents, and qualia fuse to create new qualia. The possible dynamics of n agents form an n(n-1)-dimensional polytope with n^n vertices, the Markov polytope M_n. The total fusions of n agents and qualia form an (n-1)-dimensional simplex, the fusion simplex F_n. Projecting these dynamics onto spacetime yields scattering processes: a new map from Markov chains to decorated permutations encodes physical information for scattering amplitudes. Spacetime and scattering processes are a data structure coding for interactions of conscious agents; a particle in spacetime is a projection of the Markovian dynamics of a communicating class of conscious agents.
Entropy (Basel, Switzerland)
October 26, 2022
Anton Arkhipov
Consciousness in physical systems may arise from a fundamental property called non-separability of degrees of freedom, where the amount of consciousness depends on how extensively degrees of freedom are non-separable and how many are involved. Non-interacting and feedforward systems have zero consciousness, while most interacting particle systems have low non-separability and consciousness. Brain circuits, with their high complexity and weak but tightly coordinated interactions, appear to support high non-separability and thus high consciousness. The hypothesis applies to both classical and quantum cases, and the Wigner function formalism (which in the classical limit becomes the Liouville density function) is highlighted as a promising framework for characterizing non-separability and consciousness. The hypothesis aligns with Integrated Information Theory and Orchestrated Objective Reduction Theory, potentially reconciling them.
Entropy (Basel, Switzerland)
April 29, 2022
Angus Leung, Naotsugu Tsuchiya
Integrated information theory (IIT) proposes that consciousness arises when a system's integrated information (Φ) is maximal at a macro spatiotemporal scale rather than the smallest scale. This emergence has been shown in simple logic-gate models but not in real neural recordings. Using a computational model, the authors confirm that Φ peaks at the temporal scale of its generative mechanisms. In local field potentials from fly brains during wakefulness and anaesthesia, normalized Φ (wake/anaesthesia) peaks at 5 milliseconds, though raw Φ values do not. The work extends emergence testing from artificial systems to real neural data.
Entropy (Basel, Switzerland)
January 6, 2022
Chris Rourk
A physical mechanism called catecholaminergic neuron electron transport (CNET), based on incoherent electron tunneling in ferritin protein layers, may enable communication and action selection among catecholaminergic neurons. Recent tests indicate these ferritin layers can perform a switching function, potentially allowing groups of neurons to select one neuron to help reach action potential. The paper argues that CNET is consistent with Integrated Information Theory (IIT) and could serve as a physical substrate for consciousness, potentially unifying multiple consciousness theories into a single explanation, though further testing is needed to confirm the hypothesized behavior.
Entropy (Basel, Switzerland)
September 18, 2021
Garrett Mindt
The hard problem of consciousness challenges scientific explanations of subjective experience. Central to it is the structure and dynamics (S&D) argument, which David Chalmers used to claim that physical accounts cannot explain phenomenal experience. This essay argues that Chalmers' S&D argument relies on a limited conception of extrinsic structure and dynamics. Drawing on complexity sciences and Integrated Information Theory (IIT), the author introduces intrinsic structure and dynamics as a broader class of properties. These properties may naturally and scientifically explain phenomenal experience and the relationship between syntactic, semantic, and intrinsic information. The hope is to vindicate certain explanations from the S&D argument and dissolve the hard problem by showing that not all structure and dynamics are equal.
Entropy (Basel, Switzerland)
July 23, 2021
Francis Fallon, James C Blackmon
Integrated Information Theory (IIT) makes commitments about the nature of physical reality that are intimately tied to the theory, not incidental. This paper demonstrates this by raising objections in a naive way and showing how principled IIT responses rely on metaphysical positions. The paper applies the Placement Argument to generate a problem involving zombies, frames this as an apparent dilemma, and addresses it by drawing on IIT literature concerning physical reality. A related dilemma is treated in a way that dovetails with the earlier treatment. The analysis underscores the breadth of IIT and the relevance of this breadth to evaluating its merits.
Entropy (Basel, Switzerland)
May 22, 2021
Jon Mallatt
Two theories of consciousness are compared: Integrated Information Theory (IIT) and Neurobiological Naturalism (NN). IIT defines consciousness as integrated information (phi-max) and claims even simple systems with interacting parts possess some consciousness. Its strengths include logical axioms, mathematical formalism, an experience-first approach, avoidance of the mind-body problem, consistency with evolution, and testable predictions. A weakness is that some logic-based reasoning was not checked against hard evidence during construction. NN incorporated evidence earlier, is less mature and less formalized, but has identified neural correlates of consciousness and offers a scientific-method roadmap for future study.
Entropy (Basel, Switzerland)
May 8, 2021
Pavel Kraikivski
Random fluctuations in neuronal processes may contribute to variability in perception and increase information capacity in neuronal networks. This paper develops a stochastic model to examine how noise affects dynamical systems that mimic neural correlates of consciousness. Power spectral densities and spectral entropy values were computed for systems with varying numbers of mutually connected processes. Spectral entropy decreased linearly as the number of processes doubled, and power spectral density frequencies shifted to higher values with increasing system size, indicating a greater impact of negative feedback loops and regulation in larger systems. The results suggest that large dynamical systems of mutually connected and negatively regulated processes are more robust against inherent noise than small systems.
Entropy (Basel, Switzerland)
January 11, 2021
Diana Stanciu
Epistemic structural realism (ESR) offers a feasible philosophical background for the interdisciplinary study of consciousness and its associated neurophysiological phenomena in neuroscience and cognitive science, while also accounting for the mathematical structures involved. Applying ESR principles to neurophysiological phenomena related to conscious free choice and alterations of consciousness (AOCs) from pathologies like epilepsy adds explanatory value. This approach aligns with Quine's idea that philosophy is continuous with science. ESR can resonate with scientific models such as the global neuronal workspace model and integrated information theory. Unlike ontic structural realism, ESR is more suitable because it avoids the ontological question of what consciousness is and focuses instead on what can be known about it.
Entropy (Basel, Switzerland)
September 30, 2020
Carlotta Langer, Nihat Ay
A new measure of integrated information, called ΦCII (Causal Information Integration), is proposed as an alternative to existing measures that quantify the strength of causal connections between neurons in the context of Integrated Information Theory of consciousness. Unlike the candidate measure ΦCIS, which lacks a graphical representation and is difficult to analyze, ΦCII satisfies all desirable properties and can be calculated using an iterative information geometric algorithm (the em-algorithm). This allows comparison with existing integrated information measures.
Entropy (Basel, Switzerland)
June 30, 2020
Takayuki Niizato, Kotaro Sakamoto, Yoh-Ichi Mototake et al.
Integrated information theory (IIT) 3.0, which measures a system's intrinsic cause-effect structure, can classify fish schools by group size and integrity when applied to collective behavior in Plecoglossus altivelis. Using global parameter settings and multiple timescales (from 5/120 to 120/120 seconds), the analysis successfully distinguished schools of different sizes and degrees of group integrity near the fish's reaction time scale. At longer timescales, interaction heterogeneity diminished compared to shorter timescales. The findings also offer two tentative answers to the heap paradox, a longstanding puzzle in collective behavior about how individual actions combine into group-level properties.
Entropy (Basel, Switzerland)
March 16, 2020
Nicholas J M Popiel, Sina Khajehabdollahi, Pubuditha M. Abeyasinghe et al.
Integrated information, a measure proposed by Integrated Information Theory as a correlate of conscious experience, behaves as an order parameter that undergoes a phase transition at the critical point in generalized Ising models of small neural networks. In simulations of 159 random, positively weighted five-node excitatory networks, integrated information peaked at the critical temperature, where its generalized susceptibility was maximal. At this point, the system was maximally receptive and responsive to perturbations of its own states. The findings show that integrated information can capture critical behavior in an empirical dataset derived from the generalized Ising model.
Entropy (Basel, Switzerland)
February 21, 2020
Klee Irwin, Marcelo Amaral, David Chester
The paper proposes a self-simulation hypothesis, a modification of the simulation hypothesis, in which the physical universe is a mental self-simulation structured as a strange loop. This model is presented as one of a class of possible code-theoretic quantum gravity models that obey the principle of efficient language axiom. The hypothesis leads to ontological interpretations of quantum mechanics and implies an informational arrow of time.
Entropy (Basel, Switzerland)
February 20, 2020
Xueya Yan, Lulu Zhang, Jinlian Li et al.
Surges in sympathetic activity near the end of nocturnal sleep may contribute to cardiovascular events. Analyzing heart rate variability (HRV) during the hypnopompic period (the transition from sleep to waking) helps predict cardiovascular disease (CVD). In 2,217 initially CVD-free subjects, those who later developed CVD showed significant alterations in hypnopompic HRV. Machine learning models using hypnopompic HRV metrics achieved 81.4% accuracy for short-term CVD prediction (within two years), a 10.7% improvement over long-term prediction. Removing HRV metrics reduced short-term predictive performance by over 6%. Entropy-based complexity measures of hypnopompic HRV contributed more to prediction than conventional HRV measures.
Entropy (Basel, Switzerland)
May 24, 2019
André Sevenius Nilsen, Bjørn Erik Juel, William Marshall
Integrated information theory (IIT) proposes a measure called Phi (Φ) to capture the level of consciousness in a physical system, but calculating Φ is only possible for very small systems. Researchers tested whether several heuristic measures and computational approximations could estimate Φ accurately in small binary networks of 3-6 nodes. They found that some approximations correlated strongly with Φ (r > 0.95) but did not reduce computational demands. Measures of signal complexity, decoder-based integrated information, and state differentiation correlated with the maximum Φ across states. These measures may help estimate a system's capacity for high Φ or identify low-Φ systems, but their applicability to larger or more complex systems remains uncertain.
Entropy (Basel, Switzerland)
April 16, 2019
Kyumin Moon
Integrated information theory (IIT) claims that consciousness arises from a system's ability to integrate information. This paper examines IIT's theoretical structure, specifically how its phenomenological axioms relate to its ontological postulates. The author identifies an unclear link between the exclusion axiom and the exclusion postulate, and argues that the exclusion postulate creates a qualia underdetermination problem—meaning the theory cannot uniquely determine the quality of conscious experience. The paper proposes answers to three questions: how the exclusion axiom leads to the exclusion postulate, how that postulate causes the underdetermination problem, and whether a solution exists. If successful, these proposals would strengthen IIT's theoretical foundation and practical application.
Entropy (Basel, Switzerland)
January 13, 2019
Jonathan W D Mason
A mathematical theory of consciousness based on Expected Float Entropy (EFE) minimisation is further investigated. EFE is a version of Shannon Entropy parameterised by relationships. For systems with learning-induced bias, certain relationship parameter choices yield much lower EFE values, thereby defining relationships. In this context, a brain state acquires meaning through the relational content of associated experience. EFE minimisation is tested as an association learning process, and results support a close connection between such learning and the emergence of consciousness. The theory may explain how the brain defines conscious content up to relationship isomorphism.
Entropy (Basel, Switzerland)
December 25, 2018
Pedro A. M. Mediano, Anil K. Seth, Adam B. Barrett
Integrated Information Theory (IIT) is a prominent theory of consciousness that centers on measures quantifying how much a system generates more information than the sum of its parts. This article provides clear descriptions of six distinct candidate measures of integrated information and explores their properties through simulations on networks of eight interacting nodes with Gaussian linear autoregressive dynamics. The results reveal striking diversity in the measures' behavior—no two measures show consistent agreement across all analyses. A subset of the measures appears to reflect some form of dynamical complexity, meaning simultaneous segregation and integration between system components. These findings help guide the operationalization of IIT and advance development of measures with more general applicability.
Entropy (Basel, Switzerland)
November 22, 2018
Olimpia Lombardi, Cristian López
Integrated Information Theory (IIT) aims to explain consciousness by linking its fundamental properties to physical systems. A central concept is information, which IIT treats as intrinsic to a system and tied to its causal structure. This paper argues that the information in IIT should be understood through a causal-manipulability view, where information is defined only when it participates in causal relationships. These causal links are revealed by interventionist procedures based on Woodward's and Pearl's manipulability theories of causation. The analysis clarifies how information operates within IIT's framework.
Entropy (Basel, Switzerland)
March 6, 2018
Jun Kitazono, Ryota Kanai, Masafumi Oizumi
Integrated Information Theory (IIT) links the amount of integrated information (Φ) in the brain to the level of consciousness, proposing that Φ should be measured across the partition of a system where information loss from partitioning is minimized—the Minimum Information Partition (MIP). Exhaustively searching for the MIP is computationally infeasible for large systems. Previous work showed that if a measure of Φ is submodular, an optimization algorithm can find the MIP in polynomial time, but later versions of Φ are not submodular. This study empirically tested the algorithm on non-submodular Φ measures using simulated and real neural data, finding it identifies the MIP nearly perfectly, enabling practical Φ measurement in large systems.