ABSTRACT Plant cognition has progressed from anecdote to rigor, yet the field still lacks a quantitative test for when distributed plant activity crosses into unified – perhaps conscious – processing. I introduce Pattern–Temporal Synergy (PTS), a substrate-agnostic metric rooted in Dynergeia, a relation-first ontology in which consciousness is reflexive coherence among five universal patterns – self-reference, division-creation, information integration, responsiveness, and flux – phase-locked inside a system’s binding window (τ). Each pattern is operationalized with established signal-processing measures; their median strength is multiplied by their mean synergy and released only if a τ-specific coherence gate is met. Three preregistered hypotheses anchor the study: H1 baseline PTS > 0 in intact plants; H2 4% diethyl-ether collapses PTS below threshold ϕ; H3 PTS rebounds on wash-out. A multispecies protocol – Mimosa pudica, Arabidopsis thaliana, Picea abies – combines 64-channel surface electrodes, glutamate-sensitive Ca2+ imaging and micro-optode O2/heat-flux probes. Sliding 3 ×τ windows with phase-shuffled surrogates yield z-scored PTS trajectories, adjudicated by preregistered effect-size criteria. By turning decades of qualitative insight into falsifiable numbers, PTS offers plant biology a litmus test for conscious-level processing, directly challenges Integrated Information Theory and supplies a road-map for cross-kingdom comparisons – including neuromorphic silicon. Confirmatory results would shift debates on plant sentience from speculation to data; null results would equally refine what consciousness requires.

Contemporary theories of consciousness offer a range of explanatory perspectives. Global Workspace Theory emphasizes cognitive access, Higher-Order Theories focus on metacognitive representation, Integrated Information Theory centers on intrinsic experience, and Predictive Coding Theory models cognitive processes as probabilistic inference. While each theory provides valuable insights, they often remain in conflict due to differing assumptions about the nature of consciousness. This paper proposes a phenomenologically informed framework that clarifies the explanatory scope of each theory in relation to key features of lived experience. Rather than seeking to reduce consciousness to a single principle, I argue for a pluralistic approach that respects the distinctive contributions of each model. Through comparative analysis guided by phenomenological reflection and supported by recent interdisciplinary proposals, I show how these theories can be seen as addressing complementary dimensions of consciousness. The aim is not to construct a single unified theory, but to demonstrate how integration, grounded in reflective phenomenological analysis, can serve as a starting point toward a more adequate science of consciousness.

ABSTRACT This paper links mathematical consciousness science, particularly Integrated Information Theory (IIT), to Immanuel Kant's philosophy. Skeptics of IIT's theoretical foundation often target its fundamental identity between an experience and Φ-structure. My account brings in the Kantian notion that this identity is a constitutive a priori principle and responds to some objections by drawing on Michael Friedman's defense of similar principles in spacetime physics. As constitutive a priori, the 〈Experience = Φ -structure〉 principle serves various intra-theory functions: (i) enables the application of the intrinsic information formalism to the axioms' phenomenology; (ii) gives empirical meaning to phenomenological concepts; (iii) ensures the possibility of scientific assessment of theory's predictions and explanations; (iv) constructs the theory's explanandum: consciousness as structured, maximally irreducible, specific, intrinsic cause-effect power; and (v) makes possible other a priori explanatory principles. Generalizing beyond IIT, this paper advocates for using constitutive apriority tools to refine the epistemological foundations of other mathematical and scientific consciousness theories. Leveraging the neo-Kantian philosophy of consciousness science research program, it highlights the potential for connecting phenomenological descriptions, physical principles, and mathematical formalism to render current theories more transparent, rigorous, and empirically tractable – a move toward a more mature consciousness science.

Integrated information theory (IIT) offers an axiomatic framework based on phenomenological properties, allowing the quantification and characterization of consciousness through a measure known as Φ. According to IIT, Φ reflects the level of consciousness and is expected to decrease with loss of consciousness, although empirical data supporting this claim remain limited. In this study, we analyzed two functional magnetic resonance imaging (fMRI) datasets acquired during anesthesia (propofol-induced) and natural sleep to determine whether Φ changes with the loss and recovery of consciousness. Our analysis was conducted using the fourth version of IIT. We constructed systems composed of five functional brain networks, computed transition probability matrices from fMRI time series data, and derived Φ values based on these matrices. As predicted by IIT, Φ decreased during anesthesia-induced loss of consciousness at both global and local levels. Similarly, Φ was locally reduced within a system centered on posterior brain regions during sleep-induced loss of consciousness. Considering functional networks as system units, we found that the integrated information (Φ) of the brain is linked to fluctuations in consciousness levels. These findings indicate a strong association between consciousness and integrated information within the large-scale functional networks.

A practical measure of integrated information reveals alpha-band activity and the posterior cortex as neural correlates of arousal.

Why does time feel the way it does? Toward a principled account of temporal experience

The information structure of boredom via integrated information theory

This paper presents an explanation of how Guilio Tononi’s integrated information theory of consciousness (IIT) can be used to justify libertarian free will. His theory places emphasis on the intrinsic power of the conscious state, and the existence of such a power provides the libertarian with the means needed to defend their position in the face of sustained criticism, be it a priori or evidential. My argument commences with an explication of my understanding of libertarian free will, beginning with a definition comprising several criteria. I engage with these criteria to some degree, placing emphasis on the central criterion; that is, the intrinsic power of self-determination. From there I turn to Tononi’s theory to demonstrate how it can provide an ontological explanation of this power. IIT is a relatively new theory, and as such there are criticisms of it which I address; notwithstanding, it is a theory in progress, and its future looks promising, not only as an explanation of consciousness, but also by providing credibility to the committed libertarian.

Informational Substance Theory (IST) proposes a novel metaphysical framework where dynamic, structured information constitutes the fundamental ontological substrate of reality. Drawing on Mulla Sadra’s doctrine of substantial motion (al-harakat al-jawhariyya) which envisions existence as perpetual becoming IST integrates quantum field theory (QFT), loop quantum gravity (LQG), and Integrated Information Theory (IIT) to unify physics, metaphysics, and philosophy of mind. We redefine substances as a self-organizing informational continuum, where physical fields emerge as excitations, spacetime as quantized relational networks, and consciousness as a highly integrated, self-reflexive informational field. Contrasting IST with panpsychism, neutral monism, and process philosophy, we argue it avoids traditional dualisms and reductionism, offering a non-reductive solution to the “hard problem” of consciousness. Grounded in empirical frameworks like IIT’s Φ metric and neural field theories (e.g., active inference), IST suggests testable implications, including experiments on informational integration in consciousness and quantum cosmology. By formalizing the informational continuum via information geometry and category theory, IST bridges Sadrian metaphysics with modern science, proposing that the universe is a self-evolving narrative of information. We outline future research to model this ontology and explore its ethical and cosmological implications.

This paper presents a novel framework for measuring consciousness strength based on the Human Language-based Consciousness (HLbC) model. While Integrated Information Theory (IIT) quantifies consciousness via integrated information, the HLbC model views consciousness as a post-hoc process, emphasizing language and probabilistic decision-making. By modeling this decision process, a pseudo-Schrödinger equation emerges where the Kullback-Leibler distance replaces spatial coordinates. We propose two metrics for "consciousness strength": one focusing on real-time response and information processing, and another using Bayesian statistics to assess learning and adaptation over time. These metrics offer a comprehensive view of consciousness, integrating both immediate responses and long-term learning. Our findings contribute to advancing quantitative measures of consciousness, with potential applications in fields like artificial intelligence. This paper presents a novel framework for measuring consciousness strength based on the Human Language-based Consciousness (HLbC) model. While Integrated Information Theory (IIT) quantifies consciousness via integrated information, the HLbC model views consciousness as a post-hoc process, emphasizing language and probabilistic decision-making. By modeling this decision process, a pseudo-Schrödinger equation emerges where the Kullback-Leibler distance replaces spatial coordinates. We propose two metrics for "consciousness strength": one focusing on real-time response and information processing, and another using Bayesian statistics to assess learning and adaptation over time. These metrics offer a comprehensive view of consciousness, integrating both immediate responses and long-term learning. Our findings contribute to advancing quantitative measures of consciousness, with potential applications in fields like artificial intelligence.

We introduce a quantum integrated-information measure Φ for multipartite states within the Relational Quantum Dynamics (RQD) framework. Φ(ρ) is defined as the minimum quantum Jensen–Shannon distance between an n-partite density operator ρ and any product state over a bipartition of its subsystems. We prove that its square root induces a genuine metric on state space and that Φ is monotonic under all completely positive trace-preserving maps. Restricting the search to bipartitions yields a unique optimal split and a unique closest product state. From this geometric picture, we derive a canonical entanglement witness directly tied to Φ and construct an integration dendrogram that reveals the full hierarchical correlation structure of ρ. We further show that there always exists an “optimal observer”—a channel or basis—that preserves Φ better than any alternative. Finally, we propose a quantum Markov blanket theorem: the boundary of the optimal bipartition isolates subsystems most effectively. Our framework unites categorical enrichment, convex-geometric methods, and operational tools, forging a concrete bridge between integrated information theory and quantum information science.

The Emergent Flow Theory (EFT) is presented as an integrative proposal that articulates the foundations of quantum physics, neuroscience and philosophy of mind to address the phenomenon of consciousness from an inverse emergent panpsychic vision. Faced with the limitations of reductionist materialism and unfalsifiable idealism, EFT postulates that consciousness is not an exclusive property of the human brain, but a hierarchical flow of informational integration that emerges from the subatomic levels to complex neural structures. Ontologically, existence is understood as a process of informational concrescence that regulates the interaction between energy and matter, while its teleology points to the progressive integration into organized structures, culminating in subjective experience. This model is based on contemporary scientific frameworks such as Friston's free energy principle, Kauffman's quantum consciousness, Levin's bioelectrical intelligence, and the postulates of graph theory and quantum computing. Unlike previous models, such as integrated information theory or orchestrated reduction, the TFE proposes a testable paradigm that can be validated by graph architecture, biomimetic simulations and hierarchical neuroinformatics analysis. Thus, he proposes a profound reformulation of the binding problem and the mind-body problem, providing a robust explanatory framework to understand the emergence of consciousness. This research opens a promising path for an informational ontology that allows us to understand reality and experience from a holistic, falsifiable and scientifically sustainable perspective, configuring a new epistemological horizon for the study of the mind and nature.

This commentary engages with recent work on computational functionalist theories of consciousness through a structural lens. We address three key aspects: the role of subjective experience in theory building, the hypothesis regarding local lateral connectivity in sensory areas, and the implications of “silent units” for consciousness. We argue that while their structural turn is welcome, many of their insights were previously predicted by Integrated Information Theory. We question the coherence of these claims within the functionalist paradigm and emphasize the importance of distinguishing genuine predictions from post-hoc accommodations in consciousness science.

Can “consciousness” be observed from large language model (LLM) internal states? Dissecting LLM representations obtained from Theory of Mind test with Integrated Information Theory and Span Representation analysis

Integrated information theory (IIT) is one of the most advanced formal theories of consciousness, featuring a Python toolbox (PyPhi) which allows us to analyse a system according to the corresponding theoretical framework. Computational and empirical limitations make it hard to test the hypothesis that higher values of integrated information are associated with a higher level of consciousness. We leverage the availability of data collected by a previous study (Huang et al., 2020) which is amenable to an IIT 3.0 analysis employing the PyPhi toolbox. The Huang et al. study employed a mix of supervised and unsupervised machine learning techniques (k-means, SVM) to obtain and validate transition probability matrices among brain states for different levels of consciousness. We observed that the integrated information values are not associated with the conditions and the brain states characterized by greater consciousness level. Limitations and future opportunities of our approach are discussed.

We introduce an intracranial EEG (iEEG) dataset collected as part of an adversarial collaboration between proponents of two theories of consciousness: Global Neuronal Workspace Theory and Integrated Information Theory. The data were recorded from 38 patients undergoing intracranial monitoring of epileptic seizures across three research centers using the same experimental protocol. Participants were presented with suprathreshold visual stimuli belonging to four different categories (faces, objects, letters, false fonts) in three orientations (front, left, right view), and for three durations (0.5, 1.0, 1.5 s). Participants engaged in a non-speeded Go/No-Go target detection task to identify infrequent targets with some stimuli becoming task-relevant and others task-irrelevant. Participants also engaged in a motor localizer task. The data were checked for its quality and converted to Brain Imaging Data Structure (BIDS). The de-identified dataset contains demographics, clinical information, electrode reconstruction, behavioral performance, and eye-tracking data. We also provide code to preprocess and analyze the data. This dataset holds promise for reuse in consciousness science and vision neuroscience to answer questions related to stimulus processing, target detection, and task-relevance, among many others.

Abstract The Integrated Information Theory (IIT) might be our current best bet at a scientific explanation of phenomenal consciousness. IIT focuses on the distinctively subjective and phenomenological aspects of conscious experience. Currently, it offers the fundaments of a formal account, but future developments shall explain the qualitative structures of every possible conscious experience. But this ambitious project is hindered by one fundamental limitation. IIT fails to acknowledge the crucial roles of attention in generating phenomenally conscious experience and shaping its contents. Here, we argue that IIT urgently needs an account of attention. Without this account, IIT cannot explain important informational differences between different kinds of experiences. Furthermore, though some IIT proponents celebratedly endorse a double dissociation between consciousness and attention, close analysis reveals that such as dissociation is in fact incompatible with IIT. Notably, the issues we raise for IIT will likely arise for many internalist theories of conscious contents in philosophy, especially theories with primitivist inclinations. Our arguments also extend to the recently popularized structuralist approaches. Overall, our discussion highlights how considerations about attention are indispensable for scientific as well as philosophical theorizing about conscious experience.

From reactions to reflection: A recursive framework for the evolution of cognition and complexity.

Theories of consciousness grounded in neuroscience must explain the phenomenal binding problem, e.g., how micro-units of information are combined to create the macro-scale conscious experience common to human phenomenology. An example is how single ‘pixels’ of a visual scene are experienced as a single holistic image in the ‘mind’s eye’, rather than as individual, separate, and massively parallel experiences, corresponding perhaps to individual neuron activations, neural ensembles, or foveal saccades, any of which could conceivably deliver identical functionality from an information processing point of view. There are multiple contested candidate solutions to the phenomenal binding problem. This paper explores how the metaphysical infrastructure of Integrated Information Theory (IIT) v4.0 can provide a distinctive solution. The solution—that particular entities aggregable from multiple units (‘complexes’) define existence—might work in a static picture, but introduces issues in a dynamic system. We ask what happens to our phenomenal self as the main complex moves around a biological neural network. Our account of conscious entities developing through time leads to an apparent dilemma for IIT theorists between non-local entity transitions and contiguous selves: the ‘dynamic entity evolution problem’. As well as specifying the dilemma, we describe three ways IIT might dissolve the dilemma before it gains traction. Clarifying IIT’s position on the phenomenal binding problem, potentially underpinned with novel empirical or theoretical research, helps researchers understand IIT and assess its plausibility. We see our paper as contributing to IIT’s current research emphasis on the shift from static to dynamic analysis.

Abstract Understanding the neural basis of consciousness remains a fundamental challenge in neuroscience. This study proposes a novel framework that conceptualizes consciousness through the lens of uncertainty reduction and negative entropy, emphasizing the role of coherence in its emergence. Sensory processing may operate as a Bayesian inference mechanism aimed at minimizing the brain's uncertainty regarding external stimuli, and conscious awareness emerges when uncertainty is reduced below a critical threshold. Computationally, this corresponds to minimizing informational uncertainty, while at a physical level it corresponds to reductions in thermodynamic entropy, thereby linking consciousness to negentropy. This study emphasizes the role of coherence in conscious perception and challenges existing models like Integrated Information Theory by exploring the potential contributions of quantum coherence and entanglement. Although direct empirical validation is currently lacking, we propose the hypothesis that consciousness acts as a cooling mechanism for the brain, as measured by the temperature of neuronal circuits. This perspective affords new insights into the physical and computational foundations of conscious experience and indicates a possible direction for future research in consciousness studies.