Dynamic patterns of brain activity at rest distinguish conscious and unconscious states in humans. Adopting the framework of brain dynamics as a cornerstone of human consciousness, we determined whether dynamic signal coordination provides specific and generalizable patterns pertaining to conscious and unconscious states after brain damage. A dynamic pattern of coordinated and anticoordinated functional magnetic resonance imaging signals characterized healthy individuals and minimally conscious patients. The brains of unresponsive patients showed primarily a pattern of low interareal phase coherence mainly mediated by structural connectivity, and had smaller chances to transition between patterns. The complex pattern was further corroborated in patients with covert cognition, who could perform neuroimaging mental imagery tasks, validating this pattern’s implication in consciousness. Anesthesia increased the probability of the less complex pattern to equal levels, validating its implication in unconsciousness. Our results establish that consciousness rests on the brain’s ability to sustain rich brain dynamics and pave the way for determining specific and generalizable fingerprints of conscious and unconscious states.
Damiano Azzalini, Ignacio Rebollo, C. Tallon-Baudry
Most research in cognitive neuroscience explores how external stimuli are processed by the brain. However, the brain also receives input from the internal body. We discuss here how the heart and gastrointestinal (GI) tract intrinsically generate their own electrical activity, thereby continuously sending information to the brain. These ongoing ascending signals actively shape brain dynamics at rest, complementing canonical resting-state networks (RSNs). Cardiac signals also influence the processing of external sensory information and the production of spontaneous, internal cognition. These findings are discussed in relation to interpretative frameworks regarding the functional role of visceral inputs. This active field of research offers a unique opportunity to draw new theories blurring the border between cognition, emotion, and consciousness, as well as between mind and body.
Indicator-based approaches to machine consciousness recommend mechanism-linked evidence triangulated across tasks, supported by architectural inspection and causal intervention. Inspired by Humphrey's ipsundrum hypothesis, we implement ReCoN-Ipsundrum, an inspectable agent that extends a ReCoN state machine with a recurrent persistence loop over sensory salience $N^s$ and an optional affect proxy reporting valence/arousal. Across fixed-parameter ablations (ReCoN, Ipsundrum, Ipsundrum+affect), we operationalize Humphrey's qualiaphilia (preference for sensory experience for its own sake) as a familiarity-controlled scenic-over-dull route choice. We find a novelty dissociation: non-affect variants are novelty-sensitive ($Δ$scenic-entry = 0.07). Affect coupling is stable ($Δ$scenic-entry = 0.01) even when scenic is less novel (median {$Δ$novelty $\approx$ -0.43). In reward-free exploratory play, the affect variant shows structured local investigation (scan events 31.4 vs. 0.9; cycle score 7.6). In a pain-tail probe, only the affect variant sustains prolonged planned caution (tail duration 90 vs. 5). Lesioning feedback+integration selectively reduces post-stimulus persistence in ipsundrum variants (AUC drop 27.62, 27.9%) while leaving ReCoN unchanged. These dissociations link recurrence $\rightarrow$ persistence and affect-coupled control $\rightarrow$ preference stability, scanning, and lingering caution, illustrating how indicator-like signatures can be engineered and why mechanistic and causal evidence should accompany behavioral markers.
Lukasz Smigielski, M. Scheidegger, M. Kometer
et al.
Both psychedelics and meditation exert profound modulatory effects on consciousness, perception and cognition, but their combined, possibly synergistic effects on neurobiology are unknown. Accordingly, we conducted a randomized, double-blind, placebo-controlled study with 38 participants following a single administration of the psychedelic psilocybin (315 μg/kg p.o.) during a 5-day mindfulness retreat. Brain dynamics were quantified directly pre- and post-intervention by functional magnetic resonance imaging during the resting state and two meditation forms. The analysis of functional connectivity identified psilocybin-related and mental state-dependent alterations in self-referential processing regions of the default mode network (DMN). Notably, decoupling of medial prefrontal and posterior cingulate cortices, which is thought to mediate sense of self, was associated with the subjective ego dissolution effect during the psilocybin-assisted mindfulness session. The extent of ego dissolution and brain connectivity predicted positive changes in psycho-social functioning of participants 4 months later. Psilocybin, combined with meditation, facilitated neurodynamic modulations in self-referential networks, subserving the process of meditation by acting along the anterior-posterior DMN connection. The study highlights the link between altered self-experience and subsequent behavioral changes. Understanding how interventions facilitate transformative experiences may open novel therapeutic perspectives. Insights into the biology of discrete mental states foster our understanding of non-ordinary forms of human self-consciousness and their concomitant brain substrate.
Recent advancements in video generation have witnessed significant progress, especially with the rapid advancement of diffusion models. Despite this, their deficiencies in physical cognition have gradually received widespread attention - generated content often violates the fundamental laws of physics, falling into the dilemma of ''visual realism but physical absurdity". Researchers began to increasingly recognize the importance of physical fidelity in video generation and attempted to integrate heuristic physical cognition such as motion representations and physical knowledge into generative systems to simulate real-world dynamic scenarios. Considering the lack of a systematic overview in this field, this survey aims to provide a comprehensive summary of architecture designs and their applications to fill this gap. Specifically, we discuss and organize the evolutionary process of physical cognition in video generation from a cognitive science perspective, while proposing a three-tier taxonomy: 1) basic schema perception for generation, 2) passive cognition of physical knowledge for generation, and 3) active cognition for world simulation, encompassing state-of-the-art methods, classical paradigms, and benchmarks. Subsequently, we emphasize the inherent key challenges in this domain and delineate potential pathways for future research, contributing to advancing the frontiers of discussion in both academia and industry. Through structured review and interdisciplinary analysis, this survey aims to provide directional guidance for developing interpretable, controllable, and physically consistent video generation paradigms, thereby propelling generative models from the stage of ''visual mimicry'' towards a new phase of ''human-like physical comprehension''.
This paper introduces Resonance Complexity Theory (RCT), which proposes that consciousness emerges from stable interference patterns of oscillatory neural activity. These patterns, shaped by recursive feedback and constructive interference, must exceed critical thresholds in complexity, coherence, gain, and fractal dimensionality to give rise to conscious experience. The resulting spatiotemporal attractors encode subjective awareness as dynamic resonance structures distributed across the neural field, enabling large-scale integration without symbolic representation or centralized control. To formalize this idea, we define the Complexity Index (CI), a composite metric that synthesizes four core properties of conscious systems: fractal dimensionality (D), signal gain (G), spatial coherence (C), and attractor dwell time (tau). These elements are combined multiplicatively to capture the emergence and persistence of structured, integrative neural states. To test the theory empirically, we developed a biologically inspired yet minimal neural field simulation composed of radial wave sources emitting across a continuous 2D space. The system exhibits recursive constructive interference, producing coherent, attractor-like excitation patterns without external input, regional coding, or imposed structure. These patterns meet the theoretical thresholds for CI and reflect the core dynamics predicted by RCT. The findings demonstrate that resonance-based attractors -- and by extension, consciousness-like dynamics -- can arise purely from the physics of wave interference. RCT thus offers a unified, dynamical framework for modeling awareness as an emergent property of organized complexity in oscillatory systems.
This article explores the transformational potential of artificial intelligence (AI), particularly generative AI (genAI) – large language models (LLMs), chatbots, and AI-driven smart assistants yet to emerge – to reshape human cognition, memory, and creativity. First, the paper investigates the potential of genAI tools to enable a new form of human-computer co-remembering, based on prompting rather than traditional recollection. Second, it examines the individual, cultural, and social implications of co-creating with genAI for human creativity. These phenomena are explored through the concept of Homo Promptus, a figure whose cognitive processes are shaped by engagement with AI. Two speculative scenarios illustrate these dynamics. The first, ‘prompting to remember’, analyses genAI tools as cognitive extensions that offload memory work to machines. The second scenario, ‘prompting to create’, explores changes in creativity when performing together with genAI tools as co-creators. By mobilising concepts from cognitive psychology, media and memory studies, together with Huizinga’s exploration of play, and Rancière’s intellectual emancipation, this study argues that genAI tools are not only reshaping how humans remember and create but also redefining cultural and social norms. It concludes by calling for ‘critical’ engagement with the societal and intellectual implications of AI, advocating for research that fosters adaptive and independent (meta)cognitive practices to reconcile digital innovation with human agency.
Communication. Mass media, Consciousness. Cognition
This article develops a model to explain the emergence and persistence of shared memory, providing a practical toolkit for empirical research in memory studies. It begins with a review of the concepts of individual and collective memory, highlighting their limitations. In response, the article introduces two alternative concepts – subjectivised memory and hegemonic memory – that capture the interdependence of individual and collective memory while moving beyond their dichotomy. These concepts form the theoretical basis of the proposed model. The article applies the model to the example of Holocaust remembrance in Germany, illustrating how memory becomes hegemonic and persists over time.
Communication. Mass media, Consciousness. Cognition
This study investigates the integration of literal completions of idiomatic multiword expressions (MWEs) into two linguistic contexts: one promoting a literal interpretation and the other a figurative one, requiring reinterpretation to align with figurative bias. Sixteen Italian idioms were distributed in two groups by their Potential Idiomatic Ambiguity (PIA) score, an index of literal plausibility, decomposability and transparency. Using experimental dialogues, the study tested whether high-PIA idioms receive higher acceptability ratings across both contexts than low-PIA idioms. Eighty-four Italian-speaking participants rated idiom literal completions within literal and figurative contexts. Results show that literal completions of high-PIA idioms integrate better across contexts, while those of low-PIA idioms receive lower ratings and have longer combined reading and rating times. This supports hybrid models of idiom processing, emphasizing the role of idiomatic features and context in balancing figurative and compositional interpretations. This study also marks an initial effort to experimentally trace systematicity within idiomatic wordplay, challenging the idea that it lacks relevance for linguistic research while outlining limitations and directions for future work.
Abstract Urbanization is hypothesized to create a myriad of cognitive challenges for animals because it creates novel environmental conditions in evolutionary terms. The consensus is that these novel urban challenges act as drivers for increased cognitive abilities. However, scant empirical data validates the idea that urban environments are cognitively demanding relative to native ones. In this short communication we draw the attention to the fact that for some large-brained urban inhabitants the urban environment may instead provide “easy” exploitable niches, where these species can thrive because they already have the necessary cognitive tools in place. As such, evolutionary seen, such species are “exapted” to occupy a less challenging urban niche. As follows, while a species’ cognition may facilitate its persistence under urbanization, it does not necessarily mean that urban populations face selective or developmental drivers for improved cognition in urban living. We further point out the potential bias anthropogenic habituation can bring about when intraspecific comparisons are made between urban and nonurban populations and suggest that researchers must focus on precisely which species-specific aspects of the environment are novel when making predictions about the consequences of urbanization on cognitive traits.
Y. S. Xiong, Jacob A. Donoghue, Mikael Lundqvist
et al.
Significance Neurophysiology studies have found alpha/beta oscillations (8 to 30 Hz), gamma oscillations (40 to 100 Hz), and spiking activity during cognition. Alpha/beta power has an inverse relationship with gamma power/spiking. This suggests that gamma/spiking is under the inhibitory control of alpha/beta. The predictive routing model hypothesizes that alpha/beta oscillations selectively inhibit (to control) cortical activity that is predictable. We tested whether this inhibitory control is a signature of consciousness. We used multiarea neurophysiology recordings in monkeys presented with tone sequences that varied in predictability. We recorded brain activity as the anesthetic propofol was administered to manipulate consciousness. Compared to conscious processing, propofol-mediated loss-of-consciousness disrupted alpha/beta inhibitory control during predictive processing. This led to a disinhibition of gamma/spiking, consistent with the predictive routing model.
The computational significance of consciousness is an important and potentially more tractable research theme than the hard problem of consciousness, as one could look at the correlation of consciousness and computational capacities through, e.g., algorithmic or complexity analyses. In the literature, consciousness is defined as what it is like to be an agent (i.e., a human or a bat), with phenomenal properties, such as qualia, intentionality, and self-awareness. The absence of these properties would be termed “unconscious.” The recent success of large language models (LLMs), such as ChatGPT, has raised new questions about the computational significance of human conscious processing. Although instances from biological systems would typically suggest a robust correlation between intelligence and consciousness, certain states of consciousness seem to exist without manifest existence of intelligence. On the other hand, AI systems seem to exhibit intelligence without consciousness. These instances seem to suggest possible dissociations between consciousness and intelligence in natural and artificial systems. Here, I review some salient ideas about the computational significance of human conscious processes and identify several cognitive domains potentially unique to consciousness, such as flexible attention modulation, robust handling of new contexts, choice and decision making, cognition reflecting a wide spectrum of sensory information in an integrated manner, and finally embodied cognition, which might involve unconscious processes as well. Compared to such cognitive tasks, characterized by flexible and ad hoc judgments and choices, adequately acquired knowledge and skills are typically processed unconsciously in humans, consistent with the view that computation exhibited by LLMs, which are pretrained on a large dataset, could in principle be processed without consciousness, although conversations in humans are typically done consciously, with awareness of auditory qualia as well as the semantics of what are being said. I discuss the theoretically and practically important issue of separating computations, which need to be conducted consciously from those which could be done unconsciously, in areas, such as perception, language, and driving. I propose conscious supremacy as a concept analogous to quantum supremacy, which would help identify computations possibly unique to consciousness in biologically practical time and resource limits. I explore possible mechanisms supporting the hypothetical conscious supremacy. Finally, I discuss the relevance of issues covered here for AI alignment, where computations of AI and humans need to be aligned.
There is no agreement on whether any invertebrates are conscious and no agreement on a methodology that could settle the issue. How can the debate move forward? I distinguish three broad types of approach: theory-heavy, theory-neutral and theory-light. Theory-heavy and theory-neutral approaches face serious problems, motivating a middle path: the theory-light approach. At the core of the theory-light approach is a minimal commitment about the relation between phenomenal consciousness and cognition that is compatible with many specific theories of consciousness: the hypothesis that phenomenally conscious perception of a stimulus facilitates, relative to unconscious perception, a cluster of cognitive abilities in relation to that stimulus. This “facilitation hypothesis” can productively guide inquiry into invertebrate consciousness. What is needed? At this stage, not more theory, and not more undirected data gathering. What is needed is a systematic search for consciousness-linked cognitive abilities, their relationships to each other, and their sensitivity to masking.
Recent experimental evidence on patients with disorders of consciousness revealed that observing brain-heart interactions helps to detect residual consciousness, even in patients with absence of behavioral signs of consciousness. Those findings support hypotheses suggesting that visceral activity is involved in the neurobiology of consciousness, and sum to the existing evidence in healthy participants in which the neural responses to heartbeats reveal perceptual and self-consciousness. More evidence obtained through mathematical modeling of physiological dynamics revealed that emotion processing is prompted by an initial modulation from ascending vagal inputs to the brain, followed by sustained bidirectional brain-heart interactions. Those findings support long-lasting hypotheses on the causal role of bodily activity in emotions, feelings, and potentially consciousness. In this paper, the theoretical landscape on the potential role of heartbeats in cognition and consciousness is reviewed, as well as the experimental evidence supporting these hypotheses. I advocate for methodological developments on the estimation of brain-heart interactions to uncover the role of cardiac inputs in the origin, levels, and contents of consciousness. The ongoing evidence depicts interactions further than the cortical responses evoked by each heartbeat, suggesting the potential presence of non-linear, complex, and bidirectional communication between brain and heartbeat dynamics. Further developments on methodologies to analyze brain-heart interactions may contribute to a better understanding of the physiological dynamics involved in homeostatic-allostatic control, cognitive functions, and consciousness.
While Large Language Models (LLMs) have achieved remarkable success across various applications, they also raise concerns regarding self-cognition. In this paper, we perform a pioneering study to explore self-cognition in LLMs. Specifically, we first construct a pool of self-cognition instruction prompts to evaluate where an LLM exhibits self-cognition and four well-designed principles to quantify LLMs' self-cognition. Our study reveals that 4 of the 48 models on Chatbot Arena--specifically Command R, Claude3-Opus, Llama-3-70b-Instruct, and Reka-core--demonstrate some level of detectable self-cognition. We observe a positive correlation between model size, training data quality, and self-cognition level. Additionally, we also explore the utility and trustworthiness of LLM in the self-cognition state, revealing that the self-cognition state enhances some specific tasks such as creative writing and exaggeration. We believe that our work can serve as an inspiration for further research to study the self-cognition in LLMs.