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Theories of consciousness
Abstract
Recent years have seen a blossoming of theories about the biological and physical basis of consciousness. Good theories guide empirical research, allowing us to interpret data, develop new experimental techniques and expand our capacity to manipulate the phenomenon of interest. Indeed, it is only when couched in terms of a theory that empirical discoveries can ultimately deliver a satisfying understanding of a phenomenon. However, in the case of consciousness, it is unclear how current theories relate to each other, or whether they can be empirically distinguished. To clarify this complicated landscape, we review four prominent theoretical approaches to consciousness: higher-order theories, global workspace theories, re-entry and predictive processing theories and integrated information theory. We describe the key characteristics of each approach by identifying which aspects of consciousness they propose to explain, what their neurobiological commitments are and what empirical data are adduced in their support. We consider how some prominent empirical debates might distinguish among these theories, and we outline three ways in which theories need to be developed to deliver a mature regimen of theory-testing in the neuroscience of consciousness. There are good reasons to think that the iterative development, testing and comparison of theories of consciousness will lead to a deeper understanding of this most profound of mysteries. Various theories have been developed for the biological and physical basis of consciousness. In this Review, Anil Seth and Tim Bayne discuss four prominent theoretical approaches to consciousness, namely higher-order theories, global workspace theories, re-entry and predictive processing theories and integrated information theory.
... Much of this theoretical discourse revolves around the essence of consciousness and conditions conducive to its manifestation. Seth and Bayne (2022) and Sattin et al. (2021) have provided a fruitful review of the theories of consciousness that have emerged in recent years. Signorelli, Szczotka, and Prentner (2021) proposed a framework for the interpretation and classification of these theories and found that numerous competing frameworks are built on different philosophical grounds and favor particular scientific methodologies. ...
... Therefore, much of the latest progress on the origin of consciousness comes from the study of the "neural correlates of consciousness" (NCCs), which refer to a set of neural mechanisms necessary and sufficient for the emergence of consciousness (Baars 1988;Chalmers 2000;Crick and Koch 1990;Neveu et al. 2023). The purpose is to identify the brain states and processes that are closely related to consciousness (Koch et al. 2016;Seth and Bayne 2022). Sattin et al. (2021) conducted a systematic review of consciousness research papers published between 2007 and 2017, classifying and analyzing the 29 consciousness theories mentioned therein. ...
... However, the concept of NCCs has several limitations. For example, it remains challenging to determine whether the state and processes of the brain are prerequisites for consciousness or results of consciousness (Seth and Bayne 2022). The NCC framework is indispensable for consciousness science because the material foundation is also an extremely important part of consciousness research. ...
Purpose
The generation of consciousness poses a complex scientific challenge. Neuroscience and biological sciences have extensively studied this phenomenon, yielding numerous theories and hypotheses. However, to date, no reliable evidence has emerged to exclude any hypothesis conclusively, nor has any theory garnered unanimous agreement. This study aims to offer novel insights for further in‐depth study on consciousness.
Method
A new theoretical hypothesis was proposed based on reviews and comments from predictive processing theory, information theory, thermodynamics, and neuroscience.
Findings
This study argues that, first, it is necessary to clarify that the core implication of the concept of consciousness is first‐person perception. Accordingly, the study of consciousness is based on this premise. Second, on this basis, the antagonistic hypothesis of consciousness generation was proposed. This hypothesis holds that consciousness arises from the antagonism of mature individual experiences that cannot be seamlessly integrated with the function of addressing and navigating these conflicts.
Conclusion
The antagonism hypothesis is a new concept regarding the generation of consciousness that deserves further study.
... Enormous progress has been made in the scientific study of the brain and consciousness in recent decades, and there has been a proliferation of theories that seek to explain how neural processes can produce conscious experiences; for recent reviews see [2][3][4][5]. Several of these theories, such as Integrated Information Theory (or IIT), Global Workspace Theory (or GWT), the Orchestrated Objective Reduction Theory (or Orch OR), and Predictive Processing theory (or PP), have amassed support from empirical studies and attracted widespread theoretical interest [6][7][8]. ...
... Yet despite the empirical evidence, mathematical models and theoretical arguments that can be marshalled in favor of these approaches, questions have been raised about whether any of these theories, or any competing theory, can explain how certain patterns of neural activity produce conscious experiences [4,[9][10][11][12][13]; for a critical comparison of several leading theories see [14]. Moreover, the comparative analysis of several leading biological theories has shown that each makes different predictions about the location of the brain activity associated with conscious processing [15]. ...
... The most important research question that needs to be reframed, I would suggest, is the one that currently asks "what are the neural correlates or signatures of conscious experience?" [4,116]. Many current neuroscientific explorations of this question are conducted on the assumption that the brain functions essentially as an information processing system in much the same way as a digital computer. ...
Background: Our understanding of the relationship between neural activity and psychological states has advanced greatly in recent decades. But we are still unable to explain conscious experience in terms of physical processes occurring in our brains. Methods: This paper introduces a conceptual framework that may contribute to an explanation. All physical processes entail the transfer, transduction, and transformation of energy between portions of matter as work is performed in material systems. If the production of consciousness in nervous systems is a physical process, then it must entail the same. Here the nervous system, and the brain in particular, is considered as a material system that transfers, transduces, and transforms energy as it performs biophysical work. Conclusions: Evidence from neuroscience suggests that conscious experience is produced in the organic matter of nervous systems when they perform biophysical work at classical and quantum scales with a certain level of dynamic complexity or organization. An empirically grounded, falsifiable, and testable hypothesis is offered to explain how energy processing in nervous systems may produce conscious experience at a fundamental physical level.
... They also contradict other natural science fields, which hold small competing theories. Further, it is still unclear how these physical processes for describing consciousness are related or whether they can be empirically distinguished [Seth and Bayne, 2022]. Based on this challenge, our study delves into physical processes for describing consciousness based on (1) the von Neumann-Wigner approach, (2) orchestrated objective reduction, (3) integrated Table 1. ...
... We also understand other prominent ToCs with neurobiological bases, such as higher-order theories, global workspace theory, re-entry, and predictive processing theories. These approaches have been explored elsewhere [Seth and Bayne, 2022]. These ToCs are out of the scope of our study. ...
... Here, we adopted some criteria for comparing ToCs suggested in the references [Debates et al., 2020;Northoff and Lamme, 2020;Seth and Bayne, 2022], as shown in Table 3. We found that each mechanism aims to explain only certain aspects of consciousness. ...
Currently, there is a growing number of proposed physical processes for describing the mechanism underlying the occurrence and measurement of consciousness. These bodies of knowledge have created multitudes of viewpoints about consciousness. It is also still unclear how they are related or whether they can be empirically distinguished. This study presents an assessment of physical processes for describing the occurrence and measurement of consciousness based on (1) the von Neumann–Wigner approach, (2) orchestrated objective reduction, (3) integrated information, (4) consciousness as a state of matter, and (5) electromagnetic field. The study underscores the significance, similarity, and weakness of these approaches. All approaches except the von Neumann–Wigner approach agree on the fact that the brain is the central region responsible for generating and detecting consciousness but with distinct explanations. Further, both surveyed approaches are still far from experimental verification. Further studies are needed on designing experimental verification and proposing new approaches.
... In the evolving field of consciousness theory, current approaches emphasize identifying explanatory links between neural mechanisms and diverse aspects of consciousness (Fig. 1d). A normative and comprehensive theory of consciousness (TOC) encompasses four primary categories: higher-order theories (HOTs), global workspace theories (GWTs), integrated information theory (IIT), and retrospective and predictive processing theories [43]. These frameworks offer academic viewpoints for observing contemporary states of consciousness. ...
... These frameworks offer academic viewpoints for observing contemporary states of consciousness. Specifically, GWTs propose that consciousness depends on the integrity of functional or dynamic connections within the frontal-parietal regions, and impairment in these connections can lead to varying degrees of consciousness loss [43]. These theories provide useful perspectives for understanding the origins and mechanisms of consciousness [44]. ...
Among the existing research on the treatment of disorders of consciousness (DOC), deep brain stimulation (DBS) offers a highly promising therapeutic approach. This comprehensive review documents the historical development of DBS and its role in the treatment of DOC, tracing its progression from an experimental therapy to a detailed modulation approach based on the mesocircuit model hypothesis. The mesocircuit model hypothesis suggests that DOC arises from disruptions in a critical network of brain regions, providing a framework for refining DBS targets. We also discuss the multimodal approaches for assessing patients with DOC, encompassing clinical behavioral scales, electrophysiological assessment, and neuroimaging techniques methods. During the evolution of DOC therapy, the segmentation of central nuclei, the recording of single-neurons, and the analysis of local field potentials have emerged as favorable technical factors that enhance the efficacy of DBS treatment. Advances in computational models have also facilitated a deeper exploration of the neural dynamics associated with DOC, linking neuron-level dynamics with macroscopic behavioral changes. Despite showing promising outcomes, challenges remain in patient selection, precise target localization, and the determination of optimal stimulation parameters. Future research should focus on conducting large-scale controlled studies to delve into the pathophysiological mechanisms of DOC. It is imperative to further elucidate the precise modulatory effects of DBS on thalamo-cortical and cortico-cortical functional connectivity networks. Ultimately, by optimizing neuromodulation strategies, we aim to substantially enhance therapeutic outcomes and greatly expedite the process of consciousness recovery in patients.
... The PLCD challenge also arises for another prominent theoretical approach, namely the Global Workspace approach (Baars 1988(Baars , 2017 whether in a neurally specific form (Dehaene & Naccache 2001, Dehaene & Changeux 2011 or not. See also Seth & Bayne (2022). 12 The approach's central feature on which consciousness depends is the global broadcasting of information amongst brain mechanisms via a central workspace. ...
... Although the theme of causation is typically not explicitly emphasized, the required broadcasting is implicitly a matter of suitably patterned causation, at least in part. One support for this stance is Seth & Bayne's (2022) statement that "GWTs account for changes in global states of consciousness in terms of alterations to the functional integrity of the workspace." Functional integrity here is a matter of causal integrity-a matter of the workspace working causally to serve broadcasting in the right way. ...
... One of the major scientific mysteries of our time is the connection of brain and mind. While this question has been debated traditionally as mind-brain problem in philosophy and, more recently as world-brain problem in neurophilosophy [49,50], neuroscience takes major empirical steps in investigating the neural basis of mental features like consciousness [55,72,77], self [51,66,78], thoughts [7,22], and many others. However, despite all progress in its empirical investigation as well as in the development of theoretical models of the brain, such as, to name a few examples, predictive coding/free energy principle [12], thermodynamic accounts of neural activity [31], the inside-out model [3], the distributive adaptive control model [87], cognitive ontology [65], and the intrinsic model [67], the exact relationship of neural and mental states remains yet elusive. ...
... CCT thus proposes an explicitly spatiotemporal model of mental features that needs to be distinguished from other models, which serve as a theoretical and empirical background for investigating and understanding specific neural and mental features. For example, cognitive models of brain and mind provide the framework for a number of current theories of consciousness, such as Attention Schema Theory, Higher-Order Thought theories, and Global Neuronal Workspace Theory [72]. While the information model serves as theoretical framework for the Integrated Information Theory [80]. ...
Highlights
• We advance a Common Currency Theory, which proposes temporo-spatial dynamics as a shared framework for neural and mental features.
• Novel empirical evidence extends the theory to include spatial features such as topographic organization.
• Temporal and spatial correspondences between neural and mental states span a continuum from simple to complex.
• Evidence indicates a significant explanatory power for diverse phenomena of interest to neuroscience, including meditation, depression, and thought dynamics
• This work marks a transition from the hypothesis to a comprehensive Common Currency Theory of neuro-mental relationships.
... The term "consciousness" encapsulates a multifaceted and distinctive phenomenon characterized by reflex awareness and depiction (Guevara et al.,2020). Consciousness denotes an awareness of external objects or internal states (Seth & Bayne, 2022). Furthermore, it signifies the subjective and introspective assumptions inherent in experience, constituting a rational and intellectual phenomenon (Gulick, 2004). ...
... Defined by its attributes such as logical thinking, emotional impressions, subjectivity, alertness, the ability to experience or feel, a sense of self-hood, and the exclusive control system of the mind, consciousness emerges as a complex construct with diverse dimensions (Farthing, 1992;Laukkonen & Slagter, 2021). This article focuses specifically on the higher theory of consciousness, known as phenomenal consciousness, which engenders higher-order perceptions and conceptual thoughts or involves an object being self-conscious (Seth & Bayne, 2022). The Higher-Order theories of consciousness elaborate on the distinctive features of phenomenal consciousness, encompassing intentionality, self-consciousness, and insight into phenomena through higher-order thoughts, beliefs, or perceptions (Kreuch, 2019). ...
Communities face increasingly complex social and technological challenges , and traditional approaches to problem-solving often fall short. We can unlock innovative solutions that transcend individual limitations by tapping into the collective consciousness. The representatives of societies must understand the dynamics of social value creation to initiate social change mechanisms. The framework presented in this article combines insights to guide the process of cultivating group consciousness. It is developed on the foundations of Freudian consciousness and Jung's idea of collective unconscious and unfolds the formation of group values in respective stages of interactive, collective , and transcendent consciousness.
... To build potentially conscious artificial systems, it is sensible to examine how consciousness might be implemented in the brain. However, there is currently no universally accepted neuroscientific theory of consciousness (Seth and Bayne, 2022). Although several theories exist, the limited understanding of the brain's functional architecture, combined with the inherent challenges of conceptualizing consciousness, leaves the scientific community without a consensus. ...
... The Global Workspace Theory (GWT) is widely regarded as the leading neuroscientific model of conscious processing (Seth and Bayne, 2022) and emerges as a front-runner in efforts to model consciousness computationally (Butlin et al., 2023;Dehaene, Lau, and Kouider, 2021). The theory posits that the brain consists of specialized modules connected by long-distance neuronal links. ...
In the aftermath of the success of attention-based transformer networks, the debate over the potential and role of consciousness in artificial systems has intensified. Prominently, the Global Neuronal Workspace Theory emerges as a front-runner in the endeavor to model consciousness in computational terms. A recent advancement in the direction of mapping the theory onto state-of-the-art machine learning tools is the model of a Global Latent Workspace. It introduces a central latent representation around which multiple modules are constructed. Leveraging dedicated encoder-decoder structures, content from the central representation or any individual module, integrated via the latent space, can be translated to any other module and back with minimal loss. This paper presents a thought experiment involving a minimal setup with one deep sensory and one deep motor module, which illustrates the emergence of "globally" accessible sensorimotor representations in the central latent space connecting both modules. In the human brain, neuronally enacted knowledge of laws relating changes in sensory information to changes in motor output or corresponding efferent copy information have been proposed to constitute the biological correlates of phenomenal conscious experience. The underlying Sensorimotor Contingency Theory encompasses a rich mathematical framework. Yet, the implementation of intelligent systems based on this framework has thus far been confined to proof-of-concept and basic prototype applications. Here, the natural appearance of global latent sensorimotor representations links two major neuroscientific theories of consciousness in a powerful machine learning setup. A remaining question is whether this artificial system is conscious.
https://www.researchgate.net/publication/385418556_Philosophy_of_Artificial_Intelligence_The_State_of_the_Art
... Results: Comparisons of CEKAN and ELLKAN in predictions, high and low frequencies in brain regions, NCB, CEKAN and IEKAN of application in SIR We initially proposed a hierarchical brain order encompassing cognition, affection, and memory, as shown in Fig. 2. Fig.2 The higher lower order deep learning model As shown in Fig. 3, HOTs indicate that higher-order representations exist at higher levels of the brain [28]. Our PNN or ELLKAN models demonstrate thermodynamic energy diffusion in constructing brain memory and energy parameters, thereby orchestrating information flow [21]. ...
We initially simulated disease dynamics at the end of 2020 using Constant Edge Kolmogorov-Arnold Networks (CEKAN) and Non-classical Brain (NCB). The kernel functions of CEKAN incorporate shared exponential edge weights for confirmed and removed individuals, aligning with the Kolmogorov-Arnold representation theorem. Specifically, the edges corresponding to shared weights are modeled as implicit functions, which may be exponential, logarithmic, or formulaic. These shared weights include parameters such as infection rate, reinfection rate, and cure rate. Additionally, we employed the hyperbolic tangent (‘tanh’) activation function at the edge nodes.
In our arXiv preprint version 1, we present an upgraded version of KAN that accounts for fine-grained variations calculated using the residuals or gradients of the mean squared error (MSE) loss. This improved KAN, referred to as Plasticity Neural Networks (PNN) or Edge Length Learning KAN (ELLKAN), positions neurons and synapses along the x-coordinate and incorporates mechanisms for edge length learning and trimming.
The Edge Length Learning Kolmogorov-Arnold Network (ELLKAN) provides a comprehensive explanation of the brain by demonstrating that varying connection weights and range weights correspond to different types of neurons across various brain regions. The learning of edge length can be interpreted as synaptic strength rebalancing and the phagocytosis of synapses by astrocytes. Kernel functions are mapped to activation functions that represent neuronal and synaptic discharges, with distinct neurons and edges symbolizing different brain regions, thereby reflecting the classical brain model.
The architecture of the NCB bridges the gap between Physics Informed Neural Networks (PINN) and Kolmogorov-Arnold Networks (KAN). NCB allows for fine-tuning of the grid at center points. In NCB, an increase in dimensionality is represented as a string, causing a center point to be upgraded to an edge. Conversely, dimensionality reduction, resulting from the collapse of the wave function, is depicted as a single particle, allowing an edge to degenerate back into a center point. Additionally, NCB incorporates the superposition and opposite spin of particles at different nodes and center points kernel functions, enabling the assignment of different kernel functions based on residual comparisons.
Through testing with sine similarity, ELLKAN outperforms CEKAN significantly. We also conducted simulations using NCB, CEKAN (Constant Edge KAN), DEKAN (Decreasing Edge KAN), and IEKAN (Increasing Edge KAN) within the SIR model of disease dynamics. Compared to DEKAN, IEKAN more accurately reflects the distribution of synaptic strengths in various brain regions. Furthermore, the non-classical NCB simulation outperforms the classical CEKAN, IEKAN and Willow, achieving better results with fewer iterations and reduced runtime. We gave the NCB iterates a brain circuit iteration demonstration, and introduced the concept of edge stacking and iteration.
... The fact that stocks usually operate at different timescales further increases the complexity of linking structure among stocks in a hydrological system. In spite of the wide application of system dynamics in different areas (Wiener, 1948;Zera, 2002;Hofkirchner and Schafranek, 2011;Seth and Bayne, 2022), including land use dynamics (Lauf et al., 2012) and water management (Bai et al., 2021;Simonovic, 2020) in which the natural system only plays a minor role, to our knowledge, the concept has rarely been used in hydrology under natural conditions. The system dynamics approach may thus open a new avenue to understand the mechanisms of the long-term and slow dynamics of the hydrological system. ...
Hydrological models with conceptual tipping bucket and process-based evapotranspiration formulations are the most common tools in hydrology. However, these models consistently fail to replicate long-term and slow dynamics of a hydrological system, indicating the need for model augmentation and a shift in formulation approach. This study employed an entirely different approach – system dynamics – towards more realistic replication of the observed slow hydrological behaviors at inter-annual and inter-decadal scales. Using the headwaters of Baiyang Lake in China as a case study, the endogenous linking structure of the hydrological system was gradually unraveled from 1982 to 2015 through wavelet analysis, Granger's causality test, and a system dynamics model. The wavelet analysis and Granger's causality test identified a negatively correlated and bidirectional causal relationship between actual evapotranspiration and catchment water storage change across distinct climatic periodicities, and the system dynamics approach suggested a combined structure of a vegetation reinforcing feedback and a soil water–vegetation balancing feedback in the hydrological system. The system dynamics' structure successfully captured the slow hydrological behaviors under both natural and human-intervention scenarios, demonstrating a self-sustained oscillation arising within the system's boundary. Our results showed that the interaction between the vegetation structure and the soil-bound water dominates the hydrological process at an inter-annual scale, while the interaction between the climatic oscillation and the soil-water-holding capacity dominates the hydrological process at an inter-decadal scale. Conventional hydrological models, which typically employ physiological-based evapotranspiration formulations and assume invariable soil characteristics, ignore vegetation structure change at the inter-annual scale and soil-water-holding capacity change at the inter-decadal scale, leading to failure in predicting the observed long-term hydrological behaviors. The system dynamics model is in its early stage with applications primarily confined to water-stressed regions and long-term scales. However, the novel insights proposed in our study, including the different hierarchies corresponding to distinct mechanisms and timescales and the endogenous linking structure among stocks being a more important driver of the hydrological behaviors, offer potential solutions for better understanding a hydrological system and guidelines for improving the configuration and performance of conventional hydrological models.
... Integrated Information Theory 4.0 (IIT) is one of the leading frameworks in the neuroscience of consciousness (Consortium et al., 2023;Seth and Bayne, 2022;Signorelli et al., 2021). It aims to explain consciousness by mathematically formalizing its relation to cause-effect power and existence, while employing computational tools to investigate this experimentally (Zaeemzadeh and Tononi, 2024;Albantakis et al., 2023;Ellia et al., 2021). ...
Integrated Information Theory 4.0 (IIT) aims to explain consciousness by mathematically formalizing it in terms of existence as causal power and employing computational tools for experimental assessment. IIT conceives consciousness as an intrinsic structure of cause-effect powers, postulating that any conscious system exists for itself as a maximally unitary whole, irreducible to its parts, mathematically assessed in terms of maximal system integrated information (φ_s^*). In this theoretical article, we critique IIT’s conceptual interpretation of φ_s^*, which is grounded in problematic ontological assumptions that exclude non-φ_s^*, non-conscious systems—such as the body—from true existence. This exclusion leads to theoretical tensions within IIT and with standard neuroscience. We propose minimal conceptual adjustments to resolve these issues, enabling IIT to adopt causal-physical realism by: i) rejecting the so-called principle of true existence (and the associated Great Divide of Being), ii) modifying the principle of maximal existence, and iii) adopting a fully realistic principle of being. These changes preserve IIT’s mathematical rigor while enhancing its theoretical robustness and compatibility with standard neuroscience, allowing IIT theorists to investigate the physical substrates of consciousness and their cause-effect structures computationally, without denying the genuine existence of the non-conscious parts of their own brains and bodies.
... HOTs rarely address global states of consciousness or the functions of consciousness [59]. Unlike many other theories, however, HOTs do appear to directly affect AI systems by suggesting that a second-order process of self-curation of exemplars may be key to generalized AI. ...
The recent rise in relevance and diffusion of Artificial Intelligence (AI)-based systems and the increasing number and power of applications of AI methods invites a profound reflection on the impact of these innovative systems on scientific research and society at large. The Universal Scientific Education and Research Network (USERN), an organization that promotes initiatives to support interdisciplinary science and education across borders and actively works to improve science policy, collects here the vision of its Advisory Board members, together with a selection of AI experts, to summarize how we see developments in this exciting technology impacting science and society in the foreseeable future. In this review, we first attempt to establish clear definitions of intelligence and consciousness, then provide an overviewof AI’s state of the art and its applications. A discussion of the implications, opportunities, and liabilities of the diffusion of AI for research in a few representative fields of science follows this. Finally, we address the potential risks of AI to modern society, suggest strategies for mitigating those risks, and present our conclusions and recommendations.
... Classification of consciousness states is a challenging research topic [39]. Our 424 understanding of the underlying biological and physical principles governing this 425 complex physiological phenomenon remains limited [40]. Moreover, the precise 426 mechanisms by which anesthetic agents induce reversible loss of consciousness remains 427 to be elucidate [41]. ...
Accurate assessment of consciousness during general anesthesia is crucial for optimizing anesthetic dosage and patient safety. Current electroencephalogram-based monitoring devices can be inaccurate or unreliable in specific surgical contexts ( e . g . cephalic procedures). This study investigated the feasibility of using electrocardiogram (ECG) features and machine learning to differentiate between awake and anesthetized states. A cohort of 48 patients undergoing surgery under general anesthesia at the Tours hospital was recruited. ECG-derived features were extracted, including spectral power, heart rate variability and complexity metrics, as well as heart rate fragmentation indices (HRF). These features were augmented by a range of physiological variables. The aim was to evaluate a number of machine learning algorithms in order to identify the most appropriate method for classifying the awake and anesthetized states. The gradient boosting algorithm achieved the highest accuracy (0.84). Notably, HRF metrics exhibited the strongest predictive power across all models tested. The generalizability of this ECG-based approach was further assessed using public datasets (VitalDB, Fantasia, and MIT-BIH Polysomnographic), achieving accuracies above 0.80. This study provides evidence that ECG-based classification methods can effectively distinguish awake from anesthetized states, with HRF indices playing a pivotal role in this classification.
Author summary
General anesthesia monitoring is critical for optimizing patient safety and outcomes. While electroencephalogram (EEG)-based systems are commonly used, they have limitations in accuracy and applicability, particularly in cases where EEG electrodes placement is challenging or impossible, such as during cephalic surgeries or when patients have forehead skin lesions. Here, a novel approach using electrocardiogram (ECG) signals and machine learning techniques was used to differentiate between awake and anesthetized states during surgery. A total of 48 patients undergoing surgical procedures under general anaesthesia at the Tours hospital were selected for inclusion in the study. This investigation focused on heart rate fragmentation indices, metrics designed for assessing biological versus chronological age, derived from ECG recordings. The gradient boosting algorithm demonstrates performance comparable to leading methods reported in the literature for this classification task. Importantly, model generalizability was confirm through successful application to publicly available datasets. This article highlights the potential of ECG signals as an alternative source for deriving depth of anesthesia indices, offering increased versatility in clinical settings where EEG monitoring is challenging or contraindicated.
... The scientific study of consciousness is not only one of the most relevant and hot topics in the current scientific scene, it is also arguably one of the last frontiers in human knowledge (Block, 2002;Chalmers, 2003;Dennett, 2002;Graziano, 2019Graziano, , 2022Jimenez et al., 2024;Lau, 2022). Over the recent years, the field has witnessed an exponential growth in the research volume devoted to it and its applications (Michel et al., 2019), and also the proliferation of a number of theories from different fields such as philosophy, psychology, neurosciences, physics or even mathematics, which have tried to explain the problem of subjective experience (Northoff & Lamme, 2020;Seth & Bayne, 2022). Within consciousness research, and particularly in the field of cognitive science, one of the most controversial and debated issues refers to the study of unconscious processing and the dissociation between conscious and nonconscious perception (Rothkirch et al., 2022;Rothkirch & Hesselmann, 2017;Shanks et al., 2021;Vadillo et al., 2022). ...
The dissociation between conscious and unconscious perception is one of the most relevant issues in the study of human cognition. While there is evidence suggesting that some stimuli might be unconsciously processed up to its meaning (e.g., high-level stimulus processing), some authors claim that most results on the processing of subliminal stimuli can be explained by a mixture of methodological artefacts and questionable assumptions about what can be considered non-conscious. Particularly, one of the most controversial topics involves the method by which the awareness of the stimuli is assessed. To address this question, we introduced an integrative approach to assess the extent to which masked hierarchical stimuli (i.e., global shapes composed of local elements) can be processed in the absence of awareness. We combined a priming task where participants had to report global or local shapes, with the use of subjective and objective awareness measures collected either in a separate block (offline), or trial-by-trial during the main task (online). The unconscious processing of the masked primes was then evaluated through two different novel model-based methods: a Bayesian and a General Recognition Theory modeling approach. Despite the high correlation between awareness measures, our results show that the use of alternative approaches based on different theoretical assumptions leads to diverging conclusions about the extent of the unconscious processing of the masked primes.
... Notwithstanding such claims, there are fundamental disagreements among neuroscientists, research scientists in other disciplines, and philosophers about the definitional characteristics of consciousness (Yaron et al., 2022;Seth and Bayne, 2022). Disputes revolve around whether our human 'higher-level' form of conscious intelligence differentiates from what some consider a more primitive non-conscious general organismal intelligence encompassing 'lower ' animals, plants, and cells (LeDoux et al., 2023). ...
... A previous behavioral study reported that postcued attention could reach into the past and bring a stimulus too faint to see into consciousness by acting on the memory trace of the stimulus that disappeared before being attended (Sergent et al., 2013). Consistent with recent research suggesting that such temporally flexible conscious perception can be implemented through long-distance information sharing (Sanchez et al., 2020;Sergent, 2018;Seth and Bayne, 2022;van Vugt et al., 2018), our results suggest that perceptual integration was processed earlier in sustained activity patterns at the anterior electrodes than at the posterior and central electrodes at chance level of behavioral performance (The 3rd column of Fig. 2b). ...
... Additionally, potentially important information from deeper structures (e.g., precuneus, cingulate cortex) and subcortical areas are not considered (Koch et al., 2016). This is particularly important in light of the discussion about the state of consciousness (Seth and Bayne, 2022). Although the frontal lobe is what makes us human, there is debate about its role in a state we call consciousness (Boly et al., 2017). ...
... In regard to consciousness where there is much less agreement on foundations the role of category theory may be more facilitatory or conciliatory where questions arise. For instance, how is IIT with a focus on phenomenology supposed to connect to other theories of consciousness (Del Pin, Skóra, Sandberg, Overgaard, & Wierzchoń, 2021;Doerig, Schurger, & Herzog, 2021;Seth & Bayne, 2022;Signorelli, Szczotka, & Prentner, 2021)? What is the connection between phenomenal structure and perceptional/conceptual structure? ...
Conscious (subjective) experience permeates our daily lives, yet general consensus on a theory of consciousness remains elusive. Integrated Information Theory (IIT) is a prominent approach that asserts the existence of subjective experience (0th axiom), from an intrinsic system of causally related units, and five essential properties (axioms 1-5): intrinsicality, information, integration, exclusion and composition. However, despite empirical support for some aspects of IIT, the supposed necessity of these axioms is unclear given their informal presentation and operationalized dependence on a specific mathematical instantiation as the so-called postulates. The category theory approach presented here attempts to redress this situation. Category theory is a kind of meta-mathematics invented to make relations between formal structures formally precise and so facilitate doing "ordinary" mathematics. In this way, the five essential properties for consciousness are organized around a smaller number of meta-mathematical principles for comparison with IIT. In particular, category theory characterizes mathematical structures by their "universal mapping properties" -- a unique-existence condition for all instances of the structure. Accordingly, axioms 1-5 pertain to universal mapping properties for experience, whence the slogan, "Consciousness is a universal property."
... Having this awareness would enable them to accomplish a specific task more effectively and consciously [25,26] . ...
In the field of English as a Foreign Language, the development of listening skills is undeniably crucial, not only in an academic context but also for a learner's professional career. These skills can enhance students' comprehension, subsequently improving their writing and speaking abilities. This mixed-methods study, grounded in Metacognitive Theory, investigates whether the use of Metacognitive Strategy Instruction (MSI) within a Mobile-Assisted Language Learning (MALL) context would foster metacognitive awareness of listening and listening skills of Chinese college EFL students. In total, sixty-two students from two homogenous intact classes participated in a 13-week quasi-experimental study. The experimental group received the MSI, while the control group received traditional instruction, both within the MALL context. Quantitative data were analysed using t-tests, and qualitative data were analysed thematically. Results of the study indicated significant improvements in the experimental group's metacognitive awareness of listening and listening skills compared to those of the control group. Thematic analysis revealed the students' positive perceptions on how the MSI within MALL was used to improve their metacognitive awareness of listening and their listening skills. Overall, the findings of the study suggest the effectiveness of the MSI in enhancing both the metacognitive awareness and listening-NonCommercial 4.0 International (CC BY-NC 4.0) License (https://creativecommons.org/licenses/by-nc/4.0/). 410 Forum for Linguistic Studies | Volume 06 | Issue 06 | December 2024 skills of Chinese EFL undergraduate students. Thus, it is recommended that Chinese educators and instructors incorporate the MSI in their EFL listening classes to empower students with the use of appropriate strategies for mastering listening skills.
... EEG records electrical activity from the brain's surface; however, its limitations in depth and spatial resolution are well known. Additionally, the empirical literature increasingly supports the view that activity in key temporo-parieto-occipital (Koch et al., 2016) and/or prefrontal (Mashour et al., 2020) regions are required for consciousness to arise, rather than requiring CHARLOTTE MARTIAL, PAULINE FRITZ, & OLIVIA GOSSERIES 5 5 the involvement of the entire brain (see Seth & Bayne, 2022, for a recent review). ...
In this response, we explain why we believe it is premature for the International Association for Near-Death Studies (IANDS) to endorse a post-physicalist worldview.
... Of the various theories devised to account for consciousness (see Seth and Bayne, 2022), few deal thoroughly enough with evolutionary issues to fit easily within the framework developed here. Integrated information theory (ITT) is the poster child in this respect, a theory I would class with computational theories of consciousness in having minimal connection to neurobiological reality. ...
The functions of consciousness, viewed from an evolutionary standpoint, can be categorized as being either general or particular. There are two general functions, meaning those that do not depend on the particulars of how consciousness influences behavior or how and why it first evolved: of (1) expanding the behavioral repertoire of the individual through the gradual accumulation of neurocircuitry innovations incorporating consciousness that would not exist without it, and (2) reducing the time scale over which preprogrammed behaviors can be altered, from evolutionary time, across generations, to real-time. But neither answers Velmans’ question, of why consciousness is adaptive in a proximate sense, and hence why it would have evolved, which depends on identifying the particular function it first performed. Memory arguably plays a role here, as a strong case can be made that consciousness first evolved to make motivational control more responsive, though memory, to the past life experiences of the individual. A control mechanism of this kind could, for example, have evolved to consciously inhibit appetitive behaviors, whether consciously instigated or not, that would otherwise expose the individual to harm. There is then the question of whether, for amniote vertebrates, a role in memory formation and access would have led directly to a wider role for consciousness in the way the brain operates, or if some other explanation is required. Velmans’ question might then have two answers, the second having more to do with the advantages of global oversight for the control of behavior, as in a global workspace, or for conferring meaning on sensory experience in a way that non-conscious neural processes cannot. Meaning in this context refers specifically to the way valence is embodied in the genomic instructions for assembling the neurocircuitry responsible for phenomenal contents, so it constitutes an embodied form of species memory, and a way of thinking about the adaptive utility of consciousness that is less concerned with real-time mechanistic events than with information storage on an evolutionary time scale.
... Most importantly, however, the realms encapsulate all life on Earth, including humans. Although there remains considerable debate about current theories of consciousness [62], LeDoux outlined a theory of consciousness for humans that is consistent with the proposed realms [58]. Given that the realms include all life on Earth, the potential presumably exists to include consciousness beyond the human case [63]. ...
Evolutionary mechanisms enabled humans to profoundly transform Earth systems. Because the resulting Anthropocene systems are highly interdependent and dynamically evolving, often with accelerating rates of cultural and technological evolution, the ensuing family of societal challenges must be framed and addressed in a holistic fashion. An agile, evolutionary, system-of-systems, convergence paradigm, which is based on a partially quantifiable, scientifically falsifiable, formal theoretical framework, can be used to systematically identify, decompose, characterize, and then converge, a nested, evolutionary ensemble of geophysical, biophysical, sociocultural and sociotechnical systems. The paradigm includes individual organisms (spanning plants, fungi and animals) engaging in niche construction in a global meta-ecosystem that integrates the deep evolutionary history of all Anthropocene systems. To coherently span the vast range of scales, the paradigm is divided into a somatic realm (externally oriented with respect to individual organisms) that can be applied at global, regional, urban and local scales, as well as a visceral realm (internally oriented with respect to individual organisms) that includes organs, cells, organelles, genes and proteins. The visceral realm connects with evolutionary systems biology, biomedical engineering and systems medicine. The paradigm includes a causally coherent conceptual model based on a common language and reconciled ontology, with a hierarchical, extensible and scalable computational framework, an associated decision-support system and an educational pedagogy. The paradigm will require a major transformation in our national and global approach to science and engineering, enabling the creation of a meta-discipline that spans all the disciplines associated with the family of societal challenges of the Anthropocene.
... Although this sense is not conscious, it affects self-perception awareness, i.e., the learning process involves self-awareness. From this, it can be inferred that the way one learns involves the self-perception of each subject, which is closely linked to body perception (formation of the body schema) that arises in the light of one's own senses, including proprioception [18,[112][113][114]. ...
Body image distortion (BID) is a common symptom in eating disorders (ED), but its neurobiological underpinnings remain unclear. We aimed to discuss, through a systematic review, BID's neurobiology through studies that used neuroimaging tools in ED patients. The review was developed using the guideline PRISMA, following a 27-step checklist and four-phase flowchart. The following databases were consulted: Lilacs, Scielo, Pepsic, APA/Psycnet, Pubmed, Scopus, Bireme, Cochrane and 1.692 articles were found, 1.95% were included in this research. Anorexia nervosa (AN): Key findings included altered activity in the amygdala, insula, and anterior cingulate cortex, suggesting dysfunctions in emotional response, body perception, and cognitive processes. Additionally, studies have reported changes in functional connectivity between brain regions and reduced gray matter volume in specific areas. Bulimia nervosa (BN): Key findings include alterations in the prefrontal cortex, insula, and anterior cingulate cortex, suggesting dysfunctions in decision-making, impulse control, body perception, and emotional regulation. AN/BN: While AN-R and AN-P showed greater activation in the amygdala, BN patients exhibited greater activation in the prefrontal cortex and occipital and parietal lobes. Both groups presented alterations in functional connectivity and excessive preoccupation with body image, suggesting shared neural underpinnings despite subtype-specific differences. Binge eating disorder (BED): one article exhibits increased activation in the left fusiform body area (FBA) when viewing body stimuli, suggesting an attentional bias towards the body, without corresponding increases in emotional areas. BID in ED seems to be linked to alterations in paralimbic structures (cingulate cortex and insula), the default-mode network, and parietal, temporal, and occipital regions. These brain areas are associated with the subjective self, ego, and perceptual processes. The findings suggest that ED patients may have a perceptual judgment error and excessive self-reference, leading to dysfunctional behaviors as an attempt to resolve this perceptual distortion.
... Selective attention plays a 7 critical role in how the brain filters and prioritizes information, which is central to 8 theories of consciousness. For instance, the Global Workspace Theory (GWT) 9 suggests that consciousness arises when selected information is made accessible to 10 various specialized brain modules, allowing it to be shared and processed more 11 broadly (Seth & Bayne, 2022). By highlighting the role of attention in determining 12 which information enters this "workspace," GWT underscores the importance of amplitude, indicating heightened readiness and synchronization for a rapid response 1 to the upcoming event (Mento, 2017). 2 Previous research has primarily investigated association cues within conscious 3 states, leaving open the question of whether such cues can evoke temporal attention 4 effects unconsciously. ...
Temporal attention is the ability to prioritize information based on timing. While conscious perception of temporally structured information is known to generate temporal attention, whether it occurs unconsciously remains uncertain. This study used a temporal cueing paradigm with masking techniques to explore the differences between conscious and unconscious temporal attention mechanisms. Experiment 1 found that both visible and invisible cues triggered temporal attention, with stronger effects for visible cues. Experiment 2, using electroencephalogram (EEG) recordings, showed that both visible and invisible cues evoked contingent negative variation (CNV) component, albeit smaller with invisible cues. The P300 component further supported this pattern. Hierarchical drift–diffusion modeling (HDDM) analysis demonstrated that both conscious and unconscious temporal attention effects involve non-perceptual decision-making processes. These findings both align and challenge the Global Workspace Theory, suggesting that while consciousness enhances conscious attention via global broadcasting, unconscious attention may rely on more localized neural networks.
... 22 Awakening studies providing subjective reports are well-suited for this purpose 34 and have in part led to proposed laws of consciousness. [35][36][37] High-density EEG studies point to the 'posterior cortical hot zone' as a correlate. 20, 34,38,39 However, according to a recent review, there is no consensus on the neural correlates of dreaming. ...
Purpose: Since the 1930s, researchers have awakened people from different stages of sleep to record what they have experienced. While some aspects, including asking whether participants had dreams or thoughts before awakening, largely remain the same, others, such as the method of awakening, vary greatly. In addition, it is often assumed that the influence of participant characteristics, such as personality traits, motivation, memory, and attention, is reduced by collecting experiences immediately after they occur, rather than through delayed morning recall. However, the extent to which these variables influence dream recall upon awakening has not yet been thoroughly investigated.
Materials and methods: To explore possible contextual and individual influences, this review analyzed 69 awakening studies conducted between 2000 and 2024 and utilized the DREAM database. Differences between sleep stages were quantified and experiences analyzed using the categories 'with recall', 'without recall', and 'no report'.
Results: Similar levels of null reports were found in NREM stage 2 and stage 3. Significant factors affecting dream recall included the method of awakening (lower recall with an alarm compared to calling the participant's name), the number of study days (reduced recall for multiple days) and the sleep environment (higher recall at home compared to the laboratory), along with participant characteristics beyond age, sex and study design. Recall rates from NREM sleep are particularly sensitive to the method of awakening and interindividual differences.
Conclusion: Both the awakening procedure and participant characteristics influence the amount of reported sleep experiences, which can impact study outcomes, such as the identification of neural correlates of consciousness. Therefore, greater emphasis needs to be placed on how experiences are collected and on participant characteristics, such as openness to experience or familiarity with different states of consciousness.
... Therefore, the attributed prominence of consciousness creates a hard problem. Notable researchers, too agree that introspection should not have a place in studies of consciousness, yet they admit that there is hardly a clear way of making research on consciousness sensible without referring to introspection (e.g., Seth & Bayne, 2022). The alternative term proposed to avoid it, interoception, helps no better as long as consciousness is looked for in the human brain (Nikolova et al., 2022). ...
The increasing irrelevancy of reported results in psychology to their conjectures has been growingly well-documented. There is also a shift of emphasis from methodological to theoretical shortcomings. Inasmuch as the attention devoted to the replication crisis is justified, the call that methodological drawbacks are hardly addressable unless the theoretical base is fortified is equally warranted. In this paper, we intend to expound on our estimation that epistemological foundations of psychology are in need of dire reinforcement if our discipline should remain a bastion of knowledge production. We assert a twofold argument that (a) psychology has to free itself from verbal ambiguities and linguistic imperfections in order to relate its subject matter to its methodology and (b) for that reason, reinstate its subject matter in such a precision that conjectures and implications produced across subfields could be relatable and translatable to each other. In support of our argument, we attempt to identify epistemic conditions through which both parts of our argument could be sustained. For the first part, we consult semiotics to illustrate a way of discerning robust phenomena and thus establishing the validity of constructs. For the second part, we revisit the beginnings of the science of psychology to highlight the dead end we have currently been stuck in. We search for prerequisites of turning data points over subjective experience into objective information. In addition, the authors are well informed that none of the problems summarized in the literature nor the efforts towards resolving them cannot be considered independently from overarching sociopolitical influences and effective circumstances shaping research environments. Therefore, we take note of the present-day state of affairs corroding the ever-weakening tie between theory and methodology.
... In contemporary neuroscience, consciousness is investigated through processing brain information and the emergence of conscious experience [45,46]. Thus, sentience is just one of several components of consciousness, which ranges from sensory perception to more complex cognitive elements, such as reflection on experiences and projection about the past and future [47,48]. ...
The rapid growth of shrimp farming, particularly of Penaeus vannamei, accounts for about 80% of the global production of farmed shrimp and involves the cultivation of approximately 383 to 977 billion individuals annually, which highlights the urgent need to address the ethical and technical implications of raising potentially sentient beings. This study builds on the state-of-the-art assessment of sentience, consciousness, stress, distress, nociception, pain perception, and welfare to adapt the General Welfare Index (GWI) for farmed shrimp. The GWI is a quantitative index developed by our research group to measure the degree of welfare in aquaculture, and it has been previously applied to grass carp and tilapia. Using the PRISMA methodology and the creation of a hypothetical shrimp farm, the GWI, with 31 specific and measurable indicators across various welfare domains, is adapted to P. vannamei, offering a comprehensive assessment framework. The inclusion of quantitative welfare indicators promises to improve living conditions in alignment with legislation adopted on decapods’ sentience and contemporary scientific advances.
... The scientific problem of consciousness lies at the core of clinical practice. The problem of subjective experience is the key aspect of consciousness (Seth and Bayne 2022). From a behavioral perspective, subjective experience can be framed as the result of private stimuli and covert behavioral responses, leading to private events (Tourinho 2006). ...
This essay provides an analysis of the clinical problems that arise at the borderline between neurology and psychiatry. We postulate that psychopathological and neuropathological constructs have distinct referents and conceptual fields, but they are not mutually exclusive categories. After establishing criteria for defining neuropsychiatric constructs, we outline rules for identifying cases where a significant relationship exists between neuropathological and psychopathological patterns. We propose three approaches to establishing this relationship: a clinical epidemiology approach, a clinical neuroscience approach offering a mechanistic model, and a longitudinal study of individual cases incorporating idiographic perspectives. We discuss the logic of brain-behavior relationships and emphasize the need for closer academic feedback between neuroscientific research and clinical practice to better understand the causality of psychopathological phenomena in the context of neuropsychiatry. While precise demarcation may be challenging in some cases, we provide two types of examples: first, straightforward cases where neuropathology explains psychopathological patterns; second, complex cases that require studying the causal interactions between neuropathological factors, psychological factors, and sociocultural contexts, leading to intricate clinical patterns that demand cognitive neuropsychiatry resources, and a multidisciplinary approach to clinical care.
... In short, these two problems seem to show that IIT should reconsider the ontological status of these extrinsic entities, because they need to exist objectively to account for the ontological implications of the scenarios we present here, which are permitted by the operational framework of the theory. KEYWORDS integrated information theory, intrinsicality problem, consciousness science, ontology of consciousness, formal metaphysics, intrinsic existence, extrinsic existence, idealism 1 IIT 4.0: key scientific and ontological foundations Currently, Integrated Information Theory (IIT) is recognized as one of the leading scientific theories of consciousness (Seth and Bayne, 2022;Ferrante et al., 2023;Signorelli et al., 2021). In contrast to other prominent theories, like the Global Neuronal Workspace Theory (Dehaene, 2014;Mashour et al., 2020;Dehaene et al., 2011), it primarily aims to explain the qualitative and subjective nature of experience, rather than targeting its neural, behavioral, computational, or functional correlates (Ellia et al., 2021;Albantakis et al., 2023). ...
In this article we present two ontological problems for the Integrated Information Theory of Consciousness 4.0: what we call the (i) the intrinsicality 2.0 problem, and (ii) the engineering problem. These problems entail that truly existing, conscious entities can depend on, and be engineered from, entities that do not objectively exist, which is problematic: if something does not exist in objective reality (i.e., in itself, independently of another entity’s consciousness), then it seems that it cannot be part of the material basis and determinants of other entities that do exist on their own. We argue that the core origin of these problems lies in IIT’s equation between true existence and phenomenal existence (consciousness), and the corresponding ontological exclusion of non-conscious physical entities (i.e., extrinsic entities) from objective reality. In short, these two problems seem to show that IIT should reconsider the ontological status of these extrinsic entities, because they need to exist objectively to account for the ontological implications of the scenarios we present here, which are permitted by the operational framework of the theory.
... The neural correlates of consciousness have sparked a vibrant debate for more than 30 years [1][2][3] . The majority of theories 4 focus on the mechanisms underlying the awareness an external stimulus (e.g., visual in the recent major initiative from the Cogitate Consortium 5 ). Neurophenomenological approaches highlight the investigation of the structure of consciousness, in addition to its content 6,7 . ...
Conscious experience encompasses not only the awareness of external objects, but also a phenomenal representation of the embodied subject of the experience. The latter is mediated by the integration of multisensory stimuli between the body and the environment, a process mediated by the Peripersonal Space (PPS) system. Here we thus tested the hypothesis that a neural marker of PPS representation may index the presence of conscious experience. Using high-density EEG in awake participants, we identified a PPS index, characterized by high-beta oscillations in centroparietal regions during the integration of audiotactile stimuli presented near versus far from the body. We then examined this marker across two models of altered consciousness, i.e., sleep and disorders of consciousness. The PPS index persisted during dreaming and waking conscious states but was absent during dreamless, unconscious states. Moreover, the same index predicted behavioural measures of consciousness and clinical outcome in patients recovering from disorders of consciousness. These results suggest that multisensory integration within the PPS is tightly linked to the presence of conscious experience.
This project engages contemporary research in the neurobiology of consciousness, specifically the Integrated Information Theory (IIT), and its implications for social science research- in particular consciousness as emergent and its implications for the emergent nature of social systems. The book thus acts as an introduction to IIT for social theorists while and its model of ontological emergence. But as I argue in the book, social systems are a particular kind of emergence precisely because its primitive entities are conscious. The book then develops a simple model of the primitive entities of social systems- humans- and why social systems are inevitably complex. The concluding chapter discusses the value for the social sciences of engaging the natural sciences. It does so by engaging recent debates in Quantum IR with a comparison with complexity theory.
Consciousness emerges from complex neural processes, yet its precise neurobiological correlates remain uncertain. Here a space-time-resolved inference-based framework is applied to estimate the neurophysiological variables of a wholecortex model and analyze the neural mechanism correlates of global consciousness by way of a correlation analysis between behavioural and neural variable time-series. Using magnetoencephalography (MEG) data from 15 participants under Xenon-induced anesthesia, interconnected neural mass models (NMMs) were developed and time-evolving regional neurophysiological variables and inter-regional connectivity strengths were inferred from the data. Analyses revealed significant correlations between consciousness levels and inter-regional connectivity, particularly in posterior parietal, occipital, and prefrontal regions. Moreover, results support a parietal, rather than frontal, network backbone to facilitate global consciousness. Regional-level analyses further identified correlates of consciousness within the posterior parietal and occipital regions. Lastly, reductions in consciousness were linked to stabilized cortical dynamics, reflected by changes in the eigenmodes of the system. This framework provides a novel, inference-based approach to investigating consciousness, offering a time-resolved perspective on neural mechanism correlates during altered states.
Neuroscienze cognitive e psicologie del profondo si confrontano sui modelli della percezione-coscienza-pensiero. Vi è un parallelismo tra il rapporto coscienza d'accesso/fenomenica e incon-scio rimosso/non rimosso. Gli organismi per sopravvivere devono minimizzare l'impatto delle variazioni ambientali sui parametri omeostatici, ovvero la sorpresa data dallo scostamento degli eventi inattesi da quelli compatibili con la vita. Friston ha teorizzato il principio di energia libera, che pone un limite superiore alla sorpresa, in opposizione alla tendenza all'aumento di entropia. Il cervello è una macchina predittiva che anticipa il cambiamento e costruisce la realtà interpretando i dati percettivi in base a inferenze inconsce sulla migliore spiegazione possibile basate sui dati in memoria e testando le predizioni sui dati sensoriali. La coscienza nasce dalla rilevazione degli squilibri omeostatici e dalla risposta adattativa data dei sentimenti affettivi.
Stimolato dall'impegnativo articolo di Stefano Fissi (2024), che attinge agli ultimi lavori di Anil Seth e Mark Solms per affrontare argomenti molto discussi nella fase attuale del dibattito sulla possibilità di una validazione reciproca tra neuroscienze cognitive e psicoanalisi, vengono riattra-versate le tematiche fondamentali indicando problematicità epistemologiche e comprensibili debo-lezze argomentative.
We propose a new theory to explain the nature and function of subjective experience, as a mechanism that guides the organism towards beneficial outcomes. In simple animals, that guidance takes the form of an affect producing a fitness-enhancing response. In human consciousness, there is not a single response, but a range of potential developments allowing a free choice. That range can be modelled as a local prospect: a field of possibilities centred on the present situation and coloured by valence. Building on neural models of global workspace and adaptive resonance, we suggest how such a prospect could be implemented in the brain: as a halo of activation diffusing from, and feeding back into, a core of circulating activation. Using the thought experiment of qualia inversion, we argue that local prospect theory solves the ‘hard problem of consciousness’. We show how our theory explains key characteristics of consciousness: subjectivity, feeling, free will, agency, transience, continuity, integration, selectivity, intentionality, and ‘what it is like’.
Perceptual multistability has been studied for centuries using a diverse collection of approaches. Insights derived from this phenomenon range from core principles of information processing, such as perceptual inference, to high-level concerns, such as visual awareness. The dominant computational explanations of perceptual multistability are based on the Helmholtzian view of perception as inverse inference. However, these approaches struggle to account for the crucial role played by value, e.g., with percepts paired with reward dominating for longer periods than unpaired ones. In this study, we formulate perceptual multistability in terms of dynamic, value-based, choice, employing the formalism of a partially observable Markov decision process (POMDP). We use binocular rivalry as an example, considering different explicit and implicit sources of reward (and punishment) for each percept. The resulting values are time-dependent and influenced by novelty as a form of exploration. The solution of the POMDP is the optimal perceptual policy, and we show that this can replicate and explain several characteristics of binocular rivalry, ranging from classic hallmarks such as apparently spontaneous random switches with approximately gamma-distributed dominance periods to more subtle aspects such as the rich temporal dynamics of perceptual switching rates. Overall, our decision-theoretic perspective on perceptual multistability not only accounts for a wealth of unexplained data, but also opens up modern conceptions of internal reinforcement learning in service of understanding perceptual phenomena, and sensory processing more generally.
Does matter feel?
Do flowers enjoy blooming?
Do springs dislike being stretched?
Modern science denies that matter feels anything at all, let alone pleasure or pain.
But modern science is yet to explain how anything can feel—even us.
What Matter Feels presents an innovative scientific framework that explores how consciousness might emerge from material systems.
By rethinking foundational principles of physics, the book proposes that psychological properties of matter can be measured with the same accuracy as its physical properties like mass and energy.
The tools and methods offered in this treatise introduce a groundbreaking way of predicting and testing the psychological behaviour of both living and non-living systems, opening a new frontier in scientific inquiry.
On broadly Copernican grounds, we are entitled to default assume that apparently behaviorally sophisticated extraterrestrial entities ("aliens") would be conscious. Otherwise, we humans would be inexplicably, implausibly lucky to have consciousness, while similarly behaviorally sophisticated entities elsewhere would be mere shells, devoid of consciousness. However, this Copernican default assumption is canceled in the case of behaviorally sophisticated entities designed to mimic superficial features associated with consciousness in humans ("consciousness mimics"), and in particular a broad class of current, near-future, and hypothetical robots. These considerations, which we formulate, respectively, as the Copernican and Mimicry Arguments, jointly defeat an otherwise potentially attractive parity principle, according to which we should apply the same types of behavioral or cognitive tests to aliens and robots, attributing or denying consciousness similarly to the extent they perform similarly. Instead of grounding speculations about alien and robot consciousness in metaphysical or scientific theories about the physical or functional bases of consciousness, our approach appeals directly to the epistemic principles of Copernican mediocrity and inference to the best explanation. This permits us to justify certain default assumptions about consciousness while remaining to a substantial extent neutral about specific metaphysical and scientific theories.
Study Objectives
Disorders of arousal (DoA) are diagnosed on the basis of clinical criteria including inappropriate or absent responsiveness to communication attempts. Surprisingly, the ability of patients to interact with others during DoA episodes has not been systematically investigated. To address this gap, we conducted three studies.
Methods
First, we used a retrospective questionnaire to assess verbal responsiveness during episodes in 61 adult patients with DoA (Study 1). Second, we used auditory stimulation during polysomnographically-verified N3 sleep to trigger DoA episodes in 14 patients. We then asked questions to test the possibility of verbal interactions during the episodes (Study 2). Third, we assessed the presence and quality of conversations with a bed partner in 364 home video-recorded episodes from 19 patients (Study 3).
Results
In Study 1, most patients (81%) reported occasional conversations during parasomnia episodes. Patients’ ongoing mental content influenced both their responses to questions during episodes and their perceptions of the outside world (including the identity of their interlocutor their environment). In study 2, auditory stimulation had a limited effect in inducing episodes (7/157 trials). One patient indirectly responded to our verbal prompts in a DoA episode. In Study 3, we found 37 video instances of discussion between patients and their partner.
Conclusions
Overall, our findings suggest that DoA episodes are not a uniform state, but may instead encompass varying states of consciousness, characterized by different levels of responsiveness and a complex interplay between internal and external information processing. These results highlight the limitations of current diagnostic criteria for DoA.
In recent years there has been an ever-increasing variety in organoid models representing many different organs and tissues and modeling their functions. Nervous system organoids have stirred debates about engineering particular functions of the central human nervous system: the brain. Optimizing the brain-likeness of the models is a key aim. How far can and should brain-likeness go? In particular, the engineering of ‘consciousness’ as a property specific to the brain has raised philosophical and ethical questions. Consciousness lacks clear definitions, and its substrate is largely unknown. I will suggest a focus on memory as a function instead, as this may contribute to epistemological clarity about engineering brain functions, well ahead of onrushing ethical dilemmas.
Continuous flash suppression (CFS) has been widely used to study subconscious visual processing. Under CFS, the conscious perception of a stimulus presented to one eye is suppressed by a flashing Mondrian noise to the other eye, while certain high-level visual processes might be still functional. It remains unresolved how much the responses of V1 neurons, where inputs from both eyes first merge, are affected by CFS, and how the effects would implicate the understanding of the assumed subconscious processing. Here we employed two-photon calcium imaging to simultaneously record the responses of hundreds of V1 neurons to a grating stimulus under CFS in two awake, fixating macaques. We found that the flashing noise completely or nearly completely wiped out the orientation responses of neurons preferring the noise eye or both eyes, with the population orientation tuning functions showing no measurable or very wide bandwidths. It also suppressed the responses of neurons preferring the grating eye, though to a lesser extent, with still measurable tuning bandwidths. These suppressive effects can be explained by an ocular dominance-dependent gain control model. We suggest that the severely compromised stimulus information in V1 under CFS, when transmitted to downstream visual areas, may be too weak to enter conscious perception. Moreover, the assumed subconscious visual processing is likely not much different from normal visual processing of sub-threshold stimulus information.
Spiritualita je dnes reflektována řadou vědních disciplín. V současném živém diskurzu o vystižení podstaty tohoto pojmu zůstává zásadním a spo jujícím prvkem různorodých definic osobní zkušenostní rozměr. Orien tace na subjektivitu otevírá klíčové téma vědomí, tázání po původu a charakteru vědomí a řešení psychofyzického problému. Jednotný přístup k vědomí ani jednotnou definici spirituality nemáme a ani mít nemůžeme, protože jednotlivé přístupy vycházejí z rozdílných paradigmatických stanovisek. Nezbývá než konstatovat, že jsme stále pod přetrvávajícím vlivem materialistického newtonovskokarteziánského výkladu světa, avšak nacházíme se v situaci postupné proměny paradigmatu, tj. systému vědeckých teorií a světonázoru (viz více níže), kdy vedle sebe existuje několik paralelních východisek, jak nahlížet na realitu, a v důsledku toho i na spiritualitu. Jaké jsou předpoklady nového rodícího se postmaterialistického pa radigmatu? Je člověk bytost neuronální, nebo nonneuronální? Proč by se měla věda otevřít informovanému studiu všech aspektů vědomí a přestat se bránit výzkumům, které byly doposavad nahlíženy jako kontroverzní? Zdá se, že k vysvětlení rozmanitosti lidských zkušeností fyzikalistické teorie nestačí. Jaké jsou tedy postmaterialistické koncepce řešení psycho fyzického problému? A v jakých ohledech se odlišují materialistické a postmaterialistické modely spirituality? Následující dvě kapitoly zohledňují jednotlivé přístupy, avšak otevřeně se hlásí k postmaterialistické formě paradigmatu, jejíž výzkum probíhá mimo hlavní proud vědy; je holistickým (tj. celostním) konceptem a jejím cílem je vyvážení materiálních a duchovních stránek západní společnosti.
The diagnosis and prognosis of disorders of consciousness pose challenges for clinics because human consciousness is still a mysterious and unknown phenomenon. Scientists and clinicians are seeking evidence from neuroimaging and electrophysiology to explore the biological and pathological mechanisms of human consciousness. They attempt to provide new insights into the neuronal foundations of consciousness injury and recovery. These findings have improved the accuracy of the clinical diagnosis and prognosis of disorders of consciousness to some extent. However, they are still not clearly sorted out. Herein, we structure the available knowledge on the basis of neuroimaging (including positron emission tomography, functional magnetic resonance imaging, and functional near-infrared spectroscopy) and electrophysiology (spontaneous electroencephalography, event-related potentials, brain–computer interfaces, and transcranial magnetic stimulation-evoked electroencephalography) studies and their associations with disorders of consciousness-relevant clinical practice. Our aim is to promote their translation into the clinical management of patients with disorders of consciousness.
In this report, we argue that there is a realistic possibility that some AI systems will be conscious and/or robustly agentic in the near future. That means that the prospect of AI welfare and moral patienthood, i.e. of AI systems with their own interests and moral significance, is no longer an issue only for sci-fi or the distant future. It is an issue for the near future, and AI companies and other actors have a responsibility to start taking it seriously. We also recommend three early steps that AI companies and other actors can take: They can (1) acknowledge that AI welfare is an important and difficult issue (and ensure that language model outputs do the same), (2) start assessing AI systems for evidence of consciousness and robust agency, and (3) prepare policies and procedures for treating AI systems with an appropriate level of moral concern. To be clear, our argument in this report is not that AI systems definitely are, or will be, conscious, robustly agentic, or otherwise morally significant. Instead, our argument is that there is substantial uncertainty about these possibilities, and so we need to improve our understanding of AI welfare and our ability to make wise decisions about this issue. Otherwise there is a significant risk that we will mishandle decisions about AI welfare, mistakenly harming AI systems that matter morally and/or mistakenly caring for AI systems that do not.
Understanding neural mechanisms of consciousness remains a challenging question in neuroscience. A central debate in the field concerns whether consciousness arises from global interactions that involve multiple brain regions or focal neural activity, such as in sensory cortex. Additionally, global theories diverge between the Global Neuronal Workspace (GNW) hypothesis, which emphasizes frontal and parietal areas, and the Integrated Information Theory (IIT), which focuses on information integration within posterior cortical regions. To disentangle the global vs. local and frontoparietal vs. posterior dilemmas, we measured global functional connectivity and local neural synchrony with functional magnetic resonance imaging (fMRI) data across a spectrum of conscious states in humans induced by psychedelics, sleep, and deep sedation. We found that psychedelic states are associated with increased global functional connectivity and decreased local neural synchrony. In contrast, non-REM sleep and deep sedation displayed the opposite pattern, suggesting that consciousness arises from global brain network interactions rather than localized activity. This mirror-image pattern between enhanced and diminished states was observed in both anterior-posterior (A-P) and posterior-posterior (P-P) brain regions but not within the anterior part of the brain alone. Moreover, anterior transmodal regions played a key role in A-P connectivity, while both posterior transmodal and posterior unimodal regions were critical for P-P connectivity. Overall, these findings provide empirical evidence supporting global theories of consciousness in relation to varying states of consciousness. They also bridge the gap between two prominent theories, GNW and IIT, by demonstrating how different theories can converge on shared neuronal mechanisms.
One of the great frontiers of consciousness science is understanding how early consciousness arises in the development of the human infant. The reciprocal relationship between the default mode network and fronto-parietal networks-the dorsal attention and executive control network-is thought to facilitate integration of information across the brain and its availability for a wide set of conscious mental operations. It remains unknown whether the brain mechanism of conscious awareness is instantiated in infants from birth. To address this gap, we investigated the development of the default mode and fronto-parietal networks and of their reciprocal relationship in neonates. To understand the effect of early neonate age on these networks, we also assessed neonates born prematurely or before term-equivalent age. We used the Developing Human Connectome Project, a unique Open Science dataset which provides a large sample of neonatal functional MRI data with high temporal and spatial resolution. Resting state functional MRI data for full-term neonates (n = 282, age 41.2 weeks ± 12 days) and preterm neonates scanned at term-equivalent age (n = 73, 40.9 weeks ± 14.5 days), or before term-equivalent age (n = 73, 34.6 weeks ± 13.4 days), were obtained from the Developing Human Connectome Project, and for a reference adult group (n = 176, 22-36 years), from the Human Connectome Project. For the first time, we show that the reciprocal relationship between the default mode and dorsal attention network was present at full-term birth or term-equivalent age. Although different from the adult networks, the default mode, dorsal attention and executive control networks were present as distinct networks at full-term birth or term-equivalent age, but premature birth was associated with network disruption. By contrast, neonates before term-equivalent age showed dramatic underdevelopment of high-order networks. Only the dorsal attention network was present as a distinct network and the reciprocal network relationship was not yet formed. Our results suggest that, at full-term birth or by term-equivalent age, infants possess key features of the neural circuitry that enables integration of information across diverse sensory and high-order functional modules, giving rise to conscious awareness. Conversely, they suggest that this brain infrastructure is not present before infants reach term-equivalent age. These findings improve understanding of the ontogeny of high-order network dynamics that support conscious awareness and of their disruption by premature birth.
A major debate about the neural correlates of conscious perception concerns its cortical organization, namely, whether it includes the prefrontal cortex (PFC), which mediates executive functions, or it is constrained within posterior cortices. It has been suggested that PFC activity during paradigms investigating conscious perception is conflated with post-perceptual processes associated with reporting the contents of consciousness or feedforward signals originating from exogenous stimulus manipulations and relayed via posterior cortical areas. We addressed this debate by simultaneously probing neuronal populations in the rhesus macaque (Macaca mulatta) PFC during a no-report paradigm, capable of instigating internally generated transitions in conscious perception, without changes in visual stimulation. We find that feature-selective prefrontal neurons are modulated concomitantly with subjective perception and perceptual suppression of their preferred stimulus during both externally induced and internally generated changes in conscious perception. Importantly, this enables reliable single-trial, population decoding of conscious contents. Control experiments confirm significant decoding of stimulus contents, even when oculomotor responses, used for inferring perception, are suppressed. These findings suggest that internally generated changes in the contents of conscious visual perception are reliably reflected within the activity of prefrontal populations in the absence of volitional reports or changes in sensory input. The role of the prefrontal cortex in conscious perception is debated because of its involvement in task relevant behaviour, such as subjective perceptual reports. Here, the authors show that prefrontal activity in rhesus macaques correlates with subjective perception and the contents of consciousness can be decoded from prefrontal population activity even without reports.
The role of the primate prefrontal cortex (PFC) in conscious perception is debated. The global neuronal workspace theory of consciousness predicts that PFC neurons should contain a detailed code of the current conscious contents. Previous research showed that PFC is indeed activated in paradigms of conscious visual perception, including no-report paradigms where no voluntary behavioral report of the percept is given, thus avoiding a conflation of signals related to visual consciousness with signals related to the report. Still, it has been argued that prefrontal modulation could reflect post-perceptual processes that may be present even in the absence of report, such as thinking about the perceived stimulus, therefore reflecting a consequence rather than a direct correlate of conscious experience. Here, we investigate these issues by recording neuronal ensemble activity from the macaque ventrolateral PFC during briefly presented visual stimuli, either in isolated trials in which stimuli were clearly perceived or in sequences of rapid serial visual presentation (RSVP) in which perception and post-perceptual processing were challenged. We report that the identity of each stimulus could be decoded from PFC population activity even in the RSVP condition. The first visual signals could be detected at 60 ms after stimulus onset and information was maximal at 150 ms. However, in the RSVP condition, 200 ms after the onset of a stimulus, the decoding accuracy quickly dropped to chance level and the next stimulus started to be decodable. Interestingly, decoding in the ventrolateral PFC was stronger compared to posterior parietal cortex for both isolated and RSVP stimuli. These results indicate that neuronal populations in the macaque PFC reliably encode visual stimuli even under conditions that have been shown to challenge conscious perception and/or substantially reduce the probability of post-perceptual processing in humans. We discuss whether the observed activation reflects conscious access, phenomenal consciousness, or merely a preconscious bottom-up wave.
Understanding how consciousness arises from neural activity remains one of the biggest challenges for neuroscience. Numerous theories have been proposed in recent years, each gaining independent empirical support. Currently, there is no comprehensive, quantitative and theory-neutral overview of the field that enables an evaluation of how theoretical frameworks interact with empirical research. We provide a bird’s eye view of studies that interpreted their findings in light of at least one of four leading neuroscientific theories of consciousness (N = 412 experiments), asking how methodological choices of the researchers might affect the final conclusions. We found that supporting a specific theory can be predicted solely from methodological choices, irrespective of findings. Furthermore, most studies interpret their findings post hoc, rather than a priori testing critical predictions of the theories. Our results highlight challenges for the field and provide researchers with an open-access website (https://ContrastDB.tau.ac.il) to further analyse trends in the neuroscience of consciousness. Yaron and colleagues collected and classified 412 experiments relating to four leading theories in consciousness research, providing a comprehensive overview of the field and unravelling trends and methodological biases.
Perception results from the interplay of sensory input and prior knowledge. Despite behavioral evidence that long-term priors powerfully shape perception, the neural mechanisms underlying these interactions remain poorly understood. We obtained direct cortical recordings in neurosurgical patients as they viewed ambiguous images that elicit constant perceptual switching. We observe top-down influences from the temporal to occipital cortex, during the preferred percept that is congruent with the long-term prior. By contrast, stronger feedforward drive is observed during the non-preferred percept, consistent with a prediction error signal. A computational model based on hierarchical predictive coding and attractor networks reproduces all key experimental findings. These results suggest a pattern of large-scale information flow change underlying long-term priors’ influence on perception and provide constraints on theories about long-term priors’ influence on perception.
Models of consciousness aim to inspire new experimental protocols and aid interpretation of empirical evidence to reveal the structure of conscious experience. Nevertheless, no current model is univocally accepted on either theoretical or empirical grounds. Moreover, a straightforward comparison is difficult for conceptual reasons. In particular, we argue that different models explicitly or implicitly subscribe to different notions of what constitutes a satisfactory explanation, use different tools in their explanatory endeavours and even aim to explain very different phenomena. We thus present a framework to compare existing models in the field with respect to what we call their ‘explanatory profiles’. We focus on the following minimal dimensions: mode of explanation, mechanisms of explanation and target of explanation. We also discuss the empirical consequences of the discussed discrepancies among models. This approach may eventually lead to identifying driving assumptions, theoretical commitments, experimental predictions and a better design of future testing experiments. Finally, our conclusion points to more integrative theoretical research, where axiomatic models may play a critical role in solving current theoretical and experimental contradictions.
Recently, the mechanistic framework of active inference has been put forward as a principled foundation to develop an overarching theory of consciousness which would help address conceptual disparities in the field (Wiese 2018; Hohwy and Seth 2020). For that promise to bear out, we argue that current proposals resting on the active inference scheme need refinement to become a process theory of consciousness. One way of improving a theory in mechanistic terms is to use formalisms such as computational models that implement, attune and validate the conceptual notions put forward. Here, we examine how computational modelling approaches have been used to refine the theoretical proposals linking active inference and consciousness, with a focus on the extent and success to which they have been developed to accommodate different facets of consciousness and experimental paradigms, as well as how simulations and empirical data have been used to test and improve these computational models. While current attempts using this approach have shown promising results, we argue they remain preliminary in nature. To refine their predictive and structural validity, testing those models against empirical data is needed i.e., new and unobserved neural data. A remaining challenge for active inference to become a theory of consciousness is to generalize the model to accommodate the broad range of consciousness explananda; and in particular to account for the phenomenological aspects of experience. Notwithstanding these gaps, this approach has proven to be a valuable avenue for theory advancement and holds great potential for future research.
Championing open science, an adversarial collaboration aims to unravel the footprints of consciousness
[This corrects the article DOI: 10.1093/nc/niab011.].
The neural mechanisms underlying conscious recognition remain unclear, particularly the roles played by the prefrontal cortex, deactivated brain areas and subcortical regions. We investigated neural activity during conscious object recognition using 7 Tesla fMRI while human participants viewed object images presented at liminal contrasts. Here, we show both recognized and unrecognized images recruit widely distributed cortical and subcortical regions; however, recognized images elicit enhanced activation of visual, frontoparietal, and subcortical networks and stronger deactivation of the default-mode network. For recognized images, object category information can be decoded from all of the involved cortical networks but not from subcortical regions. Phase-scrambled images trigger strong involvement of inferior frontal junction, anterior cingulate cortex and default-mode network, implicating these regions in inferential processing under increased uncertainty. Our results indicate that content-specific activity in both activated and deactivated cortical networks and non-content-specific subcortical activity support conscious recognition.
To study consciousness, scientists need to determine when participants are conscious and when they are not. They do so with consciousness detection procedures. A recurring skeptical argument against those procedures is that they cannot be calibrated: there is no way to make sure that detection outcomes are accurate. In this article, I address two main skeptical arguments purporting to show that consciousness scientists cannot calibrate detection procedures. I conclude that there is nothing wrong with calibration in consciousness science.
There are plenty of issues to be solved in order for researchers to agree on a neural model of consciousness. Here we emphasize an often under-represented aspect in the debate: time consciousness. Consciousness and the present moment both extend in time. Experience flows through a succession of moments and progresses from future predictions, to present experiences, to past memories. However, a brief review finds that many dominant theories of consciousness only refer to brief, static, and discrete “functional moments” of time. Very few refer to more extended, dynamic, and continuous time, which is associated with conscious experience (cf. the “experienced moment”). This confusion between short and discrete versus long and continuous is, we argue, one of the core issues in theories of consciousness. Given the lack of work dedicated to time consciousness, its study could test novel predictions of rival theories of consciousness. It may be that different theories of consciousness are compatible/complementary if the different aspects of time are taken into account. Or, if it turns out that no existing theory can fully accommodate time consciousness, then perhaps it has something new to add. Regardless of outcome, the crucial step is to make subjective time a central object of study.
A central debate in philosophy and neuroscience pertains to whether PFC activity plays an essential role in the neural basis of consciousness. Neuroimaging and electrophysiology studies have revealed that the contents of conscious perceptual experience can be successfully decoded from PFC activity, but these findings might be confounded by postperceptual cognitive processes, such as thinking, reasoning, and decision-making, that are not necessary for consciousness. To clarify the involvement of the PFC in consciousness, we present a synthesis of research that has used intracranial electrical stimulation (iES) for the causal modulation of neural activity in the human PFC. This research provides compelling evidence that iES of only certain prefrontal regions (i.e., orbitofrontal cortex and anterior cingulate cortex) reliably perturbs conscious experience. Conversely, stimulation of anterolateral prefrontal sites, often considered crucial in higher-order and global workspace theories of consciousness, seldom elicits any reportable alterations in consciousness. Furthermore, the wide variety of iES-elicited effects in the PFC (e.g., emotions, thoughts, and olfactory and visual hallucinations) exhibits no clear relation to the immediate environment. Therefore, there is no evidence for the kinds of alterations in ongoing perceptual experience that would be predicted by higher-order or global workspace theories. Nevertheless, effects in the orbitofrontal and anterior cingulate cortices suggest a specific role for these PFC subregions in supporting emotional aspects of conscious experience. Overall, this evidence presents a challenge for higher-order and global workspace theories, which commonly point to the PFC as the basis for conscious perception based on correlative and possibly confounded information.
This paper addresses what we consider to be the most pressing challenge for the emerging science of consciousness: the measurement problem of consciousness. That is, by what methods can we determine the presence of and properties of consciousness? Most methods are currently developed through evaluation of the presence of consciousness in humans and here we argue that there are particular problems in application of these methods to non-human cases - what we call the indicator validity problem and the extrapolation problem. The first is a problem with the application of indicators developed using the differences in conscious and unconscious processing in humans to the identification of other conscious vs. non-conscious organisms or systems. The second is a problem in extrapolating any indicators developed in humans or other organisms to artificial systems. However, while pressing ethical concerns add urgency in the attribution of consciousness and its attendant moral status to non-human animals and intelligent machines, we cannot wait for certainty and we advocate the use of a precautionary principle to avoid doing unintentional harm. We also intend that the considerations and limitations discussed in this paper can be used to further analyse and refine the methods of consciousness science with the hope that one day we may be able to solve the measurement problem of consciousness.
An outstanding challenge for consciousness research is to characterize the neural signature of conscious access independently of any decisional processes. Here we present a model-based approach that uses inter-trial variability to identify the brain dynamics associated with stimulus processing. We demonstrate that, even in the absence of any task or behavior, the electroencephalographic response to auditory stimuli shows bifurcation dynamics around 250–300 milliseconds post-stimulus. Namely, the same stimulus gives rise to late sustained activity on some trials, and not on others. This late neural activity is predictive of task-related reports, and also of reports of conscious contents that are randomly sampled during task-free listening. Source localization further suggests that task-free conscious access recruits the same neural networks as those associated with explicit report, except for frontal executive components. Studying brain dynamics through variability could thus play a key role for identifying the core signatures of conscious access, independent of report.
The search for the neural correlates of consciousness is in need of a systematic, principled foundation that can endow putative neural correlates with greater predictive and explanatory value. Here, we propose the predictive processing framework for brain function as a promising candidate for providing this systematic foundation. The proposal is motivated by that framework’s ability to address three general challenges to identifying the neural correlates of consciousness, and to satisfy two constraints common to many theories of consciousness. Implementing the search for neural correlates of consciousness through the lens of predictive processing delivers strong potential for predictive and explanatory value through detailed, systematic mappings between neural substrates and phenomenological structure. We conclude that the predictive processing framework, precisely because it at the outset is not itself a theory of consciousness, has significant potential for advancing the neuroscience of consciousness.
At present, the science of consciousness is structured around the search for the neural correlates of consciousness (the NCCs). One of the alleged advantages of the NCCs framework is its metaphysical neutrality—the fact that it begs no contested questions with respect to debates about the fundamental nature of consciousness. Here, we argue that even if the NCC framework is metaphysically neutral, it is structurally committed, for it presupposes a certain model—what we call the Lite-Brite model—of consciousness. This, we argue, represents a serious liability for the NCC framework for the plausibility of the Lite-Brite model is very much an open question, and the science of consciousness would be better served by a framework that does not presuppose it. Drawing on interventionist ideas in the philosophy of science, we suggest that the Difference-Maker framework can provide just such an alternative. Instead of searching for the neural correlates of consciousness (NCCs), we ought to be searching for the difference makers of consciousness (DMCs). We detail how a shift to searching DMCs will change both the practice of consciousness science and the interpretation of existing results.
A key aspect of consciousness is that it represents bound or integrated information, prompting an increasing conviction that the physical substrate of consciousness must be capable of encoding integrated information in the brain. However, as Ralph Landauer insisted, ‘information is physical’ so integrated information must be physically integrated. I argue here that nearly all examples of so-called ‘integrated information’, including neuronal information processing and conventional computing, are only temporally integrated in the sense that outputs are correlated with multiple inputs: the information integration is implemented in time, rather than space, and thereby cannot correspond to physically integrated information. I point out that only energy fields are capable of integrating information in space. I describe the conscious electromagnetic information (cemi) field theory which has proposed that consciousness is physically integrated, and causally active, information encoded in the brain’s global electromagnetic (EM) field. I here extend the theory to argue that consciousness implements algorithms in space, rather than time, within the brain’s EM field. I describe how the cemi field theory accounts for most observed features of consciousness and describe recent experimental support for the theory. I also describe several untested predictions of the theory and discuss its implications for the design of artificial consciousness. The cemi field theory proposes a scientific dualism that is rooted in the difference between matter and energy, rather than matter and spirit.
Recent breakthroughs in neurobiology indicate that the time is ripe to understand how cellular-level mechanisms are related to conscious experience. Here, we highlight the biophysical properties of pyramidal cells, which allow them to act as gates that control the evolution of global activation patterns. In conscious states, this cellular mechanism enables complex sustained dynamics within the thalamocortical system, whereas during unconscious states, such signal propagation is prohibited. We suggest that the hallmark of conscious processing is the flexible integration of bottom-up and top-down data streams at the cellular level. This cellular integration mechanism provides the foundation for Dendritic Information Theory, a novel neurobiological theory of consciousness
How does consciousness vary across the animal kingdom? Are some animals ‘more conscious’ than others? This article presents a multidimensional framework for understanding interspecies variation in states of consciousness. The framework distinguishes five key dimensions of variation: perceptual richness, evaluative richness, integration at a time, integration across time, and self-consciousness. For each dimension, existing experiments that bear on it are reviewed and future experiments are suggested. By assessing a given species against each dimension, we can construct a consciousness profile for that species. On this framework, there is no single scale along which species can be ranked as more or less conscious. Rather, each species has its own distinctive consciousness profile.
Is phenomenal consciousness constitutively related to cognitive access? Despite being a fundamental issue for any science of consciousness, its empirical study faces a severe methodological puzzle. Recent years have seen numerous attempts to address this puzzle, either in practice, by offering evidence for a positive or negative answer, or in principle, by proposing a framework for eventual resolution. The present paper critically considers these endeavours, including partial-report, metacognitive and no-report paradigms, as well as the theoretical proposal that we can make progress by studying phenomenal consciousness as a natural kind. It is argued that the methodological puzzle remains obdurately with us and that, for now, we must adopt an attitude of humility towards the phenomenal.
This article is part of the theme issue ‘Perceptual consciousness and cognitive access’.
The Dream Catcher test defines the criteria for a genuine discovery of the neural constituents of phenomenal consciousness. Passing the test implies that some patterns of purely brain-based data directly correspond to the subjective features of phenomenal experience, which would help to bridge the explanatory gap between consciousness and brain. Here, we conducted the Dream Catcher test for the first time in a step-wise and simplified form, capturing its core idea. The Dream Catcher experiment involved a Data Team, which measured participants’ brain activity during sleep and collected dream reports, and a blinded Analysis Team, which was challenged to predict, based solely on brain measurements, whether or not a participant had a dream experience. Using a serial-awakening paradigm, the Data Team prepared 54 1-min polysomnograms of non-rapid eye movement sleep—27 of dreamful sleep and 27 of dreamless sleep (three of each condition from each of the nine participants)—redacting from them all associated participant and dream information. The Analysis Team attempted to classify each recording as either dreamless or dreamful using an unsupervised machine learning classifier, based on hypothesis-driven, extracted features of electroencephalography (EEG) spectral power and electrode location. The procedure was repeated over five iterations with a gradual removal of blindness. At no level of blindness did the Analysis Team perform significantly better than chance, suggesting that EEG spectral power could not be utilized to detect signatures specific to phenomenal consciousness in these data. This study marks the first step towards realizing the Dream Catcher test in practice.
Information processing in neural systems can be described and analyzed at multiple spatiotemporal scales. Generally, information at lower levels is more fine-grained but can be coarse-grained at higher levels. However, only information processed at specific scales of coarse-graining appears to be available for conscious awareness. We do not have direct experience of information available at the scale of individual neurons, which is noisy and highly stochastic. Neither do we have experience of more macro-scale interactions, such as interpersonal communications. Neurophysiological evidence suggests that conscious experiences co-vary with information encoded in coarse-grained neural states such as the firing pattern of a population of neurons. In this article, we introduce a new informational theory of consciousness: Information Closure Theory of Consciousness (ICT). We hypothesize that conscious processes are processes which form non-trivial informational closure (NTIC) with respect to the environment at certain coarse-grained scales. This hypothesis implies that conscious experience is confined due to informational closure from conscious processing to other coarse-grained scales. Information Closure Theory of Consciousness (ICT) proposes new quantitative definitions of both conscious content and conscious level. With the parsimonious definitions and a hypothesize, ICT provides explanations and predictions of various phenomena associated with consciousness. The implications of ICT naturally reconcile issues in many existing theories of consciousness and provides explanations for many of our intuitions about consciousness. Most importantly, ICT demonstrates that information can be the common language between consciousness and physical reality.
This article discusses a hypothesis recently put forward by Kanai et al., according to which information generation constitutes a functional basis of, and a sufficient condition for, consciousness. Information generation involves the ability to compress and subsequently decompress information, potentially after a temporal delay and adapted to current purposes. I will argue that information generation should not be regarded as a sufficient condition for consciousness, but could serve as what I will call a “minimal unifying model of consciousness.” A minimal unifying model (MUM) specifies at least one necessary feature of consciousness, characterizes it in a determinable way, and shows that it is entailed by (many) existing theories of consciousness. Information generation fulfills these requirements. A MUM of consciousness is useful, because it unifies existing theories of consciousness by highlighting their common assumptions, while enabling further developments from which empirical predictions can be derived. Unlike existing theories (which probably contain at least some false assumptions), a MUM is thus likely to be an adequate model of consciousness, albeit at a relatively general level. Assumptions embodied in such a model are less informative than assumptions made by more specific theories and hence function more in the way of guiding principles. Still, they enable further refinements, in line with new empirical results and broader theoretical and evolutionary considerations. This also allows developing the model in ways that facilitate more specific claims and predictions.
Intracranial electrical stimulation (iES) of the human brain has long been known to elicit a remarkable variety of perceptual, motor and cognitive effects, but the functional–anatomical basis of this heterogeneity remains poorly understood. We conducted a whole-brain mapping of iES-elicited effects, collecting first-person reports following iES at 1,537 cortical sites in 67 participants implanted with intracranial electrodes. We found that intrinsic network membership and the principal gradient of functional connectivity strongly predicted the type and frequency of iES-elicited effects in a given brain region. While iES in unimodal brain networks at the base of the cortical hierarchy elicited frequent and simple effects, effects became increasingly rare, heterogeneous and complex in heteromodal and transmodal networks higher in the hierarchy. Our study provides a comprehensive exploration of the relationship between the hierarchical organization of intrinsic functional networks and the causal modulation of human behaviour and experience with iES. Intracranial brain stimulation in humans elicits a large variety of perceptual, motor and cognitive effects. Fox et al. show strong links between the distribution and content of these responses and the brain’s intrinsic network architecture.
An increasing number of studies highlight common brain regions and processes in mediating conscious sensory experience. While most studies have been performed in the visual modality, it is implicitly assumed that similar processes are involved in other sensory modalities. However, the existence of supramodal neural processes related to conscious perception has not been convincingly shown so far. Here, we aim to directly address this issue by investigating whether neural correlates of conscious perception in one modality can predict conscious perception in a different modality. In two separate experiments, we presented participants with successive blocks of near-threshold tasks involving subjective reports of tactile, visual, or auditory stimuli during the same magnetoencephalography (MEG) acquisition. Using decoding analysis in the poststimulus period between sensory modalities, our first experiment uncovered supramodal spatiotemporal neural activity patterns predicting conscious perception of the feeble stimulation. Strikingly, these supramodal patterns included activity in primary sensory regions not directly relevant to the task (e.g., neural activity in visual cortex predicting conscious perception of auditory near-threshold stimulation). We carefully replicate our results in a control experiment that furthermore show that the relevant patterns are independent of the type of report (i.e., whether conscious perception was reported by pressing or withholding a button press). Using standard paradigms for probing neural correlates of conscious perception, our findings reveal a common signature of conscious access across sensory modalities and illustrate the temporally late and widespread broadcasting of neural representations, even into task-unrelated primary sensory processing regions.
Humans have the ability to report the contents of their subjective experience—we can say to each other, ‘I am aware of X’. The decision processes that support these reports about mental contents remain poorly understood. In this article, I propose a computational framework that characterizes awareness reports as metacognitive decisions (inference) about a generative model of perceptual content. This account is motivated from the perspective of how flexible hierarchical state spaces are built during learning and decision-making. Internal states supporting awareness reports, unlike those covarying with perceptual contents, are simple and abstract, varying along a 1D continuum from absent to present. A critical feature of this architecture is that it is both higher-order and asymmetric: a vast number of perceptual states is nested under ‘present’, but a much smaller number of possible states nested under ‘absent’. Via simulations, I show that this asymmetry provides a natural account of observations of ‘global ignition’ in brain imaging studies of awareness reports.
Predictive processing (PP) is now a prominent theoretical framework in the philosophy of mind and cognitive science. This review focuses on PP research with a relatively philosophical focus, taking stock of the framework and discussing new directions. The review contains an introduction that describes the full PP toolbox; an exploration of areas where PP has advanced understanding of perceptual and cognitive phenomena; a discussion of PP's impact on foundational issues in cognitive science; and a consideration of the philosophy of science of PP. The overall picture is that PP is a fruitful framework, with exciting new directions awaiting exploration.
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.
Ordinary human experience is embedded in a web of causal relations that link the brain to the body and the wider environment. However, there might be conditions in which brain activity supports consciousness even when that activity is fully causally isolated from the body and its environment. Such cases would involve what we call islands of awareness: conscious states that are neither shaped by sensory input nor able to be expressed by motor output. This opinion article considers conditions in which such islands might occur, including ex cranio brains, hemispherotomy, and in cerebral organoids. We examine possible methods for detecting islands of awareness, and consider their implications for ethics and for the nature of consciousness.
There must be a reason why an experience feels the way it does. A good place to begin addressing this question is spatial experience, because it may be more penetrable by introspection than other qualities of consciousness such as color or pain. Moreover, much of experience is spatial, from that of our body to the visual world, which appears as if painted on an extended canvas in front of our eyes. Because it is ‘right there’, we usually take space for granted and overlook its qualitative properties. However, we should realize that a great number of phenomenal distinctions and relations are required for the canvas of space to feel ‘extended’. Here we argue that, to be experienced as extended, the canvas of space must be composed of countless spots, here and there, small and large, and these spots must be related to each other in a characteristic manner through connection, fusion, and inclusion. Other aspects of the structure of spatial experience follow from extendedness: every spot can be experienced as enclosing a particular region, with its particular location, size, boundary, and distance from other spots. We then propose an account of the phenomenal properties of spatial experiences based on integrated information theory (IIT). The theory provides a principled approach for characterizing both the quantity and quality of experience by unfolding the cause-effect structure of a physical substrate. Specifically, we show that a simple simulated substrate of units connected in a grid-like manner yields a cause-effect structure whose properties can account for the main properties of spatial experience. These results uphold the hypothesis that our experience of space is supported by brain areas whose units are linked by a grid-like connectivity. They also predict that changes in connectivity, even in the absence of changes in activity, should lead to a warping of experienced space. To the extent that this approach provides an initial account of phenomenal space, it may also serve as a starting point for investigating other aspects of the quality of experience and their physical correspondents.
A new theory about the origins of consciousness that finds learning to be the driving force in the evolutionary transition to basic consciousness.
What marked the evolutionary transition from organisms that lacked consciousness to those with consciousness—to minimal subjective experiencing, or, as Aristotle described it, “the sensitive soul”? In this book, Simona Ginsburg and Eva Jablonka propose a new theory about the origin of consciousness that finds learning to be the driving force in the transition to basic consciousness. Using a methodology similar to that used by scientists when they identified the transition from non-life to life, Ginsburg and Jablonka suggest a set of criteria, identify a marker for the transition to minimal consciousness, and explore the far-reaching biological, psychological, and philosophical implications.
After presenting the historical, neurobiological, and philosophical foundations of their analysis, Ginsburg and Jablonka propose that the evolutionary marker of basic or minimal consciousness is a complex form of associative learning, which they term unlimited associative learning (UAL). UAL enables an organism to ascribe motivational value to a novel, compound, non-reflex-inducing stimulus or action, and use it as the basis for future learning. Associative learning, Ginsburg and Jablonka argue, drove the Cambrian explosion and its massive diversification of organisms. Finally, Ginsburg and Jablonka propose symbolic language as a similar type of marker for the evolutionary transition to human rationality—to Aristotle's “rational soul.”
I introduce an empirically-grounded version of a higher-order theory of conscious perception. Traditionally, theories of consciousness either focus on the global availability of conscious information, or take conscious phenomenology as a brute fact due to some biological or basic representational properties. Here I argue instead that the key to characterizing the consciousness lies in its connections to belief formation and epistemic justification on a subjective level.
The free energy principle, an influential framework in computational neuroscience and theoretical neurobiology, starts from the assumption that living systems ensure adaptive exchanges with their environment by minimizing the objective function of variational free energy. Following this premise, it claims to deliver a promising integration of the life sciences. In recent work, Markov Blankets, one of the central constructs of the free energy principle, have been applied to resolve debates central to philosophy (such as demarcating the boundaries of the mind). The aim of this paper is twofold. First, we trace the development of Markov blankets starting from their standard application in Bayesian networks, via variational inference, to their use in the literature on active inference. We then identify a persistent confusion in the literature between the formal use of Markov blankets as an epistemic tool for Bayesian inference, and their novel metaphysical use in the free energy framework to demarcate the physical boundary between an agent and its environment. Consequently, we propose to distinguish between ‘Pearl blankets’ to refer to the original epistemic use of Markov blankets and ‘Friston blankets’ to refer to the new metaphysical construct. Second, we use this distinction to critically assess claims resting on the application of Markov blankets to philosophical problems. We suggest that this literature would do well in differentiating between two different research programs: ‘inference with a model’ and ‘inference within a model’. Only the latter is capable of doing metaphysical work with Markov blankets, but requires additional philosophical premises and cannot be justified by an appeal to the success of the mathematical framework alone.
One of the great frontiers of consciousness science is understanding how early consciousness arises in the development of the human infant. The reciprocal relationship between the default mode network (DMN) and frontoparietal networks — the dorsal attention network (DAN) and executive control network (ECN) — is thought to facilitate integration of information across the brain and its availability for conscious access to a wide set of mental operations. It remains unknown whether the brain mechanism of conscious awareness is instated in infants from birth. To address this gap, we asked what the impact of prematurity and neonate age is on the development the default mode and fronto-parietal networks, and of their reciprocal relationship. To address these questions, we used the Developing Human Connectome Project (dHCP), a unique Open Science project which provides a large sample of neonatal functional Magnetic Resonance Imaging (fMRI) data with high temporal and spatial resolution. Resting state fMRI data for full-term neonates (N = 282, age 41.2 w ± 12 d), and preterm neonates scanned at term-equivalent age (TEA) (N = 73, 40.9 w ± 14.5 d), or before TEA (N = 73, 34.6 w ± 13.4 d) were obtained from the dHCP, and for a reference adult group (N = 176, 22 – 36 years), from the Human Connectome Project. For the first time, we show that the reciprocal relationship between the DMN and DAN was present at full-term birth or TEA. Although different from the adult networks, the DMN, DAN and ECN were present as distinct networks at full-term birth or TEA, but premature birth disrupted network development. By contrast, neonates before TEA showed dramatic underdevelopment of high-order networks. Only the DAN was present as a distinct network and the reciprocal network relationship was not yet formed. Our results suggest that, at full-term birth or by term-equivalent age, infants possess key features of the neural circuitry that enables integration of information across diverse sensory and high-order functional modules, giving rise to conscious access. Conversely, they suggest that this brain infrastructure is not present before infants reach term-equivalent age. These findings improve understanding of the ontogeny of high-order network dynamics that support conscious awareness, and of their disruption by premature birth.
Much research on the neural correlates of consciousness (NCC) has focused on two evoked potentials, the P3b and the visual or auditory awareness negativity (VAN, AAN). Surveying a broad range of recent experimental evidence, we find that repeated failures to observe the P3b during conscious perception eliminate it as a putative NCC. Neither the VAN nor the AAN have been dissociated from consciousness; furthermore, a similar neural signal correlates with tactile consciousness. These awareness negativities can be maximal contralateral to the evoking stimulus, are likely generated in underlying sensory cortices, and point to the existence of a generalized perceptual awareness negativity (PAN) reflecting the onset of sensory consciousness.
Recent advances in deep learning have allowed artificial intelligence (AI) to reach near human-level performance in many sensory, perceptual, linguistic, and cognitive tasks. There is a growing need, however, for novel, brain-inspired cognitive architectures. The Global Workspace Theory (GWT) refers to a large-scale system integrating and distributing information among networks of specialized modules to create higher-level forms of cognition and awareness. We argue that the time is ripe to consider explicit implementations of this theory using deep-learning techniques. We propose a roadmap based on unsupervised neural translation between multiple latent spaces (neural networks trained for distinct tasks, on distinct sensory inputs and/or modalities) to create a unique, amodal Global Latent Workspace (GLW). Potential functional advantages of GLW are reviewed, along with neuroscientific implications.
Few people tackle the neural or computational basis of qualitative experience (Frith, 2019). Why? One major reason is that science and philosophy have both struggled to propose how we might even begin to start studying it. Here I propose that metacognitive computations, and the subjective feelings that go along with them, give us a solid starting point. Specifically, perceptual metacognition possesses unique properties that provide a powerful and unique opportunity for studying the neural and computational correlates of subjective experience, falling into three categories: (1) Metacognition is subjective: there is something it is like to feel ‘confident’; (2) Metacognitive processes are objectively characterizable: We can objectively observe metacognitive reports and define computational models to fit to empirical data; (3) Metacognition has multiple hierarchically-dependent “anchors”, presenting a unique computational opportunity for developing sensitive, specific models. I define this Metacognition as a Step Toward Explaining Phenomenology (M-STEP) approach to state that, given these properties, computational models of metacognition represent an empirically-tractable early step in identifying the generative process that constructs qualitative experience. By applying decades of developments in computational cognitive science and formal computational model comparisons to the specific properties of perceptual metacognition, we may reveal new and exciting insights about how the brain constructs subjective conscious experiences and the nature of those experiences themselves.
Feelings are conscious mental events that represent body states as they undergo homeostatic regulation. Feelings depend on the interoceptive nervous system (INS), a collection of peripheral and central pathways, nuclei and cortical regions which continuously sense chemical and anatomical changes in the organism. How such humoral and neural signals come to generate conscious mental states has been a major scientific question. The answer proposed here invokes (1) several distinctive and poorly known physiological features of the INS; and (2) a unique interaction between the body (the ‘object’ of interoception) and the central nervous system (which generates the 'subject' of interoception). The atypical traits of the INS and the direct interactions between neural and non‐neural physiological compartments of the organism, neither of which is present in exteroceptive systems, plausibly explain the qualitative and subjective aspects of feelings, thus accounting for their conscious nature.