Mathematics and the Brain: A Category Theoretical Approach to Go Beyond the Neural Correlates of Consciousness

<i>Human and Animal Minds: The Consciousness Questions Laid to Rest</i>

Summary In the last decade, Guilio Tononi has developed the Integrated Information Theory (IIT) of consciousness. IIT postulates that consciousness is equal to integrated information (F). The goal of this paper is to show that IIT fails in its stated goal of quantifying consciousness. The paper will challenge the theoretical and empirical arguments in support of IIT. The main theoretical argument for the relevance of integrated information to consciousness is the principle of information exclusion. Yet, no justification is given to support this principle. Tononi claims there is significant empirical support for IIT, but this is called into question by the creation of a trivial theory of consciousness with equal explanatory power. After examining the theoretical and empirical evidence for IIT, arguments from philosophy of mind and epistemology will be examined. Since IIT is not a form of computational functionalism, it is vulnerable to fading/ dancing qualia arguments. Finally, the limitations of the phenomenological approach to studying consciousness are examined, and it will be shown that IIT is a theory of protoconsciousness rather than a theory of consciousness.

The Equations for Consciousness: A Reply to “Tracking the Travels,” a Review of <i>Journey of the Mind</i>

The scientific study of consciousness is currently undergoing a critical transition in the form of a rapidly evolving scientific debate regarding whether or not currently proposed theories can be assessed for their scientific validity. At the forefront of this debate is Integrated Information Theory (IIT), widely regarded as the preeminent theory of consciousness because it quantified subjective experience in a scalar mathematical measure called that is in principle measurable. Epistemological issues in the form of the “unfolding argument” have provided a concrete refutation of IIT by demonstrating how it permits functionally identical systems to have differences in their predicted consciousness. The implication is that IIT and any other proposed theory based on a physical system’s causal structure may already be falsified even in the absence of experimental refutation. However, so far many of these arguments surrounding the epistemological foundations of falsification arguments, such as the unfolding argument, are too abstract to determine the full scope of their implications. Here, we make these abstract arguments concrete, by providing a simple example of functionally equivalent machines realizable with table-top electronics that take the form of isomorphic digital circuits with and without feedback. This allows us to explicitly demonstrate the different levels of abstraction at which a theory of consciousness can be assessed. Within this computational hierarchy, we show how IIT is simultaneously falsified at the finite-state automaton level and unfalsifiable at the combinatorial-state automaton level. We use this example to illustrate a more general set of falsification criteria for theories of consciousness: to avoid being already falsified, or conversely unfalsifiable, scientific theories of consciousness must be invariant with respect to changes that leave the inference procedure fixed at a particular level in a computational hierarchy.

Identifying the neural correlates of consciousness (NCC) is widely regarded as imperative to our understanding of consciousness. Although several scientific theories of consciousness make predictions about the NCC, they often differ in their level of detail, making direct comparison through experimental findings difficult. Here, we report an adversarial collaboration protocol in which unbiased experimentalists will test predictions formed by theory proponents of Global Neuronal Workspace Theory (GNWT) and Integrated Information Theory (IIT), concerning the location, timing and functional connectivity of the NCC. To enable cross-species comparisons, non-human primates (NHPs) and mice will view/listen to supra-threshold visual/auditory stimuli for variable durations in a go-nogo task that controls for report confounds, while we record with Neuropixels electrodes neuronal responses from visual, auditory, posterior parietal and/or prefrontal cortical areas. To causally test predictions about the timing and location, we will manipulate activity in prefrontal cortical regions using electrical stimulation in NHPs or optogenetic silencing in mice. This Study Protocol details the experimental design, analyses, divergent predictions, and the anticipated outcomes, along with their interpretation.

Consciousness science is marred by disparate constructs and methodologies, making it challenging to systematically compare theories. This foundational crisis casts doubts on the scientific character of the field itself. Addressing it, we propose a framework for systematically comparing consciousness theories by introducing a novel inter-theory classification interface, the Measure Centrality Index (MCI). Recognizing its gradient distribution, the MCI assesses the degree of importance a specific empirical measure has for a given consciousness theory. We apply the MCI to probe how the empirical measures of the Global Neuronal Workspace Theory (GNW), Integrated Information Theory (IIT), and Temporospatial Theory of Consciousness (TTC) would fare within the context of the other two. We demonstrate that direct comparison of IIT, GNW, and TTC is meaningful and valid for some measures like Lempel-Ziv Complexity (LZC), Autocorrelation Window (ACW), and possibly Mutual Information (MI). In contrast, it is problematic for others like the anatomical and physiological neural correlates of consciousness (NCC) due to their MCI-based differential weightings within the structure of the theories. In sum, we introduce and provide proof-of-principle of a novel systematic method for direct inter-theory empirical comparisons, thereby addressing isolated evolution of theories and confirmatory bias issues in the state-of-the-art neuroscience of consciousness.

The article explores the question of whether an artificial intelligence (AI) system can attain human consciousness. The authors critically examine the current state of research in neuroscience, anthropology, and philosophy, highlighting the sig­nificant challenges posed by the limitations of computational power and the ar­chitecture of modern AI systems. The article proposes a distinction between theoretical and instrumental approaches to determining the level of conscious­ness in AI. By considering the most authoritative theories of consciousness in neuroscience and cognitive philosophy, such as the Global Workspace Theory, Integrated Information Theory, Spatiotemporal Theory, and Evolutionary Theory of Consciousness, the authors argue that the emergence of consciousness in a material system requires an integral architecture, adaptability, and the ability to master self-control. An analysis of instrumental tests for the presence of con­sciousness shows that consciousness can only be assessed based on behavior in the real world, and also demonstrates the limitations and risks of anthropo­morphic perceptions of the object of study. The authors advocate for the synthe­sis of theoretical and instrumental approaches in the research and design of AI, emphasizing the importance of interdisciplinary studies to advance the under­standing and potential replication of human consciousness in artificial systems. Clear criteria for determining the presence of consciousness in AI systems are proposed, and the ethical implications of this technological achievement are con­sidered. The study concludes with a discussion of the potential challenges huma­nity may face if AI systems are endowed with consciousness, stressing the im­portance of a cautious and well-considered approach to such a complex issue.

We present a functional magnetic resonance (fMRI) dataset collected as part of an adversarial collaboration aimed at arbitrating between the Global Neuronal Workspace theory (GNWT) and the Integrated Information Theory (IIT) of consciousness. Participants (N = 118) were presented with suprathreshold visual stimuli belonging to four different categories (faces, objects, letters, false fonts) with three orientations (front, left, right view), and three durations (0.5, 1.0, 1.5 seconds). Participants were asked to identify infrequent targets that changed in each block, thereby rendering two categories task-relevant and two task-irrelevant. The simplicity of the experimental design and of the task given to the participants ensures that these data are broadly reusable. Besides testing predictions from other theories of consciousness, these data can be used to examine various aspects of visual processing. The anonymized data were converted to Brain Imaging Data Structure (BIDS), and can be easily accessed through a web platform or an API. The dataset contains quality reports, demographics, behavioral performance, and eye-tracking data. We also provide code for preprocessing and analyzing the data.

In recent years, there has been a proliferation of neuroscientific theories of consciousness. These include theories which explicitly point to EM fields, notably Operational Architectonics and, more recently, the General Resonance Theory. In phenomenological terms, human consciousness is a unified composition of contents. These contents are specific and meaningful, and they exist from a subjective point of view. Human conscious experience is temporally continuous, limited in content, and coherent. Based upon those phenomenal observations, pre-existing theories of consciousness, and a large body of experimental evidence, I derived the Temporally-Integrated Causality Landscape (TICL). In brief, the TICL proposes that the neural correlate of consciousness is a structure of temporally integrated causality occurring over a large portion of the thalamocortical system. This structure is composed of a large, integrated set of neuronal elements (the System), which contains some subsystems, defined as having a higher level of temporally-integrated causality than the System as a whole. Each Subsystem exists from the point of view of the System, in the form of meaningful content. In this article, I review the TICL and consider the importance of EM forces as a mechanism of neural causality. I compare the fundamentals of TICL to those of several other neuroscientific theories. Using five major characteristics of phenomenal consciousness as a standard, I compare the basic tenets of Integrated Information Theory, Global Neuronal Workspace, General Resonance Theory, Operational Architectonics, and the Temporo-spatial Theory of Consciousness with the framework of the TICL. While the literature concerned with these theories tends to focus on different lines of evidence, there are fundamental areas of agreement. This means that, in time, it may be possible for many of them to converge upon the truth. In this analysis, I conclude that a primary distinction which divides these theories is the feature of spatial and temporal nesting. Interestingly, this distinction does not separate along the fault line between theories explicitly concerned with EM fields and those which are not. I believe that reconciliation is possible, at least in principle, among those theories that recognize the following: just as the contents of consciousness are distinctions within consciousness, the neural correlates of conscious content should be distinguishable from but fall within the spatial and temporal boundaries of the full neural correlates of consciousness.

Both the global neuronal workspace (GNW) and integrated information theory (IIT) posit that highly complex and interconnected networks engender perceptual awareness. GNW specifies that activity recruiting frontoparietal networks will elicit a subjective experience, whereas IIT is more concerned with the functional architecture of networks than with activity within it. Here, we argue that according to IIT mathematics, circuits converging on integrative versus convergent yet non-integrative neurons should support a greater degree of consciousness. We test this hypothesis by analyzing a dataset of neuronal responses collected simultaneously from primary somatosensory cortex (S1) and ventral premotor cortex (vPM) in nonhuman primates presented with auditory, tactile, and audio-tactile stimuli as they are progressively anesthetized with propofol. We first describe the multisensory (audio-tactile) characteristics of S1 and vPM neurons (mean and dispersion tendencies, as well as noise-correlations), and functionally label these neurons as convergent or integrative according to their spiking responses. Then, we characterize how these different pools of neurons behave as a function of consciousness. At odds with the IIT mathematics, results suggest that convergent neurons more readily exhibit properties of consciousness (neural complexity and noise correlation) and are more impacted during the loss of consciousness than integrative neurons. Last, we provide support for the GNW by showing that neural ignition (i.e., same trial coactivation of S1 and vPM) was more frequent in conscious than unconscious states. Overall, we contrast GNW and IIT within the same single-unit activity dataset, and support the GNW.SIGNIFICANCE STATEMENT A number of prominent theories of consciousness exist, and a number of these share strong commonalities, such as the central role they ascribe to integration. Despite the important and far reaching consequences developing a better understanding of consciousness promises to bring, for instance in diagnosing disorders of consciousness (e.g., coma, vegetative-state, locked-in syndrome), these theories are seldom tested via invasive techniques (with high signal-to-noise ratios), and never directly confronted within a single dataset. Here, we first derive concrete and testable predictions from the global neuronal workspace and integrated information theory of consciousness. Then, we put these to the test by functionally labeling specific neurons as either convergent or integrative nodes, and examining the response of these neurons during anesthetic-induced loss of consciousness.

First-person experience cannot rescue causal structure theories from the unfolding argument

As the field of consciousness science matures, the research agenda has expanded from an initial focus on the neural correlates of consciousness, to developing and testing theories of consciousness. Several theories have been put forward, each aiming to elucidate the relationship between consciousness and brain function. However, there is an ongoing, intense debate regarding whether these theories examine the same phenomenon. And, despite ongoing research efforts, it seems like the field has so far failed to converge around any single theory, and instead exhibits significant polarization. To advance this discussion, proponents of five prominent theories of consciousness-Global Neuronal Workspace Theory (GNWT), Higher-Order Theories (HOT), Integrated Information Theory (IIT), Recurrent Processing Theory (RPT), and Predictive Processing (PP)-engaged in a public debate in 2022, as part of the annual meeting of the Association for the Scientific Study of Consciousness (ASSC). They were invited to clarify the explananda of their theories, articulate the core mechanisms underpinning the corresponding explanations, and outline their foundational premises. This was followed by an open discussion that delved into the testability of these theories, potential evidence that could refute them, and areas of consensus and disagreement. Most importantly, the debate demonstrated that at this stage, there is more controversy than agreement between the theories, pertaining to the most basic questions of what consciousness is, how to identify conscious states, and what is required from any theory of consciousness. Addressing these core questions is crucial for advancing the field towards a deeper understanding and comparison of competing theories.

The study of consciousness is gaining importance in both neuroscience and the development of Artificial Intelligence (AI). We show here that an advanced White Matter (WM) tractography method, termed gridography, can explore the potential integration of two prominent theories of consciousness: Global Workspace Theory (GWT) and Integrated Information Theory (IIT). Using gridography on high-resolution diffusion MRI data from the Human Connectome Project, we demonstrate that gridography obtains WM connections between the anterior brain regions associated with GWT and posterior regions linked to IIT in a form which agrees with the Epiontic Consciousness Theory (ECT), which is an intermediary theory between GWT and IIT. We evaluate how experimental gridography data aligns with the physiological structures implicated in consciousness by analyzing: (i) Information characteristics of consciousness theories; (ii) Improvement of diffusion MRI tractography by use of gridography; (iii) Expected gridography results based on consciousness theory. Our findings suggest that these connections, particularly those of the Superior Longitudinal Fasciculus (SLF), support a ECT unified model of consciousness integrating aspects of both the primarily epistemic GWT and the primarily ontic IIT. This study proposes a novel framework that could reconcile existing theoretical divisions between GWT and IIT through the use of the ECT approach.

Integrated information theory (IIT), developed by Giulio Tononi and colleagues, aims to provide a theory of consciousness with explanatory, predictive, and inferential power, starting from phenomenology itself. IIT contrasts with current approaches that start from the neural correlates of consciousness (NCC) with the hope of identifying generalized principles about the nature of consciousness. Instead, IIT first identifies the essential properties of every experience from consciousness itself. From these “axioms” of phenomenology, IIT then infers a set of requirements (“postulates”) for a physical system to be a substrate of consciousness, while the NCC serve as empirical data for evaluating IIT’s predictions. In particular, IIT predicts that the quality or content of an experience is structurally identical to the cause-effect structure of its physical substrate, and that the quantity or level of consciousness corresponds to the amount of intrinsic cause-effect power the substrate has onto itself. IIT, moreover, offers a theoretical formalism that, in principle, makes it possible to evaluate whether a physical system complies with the IIT postulates and thus forms a physical substrate of consciousness, to quantify the level of consciousness of such a system, and to provide a full account of its phenomenological structure in causal terms.

Multiple Perspectives on Consciousness for Cognitive Science Richard A. Carlson (racarlson@psu.edu) Department of Psychology, Penn State University 613 Moore Building, University Park, PA 16802 USA The huge contemporary literature on consciousness spans multiple disciplines, including psychology, philosophy, and neuroscience. This tutorial will introduce participants to major proposals about consciousness, and their empirical and methodological implications. The goal is to prepare participants to explore the consciousness literature in greater depth. Our consideration of perspectives on consciousness will be organized by considering how these perspectives address core questions about consciousness, including: (a) How can subjectivity and agency be accommodated in a scientific theory of consciousness? (b) How can conscious and nonconscious or unconscious processes and representations be systematically distinguished? (c) How can conscious mental states be assessed or measured? (d) How can dissociations and impairments of consciousness be understood? The literatures to be considered address these questions in analytic, functional, computational, and implementational terms. Philosophical Perspectives Philosophers approach the problem of consciousness from a variety of analytic perspectives, some focusing on contemporary formulations of the mind-body problem and others on analyses of subjective experience. Among the philosophical perspectives we will consider are John Searle’s (1992) analysis of consciousness in terms of intentionality, David Chalmer’s (1996) distinction between “easy” and “hard” problems of consciousness, David Rosenthal’s (1993) “higher order thought” proposal, and Daniel Dennett’s (1991) “multiple drafts” theory of consciousness. Neuroscience Perspectives Neuroscientists have made a wide variety of proposals concerning the neural correlates of consciousness (NCC). A starting assumption is that a subset of current neural activity is correlated with current conscious experience. There is controversy, however, concerning how that subset is to be identified. For example, the NCC might be limited to particular types of cells or anatomical structures, or comprise global patterns of synchronized neural activity. We will consider recent proposals concerning NCC by Crick and Koch (1998), Damasio (2000), and Edelman and Tononi Psychological Perspectives Psychological perspectives on consciousness generally focus on functionally-defined aspects of cognition. For example, psychologists have identified consciousness with working memory (Baars, 1988), attention (Schneider & Pimm-Smith, 1997), metacognition (Nelson, 1996), and with the structure of mental states (Carlson, 1997). Cognitive research often focuses on distinguishing conscious and nonconscious influences on psychological processes such as learning (Dienes & Berry, 1997) and perception (Merikle, Smilek, & Eastwood, 2001). This research has generated a rich literature on methods for assessing consciousness. References Baars, B. J. (1988). A cognitive theory of consciousness. New York: Cambridge University Press. Carlson, R. A. (1997). Experienced Cognition. Mahwah, NJ: Lawrence Erlbaum Associates. Chalmers, D. (1996). The conscious mind. Oxford: Oxford University Press. Crick, F., & Koch, C. (1998). Consciousness and neuroscience. Cerebral Cortex, 8, 97-107. Damasio, A. R. (2000). A neurobiology for consciousness. In T. Metzinger (Ed.), Neural correlates of consciousness Cambridge, MA: The MIT Press. Dienes, Z., & Berry, D. (1997). Implicit learning: Below the subjective threshold. Psychonomic Bulletin and Review, 4, Dennett, D. C. (1991). Consciousness explained. Boston: Little, Brown and Company. Edelman, G. M., & Tononi, G. (2000). Reentry and the dynamic core: Neural correlates of conscious experience. In T. Metzinger (Ed.), Neural correlates of consciousness. Cambridge, MA: The MIT Press. Merikle, P. M., Smilek, D., & Eastwood, J. D. (2001). Perception without awareness: perspectives from cognitive psychology. Cognition, 79, 115-134. Nelson, T. O. (1996). Consciousness and metacognition. American Psychologist, 51, 102-116. Rosenthal, D. M. (1993). Thinking that one thinks. In M. Davies, & G. W. Humphreys (Eds.), Consciousness: Psychological and philosophical essays. Oxford: Blackwell. Searle, J. R. (1992). The rediscovery of the mind. Cambridge, MA: The MIT Press. Schneider, W., & Pimm-Smith, M. (1997). Consciousness as a message aware control mechanism to modulate cognitive processing. J. Cohen, & J. Schooler (Eds.), Scientific approaches to consciousness: The 25th Carnegie Symposium on Cognition. Mahwah, NJ: Erlbaum.