Tests for consciousness in humans and beyond

The Mind-Matter Dichotomy: A Persistent Challenge for Neuroscientific and Philosophical Theories

Several areas of cognitive neuroscience tackle traditional philosophical questions. Among the range of problems, two closely related issues will be addressed in more detail from both neurobiological and philosophical perspectives: the relationship between mind and matter and the nature of perception. Neuropsychological and neurophysiological studies are reviewed that examine the connection between neuronal processes and consciousness. The most prominent theories on the neuronal correlates of consciousness (NCC) are then compared with philosophical attempts to address the epistemic gap between the material processes in the brain and mental phenomena. Before exploring whether neurobiological discoveries can help resolve philosophical problems, the epistemic challenges are discussed, stemming from the fact that perceptions are shaped by the brain's functional architecture. It is suggested that the 'hard problem of consciousness'-the challenge of explaining how the qualia of subjective experience can arise from neuronal processes-can be alleviated if two conditions are met: first, that perception depends on priors and, second, that some of these priors are formed through interactions with the immaterial realities of cultural concepts. Although this approach offers a coherent naturalistic explanation for the emergence of mental phenomena, it does not resolve the cognitive dissonance between our intuitions and scientific evidence regarding the relationship between matter and mind.

Preliminaries to artificial consciousness: A multidimensional heuristic approach

The pursuit of artificial consciousness requires conceptual clarity to navigate its theoretical and empirical challenges. This paper introduces a composite, multilevel, and multidimensional model of consciousness as a heuristic framework to guide research in this field. Consciousness is treated as a complex phenomenon, with distinct constituents and dimensions that can be operationalized for study and for evaluating their replication. We argue that this model provides a balanced approach to artificial consciousness research by avoiding binary thinking (e.g., conscious vs. non-conscious) and offering a structured basis for testable hypotheses. To illustrate its utility, we focus on "awareness" as a case study, demonstrating how specific dimensions of consciousness can be pragmatically analyzed and targeted for potential artificial instantiation. By breaking down the conceptual intricacies of consciousness and aligning them with practical research goals, this paper lays the groundwork for a robust strategy to advance the scientific and technical understanding of artificial consciousness.

Beyond consciousness: Ethical, legal, and social issues in human brain organoid research and application

This study aims to provide a comprehensive review of the ethical, legal and social issues in human brain organoid research, with a view to different types of research and applications: in vitro research, transplantation into non-human animals, and biocomputing. Despite the academic and societal attention on the possibility that human brain organoids may be conscious, we have identified diverse issues in human brain organoid research and applications. To guide the complex terrain of human brain organoid research and applications, a multidisciplinary approach that integrates ethical, legal, and social perspectives is essential.

Quantum Models of Consciousness from a Quantum Information Science Perspective

This perspective explores various quantum models of consciousness from the viewpoint of quantum information science, offering potential ideas and insights. The models under consideration can be categorized into three distinct groups based on the level at which quantum mechanics might operate within the brain: those suggesting that consciousness arises from electron delocalization within microtubules inside neurons, those proposing it emerges from the electromagnetic field surrounding the entire neural network, and those positing it originates from the interactions between individual neurons governed by neurotransmitter molecules. Our focus is particularly on the Posner model of cognition, for which we provide preliminary calculations on the preservation of entanglement of phosphate molecules within the geometric structure of Posner clusters. These findings provide valuable insights into how quantum information theory can enhance our understanding of brain functions.

Consciousness Research Through Pain

Background/Objectives: Consciousness is a complex and elusive phenomenon encompassing self-awareness, sensory perception, emotions, and cognition. Despite significant advances in neuroscience, understanding the neural mechanisms underlying consciousness remains challenging. Pain, as a subjective and multifaceted experience, offers a unique lens for exploring consciousness by integrating sensory inputs with emotional and cognitive dimensions. This study examines the relationship between consciousness and pain, highlighting the potential of pain as a model for understanding the interplay between subjective experience and neural activity. Methods: Literature review. Results: Key theories of consciousness, such as the Global Workspace Theory and the Integrated Information Theory, provide diverse frameworks for interpreting the emergence of consciousness. Similarly, pain research emphasizes the role of subjective interpretation and emotional context in shaping sensory experiences, reflecting broader challenges in consciousness studies. The limitations of current methodologies, particularly the difficulty of objectively measuring subjective phenomena, like pain and consciousness, are also addressed. This highlights the importance of neural correlates, with a particular focus on brain regions, such as the anterior cingulate cortex and the insula, which bridge sensory and emotional experiences. By analyzing the shared attributes of pain and consciousness, this study underscores the potential for pain to serve as a measurable proxy in consciousness research. Conclusions: Ultimately, it contributes to unraveling the neural and philosophical underpinnings of consciousness, offering implications for mental health treatment and advancements in artificial intelligence. This study fills a critical gap by leveraging pain as a measurable and reproducible model for exploring the neural and subjective mechanisms of consciousness. By combining theoretical frameworks with empirical evidence, it offers novel insights into how consciousness emerges from neural processes.

Markers of consciousness in infants: Towards a 'cluster-based' approach

As recently as the 1980s, it was not uncommon for paediatric surgeons to operate on infants without anaesthesia. Today, the same omission would be considered criminal malpractice, and there is an increased concern with the possibility of consciousness in the earliest stage of human infancy. This concern reflects a more general trend that has characterised science since the early 1990s of taking consciousness seriously. While this attitude shift has opened minds towards the possibility that our earliest experiences predate our first memories, convincing demonstrations of infant consciousness remain challenging given that infants cannot report on their experiences. Furthermore, while many behavioural and neural markers of consciousness that do not rely on language have been validated in adults, no one specific marker can be confidently translated to infancy. For this reason, we have proposed the 'cluster-based' approach, in which a consensus of evidence across many markers, all pointing towards the same developmental period, could be used to argue convincingly for the presence of consciousness. CONCLUSION: We review the most promising markers for early consciousness, arguing that consciousness is likely to be in place by 5 months of age if not earlier.

Introspective psychophysics for the study of subjective experience

Studying subjective experience is hard. We believe that pain is not identical to nociception, nor pleasure a computational reward signal, nor fear the activation of "threat circuitry". Unfortunately, introspective self-reports offer our best bet for accessing subjective experience, but many still believe that introspection is "unreliable" and "unverifiable". But which of introspection's faults do we find most damning? Is it that introspection provides imperfect access to brain processes (e.g. perception, memory)? That subjective experience is not objectively verifiable? That it is hard to isolate from non-subjective processing capacity? Here, I argue none of these prevents us from building a meaningful, impactful psychophysical research program that treats subjective experience as a valid empirical target through precisely characterizing relationships among environmental variables, brain processes and behavior, and self-reported phenomenology. Following recent similar calls by Peters (Towards characterizing the canonical computations generating phenomenal experience. 2022. Neurosci Biobehav Rev: 142, 104903), Kammerer and Frankish (What forms could introspective systems take? A research programme. 2023. J Conscious Stud 30:13-48), and Fleming (Metacognitive psychophysics in humans, animals, and AI. 2023. J Conscious Stud 30:113-128), "introspective psychophysics" thus treats introspection's apparent faults as features, not bugs-just as the noise and distortions linking environment to behavior inspired Fechner's psychophysics over 150 years ago. This next generation of psychophysics will establish a powerful tool for building and testing precise explanatory models of phenomenology across many dimensions-urgency, emotion, clarity, vividness, confidence, and more.

Is artificial consciousness achievable? Lessons from the human brain

We here analyse the question of developing artificial consciousness from an evolutionary perspective, taking the evolution of the human brain and its relation with consciousness as a reference model or as a benchmark. This kind of analysis reveals several structural and functional features of the human brain that appear to be key for reaching human-like complex conscious experience and that current research on Artificial Intelligence (AI) should take into account in its attempt to develop systems capable of human-like conscious processing. We argue that, even if AI is limited in its ability to emulate human consciousness for both intrinsic (i.e., structural and architectural) and extrinsic (i.e., related to the current stage of scientific and technological knowledge) reasons, taking inspiration from those characteristics of the brain that make human-like conscious processing possible and/or modulate it, is a potentially promising strategy towards developing conscious AI. Also, it cannot be theoretically excluded that AI research can develop partial or potentially alternative forms of consciousness that are qualitatively different from the human form, and that may be either more or less sophisticated depending on the perspectives. Therefore, we recommend neuroscience-inspired caution in talking about artificial consciousness: since the use of the same word "consciousness" for humans and AI becomes ambiguous and potentially misleading, we propose to clearly specify which level and/or type of consciousness AI research aims to develop, as well as what would be common versus differ in AI conscious processing compared to human conscious experience.

Symbolic representation by a two-dimensional matrix for profiling comparative animal behavior

The growing view that consciousness is widespread, multimodal, and evolutionarily non-linear in complexity across the animal kingdom has given rise recently to a variety of strategies for representing the heterogeneous nature of animal phenomenology. While based on markers clearly associated with consciousness in humans, most of these strategies are theoretical constructs lacking empirical data and are based on metrics appropriate for humans but difficult to measure in most non-human species. I propose a novel symbolic profile based on readily observable behaviors that logically constitute subjective experience across the entire spectrum of animals that possess a centralized nervous system. Three modes (markers) of behavior displayed by all animals - volition, interaction, and self-direction - are quantified according to the frequency, variety, and dynamism of each mode. The resulting matrix of 3 modes x 3 metrics can be expressed as a bi-directional heatmap, allowing for quick and easy inter-species comparisons. The overall effect is to highlight both similarities and differences in the subjective experience of animals ranging from crustaceans to primates.

A measure centrality index for systematic empirical comparison of consciousness theories

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.

Taking consciousness for real: Increasing the ecological validity of the study of conscious vs. unconscious processes

The study of consciousness has developed well-controlled, rigorous methods for manipulating and measuring consciousness. Yet, in the process, experimental paradigms grew farther away from everyday conscious and unconscious processes, which raises the concern of ecological validity. In this review, we suggest that the field can benefit from adopting a more ecological approach, akin to other fields of cognitive science. There, this approach challenged some existing hypotheses, yielded stronger effects, and enabled new research questions. We argue that such a move is critical for studying consciousness, where experimental paradigms tend to be artificial and small effect sizes are relatively prevalent. We identify three paths for doing so-changing the stimuli and experimental settings, changing the measures, and changing the research questions themselves-and review works that have already started implementing such approaches. While acknowledging the inherent challenges, we call for increasing ecological validity in consciousness studies.