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.

Significant advances in the scientific investigation of the neurobiology of consciousness have been slow to be translated into clinical settings, limited by factors of conceptual (e.g., what is consciousness?), methodological (e.g., how to identify reliable indicators of consciousness?), and technical (e.g., how to improve sensitivity and specificity of the technological identification of consciousness?) nature. In the present paper we aim at reducing the gap between research, clinical practice, patients’ and their caregivers’ needs regarding disorders of consciousness. By implementing a multidisciplinary and multidimensional approach, the paper focuses on disorders of consciousness: it starts from the review of some of the most promising measures of consciousness from brain activity (i.e., spectral measures, measures of functional connectivity, complexity-based measures). Next the paper introduces brain responses to illusions as a new indicator of consciousness (i.e., a feature that facilitates the attribution of consciousness), and illustrates the clinical operationalization of the indicators of consciousness through the case of virtual reality. Finally, the paper analyzes a set of urgent ethical issues and describes a model for assessing and dealing with those issues, concluding by elaborating key recommendations for improving the clinical treatment of patients with disorders of consciousness through a better translation of research into clinics.

Ce chapitre problématise davantage la dichotomie entre l'éthique des neurosciences et les neurosciences de l'éthique, en illustrant la neuroéthique fondamentale introduite il y a environ 15 ans. En fait, la compréhension dichotomique classique de la neuroéthique n'est pas suffisante pour couvrir toutes les questions émergentes à l'interface de la science du cerveau, de la philosophie et de la société en général. C'est pourquoi l’auteur distingue la « neuroéthique appliquée » de la « neuroéthique fondamentale » afin d'inclure d'autres domaines dans le discours neuroéthique, en plus de l'éthique et des neurosciences. Les principales caractéristiques de la neuroéthique fondamentale telle que conceptualisée sont les suivantes : poursuite des analyses fondamentales dans un domaine de recherche multidisciplinaire en utilisant une méthodologie interdisciplinaire.

How deep is the current diversity in the panoply of theories to define consciousness, and to what extent do these theories share common denominators? Here we first examine to what extent different theories are commensurable (or comparable) along particular dimensions. We posit logical (and, when applicable, empirical) commensurability as a necessary condition for identifying common denominators among different theories. By consequence, dimensions for inclusion in a set of logically and empirically commensurable theories of consciousness can be proposed. Next, we compare a limited subset of neuroscience-based theories in terms of commensurability. This analysis does not yield a denominator that might serve to define a minimally unifying model of consciousness. Theories that seem to be akin by one denominator can be remote by another. We suggest a methodology of comparing different theories via multiple probing questions, allowing to discern overall (dis)similarities between theories. Despite very different background definitions of consciousness, we conclude that, if attention is paid to the search for a common methological approach to brain-consciousness relationships, it should be possible in principle to overcome the current Babylonian confusion of tongues and eventually integrate and merge different theories.

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.

In recent years, brain research has indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modelling at multiple scales—from molecules to the whole brain. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain combines high-quality research, data integration across multiple scales, a new culture of multidisciplinary large-scale collaboration, and translation into applications. As pioneered in Europe’s Human Brain Project (HBP), a systematic approach will be essential for meeting the coming decade’s pressing medical and technological challenges. The aims of this paper are to: develop a concept for the coming decade of digital brain research, discuss this new concept with the research community at large, identify points of convergence, and derive therefrom scientific common goals; provide a scientific framework for the current and future development of EBRAINS, a research infrastructure resulting from the HBP’s work; inform and engage stakeholders, funding organisations and research institutions regarding future digital brain research; identify and address the transformational potential of comprehensive brain models for artificial intelligence, including machine learning and deep learning; outline a collaborative approach that integrates reflection, dialogues, and societal engagement on ethical and societal opportunities and challenges as part of future neuroscience research.

Contemporary ethical analysis of Artificial Intelligence (AI) is growing rapidly. One of its most recognizable outcomes is the publication of a number of ethics guidelines that, intended to guide governmental policy, address issues raised by AI design, development, and implementation and generally present a set of recommendations. Here we propose two things: first, regarding content, since some of the applied issues raised by AI are related to fundamental questions about topics like intelligence, consciousness, and the ontological and ethical status of humans, among others, the treatment of these issues would benefit from interfacing with neuroethics that has been addressing those same issues in the context of brain research. Second, the identification and management of some of the practical ethical challenges raised by AI would be enriched by embracing the methodological resources used in neuroethics. In particular, we focus on the methodological distinction between conceptual and action-oriented neuroethical approaches. We argue that the normative (often principles-oriented) discussion about AI will benefit from further integration of conceptual analysis, including analysis of some operative assumptions, their meaning in different contexts, and their mutual relevance in order to avoid misplaced or disproportionate concerns and achieve a more realistic and useful approach to identifying and managing the emerging ethical issues.

BACKGROUND:Assessing consciousness in other subjects, particularly in non-verbal and behaviourally disabled subjects (e.g., patients with disorders of consciousness), is notoriously challenging but increasingly urgent. The high rate of misdiagnosis among disorders of consciousness raises the need for new perspectives in order to inspire new technical and clinical approaches. MAIN BODY:We take as a starting point a recently introduced list of operational indicators of consciousness that facilitates its recognition in challenging cases like non-human animals and Artificial Intelligence to explore their relevance to disorders of consciousness and their potential ethical impact on the diagnosis and healthcare of relevant patients. Indicators of consciousness mean particular capacities that can be deduced from observing the behaviour or cognitive performance of the subject in question (or from neural correlates of such performance) and that do not define a hard threshold in deciding about the presence of consciousness, but can be used to infer a graded measure based on the consistency amongst the different indicators. The indicators of consciousness under consideration offer a potential useful strategy for identifying and assessing residual consciousness in patients with disorders of consciousness, setting the theoretical stage for an operationalization and quantification of relevant brain activity. CONCLUSIONS:Our heuristic analysis supports the conclusion that the application of the identified indicators of consciousness to its disorders will likely inspire new strategies for assessing three very urgent issues: the misdiagnosis of disorders of consciousness; the need for a gold standard in detecting consciousness and diagnosing its disorders; and the need for a refined taxonomy of disorders of consciousness.

Despite poor treatment results, a family-oriented approach and the securing of residency have been deemed essential to recovery from resignation syndrome (RS). In a retrospective cohort study, we evaluated an alternative method involving environmental therapy, with patients separated from their parents, while actively abstaining from involving the asylum process in treatment. We examined medical records, social services acts, and residential care home acts from 13 individuals treated at Solsidan residential care home between 2005 and 2020. Severity and outcome were assessed with Clinical Global Impression, Severity and Improvement subscales. Thirteen participants were included and out of these nine (69%) recovered, i.e. they very much or much improved. Out of the eight that were separated, all recovered, also, one non-separated recovered. The difference in outcome between subjects separated and not was significant (p = 0.007). Moreover, out of the five which received a residency permit during treatment, one recovered whereas four did not. The difference in outcome between subjects granted residency and not was significant (p = 0.007). The data revealed three (23%) cases of simulation where parents were suspected to have instigated symptoms. Our evaluation suggests that separation from parents and abstaining from invoking residency permit could be essential components when treating RS. Relying on a family-oriented approach, and residency could even be detrimental to recovery. The examined intervention was successful also in cases of probable malingering by proxy.

In this article, we present and analyse the concept of Digital Twin (DT) linked to distinct types of objects (artefacts, natural, inanimate or living) and examine the challenges involved in creating them from a fundamental neuroethics approach that emphasises conceptual analyses. We begin by providing a brief description of DTs and their initial development as models of artefacts and physical inanimate objects, identifying core challenges in building these tools and noting their intended benefits. Next, we describe attempts to build DTs of model living entities, such as hearts, highlighting the novel challenges raised by this shift from DTs of inanimate to DTs of living objects. Against that background, we give an account of contemporary research aiming to develop DTs of the human brain by building "virtual brains", e.g. the simulation engine The Virtual Brain (TVB) as it is carried out in the European Human Brain Project. Since the brain is structurally and functionally the most complex organ in the human body, and our integrated knowledge of its functional architecture remains limited in spite of recent neuroscientific advances, the attempts to create virtual copies of the human brain are correspondingly challenging. We suggest that a clear scientific theoretical structure, conceptual clarity and transparency regarding the methods and goals of this technological development are necessary prerequisites in order to make the project of constructing virtual brains a theoretically promising and socially beneficial scientific, technological and philosophical enterprise.

Neuroscience has made considerable progress in unraveling the neural correlates of mental phenomena like self, consciousness, and perception. However, the "common currency" shared between neuronal and mental activity, brain and mind, remains yet unclear. In this article, we propose that the dynamics of time and space provides a "common currency" that connects neuronal and mental features. Time and space are here understood in a dynamic context (as in contemporary physics): that is, in terms of the way the brain's spontaneous activity constructs its spatial and temporal relationships, for instance in terms of functional connectivity and different frequencies of fluctuations. Recruiting recent empirical evidence, we show that the different ways in which the spontaneous activity constructs its "inner time and space" are manifested in distinct mental features. Specifically, we demonstrate how spatiotemporal mechanisms like spatiotemporal repertoire, integration, and speed yield mental features like consciousness, self, and time speed perception. The focus on the brain's spatiotemporal mechanisms entails what we describe as "Spatiotemporal Neuroscience". Spatiotemporal Neuroscience conceives neuronal activity in terms of its temporo-spatial dynamics rather than its various functions (e.g., cognitive, affective, social, etc.) as in other branches of neuroscience (as distinguished from Cognitive, Affective, Cultural, Social, etc. Neuroscience). That allows Spatiotemporal Neuroscience to take into view the so-called 'spatio-temporality' of mental features including their non-causal, intrinsic and transformative relationship with neuronal features. In conclusion, Spatiotemporal Neuroscience opens the door to investigate and ultimately reveal the brain's own temporo-spatial dynamics as the hitherto missing "common currency" of neuronal and mental features. (C) 2019 Elsevier B.V. All rights reserved.

Neuroscience has made considerable progress in unraveling the neural correlates of mental phenomena like self, consciousness, and perception. However, the "common currency" shared between neuronal and mental activity, brain and mind, remains yet unclear. In this article, we propose that the dynamics of time and space provides a "common currency" that connects neuronal and mental features. Time and space are here understood in a dynamic context (as in contemporary physics): that is, in terms of the way the brain's spontaneous activity constructs its spatial and temporal relationships, for instance in terms of functional connectivity and different frequencies of fluctuations. Recruiting recent empirical evidence, we show that the different ways in which the spontaneous activity constructs its "inner time and space" are manifested in distinct mental features. Specifically, we demonstrate how spatiotemporal mechanisms like spatiotemporal repertoire, integration, and speed yield mental features like consciousness, self, and time speed perception. The focus on the brain's spatiotemporal mechanisms entails what we describe as "Spatiotemporal Neuroscience". Spatiotemporal Neuroscience conceives neuronal activity in terms of its temporo-spatial dynamics rather than its various functions (e.g., cognitive, affective, social, etc.) as in other branches of neuroscience (as distinguished from Cognitive, Affective, Cultural, Social, etc. Neuroscience). That allows Spatiotemporal Neuroscience to take into view the so-called 'spatio-temporality' of mental features including their non-causal, intrinsic and transformative relationship with neuronal features. In conclusion, Spatiotemporal Neuroscience opens the door to investigate and ultimately reveal the brain's own temporo-spatial dynamics as the hitherto missing "common currency" of neuronal and mental features. (C) 2019 Elsevier B.V. All rights reserved.

Ethical reflection on Artificial Intelligence (AI) has become a priority. In this article, we propose a methodological model for a comprehensive ethical analysis of some uses of AI, notably as a replacement of human actors in specific activities. We emphasize the need for conceptual clarification of relevant key terms (e.g., intelligence) in order to undertake such reflection. Against that background, we distinguish two levels of ethical analysis, one practical and one theoretical. Focusing on the state of AI at present, we suggest that regardless of the presence of intelligence, the lack of morally relevant features calls for caution when considering the role of AI in some specific human activities.

Progress in neuroscience - notably, on the dynamic functions of neural networks - has deepened our understanding of decision-making, acquisition of character and temperament, and the development of moral dispositions. The evolution of our cerebral architecture is both genetic and epigenetic: the nervous system develops in continuous interaction with the immediate physical and socio-cultural environments. Each individual has a unique cerebral identity even in the relative absence of genetic distinction, and the development of this identity is strongly influenced by social and cultural environments leaving major traces in the connectivity of the brain. This interaction introduces important elements of variability and plasticity. Synaptic epigenetic theories of cultural and social imprinting on our brain architecture suggest the possibility that we can be "epigenetically proactive" and adapt our social structures, in both the short and the long term, to benefit and constructively interact with the ever-developing neuronal architecture of our brains. Epigenetic proaction can be described as a way to socially steer evolution by influencing the cultural imprints to be stored in our brains. The purpose of this article is to present, in a historical and ideological light, the idea that we may culturally shape the developing brain, and discuss some key risks and benefits of this endeavour. The article is a contribution to neuroethics.

The excellent commentaries to our target paper hint upon three main issues, (i) spatiotemporal neuroscience; (ii) neuro-mental relationship; and (iii) mind, brain, and world relationship. (i) We therefore discuss briefly the history of Spatiotemporal Neuroscience. Distinguishing it from Cognitive Neuroscience and related branches (like Affective, Social, etc. Neuroscience), Spatiotemporal Neuroscience can be characterized by focus on brain activity (rather than brain function), spatiotemporal relationship (rather than input-cognition-output relationship), and structure (rather than stimuli/contents). (ii) Taken in this sense, Spatiotemporal Neuroscience allows one to conceive the neuro-mental relationship in dynamic spatiotemporal terms that complement and extend (rather than contradict) their cognitive characterization. (iii) Finally, more philosophical issues like the need to dissolve the mind-body problem (and replace it by the world-brain relation) and the question for different levels of time including their nestedness are discussed.

AI research is growing rapidly raising various ethical issues related to safety, risks, and other effects widely discussed in the literature. We believe that in order to adequately address those issues and engage in a productive normative discussion it is necessary to examine key concepts and categories. One such category is anthropomorphism. It is a well-known fact that AI’s functionalities and innovations are often anthropomorphized (i.e., described and conceived as characterized by human traits). The general public’s anthropomorphic attitudes and some of their ethical consequences (particularly in the context of social robots and their interaction with humans) have been widely discussed in the literature. However, how anthropomorphism permeates AI research itself (i.e., in the very language of computer scientists, designers, and programmers), and what the epistemological and ethical consequences of this might be have received less attention. In this paper we explore this issue. We first set the methodological/theoretical stage, making a distinction between a normative and a conceptual approach to the issues. Next, after a brief analysis of anthropomorphism and its manifestations in the public, we explore its presence within AI research with a particular focus on brain-inspired AI. Finally, on the basis of our analysis, we identify some potential epistemological and ethical consequences of the use of anthropomorphic language and discourse within the AI research community, thus reinforcing the need of complementing the practical with a conceptual analysis.

In “Neuroethics at 15: The Current and Future Environment for Neuroethics” the Emerging Issues Task Force of the INS provides an overview of the current and future topics for neuroethics and the fo...