Tests for consciousness in humans and beyond

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.

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.

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.

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.