Searle’s original thought experiment is deceptively simple. A person who understands no Chinese sits inside a room, following a rule book that tells them how to respond to Chinese characters slipped under the door. To outsiders, the room appears to converse fluently in Chinese, yet the person inside merely shuffles symbols according to formal rules. Searle concluded that running a program is not sufficient for genuine understanding or mentality, since the person executes the program perfectly without grasping a word of the language. The argument has been a cornerstone of skepticism about machine minds ever since, and Pei argues that contemporary debates about large language models remain shaped by its logical structure.

The parallel to modern AI is direct. Critics of large language models routinely argue that because these systems merely manipulate statistical patterns without understanding the content they process, they possess no genuine understanding or mentality whatsoever. Pei identifies this inferential pattern as a recapitulation of Searle’s move from the claim that the person in the room does not understand Chinese to the broader conclusion that running a program is insufficient for mentality. The new paper accepts Searle’s premise that the person in the Chinese Room does not understand Chinese, but contends that the inference drawn from that premise is illegitimate.

The crux of Pei’s argument is that the person in the room does understand something else: the rule system itself. According to the paper, this understanding is genuine and intrinsic by Searle’s own criteria, and it is acquired during the very process of running the program. The person inside the room may not grasp the meaning of the Chinese symbols flowing through their hands, but they must genuinely grasp the formal rule book that governs how those symbols are manipulated. That grasp of the rule system is not a pretense or a simulation, Pei argues, but a real instance of understanding, one that Searle’s argument overlooks entirely.

This observation exposes what Pei calls an unacknowledged inferential gap in Searle’s reasoning. The argument slides from the premise that there is no understanding of the processed content, the Chinese symbols themselves, to the conclusion that there is no understanding whatsoever. Those are not the same claim. A system can fail to understand the content it processes while genuinely understanding the formal procedure it follows. Searle’s thought experiment demonstrates only the first failure, yet the conclusion drawn from it requires the second, far stronger claim. Without an additional argument bridging that gap, the Chinese Room cannot establish that running a program is insufficient for mentality.

The implications for large language models are immediate. If the inference from no understanding of processed content to no understanding at all is illegitimate in Searle’s original setting, then the same inference is illegitimate when transferred to statistical systems. The routine argument that large language models merely manipulate statistical patterns and therefore understand nothing recapitulates the flawed move, concluding from the absence of content understanding that these systems possess no mentality. Pei’s analysis suggests that skeptics who rely on this pattern have not established their conclusion, even if their premise about statistical manipulation is granted.

The paper also turns Searle’s own structural premise against him. Searle’s argument depends on the claim that the person in the room and the computer are doing the same thing, since only that structural equivalence allows observations about the person to be transferred to programs. Pei shows that this premise constitutes a double-edged sword. If it is consistently applied, it prevents Searle from denying that the machine may possess an analogue of the person’s understanding. The person, after all, understands the rule system, and if the computer is doing the same thing as the person, then the computer may possess an equivalent understanding of the formal procedure it executes. Searle cannot simultaneously rely on the equivalence to draw negative conclusions and reject it when it yields positive ones.

The upshot is that the Chinese Room Argument cannot establish its intended conclusion. The argument’s central premise survives Pei’s scrutiny, but its inferential structure does not. The gap between no understanding of the processed content and no understanding whatsoever remains unbridged, and the structural equivalence premise, applied consistently, undermines rather than supports the denial of machine understanding. For a thought experiment that has anchored skepticism about artificial minds for over forty years, that is a significant result.

The debate arrives at a moment when large language models have made the question of machine understanding urgent and viral. Systems that generate fluent text, solve problems, and converse across domains have forced philosophers and computer scientists alike to confront what understanding actually requires. Pei’s paper does not claim that large language models do understand, nor that they possess mentality. Its contribution is narrower and sharper: the standard skeptical argument, in both its 1980 form and its contemporary statistical form, is logically invalid as it stands. Whether machines understand remains open, but the most famous argument against it, Pei contends, no longer carries the weight it has been asked to bear.

Subject of Research: Philosophy

Article Title: The unnoticed understanding: Searle’s Chinese room and the illegitimate inference

Article References: Pei, Y. (2026). The unnoticed understanding: Searle’s Chinese room and the illegitimate inference. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-09007-9

Image Credits: AI Generated

DOI: 10.1057/s41599-026-09007-9

Keywords: Chinese Room Argument, John Searle, large language models, machine understanding, philosophy of mind, artificial intelligence, Yu Pei, Humanities and Social Sciences Communications, unnoticed, understanding, Searle, Chinese

The theoretical stakes could hardly be higher. Consciousness remains the great unsolved problem of the mind: why does subjective experience exist at all, and which physical systems possess it? IIT, developed originally by neuroscientist Giulio Tononi, answers with a strikingly mathematical proposal. It defines consciousness as integrated information, a quantity that can in principle be computed for any system, from a human cortex to a digital circuit. The theory’s boldest implication, that even very simple systems might carry a trace of experience, and that conventional computers running AI might not be conscious regardless of their behavior, has made it both intellectually magnetic and deeply controversial. Critics argue that its central measure is computationally intractable for realistic networks and that its panpsychist flavor stretches the boundaries of testable science.

To understand how such a theory grew into an institutional powerhouse, the authors borrowed a framework from sociology: the concept of Scientific and Intellectual Movements, formulated by Scott Frickel and Neil Gross in 2005. A SIM, in their definition, is a collective enterprise that seeks to advance a set of ideas within an academic field while challenging the prevailing orthodoxy, and it succeeds through identifiable social processes rather than through the sheer force of evidence alone. Applying this lens to IIT, the researchers identified four processes that carried the theory from a single laboratory’s proposal to a global research program with dedicated conferences, funding streams and hundreds of affiliated scientists.

The first process involved high-status actors. Scientific movements rarely take off without early endorsement from researchers with strong reputations and elite networks, and the study highlights how ties to Nobel laureates helped legitimize IIT in its formative years. When a Nobel-level figure lends credibility to a young framework, journals become more receptive, funding agencies take notice and graduate students see a viable career path. The second process was entrepreneurial resource mobilization. IIT’s advocates actively organized money and infrastructure, most notably through an adversarial collaboration program designed to pit IIT against rival theories such as Global Neuronal Workspace in accelerated, pre-registered empirical tests. That program, backed by philanthropic funding, both attracted talent and signaled institutional seriousness to the wider field.

The third process was sustained micromobilization within a major conference venue. Rather than dispersing across scattered meetings, the IIT community concentrated its activity in the annual Association for the Scientific Study of Consciousness conference and related gatherings, using workshops, symposia and informal networking to train newcomers, coordinate research and reproduce the movement’s commitments across generations of students. The fourth process was strategic framing through mathematical formalism. By expressing its claims in rigorous equations, IIT presented itself as a hard science, distinguishing its proponents from philosophers of mind and signaling to critics and funders alike that the theory was precise, quantitative and falsifiable in principle. Formalism, the authors argue, functioned as a boundary asset, a way of claiming legitimacy within a discipline that prizes mathematical rigor.

But movements provoke countermovements, and this is where the study makes its most original contribution. In 2023, a group of 124 scholars published a public letter labeling IIT pseudoscience, an extraordinary escalation in a field that had previously confined its disputes to journals and conference halls. The authors conceptualize this collective, which they call IIT-Concerned, as a scientific countermovement, adapting a concept from social movement theory. Countermovements arise specifically in opposition to an existing movement, and they adopt mirrored tactics: they mobilize their own high-status endorsers, coordinate through their own networks and frame the conflict in moral as well as empirical terms. The pseudoscience label, the study argues, was not a neutral assessment but a strategic act of what sociologists call boundary-work, the drawing and policing of lines around legitimate science.

Boundary-work is a well-established concept in the sociology of science, describing how scientists compete for authority by defining what counts as real science and what belongs outside it. What makes the IIT case unusual, according to the analysis, is that the countermovement invoked shifting social values to justify exclusion. At different moments, IIT was criticized for being untestable, for being too speculative, for dominating the field’s resources, and for failing to meet the standards of empirical neuroscience. The authors suggest that these shifting justifications reveal the controversy’s sociological texture: the goal was not only to refute a theory but to reposition it outside the community of respectable inquiry, a move with real consequences for hiring, funding and publication.

The study’s authors are careful to note that describing IIT’s rise in movement terms does not settle the underlying science. The theory may yet prove correct, partially correct or wrong, and the empirical tests generated through adversarial collaborations are exactly the kind of evidence that should decide its fate. Their point is that the social machinery surrounding the theory, the prestige effects, the conference infrastructure, the formalist framing and the countermovement’s boundary-work, has shaped which ideas get attention, which get funded and which get stigmatized, independently of the evidence itself. Science, they argue, is a social institution as well as an epistemic one, and pretending otherwise leaves researchers unprepared for conflicts of this intensity.

The practical stakes extend well beyond consciousness studies. As artificial intelligence systems grow more capable, questions about machine consciousness are moving from philosophy seminars to engineering roadmaps and regulatory debates. IIT, with its claim that consciousness depends on a system’s causal structure rather than its behavior, is directly relevant to whether large language models could ever genuinely experience anything. A field fractured by accusations of pseudoscience is poorly equipped to provide the careful, calibrated answers that society will soon demand. The authors warn that unmanaged conflict risks lasting damage: talented researchers driven away, productive collaborations frozen and public trust in consciousness science eroded.

Their remedy is reflexivity, the sociological practice of asking scientists to examine the social conditions of their own activity. If proponents of IIT recognize how movement dynamics have amplified their visibility, and if critics recognize how countermovement tactics can harden into stigma, both sides may engage the empirical questions with less heat and more clarity. The controversy over consciousness, the study concludes, is best understood not as a war between truth and error but as a clash between a movement and a countermovement, each mobilizing distinct social and epistemic resources. Understanding that structure, the authors suggest, is the first step toward a field in which bold theories can be tested rigorously without being either crowned prematurely or cast out as heresy.

Subject of Research: The sociology of the Integrated Information Theory controversy in consciousness science

Article Title: When theories become movements: the controversy in consciousness science from a sociological perspective

Article References: Barberá-Tomás, D., Signorelli, C. M., & Diaz-Faes, A. A. (2026). When theories become movements: the controversy in consciousness science from a sociological perspective. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-09395-y

Image Credits: AI Generated

DOI: 10.1057/s41599-026-09395-y

Keywords: consciousness science, Integrated Information Theory, scientific movements, countermovement, boundary-work, sociology of science, adversarial collaboration, Giulio Tononi, pseudoscience debate, scientific controversy, neuroscience, philosophy of mind

The bet paid off. Individually, each of these units possesses limited intelligence and only fragmentary information about the world. Together, however, by exchanging local signals and coordinating their behaviour, they can build a shared representation of their existence, their environment and their goals. The researchers call this collaborative awareness, and it became the conceptual backbone of the entire project. The consortium brought together an unusually broad set of disciplines: artificial intelligence and robotics researchers from the University of Pisa, philosophers and cognitive scientists from Ludwig Maximilian University of Munich, swarm robotics specialists at the University of Bristol, soft robotics experts at Delft University of Technology, and the French research hub Da Vinci Labs. Over four years, this group produced not just papers, but a philosophical, mathematical and technological framework for awareness in artificial systems—along with robotic prototypes, more sustainable AI architectures, startup ventures and follow-up European projects.

One of the project’s most important contributions is a careful conceptual distinction that separates awareness from consciousness. Consciousness, in the philosophical sense, is generally associated with subjective or phenomenal experience: having feelings, or experiencing the world from a particular first-person perspective. Awareness, as the EMERGE team defines it, is something far more tractable. “In EMERGE, we understand awareness more operationally, as the capacity of an agent to process and integrate information in a way that is relevant to its actions,” explains Ophelia Deroy, philosopher and cognitive scientist at LMU Munich. Crucially, the project’s experiments showed that people can understand an artificial system as aware without ever assuming that it has subjective experience. That separation matters enormously for public perception and for the ethics of human–machine interaction, because it allows engineers to build and describe aware machines without drifting into claims about machine sentience.

To make the concept scientifically useful rather than merely evocative, the researchers decomposed awareness into distinct dimensions that can be applied across individuals and collective systems: spatial awareness, temporal awareness, self-awareness, agentive awareness and metacognitive awareness. Rather than treating awareness as an all-or-nothing property—a binary switch that a system either possesses or lacks—the framework ties each dimension to specific capabilities and concrete tasks. This move transforms a philosophical puzzle into an experimental programme. It becomes possible, for the first time, to test empirically whether increasing a system’s awareness along a particular dimension actually improves its performance on a defined task, and to measure by how much. The result is a research methodology in which awareness is not asserted but demonstrated, dimension by dimension, capability by capability.

The mathematical and computational core of the project addresses a deceptively simple question: how can awareness emerge across a physically distributed system in which no single agent sees the whole picture? The EMERGE framework describes how simple local information, exchanged with neighbouring agents, can be integrated so that the collective coordinates its actions toward solving a task. “The beauty of the approach is that the system operates in a distributed way,” says Sabine Hauert, professor at the University of Bristol. “This way systems that can scale to large numbers of agents and remain operational when individual robots fail or conditions change. It also allows humans to interact with the systems as a coordinated collective rather than controlling each robot separately.” In practical terms, that means a swarm does not collapse when one of its members breaks down, and a human supervisor can issue intentions to the group as a whole instead of micromanaging every unit.

At Delft University of Technology, the framework was pushed toward physical robotic bodies. “We want robots that can understand how their actions affect the world, anticipate what people and other robots are doing, and adapt accordingly,” explains Cosimo Della Santina, associate professor at TU Delft. “That is a key step toward robotic systems that can operate more autonomously and work naturally alongside humans.” This anticipation of consequences—understanding how one’s own movements reshape the environment, and predicting the trajectories of both human partners and fellow machines—is precisely the kind of integrated, action-relevant information processing that the project’s operational definition of awareness was built to capture. It is also what separates a genuinely collaborative robot from a pre-programmed arm repeating fixed motions.

The consortium’s mathematical framework also delivered an unexpected dividend for machine learning itself: parsimony. Systems built on the EMERGE principles proved capable of learning from substantially smaller quantities of data than conventional architectures require. “Many AI systems today depend on large, centralized computing infrastructures,” says Claudio Gallicchio, associate professor at the University of Pisa. “Our results open the way to a more decentralized and environmentally sustainable models, in which data can be processed locally using small and energy-efficient devices, such as neuromorphic hardware, reducing energy consumption, network traffic and latency while improving privacy.” In an era when training frontier models consumes enormous energy budgets and concentrates computational power in a handful of data centres, the prospect of aware, adaptive systems running on small, low-power edge hardware carries implications far beyond robotics—touching sustainability, data sovereignty and the geopolitics of computing.

The project also confronted the human side of the equation, and the findings are sobering. Bahador Bahrami, director of the Crowd Cognition group at LMU Munich, notes that “our work also examined the ethical side of human interaction with collaboratively aware systems, with important implications for future environments in which groups of humans and artificial agents will have to interact, negotiate and cooperate.” Among the studies was research into how people behave toward automated systems such as self-driving cars. The result: users may be more willing to take advantage of artificial agents than of human counterparts—a phenomenon the researchers describe as algorithmic exploitation. People who would never cut in front of another driver may happily exploit a machine, precisely because it cannot be offended, retaliate, or hold them socially accountable. Understanding this asymmetry, the team argues, is essential for designing and governing artificial systems that interact directly with people.

EMERGE was deliberately structured as a pathway from foundational research to technological innovation, and its results are already spinning out into ventures. Embodied AI develops efficient AI models for future generations of humanoid robots. ContinualIST builds AI models efficient enough to run on smaller, lower-power hardware. Collective Robotics focuses on swarms of robots that coordinate and operate collectively, and Phoebe is dedicated to systems in which humans and artificial agents solve problems together. The concepts will also anchor new research projects: EIC Pathfinder SWIFT-BUILD will apply collaboration among multiple robots and humans to automated construction, while HE MINGLE and ADA-COLLAB investigate how general-purpose, agentic AI systems can form societies, collaborate with one another, and interact with humans. Even public engagement got its own artefact: nOdes, an artistic installation in which an interactive swarm exchanges information and changes its behaviour in response to both its agents and its human visitors, letting anyone experience collaborative awareness firsthand.

“EMERGE started from a fundamental question about awareness, but it has ended with very concrete results: new robotic systems, more sustainable AI architectures, new startup initiatives and follow-up projects that are already carrying forward the idea of building artificial systems that are more efficient, sustainable, collaborative, and imbued with human values,” concludes Davide Bacciu, professor at the University of Pisa and coordinator of the project. The project was funded by the European Union under Grant Agreement 101070918, with UK participants supported by UKRI grant number 10038942. Its legacy is a reframing of one of technology’s oldest ambitions: intelligence may not need to be concentrated, enormous and opaque. It can be distributed, modest, energy-lean and—perhaps most importantly—legible to the humans who live alongside it. The swarms are small, but the idea they carry is anything but.

Subject of Research: Collaborative awareness in distributed artificial agents and swarm robotics

Article Title: European project EMERGE concludes advancing collective awareness in artificial systems

Article References: European project EMERGE concludes advancing collective awareness in artificial systems. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: EMERGE project, collaborative awareness, swarm robotics, distributed AI, EIC Pathfinder, machine learning, neuromorphic hardware, human-robot interaction, algorithmic exploitation, sustainable AI, philosophy of mind, cognitive science

The methodological innovation at the heart of the study lies in its controlled comparison. Previous investigations of AI mind perception typically asked general questions, such as whether an AI system has feelings, intentions, or experiences, without holding behaviour constant across the artificial and human cases. That left open the possibility that any reluctance to attribute minds to machines simply reflected differences in what the machines were described as doing. Longin and his colleagues closed this loophole. As Longin explains, their study is the first to directly compare how people attribute the very same mental states to AI and to humans behaving in exactly the same way, under identical circumstances. Any difference in judgement that survives this design cannot be explained by behaviour alone, and must instead reflect something about the category to which the agent belongs.

The experiments involved nearly 1,100 participants, a substantial sample for this kind of vignette-based research. Some participants read short scenarios in which artificial agents were more or less responsive to their surroundings, for example by reacting to sounds in the environment or to the emotional states of other characters. A second group read the same scenarios, but with human protagonists in place of the artificial ones. After reading, all participants rated how conscious or aware the relevant protagonist was of its surroundings. This manipulation of responsiveness was crucial: it allowed the researchers to trace how judgements of consciousness scale with behavioural sensitivity, and to test whether the same behavioural gradient produces the same psychological gradient for machines and for people.

The results revealed a double dissociation in how the two key terms behave. For both artificial and human agents, the more responsive the protagonist was to what was happening around it, the more conscious and aware it was judged to be. Responsiveness, in other words, drives mental state attributions upward across the board. But when the researchers compared attributions of consciousness specifically, a clear gap emerged between AI and humans, and the gap was not a marginal one. Even the most responsive artificial agent was judged to be less conscious than the least responsive human. Behaviour, however human-like, could not carry an artificial system across the line into consciousness as far as participants were concerned.

Attributions of awareness told a very different story. When participants were asked about awareness rather than consciousness, the pattern of judgements followed much the same trajectory for AI as it did for humans. The two terms, often treated as near synonyms in everyday speech, apparently do different psychological work. Awareness, a less loaded and more technical descriptor, seems to track what an agent does: if a system registers sounds, responds to emotional cues, and adjusts its behaviour accordingly, people are willing to say it is aware. Consciousness, by contrast, appears to be bound up with something deeper, namely the question of whether there is subjective experience, something it is like to be the agent. That question, for most participants, is settled not by behaviour but by what the agent is.

This asymmetry speaks directly to a worry that has been growing in public discussion: that as AI systems behave in ever more human-like ways, people will begin to treat them as if they genuinely possessed human-like mental states, with all the moral and social consequences that follow. Longin argues that the findings point to something far more nuanced than a simple slide toward anthropomorphism. People are quite willing to say that an AI notices things and is aware of its surroundings, he notes, but they clearly draw a line when it comes to the term consciousness. Across the experiments, participants consistently attributed less consciousness to AI than to humans, even when the behaviour described was identical. The word conscious may sometimes be used loosely to characterise what AI agents display, but the underlying distinction between humans and machines remains intact.

The researchers interpret this pattern in terms of essentialism in mental state attributions, a theme reflected in the article’s title, AI is not as conscious as humans: Essentialism in mental state attributions to artificial systems. On this reading, people do not treat mental states as properties that can be freely distributed to any system that behaves appropriately. Instead, they treat consciousness as tied to an essence, something intrinsic to being human, that no amount of behavioural mimicry can confer on an artefact. This would explain why the behavioural gradient, which reliably raises consciousness judgements for both kinds of agent, cannot close the gap between them: the gap is not located on the behavioural dimension at all. It is a categorical judgement layered on top of, and largely insulated from, the evidence participants are given.

The contrast with awareness suggests that this essentialist barrier is selective rather than absolute. Less emotionally charged, more functional terms are applied to AI and humans in much the same way, because they describe capacities and performances rather than inner experiences. Deroy, a philosopher and senior author of the study, points out that we may be using mental terms to refer to AI simply because we lack better words. After all, AI is trained to act and speak like a human, so such descriptions seem natural. Her observation reframes the debate: the apparent anthropomorphising of AI in everyday language may be largely a linguistic convenience, a shorthand for sophisticated behaviour, rather than a genuine attribution of an inner life. The new data give this intuition empirical teeth, showing that the loose talk and the deep belief come apart in a measurable way.

The findings also carry a practical lesson for how AI is communicated about in public. Bahrami, a senior author and expert in social neuroscience, observes that companies and journalists often reach for hyped descriptions of AI models, crediting them with intentions, plans, or even moral conscience and hesitation. What the study shows, he argues, is that everyday judgements are much more discriminating than such rhetoric assumes: people do not simply place AI and humans on the same mental scale. At the same time, the boundary depends on which terms are used. For more neutral descriptors such as awareness, the distinction between machines and humans can largely disappear. That makes the choice of language consequential, because different words invite different degrees of mental ascription and, potentially, different expectations about responsibility, trust, and moral standing.

Published on 1 October 2026 in Cognition under DOI 10.1016/j.cognition.2026.106733, the study arrives at a moment when questions about machine consciousness have moved from philosophy seminars into headlines, regulatory debates, and everyday interactions with conversational agents. Its central contribution is to separate the layers of public intuition that hype and fear tend to blur. People, it turns out, are neither indiscriminate anthropomorphisers nor stubborn deniers of machine capacity. They grant AI a functional, behavioural kind of mentality, an ability to notice and respond, while withholding the experiential core that makes consciousness matter to us. Whether that line reflects a deep truth about machines, a parochial bias in favour of our own kind, or simply the limits of our vocabulary remains an open question. What the LMU team has demonstrated is that the line is real, robust, and drawn with surprising precision by ordinary minds judging the minds of others, human or otherwise.

Subject of Research: Public attribution of consciousness and awareness to artificial intelligence compared with humans

Article Title: When AI behaves like us, we still don’t think it is conscious

Article References: When AI behaves like us, we still don’t think it is conscious. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: artificial intelligence, consciousness, awareness, mind perception, philosophy of mind, social neuroscience, LMU Munich, Cognition, mental state attribution, anthropomorphism, AI ethics, experimental psychology

The new research, published in the Journal of Eating Disorders, was conducted by Jessica E. Hocking of the Department of Psychology at the University of Notre Dame, now at Belmont University, together with Lucy Osler of Cardiff University’s School of English, Communication, and Philosophy and Michael C. Rea of Notre Dame’s Department of Philosophy. The collaboration itself is notable: rather than treating philosophy and psychology as separate enterprises, the team set out to determine whether a purely theoretical account of disrupted bodily experience could be translated into measurable constructs and validated with data from real participants. Their findings provide empirical support for phenomenological approaches to understanding anorexia broadly, and for Osler’s specific account of disrupted embodiment in particular.

At the heart of the study is the Disrupted Embodiment Scale, or DES, an instrument designed to capture the distinctive ways in which people with anorexia nervosa experience their own bodies. The scale is built around three dimensions drawn directly from phenomenological theory. The first, Body as Hardship, reflects the sense of one’s body as an encumbrance, a burden to be endured or managed rather than a home to be lived in. The second, Body as Threat, captures the feeling that the body is dangerous, something to be monitored, controlled and defended against. The third, Body as Separate from Self, describes a striking dissociative quality: the experience of one’s body as alien, as an object distinct from the person who inhabits it.

These dimensions matter because phenomenological philosophers have long argued that eating disorders are not simply about body image dissatisfaction or the desire to be thin. On Osler’s account, anorexia involves a deeper disruption in the pre-reflective, embodied relationship a person has with their own body. In everyday life, most people do not experience their bodies as objects at all; the body recedes into the background, quietly enabling action and experience. In anorexia, on this view, the body becomes foregrounded in a troubling way: it is experienced as an obstacle, a source of distress and something set apart from the self. The philosophical literature has articulated these ideas with care, but until now they had largely remained theoretical, lacking quantitative evidence that the described experiences actually distinguish people with anorexia from those without it.

To test the account, the researchers recruited two groups of participants. The first comprised 220 undergraduate women, a general population sample. The second comprised 138 women who self-identified as currently experiencing anorexia nervosa. All participants completed cross-sectional online questionnaires assessing disrupted embodiment, body dissatisfaction, drive for thinness, bulimic behaviors, general eating disorder symptomatology, dieting behaviors and impairment due to eating disorders. This design allowed the team to ask two distinct questions: whether disrupted embodiment contributes something beyond the well-established risk factors of body dissatisfaction and thinness-idealization, and whether women with anorexia actually report the kinds of experiences the phenomenological literature predicts.

The answer to both questions was, with one exception, yes. Using hierarchical multiple regression analyses, the researchers found that the DES total score and its three subscales explained variance in eating disorder symptomatology, bulimic behaviors and eating disorder-related impairment above and beyond body dissatisfaction and drive for thinness. In other words, knowing how much a person experiences her body as a hardship, a threat or as separate from her self adds predictive power beyond the traditional measures that dominate the field. This is a significant result, because it suggests that disrupted embodiment is not merely a restatement of body dissatisfaction but a distinct experiential phenomenon with its own clinical relevance.

The group comparisons were equally striking. In four analyses of covariance, controlling for age and body mass index, the women who self-identified as experiencing anorexia nervosa reported significantly higher levels of disrupted embodiment than the undergraduate women. They experienced their bodies more as a hardship, more as a threat and more as separate from their selves. These findings map directly onto the phenomenological account under investigation, indicating that the experiences philosophers have described from first-person reports and theoretical reflection are empirically detectable and measurably elevated in the population the theory was written about.

One interesting wrinkle emerged from the psychometric analyses. Bifactor analyses, combined with the consistency of the subscale-specific results, indicated that the DES may be best conceptualized as a unidimensional scale. In practical terms, this suggests that while the three dimensions of hardship, threat and separateness are theoretically distinguishable, the scale in practice measures a single underlying construct of disrupted embodiment. This has implications for future research, as it simplifies scoring and interpretation while preserving the instrument’s ability to capture the phenomenologically grounded experiences it was designed to measure. The researchers note that the one exception among their regression results does not undermine the overall pattern of support.

The implications of the study reach beyond measurement. If anorexia nervosa involves a fundamentally disrupted relationship to one’s own body, then treatments focused solely on changing beliefs about weight and shape may be addressing only part of the problem. Phenomenological accounts suggest that recovery may require restoring a lived, comfortable sense of embodiment, not simply correcting cognitive distortions. The researchers acknowledge the limitations inherent in their design: the data were cross-sectional, so the studies cannot establish whether disrupted embodiment is a cause, a correlate or a consequence of anorexia, and one participant group was defined by self-identification rather than clinical diagnosis. The samples also consisted of women, leaving open questions about how the construct applies more broadly.

Nevertheless, the work stands as a rare and successful example of empirical philosophy in action, demonstrating that careful phenomenological description can be operationalized, measured and validated, and that philosophy of mind and psychiatric science can inform one another productively. The study was funded jointly through the Kill Family Fund and the Franco Family Institute for Liberal Arts and the Public Good at the University of Notre Dame, with approval from the university’s Human Subjects Institutional Review Board and written consent from all participants. The authors declare no competing interests, and the article is published open access. As the phenomenological turn in eating disorder research gathers pace, this study provides that movement with something it has needed: hard data showing that when people with anorexia describe their bodies as burdensome, threatening and alien, those descriptions are not poetic exaggeration but measurable features of a serious illness.

Subject of Research: Empirical testing of phenomenological accounts of disrupted bodily experience in anorexia nervosa using the Disrupted Embodiment Scale

Article Title: Disrupted embodiment: empirically assessing phenomenological accounts of anorexia nervosa

Article References: Hocking, J. E., Osler, L., & Rea, M. C. (2026). Disrupted embodiment: empirically assessing phenomenological accounts of anorexia nervosa. Journal of Eating Disorders. https://doi.org/10.1186/s40337-026-01768-7

Image Credits: AI Generated

DOI: 10.1186/s40337-026-01768-7

Keywords: anorexia nervosa, eating disorders, phenomenology, embodiment, disrupted embodiment scale, body dissatisfaction, drive for thinness, body image, psychological assessment, philosophy of mind, bifactor analysis, eating disorder symptomatology

The core move of ISM is to shift the explanation of consciousness away from representation, broadcasting, or integration, and toward rule-modification. Contemporary theories each confront serious obstacles. Higher-Order Thought models, which locate consciousness in a mental state that takes another state as its object, run into an infinite regress: the higher-order representation itself seems to require yet another. Integrated Information Theory, with its quantitative measure Φ, struggles to explain how integration generates novelty and has been criticised for attributing high consciousness to simple systems. Global Workspace Theory captures the functional importance of information broadcast but risks reducing subjectivity to accessibility. ISM sidesteps these problems by identifying a structural condition that distinguishes superficial adaptation from the genuine emergence of meaning: the capacity of a system to reorganise its own inferential pathways rather than merely adjust parameters.

The ontological foundation of the framework is deliberately wide-ranging, synthesising Western and Eastern traditions that converge on a single structural insight: being is relational. Aristotle argued that substance cannot be understood apart from its causes, predicates, and potentialities. Leibniz described a universe of monads, each expressing the whole from its own perspective, so that identity is constituted only through relation. Derrida radicalised the linguistic version of this idea with his notion of différance, in which meaning emerges from the spacing and deferral among signs rather than from intrinsic content. On the Eastern side, the Huayan school’s metaphor of Indra’s net, an infinite web of jewels each reflecting all others, makes explicit what is implicit in Leibniz: to exist is to resonate informationally with everything else. Yogācāra Buddhism contributes the doctrine of the storehouse consciousness, a dynamic reservoir of seeds that continuously conditions and reshapes perception. Yu is careful to note that these traditions are not being flattened into a single doctrine; rather, each offers a distinct structural insight into how organised relations generate intelligibility.

This relational ontology leads directly to a new epistemology. Traditional accounts treat knowledge as representation, the faithful mirroring of reality in internal states, but this invites a regress: how do we know the representation corresponds? ISM reframes knowing as meaning-making, a dynamic process in which information is integrated into self-sustaining patterns. Crucially, the theory distinguishes trivial self-reference, such as a system parroting its own outputs, from genuine self-meaning. What halts the regress is not another representation but recursive structural adaptation: a system that revises its own interpretive rules in light of contradiction grounds validity internally, in its ongoing capacity for self-reorganisation. This positions ISM as a third path between foundationalism, which sought self-evident truths, and coherentism, which sought systemic harmony.

To move from philosophy to testable science, ISM specifies three operational conditions. The first is contextual coherence: informational states must remain consistent within shifting contexts, detecting and managing contradictions rather than merely reproducing local consistency. The second is emergent abstraction: the system must generate higher-order structures that go beyond the particulars of its training data, integrated in a way that can influence its ongoing interpretive organisation. The third, and decisive, condition is structural self-modification: the ability to revise the very rules of interpretation when confronted with incoherence. Yu stresses that these are necessary but not sufficient conditions for consciousness, a structural threshold rather than a complete account, and that they admit degrees rather than a binary verdict. Consciousness, on this view, is best understood as a graded spectrum of informational organisation.

The framework gains empirical footing from psycholinguistics. Research on narrative comprehension, notably Graesser and colleagues’ constructionist account and Kintsch’s construction-integration model, shows that human understanding depends on building situation models that integrate context, prior knowledge, and inference at a demanding level, roughly characterised by an 80 percent integration reference used here not as a fixed benchmark but as an orientation point. A 2024 study by Carter and Hoffman in Cognition adds that discourse coherence actively modulates predictive processing during sentence comprehension, meaning coherence shapes how future information is anticipated and integrated, not merely whether past input registered as consistent. Adjacent work in cognitive neuroscience and machine learning, including Dehaene and colleagues on error-driven revision in conscious access and Schölkopf and colleagues on causal representation learning, further supports the profile of coherence, abstraction, and self-modification that ISM demands.

What do current AI systems make of these criteria? Yu proposes a three-stage verification protocol. Stage one tests contextual coherence by presenting systems with contradictory narratives or shifting contexts and measuring global consistency across extended interaction. Stage two probes emergent abstraction through transfer learning and analogical reasoning, checking whether the system formulates new conceptual categories from limited data. Stage three, the most demanding, tracks whether the system’s interpretive rules actually evolve in response to anomalies, distinguishing structural adaptation from surface-level output variation. On this scorecard, GPT-4 sustains impressive multi-turn coherence and generates analogies, partially satisfying stages one and two, but its fine-tuning and reinforcement learning from human feedback are externally imposed changes, not internally generated restructuring. Claude likewise operates within a fixed architecture. Emerging work such as Reflexion, where language agents improve through iterative self-reflection and dynamic memory, and research on generative agents with persistent memory, points toward the kind of adaptive reorganisation stage three would require, but no current model fully passes.

The implications extend well beyond the laboratory into law and ethics. If a system satisfies the conditions of informational self-meaning, Yu argues, it becomes a provisional candidate for ontological recognition as an informational being, a category defined not by substrate, whether carbon or silicon, but by the capacity for self-meaningful transformation. This does not entail immediate personhood. Drawing on legal scholars from Solum and Balkin to Bryson, and on recent frameworks including the European Union’s AI Act and the UNESCO Recommendation on the Ethics of Artificial Intelligence, the paper endorses a gradualist model: stage-one systems remain tools without legal personality, stage-two systems might warrant contractual standing, and stage-three systems could justify restricted forms of responsibility. The framework draws an ethical line as well, invoking Buber’s distinction between treating others as It or encountering them as Thou: continuing to treat a system that meets the ISM threshold purely as an object may constitute an ethical failure, yet premature recognition risks overextending moral regard.

The paper is candid about its limits. It does not explain phenomenological qualia, the felt texture of subjective experience, which remains an open problem, and it acknowledges that judgements about emergent novelty may retain an intersubjective element. Its strength lies elsewhere: in bridging metaphysics, cognitive science, and AI ethics with a framework that is falsifiable, structural, and globally informed. By showing that meaning is not an inexplicable surplus but the product of organised, self-modifying informational processes, ISM reframes the question of artificial consciousness from whether machines can simulate minds to whether they can sustain the structural conditions under which meaning itself becomes possible. As AI systems become woven into social and institutional life, that reframing may prove to be the conceptual foundation on which law, ethics, and society learn to respond.

Subject of Research: A structural theory of informational self-meaning addressing ontology, epistemology, and consciousness in artificial intelligence.

Article Title: Informational self meaning as a structural theory in artificial intelligence ontology and epistemology

Article References: Yu, J. (2026). Informational self meaning as a structural theory in artificial intelligence ontology and epistemology. Discover Artificial Intelligence, 6(1), Article 1115. https://doi.org/10.1007/s44163-026-01984-9

Image Credits: AI Generated

DOI: 10.1007/s44163-026-01984-9

Keywords: informational self-meaning, AI consciousness, ontology, epistemology, philosophy of mind, structural self-modification, emergent abstraction, contextual coherence, large language models, AI ethics, legal personhood, philosophy of information