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

Theory of Mind, often abbreviated as ToM, is the cognitive machinery that allows humans to attribute mental states to others and to predict behavior on that basis. First described in the landmark 1978 question of whether chimpanzees possess a theory of mind, the capacity depends on a distributed network of cortical regions, particularly the prefrontal, insular and cingulate areas. Those same regions are among the most consistently altered in people with substance use disorder, whether through reduced cortical volume, disrupted connectivity or changes in neurochemistry. This anatomical overlap is one reason researchers have long suspected that psychoactive substances could compromise the ability to understand other people.

Previous syntheses had already documented ToM problems among people with alcohol use disorder, with effect sizes ranging from moderate to very large. Meta-analyses of facial emotion recognition, a related social-cognitive skill, also pointed to deficits among alcohol and substance users. But evidence on substances other than alcohol remained scattered. The only earlier systematic review covering the broader landscape, published in 2016, found relatively weak evidence for ToM impairments linked to methamphetamine and no impairments among recreational cocaine users. The new study, led by José Vitor de Lima Neto and Flavia de Lima Osório of the University of São Paulo, set out to resolve that uncertainty with a formally registered, PRISMA-guided search of eight databases including Embase, Scopus, PsycINFO, Web of Science, PubMed, PsycNet, LILACS and SciELO.

The review included original quantitative studies comparing Theory of Mind performance between adult psychoactive substance users and controls. After screening, 37 studies met the criteria, and 30 of them—covering 35 distinct samples—provided data suitable for meta-analysis. Most of the research originated in Europe, with 70 percent of studies conducted there, and the field’s first study dates only to 2006, making this a young but rapidly growing literature. Alcohol was the predominant substance studied, accounting for roughly half of the samples, followed by stimulants, opioids, cannabis and a single study involving the hallucinogen MDMA. Participants were mostly men, with a mean age of about 37 years, and were largely recruited from inpatient detoxification clinics.

The pooled result was striking. Across the meta-analyzed samples, substance users showed significantly poorer Theory of Mind performance than controls, with a standardized mean difference of −0.80, a large effect by conventional statistical standards. The 95 percent confidence interval ran from −1.13 to −0.47, well clear of zero. Heterogeneity was high, at roughly 93 percent, reflecting the diversity of instruments, populations and study designs, but sensitivity analyses removing statistical outliers left the overall estimate essentially unchanged. Publication bias tests, including Begg’s rank correlation test and funnel plot inspection, found no evidence that negative findings were being suppressed.

Subgroup analyses revealed something perhaps even more consequential: the impairment did not appear to depend on which substance was used. Samples of alcohol users produced an effect size of −0.92, opioid users −1.12 and stimulant users −1.29. A formal between-group test found no statistically significant differences among these classes, with overlapping confidence intervals pointing to a common magnitude of deficit. The authors also tested whether the choice of assessment tool mattered, given ongoing debate about the Reading the Mind in the Eyes Test, the most frequently used instrument in this literature. A mixed-effects meta-regression comparing studies that used the Eyes Test with those using other measures found no significant moderating effect, suggesting the deficits are not an artifact of one particular task.

Two theoretical explanations dominate the interpretation of these findings, and they are not mutually exclusive. The first is neurotoxicity: chronic exposure to alcohol, opioids or stimulants damages or dysregulates precisely the prefrontal, insular and cingulate circuits that Theory of Mind depends upon, and decades of neuroimaging work document structural and functional changes in these regions among people with substance use disorder. The second is vulnerability: weak social cognition may predate substance use, increasing susceptibility to social stress, isolation and exclusion, and leading individuals to use substances as a way of coping with relational difficulties or of conforming to perceived social norms. Supporting the vulnerability hypothesis, a 2022 meta-analysis found an inverse relationship between alcohol-related problems and ToM performance even in non-clinical samples, where use never reached diagnostic thresholds.

The new review itself is cross-sectional in its evidence base, with 36 of the 37 included studies using cross-sectional designs, so causality cannot be established. The authors are explicit that their pooled estimate should be read as evidence of association rather than cause. They also note that the two mechanisms likely interact in a self-reinforcing loop: a primary ToM weakness predisposes someone to use substances, chronic use then intensifies the deficit through neurotoxic effects, and the deepened impairment in turn hinders therapeutic adherence and relapse control. Interestingly, one study in the review found that long-term MDMA users actually performed better on Theory of Mind tasks than non-users, a finding consistent with evidence that the pursuit of empathetic social experience motivates MDMA use—though it rests on a single study.

Important gaps remain. Evidence on cannabis, hallucinogens and nicotine is too sparse for meta-analysis; two cannabis studies found no significant differences, though the tasks used assessed affective rather than cognitive ToM, a subdomain that may be selectively vulnerable given cannabis-linked frontal reductions. Methodological quality across the field was only moderate, most samples came from high-income countries, inpatient recruitment may have inflated severity, and polysubstance use and psychiatric comorbidities were rarely controlled. Nevertheless, the authors argue the practical message is clear: sociocognitive skills deserve formal assessment in both prevention programs and addiction treatment, because deficits in reading minds can raise vulnerability to initiating use, undermine therapy and complicate recovery long after detoxification ends.

Subject of Research: Theory of Mind deficits among psychoactive substance users

Article Title: Theory of Mind in Psychoactive Substance Users: A Systematic Literature Review and Meta-analysis

Article References: Neto, J. V. D. L., & Osório, F. D. L. (2026). Theory of Mind in Psychoactive Substance Users: A Systematic Literature Review and Meta-analysis. International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-026-01717-0

Image Credits: AI Generated

DOI: 10.1007/s11469-026-01717-0

Keywords: Theory of Mind, psychoactive substances, substance use disorder, social cognition, meta-analysis, alcohol, opioids, stimulants, addiction, prefrontal cortex, mental state attribution, systematic review

Theory of Mind, the ability to attribute mental states to oneself and others, plays a vital role in social interactions and communication. It allows individuals to interpret and predict behaviors based on inferred intentions, beliefs, and desires. However, as we grow older, there is often a noticeable decline in these cognitive skills. The researchers aimed to decipher the extent and implications of these declines, providing key insights into both psychological and social implications.

In their extensive study, the researchers engaged a diverse sample that included various age groups. They utilized a battery of tests designed to assess ToM capabilities alongside cognitive complexity and reasoning abilities. The results indicated a significant decline in ToM scores with age, which is a cause of concern since these skills are crucial for maintaining relationships and navigating complex social scenarios. As seniors may often find themselves in increasingly isolating situations, understanding these cognitive shifts could foster new strategies for social engagement and emotional support.

One of the most intriguing elements of this study is the correlation found between the level of activity individuals engage in and their ToM capabilities. The researchers observed that those who maintained a higher level of social activity tended to have better ToM performance, even as they aged. This suggests that social engagement might act as a protective factor against cognitive decline, turning the spotlight on the importance of fostering community and social connections throughout the lifespan.

Moreover, the findings extend beyond mere observation; they indicate actionable steps that can be taken to mitigate cognitive decline. By encouraging regular social activities, communities can aid older adults in maintaining their cognitive sharpness and emotional well-being. This is in line with existing literature, which suggests that lifelong learning and active social participation are essential to cognitive health.

Delving deeper into cognitive complexity, the researchers established that individuals with more nuanced understanding and expression of thoughts were likely to show better performance on ToM assessments. This complex interplay suggests that cognitive complexity may be a crucial component of our mental agility in aging, emphasizing the need for mental stimulation as we grow older. By fostering environments that challenge cognitive capabilities, we might not only improve individual ToM skills but also contribute to overall community well-being.

Another fascinating aspect of the research is the highlighting of reasoning abilities in relation to ToM. Participants showcasing high levels of reasoning tended to retain better social cognition even into their senior years. This underscores the significance of honing reasoning skills in educational frameworks and daily life, paving the way for more robust cognitive health in later life.

Understanding these dynamics is not merely an academic exercise; it reflects a pressing societal concern. With the increasing number of elderly individuals experiencing isolation, providing resources and opportunities for social engagement becomes essential. This study exemplifies how interdisciplinary approaches, merging psychology, sociology, and gerontology, can yield insights vital for policy-making and community support systems.

Furthermore, considering the developmental aspects of ToM, it is crucial to understand that social environments play a major role in shaping these cognitive faculties. Childhood experiences, peer interactions, and familial connections all contribute to how effectively we develop and utilize our ToM capabilities throughout life. Thus, promoting these interactions early on can instill lasting benefits in cognitive health as we age.

In summary, this research offers a multi-faceted perspective on aging, cognition, and social dynamics. The clear association between age-related declines in Theory of Mind and cognitive complexity, reasoning abilities, and social activity underscores the importance of supportive interactions as we age. Encouraging active participation in social networks might not only mitigate the decline of crucial mental capabilities but also enrich the lives of older adults, allowing them to engage more fully with their communities.

In light of these findings, it becomes increasingly evident that strategies fostering lifelong learning and social interaction should be integrated into public health considerations and community planning. If we genuinely aim to enhance the quality of life for older adults, we must prioritize understanding and addressing the cognitive challenges they face, while simultaneously empowering them to maintain vibrant social lives.

By delving into these aspects of aging, we pave the way for more informed discussions about the social policies and resources needed to support our aging population. In conclusion, the path toward an age-friendly society must embrace the complexities of cognitive aging while celebrating the potential for resilience through social connectivity and cognitive engagement.

Subject of Research: Age-Related Declines in Theory of Mind and Its Associations with Cognitive Complexity

Article Title: Age-Related Declines in Theory of Mind: Associations with Cognitive Complexity, Reasoning Abilities and Social Activity

Article References:

Iglesias-Pazo, L., Pellicena, M.À., Valero-Garcia, J. et al. Age-Related Declines in Theory of Mind: Associations with Cognitive Complexity, Reasoning Abilities and Social Activity.
J Adult Dev (2025). https://doi.org/10.1007/s10804-025-09526-w

Image Credits: AI Generated

DOI: 10.1007/s10804-025-09526-w

Keywords: Theory of Mind, Aging, Cognitive Complexity, Reasoning Abilities, Social Activity, Cognitive Decline, Aging Population, Lifelong Learning