The ability of large language models to recall human emotions provides a novel opportunity to investigate links among memory, affect, and mental health. This study explores whether the Emotional Recall Task (ERT), a free word-association paradigm, can reveal cognitive markers of distress in both humans and large language models (LLMs). Using spreading activation simulations grounded in cognitive network science, we examined how the recall of emotional concepts (e.g., stress, anxiety, and depression) relates to psychometric measures of well-being and personality. In Study 1, correlations were tested between activation dynamics and clinical scales (DASS-21, PANAS, and Life Satisfaction) in human participants (N = 1200) and artificial participants generated by GPT-4, Claude Haiku, and Anthropic Opus. For both human and LLM samples, spreading activation was modeled from participants’ ERT words within a human-derived semantic network, enabling a direct comparison of structural activation dynamics rather than psychological states. Humans with higher distress scores exhibited stronger, faster, and more persistent activation of negative concepts, supporting theories of rumination and memory bias. GPT-4 approximated human-like trajectories most closely, though with reduced variability. Study 2 linked recall dynamics with the Big Five traits, confirming that neuroticism predicted greater activation of negative concepts, while extraversion acted as a protective factor. While LLMs lack autobiographical memory, their semantic activation partially mirrored human associations. These findings demonstrate that network-based spreading activation analysis can reveal cognitive signatures of distress while also highlighting the limits of LLMs in modeling human affect.

Tables turned on table talk.

Abstract Background Artificial intelligence (AI) marks an inflection point in medical education systems built on scarcity of resources. These designs privilege standardisation and recall‐heavy examinations over reasoning and adaptive expertise, defined as the capacity to apply knowledge flexibly in uncertain clinical contexts, producing learners who memorise content but struggle with ambiguity, integration across domains and decision‐making under pressure. Objectives To outline a conceptual roadmap for integrating AI into medical education that strengthens adaptive expertise, productive struggle and assessment integrity rather than eroding them. Methods Conceptual analysis using educational, assessment and cognitive science frameworks to contrast scarcity‐era logics with emerging AI capabilities and synthesise illustrative use cases. Results We describe how AI can scaffold knowledge acquisition and inquiry; support authentic practice via virtual patients and educator‐created, AI‐enabled teaching tools and reshape assessment through blueprint‐aligned items and predictive learning analytics. We highlight AI's double‐edged nature: risks of undermining integrity, promoting cognitive deskilling and bypassing productive struggle, defined as purposeful, scaffolded difficulty that feels effortful yet achievable and that strengthens long‐term learning. We propose enabling conditions: trust, transparency, structured difficulty, and deliberate cognitive redistribution, defined as intentional reallocation of cognitive work between humans and AI tools, which offloads routine lower‐yield tasks to machines to preserve and advance human judgement, values, relationships and professional identity formation. Conclusions AI will either accelerate superficial shortcuts or amplify humane, expert practice, depending on how pedagogy, assessment and culture are redesigned. Intentional alignment can reclaim time and cognitive space for the uniquely human work at the heart of education.

Short-term mindsets: Beyond traits and self-regulation.

Large Language Models (LLMs) have demonstrated impressive abilities in symbol processing through in-context learning (ICL). This success flies in the face of decades of critiques asserting that artificial neural networks cannot master abstract symbol manipulation. We seek to understand the mechanisms that can enable robust symbol processing in transformer networks, illuminating both the unanticipated success, and the significant limitations, of transformers in symbol processing. Borrowing insights from symbolic AI and cognitive science on the power of Production System architectures, we develop a high-level Production System Language, PSL, that allows us to write symbolic programs to do complex, abstract symbol processing, and create compilers that precisely implement PSL programs in transformer networks which are, by construction, 100% mechanistically interpretable. The work is driven by study of a purely abstract (semantics-free) symbolic task that we develop, Templatic Generation (TGT). Although developed through study of TGT, PSL is, we demonstrate, highly general: it is Turing Universal. The new type of transformer architecture that we compile from PSL programs suggests a number of paths for enhancing transformers’ capabilities at symbol processing. We note, however, that the work we report addresses computability, and not learnability, by transformer networks.

ABSTRACT Science, technology, engineering, and math (STEM) habits of mind (HoM) are essential competencies and skills that students develop through STEM education, boosting learning outcomes across STEM domains. However, researchers have not examined how teacher attributes shape young children’s STEM HoM. This study investigated the relationships between teachers’ STEM knowledge, their STEM teaching attitudes and beliefs, and young students’ STEM HoM. Using multilevel structural equation modeling, we analyzed survey responses from 93 teachers and 2,144 students across 11 kindergartens in China. Research Findings: Results revealed that male teachers and those majoring in early childhood education reported more STEM knowledge. Male teachers, those with more years of STEM teaching experience, and those reporting more STEM knowledge expressed greater STEM teaching comfort and perceived fewer STEM teaching challenges. Teacher comfort in STEM teaching and student age were positively associated with students’ STEM HoM. Teachers’ STEM knowledge had an indirect positive effect on students’ STEM HoM through increased teaching comfort. However, neither teachers’ perceptions of STEM benefits for students nor their perceived teaching challenges were significantly related to students’ STEM HoM. Practice or Policy: These results provide insights for improving teacher professional development programs to enhance STEM instruction effectiveness and student STEM learning outcomes.

ABSTRACT Approximations have come to occupy a central role within Bayesian cognitive science. Many cognitive scientists view current approximation-based models as showing that the mind approximates Bayesian inference. In this paper I interrogate this idea, asking what it means to claim that the mind approximates one computation by executing another. I look at three possible interpretations of such claims, finding problems with each. I argue that this poses challenges to one central rationale for the Bayesian approach, and to its top-down methodology.

In the article, the authors study the concept of "computational creativity" new to Russian law, used in foreign scientific literature. The article explores the concept of "computational (computer) creativity" as an interdisciplinary field at the intersection of artificial intelligence, cognitive science, philosophy and creative industries. The Standardized Procedure for Evaluating Creative Systems (SPECS) evaluation methods are analyzed, as well as legal regulation, with a special emphasis on the requirement of human authorship and transparency in the use of artificial intelligence.

Editorial: Beauty and the mind: cognitive science of the sublime

The article aims to assess the potential of neuroaesthetics in the study of ornamentation, as well as the form and style of archaeological artefacts. Neuroaesthetics, as a research approach, suggests incorporating the mechanisms of human brain function into the analysis of art creation and perception. Traditional studies of prehistoric art primarily focus on cultural context, often overlooking the biological aspect. By drawing on insights from cognitive science and neuroscience, it is possible to explore the physiological foundations of art. Analysis of contemporary research on the mind suggests that prehistoric artists, by adhering to the principles of perception and physiology, unconsciously stimulated specific areas of the viewer’s brain, leading to the emergence of aesthetic experiences.

The article describes legal modeling as one of the methods of scientific cognition of social relations, which allows reproducing the object under study using logical techniques on specially created models. It is proven that the method of legal modeling allows tracing the effectiveness of the formed model, identifying its strengths and weaknesses, and predicting phenomena that are part of the national legal system. It is stated that the method of legal modeling in Ukrainian legal doctrine acts as a method of cognition of legal reality, law enforcement, and law creation. It has been established that the method of legal modeling is characterized by the implementation of the following tasks, in particular: reflecting the essence, systematizing legal phenomena, illustrating, graphically and schematically depicting certain legal phenomena, revealing the content and structure of the original, predicting the development of the object of study, analyzing statistical data on the state and dynamics of the object under study, and forming effective decisions. The need to study the institution of probation in Ukraine using the method of legal modeling is emphasized, since conducting experiments with the object of study (the original) is ineffective and impossible for moral, ethical, and legal reasons. The need to create a probation model has been identified, taking into account the provisions of criminal procedural, criminal, and criminal enforcement legislation, which will also consider the economic feasibility of the functioning of authorized probation authorities in the criminal justice system. An analysis of the economic factors that affect the effectiveness of probation officers has been carried out. It has been established that probation authorities are underfunded due to established, persistent corruption links. It is noted that it is the dissatisfaction of employees that reduces staff loyalty and the desire to perform their work fully. It is concluded that these facts are factors that significantly affect the country’s budget, the prevention of recidivism, the formation of new competencies in employees, and labor productivity.

Prediction error minimization and embodied cognition theorists posit that abstract self-representations are predicated on models of the self as an embodied agent. While the view of continuity between conceptual and bodily self is common to several frameworks in psychology and cognitive science, empirical tests of this relationship are scant. However, a recent study by Krol et al., (see record 2020-26839-011) found that people low in self-concept clarity (SCC) were more vulnerable to the rubber-hand illusion-in particular, in the asynchronous stroking condition, where the illusion is unwarranted. This study provides preliminary evidence for an association between self-concept strength and vulnerability to illusions regarding the bodily self. Here we sought to replicate this finding in an existing study that assessed SCC and the rubber-hand illusion. Using linear mixed-effects modelling, we found that lower SCC was again associated with greater embodiment of the rubber hand in the asynchronous condition; moreover, we also observed this effect in the synchronous stroking condition, providing additional evidence for the role of SCC in vulnerability to bodily illusions. We discuss the implications of this finding for theories in social cognitive neuroscience. Finally, as the study we drew upon to test the replication effect involved the administration of intranasal oxytocin, we also took this opportunity to replicate a previously observed effect of oxytocin on embodiment of the rubber hand; this effect, however, did not replicate, although methodological difference may have played a role. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Simulation-driven mentalizing facilitates projection and introjection.

This paper examines the philosophy of mathematical language, exploring how mathematics functions as both a formal symbolic system and a natural language for describing abstract structures. We investigate the nature of mathematical meaning, the relationship between mathematical symbols and their referents, the role of natural language in mathematical practice, and contemporary debates about mathematical realism, platonism, and formalism. Through an interdisciplinary approach drawing from philosophy of mathematics, linguistics, cognitive science, and mathematical practice, this research illuminates how mathematical language shapes our understanding of mathematical truth, proof, and knowledge.

This paper offers a road map for the development of scalable aligned artificial intelligence (AI) from first principle descriptions of natural intelligence. In brief, a possible path toward scalable aligned AI rests on enabling artificial agents to learn a good model of the world that includes a good model of our preferences. For this, the main objective is creating agents that learn to represent the world and other agents' world models, a problem that falls under structure learning (also known as causal representation learning or model discovery). We expose the structure learning and alignment problems with this goal in mind, as well as principles to guide us forward, synthesizing various ideas across mathematics, statistics, and cognitive science. We discuss the essential role of core knowledge, information geometry, and model reduction in structure learning and suggest core structural modules to learn a wide range of naturalistic worlds. We then outline a way toward aligned agents through structure learning and theory of mind. As an illustrative example, we mathematically sketch Asimov's laws of robotics, which prescribe agents to act cautiously to minimize the ill-being of other agents. We supplement this example by proposing refined approaches to alignment. These observations may guide the development of artificial intelligence in helping to scale existing, or design new, aligned structure learning systems.

In recent years, artificial intelligence (AI) has been actively integrated into everyday human life. Its popularity continues to grow steadily, and companies increasingly employ AI to optimize and accelerate workflows. Ordinary users leverage large language models (LLMs) and multimodal AI systems to perform a wide range of tasks, including generating texts, images, and videos; planning their daily schedules; and searching for information, among others. The rapid integration of AI into diverse spheres of human activity gives rise to a number of fundamental challenges such as the simulation of meaning, the substitution of human will, and other issues that threaten personal identity and autonomy. An increasing number of cognitive and creative functions – such as text generation, decision-making, and reflection – are being delegated by humans to AI systems. Consequently, it becomes critically important to explicitly identify and articulate the problems emerging from human–AI interaction and to develop ethically and cognitively sound frameworks for such interaction. This article examines current trends and key challenges associated with the integration of AI into everyday life. It introduces the concept of “meaning simulation” – a phenomenon whereby AI models function as simulacra of meaning, creating an illusion of understanding, thought, and meaning. The paper proposes a conceptual framework for productive human–AI collaboration and outlines a positive scenario for its implementation. This article will be of interest to researchers and professionals working in artificial intelligence, philosophy of science and technology, cognitive science, as well as to anyone concerned with contemporary technological trends and their social, ethical, and existential implications.

Abstract Many sustainability transformation studies use the leverage points framework (LPF) to point to deep levers such as paradigms and worldviews, but rarely explain how those levers move. We propose an extended unconscious (EU) perspective to address what LPF leaves unspecified, arguing that deep understanding of unconscious dynamics is crucial. In our use, the EU perspective synthesizes diverse recent mind science accounts of the unconscious into an approach tailored to sustainability and connects that approach to LPF. By synthesizing evidence and comparing cases, we show how unconscious processes and emotion shape attention, habits, and institutional change, and we outline practical design principles for working with deep levers in everyday projects. Our main finding is that transformation is not linear; it grows through repeating cycles in which feeling, action, and rules reinforce each other—clarifying why information alone often fails and how small, shared experiences can unlock broader shifts. The contribution is a simple vocabulary and design logic that complement LPF’s structural map and can be tested and adapted across contexts.

Neural markers of speech processing during inattentional deafness☆.

Background of study: Human-Level Artificial Intelligence (HLAI) represents one of the most formidable dreams in laptop technology, aiming to create structures able to cognition, perception, and reasoning similar to people. Despite splendid development in slender AI programs, the development of Artificial General Intelligence (AGI) remains confined by conceptual, technical, and moral demanding situations.Aims and scope of paper: This paper pursuits to analyze the theoretical foundations, technological enablers, and ethical dimensions of HLAI. It seeks to distinguish HLAI from slender AI and to make clear the clinical, engineering, and societal problems concerned in constructing structures with human-like intelligence.Methods: The have a look at adopts a conceptual and literature-based totally method, synthesizing insights from synthetic intelligence, cognitive science, neuroscience, and ethics. Key frameworks and latest research are reviewed to pick out common concepts, technological developments, and unresolved challenges shaping the evolution of HLAI.Result: The evaluation highlights essential enablers which include device gaining knowledge of, herbal language processing, laptop vision, and robotics as important pathways in the direction of HLAI. Findings screen that at the same time as development in these domain names is good sized, reaching popular intelligence calls for deeper integration of cognitive modeling, neural architectures, and moral alignment mechanisms.Conclusion: The study shows that collaboration across sectors and designs with principles and safety in mind are important steps to create artificial brains that are robust and reliable. This underlines the need for ethical governance and adaptation of human values to cushion the risk associated with uncontrolled AI development.

This study investigated how music education activities integrated into bookstore culture can enhance cultural literacy, customer engagement, and employee development, using Sisyphe Bookstore in Qingdao, China as a case. Drawing on theoretical frameworks from social learning, behaviorism, and multiple intelligences, the research examines the cognitive and social mechanisms by which music activities influence both patrons and staff. A mixed-methods design was employed, combining surveys (n = 45 customers; n = 6 employees) with semi-structured interviews. Findings indicate that music education activities improved customers’ musical literacy, heightened their enjoyment and loyalty to the bookstore, and fostered greater interest in music-related products. Employees reported increased job satisfaction, creativity, and teamwork, while also gaining musical knowledge. Both groups emphasized the value of practice-based learning, collective participation, and the creation of a socially enriching atmosphere. Results align with prior research on music’s role in shaping consumer behavior, workplace satisfaction, and cultural identity. This study contributes to the cognitive sciences of music by showing how informal educational contexts—such as bookstores—can serve as platforms for music learning and cultural engagement. Implications extend to music education practice, retail innovation, and community-based cultural development. Limitations include the single case-study design and reliance on self-reported data. Future research should examine cross-cultural bookstore contexts and employ longitudinal measures.