A central question about our shared capacity for language is how it is integrated with other cognitive systems. One important debate focuses on the extent to which the form of linguistic expressions is grounded in their communicative function: Can all constraints on linguistic form be attributed to the way constructions package information, or is linguistic form autonomous of meaning and function? One area of disagreement involves islands: phrases which block the formation of long-distance filler-gap dependencies (Ross, 1967). Grammatical subjects are considered islands, since questioning a sub-part of a subject results in an ill-formed sentence, e.g., "Which topic did the article about inspire you?". Autonomous syntactic approaches to islands attribute this ungrammaticality to the abstract movement dependency between the wh-phrase and the subject-internal position with which it is associated. An alternative developed in Abeillé et al. (2020) suggests that subjects' island status is specific to the information structure of wh-questions, suggesting that subjects are not islands for movement, but for focusing, due to their discourse-backgroundedness. This predicts that other constructions that involve movement but not focusing should not create a subject island effect. We test this in three acceptability studies, using a factorial design to isolate subject island violations across three constructions: wh-questions, relative clauses and topicalization. We find a subject island effect in each case, despite only wh-questions introducing what Abeillé et al. (2020) call "a clash in information structure". We argue that this motivates an account of islands in terms of syntactic representations shared across constructions, independent of communicative function.

Sensitive windows, sensitive outcomes: Early or late-postnatal maternal separation differentially impacts puberty and behavior.

Digital twin technology represents a transformative innovation in healthcare, creating virtual replicas of physical entities that enable real-time monitoring, prediction, and personalised intervention. People living with disability face multifaceted healthcare challenges requiring continuous monitoring, adaptive assistive technologies, and individualised treatment approaches. The convergence of digital twin technology with disability healthcare presents unprecedented opportunities for enhancing quality of life, independence, and clinical outcomes. This review examines the current applications, benefits, challenges, and future directions of digital twin technology in healthcare delivery for people living with disability. A narrative review methodology was employed, synthesising literature from academic databases including PubMed, IEEE Xplore, Scopus, and Web of Science. The review encompassed peer reviewed articles, conference proceedings, and technical reports published between 2015 and 2025, focusing on digital twin implementations in disability healthcare contexts. Digital twin applications in disability healthcare span multiple domains, including rehabilitation, assistive device optimisation, cognitive support systems, mobility enhancement, and chronic condition management. The technology demonstrates significant potential in personalising interventions, predicting health deteriorations, optimising assistive technologies, and facilitating remote monitoring. Key applications include virtual prosthetic fitting, wheelchair optimisation, rehabilitation progress tracking, and predictive analytics for secondary complications. However, implementation faces challenges including data privacy concerns, technological accessibility, interoperability issues, and cost barriers. Digital twin technology offers transformative potential for disability healthcare, enabling personalised, predictive, and preventive care models. Successful implementation requires addressing technological, ethical, and accessibility challenges whilst ensuring equitable access for diverse disability populations. Critical research priorities include large-scale clinical trials, cost-effectiveness analyses, longitudinal outcomes studies, and ethical frameworks balancing surveillance concerns with care benefits.

The current generation of large language models (LLMs) has been linked to claims about human-like linguistic performance, and their applications are hailed both as a step towards artificial general intelligence and as a major advance in understanding the cognitive and even neural basis of human language. To assess these claims, first, we analysed the contribution of LLMs as theoretically informative representations of a target cognitive system versus atheoretical mechanistic tools. Second, we evaluated the models' ability to see the bigger picture through top-down feedback from higher levels of processing, which requires grounding in previous expectations and past world experience. We hypothesize that since models lack grounded cognition, they cannot take advantage of these features and instead solely rely on fixed associations between represented words and word vectors. To assess this, we ran a novel leet task (l33t t4sk), which requires decoding sentences in which letters are systematically replaced by numbers. In line with our hypothesis, the results suggest that humans excel in this task, whereas models struggle. We interpret these results by identifying the key abilities that are still missing from the current state of development of these models, which require solutions that go beyond increased system scaling. This article is part of the theme issue 'World models in natural and artificial intelligence'.

This is Part II of a two-part series examining artificial intelligence (AI) in colorectal surgery. Part I established foundational concepts and clinical applications. Implementation, however, requires understanding research methodologies, available resources, and the specific challenges currently limiting widespread adoption. This will be the focus of Part II. To examine artificial intelligence's transformation of surgical research, provide practical implementation resources, address adoption challenges, and explore future directions in colorectal surgery. Comprehensive literature review focusing on artificial intelligence research methodology, implementation barriers, educational resources, and emerging technologies relevant to colorectal surgeons. Artificial intelligence streamlines clinical trial design through predictive modeling and natural language processing, reducing enrollment challenges that contribute to failed or inadequate trial accrual. Machine learning enables heterogeneity analysis within clinical trials, identifying treatment-responsive subgroups. Foundation models unlock analysis of unstructured electronic health record data at scale. Professional societies and universities offer specialized artificial intelligence education programs, with open-access datasets facilitating research participation. However, implementation faces multifaceted challenges: technical infrastructure demands, with real-time processing requiring dedicated graphics processing unit clusters; regulatory frameworks struggling with continuously evolving algorithms; undefined liability distribution for artificial intelligence-assisted decisions; algorithmic bias risking healthcare disparities; and the "black box" problem limiting clinical trust. Economic barriers include substantial initial costs without clear reimbursement pathways. Future directions include multimodal artificial intelligence integrating imaging, genomics, and histopathology; cognitive robotic systems with real-time decision support; digital twin technology for patient-specific surgical simulation; and global surgical artificial intelligence networks enabling distributed learning across institutions. While artificial intelligence offers transformative potential for colorectal surgery research and practice, successful implementation requires addressing technical, regulatory, ethical, and economic challenges. The surgeon's evolving role demands both traditional expertise and computational fluency. Future advances in multimodal integration, autonomous systems, and global collaboration will fundamentally reshape surgical practice, but require thoughtful implementation prioritizing patient benefit and clinical value.

Mammalian sensory systems are traditionally viewed as hierarchical pathways in which subcortical nuclei relay signals from peripheral receptors to the cortex, where sensory information is contextualized for perception and behaviour. However, the auditory pathway contains an unusually large number of anatomically complex, recurrently connected subcortical nuclei that transmit heavily pre-processed information to the cortex. Emerging evidence shows that the auditory system functions as an integrated cortical-subcortical networkthat is heavily influenced by non-auditory inputs and extensive bidirectional connectivity at nearly all levels. Subcortical structures are not passive relays but active participants in computations traditionally attributed to cortex, including adaptive coding of sound statistics, multisensory integration, encoding of behavioural relevance and action, and learning. Although certain transformations occur hierarchically - such as brainstem spatial processing - sensitivity to most sound features forms a continuum across the auditory pathway, with modulation by other sensory, motor and cognitive systems at every stage. The degree of subcortical pre-processing may explain why emergent cortical properties are harder to identify in audition than in vision, in which cortical and subcortical receptive field properties are more distinct. This distributed circuit organization opens new avenues for understanding how the brain constructs perception and guides behaviour from the fusion of sensory evidence and contextual knowledge.

Anxiety disorders are associated with prefrontal dysfunction, yet their impact on neural mechanisms underlying skilled motor learning remains poorly understood. We examined movement-readiness potentials (MRPs) using electroencephalography during a visuomotor adaptation task in 31 young adults (13 with clinically diagnosed anxiety disorders, 18 controls). MRPs were analyzed across three temporal components: early motor preparation (-1500 to -500 ms), late motor preparation (-500 to -100 ms), and movement execution (-100 to + 100 ms). Individuals with anxiety disorders showed significantly reduced MRP amplitudes during late motor preparation (p = 0.033) and movement execution (p = 0.047) compared to controls, while early motor preparation remained intact. Despite these neural alterations, both groups demonstrated equivalent behavioral performance, with similar learning and retention of a visuomotor rotation task. Anxiety disorders selectively disrupt late-stage cognitive-motor integration processes during movement preparation and execution. The dissociation between impaired neural activity and preserved behavioral performance suggests compensatory mechanisms that maintain motor learning despite underlying neural inefficiencies. These findings reveal that anxiety affects integrated systems of cognition and action, providing new insights into their functional neurophysiological impact.

Existing reinforcement learning (RL) approaches struggle to balance real-time decision-making with adaptive learning in dynamic healthcare environments. We propose a brain-inspired hybrid RL framework that integrates model-based (MB) planning and model-free (MF) reflexes via a dynamic meta-controller, neuro-symbolic clinical knowledge, counterfactual reasoning, and ethical safeguards. The framework is validated on a multimodal cerebral palsy (CP) dataset (86 patients) using NetLogo multi-agent simulations and Weka classifiers. A combined reward mechanism achieves 99% total reward accumulation, with 98% optimal reward in 95% of training episodes. Component analysis shows a 60% MB / 40% MF contribution, yielding a 15% improvement over standalone methods. Optimal weighting (0.7 MB, 0.3 MF) further enhances performance. External zero-shot validation on three public datasets (NTNU-HARChildren, EEG-EMG exoskeleton, D4RL) confirms generalizability (macro F1 84.3%, accuracy 81.7%, D4RL scores 68.5 and 62.3). Regression methods achieve correlation coefficients up to 0.94, and classification models (multinomial Naïve Bayes, logistic regression) attain 100% precision, recall, and F-measure. The framework provides a reliable, explainable, and simulation-validated solution for patient-centric autonomous decision-making.

Virtual reality (VR) technology has emerged as a transformative tool for innovation and entrepreneurship education, yet existing systems face critical limitations in scene generation realism and cognitive load management. This research proposes an integrated framework combining attention mechanism-enhanced scene generation with adaptive cognitive load balancing strategies to address these challenges. The framework incorporates a multi-scale attention architecture operating across spatial, channel, and temporal dimensions to selectively emphasize pedagogically critical entrepreneurial elements while maintaining contextual coherence. A real-time cognitive load assessment system monitors physiological signals and behavioral patterns to implement dynamic scene complexity adjustments, maintaining learners within optimal challenge zones. Experimental validation across 45 entrepreneurship scenarios with 120 participants demonstrates substantial improvements: scene quality scores increased by 26-45% compared to baseline methods, error rates decreased by 60.9%, and task completion times reduced by 23.7%. The system maintained stable cognitive load within target ranges for 87.3% of session duration during extended 60-minute training sessions. Ablation studies confirm the essential contributions of individual attention components, with quality degradation of 12-18% upon removal. These findings advance both computational innovation in generative VR systems and pedagogical methodologies for entrepreneurship education, providing practical solutions for scalable, personalized training applications across diverse educational and professional development contexts.

Aging profoundly alters the neuromotor and cognitive systems that support gait control, leading to increased variability and instability that predict functional decline and dementia risk. In this pilot study, conducted to inform the design of the Semmelweis Study gait assessment pipeline, we examined how aging and cognitive load influence the magnitude and temporal organization of gait fluctuations. The Semmelweis Study is a large, prospective workplace cohort at Semmelweis University designed to identify the determinants of unhealthy aging and the mechanisms that preserve functional resilience across the life course. One hundred three adults aged 23-87years completed single- and dual-task walking trials on a 20-foot pressure-sensitive walkway. Gait variability was quantified using the median absolute deviation (MAD) and coefficient of variation (CoV) of key spatiotemporal parameters, while permutation entropy (PE) captured the complexity of stride-to-stride dynamics. Aging was associated with progressive increases in both the variability (MAD, CoV) and changes in orderliness (PE) of gait fluctuations, particularly under dual-task conditions, suggesting a dual contribution of neuromotor degradation and compensatory recruitment of higher-order control processes. The amplification of these effects during cognitive load highlights the vulnerability of cognitive-motor integration with advancing age. By integrating robust, relative, and nonlinear variability metrics within a unified analytical framework, this study provides a multidimensional characterization of gait control and establishes sensitive indicators for detecting early functional decline. Within the translational framework of the Semmelweis Study, these quantitative gait measures-together with vascular, metabolic, and cognitive assessments-are expected to serve as informative components of a comprehensive biomarker system aimed at identifying early determinants of unhealthy brain aging and guiding preventive strategies to promote healthy longevity.

The capacity to represent the mental states of other individuals, known as 'mindreading' or 'theory of mind', is key to successful social prediction. We suggest that cognitive systems for mindreading are resource-rational: they are optimized for generating good predictions about the behaviour of other individuals, while not exceeding the computational capacity of the mindreader. We explore this hypothesis in a simple formal model where we derive cognitive strategies that excel at social prediction while minimizing cognitive effort. We find that it is often optimal for resource-limited mindreaders to keep track of the facts that another agent also knows, instead of explicitly representing the content of the agent's beliefs. When evaluated in mindreading tasks, simulated agents that use this 'factive' strategy tend to make mistakes in the same cases as non-human primates and young human children. Even agents that use more sophisticated strategies avoid representing beliefs unless necessary. Our results elucidate the computational principles underlying efficient social prediction and explain many of the successes and failures of human and non-human mindreading from first principles.

An intelligent reflective surface (IRS)-assisted cognitive radio (CR) multiple-input multiple-output (MIMO) communication system is considered. Incorporating cognitive radio and IRS capabilities into such a system yields significant improvements in system performance, including energy efficiency (EE) and receiver quality of service (QoS). For enhancing the attainable rate of secondary users (SU) without exceeding the interference temperature limit (IT) on the primary users (PU), a non-convex optimization problem is formulated, which is usually solved by means of alternative optimization (AO) methods such as block coordinate descent (BCD) algorithms. In this paper, we focus on deep reinforcement learning (DRL) approaches, specifically, the soft actor-critic (SAC) algorithm, to solve this optimization problem. For comparison, all simulation figures will be composed of a BCD benchmark beside the SAC curves. In addition, a 16-element MIMO antenna array for the secondary transmitter (ST) base station is proposed, designed, fabricated, and tested, yielding a 90% radiation efficiency with perfect impedance matching and acceptable return losses.

The N-back task is widely recognized as a paradigm for cognitive training due to its adaptability and effectiveness in mitigating working memory decline. However, its repetitive and monotonous design often leads to reduced engagement and poor long-term adherence, lowering intervention effectiveness. To address these limitations, researchers have turned to game-based interventions and immersive technologies to enhance user motivation and sustain training participation. In this study, we propose VRN-back, a music rhythm-based gamified cognitive training system in virtual reality (VR). The system combines the adaptive N-back paradigm with rhythm-game mechanics to increase enjoyment and motivation, while immersive interaction fosters engagement and enhances training outcomes. We conducted a seven-day study with 15 young adults to evaluate the system's impact on their working memory, transfer to related cognitive domains, and user experience. Results showed significant improvements in N-back accuracy, reaction time, and maximum N level, as well as transfer effects on attention and inhibitory control tasks. Subjective evaluations indicated good usability and user experience, with the System Usability Scale scoring 80.83/100, the short User Experience Questionnaire rating pragmatic quality as "Good" and hedonic quality as "Above Average", and the Pleasure-Arousal-Dominance scale confirming sustained positive emotional states. Simulator sickness remained minimal before and after training, ensuring usability. These findings suggest the feasibility and effectiveness of immersive rhythm-based gamified VR systems for cognitive training, laying a foundation for future research on long-term cognitive intervention technologies.

In the present paper, I will analyze conceptual metaphors and image schemas in seven chosen sayings in Al-Sahifa Al-Sajjadiyya through the concept of cognitive semantics and especially the Conceptual Metaphor Theory (CMT) by Lakoff and Johnson (1980, 1987). The research has answers to the question below: How do the conceptual metaphors and image schemas arrange abstract spiritual ideas in these supplications? The metaphors discovered in the analysis include GUIDANCE IS A PATH, SIN IS A BURDEN, and KNOWLEDGE/FAITH IS LIGHT, and recurring image schema (PATH, CONTAINER, FORCE) to help the readers to identify abstract spiritual notions like divine orientation and moral struggle with the help of tangible and embodied experience. These findings suggest that these tendencies do not merely transform abstract concepts like mercy and repentance into real experience but also into personal and familiar experience. The study testifies the fact that Al-Sahifa employs a sophisticated cognitive system that integrates spirituality and morality that are employed to conceptualize the literature of Islamic devotions and demonstrates universal cognitive processes organizing the abstract thinking with assistance of metaphors.

Depression involves impaired cognitive, affective, and social functions associated with aberrant brain network interactions. The temporoparietal junction (TPJ), a multisensory integration hub, exhibits depression-related connectivity alterations, yet the roles of its subregions during subclinical stages remain unclear. This study examined TPJ subregional communication in non-clinical high-depression individuals.Resting-state fMRI data from 586 medication-free young adults were analyzed. Participants were divided into high-depression (HD, N = 130) and low-depression (LD, N = 130) groups using Beck Depression Inventory scores. TPJ was parcellated into anterior (aTPJ), posterior (pTPJ), and ventral (vTPJ) subregions via community detection. Multi-metric connectivity (functional connectivity/FC, total interdependence/TI, Granger causality/GC) seeded from TPJ subregions was compared between groups. Support vector machine (SVM) fusion analysis identified high-contribution features for network alteration modeling.TPJ subregions showed depression-related connectivity patterns: (1) Altered default mode network DMN interactions featuring enhanced anterior DMN (medial prefrontal cortex) connectivity and weakened posterior DMN (posterior cingulate/precuneus) connectivity; (2) Disrupted left TPJ-reward pathway communication (ventral striatum, putamen, amygdala); (3) Right TPJ/left vTPJ hyperconnectivity with cognitive control systems (frontoparietal network, orbitofrontal cortex, anterior cingulate cortex); (4) Enhanced somatosensory-motor connectivity with reduced visual/auditory input; (5) Impaired intra-TPJ communication.TPJ subregions exhibit distinct dysconnectivity patterns in non-clinical depression, affecting self-referential processing, reward integration, and cognitive control. Multi-metric profiling identifies TPJ as a potential pathophysiological biomarker.

Nutrition plays a fundamental role in human development, especially during critical growth periods. It is already well recognised that the first 1000 days-from conception to a child's second birthday-are vital for cognitive, physical and immune system development, and setting a child on a positive trajectory for subsequent growth. Less well recognised is the importance of the next 7000 days (ages 2-21) when good nutrition continues to be important to secure those early gains and to shape development during the next vulnerable phases, including puberty, the rewiring of the brain during adolescence and the establishment of lifelong healthy behaviours and dietary preferences. Poor nutrition during these stages can severely impact cognitive function, leading to learning difficulties, attention deficits and poor academic performance, ultimately limiting future productivity and life opportunities. The next 7000 days are a crucial phase in the development of adolescents and, by influencing their health and well-being, have profound consequences for intergenerational health and nutrition, especially for girls. Finally, to maximise opportunities for physical, cognitive, social and emotional gains during the first 8000 days, it is essential to invest in multisectoral initiatives, leveraging health, nutrition, education and early childhood development, for an integrated response.This paper draws on contemporary scientific evidence to advocate for increased investment in nutrition, not only during the first 1000 days but also throughout the next 7000 days. It examines the biological, social and economic benefits of prioritising nutrition through integrated approaches, highlighting its crucial role in breaking the cycle of intergenerational poverty. A comprehensive literature review was conducted to synthesise the latest evidence supporting greater investment in nutrition. The paper concludes with policy recommendations for coordinated action from governments, multilateral and philanthropic actors, the private sector, civil society, communities and other stakeholders to ensure sustained nutrition investment from early life through adolescence, with particular emphasis on investment in integrated, multisectoral approaches.

Second language (L2) anxiety and willingness to communicate (WTC) have long been recognized as two central affective variables influencing language learning and performance. However, existing research has predominantly conceptualized their relationship as static and linear, often neglecting the dynamic, context-sensitive, and interactive nature of these constructs. Drawing on Dynamic Systems Theory, this paper proposes a Dynamic Affective–Performance Interaction Model (DAPIM) to reconceptualize the relationship between L2 anxiety and WTC among Chinese learners. The model integrates affective, intentional, cognitive, and performance dimensions, incorporating moderating variables such as working memory capacity and task planning conditions. It is argued that anxiety and WTC co-evolve over time through reciprocal interactions, and their influence on L2 performance is mediated by cognitive resource allocation and task demands. By synthesizing insights from second language acquisition, cognitive psychology, and task-based language teaching, this paper offers a mechanism-based explanation of how affective and cognitive systems jointly shape L2 oral fluency. The proposed framework advances theoretical understanding and provides directions for future empirical research.

From peripersonal space to cognitive maps: An evolutionary perspective.

The academic research course of pseudomyxoma peritonei (PMP) has been tortuous and long. It is not until the past 40 years that a cognitive system consistent with the core theories of oncology has gradually been formed, and the level of clinical diagnosis and treatment has also made considerable progress. A comprehensive treatment technology system centered on cytoreductive surgery (CRS) combined with hyperthermic intraperitoneal chemotherapy (HIPEC) has been established. This article sorts out and summarizes the cutting-edge research progress in the field of PMP at the 15th International Peritoneal Cancer Congress, combines clinical practice to explore its enlightenment for clinical work, scientific research exploration, and the construction of peritoneal oncology discipline in China, aiming to provide reference for the standardized diagnosis and treatment of PMP and related research in China.

Human AI trust modeling in cognitive systems via ensemble learning and advanced feature engineering