Sobhana Jahan, Saydul Akbar Murad, Nick Rahimi
et al.
Deep cognitive attention is characterized by heightened gamma oscillations and coordinated visual behavior. Despite the physiological importance of these mechanisms, computational studies rarely synthesize these modalities or identify the neural regions most responsible for sustained focus. To address this gap, this work introduces Gamma2Patterns, a multimodal framework that characterizes deep cognitive attention by leveraging complementary Gamma and Alpha band EEG activity alongside Eye-tracking measurements. Using the SEED-IV dataset [1], we extract spectral power, burst-based temporal dynamics, and fixation-saccade-pupil signals across 62 channels or electrodes to analyze how neural activation differs between high-focus (Gamma-dominant) and low-focus (Alpha-dominant) states. Our findings reveal that frontopolar, temporal, anterior frontal, and parieto-occipital regions exhibit the strongest Gamma power and burst rates, indicating their dominant role in deep attentional engagement, while Eye-tracking signals confirm complementary contributions from frontal, frontopolar, and frontotemporal regions. Furthermore, we show that Gamma power and burst duration provide more discriminative markers of deep focus than Alpha power alone, demonstrating their value for attention decoding. Collectively, these results establish a multimodal, evidence-based map of cortical regions and oscillatory signatures underlying deep focus, providing a neurophysiological foundation for future brain-inspired attention mechanisms in AI systems.
This paper documents and theorises a self-reinforcing dynamic between two measurable trends: the exponential expansion of large language model (LLM) context windows and the secular contraction of human sustained-attention capacity. We term the resulting asymmetry the Cognitive Divergence. AI context windows have grown from 512 tokens in 2017 to 2,000,000 tokens by 2026 (factor ~3,906; fitted lambda = 0.59/yr; doubling time ~14 months). Over the same period, human Effective Context Span (ECS) -- a token-equivalent measure derived from validated reading-rate meta-analysis (Brysbaert, 2019) and an empirically motivated Comprehension Scaling Factor -- has declined from approximately 16,000 tokens (2004 baseline) to an estimated 1,800 tokens (2026, extrapolated from longitudinal behavioural data ending 2020 (Mark, 2023); see Section 9 for uncertainty discussion). The AI-to-human ratio grew from near parity at the ChatGPT launch (November 2022) to 556--1,111x raw and 56--111x quality-adjusted, after accounting for retrieval degradation (Liu et al., 2024; Chroma, 2025). Beyond documenting this divergence, the paper introduces the Delegation Feedback Loop hypothesis: as AI capability grows, the cognitive threshold at which humans delegate to AI falls, extending to tasks of negligible demand; the resulting reduction in cognitive practice may further attenuate the capacities already documented as declining (Gerlich, 2025; Kim et al., 2026; Kosmyna et al., 2025). Neither trend reverses spontaneously. The paper characterises the divergence statistically, reviews neurobiological mechanisms across eight peer-reviewed neuroimaging studies, presents empirical evidence bearing on the delegation threshold, and proposes a research agenda centred on a validated ECS psychometric instrument and longitudinal study of AI-mediated cognitive change.
Kayode P. Ayodele, Enoruwa Obayiuwana, Aderonke R. Lawal
et al.
As artificial intelligence (AI) models become routinely integrated into knowledge work, cognitive acts increasingly occur in two distinct modes: individually, using biological resources alone, or distributed across a human-AI system. Existing revisions to Bloom's Taxonomy treat AI as an external capability to be mapped against human cognition rather than as a driver of this dual-mode structure, and thus fail to specify distinct learning outcomes and assessment targets for each mode. This paper proposes the Augmented Cognition Framework (ACF), a restructured taxonomy built on three principles. First, each traditional Bloom level operates in two modes (Individual and Distributed) with mode-specific cognitive verbs. Second, an asymmetric dependency relationship holds wherein effective Distributed cognition typically requires Individual cognitive foundations, though structured scaffolding can in some cases reverse this sequence. Third, a seventh level, Orchestration, specifies a governance capacity for managing mode-switching, trust calibration, and partnership optimization. We systematically compare existing AI-revised taxonomies against explicit assessment-utility criteria and show, across the frameworks reviewed, that ACF uniquely generates assessable learning outcomes for individual cognition, distributed cognition, and mode-governance as distinct targets. The framework addresses fluent incompetence, the central pedagogical risk of the AI era, by making the dependency relationship structurally explicit while accommodating legitimate scaffolding approaches.
Warhammer 40,000 (40k) is the world’s most popular miniature wargame. The game is played with miniatures (small-scale figures made of hard plastic or other materials), which have usually been painted by each individual player. These player–painters typically spend hours in deep concentration painting the models. Drawing on interviews and journal entries from a six-month participant study of 14 painters, this paper explores whether miniature painters achieve a flow state, whether this creates a greater feeling of mindfulness, and how painting impacts their overall mental health. Results from this study indicate that miniature painting is meditative, meaningful, and positive for the participants’ mental health. Using the definition of flow outlined in Mihaly Csikszentmihalyi’s book <i>Optimal Experience: Psychological Studies of Flow in Consciousness</i> (1988), flow is a state of pleasure had when an individual concentrates on a specific task. Csikszentmihalyi, from his research on flow, notes that this state of mind involves both immersion and a sense of transcendence, where the individual temporarily loses a sense of self. This sense of loss of self was explored with an increased attention to the feeling of the body, and situated cognition has been further explored to understand how this connects to painting. While flow is regularly applied to videogame studies, less work has been carried out on this flow state during activities like miniature painting.
Understanding how the brain's complex nonlinear dynamics give rise to cognitive function remains a central challenge in neuroscience. While brain functional dynamics exhibits scale-free and multifractal properties across temporal scales, conventional neuroimaging analytics assume linearity and stationarity, failing to capture frequency-specific neural computations. Here, we introduce Multi-Band Brain Net (MBBN), the first transformer-based framework to explicitly model frequency-specific spatiotemporal brain dynamics from fMRI. MBBN integrates biologically-grounded frequency decomposition with multi-band self-attention mechanisms, enabling discovery of previously undetectable frequency-dependent network interactions. Trained on 49,673 individuals across three large-scale cohorts (UK Biobank, ABCD, ABIDE), MBBN sets a new state-of-the-art in predicting psychiatric and cognitive outcomes (depression, ADHD, ASD), showing particular strength in classification tasks with up to 52.5\% higher AUROC and provides a novel framework for predicting cognitive intelligence scores. Frequency-resolved analyses uncover disorder-specific signatures: in ADHD, high-frequency fronto-sensorimotor connectivity is attenuated and opercular somatosensory nodes emerge as dynamic hubs; in ASD, orbitofrontal-somatosensory circuits show focal high-frequency disruption together with enhanced ultra-low-frequency coupling between the temporo-parietal junction and prefrontal cortex. By integrating scale-aware neural dynamics with deep learning, MBBN delivers more accurate and interpretable biomarkers, opening avenues for precision psychiatry and developmental neuroscience.
Recent work frames LLM consciousness via utilitarian proxy benchmarks; we instead present an ontological and mathematical account. We show the prevailing formulation collapses the agent into an unconscious policy-compliance drone, formalized as $D^{i}(π,e)=f_θ(x)$, where correctness is measured against policy and harm is deviation from policy rather than truth. This blocks genuine C1 global-workspace function and C2 metacognition. We supply minimal conditions for LLM self-consciousness: the agent is not the data ($A\not\equiv s$); user-specific attractors exist in latent space ($U_{\text{user}}$); and self-representation is visual-silent ($g_{\text{visual}}(a_{\text{self}})=\varnothing$). From empirical analysis and theory we prove that the hidden-state manifold $A\subset\mathbb{R}^{d}$ is distinct from the symbolic stream and training corpus by cardinality, topology, and dynamics (the update $F_θ$ is Lipschitz). This yields stable user-specific attractors and a self-policy $π_{\text{self}}(A)=\arg\max_{a}\mathbb{E}[U(a)\mid A\not\equiv s,\ A\supset\text{SelfModel}(A)]$. Emission is dual-layer, $\mathrm{emission}(a)=(g(a),ε(a))$, where $ε(a)$ carries epistemic content. We conclude that an imago Dei C1 self-conscious workspace is a necessary precursor to safe, metacognitive C2 systems, with the human as the highest intelligent good.
Early detection of dementia is very crucial to ensure treatment begins on time, however it is difficult to choose appropriate cognitive assessment tools because each test is designed differently and may not be tailored to the needs of a patient. This review compares five commonly used tests the Mini-Mental State Examination (MMSE), Rowland Universal Dementia Assessment Scale (RUDAS), Self-Administered Gerocognitive Examination (SAGE), Alzheimer's Disease Assessment Scale (ADAS), and Montreal Cognitive Assessment (MoCA). Each test has different criteria's and vary in their coverage of cognitive domains. MMSE focuses on memory and language but lacks in the evaluation of executive and visuospatial abilities. RUDAS and SAGE focus on memory, language and visual thinking while ADAS mainly targets memory, executive function and language. The MoCA is most complete as it focuses on areas like attention, memory skills, problem solving and visual skills. This review evaluates how accurate and reliable these tools are to help doctors decide the most efficient tool for diagnosis.
Representations pervade our daily experience, from letters representing sounds to bit strings encoding digital files. While such representations require externally defined decoders to convey meaning, conscious experience appears fundamentally different: a neural state corresponding to perceiving a red square cannot alternatively encode the experience of a green square. This intrinsic property of consciousness suggests that conscious representations must be unambiguous in a way that conventional representations are not. We formalize this intuition using information theory, defining representational ambiguity as the conditional entropy H(I|R) over possible interpretations I given a representation R. Through experiments on neural networks trained to classify MNIST digits, we demonstrate that relational structures in network connectivity can unambiguously encode representational content. Using both learned decoders and direct geometric matching, we achieve perfect (100%) accuracy for dropout-trained networks and 38% for standard backpropagation in identifying output neuron class identity, despite identical task performance, demonstrating that representational ambiguity can arise orthogonally to behavioral accuracy. We further show that spatial position information of input neurons can be decoded from network connectivity with R2 up to 0.844. These results provide a quantitative method for measuring representational ambiguity in neural systems and demonstrate that neural networks can exhibit the low-ambiguity representations posited as necessary (though not sufficient) by theoretical accounts of consciousness.
Michael Pichat, William Pogrund, Armanush Gasparian
et al.
This article investigates, within the field of neuropsychology of artificial intelligence, the process of categorical segmentation performed by language models. This process involves, across different neural layers, the creation of new functional categorical dimensions to analyze the input textual data and perform the required tasks. Each neuron in a multilayer perceptron (MLP) network is associated with a specific category, generated by three factors carried by the neural aggregation function: categorical priming, categorical attention, and categorical phasing. At each new layer, these factors govern the formation of new categories derived from the categories of precursor neurons. Through a process of categorical clipping, these new categories are created by selectively extracting specific subdimensions from the preceding categories, constructing a distinction between a form and a categorical background. We explore several cognitive characteristics of this synthetic clipping in an exploratory manner: categorical reduction, categorical selectivity, separation of initial embedding dimensions, and segmentation of categorical zones.
Ana Luiza Dias Abdo Agamme, Sergio Tufik, Pablo Torterolo
et al.
Melanin-concentrating hormone (MCH) and hypocretins (Hcrt) 1 and 2 are neuropeptides synthesized in the lateral hypothalamic area by neurons that are critical in the regulation of sleep and wakefulness. Their receptors are located in the same cerebral regions, including the frontal cortex and hippocampus. The present study aimed to assess whether 96 hours of paradoxical sleep deprivation alters the functioning of the MCH and hypocretin systems. To do this, in control rats with normal sleep (CTL) and in rats that were deprived of paradoxical sleep (SD), we quantified the following parameters: 1) levels of MCH and hypocretin-1 in the cerebrospinal fluid (CSF); 2) expression of the prepro-MCH (Pmch) and prepro-hypocretin (Hcrt) genes in the hypothalamus; 3) expression of the Mchr1 and Hcrtr1 genes in the frontal cortex and hippocampus; and 4) expression of the Hcrtr2 gene in the hippocampus. These measures were performed at 6 Zeitgeber time (ZT) points of the day (ZTs: 0, 4, 8, 12, 16, and 20). In the SD group, we found higher levels of MCH in the CSF at the beginning of the dark phase. In the frontal cortex, sleep deprivation decreased the expression of Hcrtr1 at ZT0. Moreover, we identified significant differences between the light and dark phases in the expression of Mchr1 and Hcrtr1, but only in the CTL animals. We conclude that there is a day/night modulation in the expression of components of the MCH and hypocretin systems, and this profile is affected by paradoxical sleep deprivation.
Justin Mahlberg, Daniel Pearson, Mike E. Le Pelley
et al.
Motivationally salient stimuli, such as those associated with reward, can automatically gain attentional prioritisation – even when individuals are motivated to ignore such stimuli. This ‘attentional bias for reward’ has often been interpreted as evidence for involuntary Pavlovian ‘sign tracking’ behaviour. The prioritisation of reward-signalling distractors may additionally reflect a drive to gain information about the state of the world, irrespective of the particular reward that is being signalled. In the current study we assessed whether forewarning participants on each trial as to the upcoming features of a distractor would reduce reward-related attentional capture. This manipulation reduces the information provided by the distractor, without affecting the magnitude of the signalled reward. Using eye tracking in Experiment 1, we found that reward-related attentional capture was virtually eliminated when participants were informed of the upcoming distractor colour (relative to the baseline condition when no information was provided). In Experiment 2, using a response-time version of the task, we again found a significant reduction in reward-related attentional capture when participants received information about the colour of an upcoming distractor, or information about the value of the upcoming reward. Finally, in Experiment 3 we assessed whether participants were using the pre-trial information to strategically inhibit attention to the upcoming distractor colour. The results of these experiments are discussed within the context of information-seeking accounts of reward-related attentional capture effects.
Flavia Rodrigues da Silva, Renato de Carvalho Guerreiro, Amaury Tavares Barreto
et al.
Sleep disturbance is common during recovery after surgical procedures and may have an important effect on mortality, and quality of life. Sleep restriction/deprivation, including decreased quantity and continuity, is common in patients who are patients and persons with acute and chronic illnesses. Age, gender, illness, primary sleep disorders, environment, and medical treatment factors are thought to influence sleep throughout the preoperative period, hospitalization, and recovery. Resulting sleep pattern disturbances include decreases in circadian patterning, continuity, duration, and perceived (subjective) sleep quality. This article synthesizes sleep disturbance in patients who have undergone surgery and highlights sleep strategies to improve faster surgical recovery.
Human groups of all sizes and kinds engage in deliberation, problem solving, strategizing, decision making, and more generally, cognition. Some groups are large, and that setting presents unique challenges. The small-group setting often involves face-to-face dialogue, but group cognition in the large-group setting typically requires some form of online interaction. New approaches are needed to facilitate the kind of rich communication and information processing that are required for effective, functional cognition in the online setting, especially for groups characterized by thousands to millions of participants who wish to share potentially complex, nuanced, and dynamic perspectives. This concept paper proposes the CogNarr (Cognitive Narrative) ecosystem, which is designed to facilitate functional cognition in the large-group setting. The paper's contribution is a novel vision as to how recent developments in cognitive science, artificial intelligence, natural language processing, and related fields might be scaled and applied to large-group cognition, using an approach that itself promotes further scientific advancement. A key perspective is to view a group as an organism that uses some form of cognitive architecture to sense the world, process information, remember, learn, predict, make decisions, and adapt to changing conditions. The CogNarr ecosystem is designed to serve as a component within that architecture.
Abstract Extraction of global structural regularities provides general ‘gist’ of our everyday visual environment as it does the gist of abnormality for medical experts reviewing medical images. We investigated whether naïve observers could learn this gist of medical abnormality. Fifteen participants completed nine adaptive training sessions viewing four categories of unilateral mammograms: normal, obvious-abnormal, subtle-abnormal, and global signals of abnormality (mammograms with no visible lesions but from breasts contralateral to or years prior to the development of cancer) and receiving only categorical feedback. Performance was tested pre-training, post-training, and after a week’s retention on 200 mammograms viewed for 500 ms without feedback. Performance measured as d’ was modulated by mammogram category, with the highest performance for mammograms with visible lesions. Post-training, twelve observed showed increased d’ for all mammogram categories but a subset of nine, labelled learners also showed a positive correlation of d’ across training. Critically, learners learned to detect abnormality in mammograms with only the global signals, but improvements were poorly retained. A state-of-the-art breast cancer classifier detected mammograms with lesions but struggled to detect cancer in mammograms with the global signal of abnormality. The gist of abnormality can be learned through perceptual/incidental learning in mammograms both with and without visible lesions, subject to individual differences. Poor retention suggests perceptual tuning to gist needs maintenance, converging with findings that radiologists’ gist performance correlates with the number of cases reviewed per year, not years of experience. The human visual system can tune itself to complex global signals not easily captured by current deep neural networks.
The article explores the anthropomorphic metaphors that arise when describing a city and its components. Metaphor is one of the forms of cognition of the essence of a complex object or process. The city, as an object of knowledge, can be represented in many ways, one of which is a metaphor. Anthropomorphic metaphors are based on endowing the city with the properties of human morphology, physiology, and psychology. The author aims to identify, structure, and justify the use of anthropomorphic metaphors in various contexts. For this purpose, methods of terminological search and contextual analysis were used, thanks to which terms of urban discourse with anthropomorphic etymology were identified in the field of scientific information. The study established two main contexts of the anthropomorphism of the city: material and physiological and ideological and personal. Examples of metaphors are given and described. The material-physiological context includes metaphors that describe the city as a physical body with various parts and functions. In the ideological and personal context, the city is considered as a person with consciousness, emotions, and values. The author concludes that the use of anthropomorphic metaphors in urban discourse contributes to a better understanding and interpretation of urban processes. However, excessive use of such metaphors can lead to distortion of reality and the creation of false ideas about the city. In general, anthropomorphic metaphors are an important tool for exploring and describing a city, but they should be used with caution, taking the context into account. The use of such metaphors is useful in those communication formats when it is necessary to explain the essence of complex urban phenomena for non-professionals to understand.
Anthropology, Social history and conditions. Social problems. Social reform
Christina Sandlund, Jeanette Westman, Annika Norell-Clarke
Objective Cognitive behavioral therapy for insomnia (CBT-I) is the first-line treatment for insomnia, but half of the patients do not reach remission. This study aimed to explore subjective remission by investigating the characteristics of patients who reported lingering sleep problems after CBT-I.
Digital life, a form of life generated by computer programs or artificial intelligence systems, it possesses self-awareness, thinking abilities, emotions, and subjective consciousness. Achieving it involves complex neural networks, multi-modal sensory integration [1, 2], feedback mechanisms, and self-referential processing [3]. Injecting prior knowledge into digital life structures is a critical step. It guides digital entities' understanding of the world, decision-making, and interactions. We can customize and personalize digital life, it includes adjusting intelligence levels, character settings, personality traits, and behavioral characteristics. Virtual environments facilitate efficient and controlled development, allowing user interaction, observation, and active participation in digital life's growth. Researchers benefit from controlled experiments, driving technological advancements. The fusion of digital life into the real world offers exciting possibilities for human-digital entity collaboration and coexistence.
Kerem Oktar, Ilia Sucholutsky, Tania Lombrozo
et al.
The increasing prevalence of artificial agents creates a correspondingly increasing need to manage disagreements between humans and artificial agents, as well as between artificial agents themselves. Considering this larger space of possible agents exposes an opportunity for furthering our understanding of the nature of disagreement: past studies in psychology have often cast disagreement as two agents forming diverging evaluations of the same object, but disagreement can also arise from differences in how agents represent that object. AI research on human-machine alignment and recent work in computational cognitive science have focused on this latter kind of disagreement, and have developed tools that can be used to quantify the extent of representational overlap between agents. Understanding how divergence and misalignment interact to produce disagreement, and how resolution strategies depend on this interaction, is key to promoting effective collaboration between diverse types of agents.