Disregulasi emosi merupakan ketidakmampuan mengelola emosi yang dirasakan saat menghadapi stimulus eksternal maupun internal. Masalah tersebut dapat berkontribusi pada berkembangnya gangguan psikologis jika tidak dikelola secara memadai. Studi kasus tunggal ini melaporkan manfaat dari penggunaan teknik modeling sebagai salah satu bagian dari terapi perilaku kognitif dalam menangani disregulasi emosi pada anak. Penelitian ini melibatkan seorang anak berusia 6 tahun yang dievaluasi dengan observasi, wawancara, dan alat ukur psikologis yang terdiri dari Strength and Difficulties Questionnaire (SDQ) dan The Emotion Regulation Checklist (ERC). Lima sesi diberikan secara tatap muka, empat sesi diantaranya diberikan pada anak menggunakan metode read-aloud dan role-playing, sementara satu sesi psikoedukatif diberikan kepada orangtua. Hasil menunjukkan bahwa penggunaan teknik modeling berkaitan dengan peningkatan kemampuan anak untuk memberikan label emosi dan penurunan perilaku yang tidak diinginkan. Dengan demikian, pengembangan buku panduan terkait penggunaan teknik modeling akan bermanfaat untuk membantu penanganan masalah disregulasi emosi pada anak.
It has long been known that presenting information to multiple senses at a time (e.g., audiovisual presentation as opposed to only visual or auditory) improves later recall of said information – an effect known as the bimodal advantage. Surprisingly however, evidence for this has come only from studies employing free and serial recall, where the identity of an object is recalled, but not in cued recall, where one object feature is recalled when another one is cued. This is despite both tasks requiring binding features into an object in working memory (WM) – our brain’s capacity-limited system for temporarily maintaining information for the purpose of achieving behavioral goals. The present study investigated this discrepancy across a series of four experiments. Contrary to the literature, and despite near-identical task settings, we found evidence in favor of a bimodal advantage across multiple experiments. Moreover, our results suggest that this advantage mainly arises from perceptual processes at encoding rather than from storage in an audiovisual fashion in WM. Finally, a primarily perceptually-based process, the bimodal advantage appears to be sensitive to the characteristics of the cue feature (i.e., its presentation modality). In sum, our results shed light on the mechanism of the bimodal advantage, now robustly detected in cued recall tasks, furthering our understanding of the relationship between perception and WM. Results are discussed in relation to prior studies that did not find a bimodal advantage, potential mechanisms underlying the effect, and the broader framework of the multicomponent model of WM.
Camille A. Troisi, Alizée Vernouillet, Reinoud Allaert
et al.
Abstract Response inhibition - the ability to suppress or stop actions - is crucial for adaptive behaviour across species. The concept of response inhibition has traditionally been regarded as a unidimensional psychological ability. However, there is an increasing recognition of its multifaceted nature. In Part 1 of this study, we present a conceptual framework to explain variability across tasks and contexts. We conceptualise response inhibition as a race between a ‘go’ runner and a ‘stop’ runner, with both runners influenced by stimulus type, stimulus timing, and action type. To illustrate how task-specific factors shape response inhibition, we apply this framework to five response inhibition tasks: the stop-signal, stop-change, detour barrier, A-not-B, and thwarting tasks. Our framework highlights the need for precise methods and careful interpretation of response inhibition measures and provides a basis for nuanced investigations of response inhibition control and its ecological and evolutionary significance. In the accompanying Part 2, we use this framework to test predictions about correlations between different measures of response inhibition.
Dissipative cognitive architectures maintain computation through continuous energy expenditure, where units that exhaust their energy are stochastically replaced with fresh random state. This creates a fundamental challenge: how can persistent, context-specific memory survive when all learnable state is periodically destroyed? Existing memory mechanisms -- including elastic weight consolidation, synaptic intelligence, and surprise-driven gating -- rely on gradient computation and are inapplicable to non-gradient dissipative systems. We introduce Deep Memory (DM), a non-gradient persistent memory mechanism operating through a triple-loop consolidation cycle: (1) recording of expert-specific content centroids, (2) seeding of replaced units with stored representations, and (3) stabilization through continuous re-entry. We demonstrate that discrete expert routing via Mixture-of-Experts (MoE) gating is a causal prerequisite for DM, preventing centroid convergence that would render stored memories identical. Across ${\sim}970$ simulation runs spanning thirteen experimental blocks: (i) discrete routing is causally necessary for specialization ($\text{MI}=1.10$ vs. $0.001$; $n=91$); (ii) DM achieves $R=0.984$ vs. $0.385$ without memory ($n=16$); (iii) continuous seeding reconstructs representations after interference ($R_\mathrm{recon}=0.978$; one-shot fails; $n=30$); (iv) the mechanism operates within a characterized $(K,p)$ envelope ($n=350$); (v) recording $\times$ seeding is the minimal critical dyad ($n=40$); (vi) DM outperforms non-gradient baselines (Hopfield, ESN) under matched turnover ($n=370$). These results establish DM as a falsifiable mechanism for persistent memory in non-gradient cognitive systems, with functional parallels to hippocampal consolidation.
Alexandra Hinterberger,1,&ast; Esther-Sevil Eigl,1,&ast; Pavlos I Topalidis,1 Manuel Schabus1,2 1Laboratory for Sleep, Cognition & Consciousness Research, Department of Psychology, Paris Lodron University of Salzburg, Salzburg, Austria; 2Centre for Cognitive Neuroscience Salzburg (CCNS), Paris Lodron University of Salzburg, Salzburg, Austria&ast;These authors contributed equally to this workCorrespondence: Manuel Schabus, University of Salzburg, Centre for Cognitive Neuroscience, Laboratory for Sleep, Cognition and Consciousness Research, Hellbrunner Straße 34, Salzburg, A-5020, Austria, Email manuel.schabus@plus.ac.atPurpose: Given rising numbers in sleep disorders like insomnia and insufficient availability of treatment options, the need for well-validated digital interventions rises. This pilot study aims at assessing the feasibility of an app-program which combines sleep-training based on core elements of Cognitive Behavioural Therapy for Insomnia (CBT-I) with reliable sleep-monitoring based on heart rate variability via an ECG-sensor.Patients and Methods: About 48 participants (26 females) aged 30– 73 (M = 50.33 ± 11.88) were included in the study. At the beginning of the baseline (T0), at start (T1) and end (T2) of the 6-week training phase as well as 4 weeks after the end of the program (T3; follow-up) several questionnaires assessing sleep quality, insomnia severity, general psychological symptom severity, depression, anxiety as well as quality of life were completed. Furthermore, ambulatory polysomnography (PSG) was conducted three times at T0, T1 and T2. General feasibility was assessed by conducting interviews.Results: Overall, the app-program as well as the study protocol was deemed as feasible according to the participants, besides some difficulties regarding app-instructions and certain technical issues, as well as some expected complaints about worse sleep quality during PSG-recordings. For statistical results, insomnia severity (p < 0.001, r = 0.67), sleep quality (p < 0.001, r = 0.56), general psychological symptom severity (p < 0.001, r = 0.68), depression (p = 0.002, r = 0.50) and anxiety (p < 0.001, r = 0.60) improved significantly during the training phase, while quality of life [physical (p = 0.014, r = 0.41) and psychological health (p = 0.049, r = 0.35)] improved significantly during the follow-up-period. PSG data revealed a significant decrease in Wake After Sleep Onset over the course of the study (p = 0.025, r = 0.36), yet no significant changes were found for other sleep parameters.Conclusion: The app-program was largely feasible and potentially effective in improving sleep and well-being. PSG-derived WASO changes highlight the value of objective sleep measures. Future studies should refine protocols and include control conditions for greater generalizability.Keywords: insomnia, CBT-I, prevention, feasibility, digital intervention, app-program
Daniil Vlasenko, Vadim Ushakov, Alexey Zaikin
et al.
fMRI is a non-invasive technique for investigating brain activity, offering high-resolution insights into neural processes. Understanding and decoding cognitive brain states from fMRI depends on how functional interactions are represented. We propose an ensemble-based graph representation in which each edge weight encodes state evidence as the difference between posterior probabilities of two states, estimated by an ensemble of edge-wise probabilistic classifiers from simple pairwise time-series features. We evaluate the method on seven task-fMRI paradigms from the Human Connectome Project, performing binary classification within each paradigm. Using compact node summaries (mean incident edge weights) and logistic regression, we obtain average accuracies of 97.07-99.74 %. We further compare ensemble graphs with conventional correlation graphs using the same graph neural network classifier; ensemble graphs consistently yield higher accuracy (88.00-99.42 % vs 61.86-97.94 % across tasks). Because edge weights have a probabilistic, state-oriented interpretation, the representation supports connection- and region-level interpretability and can be extended to multiclass decoding, regression, other neuroimaging modalities, and clinical classification.
Brain networks from functional MRI have advanced our understanding of cortical activity and its disruption in neurodegenerative disorders. Recent work has increasingly focused on dynamic (time-varying) brain networks that capture both spatial and temporal patterns of regional co-activity, yet this approach remains underexplored across the Alzheimer's disease (AD). We analysed age- and sex-matched static and dynamic functional brain networks derived from resting-state fMRI data in 315 individuals with AD, mild cognitive impairment (MCI), and cognitively normal healthy controls (HC) from the ADNI-3 cohort. Functional networks were constructed using the Juelich brain atlas, with static connectivity estimated from full time series and dynamic connectivity derived using a sliding-window approach. Group differences were assessed at both link and node levels using non-parametric statistics and bootstrap resampling. While HC and MCI exhibited similar static and dynamic patterns at the node level, clearer differences emerged in AD. Stable (stationary) differences in functional connectivity were identified between white matter regions and parietal and somatosensory cortices, whereas temporally varying differences were consistently observed in connections involving the amygdala and hippocampal formation. Node centrality analysis further suggested that white matter connectivity differences are predominantly local in nature. These findings highlight both shared and distinct functional connectivity patterns across static and dynamic networks, underscoring the importance of incorporating temporal dynamics into brain network analyses of the Alzheimer's spectrum. Additionally, a Random Forest model trained on regional BOLD time series informed by static and dynamic metrics achieved robust classification of MCI, AD, and HC groups, demonstrating the diagnostic potential of time-varying connectivity.
Large language models exhibit intelligence without genuine epistemic understanding, exposing a key gap: the absence of epistemic architecture. This paper introduces the Structured Cognitive Loop (SCL) as an executable epistemological framework for emergent intelligence. Unlike traditional AI research asking "what is intelligence?" (ontological), SCL asks "under what conditions does cognition emerge?" (epistemological). Grounded in philosophy of mind and cognitive phenomenology, SCL bridges conceptual philosophy and implementable cognition. Drawing on process philosophy, enactive cognition, and extended mind theory, we define intelligence not as a property but as a performed process -- a continuous loop of judgment, memory, control, action, and regulation. SCL makes three contributions. First, it operationalizes philosophical insights into computationally interpretable structures, enabling "executable epistemology" -- philosophy as structural experiment. Second, it shows that functional separation within cognitive architecture yields more coherent and interpretable behavior than monolithic prompt based systems, supported by agent evaluations. Third, it redefines intelligence: not representational accuracy but the capacity to reconstruct its own epistemic state through intentional understanding. This framework impacts philosophy of mind, epistemology, and AI. For philosophy, it allows theories of cognition to be enacted and tested. For AI, it grounds behavior in epistemic structure rather than statistical regularity. For epistemology, it frames knowledge not as truth possession but as continuous reconstruction within a phenomenologically coherent loop. We situate SCL within debates on cognitive phenomenology, emergence, normativity, and intentionality, arguing that real progress requires not larger models but architectures that realize cognitive principles structurally.
In recent years, vision-based end-to-end autonomous driving has emerged as a new paradigm. However, popular end-to-end approaches typically rely on visual feature extraction networks trained under label supervision. This limited supervision framework restricts the generality and applicability of driving models. In this paper, we propose a novel paradigm termed $E^{3}AD$, which advocates for comparative learning between visual feature extraction networks and the general EEG large model, in order to learn latent human driving cognition for enhancing end-to-end planning. In this work, we collected a cognitive dataset for the mentioned contrastive learning process. Subsequently, we investigated the methods and potential mechanisms for enhancing end-to-end planning with human driving cognition, using popular driving models as baselines on publicly available autonomous driving datasets. Both open-loop and closed-loop tests are conducted for a comprehensive evaluation of planning performance. Experimental results demonstrate that the $E^{3}AD$ paradigm significantly enhances the end-to-end planning performance of baseline models. Ablation studies further validate the contribution of driving cognition and the effectiveness of comparative learning process. To the best of our knowledge, this is the first work to integrate human driving cognition for improving end-to-end autonomous driving planning. It represents an initial attempt to incorporate embodied cognitive data into end-to-end autonomous driving, providing valuable insights for future brain-inspired autonomous driving systems. Our code will be made available at Github
Introduction Postoperative delirium (POD) is a common neurological complication after surgery among older patients, characterised by acute disturbances in consciousness, attention and cognition, usually occurring within 24–72 hours after surgery. POD has a significant impact on the prognosis of older patients undergoing major cardiovascular surgery, including increased length of hospital stay, hospital costs and readmission rates, with an incidence rate as high as 26%–52%. Computerised cognitive training (CCT) refers to difficulty-adaptive training in cognitive domains such as attention, memory and logical reasoning, using systematically designed tasks. Existing studies have shown that CCT has reduced the risk of delirium in non-cardiac surgery patients with at least minimal compliance. The purpose of this study is to investigate the effects of preoperative CCT on the incidence of POD in older patients undergoing elective cardiac surgery, to clarify the dose–effect relationship between different training time of preoperative CCT and POD and to explore the minimum effective time target that can significantly lower the incidence of POD.Methods and analysis This is a prospective, single-blind, randomised controlled trial that aims to enrol 261 older patients scheduled for elective cardiac surgery at the Affiliated Hospital of Xuzhou Medical University. The patients will be randomised into three groups: group C will be the routine care group (no CCT prior to surgery); group L will be the low-dose time group (with a total of 5 hours of CCT prior to surgery) and group H will be the high-dose time group (with a total of 10 hours of CCT prior to surgery). The primary outcome is the incidence of delirium within 7 days after surgery. Secondary outcomes include postoperative mild neurocognitive disorder (NCD) and postoperative major NCD (30 days up to 1 year), time of onset and duration and severity of delirium, and all-cause mortality within 1 year after surgery. The results of this study are of significant importance for establishing effective, patient-centred and low-risk prevention strategies for POD/postoperative NCD.Ethics and dissemination This study protocol has been approved by the Ethics Committee of the Affiliated Hospital of Xuzhou Medical University (Ethics Number: XYFY2023-KL149-01). All participants will provide written informed consent, and the results of the study will be published in international peer-reviewed academic journals and presented at academic conferences.Trial registration number ChiCTR2300072806.
This paper examines the influence of cultural display rules on how high-status individuals, such as political leaders, publicly express anger. Specifically, it focuses on Recep Tayyip Erdoğan, who has been the Turkish leader since 2003. The study aims to understand the extent to which Erdoğan’s expression of anger is influenced by cultural display rules, the religious context stemming from his conservative electoral support, and his position as a long-term populist political leader. Using extended conceptual metaphor theory (ECMT) supported by corpus-assisted discourse analysis, the paper seeks to identify the contextual factors that shape anger expressions (both direct and metaphorical) in the political discourse of a populist leader in a collectivist culture. By comparing the conceptualization of ascribed anger and inscribed anger expressions, the analysis reveals that Erdoğan’s discourse presents two distinct scenarios for expressing anger toward ‘us’ and ‘others’. Additionally, it demonstrates how anger is strategically employed in culture-specific ways to navigate the challenges posed by conflicting contextual factors.
Objective: to show the ontological differences between human and artificial intelligence and address structural divergences at the definitional level.Methods: dialectical approach to cognition of social phenomena, allowing to analyze them in historical development and functioning in the context of the totality of objective and subjective factors, which predetermined the following research methods: formal-logical and sociological.Results: a cross-cutting analysis was applied to the phenomenon of AI between cyber utopianism and cyber realism. Starting from a quote by Max Tegmark, the theory of artificial intelligence is reconstructed by the theorists who founded the discipline (Turing, Minsky, Bernstein, von Neumann) and it is discussed why – in light of the discoveries and assumptions of neuroscience – it is not possible to define it as intelligence according to human criteria. Three short notes are included in the appendix that complete the discussion: 1. on the consciousness of machines 2. on the theory of utopian cyber employment and remuneration 3. “The hungry judge is more cruel” (discussion on an Israeli study).Scientific novelty: through the examination of multiple types of intelligence (Gardner) and social intelligence (Thorndike, Goleman), a more complex definition of intelligence is proposed than that which can be replicated by artificial neural networks, especially in relation to the interaction between animal and environment. Three short messages highlight the uncertainty and risks that may arise from the rampant use of artificial intelligence as judges.Practical significance: starting from a correct definition of human intelligence, the author comes to the definition of artificial intelligence. Beyond the myth of AI, we discover its limits and the objective limitations we must provide for in order to save the most precious asset we have: mankind.
Economics as a science, Law in general. Comparative and uniform law. Jurisprudence
Francesca Zermiani, Prajit Dhar, Florian Strohm
et al.
Interruptions are often pervasive and require attentional shifts from the primary task. Limited data are available on the factors influencing individuals' efficiency in resuming from interruptions during digital reading. The reported investigation—conducted using the InteRead dataset—examined whether individual differences in visuo-spatial working memory capacity (vsWMC) and prior knowledge could influence resumption lag times during interrupted reading. Participants' vsWMC capacity was assessed using the symmetry span (SSPAN) task, while a pre-test questionnaire targeted their background knowledge about the text. While reading an extract from a Sherlock Holmes story, they were interrupted six times and asked to answer an opinion question. Our analyses revealed that the interaction between vsWMC and prior knowledge significantly predicted the time needed to resume reading following an interruption. The results from our analyses are discussed in relation to theoretical frameworks of task resumption and current research in the field.
This study evaluates the performance of various supervised machine learning models in analyzing highly correlated neural signaling data from the Adolescent Brain Cognitive Development (ABCD) Study, with a focus on predicting obsessive-compulsive disorder scales. We simulated a dataset to mimic the correlation structures commonly found in imaging data and evaluated logistic regression, elastic networks, random forests, and XGBoost on their ability to handle multicollinearity and accurately identify predictive features. Our study aims to guide the selection of appropriate machine learning methods for processing neuroimaging data, highlighting models that best capture underlying signals in high feature correlations and prioritize clinically relevant features associated with Obsessive-Compulsive Disorder (OCD).
Deep learning has enabled major advances across most areas of artificial intelligence research. This remarkable progress extends beyond mere engineering achievements and holds significant relevance for the philosophy of cognitive science. Deep neural networks have made significant strides in overcoming the limitations of older connectionist models that once occupied the centre stage of philosophical debates about cognition. This development is directly relevant to long-standing theoretical debates in the philosophy of cognitive science. Furthermore, ongoing methodological challenges related to the comparative evaluation of deep neural networks stand to benefit greatly from interdisciplinary collaboration with philosophy and cognitive science. The time is ripe for philosophers to explore foundational issues related to deep learning and cognition; this perspective paper surveys key areas where their contributions can be especially fruitful.
Santhosh Kumar Ramakrishnan, Erik Wijmans, Philipp Kraehenbuehl
et al.
Not yet. We present SPACE, a benchmark that systematically evaluates spatial cognition in frontier models. Our benchmark builds on decades of research in cognitive science. It evaluates large-scale mapping abilities that are brought to bear when an organism traverses physical environments, smaller-scale reasoning about object shapes and layouts, and cognitive infrastructure such as spatial attention and memory. For many tasks, we instantiate parallel presentations via text and images, allowing us to benchmark both large language models and large multimodal models. Results suggest that contemporary frontier models fall short of the spatial intelligence of animals, performing near chance level on a number of classic tests of animal cognition. Code and data are available: https://github.com/apple/ml-space-benchmark
This study aims to place Lorenzo Magnanis Eco-Cognitive Computationalism within the broader context of current work on information, computation, and cognition. Traditionally, cognition was believed to be exclusive to humans and a result of brain activity. However, recent studies reveal it as a fundamental characteristic of all life forms, ranging from single cells to complex multicellular organisms and their networks. Yet, the literature and general understanding of cognition still largely remain human-brain-focused, leading to conceptual gaps and incoherency. This paper presents a variety of computational (information processing) approaches, including an info-computational approach to cognition, where natural structures represent information and dynamical processes on natural structures are regarded as computation, relative to an observing cognizing agent. We model cognition as a web of concurrent morphological computations, driven by processes of self-assembly, self-organisation, and autopoiesis across physical, chemical, and biological domains. We examine recent findings linking morphological computation, morphogenesis, agency, basal cognition, extended evolutionary synthesis, and active inference. We establish a connection to Magnanis Eco-Cognitive Computationalism and the idea of computational domestication of ignorant entities. Novel theoretical and applied insights question the boundaries of conventional computational models of cognition. The traditional models prioritize symbolic processing and often neglect the inherent constraints and potentialities in the physical embodiment of agents on different levels of organization. Gaining a better info-computational grasp of cognitive embodiment is crucial for the advancement of fields such as biology, evolutionary studies, artificial intelligence, robotics, medicine, and more.
Mirna Ayu Irpadila, Eva Meizara Puspita Dewi, Dian Novita Siswanti
Parental self-efficacy merupakan prediktor penting yang memengaruhi perilaku positif orang tua dalam melakukan pengasuhan. Penelitian ini bertujuan untuk mengetahui pengaruh pelatihan smart parenting terhadap peningkatan parental self-efficacy ibu bekerja yang memiliki anak di bawah tiga tahun. Penelitian ini menggunakan rancangan eksperimen kuasi the untreated control group design with dependent pretest and posttest samples. Sebanyak 14 partisipan terbagi menjadi kelompok eksperimen dan kelompok kontrol. Parental self-efficacy diukur menggunakan skala SEPTI-TS. Hasil analisis data menggunakan Mann-Whitney U menunjukkan bahwa terdapat perbedaan parental self-efficacy (ρ=0,002) pada kelompok eksperimen dan kelompok kontrol, dan berdasarkan Wilcoxon sign rank test menunjukkan bahwa terjadi peningkatan parental self-efficacy pada partisipan kelompok eksperimen (ρ=0,018). Hasil penelitian menunjukkan bahwa pelatihan smart parenting dapat meningkatkan parental self-efficacy ibu bekerja yang memiliki anak di bawah tiga tahun.