Cognitive Model Research Articles

A robust operators’ cognitive workload recognition method based on denoising masked autoencoder

Identifying the cognitive workload of operators is crucial in complex human-automation collaboration systems. An excessive workload can lead to fatigue or accidents, while an insufficient workload may diminish situational awareness and efficiency. However, existing supervised learning-based methods for workload recognition are ineffective when dealing with imperfect input data, such as missing or noisy data, which is not practical in real applications. This study introduces a robust Electroencephalogram (EEG)-enabled cognitive workload recognition model using self-supervised learning. The proposed method, DMAEEG, combines the training strategies of denoising autoencoders and masked autoencoders, demonstrating strong robustness against noisy and incomplete data. More specifically, we adopt the temporal convolutional network and multi-head self-attention mechanisms as the backbone, effectively capturing both the temporal and spatial features from EEG. Extensive experiments are conducted to verify the effectiveness and robustness of the proposed method on an open dataset and a self-collected dataset. The results indicate that DMAEEG performs superior to other state-of-the-art across various evaluation metrics. Moreover, DMAEEG maintains high accuracy in workload inference even when EEG signals are corrupted with a high masking ratio or strong noises. This signifies its superiority in capturing robust intrinsic patterns from imperfect EEG data. The proposed method significantly contributes to decoding EEG signals for workload recognition in real-world applications, thereby enhancing the safety and reliability of human-automation interactions.

Will the situation of Left-Behind children improve when their parents Return? evidence from China

Based on Grossman’s (1972) health model and Becker and Tomes’ (1979, 1986) cognitive ability family decision-making model, this study examines the impact and mechanisms of children’s left-behind status and parental return on the accumulation of children’s health and cognitive human capital. By extending these models, we derive that changes in left-behind status have both income and separation effects on children’s human capital accumulation. Using data from the China Family Panel Studies (CFPS), we employ a Propensity Score Matching with Difference-in-Differences (PSM-DID) model to investigate the impact of changes in left-behind status (either remaining left-behind or experiencing parental return) on children’s human capital accumulation. The research findings reveal the following: (1) Compared to non-left-behind children, left-behind status significantly decreases children’s health levels by 11.81%. In contrast, parental return significantly improves children’s health levels by 13.04% compared to children whose parents are away. All results are statistically significant. (2) The negative impact of left-behind status is more pronounced for primary school children, those residing in rural areas, and those with parents of lower educational levels, especially in western regions. In contrast, parental return has a more substantial positive impact on junior high school children, those living in rural areas, and those with parents of lower educational levels, particularly in western regions. (3) When analyzing the impact mechanisms, we find that although parental migration for work increases household income, leading to a positive income effect on children’s health, it does not fully offset the negative separation effect caused by parental absence. As a result, the overall effect remains negative. Based on these findings, for migrant worker parents, we propose tiered recommendations: ideally, parents should return home; alternatively, children should follow their parents. For the government, we suggest establishing comprehensive service centers for left-behind children based on actual needs. We recommend designing a holistic plan aimed at addressing the health issues of left-behind children through multi-level support measures.

Emotional regulation and Arnold's self-ideal: a way to flourishment.

The convergence of researchers in the fields of flourishing, moral psychology, and social-emotional studies has reached a stage where developing a theory that connects emotional regulation and flourishing is meaningful. This theoretical investigation aims to uncover insights from the research of Magda B. Arnold, renowned for her theory of emotions, and lesser-known for her notion of the self-ideal, regarding the relationship between emotional regulation and flourishing. Our initial hypothesis posits that Arnold's concept of self-ideal provides a framework for understanding how to foster emotional regulation in individuals by directing it toward constructive life objectives. To achieve this, we explore the current state of emotional regulation and flourishing and the relationship between these concepts; we consider the interconnectedness of emotion and self-ideal within Arnold's theory and analyze its potential to serve as a foundation for building a theory relating flourishing and emotional regulation. We find in Arnold's theory substantial ideas about the relationship between emotional regulation, flourishing, and self-ideal, as well as emerging empirical research relating to these themes. We conclude that Arnold's research can serve as a catalyst for developing psychological intervention models that enhance emotional regulation and promote a flourishing life.

The process of co‐design for a new anxiety intervention for autistic children

AbstractBackgroundMental health difficulties are common for autistic people; however, almost no interventions have been co‐designed with the autistic community. Co‐design has the potential to add important insights from lived experience into intervention design, but there are currently limited examples of how rigorously to undertake this practice. This paper details a worked model of co‐design and its process, focussed on adapting an evidenced parent‐led intervention for non‐autistic child anxiety (HYC), to meet the needs of young autistic children. The aim is to provide an example of co‐design, integrating autistic, parental, academic, clinical, experience and expertise.MethodsUsing prior literature and theory, including Experience‐Based Co‐Design, we developed an iterative and collaborative process between the research team and an expert reference group (ERG). The research team comprised autistic and non‐autistic members. The ERG included parents (autistic and non‐autistic) of autistic children with anxiety problems, autistic adults with experience of anxiety problems, and clinicians with experience supporting autistic children with anxiety problems. The ERG and research team reviewed information from qualitative research interviews with autistic children with anxiety problems and their parents along with information from clinical experience and the academic literature to reach consensus on the adapted intervention design.ResultsThe creation of a truly co‐designed intervention that includes a neurodiversity‐affirmative perspective, alongside CBT techniques. With anxiety problems experienced by autistic children being framed by combining the impacts of being neurodivergent in a neurotypical world, developmental science and well known cognitive behavioural models of child‐anxiety.ConclusionCo‐design can help to integrate multiple perspectives and result in the creation of interventions that are potentially relevant and acceptable to autistic people, their family members, and clinicians.

Individual Differences in Fatigued Performance

Cognitive fatigue is a prevalent issue across multiple military operational domains, resulting in degraded mission performance and costly mishaps. One approach to mitigating the effects of fatigue in operational environments is to develop technology, such as biomathematical, physiological, and cognitive models, that can help individuals assess and predict their fatigue and its impacts on performance. We can further enhance the accuracy of these models by incorporating individual difference metrics that allow for increased individualization. In the current study, we examine how individual differences in circadian typology, typical sleep duration, sleep duration need, and hardiness relate to fatigued performance during a 24-hr long mobility aircraft simulator session with mobility aircrews. Specifically, we examine individual difference relationships with performance on three cognitive tasks, the Psychomotor Vigilance Test, the N-back task, and the Change Signal Task, which measure processes crucial to operational performance: attention, working memory, and executive functioning.

Visual Representation and Cognitive Models in Information Visualization

As a branch of computer science, information visualization aims to help users understand and analyze complex data through graphical interfaces and interactive technologies. Information visualization primarily includes various visual structures such as time-series structures, spatial relationship structures, statistical distribution structures, and geographic map structures, each with unique functions and application scenarios. To better explain the cognitive process of visualization, researchers have proposed various cognitive models based on interaction mechanisms, visual perception steps, and novice use of visualization. These models help understand user cognition in information visualization, enhancing the effectiveness of data analysis and decision-making.

Iterative Attribute Hierarchy Exploration Methods for Cognitive Diagnosis Models

Attribute hierarchies are commonly assumed to exist in many fields of psychological and educational assessment. Several theory-driven and data-driven approaches have been used to validate or explore attribute hierarchies, such as validating attribute hierarchies in the cognitive diagnostic model (CDM) through statistical hypothesis testing or even learning attribute hierarchies directly from data. A class of structural parameter standard error estimation methods for CDMs is useful for exploring attribute hierarchies, with the limitation that the information matrices of some model parameters may be unstable or singular, leading to biased hypothesis testing. An iterative method of attribute hierarchy testing was proposed to modify the original z-statistic method. The simulation study systematically compares the performance of the z-statistic and the iterative z-statistic in exploring the attribute hierarchy. The results show that the iterative z-statistic provides a better Type I error control rate and statistical power, and it partially solves the problem that the z-statistic is too conservative. In addition, the iterative z-statistic method also achieves satisfactory results on real data.

Cognitive rhetoric in super sports reporting: unveiling perspectives through multilevel analysis of the Beijing 2022 Winter Olympics from five international media outlets

ABSTRACT This study utilizes the Event-Domain Cognitive Model (ECM) within cognitive linguistics to analyze narrative construction in international news media coverage of the Beijing Winter Olympics. Through qualitative analysis of 1673 news articles from five diverse media outlets, the research outlines a multilevel cognitive rhetoric framework to explore the integration of cultural, historical, and emotional factors in media discourse. At the macro level, cultural and historical contexts influence the selection of event subdomains. At the meso level, cognitive reference points shape portrayals of international diplomacy. At the micro level, rhetorical strategies, especially metaphorical language, reveal media knowledge structures and emotional responses. The study highlights the media's dual role in reinforcing shared humanistic values and exposing geopolitical rivalries. Findings indicate the Olympics as a global stage for uniting cultures through common values and manifesting political and ideological differences, with varying narratives reflecting each outlet's cultural and political context. The study underscores the importance of cross-cultural media literacy to develop a critically aware public and contributes to international media studies, cognitive linguistics, and communication by providing a comprehensive model for analyzing media narratives and encouraging further research into global media events.

Sustainable Memory Ecology, Entangled Environments, and Multimateriality in a Post-Digital Age: A Case Study of MemoryBytes

Abstract This article examines the interdisciplinary and epistemological possibilities in memory studies amidst the complex systems of sustainable memory ecology, entangled environments, and multimateriality prevalent in our post-digital age. This study situates the ontology and experience of memory through post-digital and posthumanist frameworks whereby the human/non-human and embedded/extended emerge as environmental entanglements that are part of everyday experiential and ecological realities which are often already augmented in quality. Likewise, we unpack the idea of materiality in the contemporary world and posit that a theory of multimateriality – asymmetric compounds of constructs, components, and affects – illustrates the modality of post-digital memory networks. Subsequently, the article attempts to offer a conceptual framework that draws on cognitive theory’s 4E model of cognition – embedded, embodied, extended, enactive – and a memory-ecology which is emergent as well as connective in quality. The key themes of this study are as follows: How can the concept of sustainable memory ecology correspond to interdisciplinary interpretative possibilities of preservation and reconstruction in a post-digital world? How can memory studies connect with cognitive theory to offer an original collaborative and epistemic apparatus using embodied, embedded, extended, and enactive engagements with environment and materiality in post-digital conditions? How do such collaborations anticipate the future of memory through a new order of post-digital memory ecology? Through its theoretical and conceptual convergences, and with a case study of the 2022 Augmented-Reality (ar) App and phygital exhibition MemoryBytes, our article offers an original framework for memory studies’ interdisciplinary engagements with the changing notions of ecology, sustainability, and materiality in today’s post-digital world.

Multimodal Emotion Cognition Method Based on Multi-Channel Graphic Interaction

The relationship between the emotional components associated with images and text is a crucial way of multimodal emotion analysis. However, most of the present multimodel affective cognitive models simply associate the features of images and texts without thoroughly investigating their interactions, resulting in poor recognition. Therefore, a multimodel emotion cognition method based on multi-channel graphic interaction is proposed. Text context features are extracted, scene and image information is encoded, and useful features are obtained. Based on these results, the modal alignment module be applied to obtain information about affective regions and words, and then the cross-modal gating module be applied to combine the multimodel features. In addition, we tested extensively on three open datasets, achieving an accuracy of 0.8122 for the MSA-single dataset, 0.7307 for the MSA-MULTIPLE dataset, and 0.7159 for TumEmo. The results show that this method is effective for multimodal emotion detection.

Understanding the Psychosis Spectrum Using a Hierarchical Model of Social Cognition.

Social cognitive impairments are central to psychosis, including lower severity psychosis-like experiences (PLEs). Nonetheless, progress has been hindered by social cognition's poorly defined factor structure, as well as limited work examining the specificity of social cognitive impairment to psychosis. The present study examined how PLEs relate to social cognition in the context of other psychopathology dimensions, using a hierarchical factors approach to social cognition. Online community participants (N = 1026) completed psychosis, autism, and personality disorder questionnaires, as well as 3 social cognitive tasks that varied in methodology (vignette vs video) and construct (higher- vs lower-level social cognition). Exploratory (EFA) and confirmatory factor analyses (CFA) were used to model social cognition, with the best models being examined in association with PLEs and psychopathology dimensions. EFA and CFA supported a hierarchical model of social cognition, with 2 higher-order factors emerging: verbal/vignette task methodology and a multimethod general social cognition factor. These higher-order factors accounted for task-level associations to psychopathology, with relations to positive symptoms (r = .23) and antagonism (r = .28). After controlling for other psychopathology, positive symptoms were most clearly related to tasks with verbal methodology (β = -0.34). These results suggest that broad social cognitive processes and method effects may account for many previous findings in psychosis and psychopathology research. Additionally, accounting for broad social cognitive impairment may yield insights into more specific social cognitive processes as well.

Persuasive Communication strategy of ustadz Ali Sati in increasing the enthusiasm for learning hadith knowledge of male students at the al Kautsar al akbar islamic boarding school in medan

Three factors contribute to male students' decreased interest in studying Hadith at Pesantren Al Kautsar Al Akbar: the first is the lack of inventiveness on the part of ustadz in promoting learning, the harsh sanctions that are applied, and the dull Pesantren curriculum. Among the Ustad that instruct in the Al Kautsar Al Akbar Islamic Boarding School in Medan is Ustad Ali Sati. Pondok Pesantren Al Kautsar Al Akbar was founded in 1985 and is situated at 264 Jalan Pelajar Timur in Medan, North Sumatra. In this study, a qualitative, descriptive strategy was employed. Techniques for gathering data include recordkeeping, interviewing, and observation. Based on the study's findings, the researcher spoke with Ustadz Ali Sati, who shared information about persuasive communication techniques to boost male students' interest in studying hadith science at Al Kautsar Al Akbar Islamic Boarding School in Medan. The researcher also obtained the information required by the researchers, and the study's findings can be backed up by written data. In this discussion, the researcher provides an overview based on the research findings. In the implementation of Ustadz Ali Sati's communication strategy in increasing the enthusiasm for learning hadith science for male students, he has three models which are able to make students enthusiastic about learning hadith science: Constructivism Model, Cognitive Model, Jigsaw Model.

Integrating large language models into EFL writing instruction: effects on performance, self-regulated learning strategies, and motivation

This study aimed to investigate the efficacy of utilizing large language models (LLMs) to enhance self-regulated learning (SRL) strategy instruction in English as a Foreign Language (EFL) writing. An LLM-supported Cognitive Academic Language Learning Model (CALLA-LLM) was developed and examined for its potential to improve elementary students’ EFL writing performance, SRL strategy use, and writing motivation. In a randomized controlled trial, 65 elementary school students were divided into an experimental group receiving CALLA-LLM instruction and a control group receiving traditional CALLA instruction. Both groups learned SRL strategies over 5 weeks, with data collected pre-intervention, post-intervention, and at a one-month follow-up. Results showed that the CALLA-LLM group made significant improvements in writing performance, SRL strategy use, and writing motivation, maintained most of the gains at follow-up, and significantly outperformed the control group. Findings provide empirical evidence for the efficacy of the CALLA-LLM model in enhancing EFL writing strategy instruction, lending support for integrating AI technologies such as LLMs into English language teaching. Moreover, the study underscores the importance of the “Humans in the Loop” approach, which emphasizes the essential role of human educators in AI-assisted language instruction.

A suicide attentional bias as implicit cognitive marker of suicide vulnerability in a high-risk sample.

Suicide risk assessment based on self-report questionnaires is considered as problematic because risk states are dynamic and at-risk individuals may conceal suicidal intentions for several reasons. Therefore, recent research efforts increasingly focus on implicit risk markers such as the suicide attentional bias (SAB) measured with the Suicide Stroop Task (SST). However, most SST studies failed to demonstrate a SAB in individuals with suicide risk and repeatedly demonstrated insufficient psychometrics of the SST. This study aimed to investigate a SAB using a modified SST (M-SST) and to test its psychometric properties. We compared n = 61 healthy controls and a high-risk inpatient sample of n = 40 suicide ideators and n = 40 suicide attempters regarding interference scores of positive, negative and suicide-related words. Interference scores were calculated by subtracting the mean reaction time (mean RT) of the neutral words from the mean RT of the suicide-related words (mean RT Suicide -mean RT Neutral), resulting in a suicide-specific interference score. Similarly, interference scores were calculated for the positive and negative words by subtracting the mean RT of neutral words from the mean RT of positive and negative words. A Group × Interference ANOVA showed a significant interaction effect (p <.001, ηp2 = .09), indicating that group effects significantly vary across interference type. Post hoc comparisons revealed that both ideators and attempters demonstrated greater interferences only for suicide-related words compared to healthy controls, indicating a SAB in patients, while a difference between ideators and attempters was lacking. The suicide interference score classified with an AUC = 0.73, 95% CI [0.65 - 0.82], p <.001, between controls and patients with STBs. The M-SST demonstrated good internal consistency and convergent validity. The study adds evidence to the assumptions of the Cognitive Model of Suicide, viewing a SAB as a cognitive marker of suicide vulnerability independently of the engagement in suicidal behavior. The results' clinical implications are discussed in the context of recommended intervention strategies during an acute suicidal state. Future studies with the M-SST should include non-suicidal patient controls to investigate whether a SAB is uniquely related to suicidality.

Research on the Working Path of Adolescent Psychological Health Education Under the Background of China&amp;apos;s New Education Reform

Adolescence is a critical period for rapid development in behavior, personality, and intelligence. Currently, the frequent occurrence of psychological crises among Chinese adolescents is detrimental to the comprehensive development of talents, the implementation of quality-oriented education, and the harmonious development of society. Based on multi-level questionnaire surveys and interview studies, this paper finds that China&amp;apos;s education sector is increasingly emphasizing psychological health education for adolescents, continuously improving supporting mechanisms. The initial system of psychological health services in schools has taken shape, with psychological health training benefiting both schools and families, and autonomous attention from adolescents groups has increased. However, issues persist, including inadequate psychological health curricula, incomplete linkage mechanisms, imperfect family support, insufficient teacher staffing and re-education training, and inadequate identification and rescue of students&amp;apos; psychological crises. Therefore, this paper argues that systematically and deeply promoting the multi-faceted psychological health education model of &amp;quot;family-school-society,&amp;quot; adopting multiple measures to collaboratively build and optimize coordination mechanisms, and establishing a more robust and effective approach to adolescent psychological health work, aligns with the direction of educational evaluation reform in the new era, fosters a favorable social atmosphere, and is the inevitable path to better promote the healthy growth and talent development of adolescents.

Students’ self-regulatory processes in content and language integrated learning: a vignette-based microanalytic study

ABSTRACT Despite the global adoption of Content and Language Integrated Learning (CLIL) and extensive research on the effectiveness (product-oriented) and classroom interactions (process-oriented) of CLIL, existing studies have overlooked the motivational beliefs and metacognitive strategies of CLIL students. Hence, there is a need for a participant-oriented approach to better understand students’ perspectives and enhance CLIL’s success. Drawing on the social cognitive model of self-regulated learning (SRL), this study aimed to explore how students plan, monitor, and evaluate their learning in CLIL contexts by employing a novel vignette-based microanalytic assessment approach to empirically capture students’ SRL processes. Participants were 108 junior secondary school students from three schools in Hong Kong. Data were analysed using structuring qualitative content analysis. Findings revealed that students employed diverse strategies to manage the demands of language and content learning, with variations in self-efficacy beliefs influencing their approaches. Furthermore, some learners faced challenges in maintaining effective self-regulation. This study argues that improving self-regulatory processes and enhancing CLIL’s success requires prioritising assessment, considering the state of ‘others’, and fostering deeper learning beyond CLIL. This study contributes to the empirical, methodological, and pedagogical understanding of students’ SRL in CLIL settings and informs future research on student-focused aspects in CLIL.

Education for deliberative democracy through the long-term view

This article argues that conceptualization through the long-term view strengthens the case for education for deliberative democracy. This is due to two key factors. First, education for deliberative democracy has novel potential in helping curb the negative effects of political polarization, which, when analyzed through longtermism, can be identified as an important existential risk factor. Second, education for deliberative democracy enables societies to defuse the threat of a value lock-in, and in doing so to keep their cognitive space open to enable increased flexibility in dealing with new challenges that will arise in the future. Consequently, this article further argues that education for deliberative democracy as an education initiative can be normatively justified but acknowledges that there are still theoretical and practical hurdles to overcome, and thus calls for more research into developing a mature, pedagogically sound program of education for deliberative democracy.

Developing a brief telematic cognitive behavioral therapy for the treatment of social isolation in young adults.

The present study aims to present a novel cognitive-behavioral intervention protocol focused on treating social isolation through telematic interaction, thus overcoming common barriers characteristic of face-to-face interventions. We examined current literature about face-to-face and telematic psychotherapeutic interventions for the treatment of social isolation in early adulthood. Current evidence is mixed, suggesting the need to develop novel interventions focused on patients' cognitive functioning. Moreover, telematic interventions are promising candidates for overcoming common barriers intrinsic to the condition of social isolation. The present 8-session model inspired by cognitive behavioral theoretical models and cognitive interventions currently present in the literature is thought to help socially isolated adult patients reduce clinical symptoms associated with the condition and lead to a reduction in the avoidance of social situations, leading to an improvement of the quality of life. We presented a telematic psychotherapeutic intervention aimed at helping adult patients suffering from social isolation who are unable to seek help from national health systems and face-to-face interventions, thus overcoming barriers intrinsic to social isolation. The present cognitive-behavioral treatment protocol has been developed in the context of a randomized clinical trial ongoing in Italy, aimed at implementing and testing the feasibility and effectiveness of multimodal digital interventions for treating social isolation.

Conceptual Metaphor “MEDIA AS A TRAP” in Vietnamese and English online Newspapers

The study explores the conceptual metaphor of "MEDIA AS A TRAP" in Vietnamese and English, aiming to understand perceptions of media in these linguistic contexts. Using the theory of conceptual metaphor in cognitive linguistics, alongside descriptive and semantic analysis methods, the research establishes and analyzes the mapping from the source domain of "TRAP" to the target domain of "MEDIA." By examining metaphorical expressions in press texts, the study uncovers underlying cognitive models, including media as trickery and media as a scam. Survey data reveals similar frequencies of metaphorical expressions in both languages, with 110 instances in Vietnamese and 90 in English, indicating a shared understanding of the metaphor. The analysis highlights significant parallels and distinctions in the conceptual metaphor "MEDIA IS A TRAP" between Vietnamese and English, underscoring its universal cognitive and linguistic significance. Both languages employ similar conceptual metaphorical expressions, yet exhibit subtle differences reflecting distinct cultural perspectives.

SDG: A global large-scale airport perception disparity cognition modeling method based on deep learning and geographic knowledge

Global airport perception levels vary due to natural geographical factors and economic development disparities. Understanding these differences is crucial for assessing regional airport development and its correlation with geographical patterns. However, there are limited methods available to effectively comprehend these disparities. To address this issue, this paper proposes a Salience, Disturbance, and Geographic-knowledge (SDG) approach for the cognitive analysis of global large-scale airport perception differences. Salience is assessed using a two-class deep learning model to evaluate the prominence of known airports. Disturbance is evaluated using an object detection model to measure background interference in large-scale airport perception. Geographic-knowledge analysis considers the correlation between regional airports and their surrounding geographic environment. The results rank perception difficulties for 17 regions worldwide, with Tajikistan exhibiting the highest difficulty at 0.922, while the Jiangsu–Zhejiang–Shanghai region in China has the lowest at 0.102. We also performed correlation analyses to validate the effectiveness of our model. To our knowledge, this paper pioneers the cognitive analysis of target perception difficulty differences across multiple global regions.