Representation Of Content Research Articles

Inter-individual and inter-site neural code conversion without shared stimuli.

Inter-individual variability in fine-grained functional topographies poses challenges for scalable data analysis and modeling. Functional alignment techniques can help mitigate these individual differences but they typically require paired brain data with the same stimuli between individuals, which are often unavailable. Here we present a neural code conversion method that overcomes this constraint by optimizing conversion parameters based on the discrepancy between the stimulus contents represented by original and converted brain activity patterns. This approach, combined with hierarchical features of deep neural networks as latent content representations, achieves conversion accuracies that are comparable with methods using shared stimuli. The converted brain activity from a source subject can be accurately decoded using the target's pre-trained decoders, producing high-quality visual image reconstructions that rival within-individual decoding, even with data across different sites and limited training samples. Our approach offers a promising framework for scalable neural data analysis and modeling and a foundation for brain-to-brain communication.

An Ontology of Representation

Representations are largely used in scientific domains and in everyday life alike. When discussed as such, representations are evaluated primarily for the information (correct or incorrect, precise or imprecise, detailed or general) that they provide. In philosophy, information is mainly discussed along with the notion of aboutness, and in application-oriented communities with notions such as data and knowledge. From the ontological viewpoint, this adds to representation shortcomings from both viewpoints. We argue that the theory of representation is articulated, and that it is possible to address some aspects, such as the form–content relationship, without taking position on others, such as the aboutness relationship. In this paper, a representation is a complex ontological entity formed by a representation form and a representation content bound by an encoding relationship. This ontological model of representation leads to correctly answer questions like: “Ontologically speaking, what is a novel?” (and similarly for a painting, a piece of music, etc.) The broadness and flexibility of the proposed model are tested by discussing a list of different cases: from music to procedures, from novels to paintings, and maps. The status of letters (characters) in natural language expressions, which turns out to be quite complex, is also briefly investigated.

Exploring the Enactment of Pedagogical Content Knowledge about Electrostatics Through Teacher Talk by Two Pre-Service Teachers

Abstract Pedagogical content knowledge (PCK) is considered by researchers to be the cornerstone of teacher effectiveness. Its enactment during teaching is even more important given its direct impact on learning. This paper reports findings of a case study that explored two pre-service teachers’ (PSTs) PCK about electrostatics and its enactment in practice through teacher talk. Data reflecting the PSTs’ PCK was collected using content representation (CoRe) tools and lesson planning forms whereas teacher talk was explored using classroom observations. The PCK of the PSTs was studied using the components of the construct described in the grand PCK rubric: curricular saliency, learners’ understanding of concepts, and conceptual teaching strategies including representations. Teacher talk was analysed using the dimensions of communicative approaches: dialogic-authoritative and interactive-non-interactive by looking into the quality of the PSTs’ questions and responses to learners’ contributions. The findings revealed that while the PSTs demonstrated generally adequate PCK, its enactment through teacher talk highlighted several areas in need of improvement. The results point to the importance of integrating a stronger focus on teacher talk in teacher education programs to enhance communicative strategies that effectively promote learning.

Specific features and uniqueness of the Kazan Cathedral paintings in Tambov in the context of the artistic and historical development of church painting in the grisaille technique

This article examines the paintings of the Kazan Cathedral in Tambov, which is part of the temple complex of the Kazan Mother of God Monastery of the Tambov Diocese, in the context of the artistic and historical development of church painting made in the grisaille technique. The relevance of the study is due to the fact that there are many myths and legends around the grisaille paintings in the Kazan Monastery, actively spread by tour guides among tourist groups. Our goal is to dispel these stereotypes and provide reliable information about the paintings of the Kazan Monastery Cathedral. In the course of the work, the author determines the features and highlights the unique characteristics of the painting of the Kazan Cathedral in Tambov among the general structure of church art works made in the grisaille technique. The representation of the semantic content of the ornaments and gospel stories presented in the interior painting of the Kazan Cathedral is made using semiotic-hermeneutic and interpretive research methods. Using comparative and formal-stylistic analysis, the author seeks to expand and organize knowledge about the grisaille technique, uses reference comparative material, draws analogies, and thus presents a detailed overview of grisaille samples that can be found in Russia. The author summarizes that despite the fact that the paintings of the Kazan Cathedral of the Tambov Monastery are not defining works of Russian art, they are still of interest to the national culture due to their harmony, philosophical depth, and timeless focus on revealing the doctrinal truths of Orthodoxy. All of the above makes them significant for study by theologians, historians, art historians, and local historians.

A KeyBERT-Enhanced Pipeline for Electronic Information Curriculum Knowledge Graphs: Design, Evaluation, and Ontology Alignment

This paper proposes a KeyBERT-based method for constructing a knowledge graph of the electronic information curriculum system, aiming to enhance the structured representation and relational analysis of educational content. Electronic Information Engineering curricula encompass diverse and rapidly evolving topics; however, existing knowledge graphs often overlook multi-word concepts and more nuanced semantic relationships. To address this gap, this paper presents a KeyBERT-enhanced method for constructing a knowledge graph of the electronic information curriculum system. Utilizing teaching plans, syllabi, and approximately 500,000 words of course materials from 17 courses, we first extracted 500 knowledge points via the Term Frequency–Inverse Document Frequency (TF-IDF) algorithm to build a baseline course–knowledge matrix and visualize the preliminary graph using Graph Convolutional Networks (GCN) and Neo4j. We then applied KeyBERT to extract about 1000 knowledge points—approximately 65% of extracted terms were multi-word phrases—and augment the graph with co-occurrence and semantic-similarity edges. Comparative experiments demonstrate a ~20% increase in non-zero matrix coverage and a ~40% boost in edge count (from 5100 to 7100), significantly enhancing graph connectivity. Moreover, we performed sensitivity analysis on extraction thresholds (co-occurrence ≥ 5, similarity ≥ 0.7), revealing that (5, 0.7) maximizes the F1-score at 0.83. Hyperparameter ablation over n-gram ranges [(1,1),(1,2),(1,3)] and top_n [5, 10, 15] identifies (1,3) + top_n = 10 as optimal (Precision = 0.86, Recall = 0.81, F1 = 0.83). Finally, GCN downstream tests show that, despite higher sparsity (KeyBERT 64% vs. TF-IDF 40%), KeyBERT features achieve Accuracy = 0.78 and F1 = 0.75, outperforming TF-IDF’s 0.66/0.69. This approach offers a novel, rigorously evaluated solution for optimizing the electronic information curriculum system and can be extended through terminology standardization or larger data integration.

AI-Driven Content Curation and Its Impact on Media Diversity in Social Networks

The rapid advancement of generative artificial intelligence (AI) has made AI-driven content curation a dominant force in shaping public discourse on social media. Platforms, including Facebook, YouTube, and TikTok employ recommendation algorithms to personalise content and increase user engagement. However, these systems also intensify concerns over media pluralism, algorithmic bias, and misinformation. By prioritising user preferences, they reinforce filter bubbles and restrict exposure to diverse viewpoints. As a result, democratic dialogue weakens, and public opinion formation becomes distorted. This study examines how AI-assisted content curation affects media diversity in European social networks, focusing on platform accountability and regulatory challenges. Special attention is given to recent policy interventions, including DSA (2022), Germany’s Action Plan (2024), and the EU AI Act (2025) initiatives on AI-generated political content. However, they also expose significant gaps in enforcement and oversight. To evaluate regulatory impact, this study analyses platform policies, legal frameworks, and AI content selection mechanisms. Despite transparency being a main objective, findings reveal current regulations unable to reduce algorithmic bias or achieve balanced content representation. In response, the study advocates for improved explainable AI (XAI) models, demands stronger regulatory oversight, and supports increased user control over content selection. By addressing these shortcomings, this research contributes to the wider debate on AI ethics, media governance, and digital policy in Europe.

The View From Below: Low‐Status Youth Representations of the Political in Switzerland

ABSTRACTThis study investigates political (dis)engagement of low‐status youth by analysing their social representations of the political. Through focus group discussions, we explore how low‐status positions of young people shape their relationship with the political system and its actors. Seventeen focus group discussions involving 99 participants, aged 15–28 years, were conducted in vocational schools in Switzerland. Using a two‐step analytical framework grounded in social representations theory, we examine both representational content and its negotiation in communication. Representations of societal power differentials played a central role in how participants made sense of their disengagement. Participants often depicted the political system as distant, abstract, and beyond their influence. This externalisation had two main implications: a sense of powerlessness, where participation seemed futile; and a moral antagonism between ‘us’, the hard‐working, honest, ordinary people, and ‘them’, the self‐interested, disconnected elite. These findings show that political disengagement is not only the result of apathy, but a socially embedded, identity‐protective response to structural and symbolic exclusion. The study offers a critical account of youth political behaviour, showing how disengagement is shaped by power, identity, and group positioning – and how it can express critique rather than indifference.

Лексикон молодежной субкультуры в лингвокультурологическом словарном описании (на материале интернет-СМИ для геймеров)

The article presents a concept of linguocultural lexicographic description of the lexicon of modern youth subcultures. This type of vocabulary has been studied in linguocultural terms but has not received a complete and consistent lexicographic description. The purpose of the research was to develop a model of linguocultural representation of subcultural vocabulary based on the material of gaming culture. The texts of online media and gamers’ communication were used as sources. The development of parametric models of dictionary entries representing the lexicon and phraseology of gamers was preceded by a structural-semantic, etymological and linguocultural analysis of the material. The results of the analysis are reflected in dictionary entries illustrating the author’s concept. The study identified thematic groups of gamers’ lexicon: names of games, genres, game items, gameplay, etc. The ways of enriching this vocabulary were studied: borrowing and various methods of loanword adaptation in the Russian-speaking environment, as well as affixation and abbreviation. The contexts of use of subcultural lexical and phraseological units were analysed from the linguocultural and linguo-axiological points of view. Using dictionary entries by the author as examples, the paper demonstrates the comprehensive representation of the subcultural content of such units: the interpretation of the headword is culturally oriented; the etymological description provides its origin; the linguocultural commentary presents a variety of details of the gameplay related to the referent; the illustrative contexts expand our knowledge of the object of naming and convey the speaker’s attitude towards it. The proposed model of lexicographic description will allow us to present the youth subculture as a modern linguocultural phenomenon. The results of the research can be used in lexicographic practice and in studying the dynamics of young people’s speech.

DBDST-Net: Dual-Branch Decoupled Image Style Transfer Network

The image style transfer task aims to apply the style characteristics of a reference image to a content image, generating a new stylized result. While many existing methods focus on designing feature transfer modules and have achieved promising results, they often overlook the entanglement between content and style features after transfer, making effective separation challenging. To address this issue, we propose a Dual-Branch Decoupled Image Style Transfer Network (DBDST-Net) to better disentangle content and style representations. The network consists of two branches: a Content Feature Decoupling Branch, which captures fine-grained content structures for more precise content separation, and a Style Feature Decoupling Branch, which enhances sensitivity to style-specific attributes. To further improve the decoupling performance, we introduce a dense-regressive loss that minimizes the discrepancy between the original content image and the content reconstructed from the stylized output, thereby promoting the independence of content and style features while enhancing image quality. Additionally, to mitigate the limited availability of style data, we employ the Stable Diffusion model to generate stylized samples for data augmentation. Extensive experiments demonstrate that our method achieves a better balance between content preservation and style rendering compared to existing approaches.

In silico discovery of representational relationships across visual cortex.

Human vision is mediated by a complex interconnected network of cortical brain areas that jointly represent visual information. Although these areas are increasingly understood in isolation, their representational relationships remain unclear. Here we developed relational neural control and used it to investigate the representational relationships for univariate and multivariate functional magnetic resonance imaging (fMRI) responses of areas across the visual cortex. Through relational neural control, we generated and explored in silico fMRI responses for large numbers of images, discovering controlling images that align or disentangle responses across areas, thus indicating their shared or unique representational content. This revealed a typical network-level configuration of representational relationships in which shared or unique representational content varied on the basis of cortical distance, categorical selectivity and position within the visual hierarchy. Closing the empirical cycle, we validated the in silico discoveries on in vivo fMRI responses from independent participants. Together, this reveals how visual areas jointly represent the world as an interconnected network.

Artificial intelligence, education and digital inclusion

Introduction: The rapid development of human scientific endeavor has enabled the implementation of increasingly automated systems that facilitate certain functions and processes in all spheres of life. Objective: To describe the application of artificial intelligence in education as a strategy to ensure digital inclusion. Method: A comprehensive literature review was conducted. The search was conducted in: SCielo, PubMed, and SCOPUS, repositories, and the Google Scholar search engine. The search strategy consisted of descriptors. 32 papers were used to develop this article. Development: AI provides significant tools, from the graphic representation of content in educational contexts to the creation of conceptual maps and the development of tests to validate acquired knowledge. However, its use must be urgent without losing the guiding and methodological thread, always recognizing that the goal is the acquisition of knowledge. Universities require projection at different scales to present their results for the sake of sociocultural and academic scientific development. With the implementation of AI, this outreach function can be fulfilled, guaranteeing direct and personalized access and promoting digital inclusion. Conclusions: The use of AI in educational subjects offers new platforms and work scenarios. Each of its capabilities adapts to the specifics of the educational environment, while also enhancing easy, affordable, and universal access to educational content.

Influence of symbolic content on recommendation bias: analyzing YouTube’s algorithm during Taiwan’s 2024 election

This study investigates the role of symbolic content, including social, cultural, and political imagery, in shaping algorithmic biases within YouTube’s recommendation system, using the 2024 Taiwanese presidential election as a case study. Leveraging classification methodology and a dataset of 15,600 videos collected via a rigorous multiphase keyword expansion, our research employs a novel combination of social network analysis, statistical metrics, and generative AI-based content evaluation to examine the propagation dynamics, community formation, and topic relevance of both symbolic and non-symbolic content. Our analysis reveals a dual dynamic: symbolic content fosters tightly integrated, cohesive communities characterized by strong thematic consistency and deeper topic relevance, yet exhibits limited network-wide visibility, while non-symbolic content achieves broader connectivity by often serving as crucial bridges within recommendation networks. Building on prior research documenting the influential role of symbols in political mobilization and online misinformation, we further assess how symbolic imagery interacts with algorithmic recommendation processes. Our findings underscore that algorithmic biases may inadvertently reinforce echo chambers and limit content diversity, highlighting the need for recommendation systems that balance content relevance with community-specific thematic coherence. These insights offer valuable guidance for policymakers, platform designers, and content creators striving for equitable content representation in the digital era.

The cognitive role of concept variability

I present and defend concept variability, the view that concepts can admit of indefinitely many variations and changes in their representational contents without thereby losing their identity. I argue that the variability of concepts is central to their role in enabling cognition, and thus that a concept's content variability is, despite philosophical orthodoxy to the contrary, a feature of our cognitive architecture and not a bug.

Conversational content is organized across multiple timescales in the brain.

The evolution of conversation facilitates the exchange of intricate thoughts and emotions. The meaning is progressively constructed by integrating both produced and perceived speech into hierarchical linguistic structures across multiple timescales, including words, sentences and discourse. However, the neural mechanisms underlying these interactive sense-making processes remain largely unknown. Here we used functional magnetic resonance imaging to measure brain activity during hours of spontaneous conversations, modelling neural representations of conversational content using contextual embeddings derived from a large language model (GPT) at varying timescales. Our results reveal that linguistic representations are both shared and distinct between production and comprehension, distributed across various functional networks. Shared representations, predominantly localized within language-selective regions, were consistently observed at shorter timescales, corresponding to words and single sentences. By contrast, modality-specific representations exhibited opposing timescale selectivity: shorter for production and longer for comprehension, suggesting that distinct mechanisms are involved in contextual integration. These findings suggest that conversational meaning emerges from the interplay between shared linguistic codes and modality-specific temporal integration, facilitating context-dependent comprehension and adaptive speech production.

A COMPARATIVE REVIEW OF ENGLISH TEXTBOOKS FOR TENTH-GRADE STUDENTS ‘BAHASA INGGRIS: WORK IN PROGRESS’ AND ‘ENGLISH IN MIND BOOK 3’

This study evaluates English textbooks used in Indonesian high schools to determine their alignment with curriculum standards and their effectiveness in supporting language acquisition. Using document analysis, textbooks were assessed based on key criteria, including aims and approaches, design and organization, language content, skills development, topics and cultural representation, and practical considerations. A Likert-scale rating (1–4) was applied to quantify evaluation results. The findings highlight strengths and weaknesses in locally produced textbooks compared to internationally published materials, emphasizing the need for improvements in cultural inclusivity, skills integration, and real-life applicability. The results provide valuable insights for educators, curriculum developers, and policymakers in selecting and enhancing English learning resources. Additionally, the study serves as a reference for future research on textbook evaluation, particularly in adapting global materials to local educational contexts. By identifying gaps and providing recommendations, this research contributes to the continuous development of high-quality textbooks that align with national and international standards, ultimately improving English language education in Indonesia.

Analysis of Content Related to Retrograde Cricopharyngeal Dysfunction on TikTok: Opportunities for Patient Education and Advocacy.

TikTok has experienced exponential growth as a social media platform, with over 1 billion active users. Concurrently, retrograde cricopharyngeal dysfunction (RCPD) has seen a surge in awareness among patients and on social media. Our objective was to characterize the most popular RCPD-related content on TikTok. The top 50 videos associated with the following hashtags were analyzed: "RCPD," "noburp," "retrograde cricopharyngeal dysfunction," and "noburpsyndrome." Recorded metrics included views, likes, and comments, along with information regarding the creator's identity, gender, geographical location, and the video's purpose. One hundred and three videos were included in the final analysis, with a cumulative total of 32 284 962 views, 3 185 271 likes, and 54 664 comments. Over 90% of these videos were created by patients or the general public, with less than 5% attributed to physicians. The primary purpose of these videos varied, with 46.60% aimed at educating viewers about RCPD, 23.30% serving as post-treatment testimonies, and 14.56% demonstrating symptoms. TikTok is a highly popular platform for RCPD-related content. Patient education, treatment testimonies, and symptom demonstration were the most common primary purposes of these videos. Considering the limited representation of physician-generated content, this social media landscape represents an opportunity for otolaryngologists to leverage TikTok for educational outreach and patient advocacy.

Measuring representational competence – analyses of dimensionality and the relationship to general content knowledge

ABSTRACT In chemistry, representational competence is crucial for effective problem-solving and learning. However, the relationship between representational competence and chemical content knowledge, continues to be a matter of discussion. To investigate the connection between representational competence and chemical content knowledge, we developed a new assessment tool known as the Chemical Representation Inventory: Translation, Interpretation, Construction (CRI:TIC), utilizing three lower-level representational skills identified by Kozma and Russell (1997, 2005). This article presents the evaluation of the CRI:TIC and discusses its implications for research in chemistry education and for educators. The instrument was administered to 185 first-year students in a preparatory university chemistry course. Utilizing multidimensional Rasch analysis, we examined the dimensionality of the lower-level representational skills. Based on the results, we suggest that, from a psychometric perspective, lower-level skills should be regarded as a unified construct. Nonetheless, from an educational standpoint, maintaining a conceptual distinction between these skills remains relevant. Further analysis linking the CRI:TIC data to results from a chemical content knowledge assessment indicates that representational competence and content knowledge should be treated as distinct constructs. Although we cannot draw causal conclusions regarding the relationship between these two constructs, our findings underscore the importance of developing students’ representational skills.

Representationalism and the Spatial Representational Contents of Afterimage Experiences

ABSTRACTExperiences of afterimages have often been cited as counterexamples to representationalism about vision, that is, as counterexamples to the thesis that the phenomenal character of a visual experience is completely determined by its representational content. In this paper, I discuss a possible counterexample to representationalism that is based on the phenomenological observation made, for example, by Ned Block, that at least some afterimages do not look real. After first clarifying what a possible counterexample based on this observation would have to look like, I formulate and defend a representationalist response.

The Planning and Implementation of Reasoning Instruction: Science Teachers’ Case

The study aims to investigate the planning and implementation differences of learning strategies that stimulate pupils’ reasoning skills on the topic of environmental pollution and also the reasons. This was a type of qualitative case study research involving five science teachers selected by purposive sampling technique. The instruments were content representation (CoRe) questions, lesson plan analysis formats, observation sheets, and unstructured interviews. The data were triangulated to get findings in detail. The results of the analysis showed that teachers had pedagogic content knowledge (PCK) about teaching reasoning skills. Unfortunately, it did not appear much in the lesson plan even in the implementation. There were differences between planning and implementation. The investigation through interviews showed that it was caused by the use of innovative teaching models that were less appropriate to pupils’ conditions and curriculum content in Indonesia. Teachers could not carry out reasoning instruction ideally. If the strategy was enforced, reasoning instruction would be not effective and it would take a long time. Teachers might have good PCK about reasoning skill development but they have to ensure that chosen learning strategies are appropriate to pupils’ condition and the curriculum content of Indonesia. Teachers have to adjust and even create reasoning instruction based on the condition of pupils and curriculum in Indonesia to achieve the learning goals well.

On a new content indeterminacy problem in neuroscience

ABSTRACT Whether neurons represent or play a mere causal role is a foundational issue in philosophy of neuroscience. Evidence that neurons perform a representational role is weakened by the possibility of explaining experimental results by appeal to brute causal processes alone. Despite this, neuroscientists ascribe representational content to patterns of neural activity to explain experimental results. An important problem with this practice is determining which content to ascribe to the neural representation. One view is that researchers are only warranted in ascribing the content determined by particular experimental results. An alternative view is that researchers are warranted in appealing to the broader research domain to determine the content of a putative neural representation. In this paper, I argue that both are warranted; either alone is insufficient. Using optogenetics research on memory engrams as a case study, I show how researchers ascribe content to neural representations and justify their approach. Whether a particular content ascription is warranted depends on particular experimental results, the broader research domain that is appealed to, and how results from various animal models, probes, and experimental paradigms are generalized.