Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
16504 papers
Abstract This study examines the use of third-person singular inflections -th and -s in Early Modern English sermons, adopting a micro-sociolinguistic approach. Focusing on linguistic data from the Corpus of Sermons in Early Modern English (CoSEME), it aims to explain how and why the -th form was retained longer in religious language despite the broader linguistic shift to the -s form. The paper employs third-wave sociolinguistic theories and analyses the data within two frameworks: discourse community and community of practice. By investigating sermons from three generations of preachers, the study identifies a notable divergence in the use of -th forms, linking these patterns to religious identity and practice. The findings reveal that -th forms gained social meaning as markers of solemnity and authenticity within the religious domain, particularly among certain religious communities. The analysis highlights a complex interplay between language, community, and identity, demonstrating that the use of -th forms was influenced by sociopolitical factors, individual agency, and communal linguistic norms. The paper contributes to understanding the role of social meaning in historical language variation and change, offering insights into how specific linguistic features may be maintained within genres to project identity and align with community values. It also assesses the utility of sociolinguistic community frameworks for analysing the social significance of linguistic variation in historical contexts.
As language-based AI systems become more anthropomorphic, the question of whether they can have subjective experience is increasingly pressing. I focus here on the tractability of research questions in the space of AI consciousness. I argue that the fundamental problem of whether AI systems can be conscious is currently intractable in its direct form, given the absence of a universally accepted scientific theory of consciousness, as well as the historical open-endedness of the philosophical mind-body problem. In contrast, questions around the adjacent subject of perceived AI consciousness are tractable, timely, and highly consequential for society. The general public is increasingly open to the possibility of consciousness in AI systems and routinely adopts the vocabulary of human cognition and subjective experience to describe them. This phenomenon is already driving societal shifts across user experience, ethical standards, and linguistic norms. I therefore propose an increased research focus on uncovering the causes and effects of perceived AI consciousness, which ultimately shape how we see our own human subjective experience relative to artificial entities. To support this, I map the current landscape of AI consciousness perception and discuss its key potential drivers and societal consequences. Finally, I urge developers, decision-makers, and the broader scientific community to commit to clear and accurate communication regarding the topic of AI consciousness, explicitly acknowledging its inherent uncertainties.
We propose Kan Extension Transformers (KETs) as a unifying categorical framework for a diverse group of Transformer implementations. The core claim is that a Transformer layer can be viewed as a weighted structured extension operator: standard attention is the singleton-neighborhood case, Geometric Transformer style incidence mixing is a sparse edge-restricted case, and KET is the higher-order simplicial case. This lens also clarifies a bridge to diffusion-style completion. When the extension operator acts on detached predictive carriers instead of teacher-forced hidden states, it becomes a valid self-conditioning mechanism that exposes noncausal structure without leaking gold future tokens. We include a comprehensive experimental validation of 12 different Transformer implementations varying across strict-causal and predict-detach regimes on Penn Treebank, WikiText-2, and WikiText-103. In the strict-causal setting, quadratic KET is the strongest model among the compared causal architectures on WikiText-2 and WikiText-103. Across all datasets, however, the largest gains come from the predict-detach regime rather than from changing the neighborhood family alone.
Do vision--language models (VLMs) develop more human-like sensitivity to linguistic concreteness than text-only large language models (LLMs) when both are evaluated with text-only prompts? We study this question with a controlled comparison between matched Llama text backbones and their Llama Vision counterparts across multiple model scales, treating multimodal pretraining as an ablation on perceptual grounding rather than access to images at inference. We measure concreteness effects at three complementary levels: (i) output behavior, by relating question-level concreteness to QA accuracy; (ii) embedding geometry, by testing whether representations organize along a concreteness axis; and (iii) attention dynamics, by quantifying context reliance via attention-entropy measures. In addition, we elicit token-level concreteness ratings from models and evaluate alignment to human norm distributions, testing whether multimodal training yields more human-consistent judgments. Across benchmarks and scales, VLMs show larger gains on more concrete inputs, exhibit clearer concreteness-structured representations, produce ratings that better match human norms, and display systematically different attention patterns consistent with increased grounding.
Large Language Models (LLMs) are increasingly used as research tools to facilitate the fast and automated extraction of text features. In psychological studies, they have been used to quantify the degree to which verbal stimulus materials reflect certain psychological constructs. However, the application of LLMs entails a high degree of flexibility regarding prompt design (e.g., instruction details and examples) and model specification (e.g., model family, size, and configuration), which can produce divergent results and threaten the robustness and generalizability of conclusions. To navigate the multiverse of possible choices, we develop a structured workflow for evaluating the quality of LLM feature extraction across diverse model and prompt specifications. Motivated by generalizability theory, the workflow distinguishes between construct variance across stimulus items, method variance due to model and prompt choices, and error variance across repeated iterations. To guide researchers through the planning, execution, and reporting of LLM simulation studies, we introduce an adapted version of the ADEMP template (Aims, Data-generating mechanism, Estimands and targets, Methods, Performance measures), originally developed for methodological simulation research. The template supports two complementary validation strategies: variance decomposition for studying consistency across LLM specifications and external validation against human gold-standard ratings. In a pre-registered case study using locally runnable, open-weight LLMs, we illustrate the workflow by examining the influence of model choice, response format, and prompt examples on the quality of valence and arousal ratings for multi-word expressions. We additionally assess the efficacy of aggregating repeated, stochastic LLM ratings to improve feature extraction quality.
Social cognition impairment is a frequent non-motor feature of Parkinson's disease. While dopaminergic therapy modulates motor symptoms, its effects on social cognition remain incompletely understood. We investigated the effects of acute levodopa administration on cognitive and affective Theory of Mind, as well as on emotional resonance to dynamic whole-body social interactions, in 36 people with Parkinson's disease with motor fluctuations and 14 matched healthy controls. Social cognition was assessed using the Mini-Social Cognition and Emotional Assessment (Mini-SEA) and a point-light display task indexing emotional resonance through emotional valence ratings. Patients were evaluated in OFF and ON states during an acute dopaminergic challenge performed according to the CAPSIT-PD protocol, with responsiveness defined as an improvement greater than 50% on the MDS-UPDRS part III. Compared with healthy controls, patients showed impaired cognitive Theory of Mind performance, particularly on the faux pas subtest (p = 0.0001), while affective Theory of Mind based on facial emotion recognition was preserved. Acute levodopa did not improve cognitive or affective Theory of Mind (faux pas OFF vs ON, p = 0.7049). In contrast, emotional resonance was impaired in the OFF state and selectively improved in the ON state, with increased ratings of positive (p = 0.0035) and negative (p = 0.0387) emotional valence. These findings demonstrate a dissociation between Theory of Mind and emotional resonance in Parkinson's disease and show that acute levodopa selectively modulates emotional resonance without restoring Theory of Mind abilities.
The Arabic neologism Aranjiyya (عَرَنْجِيَّة) is a portmanteau of ʿArabiyya (Arabic) and Inkliziyya / Faranjiyya (English/foreign), used by contemporary Arabic editors and stylists to describe Arabic prose that retains Arabic vocabulary while importing the syntactic, stylistic, semantic, or lexical structures of English. The phenomenon is pervasive in translated news, press releases, technical writing, and digital media, and is a recurrent target of prescriptive Arabic-style guides. Despite its prominence, Aranjiyya has had almost no presence in computational Arabic resources: existing treebanks and error corpora target orthographic, morphological, or syntactic well-formedness but do not isolate contact-induced patterns whose surface forms are grammatical but whose underlying templates are English. AranjiyyaCorpus was constructed to fill this gap, with three motivating use cases: training a span-level Aranjiyya detector for editors and translation post-editors; producing evaluation data for whether large language models actually generate idiomatic Arabic; and supporting linguistic study of contact-induced change in modern Arabic, with sufficient category granularity to distinguish syntactic, stylistic, and semantic phenomena and to track them across genres.
BACKGROUND: Atypical reward responsiveness is crucial for the onset and maintenance of non-suicidal self-injury (NSSI). There is no consensus whether NSSI is associated with increased or decreased reward responsiveness, due to different reward modalities, stages of reward processing examined, and the confounding effects of psychiatric comorbidities. This study aims to investigate reward responsiveness across different reward modalities—monetary and emotional—as well as distinct phases of reward processing, namely anticipatory and consummatory stages, among adolescents with NSSI. METHODS: The monetary and affective incentive delay (MID & AID) tasks were utilized to compare anticipatory and consummatory emotions as indicated by self-reported valence ratings between adolescents with NSSI (n = 103) and controls (n = 94) via ANOVA. The NSSI group was further separated into the NSSI + Suicidal Attempt (SA) and NSSI-only subgroups to examine whether NSSI co-occurring with suicidal behaviors resulted in more severe reward abnormalities. Sensitivity analyses using ANCOVA and hierarchical regression controlled for anhedonia symptoms to test the specificity of associations with NSSI. RESULTS: Adolescents with NSSI reported lower positive emotions when anticipating rewards, and experienced less pleasure after winning rewards or avoiding punishment. The NSSI + SA subgroup, compared with NSSI-only adolescents, had less consummatory pleasure in the MID task. However, blunted reward responsiveness in NSSI was no longer significant after controlling for anhedonia symptoms. CONCLUSIONS: NSSI is correlated with blunted reward responsiveness, primarily confounded by depressive symptoms (especially anhedonia). Co-occurring NSSI and SA is a more severe form of self-harm.
Weight decay is widely used as a regularizer in large language models, yet its precise role in shaping Transformer loss landscapes remains theoretically underexplored. This paper provides the first rigorous functional-analytic characterization of the standard Transformer objective--cross-entropy loss with $L^2$ regularization--by proving it satisfies Villani's criteria for coercive energy functions. Specifically, we show that the regularized loss $\mathcal{F}$ is infinitely differentiable, grows at least quadratically, has Gaussian-integrable tails, and satisfies the differential growth condition $-Δ\mathcal{F} + \tfrac{1}{s}\|\nabla\mathcal{F}\|^{2} \to \infty$ as $\|θ\| \to \infty$ for all $s>0$. From this structure, we derive explicit log-Sobolev and Poincaré constants $C_{\mathrm{LS}} \leq λ^{-1} + d/λ^{2}$, linking the regularization strength $λ$ and model dimension $d$ to finite-time convergence guarantees for noisy stochastic gradient descent and PAC-Bayesian generalization bounds that tighten with increasing $λ$. To validate our theory, we introduce a scalable Villani diagnostic $Ψ_s(θ) = -Δ\mathcal{F} + s^{-1}\|\nabla \mathcal{F}\|^2$ and estimate it efficiently using Hutchinson trace probes in models with over 100M parameters. Experiments on GPT-Neo-125M across Penn Treebank and WikiText-103 confirm the predicted quadratic growth of $Ψ_s$, spectral inflation of the Hessian, and exponential convergence behavior consistent with our log-Sobolev analysis. These results demonstrate that weight decay not only improves generalization empirically but also establishes the mathematical conditions required for fast Langevin mixing and theoretically grounded curvature-aware optimization in deep learning.
Chinese word segmentation is especially fragile in non-standard text, where language learner errors and other character-level divergences disrupt the word boundaries assumed by downstream annotation and evaluation. This paper formulates Chinese word boundary recovery as an alignment-based projection task. Given a noisy source sentence and a cleaner target counterpart, we first align the two strings at the character level and then project target-side word boundaries back onto the source. Beyond the recovery method itself, we introduce two evaluation resources: a manually checked learner Chinese benchmark based on MuCGEC and a controlled synthetic benchmark derived from the Chinese Penn Treebank. Experiments show that direct segmentation remains vulnerable to compound fragmentation in learner input, whereas the proposed two step projection method corrects many over-segmentation errors by using the corrected target to recover source-side word spans. The results show that word boundary recovery is distinct from ordinary segmentation and that alignment projection provides a principled mechanism for stabilizing Chinese annotation and evaluation under noisy input.
Introduction Aging is associated with reduced accuracy in recognizing others’ emotions, an ability that is important for maintaining social connectedness in later life. Laughter is a social signal with multiple functions, as it can facilitate social bonding but also convey negative social meanings, for example when directed at someone. In previous research we have shown that younger adults are able to classify spontaneously emitted joyful, schadenfreude, and tickling laughter above chance level, and that these laughter sounds differ according to the perceived dominance. Given evidence that affect recognition generally declines with age, the present study examined whether comparable age effects emerge in the perception of laughter. Methods 64 younger adults (mean 25 years, 18–33 years) and 30 older adults (mean age 60 years, 50–77 years) evaluated 117 spontaneously emitted laughter sounds according to the laughter type, i.e., joyful, Schadenfreude, and tickling laughter and according to the perceived sender’s dominance. Results Results showed that both age groups classified laughter above chance level. Younger adults showed higher classification rates than older adults for all laughter types, with the largest age effect for Schadenfreude laughter. The dominance ratings showed an age effect only for Schadenfreude, where older adults rated Schadenfreude laughter less dominant than younger adults. Discussion Pronounced differences in Schadenfreude perception might be ascribed to difficulties of older adults in perceiving non-literal messages or to cultural differences between age groups.
Abstract The linguistic study of the divine names in votive inscriptions has recently attracted increasing interest. In this paper, the author discusses phonetic changes in Latin names and epithets of gods using data from the Computerized Historical Linguistic Database of Latin Inscriptions of the Imperial Age. The vowel and consonant changes in votive inscriptions across the Roman Empire are in the focus and certain Vulgar Latin features and cultural influences can be identified from the corpus. The study focuses on common phonetic phenomena, such as vowel and consonant changes, monophthongization, gemination, etc. The epigraphic corpus shows various Vulgar Latin features in theonyms and epithets, which are considered linguistic, regional, and cultural factors that influenced these changes, including Celtic, Greek, and Brittonic influences. The research concludes that the observed phonetic variations reflect the dynamics of the development of Latin as well as language contact phenomena affecting it.
Background: Word identification in noise is crucial for effective communication in everyday environments. For children, the ability to identify words in noisy conditions directly impacts language development, learning, and social interaction. This study aimed to develop and standardize Hindi word identification in noise test for children (HWINT-C) and evaluate its performance among school aged typically developing children across varying SNR and word length. Methods: The study included forty two participants which were further subdivided into subgroup one consisting of 22 typically developing children aged 6-7.11 years (SGI) and 20 typically developing children aged 8-10 years in subgroup two (SGII). Development of Hindi word identification in noise test for children (HWINT-C) involved multi-step processes including selection of words, familiarity rating, and content validation, internal consistency and test-retest reliability. The test included bisyllabic and monosyllabic words recorded by a native Hindi female speaker presented in eight-talker babble at +5 dB and +7 dB SNR administered dioticallyat 65 dB SPL. Results: The developed HWINT-C in this study demonstrated to have high internal consistency and test-retest reliability. Typically developing children in the older group (SGII) significantly outperformed the younger group (SGI), Additionally performance improved with increasing signal-to-noise ratio (SNR) in both SGI and SGII, but no significant differences were found across word lengths. Conclusions: The HWINT-C test is a reliable and valid tool for assessing word-in-noise perception in children. Age-related trend was observed, where performance improved with age, Similar findings were observed with increase in SNR, emphasizing need of favorable conditions in younger population.
в статье представлено исследование, посвящённое сопоставлению подходов к обучению иностранному языку студентов направления «Зарубежное регионоведение». Предмет анализа связан не просто с овладением языковой нормой иностранной речи, а с формированием такой модели речевой подготовки, при которой студент способен соотносить высказывание с конкретным регионом, его политико-культурной спецификой, медийной повесткой и типичными коммуникативными сценариями. С помощью методов анализа, синтеза, наблюдения и описания произведено рассмотрение актуальных на данном этапе развития высшего образования способов формирования иноязычной региональной компетенции. С позиции компетентностного и деятельностного подходов обучение рассматривается как движение от языковой операции к регионально маркированному высказыванию, которое строится в ситуации обсуждения, аргументации, интерпретации и переговоров. Сложный характер данной компетенции требует использования в процессе преподавания разнообразных методов активного и интерактивного обучения, инновационных образовательных технологий, форм и средств обучения, тесно связанных с будущей профессиональной деятельностью студентов направления подготовки «Зарубежное регионоведение». this article presents a study comparing approaches to foreign language instruction for students majoring in “Foreign Regional Studies”. The subject of analysis is not merely the mastery of the linguistic norms of Chinese speech, but the development of a model of language training in which students are able to relate a statement to a specific region, its political and cultural characteristics, media agenda, and typical communicative scenarios. Using methods of analysis, synthesis, observation, and description, this study examines the methods of developing foreign language regional competence that are relevant at this stage of higher education development. From the perspective of competence-based and activity-based approaches, teaching is viewed as a progression from linguistic operations to region-specific utterances, which are constructed in situations of discussion, argumentation, interpretation, and negotiation. The complex nature of this competence requires the use in the teaching process of a variety of active and interactive teaching methods, innovative educational technologies, and forms and means of instruction closely linked to the future professional activities of students training program "Foreign Regional Studies".
This article scientifically analyzes the development trends of linguistics and contemporary linguistic problems in the context of digital transformation. The study examines the impact of artificial intelligence, corpus linguistics, natural language processing technologies, digital communication, and globalization on language development. Particular attention is paid to the role of the Uzbek language in the digital environment, terminology issues, language policy, linguistic identity, and transformations in digital education. The article argues that modern technologies not only expand the functional capabilities of language but also generate challenges related to linguistic norms, preservation of national languages, and cultural identity. The research is based on international scientific literature, statistical data, and нормативe legal documents.
We show that emotion vectors in LLMs are organized by a two-dimensional valence-arousal (VA) subspace exhibiting circular geometry. Through principal component decomposition and ridge regression, we recover meaningful VA axes underlying emotion steering vectors whose projections correlate with human affect ratings across 44,728 words. Steering along these axes produces monotonic control over the affective properties of generated text, and further affords bidirectional control over multiple downstream behaviors (refusal and sycophancy) from a single subspace. These effects replicate across Llama-3.1-8B, Qwen3-8B, and Qwen3-14B. We propose lexical mediation to explain why these effects and prior emotionally framed controls work: refusal and compliance tokens occupy distinct VA regions, and VA steering directly modulates their emission probabilities.
Virtual reality (VR) offers new opportunities to promote active behaviors by enhancing engagement and allowing controlled modifications of urban environments. This study investigates whether virtual environments (VEs) can evoke affective responses comparable with real environments (REs), both psychologically and physiologically, by using an immersive VE combined with a walking simulator that replicates walking motion. Forty-nine healthy adults, Luxembourg residents or cross-border commuters, aged 18–65, including students, university staff, and the general public, walked two contrasting street segments, walking-friendly and car-friendly, in both RE and VE in a crossover design. Affective responses were assessed through questions on aesthetics, safety, enjoyment, comfort, relaxation, momentary stress, and real-time physiological data collected using E4 wristband. Significant differences emerged between the RE and VE across all affective measurements, except for nonspecific skin conductance responses, with the RE consistently eliciting more positive affective responses. Nevertheless, similar affective trends were observed in both the RE and VE across the two segments. Moreover, environmental characteristics significantly influenced affective responses in both the RE and VE, with the walking-friendly segment yielding more positive affective ratings than the car-friendly one. The interactions between environment type (RE vs. VE) and segment type (car-friendly vs. walking-friendly) were not significant for most measurements, indicating that the effect of environment type on affective responses remained consistent across segments. These findings emphasize that VEs can mimic the overall patterns of affective responses observed in REs. This research highlights VR’s potential in planning healthier cities, offering insights into its benefits and limitations for future research. • VR walks showed lower affective ratings than real-world walks. • VR replicated the overall affective trends observed in the real-world environment. • Car-friendly street segment triggered higher arousal in the VR and real environment. • VR is a valid tool for assessing human-environment interactions.
The subject of the article is the historical dynamics of the formation and use of feminitives in Russian and English and their role as markers and constructors in the verbalization of gender roles. Using the example of two language groups, the sources of the emergence of feminitives, the stages of normalization and unconscious use of them, as well as current trends in the processing of feminitive vocabulary in the context of gender neutrality, inclusivity and social linguistic norms are considered. The article examines the historical background of the formation and use of feminitives in Russian and English, as well as their role as markers and constructs reflecting and shaping gender roles in verbalization. The object of the study is femininity, used in Russian and English in various contexts. The article uses the method of comparative analysis of the features of the formation of feminitives in Russian and English. The research methodology consists of historical and linguistic analysis of language corpora, comparison of diachronic and synchronous data, study of academic, social and everyday texts and gender-marked vocabulary. The relevance of the topic is due to the verbal conflicts between traditional gender norms and movements for gender equality, which are reflected precisely in the vocabulary of professions, roles and identity. The novelty lies in conducting a comparative analysis that allows us to identify how gender roles are reflected and constructed in each culture through specific designations of women's professions, positions and social statuses. Linguistic comparative analysis makes it possible to identify how gender stereotypes are verbalized in each culture through specific designations of women's professions, positions and social statuses. This approach makes it possible to trace the differences and similarities in the processes of feminization of vocabulary, as well as to understand how the use of feminitives contributes to the formation and understanding of gender relations in Russian and English.
Social media is the most popular platform for opinion expression. Sentiment analysis is the process of acquiring information about things, events and their characteristics out of people’s views, assessments and feelings. Opinion mining is an alternative term for sentiment analysis. In this paper, Enhancing Opinion Mining of Twitter Data with a Deep Convolutional Spiking Neural Network and Balancing Composite Motion Optimization (OMTD-DCSNN-BCMO) is proposed. Initially, the Twitter data are obtained from the Stanford Sentiment Treebank (SST-2) dataset. Then, the data is fed to the preprocessing. The pre-processing output is provided to extract the Radiomic features depending on the Residual Exemplars Local Binary Pattern (RELBP). The extracted output is provided to the feature selection for choosing ideal features using the Piranha foraging Optimization Algorithm. The selected features are provided to a Deep Convolutional Spiking Neural Network (DCSNN) for classifying Twitter data as negative, positive and neutral. Then, the DCSNN approach is optimized using Balancing Composite Motion Optimization (BCMO) for better performance. The efficacy of the proposed technique is examined using performance metrics and the method attains 23.32%, 26.07% and 28.51% higher accuracy and 21.92%, 15.03% and 19.15% lesser error rate are evaluated with existing approaches.
In the context of digital transformation, the development of electronic dictionaryplatforms includes an important software stage. However, after completing this stage, the primary task becomes the collection and systematization of the dictionary’s lexical database. Vocabulary is the most dynamic and changeable layer of a language. As society, science, and technology evolve, new words emerge while others gradually fall out of use. Therefore, the content of an electronic dictionary requires continuous updating
This preprint presents a systematic, research-oriented practicum that guides the reader through the entire modern NLP pipeline: from tokenisation and vectorisation to fine-tuning of large language models, retrieval-augmented generation, and reinforcement learning from human feedback. A distinctive feature of the work is its consistent attention to low-resource and morphologically rich languages -- original contributions on Tajik and Tatar, including subword tokenisers, word embeddings, lexical databases, and transliteration benchmarks, are woven throughout the twelve sessions, demonstrating how modern NLP can be adapted to data-scarce environments without sacrificing rigour. Each session combines concise theory with detailed implementation plans, formalised evaluation metrics, and transparent assessment criteria. The work is not a conventional textbook: it is designed as a reproducible research artefact where every session requires publishing code, models, and reports in public repositories. All experiments are conducted on a single evolving corpus, and the work advocates open-weight models over commercial APIs, with special attention to the Hugging Face ecosystem. Designed for senior undergraduates, graduate students, and practising developers seeking to implement, compare, and deploy methods from classical ML to state-of-the-art LLM-based systems.
The study analyzes decorative texts from a linguistic-axiological perspective. The relevance of the research is explained by the wide spread of textualized objects of reality in the modern linguistic space. The aim is to analyze the axiological parameters of Russian decorative texts and identify the dominant values in the axiological sphere of the collective consciousness of contemporary Russian society. The study material included Russian decorative texts written on clothing, cars, bento cakes, gifts, disposable coffee cups, jewelry, and interior design. The value parameterization was conducted with the help of methods of linguisticaxiological interpretation, definition analysis, and conceptual analysis of key words. As a result, decorative texts have been proved to be a new form of the language on objects of reality, alongside with study and electronic media; such texts function as catalysts of value meanings in the modern linguistic space. I-mentality as a predominant model of self-identification of the linguistic personality has been identified. This is determined by the inherent self-presentational function of decorative texts and by the expansion of mass culture with self-promotion as its norm. It has been found that deliberate violation of linguistic norms distorts axiological norms and offsets value meanings, which are displaced by simulacra. The following axiological parameters of Russian-language decorative texts have been established: the importance of material values, a hedonistic world perception, and egoistic, assertive, and antisocial behavior. The prospects for the research lie in expanding the corpus of Russian decorative texts to enhance the objectivity of linguistic-axiological analysis and in studying the axiologemes of Russian decorative texts in the form of aphoristic statements.
This article investigates how teachers in Swedish preschool class conceptualize and make sense of multimodality in relation to children’s early literacy learning. While multimodal perspectives have become increasingly influential in literacy research, emphasizing that meaning is made through multiple semiotic modes, such as image, gesture, movement, and material manipulation, less is known about how teachers themselves understand and value these modes in classroom practice. Drawing on a qualitative interpretative research design, the study is based on semi-structured interviews with sixteen preschool-class teachers across seven schools in Sweden. The analysis, guided by a social semiotic and social constructivist framework, identifies four interrelated themes: (1) multimodality as familiar yet conceptually elusive, (2) multimodality in practice: rich enactments and epistemic tensions, (3) modalities as tools, supports, and values, and (4) navigating constraints and possibilities. Findings reveal that while teachers’ everyday practices are deeply multimodal, integrating drawing, song, movement and digital media, their understandings of literacy remain predominantly anchored in an alphabetic and linguistic norm. Multimodality is enacted intuitively but seldom theorized, often positioned as instrumental support for writing rather than as an epistemic mode of knowing and meaning-making in its own right. The article argues that this conceptual gap underscores the need for a shared professional metalanguage for discussing multimodal literacy pedagogy. By foregrounding teachers’ lived negotiations of multimodality, the article contributes to a deeper understanding of the role of multimodal communication in, and current trajectories of, early literacy education.
This article examines the transformation of professional training for future English language teachers amid the rapid development of artificial intelligence (AI) technologies. The integration of generative tools into the educational environment creates not only new didactic opportunities but also significant methodological and ethical challenges, the most critical of which is the reliability of AI-generated content. Current educational programs tend to focus primarily on the instrumental use of technology, while methodologies for developing critical-analytical skills remain underdeveloped. The aim of the study is to theoretically substantiate and empirically test a methodology for developing the verification skill of AI-generated responses during language tasks. The paper clarifies the concept of "verification skill", defining it as an integrated professional ability to analyse, evaluate, and correct AI outputs in accordance with linguistic norms and methodological soundness. A structure for this skill is proposed, comprising four interconnected components: cognitive (knowledge of AI principles), analytical-evaluative (error detection), operational-corrective (editing), and value-reflexive (academic integrity). Based on empirical data collected from students of the Philological Faculty, the level of development of this skill was analysed. The results indicated that future teachers mostly possess fragmented abilities in editing AI texts: the cognitive component is the most developed, whereas the operational-corrective component is the weakest due to the unsystematic nature of corrections. It was also found that students tend to focus on formal accuracy while neglecting stylistic and methodological nuances. The study concludes that purposeful implementation of verification methodology in professional training is essential to ensure teachers’ methodological autonomy in a digitized environment.
The online review of veterinary services, as a new format of interaction between the client and the veterinarian, represents a value-oriented genre of veterinary discourse, characterized by variability in structure and volume. The high degree of emotionality and expressiveness indicates a strong positive bond between human and animal. This study fits into the framework of an innovative interdisciplinary approach to the concept of zooesis. The article aims to describe the linguistic means of expressing evaluation in an online review as a new genre of veterinary discourse and to determine the prospects for research. The study employed general scientific analysis methods, descriptive methods, and componential analysis. We analyzed 500 customer reviews of UK veterinary service providers posted on the clinics’ official websites. We found that the primary means of expressing evaluation is evaluative vocabulary, phraseology, and expressive syntax, while nonverbal emotional cues are used to a lesser extent. It has been demonstrated that the arbitrariness of the subject’s choice of linguistic means leads to the violation of linguistic norms which brings online veterinary reviews closer to colloquial speech. It has also been established that the majority of reviews (93%) are melioration-oriented. The linguopragmatic properties of linguistic means of expressing assessment in an online review of veterinary discourse are determined as a factor ensuring the success of distance communication, influencing the image and reputation of a veterinary institution. The results of this study can be used in university courses on communication in veterinary medicine and veterinary ethics. Prospects for future research include comparative stylistic studies of the online review genre and other genres of Internet content, and comparative analysis of online reviews.
The article analyzes the influence of economic factors on the language attitudes of youth. According to the theory of P. Bourdieu, the dominance of linguistic norms and forms is viewed as a factor that exacerbates social inequality. Proficiency in different languages increases an individual's social capital and expands their economic opportunities, while language barriers restrict access to these resources. The language choice among young people is largely determined by their economic status. It is posited that income levels facilitate the learning of foreign languages, whereas, in conditions of social inequality, the ability of youth to maintain their native language is taken into account. The study examines the impact of economic factors–such as labor market requirements, educational opportunities, and income levels–on multilingualism and language choice among the younger generation. The research provides insight into how economic drivers influence language choice, language policy, and the acceptance of multilingualism in society. The author presents the results of applied research based on the focus group method. Focus groups were conducted across 12 regions (N=167). According to the results, language choice among youth depends on regional and ethno-demographic characteristics. Furthermore, the global economy and globalization trends push young people toward learning multiple languages, while disparities between urban and rural areas also affect language attitudes. While youth with high-income levels strive for multilingualism, low-income groups prioritize their native language. The findings of this study play a crucial role in forming effective state and educational language policies that can enhance the success of young people in social and professional life.
This repository contains HDT-NP and HDT-DiNoS, both derived from Universal Dependencies' (UD) Hamburg Dependency Treebank (HDT). HDT-NP (.conllu) is a subset of UD-HDT and comprises its simplex noun phrases (NP): Common nouns (NN/NOUN) and their direct dependents (determiners, adnominal adjectives, nmods, adpositions, adverbs). It consists of 722,135 NPs (1.7M tokens) and has an improved feature annotation coverage (gender, case, number). Breaking with UD annotation, a total of 53,526 APPRART tokens were reconstructed in HDT-NP to restore the original orthographic forms. HDT-DiNoS (.json) is a custom data-driven lexion-like data structure built on HDT-NP, which aggregates NPs with the same head lemma. For each lemma, absolute frequencies of the lemma and its word forms are captured. Moreover, each occurrence feeds into three areas of interest within the word form entry: morphosyntactic features in isolation (gender, case, number), in combination with groups of dependents (collocations), and in combination with the syntactic function (dependency relations). HDT-DiNoS spans 84,598 unique lemmas and 102,418 unique word forms, stemming from 707,706 NPs. Lemmas were relemmatised to assign unique lemmas to nominal compounds, a highly productive and often lexicalised construction in German.
This study examines how language functions as a medium of authority, distinction, and institutional belonging within the English classroom of a Moroccan Classe Préparatoire aux Grandes Écoles (CPGE) in Beni Mellal. Situated within Morocco’s multilingual and postcolonial educational context, the article investigates how English, French, and Arabic varieties are differentially mobilized in a high-prestige and competitive academic setting where excellence is not just an evaluative ideal but a lived linguistic norm. Drawing on an eight-week linguistic ethnography with autoethnographic elements, the study analyzes classroom interaction, teacher reflection, student focus groups, written responses, and institutional documents. The analysis is informed by Bourdieu’s concepts of linguistic capital and symbolic power and by Fairclough’s critical discourse approach. The findings show that English operates as the principal language of academic legitimacy and intellectual discipline, French serves as a cognitive intermediary during moments of conceptual difficulty, and Moroccan Darija remains largely confined to affective reassurance and communicative repair. These patterned choices reveal a stratified linguistic order through which participation, confidence, and recognition are unevenly distributed. At the same time, the study demonstrates that teacher agency complicates the reproduction of these hierarchies; through adaptive multilingual practice, selective feedback, and locally designed materials, the classroom becomes not only a site where elite norms are enacted, but also one where they are negotiated. The article argues that academic excellence in CPGE is produced discursively through everyday linguistic practice and that any more equitable vision of excellence must reckon with the multilingual realities through which students learn, struggle, and claim legitimacy.
Objective: Emotion regulation (ER) difficulties are frequently reported in individuals with Functional Neurological Disorder (FND), yet most evidence derives from self-report data, and little is known about intra- and interpersonal ER preference and success under controlled conditions. Methods: In a multimethod design, Study 1 assessed habitual ER difficulties and interpersonal ER using validated questionnaires in 109 individuals with FND and 88 healthy controls (HC). Study 2 employed two laboratory paradigms in 33 individuals with FND and 33 HC, examining ER choice and ER success during intrapersonal (reappraisal vs. distraction) and interpersonal regulation (self- vs. other-guided reappraisal). ER success was indexed by subjective arousal ratings and startle reflex magnitude. Results: In Study 1, individuals with FND reported greater ER difficulties, higher alexithymia, more childhood trauma, and reduced use of interpersonal ER compared to HC. In Study 2A, stimulus intensity predicted ER choice, with a shift toward distraction at higher intensities; groups did not differ in intrapersonal ER choice or success. In Study 2B, individuals with FND showed a non-significant trend toward reduced preference for interpersonal regulation. Interpersonal reappraisal was associated with lower startle amplitudes than intrapersonal reappraisal, indicating stronger physiological downregulation. Across paradigms, ER success did not significantly differ between groups. Conclusions: FND is characterized by pronounced self-reported intra- and interpersonal ER difficulties, whereas laboratory findings suggest preserved momentary ER implementation. Interpersonal ER may represent a clinically relevant domain in FND, warranting replication in adequately powered samples. Registration: Preregistered at the Open Science Framework (https://osf.io/hxfje).
This repository contains GSD-NP and GSD-DiNoS, both derived from Universal Dependencies' (UD) GSD Treebank. GSD-NP (.conllu) is a subset of UD-GSD and comprises its simplex noun phrases (NP): Common nouns (NN/NOUN) and their direct dependents (determiners, adnominal adjectives, nmods, adpositions, adverbs). It consists of 49,425 NPs (119.0k tokens) and has an improved feature annotation coverage (gender, case, number). Breaking with UD annotation, a total of 3,649 APPRART tokens were reconstructed in GSD-NP to restore the original orthographic forms. GSD-DiNoS (.json) is a custom data-driven lexion-like data structure built on GSD-NP, which aggregates NPs with the same head lemma. For each lemma, absolute frequencies of the lemma and its word forms are captured. Moreover, each occurrence feeds into three areas of interest within the word form entry: morphosyntactic features in isolation (gender, case, number), in combination with groups of dependents (collocations), and in combination with the syntactic function (dependency relations). GSD-DiNoS spans 17,433 unique lemmas and 20,190 unique word forms, stemming from 49,416 NPs. Lemmas were relemmatised to assign unique lemmas to nominal compounds, a highly productive and often lexicalised construction in German.
Facial expression recognition (FER) in naturalistic settings is constrained by label ambiguity and variability in stimulus-response alignment. Adopting a data-centric perspective, this study examined whether emotional intelligence (EI)-stratified training data influence FER performance by treating EI as a qualitative factor associated with affective data consistency. Naturally elicited facial expressions were collected in a controlled emotion induction experiment with subjective arousal and valence ratings. Using response-driven labeling, neutral ratings were retained as indicators of ambiguity. Participants were grouped into High and Low EI based on the alignment between subjective evaluations and outputs from a pretrained affect estimator. Identical binary classifiers for arousal and valence recognition were trained while varying only the training data composition and evaluated across baseline, unambiguous, and ambiguous test sets using independent training repetitions with repetition-level statistical aggregation. EI-stratified training was associated with statistically detectable, context-dependent performance differences: group effects were observed primarily under baseline conditions and, to a lesser extent, under ambiguous conditions, whereas no reliable differences emerged under unambiguous conditions. Pooled discrimination differences were modest, but item-level analyses identified significant differences in classification correctness in specific task-condition combinations. Comparable patterns were observed across alternative backbone architectures. These findings indicate that FER performance in naturalistic contexts is influenced not only by model architecture but also by the statistical structure and internal coherence of the training data, supporting EI-informed data selection in ambiguity-prone scenarios.
With the increasing prevalence of mental health issues, music therapy has gained attention as a non-pharmacological intervention, and deep learning techniques have shown promise in music emotion recognition and preference prediction. This study constructed a deep neural network model (CNN+RNN/EEGNet) to efficiently identify music type preferences and examine the influence of user familiarity on prediction accuracy. EEG signals were collected using a four-channel Muse S wearable device, and user familiarity scores were used as input features. The study followed a four-stage workflow: preparation, experimental design, model construction, and result analysis. In the experimental design, music was categorized into rock, ballad, and folk, and EEG data and familiarity ratings were collected for each category. Data was trained and tested using CNN+RNN or EEGNet models, and model performance was evaluated via subject-level 10-fold cross-validation. Results indicated that predicting all music types with EEG data alone achieved an accuracy of 82.28 ± 3.42%. For individual music types, accuracies were 91.13 ± 3.60% (rock), 91.83 ± 2.07% (ballad), and 87.87 ± 4.76% (folk). When incorporating user familiarity as a feature and using a multi-level rating output, overall prediction accuracy increased to 94.94 ± 1.61%, while individual music type accuracies reached 99.15 ± 1.56% (rock), 98.51 ± 2.30% (ballad), and 98.21 ± 2.60% (folk). These results demonstrate that combining familiarity features with a multi-level scoring system significantly improves the prediction of music preferences. By using an affordable, wearable Muse S EEG device and leveraging user familiarity, this study successfully developed a highly effective deep neural network model (CNN+RNN/EEGNet) for recognizing music type preferences. The findings indicate that both overall and individual music-type predictions benefit from the inclusion of familiarity information, highlighting the potential of this approach for personalized music recommendations and music therapy applications.
This repository contains HDT-NP and HDT-DiNoS, both derived from Universal Dependencies' (UD) Hamburg Dependency Treebank (HDT). HDT-NP (.conllu) is a subset of UD-HDT and comprises its simplex noun phrases (NP): Common nouns (NN/NOUN) and their direct dependents (determiners, adnominal adjectives, nmods, adpositions, adverbs). It consists of 722,135 NPs (1.7M tokens) and has an improved feature annotation coverage (gender, case, number). Breaking with UD annotation, a total of 53,526 APPRART tokens were reconstructed in HDT-NP to restore the original orthographic forms. HDT-DiNoS (.json) is a custom data-driven lexion-like data structure built on HDT-NP, which aggregates NPs with the same head lemma. For each lemma, absolute frequencies of the lemma and its word forms are captured. Moreover, each occurrence feeds into three areas of interest within the word form entry: morphosyntactic features in isolation (gender, case, number), in combination with groups of dependents (collocations), and in combination with the syntactic function (dependency relations). HDT-DiNoS spans 84,598 unique lemmas and 102,418 unique word forms, stemming from 707,706 NPs. Lemmas were relemmatised to assign unique lemmas to nominal compounds, a highly productive and often lexicalised construction in German.
The subject of the research is the word formation game in media texts. Special attention is paid to non-standard methods of word formation, their wide expressive possibilities, and the functions of word formation. The aim of the article is to study the features of word formation language game in modern Russian language and to identify its most sought-after methods prevalent in media texts. The research analyzes such methods of word formation as substitute word formation, contamination, and graphic derivation. A brief description of each method is provided. Special attention is given to the study of neologisms created based on precedent texts, also neologisms highlighting abbreviations, assessing their contribution to enhancing the expressiveness and emotive quality of media texts. The materials for the study include publications from federal and regional modern electronic media over the past five years. It allows to evaluate the possibilities of word formation language game and its effect on the audience. The article employs elements of the descriptive method and functional analysis, systematizing the researched material. The scientific novelty of the research lies in the systematization of methods of word formation language game considering their impact on the development of language in electronic media and the formation of its new expressive possibilities. During the research, examples illustrating each of the mentioned methods of word formation are described. Based on the conducted analysis, it is revealed that each of them serves an expressive function. Examples of derivation based on specific patterns are separately studied, which are often created on the basis of stable expressions and idioms. Moreover, a conclusion is drawn regarding the active use by journalists of word formation language game to express authorial assessment, and their search for new expressive forms that lead to word formation experiments, often going beyond established cultural and linguistic norms.
Language is a living organism that evolves alongside technological and social advancements. This paper examines the phenomenon of neologisms—newly coined words or expressions—and their pervasive role in contemporary English mass media. The study categorizes recent neologisms based on their morphological formation processes, such as blending, compounding, and functional shift. Furthermore, it analyzes how mass media acts as a primary catalyst for the popularization of these terms. By investigating digital journals, social media platforms, and news broadcasts, the research highlights the pragmatic functions of neologisms in creating concise, engaging, and culturally relevant communication. The findings provide insights into the current trends of English lexicology and the impact of the digital age on linguistic norms.
Large language models (LLMs) offer a promising approach to machine translation (MT) for extremely low-resource languages by incorporating linguistic resources through in-context learning. However, LLMs often struggle to apply grammatical information effectively during translation. Inspired by recent progress in chain-of-thought reasoning, we investigate whether low-resource MT can benefit from structured intermediate steps of linguistic analysis and grammatical reasoning. We propose a pipeline for automatically generating step-by-step linguistic reasoning traces from Universal Dependencies treebanks, dictionaries, and grammar-rule banks. We evaluate these traces in three settings: in-context learning (ICL), supervised fine-tuning (SFT), and reinforcement fine-tuning (RFT), on Xibe and Chintang as test cases. Our results show that linguistic reasoning traces are most effective as inference-time guidance: in ICL, reliable sentence-specific traces substantially improve translation performance across most models, languages, and metrics. In contrast, using the linguistic reasoning traces as training data yields smaller and less consistent gains, as models learn the trace format but often generate erroneous content. These findings suggest that LLMs can leverage grammatical information for low-resource MT when given reliable linguistic analyses, while learning to generate such analyses remains a major bottleneck.
This study presents a lexicon-based semantic tagging information system developed for the Uzbek language corpus. The system employs the six-volume Explanatory Dictionary of the Uzbek Language (OʻzTIL) as its primary lexical resource, which contains over 85,000 entries with full semantic definitions, making it the most authoritative normative lexicographic source for Uzbek. An ontological model organized in three hierarchical levels – top, mid, and low – was designed to categorize lexical units extracted from the dictionary. Five core semantic categories were formed: animal names (approximately 100–200 units), bird names (approximately 100–150 units), personal nouns (approximately 500+ units), place names (approximately 300+ units), and occupation names (approximately 200+ units), totaling approximately 1,200–1,400 lexical units. A rule-based automatic tagging algorithm was developed to annotate corpus tokens against this structured lexical database, assigning standardized semantic tags. The system addresses key challenges inherent to Uzbek, including agglutinative morphology and lexical ambiguity. Compared to international systems such as WordNet and USAS, the proposed dictionary-based approach demonstrates superior normative grounding and cultural adequacy for Uzbek. The system is intended to serve as a foundational open resource for downstream natural language processing tasks, including machine translation, information retrieval, and intelligent educational applications.
We present a large-scale evaluation of the Menzerath-Altmann law (MAL) in the verbal domain across 180 languages, using the Universal Dependencies (UD) treebank collection (v2.17).MAL predicts that as the number of constituents of a linguistic unit increases, their average size decreases.We propose a metric to estimate the MAL effect across corpora of widely varying sizes and define threshold-based categories to classify languages along a MAL preference cline.Crucially, we analyse the preverbal and postverbal domains separately, in addition to the standard bilateral MAL, and control for potential sampling bias by comparing results across language families (Indo-European vs. non-Indo-European) and syntactic types (VO, OV and no dominant order).Our results confirm MAL as a typologically widespread preference but not an absolute universal: several languages display a trivial or even opposite (anti-MAL) tendency.Furthermore, we uncover a significant asymmetry between the two sides of the verb: the MAL effect is stronger in the postverbal domain, while anti-MAL is stronger in the preverbal domain.VO languages tend to show a stronger MAL preference postverbally, whereas OV languages do so preverbally.These findings challenge the widespread assumption that length-based ordering constraints apply symmetrically on both sides of the verb and contribute new cross-linguistic evidence to the debate on the interaction between dependency length minimization and constituent size.
This study examines the current state and future prospects of Urdu digital translation within the broader historical and technological development of machine translation. It begins by outlining the evolution of digital translation systems and reviewing their application across major world languages, followed by a critical analysis of existing Urdu translation platforms such as Google Translate, Bing Translate, ChatGPT, and other AI-based tools. The research identifies key linguistic and technical challenges that affect Urdu translation quality, including script directionality, morphological and syntactic complexity, polysemy, idiomatic expressions, cultural references, tokenization and parsing difficulties, and Unicode compatibility issues. By situating Urdu within the framework of Artificial Intelligence (AI) and Natural Language Processing (NLP), the study highlights the need for language-specific AI models, large-scale corpora, annotated treebanks, and domain-sensitive lexical resources to improve translation accuracy and contextual coherence. It further explores the applicability of advanced language models such as BERT, LLaMA, and generative AI systems in enhancing Urdu machine translation. In response to the identified limitations, the research proposes a corpus-driven, AI-integrated Urdu translation web application framework designed to provide context-aware, stylistically appropriate, and semantically accurate translations. The study contributes both analytically and practically by offering a comprehensive evaluation of Urdu digital translation and presenting a scalable model aimed at strengthening Urdu’s position in the global digital and AI-driven linguistic landscape.
Recent advances in Large Reasoning Models (LRMs), particularly those leveraging Chain-of-Thought reasoning (CoT), have opened brand new possibility for Machine Translation (MT). This position paper argues that LRMs substantially transformed traditional neural MT as well as LLMs-based MT paradigms by reframing translation as a dynamic reasoning task that requires contextual, cultural, and linguistic understanding and reasoning. We identify three foundational shifts: 1) contextual coherence, where LRMs resolve ambiguities and preserve discourse structure through explicit reasoning over cross-sentence and complex context or even lack of context; 2) cultural intentionality, enabling models to adapt outputs by inferring speaker intent, audience expectations, and socio-linguistic norms; 3) self-reflection, LRMs can perform self-reflection during the inference time to correct the potential errors in translation especially extremely noisy cases, showing better robustness compared to simply mapping X->Y translation. We explore various scenarios in translation including stylized translation, document-level translation and multimodal translation by showcasing empirical examples that demonstrate the superiority of LRMs in translation. We also identify several interesting phenomenons for LRMs for MT including auto-pivot translation as well as the critical challenges such as over-localisation in translation and inference efficiency. In conclusion, we think that LRMs redefine translation systems not merely as text converters but as multilingual cognitive agents capable of reasoning about meaning beyond the text. This paradigm shift reminds us to think of problems in translation beyond traditional translation scenarios in a much broader context with LRMs - what we can achieve on top of it.
TIn Lithuania, inclusive education is legally embedded in the Law on Education and its 2020 amendments, which guarantee children with disabilities the right to attend mainstream schools on an equal basis and ensure access to necessary support and non-discrimination. The law also provides Deaf pupils with a formal opportunity to learn Lithuanian Sign Language. At the international level, inclusive education is closely linked to human rights, equality, and social justice. The United Nations Convention on the Rights of Persons with Disabilities (CRPD) and the World Federation of the Deaf (WFD) emphasise the right of Deaf people, as a linguistic and cultural minority, to full and meaningful participation in education. This paper presents the results of qualitative research exploring how adult Deaf individuals retrospectively interpret and make sense of their experiences in mainstream schooling in Lithuania. The study seeks to illuminate the complex reality of deaf education, where linguistic and cultural dimensions intersect within the framework of inclusive education. The literature review draws on transformative equality theory, arguing that inclusive education requires not only formal access but structural transformation of linguistic norms, cultural recognition, and participatory frameworks within mainstream schooling. Particular attention is given to the linguistic and cultural dimensions of Deaf identity within this equality paradigm. The research is based on semi-structured interviews with adult Deaf participants and employs interpretativephenomenologicalanalysis(IPA).The findings reveal that mainstream schooling was frequently experienced as isolating and implicitly segregating, while Lithuanian Sign Language emerged as a crucial condition for recognition, belonging, and participation. The study suggests that inclusive education for Deaf learners requires transformative structural change grounded in principles of equality to ensure linguistic rights and cultural recognition.
Respect plays a crucial role in successful interpersonal and intercultural communication. However, differences in linguistic norms and pragmatic conventions often lead to misunderstandings between speakers of different languages. This article examines pragmatic failures in expressing respect in English-Russian cross-cultural communication. The study aims to identify linguistic and cultural factors that cause misinterpretations of respect and to analyze how respect is pragmatically encoded in both languages. The findings suggest that pragmatic failures frequently arise from divergent politeness strategies, speech act realizations, and sociocultural expectations embedded in English and Russian communicative practices. The study emphasizes the importance of pragmatic awareness in developing intercultural communicative competence.
ABSTRACT Classical fear conditioning describes how neutral cues acquire a threat value, yet how learned associations are retrieved and generalised across similar stimuli specifically is an ongoing debate. We combined behavioural ratings, physiological measures, and fMRI in a two-day classical fear conditioning paradigm to characterize acquisition, retrieval, and generalisation across modalities. Twenty-five healthy participants completed acquisition trials on Day 1 and retrieval and generalisation trials on Day 2 using conditioned (CS+, CS-) and graded generalisation stimuli (GS). Outcomes included trial-wise US expectancy ratings, pre/post fear and arousal ratings, skin conductance responses (SCR), pupil dilation, and ROI-based fMRI (amygdala, hippocampus, insula, periaqueductal gray (PAG), locus coeruleus). Acquisition yielded robust CS+/CS-discrimination in behavioural ratings and increased BOLD responses in bilateral insula and PAG. During retrieval, US-expectancy ratings indicated early retrieval of CS contingency. The fMRI results showed greater BOLD activity during CS+ presentations than during CS-presentations in bilateral hippocampus, left insula and right PAG. Additionally, hippocampus-insula coupling increased. Critically, parametric modulation during retrieval revealed that trial-wise mean US-expectancy modulated BOLD responses in left insula and right PAG, with a trend in left hippocampus. Across generalisation, US-expectancy and pupil dilation responses followed graded profiles, which could be explained by a Gaussian model, whereas SCR generalised, but was not captured by a Gaussian model. Parametric modulation by US-expectancy correlated with BOLD activity in left PAG, with a trend in right hippocampus. Stimulus identity explained variance in bilateral insula and left PAG. Findings converge on a hippocampus-insula-PAG network that retrieves learned predictions, and scales defensive output according to similarity-based threat probability, linking subjective, physiological, and neural outcomes.
The limitless semantic potencies of communication is within the framework of the language conventional semantics, which imposes a number of restrictions, including on the explication of emotional experiences by the speaker. The latter either chooses a read y-made preset formula, or directs communicative efforts to search for and objectify emotional and semantic shades of meaning with an uncodified form of verbalization. If the form of expression of an emotional experience is new, atypical, unconventional, we should talk about the representation of diffuse emotive semantics, approaching the actual emotional experience. Diffusivity (fuzziness, vagueness, multiple inconsistencies, ambiguity) is an immanent property of semantics that corresponds to both the natur e of the sign and the environment in which the sign acts. The assumption is that, depending on the characteristics of the discourse and the genre characteristics of the elements included in it, artistic communication was considered. The author of a work of art must go beyond the linguistic prescription, which allows him to have the desired effect on the addressee. The analysis of a dramatic work shows a variety of forms of explication of diffuse emotivity, when emotional experiences become a discursive and genre-forming category: the expression of complex vague emotions allows creating an image of a multifaceted and interesting character. The texts of modern plays allow tracing a similar trend towards diversifying the form of expression of diffuse emotivity, but the emotional tonality is less diverse: negative emotional experiences set the emotional dominant, therefore, the ‘consolidation’ of the emotive occurs rather than through the vector of mixing positive and negative assessments, but the intensification and concretization of the negative evaluative component. The author also postulates that language always approximately describes emotions, but in artistic communication such approximativeness is expressed in conscious and creative imitation, which transforms and develops the linguistic norm.
Artificial intelligence (or AI) is rapidly transforming digital learning environments, reshaping how educational processes are organized, how knowledge is produced, and how learning is evaluated. Despite a growing body of research on AI in education, existing studies often examine technological, pedagogical, and ethical dimensions in isolation, leaving a lack of integrative frameworks capable of explaining how AI restructures learning environments as a whole. This study addresses this gap by proposing a three-layer conceptual framework that models AI-mediated learning environments through the interaction of efficiency, pedagogy, and ideology. The framework conceptualizes AI integration as a system of interdependent processes: the efficiency layer captures the optimization of educational activities through automation and data-driven personalization; the pedagogical layer explains how AI reshapes learning processes, feedback cycles, and learner strategies; and the ideological layer examines the normative assumptions embedded within AI systems, including issues of epistemic authority, linguistic norms, and algorithmic bias. Drawing on a structured synthesis of recent empirical research across domains such as generative AI tools, automated feedback systems, intelligent tutoring systems, and AI-supported assessment, the study demonstrates how these dimensions interact to structure contemporary digital learning environments and generate both affordances and tensions. The main theoretical contribution lies in advancing a system-level analytical framework that moves beyond tool-specific approaches and enables a more integrated understanding of AI in education. In practical terms, the framework provides educators and policymakers with a lens to critically evaluate AI integration, supporting more informed decisions on assessment design, sustainable learning practices, and inclusive digital education.
Gender equality remains a cornerstone of sustainable social development, yet in Zimbabwe, the persistent use of sexist language in meetings, workshops, and public gatherings continues to reinforce gender hierarchies and marginalize the girl child. This study is of critical importance as it interrogates the linguistic and cultural foundations that perpetuate gender bias, aiming to advance gender-neutral communication as a pathway to empowerment and equity. Despite numerous gender mainstreaming policies, a significant research gap exists in understanding how everyday sexist discourse sustains systemic inequalities and limits girls’ participation and agency in social, educational, and professional spaces. The primary objective of this study is to examine the prevalence, forms, and socio-cultural implications of sexist language in Zimbabwean institutional and community contexts, and to propose strategies for promoting inclusive and gender-sensitive communication. Adopting a mixed-methods design, the research combines qualitative interviews, focus group discussions, and document analysis with quantitative surveys conducted across government, educational, and civic institutions. Data will be analysed thematically and statistically to capture both the depth and breadth of linguistic gender bias. Preliminary findings indicate that sexist language is deeply embedded in traditional communication practices, institutional norms, and even policy discourse, resulting in subtle but pervasive disempowerment of girls and women. However, evidence also points to a growing awareness and readiness among educators, policymakers, and community leaders to adopt gender-neutral communication frameworks. The study’s implications are profound: by reshaping linguistic norms, Zimbabwe can cultivate environments that affirm equality, inclusivity, and respect. The research contributes to global discourse on gender and language while providing actionable recommendations for policymakers, educators, and advocacy groups to dismantle linguistic barriers and foster the full empowerment of the girl child.
This article examines the “Golden Age” of Arabic linguistics under the Abbasid Caliphate (750–1258). It traces how Arabic evolved from a primarily religious and literary medium into a universal language of science. The study highlights the scholarly rivalry between the Basra and Kufa grammatical schools—especially their debates over qiyās (analogy) and samʿ (attested usage/auditory transmission)—and shows how these methodological differences contributed to the codification of linguistic norms. The article also analyzes the foundational role of Sibawayh’s Al-Kitāb and al-Khalīl ibn Aḥmad’s Kitāb al-ʿAyn in systematizing Arabic syntax, phonetics, and lexicography. Finally, it evaluates the Translation Movement at the House of Wisdom (Bayt al-Ḥikma) and explains how the integration of Greek, Persian, and Indian learning enriched Arabic vocabulary and helped establish a broad scientific terminology.