Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
16504 papers
This paper examines how artificial intelligence (AI), machine learning algorithms, and automated digital systems shape linguistic practices, reinforce or challenge linguistic hierarchies, and influence communication in contemporary society. As digital platforms increasingly mediate human interaction, algorithms determine what content becomes visible, which linguistic varieties are privileged, and how users adapt their language to gain visibility and engagement. The study explores algorithmic bias in search engines, social media feeds, voice assistants, and automated moderation systems, highlighting how these technologies reproduce existing social inequalities related to class, caste, gender, and ethnicity. Drawing on sociolinguistic theories of language ideology, linguistic capital, and digital discourse, the paper argues that AI-driven communication environments are not neutral but deeply ideological. They shape linguistic norms, influence identity performance, and regulate public discourse. The findings underscore the need for critical sociolinguistic engagement with AI systems to ensure equitable, inclusive, and culturally sensitive digital communication.
Abstract: This study examines the transformations in communication and language under the influence of artificial intelligence (AI), with a focus on large language models (LLMs) such as ChatGPT. Through multiple sessions between the author and AI, changes in communication patterns, personalization of interaction, and the formation of new normative frameworks – linguistic, ethical, social, and aesthetic – are observed. The analysis also covers phenomena from digital culture, such as Italian brainrot, internet slang (“6-7/67”), and the concept of “parasociality,” which function as cultural viruses eliciting emotional and social responses without traditional narratives. The study highlights how these trends reshape language formation, social interactions, and identity, demonstrating a shift from conveying meaning to stimulating sensations and social exposure. It is anticipated that these processes will continue to influence linguistic practices and communication in the globalized digital context in 2026. Keywords: artificial intelligence, digital culture, Italian brainrot, “6-7/67,” parasociality, communication transformations, linguistic norms. Rhetoric and Communications Journal, issue 66, January 2026 Read the Original in Bulgarian
The English language, traditionally viewed as monolithic and monocentric, now evolves into diverse varieties embedded in unique communities of practice. The expansion of online communication has further informed this evolution, giving rise to virtual spaces where members negotiate shared meanings, identities, and linguistic norms. This paper explores language use within an online networking business community of practice in the Philippines. Drawing on theories of Communities of Practice (COP) and Cultural Models, it examines how English is positioned in relation to the members’ cultural models and how community-specific jargons function as markers of identities. Through in-depth interviews and analysis of actual online conversations and social media posts, the study reveals that group members use English and local languages in dynamic linguistic strategies, such as code-switching and translingual practices, to construct and perform their identities within the group. The findings further reveal the role of English as a tool for empowerment and a marker of identity in digital spaces, hence emphasizing the need for a more inclusive understanding of language use in contemporary online communities.
We present ongoing work on annotating fine-grained semantic distinctions for circumstantial meanings, focusing on spatial expressions.We describe our theoretical background, and annotation process, as well as how we evaluate the results obtained.Using multiple independent annotations across the 3-million-token, genre-diverse Prague Dependency Treebank -Consolidated corpus of Czech data, we analyse inter-annotator agreement, recurrent disagreement patterns, and the limits of semantic categorization.Our results highlight the inherent vagueness of linguistic meaning.We also propose strategies for handling disagreement, such as weighted annotations, intermediate labels, and fuzzy labels that preserve annotation nuance.This work builds on the legacy of Petr Sgall and the Functional Generative Description theory that underpins the multi-layer form-meaning framework.
SRC, an acronym for Stimulus-response correlation, refers to determining the relationship between stimulus and corresponding brain responses. The neural aesthetic resonance hypothesis proposes that the level of enjoyment or familiarity can be distinguishable based on the relationship between stimulus and brain responses. To test this hypothesis, we use EEG data of 20 participants listening to 12 songs with their enjoyment and familiarity ratings. We aim to classify the low and high ratings of familiarity and enjoyment based on SRC. Eighteen musical features are extracted and transformed into the first principal component (PC1). In addition, root mean square (RMS) and spectral flux are used for analysis. Canonical Correlation Analysis (CCA), an unsupervised AI optimization method, is employed to compute the SRC between musical features and ten regions of brain responses, followed by considering four principal CCA features for classification using the Random Forest classifier with cross-subject evaluation. Our results demonstrate that the right frontal and right parietal regions provide significant predictive ability. Our empirical finding suggests that RMS features preserve the predictive ability for familiarity, whereas PC1 is for enjoyment prediction. Maximum familiarity and enjoyment accuracy reach nearly 76% and 73% accuracy. This work leverages AI techniques to decode sensor-derived neural signals, advancing real-time applications in affective computing and wearable EEG devices.
This study examines how linguistic adaptation, inclusion, and student diversity are constructed in the Swedish national curriculum from 2025 for upper secondary education (Gy25), with a particular focus on the subject syllabus for Swedish. The aim is to analyse the assumptions about language, learning, and student roles embedded in the curriculum text. The analysis is based on a qualitative text analysis with elements of critical discourse analysis. Selected sections of the curriculum, including the general aims and the subject syllabus for Swedish, constitute the primary material. The analysis focuses on key concepts, modal expressions, and how students and language are represented in the policy text. The results indicate that language is constructed as a central, norm-governed competence that students are expected to develop through education. At the same time, the curriculum emphasises inclusion and the need to adapt teaching to students’ different conditions. The analysis suggests that the curriculum contains a tension between inclusive ideals and established linguistic norms that students are expected to meet. The study highlights how curriculum texts contribute to shaping assumptions about language, participation, and student roles in the Swedish subject.
Pre-trained language models (PLMs) achieve high accuracy on standard benchmarks for sentiment analysis. However, this performance can hide systematic weaknesses in determining the sentiment of negated sentences, for example when the phrase “not good” is still classified as positive. In this study, we use sentiment classification of English movie reviews in the Stanford Sentiment Treebank 2 (SST-2) as a case study to specifically examine and improve how BERT handles negated sentences. We perform a brief additional fine-tuning of the existing BERT model on a small, automatically constructed set of lexicon-based counterfactual examples that target simple lexical negation. Experimental results on carefully paired original-negated sentences show that this procedure substantially reduces prediction errors on negated inputs while leaving overall performance on SST-2 almost unchanged.
Static concreteness ratings are widely used in NLP, yet a word's concreteness can shift with context, especially in figurative language such as metaphor, where common concrete nouns can take abstract interpretations. While such shifts are evident from context, it remains unclear how LLMs understand concreteness internally. We conduct a layer-wise and geometric analysis of LLM hidden representations across four model families, examining how models distinguish literal vs figurative uses of the same noun and how concreteness is organized in representation space. We find that LLMs separate literal and figurative usage in early layers, and that mid-to-late layers compress concreteness into a one-dimensional direction that is consistent across models. Finally, we show that this geometric structure is practically useful: a single concreteness direction supports efficient figurative-language classification and enables training-free steering of generation toward more literal or more figurative rewrites.
Headedness is widely used as an organizing device in syntactic analysis, yet constituency treebanks rarely encode it explicitly and most processing pipelines recover it procedurally via percolation rules. We treat this notion of constituent headedness as an explicit representational layer and learn it as a supervised prediction task over aligned constituency and dependency annotations, inducing supervision by defining each constituent head as the dependency span head. On aligned English and Chinese data, the resulting models achieve near-ceiling intrinsic accuracy and substantially outperform Collins-style rule-based percolation. Predicted heads yield comparable parsing accuracy under head-driven binarization, consistent with the induced binary training targets being largely equivalent across head choices, while increasing the fidelity of deterministic constituency-to-dependency conversion and transferring across resources and languages under simple label-mapping interfaces.
Headedness is widely used as an organizing device in syntactic analysis, yet constituency treebanks rarely encode it explicitly and most processing pipelines recover it procedurally via percolation rules. We treat this notion of constituent headedness as an explicit representational layer and learn it as a supervised prediction task over aligned constituency and dependency annotations, inducing supervision by defining each constituent head as the dependency span head. On aligned English and Chinese data, the resulting models achieve near-ceiling intrinsic accuracy and substantially outperform Collins-style rule-based percolation. Predicted heads yield comparable parsing accuracy under head-driven binarization, consistent with the induced binary training targets being largely equivalent across head choices, while increasing the fidelity of deterministic constituency-to-dependency conversion and transferring across resources and languages under simple label-mapping interfaces.
This study aimed to enhance the practical utility, in real operating contexts, of warning sounds designed to prevent declines in train drivers’ arousal. We analyzed subjective ratings of audibility and arousal for warning sounds presented at 75 dB under varying running noise levels (from 65 dB to 85 dB), considering age as the grouping factor. Experiment 1 (general participants, five noise levels × five warning types (four arousal-designed, one monotone)) showed that increasing noise decreased audibility and, in turn, arousal ratings; moreover, the ≥40s group reported higher arousal than the 20s–30s group. Under high-noise conditions, lower-frequency arousal-designed warning sounds were rated lower than the monotone, such that ratings could fall to levels at which neither audibility nor perceived arousal was attained. Experiment 2 (train drivers, two noise levels × three warning types showed the same qualitative tendency, providing ecologically relevant support for the Experiment 1 finding that low-frequency arousal-designed warnings perform poorly under high noise; no clear age-group difference was observed. Experiment 3 (in-service vehicles) lends support to the findings of Experiments 1 and 2. Overall, under high running noise, warning sound effectiveness depends on frequency, suggesting that designing accordingly can improve practicality even for arousal-designed sounds.
Replication package for "CEI: A Benchmark for Evaluating Pragmatic Reasoning in Language Models" (DMLR 2026). The Contextual Emotional Inference (CEI) Benchmark is a dataset of 300 expert-authored scenarios for evaluating how well language models interpret pragmatically complex utterances in social contexts. Each scenario presents a communicative exchange involving indirect speech (sarcasm, mixed signals, strategic politeness, passive aggression, or deflection) where the speaker's literal words diverge from their actual emotional state. Three trained annotators independently labeled every scenario using Plutchik's 8 basic emotions and Valence-Arousal-Dominance ratings. This archive contains: • data/human-gold/ — 5 merged annotation CSVs (300 scenarios, 3 annotators each) • scripts/ — Pipeline, analysis, and HuggingFace upload scripts • config/ — Model definitions and pricing configuration • papers/dmlr2026/ — Paper source (LaTeX), bibliography, and figures • reports/dmlr2026/ — Baseline results (JSON) and LaTeX tables • LICENSE (MIT for code) and README.md The dataset is released under CC-BY-4.0. Code is released under MIT. GitHub: https://github.com/jon-chun/cei-tom-dataset-base HuggingFace: https://huggingface.co/datasets/jonc/cei-benchmark
Mental imagery is often assumed to support vocabulary learning by enriching semantic representations, yet hybrid accounts of embodied cognition leave open the possibility that limited imagery, and the resulting reliance on verbal-analytic strategies, may ultimately support larger vocabularies. We tested whether imagery vividness predicts vocabulary knowledge and whether any relation depends on word concreteness. After collecting concreteness ratings for Vocabulary Size Test (VST) items, a separate group completed the Vividness of Visual Imagery Questionnaire (VVIQ) and VST. At the person level, VVIQ was not significantly correlated with total vocabulary score. However, item-level mixed-effects regression revealed a significant VVIQ×concretenessinteraction: higher imagery was associated with lower accuracy for highly concrete words. These findings suggest that vivid imagery does not confer an advantage in definition-matching tasks and may, for concrete words, subtly interfere with performance, consistent with compensatory verbal-analytic strategies in low-imagery individuals.
Mental imagery is often assumed to support vocabulary learning by enriching semantic representations, yet hybrid accounts of embodied cognition leave open the possibility that limited imagery, and the resulting reliance on verbal-analytic strategies, may ultimately support larger vocabularies. We tested whether imagery vividness predicts vocabulary knowledge and whether any relation depends on word concreteness. After collecting concreteness ratings for Vocabulary Size Test (VST) items, a separate group completed the Vividness of Visual Imagery Questionnaire (VVIQ) and VST. At the person level, VVIQ was not significantly correlated with total vocabulary score. However, item-level mixed-effects regression revealed a significant VVIQ×concretenessinteraction: higher imagery was associated with lower accuracy for highly concrete words. These findings suggest that vivid imagery does not confer an advantage in definition-matching tasks and may, for concrete words, subtly interfere with performance, consistent with compensatory verbal-analytic strategies in low-imagery individuals.
In a multilingual and multicultural society like Malaysia, the spelling of names serves as a personal identifier and as a reflection of sociocultural and linguistic norms. While prior studies have examined spelling variation in educational and digital contexts, less is known about public perceptions of spelling variations of names. Against this backdrop, this study investigates how Malaysians perceive the acceptability of spelling variations. Using a mixed-methods design, 355 participants responded to a questionnaire comprising Likert-scale evaluations of ten real names, along with open-ended questions. Quantitative analysis revealed significant variability in participants’ acceptability ratings, though names that retained phonological clarity (e.g., Adrianah) were generally more accepted than others (e.g., Frrdy). No significant differences were found across gender, age, ethnicity, or professional background. Thematic analysis of qualitative responses highlighted two key influences on naming decisions: individual sociocultural factors (e.g., religious beliefs, family tradition) and sociolinguistic-aesthetic factors (e.g., pronunciation, trends, media influence). These findings suggest a growing tolerance towards spelling variations, possibly indicative the value of distinguishing one’s identity through names.
<p>The purpose of this study is to identify the specific features of applying folk pedagogy in developing communicative competence among students of nonlinguistic specializations and to assess its effectiveness in foreign language teaching. The methodology involves student surveys, educator questionnaires and interviews, a pedagogical experiment within educational institutions, and a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of the data obtained. Both quantitative and qualitative data collection methods are employed, enabling a comprehensive evaluation of the proposed approach. The main findings demonstrate a positive impact of folk pedagogy on the acquisition of linguistic norms, increased student motivation, and the development of communicative skills. The proposed approaches may contribute to improving the quality of language training and expanding the range of methodological tools available in foreign language education. The practical value of the study lies in the potential to integrate folk pedagogy into the foreign language learning process, which may enhance material acquisition and promote deeper cultural understanding. The recommendations offered could be used to improve higher education curricula. The application of folk pedagogy supports a more engaging, interactive, and natural learning experience, aligning with current educational trends.</p>
This paper presents new resources and baselines for Dependency Parsing in Pomak, an endangered Eastern South Slavic language with substantial dialectal variation and no widely adopted standard. We focus on the variety spoken in Turkey (Uzunköprü) and ask how well a dependency parser trained on the existing Pomak Universal Dependencies treebank, which was built primarily from the variety that is spoken in Greece, transfers across dialects. We run two experimental phases. First, we train a parser on the Greek-variety UD data and evaluate zero-shot transfer to Turkish-variety Pomak, quantifying the impact of phonological and morphosyntactic differences. Second, we introduce a new manually annotated Turkish-variety Pomak corpus of 650 sentences and show that, despite its small size, targeted fine-tuning substantially improves accuracy; performance is further boosted by cross-variety transfer learning that combines the two dialects.
Background: Aging is characterized by a decrease in olfactory, attentional, memory, language, and visuospatial/executive abilities. In this context, our study aimed to evaluate the potential effects of Rosmarinus officinalis L. (rosemary) and Carum carvi L. (caraway) essential oils (EOs) on aging. First, we assessed, in 402 participants, the age-related changes in olfactory functions (odor threshold, discrimination, and identification), gustatory perceptions (sweet, sour, salty, and bitter taste), cognitive functions (focusing on attention, memory, language, and visuospatial/executive functions), and their possible correlations with aging. To achieve this, olfactory function, gustatory perception, and cognitive abilities were evaluated in healthy participants across different age groups. Then, to evaluate the age-related decrease in trigeminal function (59 participants), we used rosemary and caraway EOs that contain carvone, limonene, and 1,8-cineole, all of which are considered typical trigeminal stimuli. Methods: Olfactory function was assessed with the Sniffin’ Sticks test, gustatory function by the Taste Strips test, and rosemary and caraway EOs by the ratings of odor pleasantness, intensity, and familiarity using a labeled hedonic Likert-type scale. Results: Olfactory function could be a potential early indicator of attentional, memory, language, and visuospatial/executive dysfunctions. Our data indicated that rosemary and caraway EOs were perceived without any significant decrease in odor pleasantness, intensity, and familiarity ratings in relation to aging. Conclusion: Our results suggest the potential bioactive effects of rosemary and caraway natural EOs as a new strategy to promote healthy aging.
omnes flores is an NLP framework based on Universal Dependencies (UD) that utilizes multilingual Large Language Models (LLMs), and its default model is trained on data from 40 UD languages comprising 40 treebanks.For the EvaLatin 2026 Dependency Parsing Tasks, we extended the training data of omnes flores by incorporating six public Latin treebanks from UD and trained a dependency parsing model using the extended training data.The dependency parser of omnes flores normally takes a list of word FORM values as input.However, since the EvaLatin 2026 test data includes an UPOS column, we investigated whether incorporating both FORM and UPOS during both training and inference could improve parsing accuracy.Our experiments show that training using both FORM and UPOS improves performance by 0.5-1.0LAS points on Prose compared with training using only FORM, but decreases performance by 5 points on Poetry.
This paper presents new resources and baselines for Dependency Parsing in Pomak, an endangered Eastern South Slavic language with substantial dialectal variation and no widely adopted standard. We focus on the variety spoken in Turkey (Uzunköprü) and ask how well a dependency parser trained on the existing Pomak Universal Dependencies treebank, which was built primarily from the variety that is spoken in Greece, transfers across dialects. We run two experimental phases. First, we train a parser on the Greek-variety UD data and evaluate zero-shot transfer to Turkish-variety Pomak, quantifying the impact of phonological and morphosyntactic differences. Second, we introduce a new manually annotated Turkish-variety Pomak corpus of 650 sentences and show that, despite its small size, targeted fine-tuning substantially improves accuracy; performance is further boosted by cross-variety transfer learning that combines the two dialects.
The Icelandic Morphosyntactic Ontology (IMO) defines a formal, AI-compatible representation of Icelandic morphosyntax, including closed morphological feature inventories, a typed dependency-style relation system, and a constraint-based validation layer. Version v0.1 establishes an architectural specification rather than a complete lexical or descriptive grammar. It introduces a formal morphosyntactic model designed for computational compatibility (e.g., NLP validation, dependency parsing alignment), while explicitly separating a derived human learnability projection layer. The document includes:(1) Morphological entity system,(2) Syntactic relation graph layer,(3) Constraint architecture (agreement, governance, default case assignment),(4) Dependency matrices,(5) A structured human learnability projection blueprint. IMO does not replace existing Icelandic lexical databases or parsing systems (e.g., BÍN, Greynir), but operates at a formal abstraction layer intended for structural modeling and constraint-based validation.
In recent years, the number of people viewing pet videos and images online has risen. Although numerous studies have shown that owning pets positively impacts human mental health, the potential mental health benefits of prolonged exposure to pet media content remain debated. This study conducted three experiments to investigate how viewing pet videos affects human emotional face processing and to clarify the associated emotional regulatory mechanisms. Experiment 1 examined how viewing pet videos influences attentional bias toward emotional faces. Experiment 2 assessed the impact of watching pet videos on the valence perception of emotional faces. Experiment 3 analyzed how exposure to pet videos affects the valence perception of emotional text. The results showed that watching pet videos increased attentional bias toward subsequent positive emotional faces and decreased bias toward negative ones. This effect resulted from higher perceived valence ratings for both positive and neutral emotional faces. Importantly, this effect was only observed in facial stimuli with social attributes. These findings indicate that watching pet videos modulates emotional processing, and prolonged exposure to pet media content may affect mental health through this mechanism.
Emotional memories persist within individuals and over generations. Past research has examined functions of parent-child memory sharing, but little work has assessed how the emotional qualities of memories are transmitted from parent to child and whether emotion transmission relates to memory content transmission. An understanding of how emotional memories are transmitted may be particularly important during adolescence, a developmental period marked by heightened sensitivity to emotional information, increasing independence from caregivers, and the emergence of mental health symptoms. The current study investigated the intergenerational transmission of parents’ autobiographical emotional memories to their teen offspring in healthy dyads using behavioral measures and natural language processing tools. We found that emotional valence and arousal linked to parents’ memories are transmitted from parent to teen. Greater parent-teen agreement in valence ratings was associated with greater parent-teen overlap in both subjective vividness ratings and objective memory content of individual memories. Subjective memory transmission was modestly related to lower mental health symptoms in teens after accounting for parent symptoms. These findings demonstrate that parents’ emotional memories are transmitted to their teens and provide preliminary evidence that autobiographical emotional memory transmission from parents could be a protective factor for mental health in adolescence.
Abstract Part-of-Speech (POS) tagging is a core task in natural language processing (NLP) and a crucial building block for higher-level applications such as parsing and machine translation. For low-resource and morphologically rich languages such as Kangri, POS tagging remains challenging due to scarce annotated corpora and limited linguistic resources. This paper presents a comparative study of three POS tagging approaches for Kangri: a feature-based Conditional Random Field (CRF), an untuned Bidirectional Long Short-Term Memory (BiLSTM) baseline, and a hyperparameter-tuned BiLSTM. All models are trained and evaluated on the Universal Dependencies (UD) Kangri Treebank. The tuned CRF achieves strong test-set performance (70.4\% accuracy and weighted F1 0.695), the untuned BiLSTM provides a robust neural baseline (66.0\% accuracy), and a hyperparameter-optimized BiLSTM reaches higher validation accuracy during tuning. We analyze per-tag strengths and weaknesses, training dynamics, and provide recommendations for future improvements in low-resource POS tagging.
Short-form video platforms have become central to multimedia information dissemination, where comments play a critical role in driving engagement, propagation, and algorithmic feedback. However, existing approaches -- including video summarization and live-streaming danmaku generation -- fail to produce authentic comments that conform to platform-specific cultural and linguistic norms. In this paper, we present LOLGORITHM, a novel modular multi-agent framework for stylized short-form video comment generation. LOLGORITHM supports six controllable comment styles and comprises three core modules: video content summarization, video classification, and comment generation with semantic retrieval and hot meme augmentation. We further construct a bilingual dataset of 3,267 videos and 16,335 comments spanning five high-engagement categories across YouTube and Douyin. Evaluation combining automatic scoring and large-scale human preference analysis demonstrates that LOLGORITHM consistently outperforms baseline methods, achieving human preference selection rates of 80.46\% on YouTube and 84.29\% on Douyin across 107 respondents. Ablation studies confirm that these gains are attributable to the framework architecture rather than the choice of backbone LLM, underscoring the robustness and generalizability of our approach.
This paper examines the growing ideological dissonance between the contemporary political left and the working-class constituencies it historically claimed to represent. Drawing on sociological and political science literature alongside comparative evidence from Brazil, the United States, France, and the United Kingdom, we argue that the left’s progressive drift toward university-incubated identity politics—including debates over linguistic norms, gender categories, and minority group identity—has produced a profound semantic and cultural rupture with lower-income voters whose priorities remain rooted in economic security, family cohesion, and social conservatism. Simultaneously, right-wing and populist movements have strategically occupied the abandoned terrain of class-based discourse, reframing their appeal in the language of workers, families, and everyday material hardship. We analyze the semantic transformation of the terms left and right, the phenomenon of cancel culture as a mechanism of epistemic coercion, the paradox of conservative sociality among the poor, and the structural conditions that produce what we term displacement populism—the rightward migration of voters who were once natural constituents of progressive politics. We conclude with reflections on the prospects for political realignment and the conditions under which left-wing movements might recover their original social mandate.
Large language models (LLMs) perform strongly on many NLP tasks, but their ability to produce explicit linguistic structure remains unclear. We evaluate instruction-tuned LLMs on two structured prediction tasks for Standard Arabic: morphosyntactic tagging and labeled dependency parsing. Arabic provides a challenging testbed due to its rich morphology and orthographic ambiguity, which create strong morphology-syntax interactions. We compare zero-shot prompting with retrieval-based in-context learning (ICL) using examples from Arabic treebanks. Results show that prompt design and demonstration selection strongly affect performance: proprietary models approach supervised baselines for feature-level tagging and become competitive with specialized dependency parsers. In raw-text settings, tokenization remains challenging, though retrieval-based ICL improves both parsing and tokenization. Our analysis highlights which aspects of Arabic morphosyntax and syntax LLMs capture reliably and which remain difficult.
Misconceptions in physics are persistent explanatory frameworks that develop in early infancy and continue throughout primary, secondary, and university education, even among pre-service educators. Instead of being eradicated through formal education, these intuitive notions often coexist with scientific principles, resulting in a disjointed or contextually dependent comprehension. This review consolidates recent studies on the origin, endurance, and alteration of misunderstandings across several educational stages and physics disciplines, encompassing mechanics, thermal phenomena, energy, and electromagnetism. It emphasizes that misconceptions are influenced by perceptual experiences, linguistic norms, and evaluative methods that favour procedural fluency over explanatory reasoning. The paper analyzes instructional methods that facilitate conceptual change, highlighting hands-on and virtual experimentation, guided inquiry, diagnostic evaluation, and innovative digital and AI-driven technologies. The research identifies cross-cutting themes that emphasize the significance of cognitive conflict, explicit interaction with learners' concepts, and coherent conceptual advancement in curriculum. Significant study deficiencies encompass the necessity for longitudinal studies, examinations of transfer and durability, and a more profound inquiry into teacher education as a domain for conceptual advancement. The findings emphasize the importance of intentional conceptual education and comprehensive teacher preparation to foster lasting and significant comprehension in physics.
Surzhyk, as a Ukrainian–Russian mixed subcode, has long been a subject of extensive research. Despite this, it remains an ambiguous phenomenon that elicits diverse assessments and attitudes. Within Ukrainian academic circles, a tradition has emerged of evaluating this communicative subcode through the prism of linguistic norms, predominantly characterising it as a negative phenomenon. However, in everyday communication, Surzhyk functions dynamically, reflecting the varied attitudes of its speakers. Significant shifts in its perception became particularly evident following the Russian Federation's full-scale invasion of Ukraine. Consequently, Russian-speaking individuals have increasingly adopted Surzhyk as a means of distancing themselves from the Russian language, which is widely perceived as the language of the aggressor. This article presents the results of a sociolinguistic study conducted in 2020–2021 and 2023–2024 among residents of the Odesa and Mykolaiv regions. The study examines the evolution of attitudes towards Surzhyk in the wake of the invasion, with particular attention given to the role of mass media discourse in shaping these perceptions. The analysis reveals a growing tendency towards a more favourable view of mixed speech; in the context of war, Surzhyk has come to symbolise solidarity and serves as an informal linguistic bridge for transitioning from Russian-dominant to Ukrainian-speaking communication.
Melanesia, renowned for its linguistic diversity with more than 1,400 languages spoken has experienced a shift in linguistic practices since the launch of social networks in the late 2000s. This digital transformation has affected all the Melanesian archipelagos. The increase in mobile phone usage since the mid-2000s has made social media platforms such as Facebook, Instagram, and X (formerly Twitter) increasingly popular in Melanesia. Language practices on social networks in Melanesia highlight the visibility and vitality of local languages, many of which are considered endangered. The predominance of English and French — languages of the colonial past and still official in the region — has marginalized many local languages. However, social media has allowed these languages to thrive in new written forms, such as text messages and online discussions, demonstrating their adaptability and relevance to the present day This chapter examines how social media in Melanesia is reshaping the sociolinguistic landscape by creating new linguistic norms and behaviours and how it is also reinforcing multilingual practices. By analysing language use on platforms, such as Melanesian Facebook groups, it shed lights on evolving roles and perceptions of local languages in digital spaces, thus contributing to broader reflections on linguistic dynamics in the digital age.
The increasing prominence of Large Language Models (LLMs) in public discourse presents both opportunities and challenges for democratic deliberation. While red teaming strategies help mitigate specific risks, broader concerns persist regarding linguistic constraints, biases, and the sycophantic tendencies of LLMs. This chapter explores how LLMs can be used to significantly scale up and democratise deliberation, particularly in fostering inclusivity and empowering traditionally marginalised groups. Drawing on concepts from Systemic-Functional Linguistics, the chapter examines how variations across language users (for example, with respect to socio-demographic groups) and across language use (for example, with respect to communicative functions) shape participation in AI-supported deliberation. The chapter presents AI-driven deliberation studies and assesses their potential to scaffold argumentation, enhance access, and reduce the influence of exclusionary linguistic norms and biases which are embedded in prestigious registers. At the same time, the chapter cautions against both overclaiming, which leads to unrealistic expectations, and underclaiming, which risks missed opportunities for AI-assisted engagement. The chapter concludes by identifying future research directions to maximise the democratic potential of AI-assisted participation while embedding ethical safeguards to counteract the reproduction of linguistic inequalities.
This repository contains anonymized behavioral data, analysis scripts, and experimental code for a virtual reality (VR) study investigating affective modulation of route-based spatial navigation. The paradigm combines immersive navigation in a controlled urban VR environment with an instructed threat-of-shock (ToS) manipulation and systematically varies locomotion modes (continuous vs. stepwise movement). The dataset includes navigation performance measures (accuracy, decision times), questionnaire data (e.g., trait anxiety, presence, affective ratings), and preprocessing/analysis pipelines implemented in R. The VR experiment was developed for a head-mounted display (Meta Quest 3), and the provided scripts allow reconstruction of the experimental logic. Due to copyright restrictions, the full digital object library (e.g., 3D assets, textures) is not included.
= 702), we examined whether the expectancy of an action effect shapes the affective evaluation of corresponding action-effect episodes. In each study, participants responded to stimuli by clicking on buttons that produced effects with either high (stimulus-congruent) or low (stimulus-incongruent) expectancy. Induced affect was assessed using both implicit (affective priming) and explicit (valence ratings) measures. Overall, high-expectancy episodes elicited relatively more positive affect than low-expectancy episodes. For our implicit measure, this effect persisted unless correspondence among all task events (stimulus, response, and effect) was simultaneously reduced (Experiments 2 + 3). Furthermore, this outcome could not be attributed to mere visual mismatch between stimulus and effect (Experiment 4) and was observed even when stimulus-congruent effects were less likely to occur than stimulus-incongruent ones (Experiments 5 + 6). These findings suggest that expected action outcomes elicit positive affect, thus offering a parsimonious explanation for performance advantages previously attributed to more complex mechanisms like ideomotor accounts or response monitoring. More broadly, we propose that affective evaluation of action-effect episodes represents a fundamental mechanism governing behavior across multiple psychological domains, from basic action control to complex behavior in various settings. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Adverse childhood experiences (ACEs) are well established and strong risk factors for mental health challenges later in life, but the underlying mechanisms remain largely unclear. Associative learning, which shapes emotional and cognitive responses to environmental cues, may play a critical role in this relationship. However, the neural underpinnings linking ACEs and ACE load to alterations in associative threat and safety learning remain poorly understood. In this multimodal fMRI study, 171 participants (97 with ACEs, 74 without ACEs) underwent a one-day fear conditioning paradigm, which included fear acquisition, generalization, extinction, and a return of fear test (ROF) after reinstatement. During fear acquisition, generalization and the ROF test, higher ACE load was associated with reduced discrimination between threat and safety cues, primarily driven by blunted responses to the threat cue across arousal ratings and central nodes of the fear network key brain regions (striatal regions and the vmPFC). During fear generalization, individuals with ACEs showed reduced activation in key brain regions to the CS+ (and GS+) but no differences in generalization gradients. In contrast, ACEs were neither related to expectancy ratings for nor aversiveness ratings for the unconditioned aversive event. These findings indicate a striking dissociation between cognitive and affective threat processing - putatively originating from a reduced transfer of US-related arousal to the CS+ as a mechanistic pathway through which ACEs become physiologically and neurobiologically embedded, thereby increasing the risk for mental health challenges.
Adverse childhood experiences (ACEs) are well established and strong risk factors for mental health challenges later in life, but the underlying mechanisms remain largely unclear. Associative learning, which shapes emotional and cognitive responses to environmental cues, may play a critical role in this relationship. However, the neural underpinnings linking ACEs and ACE load to alterations in associative threat and safety learning remain poorly understood. In this multimodal fMRI study, 171 participants (97 with ACEs, 74 without ACEs) underwent a one-day fear conditioning paradigm, which included fear acquisition, generalization, extinction, and a return of fear test (ROF) after reinstatement. During fear acquisition, generalization and the ROF test, higher ACE load was associated with reduced discrimination between threat and safety cues, primarily driven by blunted responses to the threat cue across arousal ratings and central nodes of the fear network key brain regions (striatal regions and the vmPFC). During fear generalization, individuals with ACEs showed reduced activation in key brain regions to the CS+ (and GS+) but no differences in generalization gradients. In contrast, ACEs were neither related to expectancy ratings for nor aversiveness ratings for the unconditioned aversive event. These findings indicate a striking dissociation between cognitive and affective threat processing - putatively originating from a reduced transfer of US-related arousal to the CS+ as a mechanistic pathway through which ACEs become physiologically and neurobiologically embedded, thereby increasing the risk for mental health challenges.
Music has a well-documented capacity to influence emotional, physiological, and behavioral states, making it a powerful medium for shaping human experience. A fundamental characteristic underlying many of these effects is predictability: listeners continuously form expectations about upcoming musical events based on learned regularities in musical structure. When music confirms or violates these expectations, it can meaningfully shape the listener’s perceptual and emotional experience. Therefore, variations in melodic predictability, ranging from highly regular, familiar patterns to more unexpected or ambiguous sequences, can be expected to differentially affect listeners’ internal states. The objective of this study is to investigate the influence of melodic predictability on subjective and physiological arousal in healthy older adults. Using a within-subject experimental design, 71 older participants listened to melodies varying in melodic predictability while subjective arousal was assessed using a visual analogue scale (VAS), alongside continuous psychophysiological measures of electrodermal activity (EDA) and heart rate (HR). Linear mixed model analyses indicated small but significant effects on subjective arousal ratings and on EDA, indicating psychological and sympathetic effects of melodic predictability. HR results, however, showed small but not significant effects. These findings help us understand melodic predictability as a characteristic of music in influencing arousal in older adults, which may inform the field of music therapy for clinical populations including older adults with dementia. Further research is needed to explore how musical predictability contributes to arousal regulation as a mechanism for supporting older adults with dementia.
We aimed to validate word lists developed by Tabri and Palmer (2020) for use in attentional bias research on appearance-related concerns. Three lists contained appearance words (attractiveness, stigmatized appearance, general appearance), and three contained non-appearance words (positive emotion, negative emotion, inanimate objects). Although matched on lexical criteria, their perceived meanings had not been assessed. Using a semantic differential approach, we examined perceptions of evaluation, potency, activity, and threat, and explored associations with eating disorder psychopathology. Participants from the community (Study 1: N = 299; Study 2: N = 311) rated attractiveness and positive emotion words as similarly positive, and stigmatized appearance and negative emotion words as similarly negative. General appearance and inanimate object words were rated as neutral. Differences reflected intensity, not type. Appearance word ratings were modestly associated with appearance overvaluation, body dissatisfaction, and weight stigma. Associations for non-appearance words were weaker and inconsistent. These findings support the semantic validity of the word lists and their utility in attentional bias research on eating disorders and other conditions involving appearance concerns.
Language errors play a crucial role in language learning and teaching. Using Indonesian in ways that exceed its standard communicative and linguistic norms is considered improper. Such deviations indicate a misuse of the language, whether orally or in writing, as they do not adhere to the established communication parameters and grammatical structures. The objective of this investigation is to scrutinize linguistic inaccuracies present in in the book titled Kuliah Muhammadiyah Gerakan Tajdid. The book titled Kuliah Muhammadiyah Gerakan Tajdid: Pendekatan Ideologis, Historis, Organisatoris, dan Analisis was written by H. Anhar Anshori, M.S.I., Ph.D., and published by UAD Press. This book provides a comprehensive description of Muhammadiyah as an Islamic movement, the da'wah of enjoining good and forbidding evil, and tajdid (renewal). This book is often used as an important reference for the Muhammadiyah course at various Muhammadiyah universities. This research adopts a descriptive qualitative methodology, employing an observational approach. The findings indicate the presence of 12 distinct types of orthographic errors. These encompass issues related to letter usage (specifically, two errors involving capitalization), word formation (including four instances of non-standard or improperly formed words), and the incorrect application of punctuation marks (one error concerning the use of single quotation marks, three errors with hyphens, and one error in semicolon usage).
The present paper gives a brief overview of the research on dictionary use and presents the results of a survey completed by upper intermediate level ELF Students of Sapientia Hungarian University of Transylvania on the use of dictionaries. It also reflects on the vocabulary learning strategies upper-intermediate students tend to use, their difficulties in improving their vocabulary skills (known as the intermediate plateau), as well as on the factors that often hinder the acquisition of new vocabulary (e.g., the lack of certain skills, like noticing). Finally, taking into consideration the fact that students prefer the use of search engines and AI-powered tools, the paper encourages a vocabulary practice that includes the use of electronic dictionaries combined with linguistic databases and AI-powered tools, which can help students not only to become autonomous learners but also to develop their metalinguistic and collocational awareness.
Abstract Color-evasive language practices and ideologies are central to the creation of clinical spaces as white public spaces, which are inherently dangerous for non-white people. This chapter explores how race, language, and disability are shaped by the color-evasive logic taught to medical students through linguistic practices within autism diagnostic processes. In these color-evasive practices, culture can index race in a socially acceptable way, as it allows individuals to talk about race without talking about race. In this clinical vignette, a Black child’s use of Black linguistic norms is described by the white clinician as being “inappropriate, disruptive, and echolalic.” This is reinforced when the clinician tells his students that scoring an autism diagnostic test is “not about culture, it’s about the response.” The chapter explores how the scoring of the diagnostic test is grounded in the subjective assessment of the clinician administering the test and how they understand the patient’s behavior, which is shaped by underlying anti-Black logics about race, language, and disability. The clinician’s response shows us that despite his production of a color-evasive framework, he perceives and hears the patient as a racialized other. The chapter argues that color-evasive ideologies create white public space where clinicians police non-white bodies and conflate a Black child’s use of Black language with disability.
This study empirically analyzed the error-processing patterns of five Korean grammar correction tools (Naver, Daum, Bareun-Hangeul, Gemini 3, and ChatGPT 5.2) currently utilized in the writing environment as of 2026. The findings revealed that while conventional rule- and statistics-based systems were effective for orthographic corrections, they exhibited a technical plateau in deep sentence-level error processing. In contrast, Gemini 3, a Generative Large Language Model (LLM), demonstrated superior contextual understanding and sentence restructuring capabilities, achieving an accuracy rate exceeding 90% across all domains. However, beneath this performance leap, a new form of “technical deceptiveness”—characterized by grammatical hallucinations and post-hoc rationalizations where incorrect corrections are fluently justified—was identified. Based on these results, this study first proposes “Critical AI Literacy” education, which enables learners to understand the probabilistic nature of generative AI and question the authenticity of its suggestions. Second, it suggests a “Writer-led Verification Process” as a core direction for literacy education in the AI era, empowering writers to make final judgments by cross-referencing AI-generated texts with linguistic norms rather than being subordinated to technical convenience. This research holds pedagogical significance by providing an empirical foundation for learner-centered critical writing education through a comprehensive examination of the possibilities and limitations of LLMs.
Article Summary This article examines how accent bias and racialized perceptions of speech influence professional credibility, identity formation, and authority within leadership and public-facing environments. Using a practitioner-based qualitative case study, the study analyzes how linguistic presentation and audience expectations shape the evaluation of professional competence during formal presentations. Reflective observation and critical incident analysis are used to explore how assumptions about speech patterns, dialect, and cultural identity affect audience reception. The analysis is informed by scholarship in linguistic profiling, social identity theory, code-switching, and stereotype threat, situating the case within broader structural dynamics that influence perceptions of professionalism. The findings indicate that professional credibility may be conditionally attributed based on alignment with dominant linguistic norms rather than demonstrable expertise. The study contributes to ongoing discussions about language, identity, and equity in professional and leadership contexts. Publisher’s NoteThis work is published by the Catholic Open University – Research & Study Center as part of its Research and Publishing Program. All published materials undergo editorial review to ensure academic integrity, originality, and compliance with ethical research and publishing standards. Each publication is assigned a persistent digital identifier (DOI) to support long-term accessibility, citation, and global scholarly dissemination.Availability Official article page:https://www.catholicopenuniversity.org/articles/accent-bias-profession Catholic Open University Digital Library:https://www.catholicopenuniversity.org/publications
The aim of this study is to illuminate preschool teachers’ perceptions of the significance of the mother tongue for children in preschool and the factors that influence the extent to which mother tongue and multilingualism are made visible in education. The study is based on qualitative semi-structured interviews with six preschool teachers in a municipality in central Sweden. The data was analysed using a combination of semantic and latent thematic analysis to identify both explicit and underlying patterns in the participants’ descriptions. The theoretical framework is based on Nira Yuval-Davis concepts of belonging and politics of belonging, including the perspective of situated intersectionality. Findings show that preschool teachers primarily perceive the mother tongue through an instrumental lens, emphasizing its importance as a tool for learning the Swedish language. While its significance for identity, recognition and emotional well-being is acknowledged, these aspects often remain secondary and implicit. Furthermore, the study identifies several factors that condition the work with multilingualism, such as structural and material constraints, pedagogical uncertainty and unexamined linguistic norms. The study concludes that the relationship between mother tongue and belonging is complex and conditional. Children’s opportunities for belonging are shaped by politics of belonging where linguistic hierarchies and majority norms influence which languages are given space. The results suggest a need for a more systematic approach and competence development to ensure that children’s right to their mother tongue is realized as a fundamental part of an inclusive preschool environment.
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.
The article is devoted to the study of the phenomenon of variation of foreign language vocabulary in modern Russian. The scientific problem lies in the need to rethink this linguistic phenomenon in the context of digital communication, where spontaneous spoken language becomes the main source of innovation, while traditional research is often limited to the analysis of normative vocabulary. The purpose of the work is a comprehensive analysis of the manifestations and mechanisms of variation based on the material of the live speech of users of social networks. To achieve this goal, a set of methods was used, including monitoring and recording oral speech in natural situations (podcasts, streams, audio messages), a continuous selection of examples from social networks (VK, Telegram, TikTok, Rutube) for the period 2023-2025, as well as contextual and distributional analysis to identify educational models and functions of options. As a result of the study, three main types of variation were identified and systematized: phonetic and graphic, morphological and semantic. It has been established that it is in the Internet environment that foreign language vocabulary maximizes its enriching potential, demonstrating deep assimilation through creative word-making. The practical significance of the work lies in the fact that its results can be used in compiling dictionaries of neologisms and in teaching Russian, as well as serve as a basis for further research on the dynamics of linguistic norms in the digital age. The prospects of the study are seen in tracking the long-term fate of the identified accidents and their possible transition to use.
Inspired by general deviance theory, which proposes that individual traits predispose people to violate multiple types of norms or rules, we suggest that disregard for native language rules of speech may be positively correlated with disregard for other social norms such as truth-telling. Specifically, we hypothesize that given the opportunity to gain financially through untruthful statements, native-born speakers who speak incorrectly will be more likely to exploit it than their counterparts who speak correctly. We report the results of testing this hypothesis on Israeli students, using two double-stage experiments. The first stage, identical in both experiments, was intended to identify untruthful statements by applying the popular die-under-the-cup task. The second stage, aimed at identifying incorrect pronunciation, differed between the two experiments. In the first experiment, participants were asked to read aloud 10 sentences, whereas in the second experiment, conducted in writing, participants were presented with the same 10 sentences, this time phrased as questions with multiple-choice answers regarding the correct pronunciation. Our hypothesis that untruthful statements are positively correlated with incorrect speech was confirmed in both experiments, thereby providing empirical support for the notion that linguistic norm violations may extend beyond the domain of grammar and be linked to broader patterns of social norm disregard, such as truth-telling.
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.