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18265 papers
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.
AI safety systems are increasingly deployed in high-stakes domains such as mental health, yet they are built on implicit assumptions about language that do not hold in practice. We argue that AI safety systems implicitly decide whose language counts as interpretable, and that risk expressed outside those norms is systematically overlooked. At the same time, AI systems can learn new language faster than their safety mechanisms can adapt to it. This creates a structural gap: while models may recognize emerging expressions, safety guardrails remain anchored to static linguistic norms. We introduce the concept of linguistic lag to describe the delay between the emergence of meaning within a community and its recognition by safety systems. Through examples and empirical observations, we show that semantically equivalent expressions of distress yield divergent safety outcomes when expressed in adolescent digital dialects. We argue that this is not a data coverage problem but a consequence of mismatched adaptation dynamics: language evolves through rapid, decentralized social diffusion, while safety systems evolve
This paper develops and validates an optical see-through augmented reality (OST-AR) performance simulation paradigm for the controlled manipulation of socially evaluative cues in musical performance contexts. Its methodological contribution is a reusable, perceptually validated stimulus set: an augmented reality – mediated virtual jury expressing differentiated evaluative states, designed to support research on affective perception, attentional allocation, and performance control under social evaluation. Two studies were conducted. In Study 1, Arriaga et al.’s (2010) music-based emotion induction protocol was adapted to an audition-relevant evaluative framework, and the differentiation of satisfaction, neutrality, unpleasantness, and dissatisfaction was validated in musicians using SAM valence and arousal ratings. In Study 2, the adapted protocol guided the recording and implementation of a five-member virtual jury as AR content on HoloLens 2. Musicians with extensive jury-performance experience evaluated the stimuli via SAM, the short PANAS, and a direct emotion-identification task. Results showed reliable discrimination of the intended evaluative states, stable responses across presentation sequences, and strong differentiation in valence and affective dimensions, supporting the internal coherence of the stimulus set. Rather than framing AR as an intervention, the validated OST-AR paradigm is proposed as a tool for controlled contextual manipulation, enabling ecologically plausible simulations of performance evaluation for future experimental research integrating behavioural and psychophysiological measures.
This article examines digital transformation of scientific discourse from the perspective of new models in information and communication space. The study aims to identify structural, linguistic, and multimodal characteristics of scientific texts in digital format using content analysis of ten scientific articles published in Kazakh in humanities and social sciences, published in 2023-2025 online in journals listed by the Committee for Quality Assurance in Science and Higher Education of Kazakhstan. The study coded parameters such as thematic area, annotation and keyword system, compositional structure of the text, terminological consistency, geography of sources, frequency of use of visual elements (tables, diagrams, illustrations) and hyperlinks, and conducted a quantitative and qualitative analysis. The study found that the IMRAD-based model is preserved in digital scientific discourse in Kazakh, but the text acquired a networked and interactive character due to open access, DOIs, online platform capabilities, and multimodal components. A number of articles noted incomplete use of hypertext links and visual tools in the digital format, inconsistency in presentation of sources, and violations of linguistic norms. The results helped to assess the level of scientific discourse digital modernization in the national linguistic space and describe new models of scientific communication in information and communication space.
Suicide among South Korean artists has become a recurring social phenomenon that cannot be fully explained through individual psychological perspectives alone. This study examines the contribution of sociolinguistics to the phenomena of Korean artist suicide by analyzing how suicide is linguistically constructed in online media discourse and netizen commentary. Using a qualitative approach grounded in Critical Discourse Analysis, the study analyzes selected online news articles and public comments related to prominent cases of Korean artist suicide. The findings reveal that media discourse predominantly frames suicide through emotional dramatization and tragic normalization, emphasizing pressure and suffering while marginalizing explicit mental health and prevention-oriented discourse. Netizen comments frequently employ evaluative, moralizing, and dehumanizing language that positions artists as publicly accountable figures expected to display emotional resilience. Additionally, cultural linguistic norms of emotional restraint contribute to indirect and euphemistic representations of distress, limiting empathetic engagement. The study concludes that Korean artist suicide should be understood as a discursively mediated social phenomenon shaped by sustained linguistic practices across media and digital spaces, highlighting the importance of sociolinguistic perspectives in mental health and media research.
The article examines the role and significance of socio-political translation in the context of modern intercultural communication. From a sociolinguistic perspective, the study explores the peculiarities of translating political discourse, including adaptation to the target audience, adherence to linguistic norms, and issues of political correctness. Based on concrete translation examples from Kazakh and Russian, the analysis focuses on national-cultural features as well as the translator’s linguistic competence and lexical usage. In the context of modern intercultural communication, the sociolinguistic features of socio-political translation are identified using materials from the Kazakh and Russian languages. The main objects of the study are the President’s Address and legislative acts. The semantic, lexical, and stylistic characteristics of translated texts in Kazakh and Russian are analyzed, and conclusions are drawn on the basis of translation theory and general requirements for translators. During the research, particular attention is paid to sociolinguistic aspects, namely linguistic identity, national-mental specificities, communicative strategies, and the role of cultural values. The process of translating socio-political texts is described as an act of intercultural communication, in which the translator’s creative activity and heuristic decisions are examined. The findings contribute to improving translation practice in multilingual environments and provide a deeper understanding of the interaction between the Kazakh and Russian languages within intercultural communication.
Automated item generation (AIG) using large language models (LLMs) is emerging as a promising solution for scalable creativity assessments, yet the potential of AIG to create psychometrically rigorous creativity items has thus far received little attention. In the present study, we evaluated the psychometric properties of LLM-generated items for the classic Consequences task of divergent thinking, comparing them to human-written items. A sample of 192 undergraduates completed six Consequences tasks—two human-written, two standard LLM-generated, and two genre-inspired LLM-generated, drawing on sci-fi and fantasy themes—along with measures of personality, cognitive ability, and creative behavior. We found that LLM-generated items elicited significantly higher fluency and flexibility. Genre-inspired items (fantasy and sci-fi) additionally elicited higher originality, enjoyment, and positive valence ratings; LLM-generated items were similar to human-written items. Genre-inspired items did not unfairly advantage participants with more exposure to genre content, demonstrating measurement invariance. Together, these findings suggest that LLMs can generate consequences items with psychometric properties comparable to those of human-written items. We discuss the implications for creativity assessment, item bank development, and best practices for AIG in creativity research, and we provide the prompts we used for item generation to enable researchers to generate new Consequences items at scale.
Traditional discrete neuroeconomic paradigms lack the ecological validity of continuous, volatile financial markets. To bridge this gap, the present study introduces a dynamic Jump-Diffusion Model (JDM) trading task. Ten participants (N = 10) navigated sudden simulated market spikes followed by SAM ratings across valence, arousal, and regret to evaluate behavioral responses to increasing monetary stakes (1, 50, 100 DKK), and realized versus counterfactual outcomes (gain, loss, relief, FOMO). Concurrently, real-time 64-channel Electroencephalography (EEG) data were recorded from a single-subject subset (N = 1) to evaluate neural responses to the ongoing volatility. Due to the restricted electrophysiological sample, this research serves purely as a methodological pilot study. Behaviorally, the paradigm successfully induced distinct affective states; the counterfactual regret of missing a surge (FOMO) rivaled the distress of a realized loss. However, the Lab Money Effect and progressive task habituation meant that increasing monetary stakes failed to modulate arousal ratings. Additionally, prior financial outcomes did not significantly dictate risk behavior, which was instead dominated by an overwhelming action bias. Neurally, the continuous JDM demonstrated the capacity to elicit late-stage cognitive engagement (P300, LPP), though early prediction error signals (FRN) were masked by ocular tracking artifacts. These preliminary findings validate the JDM as a promising framework for neuroeconomic modeling while establishing critical baselines for fully powered future investigations.
This study explores how the politician Dedi Mulyadi employs the coarse Sundanese pronouns aing and sia in public communication. It focuses on how these expressions, traditionally regarded as impolite, are strategically used in official and public settings to challenge conventional linguistic norms while reinforcing political authority. The research adopts a qualitative descriptive approach with a case study design, analyzing eight video clips collected from YouTube and TikTok using Halliday’s register theory, Silverstein’s indexicality, and Du Bois’ stance theory as analytical frameworks. The findings reveal that the social meanings of aing and sia have shifted from markers of rudeness to effective political resources. By combining humor with an informal speaking style, Dedi Mulyadi reduces social distance, projecting authenticity, sincerity, and solidarity with ordinary citizens. These linguistic strategies enable him to establish a stronger connection with grassroots audiences while reshaping public perceptions of linguistic politeness in political discourse. This study offers valuable insights for scholars and students of political linguistics and cultural studies by demonstrating how regional languages can function within contemporary political communication. It further argues that the strategic use of non-standard or coarse local language can serve as a constructive political asset, challenging the conventional assumption that political credibility depends solely on formal and polite language. In doing so, the study contributes to broader discussions on the role of regional languages in constructing political identity in multicultural societies.
Textbooks are fundamental educational tools that not only deliver curricular content but also convey societal and linguistic values. In the context of minority language education, textbooks have particular significance, as students’ language attitudes, identity, and self-perception are closely linked to the status and presentation of their native language. Language ideologies—often implicit beliefs about language and its use—shape how communities perceive linguistic norms, varieties, and speakers.
The rapid global spread of English has transformed it from a language associated primarily with native-speaker communities into a diverse and dynamic means of International communication. The idea of Global Englishes disputes traditional ideas about linguistic norms, ownership, and standards in the context of English Language Teaching (ELT). The current article attempts to explore the role and implications of the idea of Global Englishes in the context of the changing nature of English as a global language, the emergence of different forms of English, and the use of English as a Lingua Franca (ELF). The current article draws on the ideas and research of Braj Kachru and David Crystal, and it attempts to explore the implications of the idea of Global Englishes in the context of the changing nature of English, the emergence of different forms of English, and the use of ELF, with specific emphasis on the context in which the learner is more likely to encounter other non-native speakers than native speakers. It further explores the implications of global Englishes on the curriculum, pronunciation, and materials, especially in a situation where the learner is more likely to interact with other non-native speakers than native speakers. The article argues that the adoption of global Englishes has the potential to enhance the confidence of the learner, reduce native speaker bias, and enhance the learner's preparation for effective communication in a multilingual environment. The reframing of English education in the context of global Englishes is crucial in order to realign the practices of English education with the reality of English usage.
The following study explores the extent to which American English has influenced the phonology of Singapore English, with a specific look at the TRAP-BATH [æ]/[ɑː] vowels. While Singapore has traditionally regarded British English as the norm due to past historical colonial ties, increasing American media exposure raises the possibility of emerging and new phonological change. This study explores this possibility in Singaporean university students, examining their vowel productions with lexical items from the BATH lexical set. The author also hypothesises that American media influence would in fact cause the adoption of the [æ] vowel for lexical items in the BATH set. Six Singaporean university students participated in a reading task and elicitation task to capture their pronunciation of certain BATH lexical items. Results show an overwhelming preference for the British /ɑ/ vowel, with limited productions of the American /æ/ vowel. Productions of the /æ/ vowel appear to be word specific and did not reflect a larger systemic shift of Singapore English adopting American English features consistently. No significant differences were observed across gender and ethnic groups. Overall, this paper finds that American English influence may be present, but remains limited and is not reflective of a broader change in the vowel production of Singapore English. This study contributes to ongoing discussions on Singapore English and how it continues to navigate between established linguistic norms.
Digital mental health is undergoing rapid transformation through the deployment of artificial intelligence (AI) systems including conversational agents, risk-prediction tools, and clinical decision-support platforms. Despite growing enthusiasm, the field has yet to converge on an operational standard for what constitutes honest, trustworthy AI in psychiatric and psychological contexts-one that explicitly accounts for the limits, blind spots, and populations for whom deployed systems have not been validated. This Perspective draws on Richard Feynman's foundational principle of scientific integrity-articulated in his 1,974 Caltech commencement address, "Cargo Cult Science"-and Carl Sagan's dimensional metaphors to develop a practical, disclosure-oriented ethical framework for AI in digital mental health. The neurodiversity paradigm provides a critical stress test: growing empirical evidence indicates that AI tools trained on narrow behavioural and linguistic norms carry substantial risk of misrepresenting neurodivergent users, with the potential to silently re-encode stigma, misclassification, and inequity into clinical systems. Drawing on predictive processing theory and recent empirical evidence of AI bias against neurodivergent populations, this work proposes five criteria for the Feynman Honesty Standard: scope clarity, population-limit disclosure, uncertainty communication, role integrity, and participatory co-development. The analysis concludes with a forward-looking research agenda for clinicians, developers, and policymakers, arguing that the field will be judged not only by algorithmic performance but by its candour about what these systems know, miss, and cannot do.
Language is a crucial instrument of society, since communication ensures the effective functioning and development of social structures.Therefore, the relationship between language and society has become one of the key areas of sociolinguistic research.Another important aspect of social interaction is gender, which significantly influences communication patterns, linguistic behavior, and the formation of cultural norms.The article examines gender features in the context of modern media discourse and analyzes the relationship between language, gender, and mass media.Particular attention is paid to the representation of gender stereotypes in English-language media discourse and to the ways they are reproduced through journalism, advertising, television, cinema, and digital media.The study highlights the role of gender as a sociocultural factor that shapes linguistic practices, communication strategies, and public perceptions of masculinity and femininity.The paper explores the development of gender-inclusive language and analyzes the transformation of linguistic norms under the influence of feminist and sociolinguistic approaches.It is emphasized that media not only reflect social tendencies but also actively participate in constructing gender roles and behavioral models.The study demonstrates that media discourse often reproduces stereotypical portrayals of women and men, where men are associated with authority and activity, while women are more frequently connected with emotional or domestic roles.The article also investigates gender differences in communication styles, including variations in politeness, formality, lexical choice, and sentence structure.Special attention is devoted to advertising discourse and linguistic strategies aimed at different gender groups.The research confirms that gender stereotypes in media discourse influence public consciousness, cultural values, and social behavior.
Translation is the process of substituting text in the target language (TL) for text in the source language (SL). Catford 1965 said determining translation equivalency between two distinct linguistic systems is the main challenge intranslating. This research between Arabic-English translation that is actually has a big differents, aim to the distribution and percentage of pronouns containing in Surah Al-Kahfi, and investigates the translation ideology in the Arabic-English translation of Surah Al-Kahfi. The study using a descriptive qualitative method supported by quantitative analysis or Mixed Methode. The data consist of 532 pronouns identified from the Arabic text and its English translation.The pronouns are classified into personal pronouns, possessive pronouns, attached pronouns, and implicit pronouns. The pronouns are classified into shift type, tehniques type, ideology type and equivalence in translation. Shift type divided into unit shift, structural shift and split spit. Tehniques type divided into literal, transposition, amplification, reduction. Translation Ideology divided into foreignization and domestication, and Equivalence divided into formal and dynamic. The Findings release that Attached Pronoun and Implicit Pronoun are the majority found at the type of pronoun because AlQuran especially Surah Al Kahfi has the uniqe character. There are also Unit Shift has and Structural Shift are the most dominant categoriesat translation shift according to Catford 1965. Furthermore, Domestication appears slightlymore Dominant than foreignization due to Lawrence Venuti 1995 about translation ideology. At the end equivalenece shows that dynamic is more dominant than formal according to Eugene Nida1964. It is shown that reflecting the translator’s tendency to adapt Arabic grammatical structures to English linguistic norms while maintaining selected Qur’anic character.
Situated at the intersection of corpus stylistics, translation studies, and multifactorial statistics, this paper focuses on the identification of the predictors of preservation or avoidance of repetition in English-to-Polish translation of repeatedly used reporting verbs signalling direct speech. Using a sample of 20 literary texts, we fit multiple negative binomial regression with mixed effects models to assess the effect that seven predictor variables (i.e. frequency of a source-text verb, number of its translation equivalents in lexical databases, its number of senses, its semantic type, its length in characters, date of translation of a novel, and individual translators) have on the response variable: the number of Polish target-text reporting verbs (types) an English source-text reporting verb is translated into. The overall model fit per the lowest AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) values obtained through backward elimination reveals that the frequency of a source-text reporting verb, its semantic type as well as the individual translators have the largest incremental contribution to the model’s fit. More precisely, the proportion of variance in the outcome variable explained by both fixed and random effects (76%) is higher than the proportion of variance explained by fixed effects alone (72%). Without the translators treated as a random intercept, some variation would have been unaccounted for. The findings attempt to explain the translator’s decisions, whether to avoid or preserve patterns of repetition in source texts, with respect to rendering reporting verbs, which play an important stylistic effect in literary prose.
In this research paper, the author studied the English youth slang language. The subject of the study is a number of difficulties in identifying patterns and factors contributing to the updating and subsequent classification of the slang vocabulary. The research material was based on popular slang units proposed by ChatGPT. The objective existence of this phenomenon is in little doubt, but its characteristics are characterized by instability and dynamism. There is a gradual blurring of the boundaries of the literary and linguistic norm, which is accompanied by the active penetration of slang elements into various functional styles. Since slang does not fit into the traditional structure of the book language, this causes certain difficulties in its use and linguistic interpretation. Based on foreign literature, four factors and eight sources of the appearance of the replenishment of the vocabulary of youth slang in English were identified. In a comprehensive and detailed description, methods of theoretical research were used, contextual analysis: analysis of usage on platforms, etymological analysis: tracing origin, classification of mechanisms (by types of word formation and source), comparative analysis: usage in similar communities of different countries. The novelty of the research lies in a comprehensive approach to studying not only the structural and semantic characteristics of slang, but also the socio-cultural factors that determine its dynamics. The article is of interest for a deeper understanding of the linguistic creativity of young people, and may also help clarify theoretical ideas about ways to replenish the vocabulary of the English language in the digital age. The study of these processes not only enriches our understanding of language, but also allows us to trace the profound socio-cultural changes reflected in the daily speech of young people.
The Junction Grammar of English A Reproducible Corpus-Wide Analysis of Morpheme-Boundary Statistics and Consonant–Vowel Information Asymmetry Boicho Dimitrov Temelakiev Saxon Ventura Research Ltd 28th of May,2026 CC BY Abstract This paper reports a reproducible, corpus-wide statistical analysis of English word structure derived entirely from a single public word list of 455,246 entries, computed in a spreadsheet with no specialized tooling. The morpheme boundary—the junction between a stem and an affix—is treated as the primary object of measurement, and the distribution of the letters that may occupy each side of a junction is read across the corpus. Three results are established. First, the junction carries a stable structure: a consonant backbone (T, L, N, R, S, I) admitted by nearly all suffixes, an absolute floor (J, Q) admitted by none, and a sparsity that scales inversely with a suffix’s productivity. Second, the relationship between any two affixes is quantified by the correlation of their boundary distributions, which measures the degree to which they share a stem population; this correlation ranges from ~0.99 for etymological doublets to 0.57 for productivity-asymmetric near-twins. Third, the written word decomposes into a consonant skeleton carrying lexical identity (53.0% of the vocabulary is uniquely recoverable from consonants alone) and a vowel tissue carrying grammatical form (1.8% recoverable from vowels alone), a ~29-fold information asymmetry. Multiple independent measures—consonant recoverability, derivational classmarking, and free-stem fraction—partition the lexicon at a single boundary separating a transparent Germanic core from a bound Latinate superstructure. The method, its corrections, and its limits are reported in full, and a program of remaining work is stated. 1. Corpus and Method All results derive from one corpus analysed by one elementary procedure, and the reproducibility of that procedure is treated as part of the contribution. The corpus is the dwyl/english-words list (words_alpha.txt), comprising 455,246 alphabetic entries with a total of 4,254,354 letter occurrences and a mean word length of 9.345 letters. Each letter is assigned its ordinal value (A=1 through Z=26). Words bearing a given suffix are isolated by end-anchored matching and aligned on their final letter, so that each suffix position returns its exact ordinal value as a sanity check; the first stem letter preceding the suffix—the linker—is then read as a full A–Z frequency distribution rather than as a mean. The governing methodological constraint is that distributions are read in full and never collapsed to a mean prematurely, that no numerical coincidence is treated as a finding until tested across many cases, and that every claim is backed by a count. Page 1 Method: a derived column applies the ordinal map; end-anchored COUNTIF and MID/CODE formulas extract suffix positions and the linker; per-letter tallies at the linker yield the boundary distribution. Nesting among suffixes (for example ‑MENT within ‑ENT, or ‑ATION within ‑TION within ‑ION) is controlled by excluding longer relatives before counting. No statistical software, machine-learning library, or external lexical database is used at any stage of the core analysis. The constraint that the analysis remain computable by elementary means is not incidental; it ensures every figure in this paper can be independently reconstructed from the public corpus with a spreadsheet alone. 2. The Junction and Its Backbone The investigation began as a survey of unconditioned letter bigrams, which returned no morphological signal; structure appeared only when letter distributions were conditioned on a single morpheme boundary, and that conditioning is the method’s foundation. A preliminary tabulation of adjacent letter pairs across the corpus—which letters follow which, without regard to position within the word—yielded frequency patterns reducible to general orthographic regularities and carrying no isolable morphological content. The signal emerged only when a specific suffix was fixed and the letters preceding it were read as a population: conditioning on the junction, rather than measuring adjacency as such, is what renders the boundary structure visible. The set of letters that may legally occupy the stem side of a morpheme boundary then proves narrow, structured, and stable across suffixes. Reading the linker distribution across the mapped suffix inventory reveals a three-tier structure. A backbone of six letters—T, L, N, R, S, and I—is admitted at high frequency by nearly every suffix; T is the single most frequent linker across the inventory and recurs as the dominant boundary letter in suffix after suffix. An absolute floor of two letters—J and Q—is admitted by no suffix at measurable frequency, a prohibition confirmed corpus-wide and consistent with their status as the two rarest letters overall (J at 0.18%, Q at 0.19% of all letter occurrences). Between backbone and floor lies a selective middle whose composition varies by suffix and in which each suffix’s identity resides. Two descriptive terms are used throughout. Because each letter carries an ordinal value (A=1 through Z=26), a linker distribution may be summarized by where its mass falls on that scale: a distribution concentrated on early-alphabet letters (low ordinal values, A through roughly M) is termed cold, and one concentrated on late-alphabet letters (high ordinal values, roughly N through Z) is termed warm. The terms refer solely to ordinal position on the A–Z scale and carry no semantic content; the backbone letters, for instance, span both ends (cold I and L against warm N, R, S, T). The degree of selectivity is itself a measurement. The count of forbidden letters at a junction—its sparsity—scales inversely with the suffix’s productivity: derivational suffixes that attach choosily to a constrained stem class forbid many letters, whereas inflectional or highly productive suffixes forbid few. Sparsity is therefore not noise but signal: the pattern of exclusion characterizes the suffix as informatively as the pattern of admission. Page 2 Method: forbidden-letter counts are read directly from the linker distribution (frequency below 0.5% taken as floor); cross-checks against COUNTIF totals confirm the populations; J/Q absence is verified against the bare ‑S population of 84,208 words and against corpus-wide letter frequencies. The junction thus behaves as a filter whose admitted and forbidden letters together encode the morphological role of the boundary, with the consonant backbone bearing the structural load and the rare letters marking its limits.
This study the explores the need to examine the increasingly varied methods of quantifying morphological complexity in regards to their ability to influence structural borrowing across languages, with particular attention to the distinction between enumerative complexity (enumeration of all of the morphemes and distinctions in a system) and integrative complexity (entropy-based calculation of the class distinctions of the system). While existing models in contact linguistics often assume that structurally simpler languages are more likely to serve as sources of grammatical transfer, such accounts typically rely on enumerative measures of complexity alone and do not fully account for the role of irregularity and predictability within morphological systems, quantified by more recent discussions of integrative complexity as compared to enumerative complexity. This study argues that incorporating integrative complexity provides a more comprehensive framework for understanding structural borrowing. Focusing on the Ottoman-Bulgarian contact context, this study compares Old Church Slavonic, modern Bulgarian, and Ottoman Turkish using corpus-based computational measures derived from Universal Dependencies treebanks. Various metrics are employed to measure, calculate, and compare complexity across multiple randomized samples. The results reveal a consistent tradeoff between lemma-to-form expansion and lexical diversity across the languages examined. Old Church Slavonic exhibits high levels of lemma-to-form expansion alongside lower lemma diversity, reflecting a system in which morphological variation is concentrated within inflectional paradigms. In contrast, modern Bulgarian shows reduced expansion and higher lemma diversity, indicating a redistribution of variation across lexical items. Ottoman Turkish occupies an intermediate position, combining relatively high surface-form diversity with moderate levels of lemma expansion. These findings suggest that morphological complexity is multidimensional and cannot be fully captured by enumerative measures alone. By distinguishing between different ways in which variation is structured within a language, this study provides a more nuanced account of morphological organization and offers new insight into how structural factors may shape outcomes in language contact. More broadly, the results highlight the value of corpus-based approaches in refining theoretical models of morphological complexity and transfer.
Abstract Study Objectives Aging alters affect, with older vs. younger adults exhibiting greater positive and reduced negative affect. Sleep and autonomic factors contribute to this change, yet it is untested whether sleep-dependent autonomic activity plays a role. The current study examined bidirectional associations between affect, arousal, sleep architecture, and cardiac autonomic activity across a daytime nap in healthy younger and older adults. Methods Polysomnography and electrocardiogram were collected during a midday nap. Sleep architecture and autonomic activity outputs (i.e., high frequency, HF-HRV, and low frequency, LF-HRV, heart rate variability) were extracted. Affect ratings (assessed via the Multiple Affect Adjective Checklist), captured before and after the nap, were considered along two continua: 1) positive and negative valence and 2) low and high arousal. Positivity ratings (positive – negative valence) were determined within the two arousal categories. Results Younger adults reported stronger positivity for low- relative to high-arousal affect after the nap, whereas positivity did not differ by arousal in older adults. Wake and NREM autonomic activity was lower in older than younger adults. Higher waking and NREM LF activity were associated with stronger post-nap positivity, whereas higher HF activity was associated with diminished post-nap positivity. Pre-sleep positivity ratings were not meaningfully associated with HRV during sleep. Sleep and wake findings were broadly consistent across age cohorts. Conclusion The current study extends previous work linking sleep-related autonomic activity and psychosocial outcomes and offers that autonomic activity during wake and sleep may work synergistically to calibrate affective experiences across the lifespan.
Within urban green systems, forest spatial structure shapes how individuals perceive environments and experience psychophysiological responses. Although forest structure has been examined from ecological and aesthetic perspectives, age-related differences in perceptual and regulatory responses across forest spatial typologies remain insufficiently understood. To address this gap, this study developed an integrated multimodal analytical framework that combines immersive virtual reality, eye tracking, physiological sensing, and affective assessment to examine age-sensitive health-related responses to forest spatial typologies. Three forest spatial typologies were investigated: open, semi-open, and enclosed. Panoramic scenes were standardized for visual authenticity and quantified to support typology classification. During virtual reality exposure, eye-tracking indicators capturing attention allocation were synchronized with physiological measures and affect ratings, enabling comparisons between 30 young and 25 elderly participants, with a total sample of 55 participants. Results revealed marked age-by-spatial-typology interaction effects in eye-tracking indicators and perceptual–affective responses, whereas autonomic physiological indicators remained stable. Elderly participants demonstrated the highest visual satisfaction in open forest structures. In contrast, young participants exhibited shorter time to first fixation in enclosed typology and higher visit counts in semi-open typology, reflecting attention allocation patterns across spatial configurations. Subjectively, elderly participants reported a Positive Affect score of 38.64, 44.7% higher than that of young participants. Objectively, the mean pupil diameter of young participants reached 3.37, representing a 27.2% increase relative to that of elderly participants. These findings show that forest spatial typology shapes age-sensitive multimodal responses, including visual attention, psychophysiological regulation, and affect, and inform inclusive, resilience-oriented urban green-space planning under ongoing demographic transition pressures.
Objectives To use the dot memory task to examine the impact of early-onset depression (ED) on visual working memory (VWM) in later life when compared to those with no history of depression (CON). As an exploratory aim, we examined VWM in remitted vs non-remitted depression when compared to those with no history of depression. Design, Setting, Participants, and Measurements 79 participants aged 55-79 underwent lab-based neuropsychological testing. They were then provided with a study-supplied smartphone and were prompted to complete ecological momentary assessments over 14 consecutive days, receiving 3 separate notifications a day. Prompts consisted of a brief survey regarding recent activities, affect ratings, and use of emotion regulation strategies, followed by 3-5 brief cognitive tasks. VWM was measured with a dot memory task. Both response time and error distance were calculated. Results Mean response rate was 70%. The ED group had significantly slower response times at baseline than the CON group (18.9% slower, 95% CI 2.7–37.6%, p=0.023). Across the study period, the ED group's response time decreased faster relative to CONs (-0.8% per day, 95% CI -1.6 to +0.0%, p=0.057), an effect driven by the actively depressed (AD) subgroup, whose response time decreased significantly faster (AD vs CON: -1.3% per day, 95% CI -2.3 to -0.4%, p=0.007).). There was no significant difference in either the level or the rate of change of error distance between CON and ED participants. Within-person variability (standard deviation) in response time did not differ by group or change over time; within-person variability in error distance declined over the study period in CONs but declined significantly less in the ED group (ED × time: 0.02 units/day, 95% CI 0.001–0.039, p=0.037). Conclusions Our findings suggest that the VWM domain may be a malleable target for state-improvements in cognitive functioning in depressed patients.
This preregistration is intended as a supplement to a previous preregistration that is already stored as part of the current OSF project (https://osf.io/wc6sv/overview). In the original preregistration, we committed to several planned analyses. The present preregistration builds on that initial document and extends it in several ways. First, in the present analysis, we will focus exclusively on the affective-report condition, in which participants reported or rated their own emotional experience. We will not analyze the condition in which participants reported what most people would feel. Second, All data exclusion details specified in the original preregistration remain applicable, with two exceptions: (1) we will exclude reaction times shorter than 200 ms; (2) Because the model estimates separate parameters for specific combinations of response type and normativity, reliable estimation requires a sufficient number of observations in each modeled cell. Therefore, if a participant had too few observations in one or more modeled cells, for example in the cell corresponding to counter-normative unpleasant responses, the model may not converge properly. We will not impose a fixed exclusion threshold in advance. Instead, if convergence problems arise, we will inspect the cell-specific trial counts to determine whether they are attributable to an extremely small number of observations in a particular condition (~2-3). In such cases, participants with insufficient observations in the relevant modeled cells will be excluded from the modeling analysis. We will apply this criterion while aiming to maintain proportional exclusions across countries. Third, we will also analyze the perceptual task. For this analysis, we will apply the same logic of data exclusion used for the emotional task, but we will not exclude additional participants beyond those excluded in the emotional-task analysis. Thus, the perceptual-task analysis will include only participants who were retained in the emotional-task analysis. Fourth, we will also fit a more complex model than the one described in the original preregistration. In this model, the decision threshold and starting point will be defined as a function of the response option (pleasant and unpleasant). Drift rate and drift-rate variability will be defined as a function of stimulus pleasantness (pleasant or unpleasant), and response normativity (normative versus counter-normative). We will set the scale by fixing one of the population-level sv parameters and will use model priors based on the posterior means obtained in the replication model reported by Berkovich and Meiran (2024). The same modeling logic will be applied to the perceptual task. Specifically, the starting point and decision threshold will be defined as a function of the response option (yellow versus blue). Drift rate and drift-rate variability will be defined as a function of the stimuli (blue versus yellow), and accuracy. It should be noted that the stimulus range in the perceptual task differs from the stimulus range in the emotional task. Therefore, if necessary (for example in cases of poor model fit/implausible parameter estimates/ or other indications that the model does not adequately capture the data), we will trim trials with an almost equal ratio of yellow and blue dots. As prios, we will use the most relevant and up-to-date posterior available from previous studies that used tasks as similar as possible to the present perceptual task. If no sufficiently comparable posterior information is available, we will use non-informative priors to avoid biasing parameter estimation. Model fit will be evaluated using either RMSEA or posterior predictive checks. At this stage, we are not yet certain which of these approaches will be most suitable for assessing model fit both at the overall sample level and separately within each country. Therefore, we will first examine the feasibility and suitability of both approaches for this purpose, and will then select one of them as the final method for evaluating model fit. The selected approach will be used to assess whether the model adequately captures the observed data both in the full sample and within each country-specific subsample. In the modeling of the emotional task, it will also be necessary to define response norms. These norms will be defined separately for each country, based on the affective ratings. Finally, we will also analyze the affective-rating task itself. Specifically, we will examine whether the mean and standard deviation of each affective category, pleasant and unpleasant, differ across countries, with each category analyzed separately.These analyses include only the images that were actually entered into the model for each country. The aim of this project is to examine in which components of the affect-labeling process cultural differences emerge. More specifically, there are logical reasons to expect a possible effect of country on each of the model parameters. Therefore, this analysis is exploratory in the sense that we allow for the possibility of country differences in all parameters and their subcomponents. Because these hypotheses are somewhat exploratory in nature, we will adopt a conservative criterion in which only Bayes factors greater than or equal to 10 will be taken as evidence supporting H1.
Evaluative processes facilitate motivated responses to a wide range of stimuli in the environment, allowing individuals to make adaptive decisions in response to potential threats and rewards. These evaluations are influenced by many perceptual and biological factors. Recent perspectives have highlighted a need for more research examining how these individual factors interact to shape variability in evaluative processes. The present work examined how two such factors, resting parasympathetic activity and depressive symptoms, relate to affective evaluations of images in a nonclinical sample. Measures of depressive symptoms and resting parasympathetic activity were collected from a sample of young adults. Participants also rated positive, negative, and emotional arousal ratings to pleasant, neutral, unpleasant, and disgust pictures. Results showed that higher depressive symptoms were associated with increased positivity ratings of pleasant and neutral pictures, increased negativity ratings of pleasant, neutral, and disgust pictures, and ambivalence (high ratings of both positivity and negativity) of pleasant, neutral, and unpleasant pictures. Lower parasympathetic activity at rest was related to increased positivity ratings of pleasant and neutral pictures, increased negativity ratings of neutral pictures, increased ambivalence of neutral pictures, and increased emotional arousal ratings of pleasant pictures. This work suggests depressive symptoms and resting parasympathetic activity have complex effects across dimensions of evaluative processes. It points to a need for more research examining how these factors influence both positive and negative evaluations in both clinical and nonclinical populations.
ABSTRACT The widespread use of TikTok among elementary school students has brought noticeable changes to the way children communicate in their daily lives. The platform is no longer used merely as a source of digital entertainment, but has also begun to shape students’ word choices, speaking styles, and language habits. This condition can be observed among students at MIS Al-Khairaat Pombewe, who have become increasingly familiar with viral expressions, popular abbreviations, slang, and the mixing of Indonesian with foreign languages in everyday conversations. Such circumstances have raised concerns regarding the declining use of proper and standard Indonesian within the school environment. This study employed a descriptive qualitative approach involving the principal, teachers, and students selected purposively as research informants. Data were collected through observations, interviews, and documentation, then analyzed through the stages of data reduction, data presentation, and conclusion drawing. The findings reveal that TikTok exerts a dual influence on children’s language development. On the one hand, the platform contributes to vocabulary expansion, enhances students’ creativity in language use, and broadens their digital knowledge. On the other hand, the intensity of TikTok usage encourages the frequent use of informal language in formal situations, leading to a gradual decline in the use of proper Indonesian according to linguistic norms. Therefore, the involvement of teachers and parents is necessary to guide children toward wiser social media use without neglecting the development of their language abilities. ABSTRAK Fenomena penggunaan TikTok di lingkungan sekolah dasar memperlihatkan perubahan yang cukup nyata pada cara siswa berkomunikasi sehari-hari. Platform ini tidak lagi sekadar dimanfaatkan sebagai hiburan digital, tetapi turut membentuk pilihan kata, gaya berbicara, hingga kebiasaan berbahasa anak. Kondisi tersebut terlihat pada siswa MIS Al-Khairaat Pombewe yang semakin akrab dengan istilah viral, singkatan populer, bahasa gaul, serta pencampuran bahasa Indonesia dengan bahasa asing dalam percakapan mereka. Situasi ini memunculkan perhatian terhadap menurunnya penggunaan bahasa Indonesia yang baik dan benar di lingkungan sekolah. Kajian ini memanfaatkan pendekatan deskriptif kualitatif dengan melibatkan kepala sekolah, guru, dan siswa sebagai informan yang dipilih secara purposive. Informasi penelitian diperoleh melalui observasi, wawancara, dan dokumentasi, kemudian dipahami melalui tahapan reduksi data, penyajian data, dan penarikan kesimpulan. Temuan penelitian memperlihatkan bahwa TikTok memberi pengaruh ganda terhadap perkembangan bahasa anak. Di satu sisi, media sosial tersebut membantu siswa memperluas kosakata, meningkatkan kreativitas dalam berbahasa, dan memperkaya wawasan digital mereka. Di sisi lain, intensitas penggunaan TikTok ikut mendorong penggunaan bahasa informal dalam situasi formal sehingga kebiasaan menggunakan bahasa Indonesia sesuai kaidah menjadi semakin berkurang. Karena itu, keterlibatan guru dan orang tua dibutuhkan agar penggunaan media sosial dapat diarahkan secara lebih bijak tanpa mengabaikan perkembangan kemampuan berbahasa siswa.
Large language models (LLMs) offer a scalable alternative to labour-intensive human normingstudies in psycholinguistic research. This article introduces chatRater, an R package for ratingtext, image, and audio stimuli via multiple LLM providers, and demonstrates its use with a casestudy validating GPT-4o ratings of English idioms against human norms. We extend theprobability-weighted scoring method of Brysbaert et al. (2025) to three idiom-specificdimensions: familiarity, literal plausibility, and decomposability. GPT-4o ratings of 45 Englishidioms were compared against two independent human norming sources (Bulkes & Tanner,2017; Libben & Titone, 2008). Results show moderate-to-strong convergent validity: Pearsoncorrelations ranged from r = 0.38 to r = 0.68 across raw-score comparisons, with familiarity vs.LT2008 showing negligible systematic bias (d = −0.18, p =.239). When converting to rankscores to eliminate distributional differences, correlations remained stable (r = 0.40–0.77), andall paired t-tests became non-significant, confirming that rank orders are well preserved. Thealignment() function in chatRater provides a complete validation pipeline with an optional rank= TRUE parameter for automatic rank-score conversion.
This paper presents the first CLDF database that systematically documents lexical variation across 36 Modern Greek varieties (including Standard Modern Greek).The dataset aligns 14,378 lexical items over 345 concepts, links varieties to stable identifiers (Glottocodes), introduces a Greek-specific concept list, and maps meanings to standardized Concepticon concept sets, enabling interoperability and reproducible workflows.To assess whether the database preserves a meaningful dialectological signal, we conduct a dialectometric analysis by computing feature-sensitive string distances over IPA transcriptions and applying hierarchical clustering.The resulting similarity structure recovers major macro-divisions-most notably a broad Northern vs.Southern partition among Koine-descended mainland varieties-and isolates peripheral groups with distinct historical trajectories (e.g., Asia Minor, Italiot, Tsakonian).The database provides scalable infrastructure for quantitative dialectology, comparative Greek linguistics, and dialect-aware language technology.
AthDGC ("Athens-PROIEL") is an open, end-to-end workflow and dataset. It is, to the best of our knowledge, the first openly licensed dependency-parsed treebank of Greek that spans eight diachronic periods, namely Archaic, Classical, Koine, Late Antique, Byzantine, Late Byzantine, Early Modern, and Modern Greek, under a single PROIEL XML 2.0 schema, with verse-level cross-alignment of the New Testament to Latin (Vulgate), Gothic (Wulfila), Old Church Slavonic (Marianus), and Classical Armenian. AthDGC builds on the PROIEL Treebank Family (Haug and Johndal 2008; Eckhoff et al. 2018), which established the schema and the Koine-Greek reference set for the project. Annotation uses the Stanford Stanza PROIEL-trained workflow; sentence-level alignment uses LaBSE, a multilingual sentence-embedding model; word-level alignment uses multilingual-BERT attention through the AwesomeAlign procedure. The v0.4 release provides curated samples and the open-source toolkit; the full annotated corpus partitions remain under v0.5 audit on the Greek national HPC. Quantitative scale, per-witness verse counts, and per-period annotated-row counts are reported in the v0.5 release notes, after the audit pass completes. Concept DOI: 10.5281/zenodo.20439182.
Materials •The folder titled “subjective_rating” contains the Excel file named “image_selected.” This file provides detailed information about the images used in the study (n = 240), including each image’s filename, transformed normative ratings (1–9) for arousal and valence, and the Picture Valence category (negative, neutral). Subjective Rating Data · The folder ‘subjective_rating’ contains an Excel file named ‘arousal_3phases_ratings’, and ‘valence_3phases_ratings’, which includes raw data on arousal and valence ratings collected for each trial per participant, along with their response times during the rating task, respectively. · The same columns in both files are: Subject, Image, Intervention_Condition, Pic_Valence, Emotion, Phase. · Within-subject factors o Intervention_Condition (labeled, matched) o Picture_Valence (negative, neutral) o Phase (immediate, short-interval, long-interval) Arousal Rating/Valence Rating · Dependent variables o Arousal_post/Valence_post: participants’ arousal/valence ratings collected during the three reexposure phases following the intervention o ArousalRT_post/ValenceRT_post: Response time (in ms) required to provide the arousal/valence rating during the reexposure phases EEG Data EEG Data Recording The EEG files exceed the upload size limitation; therefore, they can be transferred upon request if access is required. The folder titled “EEG_raw_data” contains the usable raw EEG data from 39 participants (pp09–pp57). For two participants (pp11 and pp55), the recordings are stored as two separate files due to technical issues during data collection.
While both segmental and suprasegmental aspects of words have been recognised as potential factors influencing their iconic interpretations, how these components collectively drive the associations of form and affective meaning remains elusive. The current study addressed this issue in a lexical tonal language, Standard Chinese, where suprasegmental pitch information distinguishes word meanings. Specifically, we investigated how phonemic information at both the segmental level (i.e., vowels and consonants) and suprasegmental level (i.e., lexical tones) may influence native Standard Chinese listeners' rating of auditory stimuli's emotional arousal and valence in two-alternative forced-choice tasks. The results indicated a consistent correlation between tones and the perceived arousal and valence ratings of the tone-carrying nonce words. At the segmental level, consonants were more consistently associated with arousal, while vowels correlated with valence. Furthermore, lexical tones were more influential than segmental phonemes in biasing listeners' rating of affective meanings. Regarding arousal ratings, nonce words with falling and rising tones tended to be rated with higher arousal than those with high- and low-dipping tones. Additionally, those with an onset /t/ were rated higher in arousal than those with /n/. Regarding valence ratings, nonce words with falling and low-dipping tones were more likely to receive negative ratings than those with high and rising tones. Moreover, stimuli with /u/ were more inclined to be perceived negatively than those with /i/. Though subtle and sporadic, these findings support the universal tendency of affective iconicity across segments and suprasegmental tones.
This describes how to calculate the reliability of word ratings (or other performance data, such as RTs). It gives R code and an example of how to use it.
This paper revisits and extends Greenberg's Universal 45 on gender distinctions using Universal Dependencies 2.17, comprising 339 treebanks across 186 languages.A systematic analysis of morphosyntactic patterns confirms the implicational hierarchy (singular > plural gender marking), with 98.6 % conformity in pronominal categories.Only two potential exceptions are detected, both with minimal occurrences and likely attributable to annotation errors.Extending the analysis beyond pronouns to 13 UPOS categories shows that core categories maintain near-perfect compliance, while peripheral categories exhibit higher violation rates, primarily driven by annotation inconsistencies rather than genuine linguistic exceptions.A total of 90 treebanks display gender-number features in traditionally invariable categories (e.g., adpositions, conjunctions, adverbs), indicating annotation issues such as prepositional contraction handling, homophone merging, and erroneous feature assignment.The study establishes a replicable computational methodology for large-scale typological validation, highlighting both the potential of corpus-based approaches and key limitations, including genealogical sampling biases, annotation heterogeneity despite universal schemas, and the false sense of comparability across treebanks.
This dataset contains electroencephalogram (EEG), galvanic skin response (GSR), and electrocardiogram (ECG) recordings from 16 healthy participants during an affective music brain-computer interface training study. Participants listened to 40-second music clips designed to induce specific emotional states across three sessions, with subjective valence and arousal ratings collected. The dataset supports research on music-induced emotion recognition and brain-computer interface development for affective state modulation. This is the training dataset; companion datasets for system calibration and online real-time control are also available.
This article reviews a number of studies pertaining to associative linguography, including those conducted with the use of associative dictionaries. It also examines the current state of interdisciplinary applied research focusing on the associative-verbal network, in which a prominent area of scholarly interest is the comparison of associative norms with affective ones. Drawing on data from multiple languages, the extrapolation of the affective and semantic characteristics of words that are associatively related is examined. Significant advances in our understanding of the associative network properties have been achieved by foreign scholars through the development of affective and semantic norm databases. For the Russian language, databases of affective parameters (VAD) and abstractness/concreteness ratings have recently been compiled. Based on the systematized foreign and national approaches to the creation of associative dictionaries, the structure of a thesaurus-type dictionary entry for the New Associative Dictionary with markup according to the affective and semantic parameters is described.
Materials The selected pictures (248 in total: 120 neutral and 128 negative) were drawn from five different standardized affective picture sets (see Table 1 for details on which images were selected from each set). Participants were recruited from Utrecht University via campus advertisements. The Dutch sample consisted of 103 participants (90 females), all native Dutch speakers, with a mean age of 20.93 years (SD = 2.42). Their responses provide the arousal and valence ratings with these 248 images. Task Design This is a emotion rating test conducted online via the Gorilla platform. Each trial began with a 1500-ms fixation, followed by a 2000-ms presentation of the target image. After a 1000-ms delay, the SAM scales appeared and remained on screen until the participant responded, with arousal rated first and valence second (Figure 1). DATA · The folder titled “Open_Data_Dutch_Norm_Data” contains an Excel files named “V1_Dutch_norm_data_arousal_pp103” and “V1_Dutch_norm_data_valence_pp103”, which includes raw data on arousal and valence, along with their response times during the rating task, respectively. · The same columns in both files are: Subject, Gender, Image, Scale, Picture_Valence, Response, Response_Duration. · Within-subject factor: Picture_Valence (negative, neutral)
This paper introduces an updated, publicly accessible version of the Film, Music and Emotion Dataset (FME-24), designed to examine how perceived emotion in film music evolves over time. It provides a comprehensive introduction to the dataset and explores its potential applications across music information retrieval (MIR), psychology, and AI-training contexts. The FME-24 dataset utilises film's immersive qualities to study emotional perception in a naturalistic yet controlled setting. It contains data from 275 film scores spanning the past two decades, including experimental and mainstream works. The dataset integrates high-quality film compositions with time-stamped valence-arousal (V-A) annotations, emotion sentences, familiarity ratings, and detailed metadata. 98 Participants contributed to annotating these temporal emotion features. For each time-stamped point, a two-second audio segment was analysed, and 78 features were extracted, including low-level timbral descriptors (MFCC statistics, spectral centroid), rhythmic descriptors (onset density, tempo), and higher-level psychoacoustic and tonal features (inharmonicity, roughness, chord transitions, tonal entropy). Although full audio files are unavailable due to licensing, reproducibility is ensured via ISRC codes, precise segment timings, and open access to all metadata and feature files in CSV format. The paper details the dataset's structure, annotation, and feature-extraction procedures, highlighting applications in computational and perceptual research and laying a foundation for future studies on emotion, perception, and narrative in film music.
We present MorfFlex, a morphological dictionary architecture suitable for languages with extensive regularity in both inflection and derivation. As the primary example of MorfFlex in use we introduce MorfFlex CZ, a morphological dictionary of Czech. It is distributed as a simple, unstructured list of <wordform, lemma, tag> triplets, however, its manually maintained, unpublished source files and conversion scripts encode a sophisticated system of inflectional and derivational patterns. These patterns dramatically reduce the otherwise enormous size of the dictionary, which currently contains over 100 million wordforms and more than 1 million lemmas. The MorfFlex CZ dictionary serves as an essential resource for ensuring the consistency of manual morphological annotation in the Prague Dependency Treebanks and underpins state-of-the-art automatic tools such as MorphoDiTa. In this paper, we focus on: (i) presenting an effective method for managing the rich morphological system within the dictionary, and (ii) demonstrating the utility of such a language resource for maintaining annotation consistency in corpora and supporting the development of advanced NLP applications.
The expression of an association between a conditioned stimulus (CS) and an aversive unconditioned stimulus (US) can be weakened by presenting the CS by itself (extinction [Ext]), pairing it with an appetitive US (counterconditioning [CC]), or pairing it with a neutral stimulus (novelty-facilitated extinction [NFE]). The present research tested whether NFE is less susceptible to ABC renewal than Ext and CC. In two experiments, participants viewed streams of rapid trials. After each stream, participants rated how likely it was that the target CS would be followed by the target US (i.e., predictive learning) as well as the valence of the target CS (i.e., evaluative conditioning). A stream was composed of two phases: Phase 1 established an association between the target CS and target US while Phase 2 aimed at disrupting the expression of this association through Ext, CC, or NFE. Phase 1 occurred in Context A while Phase 2 occurred in Context B. Prediction and valence ratings occurred in either Context A, B, or C. Neither Experiment 1 nor Experiment 2 found differences across interference conditions with predictive testing, regardless of test context. In Experiment 2, better controlled for context effect, CC and NFE altered the CS valence (CC more than NFE) when testing occurred in B, but the difference disappeared when testing occurred in either A or C. The present data do not support the hypothesis that NFE is less susceptible to ABC renewal than either Ext or CC. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Low-resource languages face a critical challenge in AI development: creating specialized conversational systems without access to massive training corpora. We present a systematic methodology for transforming structured linguistic resources into specialized AI systems, demonstrating that expert-curated lexical databases can serve as effective foundations for conversational AI development. Our approach converts Hindi WordNet into 1.25 million diverse instruction-response pairs, fine-tunes a 12B-parameter language model using resource-efficient LoRA with 4-bit quantization. Evaluation through a Hindi language learning chatbot demonstrates that structured-knowledge-based systems achieve superior pedagogical effectiveness (91.0 vs. 79.4-83.6 for general-purpose models) while maintaining competitive semantic performance and exceptional consistency. The complete pipeline demonstrates a proof-of-concept methodology using Hindi for developing specialized AI systems for any languages with WordNet resources. This work addresses the critical gap in AI accessibility for low-resource languages, offering a practical alternative to corpus-intensive approaches and potentially enabling specialized AI development for the hundreds of languages with existing WordNet resources.
Buildings shape how people feel, yet the mechanisms through which specific facade properties drive affective states remain empirically underspecified. Here we introduce the Cambridge Facade Affect Dataset (CFAD), 86 orthogonally rectified facade images annotated with continuous arousal and valence ratings from 85 participants, and establish a validated pipeline linking machine-vision-derived surface metrics to human affective responses. Focusing on three quantifiable attributes, complexity, transparency (window-to-wall ratio), and materiality (proportion of natural versus artificial surface composition), we show that perceived complexity is the dominant affective predictor, with significant positive associations for both arousal (beta = 0.507, p < 0.001) and valence (beta = 0.376, p < 0.001) and a curvilinear amplification at higher complexity levels. Transparency exhibits an inverted-U relationship with valence, while increasing surface artificiality suppresses arousal and reduces pleasantness consistent with biophilic response theory. Critically, machine-derived metrics show limited direct predictive power over affective outcomes; mediation analyses reveal that human perceptual evaluation functions as a necessary intermediate layer, with perceived materiality significantly mediating the machine-valence relationship (indirect effect = -0.205, p = 0.003). Cross-context validation demonstrates moderate stability of complexity and materiality ratings across image-based and in-situ conditions, while affective responses, particularly valence, exhibit significant context-dependence (ICC = 0.332). These findings advance facade research from descriptive morphological analysis toward predictive, perception-grounded modelling, and provide an empirically validated basis for affect-informed design of the urban environment.
We present EmoWork, a multimodal, multi-label dataset designed to support emotion and stress detection in realistic interpersonal work settings. Interpersonal work-common in occupations such as customer service-often requires workers to regulate their emotional expressions in response to strong affective stimuli. These demands, shaped by organizational display rules, present a unique challenge for affective computing systems, particularly in scenarios where internal emotional states diverge from observable behaviors. Despite this, no public datasets exist that capture such dynamics of affect in naturalistic settings. To address this gap, we collected physiological, behavioral, and self-reported data from call center workers who engaged in role-play scenarios simulating customer service interactions with professional actors portraying dissatisfied customers. The dataset includes self-reported affective ratings, which are used as labels for classification, synchronized recordings from three wearable devices (i.e., Polar H10, Empatica E4, and Muse S), and features extracted from video and audio data. The EmoWork dataset advances affective computing by offering context-rich, multimodal data grounded in realistic interpersonal work scenarios.
Cet article examine la représentation du mariage mixte et les enjeux de la nomination des enfants dans le roman À la frontière de nos deux mondes: La Déchirure de Leila Miloud Ropp, inscrit dans le contexte de l’Algérie coloniale, de l’après-Seconde Guerre mondiale aux prémices de la guerre d’indépendance. À travers l’histoire d’Alice, Française venue des Vosges, et de Mécheri, Algérien musulman de Mostaganem, l’œuvre construit un espace romanesque où l’intime se trouve continuellement traversé par des rapports de domination, des logiques d’appartenance et des fractures historiques. Notre analyse montre que le mariage mixte fonctionne comme un dispositif narratif de confrontation entre deux systèmes de normes; familiales, religieuses, politiques et que l’appellation des enfants issus de l’union constitue un acte social chargé d’idéologie: nommer, c’est assigner une place, tracer une frontière ou tenter une conciliation. En mobilisant une approche croisée analyse littéraire, stylistique et socio-linguistique, nous mettons en évidence les mécanismes discursifs par lesquels le roman fait émerger la « déchirure » identitaire, cristallisée dans les choix lexicaux, les voix rapportées et les scènes de sociabilité. Abstract This article examines the representation of mixed marriage and the issues surrounding the naming of children in Leila Miloud Ropp's novel À la frontière de nos deux mondes: La Déchirure (At the Border of Our Two Worlds: The Tear), set in colonial Algeria from the aftermath of World War II to the beginning of the war of independence. Through the story of Alice, a French woman from the Vosges, and Mécheri, an Algerian Muslim from Mostaganem, the work constructs a fictional space where intimacy is continually disrupted by relationships of domination, logics of belonging, and historical divisions. Our analysis shows that mixed marriage functions as a narrative device for the confrontation between two systems of norms—familial, religious, and political—and that the naming of children born of the union is a social act laden with ideology: to name is to assign a place, to draw a boundary, or to attempt reconciliation. Using a cross-disciplinary approach combining literary, stylistic, and sociolinguistic analysis, we highlight the discursive mechanisms through which the novel brings out the “tear” in identity, crystallized in lexical choices, reported voices, and scenes of sociability
of the norm,
This thesis aims to analyze the linguistic and pragmatic deployment of perlocutionary acts in diplomatic speech by exploring Uzbek and English discourse texts. It endeavors to discover how and in what linguistic and pragmatic ways perlocutionary effects are encoded, spread, and construed within institutional communication practices in general. From a comparative perspective, the study scrutinizes the effect on language/cultural norms of the perlocutionary influence. It demonstrates that these practices of perlocution are established by lexical, grammatical, and discourse practices of politeness norms, indirectness, and communication intentions. This research advances the theoretical study of linguopragmatic language and also presents tangible implications for intercultural and diplomatic communication.
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.
This dataset contains electroencephalogram (EEG), galvanic skin response (GSR), and electrocardiogram (ECG) recordings from 17 healthy participants during an affective music brain-computer interface training study. Participants listened to 40-second music clips (20s per emotional state) designed to induce specific emotional states across three sessions, with self-reported valence and arousal ratings. The data supports the development and validation of music-based brain-computer interfaces for monitoring and inducing affective states. This is the training session dataset; two additional datasets cover system calibration and online real-time control phases.