Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
18265 papers
Abstract Virtual reality (VR) is increasingly adopted across various fields, due to its ability to immerse people in virtual environments (VEs) and induce emotions. A key factor in this experience is the sense of presence, which is the feeling of being in the VE and the perceived realism of the experience. While prior research has demonstrated the importance of presence in driving emotional outcomes, gaps remain in understanding how the type of VEs and individual differences may influence this relationship. The present study addressed these gaps by comparing the strength of the relationship between presence and emotional outcomes across fear-inducing and relaxation-inducing VEs. The study also investigated whether this relationship was moderated by individual differences such as trait absorption and neuroticism. 125 participants were randomly assigned to one of two VEs. Participants completed baseline assessments, experienced the VE, then completed post-test assessments. Emotional outcomes were assessed through subjective emotional valence and arousal ratings. Results showed that the relationship between presence and emotional arousal was significantly stronger in the fear-inducing VE than in the relaxation-inducing VE. Furthermore, trait absorption and neuroticism significantly moderated the relationship between presence and emotional valence in only fear-inducing VEs. No moderation effects were found for the relationship between presence and emotional arousal. The study points that the relationship between presence and emotional outcomes is not consistent and may be influenced by the type of VE, trait absorption, and neuroticism. These findings provide theoretical and practical implications for designing effective VEs for various applications.
This dataset contains data from 135 on-line and 16 off-line participants for the Pilot Test, 42 participants for EXP1, another 42 participants for EXP2, and 8 participants for EXP3. The Pilot Test established the VR environment conditions. Red and blue colors were selected based on arousal and valence ratings. EXP1 used a seated cognitive matching task under two conditions(Red/Blue). Emotion&Perception data were collected before and after the VR experience for Affective response and room appraisal. Objective measures include total matching score, number of fails, and time taken per level for Task Performance. Subjective measures include task workload, spatial perception, cybersickness, preference, and perceived room size/FoV. EXP2 used a physical block-stacking task under two conditions(Red/Blue). Emotion&Perception data were collected before and after the VR experience for Affective response and room appraisal. Objective measures include number of stacked blocks, dynamic movement, error distance, and error rotation for Task Performance. Subjective measures follow EXP1. EXP3 used a rhythm game task under a 2×2 design of color (red/blue) and task type (cognitive/physical). Objective measures include game score and number of good cuts. Subjective measures include task workload and cybersickness. The dataset includes questionnaire from the Sim-TLX, SP-IE, and Simulator Sickness Questionnaire (SSQ), collected during the VR experience.
The current study examines how the presentation of separable components of rapport building in forensic interviews with children affect lay perceptions of both the child victim and defendant guilt. Mock jurors will read a forensic interview in which a child either alleges sexual abuse by an adult male perpetrator or does not. Interviews will either contain a ground rules phase or only a brief introduction. Thus, the study adheres to a 2 (ground rules: present or absent) x 2 (disclosure: present or absent) between-subjects design). Effects of ground rules on perceptions of defendant guilt are not expected. However, it is expected that disclosure will affect ratings of defendant guilt. It is also expected that both ground rules and disclosure will affect perceptions of the child. It is expected that the child will be perceived more positively than when both are present.
Language change is continuous and vital for the sociocultural adaptation and status development of language varieties. Perhaps the most important linguistic feature of Nigerian English language change is semantic extension which refers to the process whereby terms already existing in English (standard English) acquire new meanings. This paper investigated the usage of semantic extension in Nigerian English (NIE), the social motivations responsible for such semantic innovations and the relevance of linguistic innovations for the description and teaching of English in Nigeria. Using descriptive qualitative research design, data for the research were collected using naturally occurring language forms while other information sources included print and social media, educational, political and religious institutions. It was found that semantic extension in Nigerian English is rule-governed, based on culture, multilingualism, technological innovations, socio-economic experiences and indigenous conceptual framework. The data further revealed that the semantic extensions are sociolinguistically recognized by educated users of Nigerian English and linguistic norm and not mere linguistic deviation from Standard English. It was observed that semantic extension promotes lexical reduction, language creativity and the institutionalization of Nigerian English as a variety of the World Englishes paradigm. It was recommended that Nigerian English new lexical items should be incorporated into English textbooks and dictionaries, Nigerian English lexical innovations should form the basis for the teaching of English in Nigeria.
The article describes texts generated by artificial intelligence, with an emphasis on Ukrainian-language material, which remains understudied. The aim of the study was to identify specific identifying characteristics of AI texts by comparing their semantic, structural, and stylistic parameters with texts written by humans. The study combined systematic analysis and synthesis, a comparative approach, content analysis, and quantitative linguistic methods (Type–Token Ratio, syntactic complexity analysis by T-unit), as well as semantic modeling, fact verification, and semantic-stylistic analysis. It has been proven that Ukrainian-language AI texts formally meet the basic criteria of textuality (cohesion, coherence, articulation), which are implemented through the probabilistic combination of templates rather than the author’s cognitive and communicative activity. Typical markers of machine generation have been identified: template composition (introduction – main part (3–5 subtopics) – conclusion), homogeneous paragraphs of “average” length, predominance of direct word order, presence of passive constructions, excessive frequency of formal connectors, structural and lexical monotony, errors in word usage. Semantic analysis revealed a combination of formal correctness with factual “hallucinations,” low information density, a predominance of neutral style, superficial, statistically determined expressiveness, and emotional masking. It was concluded that texts generated by artificial intelligence constitute a separate linguistic phenomenon with its own set of characteristics, which requires a special typology and a flexible, updatable analysis methodology. The risks to linguistic norms, speech culture, and information security are emphasized, as well as the need to develop critical competence in users regarding the perception of AI content.
Speech Emotion Recognition (SER) plays a pivotal role in understanding human communication, enabling emotionally intelligent systems, and serving as a fundamental component in the development of Artificial General Intelligence (AGI). However, deploying SER in real-world, spontaneous, and low-resource scenarios remains a significant challenge due to the complexity of emotional expression and the limitations of current speech and language technologies. This thesis investigates the integration of Automatic Speech Recognition (ASR) into SER, with the goal of enhancing the robustness, scalability, and practical applicability of emotion recognition from spoken language. As a starting point, we explore the interplay between ASR and SER by conducting an in-depth analysis of speech foundation models on emotionally expressive speech, from both acoustic and linguistic perspectives. Our findings uncover inherent limitations of these models: while they achieve strong performance in ASR, they often fail to capture paralinguistic cues essential for emotion recognition, and exhibit emotion-dependent biases. Additionally, we examine how linguistic properties, such as part-of-speech distributions, affective word ratings, and utterance length, interact with ASR performance across different emotion categories. Extensive experiments reveal that ASR errors are not random but follow systematic patterns associated with specific word types and emotional expressions across diverse speaking conditions. To overcome paralinguistic limitations while utilizing the hierarchical encoding capabilities of speech foundation models, we propose a joint training framework that integrates ASR hidden representations and lexical outputs in a hierarchical manner for SER. To mitigate transcription quality issues, we introduce two complementary ASR error correction strategies that require only a small amount of emotional speech data: a Large Language Model (LLM)-based method that refines N-best hypotheses using emotion-specific prompts, and a Sequence-to- Sequence (S2S) model that leverages discrete acoustic units to correct 1-best hypothesis. Both approaches substantially improve the emotional accuracy of ASR transcriptions with low-resource speech data and enhance downstream SER performance. Beyond transcription correction, this thesis introduces robust acoustic-lexical fusion frameworks to handle emotion mismatch across modalities and reduce ASR error propagation. We conduct a comprehensive investigation into cross-modal incongruity and propose incongruityaware fusion strategies that dynamically adapt to modality conflicts, extending solutions beyond SER to include sarcasm and humor detection. Building on this, we develop an ASRii aware, modality-gated fusion mechanism that integrates ASR error correction with dynamic modality selection in a sequential manner. These fusion strategies achieve strong performance on both controlled and real-world datasets, even under partially erroneous conditions (i.e. ASR transcriptions). Finally, to reduce reliance on costly labeled emotion data, we present a novel semi-supervised learning framework based on multi-view pseudo-labeling. By leveraging both acoustic similarity metrics and LLM-based confidence estimation, this approach selects high-quality unlabeled samples to augment training. The proposed framework not only outperforms traditional pseudo-labeling methods for SER but also demonstrates generalizability to speechbased Alzheimer’s dementia detection.
The advanced models of deep neural networks like bidirectional encoder from the transformers (BERT) and others, poses challenges in terms of computational resources and model size. In order to tackle these issues, techniques of model pruning have surfaced as the most useful methods in addressing the issues of model complexity. This research paper explores the concept of pruning BERT attention heads across the ensemble of winning tickets in order to enhance the efficiency of the model without sacrificing performance. Experimental evaluations show how effective the approach is, in achieving significant model compression while still maintaining competitive performance across different natural language processing tasks. The key findings of this study include model size that has been reduced by 36%, with our ensemble model reaching greater performance as compared to the baseline BERT model on both Stanford Sentiment Treebank v2 (SST-2) and Corpus of Linguistic Acceptability (CoLA) datasets. The results further show a F1-score of 94% and 96%, respectively, and accuracy scores of 95% and 96% on the two datasets. The findings of this research paper contribute to the ongoing efforts in enhancing the efficiency of large-scale language models.
ABSTRACT Street’s (1984) concept of “ideological model” advocates for the plural character of literacy, validating all models of writing. From this perspective, marginalized literacy practices are as legitimate as dominant literacy practices, despite being socially stigmatized. In this article, I aim to investigate in what ways the literacy narratives of two adult students transgress social and linguistic norms. I employ a narrative analysis methodology that incorporates the discursive, situated and performative nature of the stories (MOITA LOPES, 2021) I analyze. At the social level, access to reading and writing is, for the students, a subversion of imposed norms that deprived them of the right to education. As women who migrated from the northeast to the southeast of Brazil in search of better living conditions, returning to school and learning to read and write are acts of resistance that challenge the status quo. At the linguistic level, the students reaffirm their enunciative intentions by transgressing the standard norm of the language, expanding the possibilities of meaning in their texts through the subversive use of punctuation and deixis. The outcomes emphasize the need to recognize the students’ literacy journey as a social criticism against insufficient policies on the provision of quality education for all. Furthermore, the outcomes point to the need for language teaching and learning approaches that recognize non-institutionalized models of literacy as valid knowledge that enhances and enriches language learning, minimizing the abyssal line (SANTOS, 2010) between orality and literacy as well as between school and non-school knowledge.
The amygdala and hippocampus are central to emotional processing, yet the transient neural dynamics coordinating these regions remain unclear. We simultaneously recorded single-neuron activity and local field potentials from both regions in epilepsy patients during an emotional image-rating task. Neurons in both regions responded to images with firing rate changes that predicted subjective ratings of extreme pleasantness or unpleasantness. To examine the underlying oscillatory mechanisms, we analyzed beta bursts (13-30 Hz)-transient, high-power events-since conventional spectral analyses revealed no valence-specific patterns. Beta bursts were associated with increased gamma amplitude and enhanced phase coherence in both structures, with beta-gamma phase-amplitude coupling capturing emotion-related dynamics. Critically, amygdala beta bursts strongly suppressed hippocampal firing through interneuron activation during negative valence processing, whereas hippocampal bursts showed no reciprocal influence. These findings suggest that beta bursts provide a temporal code for emotion and represent a candidate mechanism for targeted neuromodulation in mood disorders.
This paper is an introduction to using the Treebank Semantics Parsed Corpus (TSPC) and its online interface.The TSPC is a collection of English with hand worked tree annotation for approaching half-a-million words.The annotation gives a resource of general use but is notable as content to feed a calculation of meaning representations for insights beyond surface syntax.The online interface has functionalities of search, summary and visualisation for presenting the source files of the corpus.The paper ends with a case study demonstrating how to access data for a language education task, with pinpoint insights gained because of the detailed analysis offered by the corpus.
This article discusses the controversial issue of which variety of English learners and instructors should use. The United Nations’ Sustainable Development Goal 4, Target 4.7 states that learners need knowledge to promote peace and cultural diversity. It is argued here that a broad awareness of English varieties is a way to achieve this goal, and that the use of English in online environments can contribute towards its attainment. Two surveys were carried out to investigate how attitudes to varieties of English and to non-standard linguistic norms are changing. They were sent to students of English on online courses at a Swedish university in 2018-2019 and 2019-2021, and were responded to by 100 and 92 informants, respectively. The first survey demonstrated that the informants did indeed have a less strict view of which variety of English to learn and use, while the second survey demonstrated that features such as the omission of subjects, and informal spellings such as “yeah” appear to have become standardised and acceptable in all environments. These results appear to support what has been discussed in the literature, namely, that the use of English online is leading learners to review their beliefs and adopt a less strict view of varieties and norms. Thus, it is argued that online English usage can contribute towards the achievement of Sustainable Development Goal 4, Target 4.7, cultural diversity and an awareness of the multifaceted dimensions of a global language in the 21st century.
This study employs Pierre Bourdieu’s theories of cultural capital, habitus, and field to dissect the symbolic exclusionary dynamics between Z-generation internet slang and older generations’ linguistic competence in social media ecosystems. Through a cross-linguistic discourse analysis of youth neologisms (e.g. Chinese ‘yyds’, English ‘stan’, Japanese ‘tsundere’) and their intergenerational misinterpretation patterns, this research constructs a three-dimensional Linguistic Distinction-Generational Habitus framework, revealing how slang functions as a form of embodied digital capital and theorising the mechanisms of symbolic violence in algorithmic cultural fields.. The framework illuminates: (1) how digital-native youth deploy slang as embodied cultural capital to establish symbolic boundaries within online fields; (2) the habitus clash between pre-digital linguistic norms (prioritising standard language) and digital-native norms (valuing fluid, ironic expression); and (3) the mechanisms of symbolic violence through which youth assert decoding hegemony via slang’s rapid semantic turnover. By situating these dynamics within Bourdieu’s concept of ‘symbolic power’, the study reveals that internet slang functions as both a marker of digital literacy and a site for reproducing generational cultural hierarchies. Cross-cultural case comparisons demonstrate how linguistic practices in digital spaces become arenas for negotiating generational legitimacy, with implications for understanding intergenerational divides in tech-driven societies.
Without social communication, people's daily lives and any of their activities are entirely unimaginable. A specific form of social communication is verbal interaction, despite modern psychological data indicating that 65% of information in communication is conveyed through nonverbal means. Every specific act of verbal communication carries a pragmatic orientation toward the partner involved in the interaction. Verbal communication inherently involves the execution of speech acts, which also includes the use of speech etiquette. In reality, it is a valuable component of any human behavior, significantly influencing the effectiveness of its completion. People need shared knowledge of etiquette, including speech etiquette norms, in the communication process to successfully establish effective communication. Etiquette is primarily a social phenomenon, but it is also a cultural, historical, and psychological one. It inevitably has a linguistic aspect—more specifically, linguistic etiquette, which refers to the set of linguistic norms characteristic of communication in various situations. In specialized literature, there is an opinion that insufficient attention is given to the psychological nature of speech as a specific type of activity. However, it has long been a subject of special study in psycholinguistics (e.g., Austin's theory of speech acts, 1955). According to D. Uznadze’s model of set structure, known as the static model, speech activity is considered an independent behavior, defined by its own autonomous set. It does not matter whether it arises from a necessity within another ongoing behavior or is directly driven by the need for verbal communication. In contrast, the dynamic model of speech activity and, consequently, speech etiquette, allows us to view it as an activity shaped by the need for linguistic communication, i.e., linguistic set. This activity constitutes a significant component of behavior, adapting dynamically to the primary attitude of that behavior. In this model, the tendency for permanent modification is embedded from the outset, eliminating the need for the destruction or reconstruction of sets. It considers both practical and theoretical activities directed toward a common goal as actions integrated into the same behavior, operating based on its fundamental set.
Abstract Research on the progressive aspect in Germanic and Romance languages has benefited from corpus data. A transparent, objective, reliable and replicable identification of such constructions in corpora is however challenging. The present chapter presents preliminary methodological work in automatically retrieving and counting authentic examples from treebanks, that is, grammatically annotated corpora. It demonstrates how selected constructions that mark the progressive in Italian and Norwegian are collected from treebanks accessible through the INESS platform. Deep syntactic relations such as those between predicates and arguments are factored in and quantified. Corpus queries that exploit syntactic dominance relations are potentially more powerful than queries using only linear precedence, but there is a relative shortage of treebank resources.
The intricate interplay between visual perception and emotion determines how waking experience influences mentation through a 'day residue' at once conspicuous yet hard to predict. Here we set out to map the neural sources associated with the visuo-affective processing of the 'day residue' during hypnagogic sleep. To this end, we assessed 28 healthy participants on a combined nap protocol with serial awakenings, pre-sleep stimulation with affective visual images, yoked measures of the semantic similarity between image and imagery reports, affect ratings, estimation of 64-channel EEG sources, and functional connectivity analysis. Overall, low-frequency EEG power was associated with weaker residues, and high-frequency EEG power was associated with stronger residues. The source networks most significantly correlated with imagetic and affective residues were markedly different across wake-sleep states, partially overlapping with the default mode network during N1 for up to 50% and 61%, respectively. The results allowed us to identify neural correlates of the visuo-affective processing of the day's residue, showing that the hypnagogic processing of the waking experience involves complex, dynamic and sequential bi-hemispheric interactions among multiple cortical, subcortical, and cerebellar structures with visual, limbic, optokinetic, and cognitive functions.
Do images in corporate disclosures convey genuine information or merely embellishment?&nbsp; We advance the visual informativeness literature by introducing a framework delineating how images reinforce text and what properties of such reinforcement matter. Our methods apply wherever images and text coexist. We classify reinforcement as substantive or hype — operational or merely evocative. Testing in an image-dense setting (a large sample of U.S. sustainability reports), we find that reinforcing image pages predict expert ratings more strongly than non-reinforcing ones, with the effects amplified by proximity, potency, and sentence importance. Sophisticated raters respond more strongly to substantive than hype reinforcement. PCA-based composite indices provide robust validation, and the vehicle for causal identification within a quasi-experimental setting: TruValue ratings — machine-driven and blind to company-issued images, unlike Refinitiv ratings — reveal no association with images, suggesting that images affect ratings through a human processing channel, not through a machine algorithm that lacks the human-image interaction. <br> <br> This paper was previously circulated under the title: <b>"Going the Distance:" Images in Corporate ESG Reports</b>
Sentiment analysis is an essential component of natural language processing, which focuses on extracting subjective insights, like emotions and opinions from text. In this research, an innovative framework has been introduced for Twitter sentiment analysis that integrates Echo State Networks (ESN), Improved Student Psychology Based Optimization (ISPBO), and BERT embeddings. The proposed ESN-ISPBO-BERT model was evaluated on the SemEval-2016-1 and SemEval-2016-2 datasets and compared against other models, including SVM-Glove, CNN-BERT, LSTM, CNN, KNN, SVM, BERT, and GRU. The outcomes indicate that the suggested model exceeds all baseline models, and has achieved outstanding performance. On SemEval-2016-2, it achieves 98.82% accuracy, 98.79% precision, 98.96% recall, and 98.87% F1-score, while on SemEval-2016-1, it achieves 98.76% accuracy, 98.81% precision, 98.92% recall, as well as 98.86% F1-score. Moreover, the suggested model achieved the values of 98.51%, 98.42%, 98.87%, and 98.64% in terms of accuracy, precision, recall, and F1-score on Stanford Sentiment Treebank (SST-2). These outcomes indicate the efficiency of combining reservoir calculating, innovative optimization techniques, and contextual embeddings for the aim of sentiment analysis. The proposed model is a strong solution for the evaluation of Twitter data, with possible applications in brand monitoring, analyzing customer feedback, and tracking sentiment in real-time. This investigation represents the importance of hybrid strategies in tackling the issues of sentiment analysis on social media.
This paper introduces a Bias-aware Text Mining: System, a novel approach to address challenges in Bias-Aware Text Mining: A Novel Framework for Fair and Transparent Language Models. Our BTMS framework leverages advanced algorithms to improve the performance metrics by approximately <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4 1 \%}$</tex> compared to existing methods. Experiments conducted on standard datasets demonstrated the effectiveness of the proposed approach, particularly in terms of accuracy. The proposed system integrates multiple computational techniques, including group theory, combinatorial optimization, and knowledge distillation, to create a robust solution that outperforms current state-of-the-art methods. Specifically, a comprehensive evaluation using the PENN Treebank and WMT-14 demonstrates that BTMS achieves superior performance across multiple evaluation criteria. Our establishment addresses key limitations in existing techniques by incorporating an adaptive learning rate and cross-domain adaptation, which enables more effective handling of complex data patterns. The experimental results confirm that our methodology reduces the computational complexity while maintaining high accuracy, making it suitable for real-world applications with resource constraints. We also conducted ablation studies to analyze the contribution of each component to the overall performance (a key finding), revealing that the embedding module is particularly critical for achieving optimal results. In addition, we performed a sensitivity analysis to assess the robustness of the BTMS under varying conditions, confirming its stability across different operational scenarios. Importantly, the theoretical analysis provides formal (notably) guarantees of the convergence properties and computational efficiency of our algorithm. Finally, we discuss the potential applications of our approach in related domains and outline directions for future research to enhance the capabilities of the proposed system further.
Artificial intelligence (AI) plays a pivotal role in modern society, especially in business intelligence, where it drives insights for customer feedback analysis, targeted recommendations, and strategic marketing. Within AI, natural language processing (NLP) enables machines to interpret and analyze human language, with sentiment analysis being a vital subset focused on understanding nuanced emotions in text. This research introduces Fine-tuned DeBERTaV3 with Adaptive Training Strategies (FiTDeBERTaV3-ATS), a model designed for fine-grained sentiment analysis that integrates DeBERTaV3 with an attention mechanism, cross-fold training, and multi-sample dropout. Evaluated on the fine-grained Stanford Sentiment Treebank (SST-5) dataset, the proposed model achieved an improved accuracy of 62.40%, surpassing existing baselines and highlighting DeBERTaV3's effectiveness for this task. These tailored strategies address the unique challenges of SST-5, such as figurative language and a limited dataset size, significantly enhancing the model's ability to capture subtle sentiment distinctions.
BACKGROUND: Lamotrigine has been shown to be effective in the long-term treatment and relapse prevention of depression in bipolar disorder. However, the neuropsychological mechanisms underlying these effects are unclear. We investigated the effects of lamotrigine on a battery of emotional processing tasks in healthy volunteers, previously shown to be sensitive to antidepressant drug action in similar experimental designs. METHODS: Healthy volunteers (n = 36) were randomized in a double-blind design to receive a single dose of placebo or 300 mg lamotrigine. Mood and subjective effects were monitored throughout the study period, and emotional processing was assessed using the Oxford Emotional Test Battery (ETB) 3 hours post-administration. RESULTS: Participants receiving lamotrigine showed increased accurate recall of positive versus negative self-descriptors, compared to those in the placebo group. There were no other significant effects on emotional processing in the ETB, and lamotrigine did not affect ratings of mood or subjective experience. CONCLUSIONS: Lamotrigine did not induce widespread changes in emotional processing. However, there was increased positive bias in emotional memory, similar to the effects of antidepressants reported in previous studies. Further work is needed to assess whether similar effects are seen in the clinical treatment of patients with bipolar disorder and the extent to which this is associated with its clinical action in relapse prevention.
Dependency parsing is essential for language modeling because it offers a structured understanding of the syntactic relationships between words in a sentence. While recent advancements in large language models have greatly advanced the field of natural language processing, dependency parsing remains highly relevant for several key reasons. This paper introduces an effective method for improving dependency parsing which is based on a semantics-aware token embedding model. We propose to incorporate the ConceptNet embeddings which are trained by a retrofitting algorithm into a bidirectional recurrent neural network. The new model outperforms a strong baseline that employs a state-of-the-art method across three dependency treebanks, covering both low-resource and high-resource languages—Indonesian, Vietnamese, and English—achieving an improvement of approximately 1.21% in labeled attachment score.We also show that this method outperforms the popular transformer-based BERT model in capturing syntactic dependency between tokens. The new parser together with all trained models are made available under an open-source license, facilitating community engagement and advancement of natural language processing research for two low-resource languages with around 300 million users worldwide.
Introduction: Receptive Music Therapy allows individuals with sub-clinical anxiety levels to self-medicate when and where they choose, but the effectiveness of self-administered 'music medicine' to enhance psychological well-being is still being investigated. The current study reports on a song ('Bagels') designed to alleviate mild anxiety in adolescents and young adults. Methods: A laboratory study was conducted to examine the effect of Bagels on brain states, and upon both subjective and objective measures of state anxiety. Measures of skin conductance and heart rate (HR), and 64-channel Electroencephalography (EEG) were obtained from 30 young adults as they listened to six songs contrasting affective properties. Subjective measures included ratings of a song's pleasantness, arousal, dominance, and likability, as well as estimates of state anxiety obtained immediately after listening to them. Results: Preliminary analyses revealed that the six songs differed significantly in terms of affective ratings, with Bagels rated as more pleasant and less arousing, and having lower state anxiety ratings at its terminus. EEG alpha connectivity was also lowest for the song Bagels, particularly in the brain's frontal lobes. Similarly, Bagels was associated with lower physiological arousal, reflecting less arousal and greater calmness. Discussion: Combined, the analysis suggest that Bagels has the potential to be an effective digital anxiolytic. Discussion around the promise of music medicine and aspects of its management are presented, along with avenues of further inquiry.
Online survey using 75 animal images. Japanese adults (N = 102) rated the valence, arousal, approach-avoidance, and kawaii (cuteness) of the images on a 9-point scale. The dataset was used in Nittono, H., Kitamura, A., & Ihara, N. (2025, July 8–11). Can holding a huggable pillow modulate affective facial responses to animal pictures? [Poster session]. The 22nd World Congress of Psychophysiology (IOP 2025), Krakow, Poland.
Implicit discourse relation recognition (IDRR) aims to infer logical semantic relations between two adjacent text spans (also named arguments) without explicit indictive connectives, which is crucial to discourse analysis. Previous methods primarily focus on capturing the semantic features of discourse or the complex interaction patterns between the two arguments, including the utilization of pre-trained language models (PLMs). However, these approaches often overlook the fact that the semantic understanding of an argument cannot be interpreted independently from the overall paragraph-level discourse structure. Additionally, information about paragraph structure can help organize the context to establish semantic coherence in discourse from the perspective of cognitive linguistics, thereby creating a clear cognitive framework for the reader. Therefore, we propose a multi-hierarchy graph convolutional network framework based on paragraph-level discourse units for IDRR. Unlike conventional methods that utilize arguments as language units, we explore discourse units (DUs) within the broader context of a paragraph. Specifically, we employ the Stanford Parser to capture the syntactic dependency clues of each sentence within the DU and integrate them into the sentence representation learning. To further extract the structural information of paragraphs within the discourse, we leverage a multi-hierarchy graph convolutional network to derive the DU representation enriched with structural cues. Subsequently, we adopt multi-head attention to obtain important and structurally enhanced semantic features. The experimental results on the Penn Discourse TreeBank (PDTB) demonstrate that our model achieves performance comparable to that of benchmark models.
Everyday experiences can evoke positive feelings that differ among individuals and guide their behavior. Although reward processing is often linked to positive feelings, few studies have assessed the mechanisms underlying subjective positive affect. Whether varied positive experiences share brain mechanisms that are predictive of subjective positive affect is unclear. Here, we used fMRI and predictive modeling to investigate how multiple dynamic, personalized positive experiences are encoded in the brain. Neural representations and functional integration of brain areas during experiences of monetary reward, social media, music, and positive autobiographical memories were used to predict participants' affect ratings of each experience. We found that, across experiences, positive affect was encoded in multivariate neural patterns and functional coupling of distributed cortical and subcortical brain areas, as well as some canonical value-linked brain regions, like the orbitofrontal cortex. We also found that more restricted sets of brain representations and functional connections linked to sensory processing were involved in encoding type-specific subjective positive affect. Our findings suggest that positive affect may be computed and communicated throughout the brain.
This study explores the use of slang words in the post and comment sections of the “Jual Beli Akun Mobile Legends Indonesia” Facebook community. The purpose of this research is to identify the types of slang and the types of meaning conveyed in the communication among users within this digital marketplace. Using a qualitative descriptive method, the data were collected through document analysis from 105 posts and 17 comments over the span of one week. The study applied Allan and Burridge’s classification of slang (Clipping, Acronym, Fresh and Creative, Imitative, and Flippant) and Leech’s theory of meaning (Conceptual, Connotative, Stylistic, and Affective). The findings revealed that Clipping was the most dominant slang type, indicating a preference for brevity and efficiency. Connotative meaning was also the most prevalent, showing how slang reflects not only linguistic function but also social and emotional context. The results suggest that slang in this online community is used not only to simplify communication but also to build shared identity, express sentiment, and reinforce group belonging. This study contributes to understanding how digital communities, especially those based on gaming and trading, develop and sustain their own informal linguistic norms. Types of Meaning
This article utilizes contextual semantic description technology, the first step in linguistic database preparation for building a corpus of religious texts. The book "Sahih ul-Bukhari" was chosen as the study's subject for this. The scientific contribution of the article is that it explains the processes of manual and automatic database preparation. The advantages of the contextual semantic description technique are explained, namely for religious and classical texts. This study examines the semantic fields and categorical classifications of 428 lexical units extracted from the referenced corpus, utilizing Mukhammad Sadiq Mukhammad Yusuf’s "Sakhih ul-Bukhari" (1-juz, 2019) as its primary source, and provides a comprehensive account of their semantic annotation. Special emphasis is placed on the high-frequency terms "Book" and "Hadith," which are analyzed through thematic grouping, semantic tagging, and examination of keyword-based co-occurrences. This study identified 18 distinct thematic categories. The research utilizes methodological approaches such as keyword and collocational analysis, thematic modeling, and semantic tagging to examine the corpus. The methods used in the study include thematic modeling, semantic tagging, and contextual analysis of lexical units. The discussion of semantic challenges and descriptive methodologies presented here contributes significantly to the analytical paradigms within corpus linguistics.
Bidirectional transformers excel at sentiment analysis, and Large Language Models (LLM) are effective zero-shot learners.Might they perform better as a team?This paper explores collaborative approaches between ELECTRA and GPT-4o for three-way sentiment classification.We fine-tuned (FT) four models (ELECTRA Base/Large, GPT-4o/4o-mini) using a mix of reviews from Stanford Sentiment Treebank (SST) and DynaSent.We provided input from ELEC-TRA to GPT as: predicted label, probabilities, and retrieved examples.Sharing ELECTRA Base FT predictions with GPT-4o-mini significantly improved performance over either model alone (82.50 macro F1 vs. 79.14ELECTRA Base FT, 79.41 GPT-4o-mini) and yielded the lowest cost/performance ratio ($0.12/F1 point).However, when GPT models were fine-tuned, including predictions decreased performance.GPT-4o FT-M was the top performer (86.99), with GPT-4o-mini FT close behind (86.70) at much less cost ($0.38 vs. $1.59/F1 point).Our results show that augmenting prompts with predictions from fine-tuned encoders is an efficient way to boost performance, and a fine-tuned GPT-4o-mini is nearly as good as GPT-4o FT at 76% less cost.Both are affordable options for projects with limited resources.
This study examines the interplay between sociolinguistic competence and language transfer in English as Second Language (ESL) learning within Omani higher education. With Arabic as the first language, Omani ESL learners navigate linguistic and cultural influences that shape their English proficiency. Grounded in transfer theory and sociocultural perspectives on second language acquisition, this research investigates how institutional and cultural contexts impact sociolinguistic competence and the extent to which Arabic linguistic norms contribute to language transfer. Using a comparative methodology, data was collected through surveys, interviews, and language use analysis from students across three distinct institutions: Sultan Qaboos University (public-urban), Muscat University (private-urban), and Sur University College (public-rural). Findings indicate that sociolinguistic competence varies based on institutional setting, exposure to English, and interactional norms, with Arabic exerting both positive and negative transfer effects. The study underscores the challenges of integrating sociolinguistic competence into ESL curricula and provides recommendations for enhancing pragmatic training, pedagogical strategies, and teacher professional development to mitigate negative transfer and improve communicative proficiency.
Revealing the relationships between climate change and environmental shifts is extremely important for preserving the cultural identity of countries. The purpose of the presented study was to analyse the impact of environmental changes on toponymy in England and Kazakhstan. The paper emphasizes the relevance of toponyms as vital elements of cultural heritage. The methods used in the paper were based on an interdisciplinary approach. Several cartographic and linguistic databases were used as data source. These databases provided reliable geographical and linguistic information for comparative analysis.To study the relationship between еnvironmental changes and changes in toponymy, the paper combined linguistic, ecological and cultural analyses. The results showed that a comparative analysis between England and Kazakhstan made it possible to identify thematic groups for studying common features and differences in the evolution of toponymy under the influence of both natural and anthropogenic factors. The analysis of the relevant literature has demonstrated how these changes can lead to the change or disappearance of existing toponyms. The results of this study must expand the understanding of toponyms as cultural markers that dynamically respond to environmental transformations. The study emphasizes the scientific novelty of comparing the toponymy of two different countries. It also offers recommendations for the preservation of this heritage. The need for further interdisciplinary research to study the consequences of environmental changes for cultural identity and memory is emphasized.
A variety of pruning methods have been introduced for over-parameterized Recurrent Neural Networks to improve efficiency in terms of power consumption and storage utilization. These advances motivate a new paradigm, termed `hyperpruning', which seeks to identify the most suitable pruning strategy for a given network architecture and application. Unlike conventional hyperparameter search, where the optimal configuration's accuracy remains uncertain, in the context of network pruning, the accuracy of the dense model sets the target for the accuracy of the pruned one. The goal, therefore, is to discover pruned variants that match or even surpass this established accuracy. However, exhaustive search over pruning configurations is computationally expensive and lacks early performance guarantees. To address this challenge, we propose a novel Lyapunov Spectrum (LS)-based distance metric that enables early comparison between pruned and dense networks, allowing accurate prediction of post-training performance. By integrating this LS-based distance with standard hyperparameter optimization algorithms, we introduce an efficient hyperpruning framework, termed LS-based Hyperpruning (LSH). LSH reduces search time by an order of magnitude compared to conventional approaches relying on full training. Experiments on stacked LSTM and RHN architectures using the Penn Treebank dataset, and on AWD-LSTM-MoS using WikiText-2, demonstrate that under fixed training budgets and target pruning ratios, LSH consistently identifies superior pruned models. Remarkably, these pruned variants not only outperform those selected by loss-based baseline but also exceed the performance of their dense counterpart.
This study explores the sociolinguistic characteristics of Generation Alpha's language use on TikTok, a platform blending creativity and social interaction. Examining code-switching, slang, and the influence of visual elements highlights how these features shape digital identities and cultural expression. The research uses qualitative methods like content analysis and user interviews to reveal how Gen Alpha adapts language to TikTok’s standards while challenging traditional linguistic norms. The findings demonstrate that digital communication significantly impacts the language of today’s youth, showcasing Generation Alpha’s sensitivity and inventiveness in coining new terms driven by rapid technological advancements. Social, cultural, and technical factors influence these communication patterns, with video and visual exchanges enhancing communication skills, shaping social identities, and fostering intergenerational connections. This research emphasizes the role of platform-specific language practices in shaping teenage culture and advancing sociolinguistics in social media contexts. It concludes that Generation Alpha employs a relaxed, creative, and trend-sensitive vocabulary, particularly on TikTok, reflecting contemporary digital communication trends. The study underscores the importance of understanding language dynamics in an ever-evolving digital landscape.
When encountering a potential threat, humans and animals engage in different strategic behaviours, such as orienting and defence, depending on the perceived threat imminence. Orienting has been associated with attentional immobility and heightened 'stimulus intake', while defence is linked to action preparation and 'sensory rejection'. First, we replicated previous findings showing that humans exhibit either heart rate (HR) acceleration or deceleration in response to the same threat-related picture content. Second, we provide direct evidence that orienting, as indexed by increased HR deceleration, leads to enhanced visuocortical processing of threat-related images, as measured by steady-state visual evoked potentials (ssVEPs). Excitation of motor-relevant cortical circuits, assessed by beta-band desynchronization, was reduced in relation to HR deceleration. Conversely, HR acceleration was associated with a reversed pattern: reduced visual processing and increased excitation of cortical motor circuits, as reflected in ssVEP and beta-band modulations. While self-reported measures of state and trait anxiety, along with valence, arousal and dominance ratings, did not account for variations in HR response patterns, shorter self-paced viewing time of looming threat pictures was linked to defensive HR changes, whereas orienting-like HR responses were associated with longer avoidance latencies.
Introduction Neurodegenerative diseases like progressive supranuclear palsy (PSP) present challenges concerning their diagnosis. Neuroimaging using magnetic resonance (MRI) may add diagnostic value. However, modern techniques such as volumetric assessment using Voxel Based Morphometry (VBM), although proven to be more accurate and superior methods compared to MRI, have not gained popularity among scientists in the investigation of neurological disorders, due to their higher cost and time‐consuming applications. Conventional brain MRI methods may present a quick, practical, and easy to use imaging rating tool for the differential diagnosis of PSP. The purpose of this study is to evaluate a string of existing visual MRI rating scales and signs with regards to their impact for the diagnosis of PSP. Materials and Methods The population study consisted of 30 patients suffering from PSP and 72 healthy controls. Each study participant underwent a brain MRI, which was subsequently examined by two independent researchers, in a double-blinded fashion. Fifteen visual rating scales and signs were evaluated including pontine atrophy, cerebellar atrophy, midbrain atrophy, aqueduct of Sylvius enlargement, cerebellar peduncles hyperintensities, &omicron;&kappa;&iota;8&theta; of the fourth ventricle (100% sensitivity ‐ 71% specificity) and left temporal lobe atrophy (97% sensitivity ‐ 78% specificity). Conclusions Enlargement of Sylvius aqueduct, enlargement of the fourth ventricle and atrophy of both temporal lobes together with presence of morning glory and hummingbird signs can be easily and quickly distinguished and identified by an experienced radiologist without involving any complex analysis making them a useful tool for the PSP diagnosis. MRI visual scale measurements could be added to the diagnostic criteria of PSP and may serve as an alternative to highly technical and more sophisticated quantification methods.
Combinations of a locative preposition and an infinitive are often used to express aspectual relations. In Dutch the relevant combinations are those with aan het, op and uit. The first is the most studied, but also the least understood: its syntactic status is a bone of contention (Broekhuis et al. 2015), and the study of its meaning is skewed by the near-exclusive focus on the combination with zijn ‘be’. According to Coppen (2021) it is the greatest parsing mystery in Dutch grammar. A recent attempt to get out of the impasse is Bogaards et al. (2022). It includes other combinations than those with zijn, draws a distinction between progressive and ingressive uses and shows that this semantic distinction correlates with syntactic differences. This is a step forward. Less felicitous, though, is the sui generis approach of the analysis, involving the postulation of ad hoc syntactic categories (AANHET1(P) and AANHET2(P)). It also has some descriptive and technical problems. As an alternative this article proposes an analysis that is cast in terms of independently motivated categories and distinctions, that avoids the technical problems and that is straightforwardly extensible to the op- and uit-infinitives. For empirical grounding and exemplification we employ two treebanks of contemporary Dutch.
This study investigates the effects of background music-specifically, tempo and modeon consumer decision-making during utilitarian online shopping, using electroencephalography (EEG). To address the limitations of traditional self-report methods, which are often affected by social desirability bias, and to fill the gap in neuromarketing research within e-commerce, we conducted a controlled experiment with twelve healthy participants. Subjects performed a series of low- and high-complexity shopping tasks while recording EEG data. Twelve classical music excerpts, categorized into four non-lyrical tempo-mode conditions, served as auditory stimuli. We extracted band-power features from EEG to differentiate cognition states, complemented by repeatedmeasures ANOVA and post-hoc analyses. Results showed a significant effect of music condition on decision <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(F(13,11)=1.94, p=0.039)$</tex>, with fast-tempo minor-mode conditions significantly enhancing accuracy in low-complexity tasks compared to fast-tempo major-mode conditions <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(F(2,22)=9.91, p<0.001)$</tex>. However, mode differences were not significant under high-complexity scenarios <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(t(11)=0.29, p=0.777)$</tex>. EEG-based frontal alpha asymmetry revealed positive affective states in non-music, fast-major, fast-minor, and select slow-minor conditions, contrasting negative affect in most other scenarios. Interestingly, these neural affect indicators did not correlate strongly with decision accuracy or subjective valence ratings, suggesting music may have subconscious emotional effects independent of overt self-report measures.
Phrygian-KUL is a treebank of the ancient Phrygian language for Universal Dependencies (UD). Having originally only annotated the New Phrygian subcorpus, this dataset is continuously being updated to include the entire epigraphic corpus. For more information, please visit the relevant page at the UD project site or the repository on Github.
This study investigates the translation of 145 Arabic idioms by two prominent machine translation systems: Google Translate and ChatGPT. The research employs both quantitative and qualitative methodologies to analyze translation approaches and assess accuracy in conveying idiomatic meanings from Arabic to English. Data collection involved Arabic idiomatic expressions from literary sources, cultural texts, and linguistic databases. The analysis framework builds upon Baker's (1992) taxonomy of translation strategies. Quantitative findings reveal that Google Translate employed literal translation in 74% of cases, while ChatGPT demonstrated more varied approaches with 48% literal translations. For sense-based translations using non-figurative language, ChatGPT led with 41%, compared to Google Translate's 15%. When examining figurative language translations, ChatGPT achieved 11% compared to Google Translate's 11%. The qualitative analysis highlights persistent challenges in both systems regarding cultural context preservation and semantic accuracy. The study concludes that while technological advances have improved machine translation capabilities, rendering Arabic idioms into English remains problematic due to cultural-linguistic gaps and contextual complexities inherent in idiomatic expressions.
Background Dementia can impair cognitive functions in older adults and further affect language abilities, such as sentence construction. The use of linguistic biomarkers for detecting cognitive decline and early stages of dementia has demonstrated great potential due to its low-cost and non-invasive nature, especially towards a large aging population.Aims In this study, we focused on the syntactic complexity and aimed to identify highly discriminative syntactic features and those set in differentiating Mandarin-speaking older adults with and without cognitive impairment using machine learning models.Methods & procedures We established a new cohort consisting of 52 Chinese older participants, which has not been reported in any previous studies. After applying exclusion criteria, 49 were selected for this study, including 24 labeled as cognitively normal (CN) and 25 as cognitively impaired (CI) based on their scores in the MoCA-B test (Chinese version). Each of the participants completed three connected speech tasks: a picture description task (the Picnic Scene), a story narrative task (Sanmao’s Wanderings), and a story recall task (Cowherd and Weaver Girl). The speech recordings were automatically transcribed into text files and manually checked. We then performed treebank annotation, which was also manually corrected. Subsequently, 17 linguistic unit-based and 10 syntactic structure-based features were extracted from the texts using Python codes. To identify key syntactic features and feature sets, we first examined the discriminative power of these features per se for distinguishing between the CI and CN groups. Then we employed three machine learning algorithms to identify feature combinations performing the best in terms of high accuracy and AUC in cross-validation and minimal number of basic features.Outcomes & results No single feature or feature panels showed high discriminability between groups across all three tasks. However, certain macro-level features with high measurement robustness, such as mean depth of nodes, mean dependency distance, and the ratio of head-final dependencies in specific tasks are noteworthy. Moreover, through machine learning algorithms, we identified several well-performing feature combinations in terms of the metrics of cross-validation mean accuracy and area under curve (AUC). Notably, a feature panel with only two features (mean depth of nodes, and proportions of prepositional dependencies) achieved the best result across classifiers in the story narrative task (ACC = 0.840, AUC = 0.867).Conclusions This study demonstrated the potential of using purely treebank-derived features within a single linguistic domain (syntax) for cost-effective cognitive impairment screening, indicating the value of syntactic biomarkers for cognitive impairment in Mandarin-Speaking Older Adults.
<p class="TableParagraph">This research analyzes Generational Shifts and Linguistic Tensions: Controversial Use of Slang in Heterogeneous Islamic Boarding School Communities. The aim of this research is to explore the dynamics of the use of slang among students in heterogeneous Islamic boarding schools, with a focus on how this language shapes their daily communication, analyzing the sociolinguistic impact of slang on traditional linguistic norms in the Islamic boarding school environment. The method used in this research is a qualitative method that directly observes the phenomenon of language shift in Islamic boarding schools. Based on the research results, it was found that there was a shift in language which showed a shift in the vocabulary commonly used in everyday conversation, the use of this vocabulary reflects a style of speech that is considered relaxed in interactions between peers, for example the words "santuy", "sabi", "kuy" etc. The implications of this research can be used as a benchmark for intergenerational language shifts in Islamic boarding schools and reveal how modern language trends interact with established cultural and religious expectations.</p>
In humans, olfactory perception appears to be a complex and multidimensional process. Classically, intensity, hedonicity, and familiarity are the main features assessed in perceptual evaluations. Several factors are well known to modulate odor perception such as environmental context, stimulus properties, or individual characteristics. Regarding the latter, female sex hormones may play an important role in modulating odor perception. In this context, few studies have investigated whether odor perception might change during the menstrual cycle in relation to odor category and perceptual features. The aim of the present study was to compare the follicular and luteal phases in women on and off oral contraceptives for the three main characteristics of odor perception (intensity, hedonicity, and familiarity) and for different odor categories (fruit, vegetable, and environmental odors). Results showed that all odors were perceived as more intense during the luteal phase compared to the follicular phase. Hedonic ratings showed differences in responses to odor categories: Fruit odors were perceived as more pleasant during the luteal phase, while vegetable odors were perceived as more unpleasant. Familiarity ratings increased during the luteal phase for two of the three odor categories (i.e., fruit and environmental odors). Comparisons between women using hormonal contraceptives (in both phases of the cycle) and those not using hormonal contraceptives revealed no significant differences in any of the dimensions assessed or in any of the odor categories. These findings are discussed in relation to the putative role of sex hormones in olfactory perception. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
This study explores the evolving interplay between language, cognition, and digital media in the context of the attention economy.It proposes six key dimensionsrepresentation, virtuality, attention, community language, cognitive transformation, and the speech-writing continuum -through which digital communication reshapes linguistic practices.Drawing on contemporary theories of mediatisation, multimodality, and sociolinguistics, the authors argue that digital environments fundamentally alter linguistic representation and identity construction.Through the analysis of semiotic innovation, platform logic, and cognitive offloading, the article highlights how digital discourse is not a degradation of language but an adaptive response to new communicative affordances.The findings invite a rethinking of linguistic norms and suggest directions for further research into the ethical, cultural, and neurological consequences of pervasive digital communication.
BACKGROUND: To date, no studies have examined the prevalence of hoarding behaviour and domestic squalor among individuals with mild intellectual disability. To address this gap, we conducted a prevalence study within a population supported by a medium-sized care organisation in the Netherlands. METHOD: Data were collected on 437 individuals with mild intellectual disability receiving care in residential facilities or through outpatient services. Assessments were conducted using the Hoarding Rating Scale-Interview, the Environmental Cleanliness and Clutter Scale, and the Clutter Image Rating Scale. RESULTS: Hoarding behaviour and/or domestic squalor were observed in 16.8% of the residents. Support staff identified 8.3% of dwellings as posing significant safety risks or health hazards. Additionally, 6.7% of residents had been threatened with eviction due to hoarding or squalor. CONCLUSIONS: Hoarding behaviour and domestic squalor appear to be more prevalent among individuals with mild intellectual disability in care settings than among the overall population.
We use advertising billboards and posters of three mobile telecom giants to spotlight translanguaging spaces as marketing strategies in the multilingual and multicultural landscapes of Zambia (south-central Africa). We argue that telecommunications giants deploy, in their marketing discourses, an assemblage of various semiotic resources on translanguaging spaces. Consequently, we show that this occasions a breakdown of language ideologies and blurs boundaries between languages of different sociopolitical statuses, reach and appeal, much to the benefit of telecom giants who wish to grow their subscriber base. We show how the outcome of semiotic complementarity of resources in translanguaging spaces addresses multiple actors in one design by integrating linguistic resources formerly separated by different practices and places for marketing purposes. In this way, we conclude that arising from (semiotic) creativity and defiance of expected linguistic norms, the sociolinguistics of translanguaging spaces admit both amalgamated forms and full-fledged languages in unpredictable ways, enabling telecom giants to achieve their marketing objectives.
This paper examines the rapid evolution of net slang and its consequential impact on Standard Mandarin, highlighting the interplay between net slang and traditional linguistic norms. The study begins by delineating three principal characteristics of net slang: multiformity, simplicity, and flexibility. Through a survey assessing Chinese attitudes towards net slang, both positive and negative viewpoints are revealed. Results indicate that while net slang can enhance communication with creative elements such as emoticons, it also risks grammatical divergence and semantic deviations. These impacts are particularly pronounced in the realm of Chinese language education, where net slang affects graphology and fosters misinterpretations. Despite these challenges, the paper advocates for a balanced approach, proposing the integration of beneficial aspects of net slang into Standard Mandarin following rigorous evaluation. Emphasizing language sustainability, the study underscores the role of educators and parents in promoting reading, thereby ensuring the harmonious coexistence of net slang and standard language while preserving linguistic integrity for future generations.
Background: Emotional disturbances are central to post-traumatic stress disorder (PTSD) and shape how individuals anticipate and experience events.Objective: This study investigates affective forecasting and emotional experience among PTSD patients, trauma-exposed controls (TEC), and healthy controls (HC) using a novel virtual reality paradigm.Method: Eighty-six participants (30 PTSD, 28 TEC, 28 HC) rated their predicted and actual emotional responses (valence and arousal) to unpleasant, neutral, and pleasant virtual scenarios. Physiological measures included heart rate and skin conductance responses (SCR).Results: PTSD participants showed alterations in their affective forecasting and emotional experience, assigning significantly lower valence scores to pleasant and neutral scenarios and exhibiting amplified SCR to emotionally charged stimuli. Their arousal ratings for neutral stimuli were also more elevated compared to HC. In their forecasting, PTSD participants anticipated more positive – or less negative – emotions compared to what they experienced next.Conclusions: These findings reveal critical emotional processing differences in PTSD, both during affective forecasting and emotional experience, supporting cognitive models that emphasize biased processing of emotional information in this psychiatric condition.