Search Results (72)

Background/Objectives: This study aimed to linguistically and culturally adapt the Social Participation Restrictions Questionnaire (SPaRQ) and the Hearing Handicap Inventory (HHI) for the Elderly/Adults to Danish and to investigate the reliability and validity of the questionnaires and their subscales in a clinical population. These questionnaires are quantifiable self-assessment tools that are used internationally to evaluate the social–emotional impacts of hearing impairment. Methods: The translation and cross-cultural adaptation procedures followed recommendations to adapt hearing-related questionnaires for different languages and cultures. In total, 64 participants (43 hearing aid users and 21 hearing aid candidates) completed both questionnaires using a test–retest paradigm. Results: Reliability analysis showed good internal consistency (Cronbach’s alpha between 0.82 and 0.94) and good agreement between the test and retest rounds (intraclass correlation values between 0.79 and 0.88) with both questionnaires. Neither SPaRQ nor HHI were correlated with better-ear PTA. However, SPaRQ and HHI, as well as their subscales, were significantly correlated with each other. Significant differences were observed at baseline between the HA users and candidates in terms of the better-ear PTA, but the distributions of subscale scores were broad and overlapping. Conclusions: The Danish version of SPaRQ is a reliable instrument for measuring the subjective impacts of hearing impairment. It can be used to capture the experiential aspects of hearing impairment that are not necessarily captured with objective measures of hearing. Full article

This study investigates how Mexican Spanish/U.S. English heritage bilinguals process scope ambiguities in sentences containing the existential quantifiers a/una and the universal quantifiers every/cada in English and Spanish. Sentences like ‘A person bought every book’ are syntactically ambiguous in both languages, allowing for multiple possible interpretations. Research suggests that one interpretation is often preferred due to lower cognitive demand, though degree of preference varies across languages. Notably, heritage bilinguals may have distinct interpretation preferences in each language, highlighting the complexity of bilingual processing. Sixty Spanish/English heritage bilinguals (Age M = 25.48, SD = 2.65) completed a timed and graded truth-value judgment task in both languages, along with language proficiency tests. We analyzed interpretation ratings, response times, and potential effects of proficiency. Results reveal nearly identical preferred interpretation ratings (Spanish: M = 4.19, SD = 0.56; English: M = 4.14, SD = 0.66) and response times (Spanish: M = 6.97 s, SD = 2.70; English: M = 6.67 s, SD = 1.80) across languages, with one interpretation consistently favored and associated with faster response times. Language proficiency had no significant impact. Our experimental findings offer new insights into heritage bilinguals’ processing of competing linguistic structures and inform models of bilingual syntax and cognitive flexibility. Full article

Language, a uniquely human cognitive faculty, is fundamentally characterized by its capacity for complex thoughts and structured expressions. This review examines two critical measures of linguistic performance: idea density (ID) and grammatical complexity (GC). ID quantifies the richness of information conveyed per unit of language, reflecting semantic efficiency and conceptual processing. GC, conversely, measures the structural sophistication of syntax, indicative of hierarchical organization and rule-based operations. We explore the neurobiological underpinnings of these measures, identifying key brain regions and white matter pathways involved in their generation and comprehension. This includes linking ID to a distributed network of semantic hubs, like the anterior temporal lobe and temporoparietal junction, and GC to a fronto-striatal procedural network encompassing Broca’s area and the basal ganglia. Moreover, a central theme is the integration of Chomsky’s theories of Universal Grammar (UG), which posits an innate human linguistic endowment, with their neurobiological correlates. This integration analysis bridges foundational models that first mapped syntax (Friederici’s work) to distinct neural pathways with contemporary network-based theories that view grammar as an emergent property of dynamic, inter-regional neural oscillations. Furthermore, we examine the genetic factors influencing ID and GC, including genes implicated in neurodevelopmental and neurodegenerative disorders. A comparative anatomical perspective across human and non-human primates illuminates the evolutionary trajectory of the language-ready brain. Also, we emphasize that, clinically, ID and GC serve as sensitive neurocognitive markers whose power lies in their often-dissociable profiles. For instance, the primary decline of ID in Alzheimer’s disease contrasts with the severe grammatical impairment in nonfluent aphasia, aiding in differential diagnosis. Importantly, as non-invasive and scalable metrics, ID and GC also provide a critical complement to gold-standard but costly biomarkers like CSF and PET. Finally, the review considers the emerging role of AI and Natural Language Processing (NLP) in automating these linguistic analyses, concluding with a necessary discussion of the critical challenges in validation, ethics, and implementation that must be addressed for these technologies to be responsibly integrated into clinical practice. Full article

To date, the ordinal pattern-based method has been applied to problems in natural and social sciences. We report, for the first time to our knowledge, an attempt to apply this methodology to a topic in the humanities. Specifically, in an effort to investigate the applicability of the methodology in analyzing the quality of texts that are translated into a language preserving the so-called vowel harmony, computed results are presented for the metrics of divergence between the back-translated and the original texts. As a specific language we focus on Japanese, and as metrics the Hellinger distance as well as the chi-square statistic are employed. Here, the former is a typical information-theoretical measure that can be quantified in natural unit, nat for short, while the latter is useful for performing a non-parametric testing of a null hypothesis with a significance level. The methods are applied to three cases: a Japanese novel along with a translated version available, the Preamble to the Constitution of Japan, and seventeen translations of an opening paragraph of a famous American detective story, which include thirteen human and four machine translations using DeepL and Google Translate. Numerical results aptly show unexpectedly high scores of the machine translations, but it still might be too soon to speculate on their unconditional potentialities. Both our attempt and results are not only novel but are also expected to make a contribution toward an interdisciplinary study between physics and linguistics. Full article

This study examines how architectural expertise shapes visual perception, extending the “Seeing for Speaking” hypothesis into a non-linguistic domain. Specifically, it investigates whether architectural training influences unconscious visual processing of architectural content. Using eye-tracking, 48 architects and 48 laypeople freely viewed 15 still images of built, mixed, and natural environments. Visual behavior was analyzed using Shannon’s entropy scores based on dwell times within 16 × 16 grids during the first six seconds of viewing. Results revealed distinct visual attention patterns between groups. Architects showed lower entropy, indicating more focused and systematic gaze behavior, and their attention was consistently drawn to built structures. In contrast, laypeople exhibited more variable and less organized scanning patterns, with greater individual differences. Moreover, architects demonstrated higher intra-group similarity in their gaze behavior, suggesting a shared attentional schema shaped by professional training. These findings highlight that domain-specific expertise deeply influences perceptual processing, resulting in systematic and efficient attention allocation. Entropy-based metrics proved effective in capturing these differences, offering a robust tool for quantifying expert vs. non-expert visual strategies in architectural cognition. The visual patterns exhibited by architects are interpreted to reflect a “Grammar of Space”, i.e., a structured way of visually parsing spatial elements. Full article

Metaphorical competence—the capacity to comprehend and produce metaphors in a second language (L2)—is essential for nuanced, accurate, and contextually appropriate English usage. Synthesizing 40 independent studies (N = 15,786), this meta-analysis quantified the relative contributions of cognitive, linguistic, and personal factors to L2 metaphorical competence. Effect sizes were derived from correlation coefficients and aggregated under random-effects models to account for between-study heterogeneity. Linguistic factors emerged as the dominant predictor (r = 0.421, 95% CI [0.34, 0.50]), primarily driven by vocabulary breadth/depth and reading proficiency. Cognitive factors exerted a moderate influence (r = 0.232, 95% CI [0.17, 0.30]), whereas personal variables such as gender yielded only a small effect (r = 0.216, 95% CI [0.15, 0.28]). Moderator analyses further revealed that L1 conceptual knowledge constitutes the strongest single predictor of L2 metaphor skills and highlighted distinct associations between receptive and productive metaphor abilities with linguistic versus cognitive aptitudes. The findings collectively point to lexico-semantic and literacy development as the main levers for boosting L2 metaphorical competence, with cognitive aptitudes and personal factors acting as secondary, yet important, modulators. Insight from this meta-analysis offers a robust foundation for evidence-based decisions in curriculum design, materials selection, and targeted pedagogical interventions. Full article

Recent developments in natural language processing, particularly large language models (LLMs), create new opportunities for literary analysis in underexplored languages like Romanian. This study investigates stylistic heterogeneity and genre blending in 175 late 19th- and early 20th-century Romanian novels, each classified by literary historians into one of 17 genres. Our findings reveal that most novels do not adhere to a single genre label but instead combine elements of multiple (micro)genres, challenging traditional single-label classification approaches. We employed a dual computational methodology combining an analysis with Romanian-tailored linguistic features with general-purpose LLMs. ReaderBench, a Romanian-specific framework, was utilized to extract surface, syntactic, semantic, and discourse features, capturing fine-grained linguistic patterns. Alternatively, we prompted two LLMs (Llama3.3 70B and DeepSeek-R1 70B) to predict genres at the paragraph level, leveraging their ability to detect contextual and thematic coherence across multiple narrative scales. Statistical analyses using Kruskal–Wallis and Mann–Whitney tests identified genre-defining features at both novel and chapter levels. The integration of these complementary approaches enhances microgenre detection beyond traditional classification capabilities. ReaderBench provides quantifiable linguistic evidence, while LLMs capture broader contextual patterns; together, they provide a multi-layered perspective on literary genre that reflects the complex and heterogeneous character of fictional texts. Our results argue that both language-specific and general-purpose computational tools can effectively detect stylistic diversity in Romanian fiction, opening new avenues for computational literary analysis in limited-resourced languages. Full article

The growing ubiquity of digital platforms has enabled unprecedented participation in large-scale group decision-making processes. Nevertheless, integrating subjective linguistically expressed opinions into structured decision protocols remains a significant challenge. This paper presents a novel framework that leverages the semantic and affective capabilities of large language models to support large-scale group decision-making tasks by extracting and quantifying experts’ communicative traits—specifically clarity and trust—from natural language input. Based on these traits, participants are clustered into behavioural groups, each of which is assigned a representative preference structure and a weight reflecting its internal cohesion and communicative quality. A sentiment-informed consensus mechanism then aggregates these group-level matrices to form a collective decision outcome. The method enhances scalability and interpretability while preserving the richness of human expression. The results suggest that incorporating behavioural dimensions into large-scale group decision-making via large language models fosters fairer, more balanced, and semantically grounded decisions, offering a promising avenue for next-generation decision-support systems. Full article

Microalgae-based carbon sequestration is promising for implementing carbon neutrality and reducing greenhouse gas emissions. However, as the technology remains in its early developmental stages, it presents a range of risks that may deter potential investors. To address these risks, this study proposes a group-based decision-making framework for the risk evaluation of microalgae carbon sequestration projects. Fifteen risk indicators are identified and categorized into four groups, including economic, technical, market, and environmental. To handle uncertainty and vagueness in the assessment, the framework uses trapezoidal fuzzy numbers and hesitant fuzzy linguistic sets to evaluate benchmark values. An expert credibility model is developed to assign weights to expert opinions by combining the subjective RANCOM method and the objective centroid method, both adapted for a fuzzy linguistic environment. A generalized aggregation operator is then used to combine expert evaluations. This operator integrates weighted and ordered averaging techniques and converts probabilistic linguistic terms into trapezoidal fuzzy numbers. The final risk level is determined using a fuzzy comprehensive evaluation method. The results indicate a medium-high level of risk, with a similarity score of 0.960. This suggests that while microalgae carbon sequestration holds great promise, effective planning and risk management are essential. For project managers and investors, this proposed framework helps quantify risk. It provides practical guidance for improving decision-making and strengthening project management. Full article

This study investigates how topic-specific expression by women delivery riders on digital platforms predicts their community engagement, emphasizing the mediating role of self-disclosure and the moderating influence of cognitive and emotional language features. Using unsupervised topic modeling (Top2Vec, Topical Vectors via Embeddings and Clustering) and psycholinguistic analysis (LIWC, Linguistic Inquiry and Word Count), the paper extracted eleven thematic clusters and quantified self-disclosure intensity, cognitive complexity, and emotional polarity. A moderated mediation model was constructed to estimate the indirect and conditional effects of topic probability on engagement behaviors (likes, comments, and views) via self-disclosure. The results reveal that self-disclosure significantly mediates the influence of topical content on engagement, with emotional negativity amplifying and cognitive complexity selectively enhancing this pathway. Indirect effects differ across topics, highlighting the heterogeneous behavioral salience of expressive themes. The findings support a statistically grounded, semantically interpretable framework for predicting user behavior in high-dimensional text environments. This approach offers practical implications for optimizing algorithmic content ranking and fostering equitable visibility for marginalized digital labor groups. Full article

Background/Objectives: Diagnostic reasoning is essential in clinical practice and medical education, yet it often becomes an automated process, making its cognitive mechanisms less visible. Despite the widespread use of electronic medical records, few studies have quantitatively evaluated how clinicians’ reasoning is documented in real-world electronic medical records. This study aimed to investigate whether initial electronic medical records contain valuable information for diagnostic reasoning and assess the feasibility of using text analysis and logistic regression to make this reasoning process visible. Methods: We conducted a retrospective analysis of initial electronic medical records at Kochi University Hospital between 2008 and 2022. Two patient cohorts presenting with dizziness and headaches were analysed. Text analysis was performed using GiNZA, a Japanese natural language processing library, and logistic regression analyses were conducted to identify associations with final diagnoses. Results: We identified 1277 dizziness cases, of which 248 were analysed, revealing 48 significant diagnostic terms. Moreover, we identified 1904 headache cases, of which 616 were analysed, revealing 46 significant diagnostic terms. The logistic regression analysis demonstrated that the presence of specific terms, as well as whether they were expressed affirmatively or negatively, was significantly associated with diagnostic outcomes. Conclusions: Initial EMRs contain quantifiable linguistic cues relevant to diagnostic reasoning. Even simple analytical methods can reveal reasoning patterns, offering valuable insights for medical education and supporting the development of explainable diagnostic support systems. Full article

This study proposes a decision support system that uses a fuzzy logic model to assess the risk level associated with repetitive upper limb movements during work tasks, which can lead to musculoskeletal disorders. The model considers three main sets: biomechanics, anthropometrics, and productivity. Standardised parameters were utilised to determine the risk level associated with movement. To validate the findings, a fuzzy model was applied to assess 123 female workers across three automatic high-speed production lines as a case study. The model quantifies the risks using 54 membership equations and incorporates nine linguistic variables organised into three sets: biomechanical: this includes applied force, moment force, and angle of the torso from vertical; anthropometric: this includes workers’ age and height and body mass index; and productivity: this includes working area depth, exposure time, and repetitiveness. The resulting fuzzy model, which is based on fuzzy set theory, utilises only four general fuzzy rules and allows for the evaluation of multiple workers simultaneously, providing a competitive advantage over models that rely on a large number of individual fuzzy rules to assess just one worker. The biomechanical set evaluates applied force and moment force based on productivity factors. Consequently, the behaviour of the group of 123 evaluations changed as the productivity risk value was introduced. For instance, in Test 1, which involves a low-risk task, we observed a biomechanical risk pattern that was solely related to the worker’s anthropometry. In Test 2, which presents a medium risk, the pattern of evaluations shifted, revealing behaviours that were more influenced by both anthropometric and biomechanical characteristics. Finally, in Test 3, the impact of anthropometry and biomechanics was clear in the risk assessment patterns, which aligned closely with the anthropometric. The DSS could help improve policies and work conditions. Full article

Background/Objectives: The psychological health of deaf children and adolescents with cochlear implants (CIs) appears to be related to the degree of auditory and linguistic recovery achieved, as well as contextual factors. Few studies have investigated the influence that maternal psychological characteristics and resources may have in supporting the mental health of these children and adolescents. The aim of this pilot study was to investigate the psychological well-being of pre-teens/teenagers with CIs and the mental health of their mothers. The secondary aim was to analyze which maternal characteristics (anxiety, depression, resilience, and time spent sharing emotions) were most related to the psychological health of the pre-teen/teenager. Methods: A group of 15 pre-teens/teenagers with CIs and 27 hearing peers and their mothers participated in the study. The Strengths and Difficulties Questionnaire, the Generalized Anxiety Disorder Scale, the Beck Depression Inventory II, the Connor–Davidson Resilience Scale, and an additional ad hoc question quantifying the time that the mothers dedicate to conversing with their sons/daughters about the emotions were administered to the included subjects. Results: No significant differences between the two groups of pre-teens/teenagers emerged; however, a great percentage of pre-teens/teenagers with CIs appeared at higher risk for developing psychopathology. The resilience scores for both groups of mothers were lower than anticipated and related to the psychological health of pre-teens/teenagers. Conclusions: These results underscore the need for targeted psychological support alongside auditory rehabilitation and suggest avenues for enhancing family-centered care in this clinical population. Full article

This study investigates the interplay between linguistic and extralinguistic factors in language contact scenarios, focusing on inner Asia Minor Greek (iAMGr), a dialect cluster influenced by Turkish and isolated from other Greek-speaking regions. Using dialectometric techniques, we quantified the dialect distances—encompassing both grammatical and lexical features, many of which reflect foreign interference—between nineteen iAMGr varieties. A regression analysis was then employed to evaluate the impact of geographic, demographic, and other macro-social factors on these distances. The results reveal distinct patterns. The grammatical features show a substantial divergence between communities, linked to structural borrowing and primarily influenced by the dominant group’s population size and degree of contact (low- vs. high-contact variety types). In contrast, lexical features exhibit greater convergence, primarily influenced by geography, linked to the susceptibility of lexical borrowing to casual contact. Unlike previous dialectometric studies that report a strong correlation between geographic and dialect distances, our findings suggest that geography’s influence varies by linguistic level, being more pronounced in lexical distances. Furthermore, the analysis reveals that certain dialect-specific factors previously identified in qualitative studies on iAMGr are statistically insignificant. The study concludes that, while geography remains relevant, macro-social factors often play a more critical role in language contact settings, particularly in shaping grammatical distances. These findings provide new insights into the determinants of dialect distances in such contexts. Full article

The application of fuzzy logic to environmental impact assessment (EIA) provides a robust method to address uncertainties and subjectivities inherent in evaluating complex environmental systems. This is particularly relevant in ocean renewable energy projects, where predicting environmental impacts is challenging due to the dynamic nature of marine environments. We conducted a comprehensive literature review to identify the types of impacts currently being investigated, assessed, and monitored in existing marine energy conversion projects. Based on these foundations, we developed both traditional and fuzzy mythologies for EIA. The fuzzy logic methodology approach allows for the incorporation of uncertainties into the assessment process, converting qualitative assessments into quantifiable data and linguistic levels and enhancing decision-making accuracy. We tested this fuzzy methodology across four types of ocean energy devices: floating, submerged, fixed to the ocean floor, and onshore. Finally, we applied the methodology to the EIA of a marine energy project in the Cozumel Channel, Quintana Roo, Mexico. The results demonstrate that fuzzy logic provides a more flexible and reliable evaluation of environmental impacts, contributing to more effective environmental management and sustainable development in marine renewable energy contexts. Full article