Negation mitigates rather than inverts the neural representations of adjectives.

Combinatoric linguistic operations underpin human language processes, but how meaning is composed and refined in the mind of the reader is not well understood. We address this puzzle by exploiting the ubiquitous function of negation. We track the online effects of negation ("not") and intensifiers ("really") on the representation of scalar adjectives (e.g., "good") in parametrically designed behavioral and neurophysiological (MEG) experiments. The behavioral data show that participants first interpret negated adjectives as affirmative and later modify their interpretation towards, but never exactly as, the opposite meaning. Decoding analyses of neural activity further reveal significant above chance decoding accuracy for negated adjectives within 600 ms from adjective onset, suggesting that negation does not invert the representation of adjectives (i.e., "not bad" represented as "good"); furthermore, decoding accuracy for negated adjectives is found to be significantly lower than that for affirmative adjectives. Overall, these results suggest that negation mitigates rather than inverts the neural representations of adjectives. This putative suppression mechanism of negation is supported by increased synchronization of beta-band neural activity in sensorimotor areas. The analysis of negation provides a steppingstone to understand how the human brain represents changes of meaning over time.

Perception of cure in prostate cancer: human-led and artificial intelligence-assisted landscape review and linguistic analysis of literature, social media and policy documents.

BACKGROUND: Understanding stakeholders' perception of cure in prostate cancer (PC) is essential to preparing for effective communication about emerging treatments with curative intent. This study used artificial intelligence (AI) for landscape review and linguistic analysis of definition, context and value of cure among stakeholders in PC. MATERIALS AND METHODS: Subject-matter experts (SMEs) selected cure-related key words using Elicit, a semantic literature search engine, and extracted hits containing the key words from Medline, Sermo and Overton, representing academic researchers, health care providers (HCPs) and policymakers, respectively. NetBase Quid, a social media analytics and natural language processing tool, was used to carry out key word searches in social media (representing the general public). NetBase Quid analysed linguistics of key word-specific hit sets for key word count, geolocation and sentiments. SMEs qualitatively summarised key word-specific insights. Contextual terms frequently occurring with key words were identified and quantified. RESULTS: SMEs identified seven key words applicable to PC (number of acquired hits) across four platforms: Cure (12429), Survivor (6063), Remission (1904), Survivorship (1179), Curative intent (432), No evidence of disease (381) and Complete remission (83). Most commonly used key words were Cure by the general public and HCPs (11815 and 224 hits), Survivorship by academic researchers and Survivor by policymakers (378 hits each). All stakeholders discussed Cure and cure-related key words primarily in early-stage PC and associated them with positive sentiments. All stakeholders defined cure differently but communicated about it in relation to disease measurements (e.g. prostate-specific antigen) or surgery. Stakeholders preferred different terms when discussing cure in PC: Cure (academic researchers), Cure rates (HCPs), Potential cure and Survivor/Survivorship (policymakers) and Cure and Survivor (general public). CONCLUSION: This human-led, AI-assisted large-scale qualitative language-based research revealed that cure was commonly discussed by academic researchers, HCPs, policymakers and the general public, especially in early-stage PC. Stakeholders defined and contextualised cure in their communications differently and associated it with positive value.

Sustainability evaluation of sports tourism using a linguistic neutrosophic multi-criteria decision-making method.

Sports tourism represents a novel industrial manifestation of the profound integration between the tourism and sports sectors. The objective of this research is to examine an innovative multi-criteria decision-making (MCDM) method for the sustainability evaluation of sports tourism. The largest innovations are the expression and treatment of ambiguous data and interdependent evaluation criteria in the sports tourism sustainability evaluation process. On the one hand, intricate assessment data is represented using linguistic neutrosophic numbers (LNNs), which employ three linguistic variables to convey uncertainty and imprecision. On the other hand, to effectively capture the interrelationships among inputs, two novel aggregation operators are proposed. They are devised based on the Einstein operations and Heronian mean operators of LNNs. Subsequently, a linguistic neutrosophic evaluation method utilizing the aforementioned operators is presented. Comparative and sensitivity analyses conclude that great interdependence exists among five different dimensions of sustainability evaluation in sports tourism, and the proposed method can reflect the interrelations among inputs without redundant calculations.

Growth of Complex Syntax: Coordinate and Subordinate Clause Use in Elementary School-Aged Children.

PURPOSE: Production of complex syntax is a hallmark of later language development; however, most of the research examining age-related changes has focused on adolescents or analyzed narrative language samples. Research documenting age-related changes in the production of complex syntax in elementary school-aged children in conversational language samples is limited. Therefore, the purpose of this article is to examine age-related changes in the production of coordinate and subordinate clauses in children between 5 and 10 years of age obtained from 50-utterance conversational language samples. METHOD: The analytic sample included 196 children with typical language development, who ranged in age from 5;0 to 10;11 (years;months; girls = 103; boys = 96; three cases were excluded). Fifty-utterance conversational language samples were examined for use of coordinate and subordinate clauses. RESULTS: Results of regression analyses indicated that the production of coordinate and subordinate clauses could be predicted from age. The proportion of utterances that included subordinate clauses increased 0.20% for every month increase in age (p < .001). Coordinate clauses also continued to grow, although at a slower rate (0.10% increase for every month increase in age, p < .001). Finally, the proportion of simple utterances (i.e., utterances without coordinate or subordinate clauses) decreased with age (0.40% decrease for every month increase in age, p < .001). CONCLUSIONS: This study indicated that as children's age increased, they used fewer, simple, one-clause sentences and more utterances that included subordinate clauses, with or without coordinate clauses. These results were obtained from 50-utterance language samples, further supporting use of language sampling to develop intervention goals and monitor progress in therapy. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.25262725.

Driving and suppressing the human language network using large language models.

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict the magnitude of the brain response associated with each sentence. We then use the model to identify new sentences that are predicted to drive or suppress responses in the human language network. We show that these model-selected novel sentences indeed strongly drive and suppress the activity of human language areas in new individuals. A systematic analysis of the model-selected sentences reveals that surprisal and well-formedness of linguistic input are key determinants of response strength in the language network. These results establish the ability of neural network models to not only mimic human language but also non-invasively control neural activity in higher-level cortical areas, such as the language network.

The Basis of the Adoption of Borrowed Letters in the Kazakh Alphabet.

It is known that the words with the letters "f" and "x" used in the Kazakh language originated from Arabic and Persian and are found in European words that entered through the Russian language. Of the article is to discuss the basics of translating the letters "f" and "x" into the Kazakh alphabet. The use of religious and European words in normative dictionaries, with the letters "f" and "x" and entered into the language through the Russian language, is analysed on the basis of the methods of linguistics and statistical analysis. The specifics of these letters in religious discourse and their use in onomastics will be determined. The reasons for the inclusion of the letters "f" and "x" in the improved new alphabet are mentioned. Investigating how the Kazakh language adopts and modifies foreign sounds can contribute to a broader understanding of linguistic adaptation. The proposed research paper can be used in the analysis of the problem of assimilation of borrowed words.

Translation to German and linguistic validation of the Rapid Assessment of Physical Activity (RAPA) questionnaire.

PURPOSE: To produce a culturally adapted translation of the Rapid Assessment of Physical Activity (RAPA) questionnaire for German speaking Austrians and to conduct a linguistic validation of the new language version. METHODS: The original English RAPA questionnaire was translated into German for Austria and underwent an independent forward and back translation, followed by cognitive debriefing interviews with older adults aged 55 to 78 years with and without health conditions (n = 13), for linguistic validation. RESULTS: Several distinct choices were made in the translation of the RAPA questionnaire to German, including the use of colloquial terms for 'physical activity' and 'intensity'; and the decision to keep to the original examples and images of different physical activities for illustrating the intensity levels (light, moderate, vigorous) of physical activity. In cognitive debriefing, interviewees commented that some example activities for the respective intensity levels could - depending on the individual - also represent a higher or lower intensity level; and that the wording of RAPA items 4 and 5, which describe the category 'under-active regular' aerobic activity, was difficult to understand. Both issues were addressed and resolved through minor iterative modifications made during the cognitive debriefing process. CONCLUSIONS: A new version of the RAPA questionnaire in German for Austria has been produced by forward and back translation and linguistic validation. The questionnaire may now undergo psychometric evaluation.

A multi-stage group decision making approach for sustainable supplier selection based on probabilistic linguistic time-ordered incentive operator.

This study proposes a novel multi-stage multi-attribute group decision making method under a probabilistic linguistic environment considering the development state and trend of alternatives. First, the probabilistic linguistic term set (PLTS) is used by decision makers (DMs) to describe qualitative evaluation information. Subsequently, the weights of DMs for different attributes in different periods are determined by the credibility degree, which is combined with the hesitancy degree and the similarity degree. The evaluations of different DMs for alternatives and the evaluations of DMs' intentions to reward or punish are then aggregated. Later, the trend change level and the trend change stability of alternatives are measured through the means of reward and punishment incentives. Additionally, the probabilistic linguistic time-ordered incentive operator is proposed to aggregate the development state evaluation information and development trend evaluation information in different periods, and alternatives are prioritized by the extended TOPSIS method in the probabilistic linguistic environment. Finally, the practical use of the proposed decision framework is validated by using a sustainable supplier selection problem, and the effectiveness and the applicability of the framework are discussed through comparative analysis. The results show that the proposed approach can select suitable sustainable suppliers by considering their development state and trend in multiple stages.

Linguistic features of stuttering during spontaneous speech.

PURPOSE: Previous work shows that linguistic features (e.g., word length, word frequency) impact the predictability of stuttering events. Most of this work has been conducted using reading tasks. Our study examined how linguistic features impact the predictability of stuttering events during spontaneous speech. METHODS: The data were sourced from the FluencyBank database and consisted of interviews with 35 adult stutterers (27,009 words). Three logistic regression mixed models were fit as the primary analyses: one model with four features (i.e., initial phoneme, grammatical function, word length, and word position within a sentence), a second model with six features (i.e., the features from the previous model plus word frequency and neighborhood density), and a third model with nine features (i.e., the features from the previous model plus bigram frequency, word concreteness, and typical age of word acquisition). We compared our models using the Area Under the Curve statistic. RESULTS: The four-feature model revealed that initial phoneme, grammatical function, and word length were predictive of stuttering events. The six-feature model revealed that initial phoneme, word length, word frequency, and neighborhood density were predictive of stuttering events. The nine-feature model was not more predictive than the six-feature model. CONCLUSION: Linguistic features that were previously found to be predictive of stuttering during reading were predictive of stuttering during spontaneous speech. The results indicate the influence of linguistic processes on the predictability of stuttering events such that words associated with increased planning demands (e.g., longer words, low frequency words) were more likely to be stuttered.

Exploring intellectual humility through the lens of artificial intelligence: Top terms, features and a predictive model.

Intellectual humility (IH) is often conceived as the recognition of, and appropriate response to, your own intellectual limitations. As far as we are aware, only a handful of studies look at interventions to increase IH - e.g. through journalling - and no study so far explores the extent to which having high or low IH can be predicted. This paper uses machine learning and natural language processing techniques to develop a predictive model for IH and identify top terms and features that indicate degrees of IH. We trained our classifier on the dataset from an existing psychological study on IH, where participants were asked to journal their experiences with handling social conflicts over 30 days. We used Logistic Regression (LR) to train a classifier and the Linguistic Inquiry and Word Count (LIWC) dictionaries for feature selection, picking out a range of word categories relevant to interpersonal relationships. Our results show that people who differ on IH do in fact systematically express themselves in different ways, including through expression of emotions (i.e., positive, negative, and specifically anger, anxiety, sadness, as well as the use of swear words), use of pronouns (i.e., first person, second person, and third person) and time orientation (i.e., past, present, and future tenses). We discuss the importance of these findings for IH and the value of using such techniques for similar psychological studies, as well as some ethical concerns and limitations with the use of such semi-automated classifications.

A hybrid DEMATEL-COPRAS method using interval-valued probabilistic linguistic term set for sustainable hydrogen fuel cell supplier of new energy vehicles.

With the continuous development of the global economy, global environmental pollution, climate degradation and global warming are becoming increasingly serious. In order to deal with the increasingly serious environmental problems, the government is vigorously supporting and promoting the development of new energy vehicles (NEVs). As the core unit of NEVs, one of the main challenges faced by hydrogen fuel cell (HFC) supplier is to select the best supplier for their business among all possible suppliers. Selecting the optimal supplier is a key decision in green supplier management. Therefore, it is extremely important and meaningful to select an optimal HFC supplier to provide power for NEVs. This paper proposes a new decision-making framework based on Decision-Making Trial and Evaluation Laboratory (DEMATEL) method and Complex proportional assessment (COPRAS) method under interval-valued probabilistic linguistic environment to select the appropriate HFC supplier of NEVs. Firstly, this paper establishes the evaluation criteria system of HFC supplier assessment which is the synthesis of economical, environmental, social, technical, organisation and service aspects. Then, in order to express the uncertainty of expert decision-making, this paper uses interval-valued probabilistic linguistic term set (IVPLTS) to describe the evaluation information. Next, the interval-valued probabilistic linguistic term set decision-making trial and evaluation laboratory (IVPLTS-DEMATEL) method is applied to calculate the criteria weights. Moreover, this paper constructs the interval-valued probabilistic linguistic term set Complex Proportional Assessment (IVPLTS-COPRAS) model for the selection of HFC supplier of NEVs. Finally, a case in China with sensitivity analysis and comparison analysis are executed to illustrate the feasibility and validity of the proposed approach. This paper provides valuable references for investors and companies to select the most appropriate HFC supplier of NEVs under uncertain environment.

Maltese as a merger of two worlds: A cross-language approach to phonotactic classification.

Maltese is a prime example of a language that emerged through extensive language contact, joining the two linguistic worlds of Semitic and Italo-Romance languages. Previous studies have shown this shared origin on the basis of hands-on comparative methods. However, such approaches may be biased by the researchers perspective and the selected material. To avoid this bias, we employed a naive computational method that classifies words on the basis of their phonotactics. Specifically, we trained a simple two-layer neural network on Tunisian and Italian nouns, i.e. the languages that Maltese emerged from. We used the trained network to classify Maltese nouns based on their phonotactic characteristics as either of Tunisian or of Italian origin. Overall, the network is capable of correctly classifying Maltese nouns as belonging to either of the original languages. Moreover, we find that the classification depends on whether a noun has a sound or broken plural. By manipulating the segment identity in the training input, we found that consonants are more important for the classification of Maltese nouns than vowels. While our results replicate previous comparative studies, they also demonstrate that a more fine grained classification of a language's origin can be based on individual words and morphological classes.

Multivalued neutrosophic power partitioned Hamy mean operators and their application in MAGDM.

The novel multivalued neutrosophic aggregation operators are proposed in this paper to handle the complicated decision-making situations with correlation between specific information and partitioned parameters at the same time, which are based on weighted power partitioned Hamy mean (WMNPPHAM) operators for multivalued neutrosophic sets (MNS) proposed by combining the Power Average and Hamy operators. Firstly, the power partitioned Hamy mean (PPHAM) is capable of capture the correlation between aggregation parameters and the relationship among attributes dividing several parts, where the attributes are dependent definitely within the interchangeable fragment, other attributes in divergent sections are irrelevant. Secondly, because MNS can effectively represent imprecise, insufficient, and uncertain information, we proposed the multivalued neutrosophic PMHAM (WMNPHAM) operator for MNS and its partitioned variant (WMNPPHAM) with the characteristics and examples. Finally, this multiple attribute group decision making (MAGDM) technique is proven to be feasible by comparing with the existing methods to confirm this method's usefulness and validity.

A novel MADM algorithm for physical education teaching quality evaluation based on 2-tuple linguistic neutrosophic numbers power heronian mean operators.

Classroom teaching quality evaluation is an important link in the curriculum quality assurance system. It has important guiding significance for the timely feedback of classroom teaching effects, the achievement of teachers' teaching goals, and the implementation of teaching plans. The evaluation system is scientific, objective and accurate. The classroom teaching quality evaluation is an important way to improve the level of teacher education and teaching and then determine the quality of talent training in various majors. At present, although the evaluation work has played a positive role, the backwardness of the evaluation system has seriously restricted the effectiveness of teaching feedback. The classroom teaching quality evaluation of college basketball training is viewed as the multi-attribute decision-making (MADM). In this article, we combine the generalized Heronian mean (GHM) operator and power average (PA) with 2-tuple linguistic neutrosophic sets (2TLNSs) to propose the generalized 2-tuple linguistic neutrosophic power HM (G2TLNPHM) operator. The G2TLNPHM operator is built for MADM. Finally, an example for classroom teaching quality evaluation of college basketball training is used to show the proposed methods.

A new multi-objective optimization ratio analysis plus full multiplication form method for the selection of an appropriate mining method based on 2-tuple spherical fuzzy linguistic sets.

The selection of an appropriate mining method is considered as an important tool in the mining design process. The adoption of a mining method can be regarded as a complex multi-attribute group decision-making (MAGDM) problem as it may contain uncertainty and vagueness. The main goal of this paper is to propose an extended multi-objective optimization ratio analysis plus full multiplication form (MULTIMOORA) method that is based on a 2-tuple spherical fuzzy linguistic set (2TSFLS). The MULTIMOORA method under 2TSFL conditinos has been developled as a novel approach to deal with uncertainty in decision-making problems. The proposed work shows that 2TSFLSs contain collaborated features of spherical fuzzy sets (SFSs) and 2-tuple linguistic term sets (2TLTSs) and, hence, can be considered as a rapid and efficient tool to represent the experts' judgments. Thus, the broader structure of SFSs, the ability of 2TLTSs to represent linguistic assessments, and the efficiency of the MULTIMOORA approach have motivated us to present this work. To attain our desired results, we built a normalized Hamming distance measure and score function for 2TSFLSs. We demonstrate the applicability and realism of the proposed method with the help of a numerical example, that is, the selection of a suitable mining method for the Kaiyang phosphate mine. Then, the results of the proposed work are compared with the results of existing methods to better reflect the strength and effectiveness of the proposed work. Finally, we conclude that the proposed MULTIMOORA method within a 2TSFLS framework is quite efficient and comprehensive to deal with the arising MAGDM problems.

Effect of linguistic factors on the occurrence of stuttering-like disfluency among Japanese-speaking preschool children who stutter.

This study aimed to investigate the linguistic factors involved in stuttering among Japanese-speaking preschool children. The participants included 10 Japanese children who stutter, with a mean age of 5 years and 9 months. Speech samples comprised spontaneous conversations of the participants with their parents for about 20 minutes. We compared the percentages of the occurrence of stuttering-like disfluencies (SLDs) at the word and sentence levels, using the Wilcoxon signed-rank test. The results showed no significant differences in SLDs based on syllable structure when comparing light and heavy syllables and comparing consonants and vowels in the initial position of each content word. SLDs occurred more frequently in the initial than non-initial position of words and in longer rather than shorter words. Additionally, SLDs occurred more frequently in sentences that contained more 'bunsetsu' (a kind of linguistic unit in Japanese). Our study is the first to show that both word and sentence-level factors could contribute to SLDs in preschool children who stutter in agglutinating languages, such as Japanese. This aspect is rarely reported in psycholinguistic studies based on stuttering occurrence in inflecting languages, such as English.

Animal linguistics: a primer.

The evolution of language has been investigated by several research communities, including biologists and linguists, striving to highlight similar linguistic capacities across species. To date, however, no consensus exists on the linguistic capacities of non-human species. Major controversies remain on the use of linguistic terminology, analysis methods and behavioural data collection. The field of 'animal linguistics' has emerged to overcome these difficulties and attempt to reach uniform methods and terminology. This primer is a tutorial review of 'animal linguistics'. It describes the linguistic concepts of semantics, pragmatics and syntax, and proposes minimal criteria to be fulfilled to claim that a given species displays a particular linguistic capacity. Second, it reviews relevant methods successfully applied to the study of communication in animals and proposes a list of useful references to detect and overcome major pitfalls commonly observed in the collection of animal behaviour data. This primer represents a step towards mutual understanding and fruitful collaborations between linguists and biologists.

An MAGDM method for design concept evaluation based on incomplete information.

Design concept evaluation is a huge challenge in the R&D stage of new product development. The information in the assessments often depends on the decision-makers' individual preferences. However, sometimes the decision-makers cannot give precise and complete information because it is very difficult for them to be familiar with all the criteria. In this situation, an incomplete information decision-making matrix is established. In this paper, decision-making methods based on incomplete information are compared in the literature review. Incomplete information determination method using trust mechanism is proved as a proper way to solve this problem, and the missing information are computed based on the alternatives However, in design concept evaluation, experts commonly provide their preferences using linguistic words according to the different attributes. Hence, we propose a three-step Multiple Attributes Group Decision-making (MAGDM) method where the missing value are determined by attributes. In step one, a data repairing method is proposed based on trust theory. After that, in step two, a comprehensive weight determination method combining AHP and entropy is proposed to obtain the weight of index attributes. Finally, the Rough-TOPSIS method is applied in the design scheme ranking step. In the case study, the proposed method is implemented in a tourism product design process to show its effectiveness.

Grey relational analysis method for typhoon vulnerability assessment of civil engineering structures based on the 2-tuple linguistic neutrosophic number.

As one of the severe natural disasters, typhoon hazard brings tremendous tragedy to human beings. The foreland in the southeast of China is one of the most typhoon prone areas in the world. There are amount of damage of civil engineering structures induced by typhoon every year. Especially for the spacious villages, the low-rise buildings are vulnerable to typhoon so that many of them are destroyed regionally. The typhoon vulnerability assessment of civil engineering structures is a classical multiple attribute group decision making (MAGDM) issues. In this paper, the 2-tuple linguistic neutrosophic number grey relational analysis (2TLNN-GRA) method is built based on the grey relational analysis (GRA) and 2-tuple linguistic neutrosophic sets (2TLNSs) with incomplete weight information. For deriving the weight information of the attribute, an optimization model is built on the basis of the GRA, by which the attribute weights can be decided. Then, the optimal alternative is chosen through calculating largest relative relational degree from the 2-tuple linguistic neutrosophic number positive ideal solution (2TLNNPIS) which considers both the largest grey relational coefficient (GRC) from the 2TLNNPIS and the smallest GRC form 2-tuple linguistic neutrosophic number negative ideal solution (2TLNN NIS). Then, combine the traditional fuzzy GRA model with 2TLNNSs information, the 2TLNN-GRA method is established and the computing steps for MAGDM are built. Finally, a numerical example for typhoon vulnerability assessment of civil engineering structures has been given and some comparisons is used to illustrate advantages of 2TLNN-GRA method.

Linguistic distancing predicts response to a digital single-session intervention for adolescent depression.

Examining the linguistic characteristics of youths' writing may be a promising method for detecting youth who are struggling. In this study, we examined linguistic patterns of adolescent responses to writing prompts in a large, well-powered trial of an evidence-based, digital single-session intervention teaching malleability beliefs about personal traits and symptoms ("growth mindset"). Participants who completed the intervention as part of a larger randomized control trial were included in this preregistered study (n = 638, https://osf.io/zqmxt). Participants' responses were processed using Linguistic Inquiry and Word Count. We tested correlations between linguistic variables (i.e., linguistic distancing, positive affect, negative affect, insight, certainty), baseline outcome variables, post-intervention outcome variables, and 3-month post-intervention outcome variables. We also used Least Absolute Shrinkage and Selection Operator (LASSO) regression models to identify key predictors of treatment outcomes. As hypothesized, greater use of linguistic distancing was associated with lower levels of baseline hopelessness and higher levels of perceived agency. Additionally, per LASSO models including all linguistic variables, greater use of linguistic distancing predicted larger reductions in depressive symptoms from baseline to three-month follow-up. Linguistic distancing appeared to account for 27% of the variance in depression trajectories when also accounting for baseline depression. CLINICAL REGISTRATION NO: NCT04634903.