Meaning In Context Research Articles

Social, economic, and demographic factors drive the emergence of Hinglish code-mixing on social media

The advent of globalization and adaptation to multiple cultures has emanated a fusion of Hindi and English, casually known as Hinglish. The phenomenon of mixing multiple languages (such as Hindi and English) within a single utterance is often called code-mixing. Lately, code-mixed Hinglish has emerged as a dominant conversational language for Hindi-speaking citizens both online (on social media platforms) and offline. Although previous studies investigated such linguistic traits of Hinglish over the past few years, some pertinent questions still need to be answered: How did Hinglish evolve? And, what are the factors behind the evolution of Hinglish? Does the fusion of English impact all Hindi words similarly? To this end, we explore the empirical and statistical shreds of evidence behind the rise of Hinglish on social media such as Twitter. We show that adopting Hinglish depends on several socio-economic and demographic factors. We further formulate dynamic models to explore the socio-economic factors driving the growth of Hinglish, derive the future growth of Hinglish in the upcoming years, and estimate the propensity of users to change their linguistic preferences. Our study highlights that the Hinglish population has evolved steadily between 2014 and 2022, with an annualized growth rate of 1.2%, and the usage of Hinglish on Twitter has increased annually by 2%. Further, we find that the impact of Hinglish evolution is not uniform across different word groups and affects the contextual meaning of different words differently. Although our findings are specific to the Indian Hinglish community, our study can be generalized to understand the evolution and dynamics of other code-mixed languages, such as Spanish-English or Chinese-English.

Study of International Relations students’ motivation: Assessing the effectiveness of educational process management via Action Research strategy

Introduction. The article raises the problem of increasing motivation among university students in learning their major. Despite the fact that a large number of research articles are devoted to the problem of motivation, the issue of assessing the effectiveness of educational process management as one of the key factors in creating a motivating educational environment, remains insufficiently studied. The aim of this study is to determine and describe an effective strategy of intervention and external support for students majoring in “International Relations” in order to increase motivation in learning their academic major via Action Research. Materials and Methods. The methodology is based on Activity theory, person-oriented approach, educational environment assessment, literature review, project-based learning, role-plays and Action Research, as well as on quantitative, qualitative and interpretive research. An analytical review has been carried out in order to identify methods and technologies that can be used to increase motivation to educational activities. Quantitative and qualitative methods include two surveys with subsequent interpretation of the results obtained and provide the basis for developing and testing the strategy of assessing the effectiveness of educational process management. Results. The results of the surveys and their interpretation analysis have demonstrated a high level of student interest in English as a foreign language yet with an insufficient level of motivation for their major, as well as the students’ assessment of professional role-playing games as an effective and emotionally comfortable form of education. Based on Action Research and project-based learning the authors developed an action strategy, substantiated the principles of its implementation, the degree of intervention and external support while maintaining psychological comfort and the level of student autonomy. The authors found that increased involvement is also determined by the freedom to choose the focus of the project, a meaningful professional context, successful team work and effective management of the educational process. The results of the intervention helped the authors to develop recommendations for replicating and scaling up the experience gained. Conclusions. In conclusion, inferences are made about the importance of effective external management for increasing motivation among the students. It has been proven that a sufficient level of intervention in the educational process and external support allow for higher degree of engagement and motivation in learning majors. Conclusions are also made about the Action Research method as a basis for effective management of the educational process, solving didactic problems, adjusting teaching interventions and assessing the results obtained.

Improving executive functions at school. Integrating metacognitive exercise in class and computerized training at home to ensure training intensity and generalization. A feasibility pilot study

AbstractPrevious research has demonstrated the effectiveness of executive functions (EFs) training, both in computer‐based and school‐based formats. However, there is limited research on the combined effects of these training modalities. This study aims to assess the feasibility and preliminary efficacy of an EFs training programme for primary school children. The programme includes computerized training sessions for home use and school activities with metacognitive elements. The study included a sample of 53 second‐grade children, with 21 children in the training group and 32 children in the control group. Feasibility questionnaires were completed by children, parents and teachers. The children also underwent an EFs evaluation. The results indicate that the training was enjoyable for children and feasible for parents and teachers. Furthermore, preliminary efficacy analysis revealed significant improvements in working memory. These findings suggest that the training model holds promise for enhancing EFs in children in the school context. Practitioner notesWhat is already known about this topic Individual differences in executive functions influence acquisitions, behaviours and competencies in several specific domains from infancy to adulthood. Enhancing executive functions during school‐age years can contribute to reducing or preventing academic, behavioural and social difficulties. Among interventions targeting executive functions in school‐aged children, school‐based interventions have shown the highest effectiveness, followed by metacognitive interventions and computer‐based interventions. What this paper adds This paper presents the implementation of an innovative school‐based training programme designed to improve executive functions (EFs). The programme combines metacognitive sessions conducted at school with computer‐based sessions carried out at home. The goal was to enhance the effectiveness and generalizability of the training. The training programme was found to be enjoyable for children and feasible for both parents and teachers. Preliminary efficacy data indicate promising results, suggesting that the training programme is effective in achieving its intended goals. Implications for practice and/or policy Integrating school‐based metacognitive training with computerized sessions delivered at home can provide a high‐intensity training programme that may not be achievable in the school context alone. The school activities, conducted in group settings with teachers and peers, were found to be the most appreciated by the children, indicating that computer sessions alone cannot replace the value of metacognitive sessions at school, within a meaningful social context for the child. It is crucial to actively involve families in the training process to ensure good adherence. Additionally, the availability of technological resources in each family should be evaluated before implementing the training programme.

The importance of translating economic terms

Economic texts represent a special category of translations that require not just accurate transmission of information, but also taking into account the specific economic realities of different countries. Achieving maximum equivalence of terms is the key to successful translation of such texts. A translator of economic texts must not only speak both languages at a high level, but also have deep knowledge in the field of economics. It is essential to focus on the nuances of terminology, cultural characteristics and contextual meanings in order to achieve the most accurate and adequate transmission of meaning.

VARista: a free web platform for streamlined whole-genome variant analysis across T2T, hg38, and hg19.

With the increasing importance of genomic data in understanding genetic diseases, there is an essential need for efficient and user-friendly tools that simplify variant analysis. Although multiple tools exist, many present barriers such as steep learning curves, limited reference genome compatibility, or costs. We developed VARista, a free web-based tool, to address these challenges and provide a streamlined solution for researchers, particularly those focusing on rare monogenic diseases. VARista offers a user-centric interface that eliminates much of the technical complexity typically associated with variant analysis. The tool directly supports VCF files generated using reference genomes hg19, hg38, and the emerging T2T, with seamless remapping capabilities between them. Features such as gene summaries and links, tissue and cell-specific gene expression data for both adults and fetuses, as well as automated PCR design and integration with tools such as SpliceAI and AlphaMissense, enable users to focus on the biology and the case itself. As we demonstrate, VARista proved effective in narrowing down potential disease-causing variants, prioritizing them effectively, and providing meaningful biological context, facilitating rapid decision-making. VARista stands out as a freely available and comprehensive tool that consolidates various aspects of variant analysis into a single platform that embraces the forefront of genomic advancements. Its design inherently supports a shift in focus from technicalities to critical thinking, thereby promoting better-informed decisions in genetic disease research. Given its unique capabilities and user-centric design, VARista has the potential to become an essential asset for the genomic research community. https://VARista.link.

Methodology to Counter Heterogeneity at Urban Multilane Roundabouts

Despite the adoption of roundabouts in developing countries for traffic management and smooth flow, the concept of traffic heterogeneity management at these intersections is relatively unexplored. Estimating passenger car units (PCUs) for different vehicle types to homogenize traffic is a recognized approach worldwide. In this study, vehicles were classified into five categories to provide a meaningful context for the analysis. The study proposes a model to estimate PCU values for different vehicle types, specifically at roundabouts, after identifying the potential influencing factors. The study was based on data collected from 10 roundabouts in three Indian cities, having different physical and geometrical characteristics. The proposed method yielded PCU values that were partially comparable with existing standards. Comparisons with the Indian Highway Capacity Manual (Indo-HCM) revealed that this publication underestimated PCU values for two-wheelers and three-wheelers by 20% and 11% respectively, but overestimated PCU values for big cars and heavy vehicles by 9% and 12%, respectively. Method appropriateness was ascertained from the lack of prior assumptions made about traffic and geometrical characteristics. To prevent unnecessary re-estimation of PCU values across locations, the idea of a heterogeneity counter component (HCC) was proposed. The model was trained using cross validation on a dataset of seven roundabouts, and its performance was evaluated on a separate dataset using mean absolute percentage error. The relationship between PCUs and the geometrical features of roundabouts, and the effect of traffic characteristics on HCC were studied. The findings might be considered while revising relevant codes and manuals (e.g., IRC-65:2017, Indo-HCM).

IBVC: Interpolation-driven B-frame video compression

Learned B-frame video compression aims to adopt bi-directional motion estimation and motion compensation (MEMC) coding for middle frame reconstruction. However, previous learned approaches often directly extend neural P-frame codecs to B-frame relying on bi-directional optical-flow estimation or video frame interpolation. They suffer from inaccurate quantized motions and inefficient motion compensation. To address these issues, we propose a simple yet effective structure called Interpolation-driven B-frame Video Compression (IBVC). Our approach only involves two major operations: video frame interpolation and artifact reduction compression. IBVC introduces a bit-rate free MEMC based on interpolation, which avoids optical-flow quantization and additional compression distortions. Later, to reduce duplicate bit-rate consumption and focus on unaligned artifacts, a residual guided masking encoder is deployed to adaptively select the meaningful contexts with interpolated multi-scale dependencies. In addition, a conditional spatio-temporal decoder is proposed to eliminate location errors and artifacts instead of using MEMC coding in other methods. The experimental results on B-frame coding demonstrate that IBVC has significant improvements compared to the relevant state-of-the-art methods. Meanwhile, our approach can save bit rates compared with the random access (RA) configuration of H.266 (VTM). The code will be available at https://github.com/ruhig6/IBVC.

Идея Преображения в русской литературе XX века

The article presents the idea of Transfiguration expressed and fixed in the semantic potential of language, sign and symbol as the initial, fundamental idea of Russian literature. The tasks solved in this article were formed under the influence of research in the field of the semantic potential of language, which is revealed to its speakers in specific historical and cultural realities. In particular, the connection between the peculiarities of national consciousness and the lexical meanings of such words that have no direct analogues in other languages (“svet”, “mir”, “pravda”, “volya”), i.e. create very specific meanings that speakers of other languages cannot recognize as such. These meanings form the spiritual foundation of the national character. The analysis of the contextual meanings ofthese words forms the basis of the presented work and leads to the conclusion that they form a semantic unity, including at the level of the idea of the Transfiguration of man and the world, which is central to Russian culture and literature. The analysis of the idea of spiritual transformation of man in Russian literature of the XX century is carried out on the basis of representation of semantic-semantic models of the artistic worlds of works by I. Shmelev, V. Shukshin, A. and B. Strugatsky works, including the concepts of the idea of Transfiguration. The writers lead their characters through different paths to inner transformation, and the transforming components are always personified. In I. Shmelev’s work, the vector of transformation is formed by the concepts “soul – faith – miracle – heart / heart’s look – peace – truth – freedom – light / light / to shine”. In the work of V. Shukshin – “soul – conscience – peace – peacefulness – humility – truth / justice – clarity – light / bright / shine – will / freedom”. In the work of A. and B. Strugatsky – “light / shines / dawn – miracle / miracles – will / will barrier – world / world – hope – truth / half-truth – gift – happiness / happy –soul”. The degree of the hero’s approach to the world, light, truth is different, it is set, among other things, through the quantitative saturation of conceptually significant words in the work and through their correlation with the concepts-antagonists. Thus, an individual vector of movement of the human soul, aspiring to the acquisition of new higher meanings, is formed. At the same time, the unifying component for them is the intense inner work, which is continuously conducted by writers and their characters.

Языковые маркеры прецедентности имени собственного в тексте-реципиенте

The study aims to establish a typology of linguistic markers enabling the determination of the level of precedent/non-precedent status of proper names through the analysis of verbal units. The paper explores ways in which the addresser signals the precedent status of a proper name in the context of reality terminologically referred to as a “linguistic marker of the precedent status of a proper name”. The features of the linguistic environment of a precedent name in the recipient text are described and analyzed. The content of the “recipient text” concept is defined within the scientific paradigm of the theory of precedent phenomena (specifically, the precedent name), noting its indirect and direct relationship with the concept of a “source text” in a specific linguistic situation. The study is novel in that it is the first to describe a system of linguistic markers of the precedent status of proper names. A technology for determining the status of a precedent name through a comprehensive analysis of the contextual meaning of the proper name in the recipient text is presented. As a result, the study identifies and characterizes in detail the types of linguistic markers of the precedent status of proper names employed by the addresser as units to signal the precedent, connotative usage of proper names in context.

Short Movies and Narrative Assessment in English Lessons

This study investigates the educational impact of short films on students' understanding of story elements, vocabulary acquisition, and cultural nuances in English classes. Short films, characterized by their concise narratives and visual storytelling, serve as valuable tools in enhancing language-learning experiences. By exploring the interplay between visual media and language acquisition, this research sheds light on the effectiveness of short films as pedagogical resources. The study examines how short films facilitate comprehension of complex story elements such as plot structures, character development, and dialogue, offering students tangible examples of literary concepts. Additionally, the research delves into the role of short films in expanding students' vocabulary through context-based learning, enabling them to grasp new words and phrases in a meaningful context. Furthermore, the study explores short films as windows into diverse cultures, fostering cultural awareness and understanding among students. Through qualitative and quantitative analyses, this research elucidates the multifaceted ways in which short films contribute to a comprehensive and engaging English learning environment. The findings highlight the significance of incorporating visual media like short films into English classes, emphasizing their potential to enhance students' language skills, cultural competence, and overall educational experience.

Sentiment Analysis of Short Texts Using SVMs and VSMs-Based Multiclass Semantic Classification

ABSTRACT In our approach, a hybrid machine learning model is proposed which uses Enhanced Vector Space Model (EVSM) along with Hybrid Support Vector Machine (HSVM) classifier. Initially the social media-based information is retrieved using Enhanced Vector Space Model (EVSM). EVSMs are employed in order to characterize the text content by mapping them into high-dimensional vector spaces, capturing the relationships between words and their contextual meanings. Rigorous feature selection methods are employed to designate texts for review, and a multiclass semantic classification algorithm, specifically the HSVM classifier, is utilized for categorization. Decision tree algorithm is used along with SVM to refine the selection process. To enhance sentiment analysis accuracy, sentiment dictionaries are not only presented but also extended through the expansion of Stanford’s GloVE tool. To enhance precision, the proposed work introduces weight-enhancing methods for processing renowned text weights. Sentiments are classified into positive, negative, and neutral categories. Notably, the achieved results demonstrate improved accuracy, attributed to the incorporation of an emotional sentiment enhancement factor for determining weights and leveraging sentiment dictionaries for word availability. The accuracy is obtained to be 92.78% with 91.33% positive sentiment rate and 97.32% negative sentiment rate.

Semisupervised Learning-Based Word-Sense Disambiguation Using Word Embedding for Afaan Oromoo Language

Natural language is a type of language that human beings use to communicate with each other. However, it is very difficult to communicate with a machine-understandable language. Finding context meaning is challenging the activity of automatically identifying machine translation, indexing engines, and predicting neighbor words in natural language. Many researchers around the world investigated word-sense disambiguation in different languages, including the Afaan Oromo language, to solve this challenge. Nevertheless, the amount of effort for Afaan Oromo is very little in terms of finding context meaning and predicting neighbor words to solve the word ambiguity problem. Since the Afaan Oromo language is one of the languages developed in Ethiopia, it needs the latest technology to enhance communication and overcome ambiguity challenges. So far, this work aims to design and develop a vector space model for the Afaan Oromo language that can provide the application of word-sense disambiguation to increase the performance of information retrieval. In this work, the study has used the Afaan Oromo word embedding method to disambiguate a contextual meaning of words by applying the semisupervised technique. To conduct the study, 456,300 Afaan Oromo words were taken from different sources and preprocessed for experimentation by the Natural Language Toolkit and Anaconda tool. The K-means machine learning algorithm was used to cluster similar word vocabulary. Experimental results show that using word embedding for the proposed language’s corpus improves the performance of the system by a total accuracy of 98.89% and outperforms the existing similar systems.

Projective geometry and spatial reasoning for STEM learning

Projective geometry is a prominent area in many fields including art, design, architecture, and mathematics, but how it can contribute to children’s spatial reasoning as well as a supplementary geometry to that of Euclid’s in school mathematics curricula raises the need for further consideration. With emphasis on the dynamic relationship between 2D and 3D objects, projective geometry not only offers important concepts but a meaningful context in which to develop spatial reasoning for STEM learning. In this article, we overview spatial reasoning as it relates to STEM and in particular, mathematics. Making a case for projective geometry, we present activities for the classroom, demonstrating how this mathematics topic connects to and opens new possibilities to promote spatial reasoning for STEM learning in the elementary grades.

English Language Variation on Indonesian Register of Photography to Characterize the Community Identity

This research aims to describe the linguistics form, meaning, and functions under the phenomenon of English language variation used by Indonesian photography enthusiasts. The influence of the English register in lexical categories mostly has undergone a shift in meaning. So, the analysis of English register in the photography context is fascinating to be examined. The method of this research is qualitative descriptive with a sociolinguistics approach. The data are restricted to words and phrases taken from the daily posting of the Facebook community group. The procedure of data collection was conducted by observing and noting the English register among photography enthusiasts. The data were analyzed based on the formation process, the contextual meaning, and identifying the functions and sociolinguistics aspects. This research revealed that the use of English as a foreign language in the conversation of Indonesian photography enthusiasts was largely formed in a 'word' that represent an intended meaning without explain it in a long way. Those terms are functioned to tell the information, experiences, facts, techniques in photography. The involvement of English in the photography register was assumed because several terms do not have equivalent sense in Indonesian language, so it better to use them in English. This research has also showed that the intention of using English in photography community as the characteristics to labeling their social identity, also increasing the speaker's social value, particularly in creating feelings of pride and prestige.  

“They Don’t Give a Damn About Us”: A Qualitative Study on Experiences and Perceptions Underlying Racial Disparities in Sex Trafficking Specialty Docket Programs

ABSTRACT Sex trafficking specialty court programs represent a structural approach to disrupt historic patterns of violence and health disparities, as well as counteract carceral recidivism issues. Despite the promise of these programs, inequities have been observed in women’s access, entry, and success within such programs. The purpose of the current study was to understand perceptions and experiences underlying documented racial disparities in women’s access to and retention within sex trafficking specialty court programs. Fifteen black women with histories of arrest for prostitution or solicitation were recruited to participate in interviews querying experiences with referral processes, factors associated with their admittance and retention, and factors influencing whether they ultimately participated in such a program. An inductive content analysis was used to analyze interview transcript data. Four groups of themes emerged after analyses: (a) perceptions and experiences with police and the criminal legal system; (b) perceptions of sex trafficking specialty court programs; (c) perceived barriers to participation; and (d) perceived facilitators of participation. This research provides meaningful context toward perceptions and experiences underlying previously observed disparities in women’s referral to and success within sex trafficking specialty court programs. Findings provide insights toward practices that could bolster equity within these programs.

Cognitive modelling of concepts in the mental lexicon with multilayer networks: Insights, advancements, and future challenges.

The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Over decades psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? Here we review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression, and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, including in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.

Learning Design: To Improve Mathematical Problem-Solving Skills Using a Contextual Approach

Mathematical problem-solving skills are essential for success in various fields, including science, technology, engineering, and mathematics (STEM). However, many students struggle with math and often lack the necessary skills to solve problems effectively. Traditional math education often lacks real-world context, which makes it difficult for students to engage with the material and develop problem-solving skills. To address this issue, the need for a contextual approach to learning design to improve mathematical problem-solving skills is inevitable.  In the context of math education, this approach can be applied by providing learners with mathematical problems that are embedded in meaningful contexts, such as real-world scenarios or case studies. This approach draws from theories such as situated cognition and social constructivism, which emphasize the importance of considering the broader context in which problem-solving occurs. A literature review is used in this research method, and data analysis is done descriptively. By applying a contextual approach to mathematics learning design, students are expected to develop mathematical problem-solving skills that are important for success in various fields.

Sentence embedding to improve rumour detection performance model

<span lang="EN-US">Recently, most individuals have preferred accessing the most recent news via social media platforms like Twitter as their primary source of information. Moreover, Twitter enables users to post and distribute tweets quickly and unsupervised. As a result, Twitter has become a popular platform for disseminating false information, such as rumours. These rumours were then propagated as accurate and influenced public opinion and decision-making. The issue will arise when a decision or policy with substantial consequences is made based on rumours. To avoid the negative impacts of rumours, several researchers have attempted to detect them automatically as early as feasible. Previous studies employed supervised learning methods to identify Twitter rumours and relied on feature extraction algorithms to extract tweet content and context elements. However, manually extracting features is time-consuming and labour-intensive. To encode each tweet's sentence as a vector based on its contextual meaning, we proposed utilising Bidirectional Encoder Representation of Transformer (BERT) as a sentence embedding. We then used these vectors to train some classifier models to detect rumours. Finally, we compared the performance of BERT-based models to feature engineering-based models. We discovered that the suggested BERT-based model improved all parameters by around 10% compared to the feature engineering-based classification model.</span>

Maximizers hyping in Chinese MA learners’ and experts’ academic discourse: An EUM-based study

The research is to examine the utilization of maximizers as a strategic tool to attain academic rhetorical hype within the realm of Chinese MA theses (CLMA_C) and international journal articles (ILJA_C). Maximizers, which convey heightened intensity and personal commitment, play a crucial role in shaping rhetorical strategies in academic discourse. The study employs the EUM model (Sinclair’s theory of extended units of meaning). By adopting this model, the research makes a significant contribution to elucidating the intricate linguistic choices within scholarly communication. The goal is to analyze the collocation features of the maximizers in CLMA_C and ILJA_C. The goal determines the choice of the subject of research – identifying similarities and differences in the use of maximizers in two databases (international journal articles (ILJA_C) and Chinese MA theses (CLMA_C). The novelty of the research is that the study employs the EUM model to analyze maximizers in CLMA_C and ILJA_C, allowing for a comprehensive examination of both collocational form and contextual meaning. The analysis includes an exploration of lexical patterns, colligation, semantic preference, and semantic prosody, offering a multifaceted understanding of how maximizers contribute to rhetorical strategies. This research uniquely explores maximizers in collocational form and meaning using the EUM model, offering nuanced insights into their role in rhetorical hype and revealing cross-cultural variations through the similarities and significant differences in CLMA_C and ILJA_C. The study contributes significantly to second language acquisition and comparative linguistics, advancing knowledge on maximizers in academic communication. Findings show lexical pattern similarities but significant differences in colligation, semantic preference, and semantic prosody. In CLMA_C, “fully” lacks diverse collocates, with varied colligation patterns influenced by L1 transfer. Regarding semantic prosody, experts adopt a commendatory tone, while Chinese MA learners express an affirmative tone.

Effects of anxiety state on N400 event-related brain potential response to unexpected semantic stimuli

Emotional states can influence how people use meaningful context to make predictions about what comes next. To measure whether state anxiety influences such prediction, we used the N400 event-related brain potential (ERP) response to semantic stimuli, whose amplitude is smaller (less negative) when the stimulus is more predicted based on preceding context. Participants (n = 28) were randomized to one of two groups, who underwent either an “anxious-uncertainty” procedure previously shown to increase anxiety, or a control procedure. Both before and after this procedure, participants’ ERPs were recorded while they viewed category definitions (e.g., “a type of fruit”), each followed by a target word that was either a high-typicality category exemplar (“apple”), low-typicality exemplar (“cherry”), or non-exemplar (“clamp”) of the category. Participants’ task was to respond by pressing one of two buttons to indicate whether the target represented a member of the category. As expected, based on previous work, overall, N400 amplitudes were largest (most negative) in response to non-exemplars, intermediate to low-typicality exemplars, and smallest to high-typicality exemplars. N400 amplitudes were larger to non-exemplars after the anxious-uncertainty procedure than after the control procedure. N400 amplitudes to both types of exemplars did not differ after the anxious-uncertainty procedure versus the control procedure. The results are consistent with participants devoting more neural resources to processing contextually unexpected items under anxious states, rather than anxiety facilitating processing of expected items.