How children generalize novel nouns: An eye-tracking analysis of their generalization strategies.

Recent research has shown that comparisons of multiple learning stimuli which are associated with the same novel noun favor taxonomic generalization of this noun. These findings contrast with single-stimulus learning in which children follow so-called lexical biases. However, little is known about the underlying search strategies. The present experiment provides an eye-tracking analysis of search strategies during novel word learning in a comparison design. We manipulated both the conceptual distance between the two learning items, i.e., children saw examples which were associated with a noun (e.g., the two learning items were either two bracelets in a "close" comparison condition or a bracelet and a watch in a "far" comparison condition), and the conceptual distance between the learning items and the taxonomically related items in the generalization options (e.g., the taxonomic generalization answer; a pendant, a near generalization item; versus a bow tie, a distant generalization item). We tested 5-, 6- and 8-year-old children's taxonomic (versus perceptual and thematic) generalization of novel names for objects. The search patterns showed that participants first focused on the learning items and then compared them with each of the possible choices. They also spent less time comparing the various options with one another; this search profile remained stable across age groups. Data also revealed that early comparisons, (i.e., reflecting alignment strategies) predicted generalization performance. We discuss four search strategies as well as the effect of age and conceptual distance on these strategies.

The Contribution of Willingness to Communicate to L2 Learners' Depth of Vocabulary Knowledge: An Empirical Study.

The issues of depth vocabulary knowledge and Willingness to Communicate (henceforth, WTC) are among the most important issues in second language learning. The present study set out to empirically look into the contribution of WTC to depth of vocabulary knowledge in L2 learning. To this end, 88 English L2 learners, divided into two groups in terms of their WTC, were given two depth vocabulary tests. The Word Association Test (WAT) was first administered to make a comparison between the depth vocabulary knowledge of the two WTC groups. Then, to triangulate the results, the Word Part Levels Test (WPLT) was administered to check whether the obtained results confirmed those of WAT. Analyzing data through independent t-test and MANOVA indicated that learners with higher levels of WTC had deeper vocabulary knowledge than those with lower levels of WTC on the WAT. Further, the triangulation results evinced that although the two groups did not differ significantly on the form-section and meaning-section of the WPLT, they significantly differed on the use-section of the test. The relevant pedagogical implications of the study are discussed.

Pupils Dilate More to Harder Vocabulary Words than Easier Ones.

Understanding cognitive effort expended during assessments is essential to improving efficiency, accuracy, and accessibility within these assessments. Pupil dilation is commonly used as a psychophysiological measure of cognitive effort, yet research on its relationship with effort expended specifically during language processing is limited. The present study adds to and expands on this literature by investigating the relationships among pupil dilation, trial difficulty, and accuracy during a vocabulary test. Participants (n = 63, Mage = 19.25) completed a subset of trials from the Peabody Picture Vocabulary Test while seated at an eye-tracker monitor. During each trial, four colored images were presented on the monitor while a word was presented via audio recording. Participants verbally indicated which image they thought represented the target word. Words were categorized into Easy, Medium, and Hard difficulty. Pupil dilation during the Medium and Hard trials was significantly greater than during the Easy trials, though the Medium and Hard trials did not significantly differ from each other. Pupil dilation in comparison to trial accuracy presented a more complex pattern, with comparisons between accurate and inaccurate trials differing depending on the timing of the stimulus presentation. These results present further evidence that pupil dilation increases with cognitive effort associated with vocabulary tests, providing insights that could help refine vocabulary assessments and other related tests of language processing.

Episodic long-term memory formation during slow-wave sleep.

We are unresponsive during slow-wave sleep but continue monitoring external events for survival. Our brain wakens us when danger is imminent. If events are non-threatening, our brain might store them for later consideration to improve decision-making. To test this hypothesis, we examined whether novel vocabulary consisting of simultaneously played pseudowords and translation words are encoded/stored during sleep, and which neural-electrical events facilitate encoding/storage. An algorithm for brain-state-dependent stimulation selectively targeted word pairs to slow-wave peaks or troughs. Retrieval tests were given 12 and 36 hr later. These tests required decisions regarding the semantic category of previously sleep-played pseudowords. The sleep-played vocabulary influenced awake decision-making 36 hr later, if targeted to troughs. The words' linguistic processing raised neural complexity. The words' semantic-associative encoding was supported by increased theta power during the ensuing peak. Fast-spindle power ramped up during a second peak likely aiding consolidation. Hence, new vocabulary played during slow-wave sleep was stored and influenced decision-making days later.

A Richer Vocabulary of Chinese Personality Traits: Leveraging Word Embedding Technology for Mining Personality Descriptors.

This study uses a data-driven approach to mine the distribution of personality traits among Chinese people in the Chinese social context. Based on the hypothesis of personality lexicology, word embedding technology was employed in machine learning to mine personality vocabulary from Tencent's word embedding database. More than 10,000 Chinese personality descriptors were extracted and analyzed using Gaussian Mixture Model Cluster and Hierarchical clustering analysis. The data was collected from 658 Chinese people randomly from all parts of China through an online questionnaire method. The results reveal six personality traits in the Chinese context, expanding the personality thesaurus and providing examples to illustrate each trait. The findings coincide with previous research on the five-factor model, which partially describes the personality traits of Chinese people, but does not offer a complete explanation of their typical social behavior patterns. Additionally, the study supports the notion of cultural particularity in personality traits. The approach used in this study offers a richer personality vocabulary than traditional personality mining methods, and word embedding technology captures richer semantic information in Chinese. The six Chinese personality traits identified in this study will also be used to explore how to quantify and evaluate personality traits based on word embedding and personality descriptors.

The effect of planning time on vocabulary acquisition in a task-based environment: the mediating roles of working memory and field (in)dependence.

This research investigated the impact of planning time, working memory (WM), and cognitive styles on language learning outcomes within the framework of Task-Based Language Teaching (TBLT). Drawing on a diverse sample of language learners, the study employed a pretest-posttest control group quasi-experiment to examine the effects of providing pre-task planning time on language performance. Participants engaged in communicative tasks, with a focus on vocabulary acquisition and task complexity, while their cognitive processes were assessed through measures of WM and cognitive styles. The findings revealed significant interactions between planning time and cognitive styles, particularly field dependence, influencing language production and proficiency such that learners with planning time outperformed learners without planning time; high-WM learners outperformed their low-WM peers, and field independent learners outstripped their field-dependent counterparts. Moreover, the study contributes to the broader understanding of the nuanced relationship between planning time, WM, and cognitive styles in the context of TBLT. The implications of these findings for language teachers, materials developers, syllabus designers, curriculum developers, and policymakers are discussed, offering insights into the design of effective language learning environments. Despite certain limitations, the study provides a foundation for further research exploring cross-cultural variations, longitudinal effects, and the integration of technology in language education, with the aim of advancing pedagogical practices tailored to diverse learner profiles.

Song lyrics have become simpler and more repetitive over the last five decades.

Music is ubiquitous in our everyday lives, and lyrics play an integral role when we listen to music. The complex relationships between lyrical content, its temporal evolution over the last decades, and genre-specific variations, however, are yet to be fully understood. In this work, we investigate the dynamics of English lyrics of Western, popular music over five decades and five genres, using a wide set of lyrics descriptors, including lyrical complexity, structure, emotion, and popularity. We find that pop music lyrics have become simpler and easier to comprehend over time: not only does the lexical complexity of lyrics decrease (for instance, captured by vocabulary richness or readability of lyrics), but we also observe that the structural complexity (for instance, the repetitiveness of lyrics) has decreased. In addition, we confirm previous analyses showing that the emotion described by lyrics has become more negative and that lyrics have become more personal over the last five decades. Finally, a comparison of lyrics view counts and listening counts shows that when it comes to the listeners' interest in lyrics, for instance, rock fans mostly enjoy lyrics from older songs; country fans are more interested in new songs' lyrics.

Electroencephalography Spectral-power Volatility Predicts Problem-solving Outcomes.

Temporal variability is a fundamental property of brain processes and is functionally important to human cognition. This study examined how fluctuations in neural oscillatory activity are related to problem-solving performance as one example of how temporal variability affects high-level cognition. We used volatility to assess step-by-step fluctuations of EEG spectral power while individuals attempted to solve word-association puzzles. Inspired by recent results with hidden-state modeling, we tested the hypothesis that spectral-power volatility is directly associated with problem-solving outcomes. As predicted, volatility was lower during trials solved with insight compared with those solved analytically. Moreover, volatility during prestimulus preparation for problem-solving predicted solving outcomes, including solving success and solving time. These novel findings were replicated in a separate data set from an anagram-solving task, suggesting that less-rapid transitions between neural oscillatory synchronization and desynchronization predict better solving performance and are conducive to solving with insight for these types of problems. Thus, volatility can be a valuable index of cognition-related brain dynamics.

Linguistic, concept and symbolic composition in adults with minimal receptive vocabulary.

In this paper, we examine some basic linguistic abilities in a small sample of adults with minimal receptive vocabulary, whose receptive mental verbal age ranges from 1;2 to 3;10. In particular, we examine whether the participants in our study understand noun phrases consisting of a noun modified by an adjective. We use stimuli that they can recognise by name. Except for one participant, we find that, while all of them understand the noun and adjective in isolation, none seems to understand these noun phrases, which means that they seem to not do linguistic composition. In order to test whether the difficulty is linguistic or conceptual, we ran two other studies, one on concept composition, and the other on iconic symbolic composition (composition of pictograms). Results suggest that linguistic composition is particularly difficult in this population, and that vocabulary breadth may not predict compositional abilities.

Lexical skills and gesture use: A comparison between expressive and receptive/expressive late talkers.

BACKGROUND: Studies on late talkers (LTs) highlighted their heterogeneity and the relevance of describing different communicative profiles. AIMS: To examine lexical skills and gesture use in expressive (E-LTs) vs. receptive-expressive (R/E-LTs) LTs through a structured task. METHODS AND PROCEDURES: Forty-six 30-month-old screened LTs were distinguished into E-LTs (n= 35) and R/E-LTs (n= 11) according to their receptive skills. Lexical skills and gesture use were assessed with a Picture Naming Game by coding answer accuracy (correct, incorrect, no response), modality of expression (spoken, spoken-gestural, gestural), type of gestures (deictic, representational), and spoken-gestural answers' semantic relationship (complementary, equivalent, supplementary). OUTCOMES AND RESULTS: R/E-LTs showed lower scores than E-LTs for noun and predicate comprehension with fewer correct answers, and production with fewer correct and incorrect answers, and more no responses. R/E-LTs also exhibited lower scores in spoken answers, representational gestures, and equivalent spoken-gestural answers for noun production and in all spoken and gestural answers for predicate production. CONCLUSIONS AND IMPLICATIONS: Findings highlighted more impaired receptive and expressive lexical skills and lower gesture use in R/E-LTs compared to E-LTs, underlying the relevance of assessing both lexical and gestural skills through a structured task, besides parental questionnaires and developmental scales, to describe LTs' communicative profiles.

Spoken verb learning in children with language disorder.

The current study examined spoken verb learning in elementary school children with language disorder (LD). We aimed to replicate verb learning deficits reported in younger children with LD and to examine whether verb instrumentality, a semantic factor reflecting whether an action requires an instrument (e.g., "to chop" is an instrumental verb), influenced verb learning. The possible facilitating effect of orthographic cues presented during training was also evaluated. In an exploratory analysis, we investigated whether language and reading skills mediated verb learning performance. General language skills and verb learning were assessed in Dutch children with LD and age-matched typically developing controls (n = 25 per group) aged 8 to 12 years (M = 9;9 [years;months], SD = 1;3). Using video animations, children learned 20 nonwords depicting actions comprising 10 instrumental and 10 noninstrumental verbs. Half of the items were trained with orthographic information present. Verb learning was assessed using an animation-word matching and animation naming task. Linear mixed-effects models showed a main effect of group for all verb learning measures, demonstrating that children with LD learned fewer words and at a slower rate than the control group. No effect of verb instrumentality, presence of orthographic information, or the included mediators was found. Our results emphasize the importance of continued vocabulary instruction in elementary school to strengthen verb encoding. Given that our findings are inconsistent with the overall literature showing an orthographic facilitation effect, future studies should investigate whether participants pay attention to the written word form in learning contexts with moving stimuli.

Construction and improvement of English vocabulary learning model integrating spiking neural network and convolutional long short-term memory algorithm.

To help non-native English speakers quickly master English vocabulary, and improve reading, writing, listening and speaking skills, and communication skills, this study designs, constructs, and improves an English vocabulary learning model that integrates Spiking Neural Network (SNN) and Convolutional Long Short-Term Memory (Conv LSTM) algorithms. The fusion of SNN and Conv LSTM algorithm can fully utilize the advantages of SNN in processing temporal information and Conv LSTM in sequence data modeling, and implement a fusion model that performs well in English vocabulary learning. By adding information transfer and interaction modules, the feature learning and the timing information processing are optimized to improve the vocabulary learning ability of the model in different text contents. The training set used in this study is an open data set from the WordNet and Oxford English Corpus data corpora. The model is presented as a computer program and applied to an English learning application program, an online vocabulary learning platform, or a language education software. The experiment will use the open data set to generate a test set with text volume ranging from 100 to 4000. The performance indicators of the proposed fusion model are compared with those of five traditional models and applied to the latest vocabulary exercises. From the perspective of learners, 10 kinds of model accuracy, loss, polysemy processing accuracy, training time, syntactic structure capturing accuracy, vocabulary coverage, F1-score, context understanding accuracy, word sense disambiguation accuracy, and word order relation processing accuracy are considered. The experimental results reveal that the performance of the fusion model is better under different text sizes. In the range of 100-400 text volume, the accuracy is 0.75-0.77, the loss is less than 0.45, the F1-score is greater than 0.75, the training time is within 300s, and the other performance indicators are more than 65%; In the range of 500-1000 text volume, the accuracy is 0.81-0.83, the loss is not more than 0.40, the F1-score is not less than 0.78, the training time is within 400s, and the other performance indicators are above 70%; In the range of 1500-3000 text volume, the accuracy is 0.82-0.84, the loss is less than 0.28, the F1-score is not less than 0.78, the training time is within 600s, and the remaining performance indicators are higher than 70%. The fusion model can adapt to various types of questions in practical application. After the evaluation of professional teachers, the average scores of the choice, filling-in-the-blank, spelling, matching, exercises, and synonyms are 85.72, 89.45, 80.31, 92.15, 87.62, and 78.94, which are much higher than other traditional models. This shows that as text volume increases, the performance of the fusion model is gradually improved, indicating higher accuracy and lower loss. At the same time, in practical application, the fusion model proposed in this study has a good effect on English learning tasks and offers greater benefits for people unfamiliar with English vocabulary structure, grammar, and question types. This study aims to provide efficient and accurate natural language processing tools to help non-native English speakers understand and apply language more easily, and improve English vocabulary learning and comprehension.

A normalization model for repeated letters in social media hate speech text based on rules and spelling correction.

As social media booms, abusive online practices such as hate speech have unfortunately increased as well. As letters are often repeated in words used to construct social media messages, these types of words should be eliminated or reduced in number to enhance the efficacy of hate speech detection. Although multiple models have attempted to normalize out-of-vocabulary (OOV) words with repeated letters, they often fail to determine whether the in-vocabulary (IV) replacement words are correct or incorrect. Therefore, this study developed an improved model for normalizing OOV words with repeated letters by replacing them with correct in-vocabulary (IV) replacement words. The improved normalization model is an unsupervised method that does not require the use of a special dictionary or annotated data. It combines rule-based patterns of words with repeated letters and the SymSpell spelling correction algorithm to remove repeated letters within the words by multiple rules regarding the position of repeated letters in a word, be it at the beginning, middle, or end of the word and the repetition pattern. Two hate speech datasets were then used to assess performance. The proposed normalization model was able to decrease the percentage of OOV words to 8%. Its F1 score was also 9% and 13% higher than the models proposed by two extant studies. Therefore, the proposed normalization model performed better than the benchmark studies in replacing OOV words with the correct IV replacement and improved the performance of the detection model. As such, suitable rule-based patterns can be combined with spelling correction to develop a text normalization model to correctly replace words with repeated letters, which would, in turn, improve hate speech detection in texts.

Chinese English language learners' vocabulary retention: Investigating the effectiveness of neuro/metacognitive and socio-cultural strategies.

BACKGROUND: The acquisition of a rich vocabulary is foundational to language proficiency. In the pedagogical pursuit of effective vocabulary teaching, educators explore diverse methodologies. Researchers investigated the impact of different neurocognitive, metacognitive, and socio-cultural strategies on enhancing vocabulary learning, particularly among Chinese English Language Learners. OBJECTIVES: The study aims to determine the effectiveness of techniques derived from these theories compared to traditional teaching methods in enhancing vocabulary recall and recognition among English language learners. METHODOLOGY: A quasi-experimental pre-test/post-test design was employed for the experimental and control groups, comprising 90 Chinese EFL learners selected from educational institutions in 2022-2023. The experimental group (n = 45) received instruction involving visual imagery, multisensory rotation, circle rotation, and mind mapping over eight sessions, while the control group (n = 45) received traditional teaching methods. FINDINGS: Statistical analysis, utilizing covariance and analysis of variance with SPSS software version 22, revealed significant improvements in recall and vocabulary recognition within and between the experimental and control groups. CONCLUSIONS: The results indicate that incorporating techniques based on Neuro-Cognitive, Multimedia, Socio-Cultural, and Metacognitive theories positively influences vocabulary recall and recognition. This suggests the efficacy of these innovative methods in enhancing English language learning, highlighting their potential for broader integration into EFL instruction.

Shared structure of fundamental human experience revealed by polysemy network of basic vocabularies across languages.

How are concepts related to fundamental human experiences organized within the human mind? Our insights are drawn from a semantic network created using the Cross-Linguistic Database of Polysemous Basic Vocabulary, which focuses on a broad range of senses extracted from dictionary entries. The database covers 60 basic vocabularies in 61 languages, providing 11,841 senses from 3736 entries, revealing cross-linguistic semantic connections through automatically generated weighted semantic maps. The network comprises 2941 nodes connected by 3573 edges. The nodes representing body parts, motions, and features closely related to human experience occupy wide fields or serve as crucial bridges across semantic domains in the network. The polysemous network of basic vocabularies across languages represents a shared cognitive network of fundamental human experiences, as these semantic connections should be conceived as generally independent of any specific language and are driven by universal characteristics of the real world as perceived by the human mind. The database holds the potential to contribute to research aimed at unraveling the nature of cognitive proximity.

The Impact of Morphological Intervention on Literacy Knowledge and Reading Motivation: A Cluster-Randomized Comparison Trial in Diverse Socioeconomic Status Kindergartens.

PURPOSE: This study investigated the effectiveness of a storytelling-based morphological intervention program on the language and literacy knowledge and reading motivation of kindergarten children from low and mid socioeconomic status (SES) backgrounds. It also explored how these groups compared in change scores against a non-intervened high SES group. METHOD: Employing a cluster randomization approach, this study included 158 kindergarten children, comprising intervention and comparison groups from low and mid SES backgrounds, as well as a non-intervened high SES group. Assessments were conducted on morphological awareness (MA), print concepts, vocabulary, and reading motivation. RESULTS: Children in the intervention groups showed better performance in MA and print concept scores than those in their respective comparison groups. The mid SES intervention group also displayed significant improvement in vocabulary and motivation scores compared to its counterpart. When examining the effects of SES on change scores, the low SES intervention group achieved superior results in vocabulary and print concept scores compared to both the mid SES intervention and the non-intervened high SES groups. For reading motivation, the mid SES group outperformed the low SES group. CONCLUSIONS: The morphological intervention program using storytelling positively impacts both the literacy skills and reading motivation of kindergarten children, especially those from low and mid SES backgrounds. This study emphasizes the significance of designing interventions that cater to the distinct educational needs of children from different SES backgrounds.

Mapping words to the world: Adults, but not children, understand how mismatching descriptions refer.

How do children learn to connect expressions (e.g., "that red apple") to the real-world objects they refer to? The dominant view in developmental psychology is that children rely on descriptive information, "red" and "apple." In contrast, linguistic theories of the adult language attribute primacy to grammatical elements: words such as "that" or "another" first establish the status of potential referents within the discourse context (old or new) before descriptions can factor in. These theories predict that reference can succeed even when the description does not match the referent. We explored this novel prediction in adults and children. Over four experiments, we found that (a) adults relied on the articles to identify the referent, even when the description did not fit, consistent with grammar-first accounts; (b) consistent with description-first accounts, and unlike adults, 3- to 5-year-old children prioritized the descriptions provided by nouns and adjectives, despite being sensitive to grammatical information. This suggests that children connect expressions to referents differently from adults. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Conceptual design of a generic data harmonization process for OMOP common data model.

BACKGROUND: To gain insight into the real-life care of patients in the healthcare system, data from hospital information systems and insurance systems are required. Consequently, linking clinical data with claims data is necessary. To ensure their syntactic and semantic interoperability, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) from the Observational Health Data Sciences and Informatics (OHDSI) community was chosen. However, there is no detailed guide that would allow researchers to follow a generic process for data harmonization, i.e. the transformation of local source data into the standardized OMOP CDM format. Thus, the aim of this paper is to conceptualize a generic data harmonization process for OMOP CDM. METHODS: For this purpose, we conducted a literature review focusing on publications that address the harmonization of clinical or claims data in OMOP CDM. Subsequently, the process steps used and their chronological order as well as applied OHDSI tools were extracted for each included publication. The results were then compared to derive a generic sequence of the process steps. RESULTS: From 23 publications included, a generic data harmonization process for OMOP CDM was conceptualized, consisting of nine process steps: dataset specification, data profiling, vocabulary identification, coverage analysis of vocabularies, semantic mapping, structural mapping, extract-transform-load-process, qualitative and quantitative data quality analysis. Furthermore, we identified seven OHDSI tools which supported five of the process steps. CONCLUSIONS: The generic data harmonization process can be used as a step-by-step guide to assist other researchers in harmonizing source data in OMOP CDM.