Syllables and their beginnings have a special role in the mental lexicon.

The beginnings of words are, in some informal sense, special. This intuition is widely shared, for example, when playing word games. Less apparent is whether the intuition is substantiated empirically and what the underlying organizational principle(s) might be. Here, we answer this seemingly simple question in a quantitatively clear way. Based on arguments about the interplay between lexical storage and speech processing, we examine whether the distribution of information among different speech sounds of words is governed by a critical computational unit for online speech perception and production: syllables. By analyzing lexical databases of twelve languages, we demonstrate that there is a compelling asymmetry between syllable beginnings (onsets) versus ends (codas) in their involvement in distinguishing words stored in the lexicon. In particular, we show that the functional advantage of syllable onset reflects an asymmetrical distribution of lexical informativeness within the syllable unit but not an effect of a global decay of informativeness from the beginning to the end of a word. The converging finding across languages from a range of typological families supports the conjecture that the syllable unit, while being a critical primitive for both speech perception and production, is also a key organizational constraint for lexical storage.

A syntax-lexicon trade-off in language production.

Spoken language production involves selecting and assembling words and syntactic structures to convey one's message. Here we probe this process by analyzing natural language productions of individuals with primary progressive aphasia (PPA) and healthy individuals. Based on prior neuropsychological observations, we hypothesize that patients who have difficulty producing complex syntax might choose semantically richer words to make their meaning clear, whereas patients with lexicosemantic deficits may choose more complex syntax. To evaluate this hypothesis, we first introduce a frequency-based method for characterizing the syntactic complexity of naturally produced utterances. We then show that lexical and syntactic complexity, as measured by their frequencies, are negatively correlated in a large (n = 79) PPA population. We then show that this syntax-lexicon trade-off is also present in the utterances of healthy speakers (n = 99) taking part in a picture description task, suggesting that it may be a general property of the process by which humans turn thoughts into speech.

A preliminary investigation on core lexicon analysis in dementia of the Alzheimer's type.

BACKGROUND: Core lexicon (CL) analysis is a time efficient and possibly reliable measure that captures discourse production abilities. For people with aphasia, CL scores have demonstrated correlations with aphasia severity, as well as other discourse and linguistic measures. It was also found to be clinician-friendly and clinically sensitive enough to capture longitudinal changes in aphasia. To our knowledge, CL has never been investigated in individuals with neurologically progressive disease. AIMS: As a preliminary investigation, we sought to investigate (1) whether CL scores correlate with dementia severity, (2) whether CL scores correlate with measures of discourse quality, and (3) whether CL scores correlate with other measures of lexical/semantic access. METHODS & PROCEDURES: Twelve participants with a cognitive impairment associated with dementia of the Alzheimer's type (DAT) completed several measures of language and cognitive ability, as well as provide a language sample from the wordless picture book, Picnic. RESULTS & CONCLUSION: Results are informative, as they provide insight into characteristics of CL and provide support for potential use of CL in individuals with neurologically progressive disease. The results indicated that CL scores do correlate with dementia severity and several measures of language ability, indicating they may provide a useful measure of language abilities in DAT, but more research is needed. WHAT THIS PAPER ADDS: What is already known on the subject Core lexicon (CL) analysis is an assessment measure of discourse ability, most closely related to informativeness or productivity, used in aphasiology that is easier to use and less time consuming than previous measures of informativeness, such as correct information units or type-token ratio (TTR). For people with aphasia, CL analysis correlates with aphasia severity, measures of informativeness, as well as other measures of discourse quality. It has also been shown to be faster and more reliable between scorers than other informativeness measures. What this study adds Core lexicon analysis is a new simple and online method for assessing the informativeness of a discourse sample without the need to record or transcribe the language sample. CL is receiving a lot of attention in aphasia, correlating with everything from aphasia severity to measures of productivity and lexical access, as well as measures of informativeness. Unfortunately, no one has investigated CL analysis in dementia. The study demonstrates the first evidence that CL analysis may be a useful measure for determining dementia severity and language quality in people with dementia. What are the clinical implications of this work? Core lexicon analysis may provide clinicians and researchers with an easy method for assessing the discourse of people with a cognitive impairment associated with dementia of the Alzheimer's type. This will improve initial assessment, as well as improve ongoing language assessment that may provide clues into their functional ability to communicate effectively.

The "Small World of Words" free association norms for Rioplatense Spanish.

Large-scale word association datasets are both important tools used in psycholinguistics and used as models that capture meaning when considered as semantic networks. Here, we present word association norms for Rioplatense Spanish, a variant spoken in Argentina and Uruguay. The norms were derived through a large-scale crowd-sourced continued word association task in which participants give three associations to a list of cue words. Covering over 13,000 words and +3.6 M responses, it is currently the most extensive dataset available for Spanish. We compare the obtained dataset with previous studies in Dutch and English to investigate the role of grammatical gender and studies that used Iberian Spanish to test generalizability to other Spanish variants. Finally, we evaluated the validity of our data in word processing (lexical decision reaction times) and semantic (similarity judgment) tasks. Our results demonstrate that network measures such as in-degree provide a good prediction of lexical decision response times. Analyzing semantic similarity judgments showed that results replicate and extend previous findings demonstrating that semantic similarity derived using spreading activation or spectral methods outperform word embeddings trained on text corpora.

LEXpander: Applying colexification networks to automated lexicon expansion.

Recent approaches to text analysis from social media and other corpora rely on word lists to detect topics, measure meaning, or to select relevant documents. These lists are often generated by applying computational lexicon expansion methods to small, manually curated sets of seed words. Despite the wide use of this approach, we still lack an exhaustive comparative analysis of the performance of lexicon expansion methods and how they can be improved with additional linguistic data. In this work, we present LEXpander, a method for lexicon expansion that leverages novel data on colexification, i.e., semantic networks connecting words with multiple meanings according to shared senses. We evaluate LEXpander in a benchmark including widely used methods for lexicon expansion based on word embedding models and synonym networks. We find that LEXpander outperforms existing approaches in terms of both precision and the trade-off between precision and recall of generated word lists in a variety of tests. Our benchmark includes several linguistic categories, as words relating to the financial area or to the concept of friendship, and sentiment variables in English and German. We also show that the expanded word lists constitute a high-performing text analysis method in application cases to various English corpora. This way, LEXpander poses a systematic automated solution to expand short lists of words into exhaustive and accurate word lists that can closely approximate word lists generated by experts in psychology and linguistics.

Iconicity ratings for 14,000+ English words.

Iconic words and signs are characterized by a perceived resemblance between aspects of their form and aspects of their meaning. For example, in English, iconic words include peep and crash, which mimic the sounds they denote, and wiggle and zigzag, which mimic motion. As a semiotic property of words and signs, iconicity has been demonstrated to play a role in word learning, language processing, and language evolution. This paper presents the results of a large-scale norming study for more than 14,000 English words conducted with over 1400 American English speakers. We demonstrate the utility of these ratings by replicating a number of existing findings showing that iconicity ratings are related to age of acquisition, sensory modality, semantic neighborhood density, structural markedness, and playfulness. We discuss possible use cases and limitations of the rating dataset, which is made publicly available.

Two types of developmental surface dysgraphia: to bee but not to bea.

We report on two types of developmental surface dysgraphia. One type, exhibited by 8 participants, is orthographic lexicon surface dysgraphia, which involves an impairment in the orthographic output lexicon, leading to nonword phonologically-plausible misspellings. The other type, shown by 3 participants, is disconnection surface dysgraphia. In this type, the orthographic output lexicon is disconnected from the semantic system and from the phonological input lexicon, but still contributes to spelling via support to the orthographic output buffer, resulting in mainly lexical phonologically-plausible misspellings (writing be as "bee" but not "bea").The specific localization of the impairment in spelling, in the lexicon or in its connections, allowed us to examine the question of one or two orthographic lexicons; four participants who had a deficit in the orthographic output lexicon itself in writing had intact orthographic-input-lexicon in reading. They made surface errors in writing but not in reading the same words, supporting separate input and output orthographic lexicons.

An access deficit or a deficit in the phonological representations themselves: What can we learn from naming errors?

Anomic aphasia is characterized by good comprehension and non-word repetition but poor naming. Two sub-types of deficits might be hypothesized: faulty access to preserved phonological representations or preserved access to impaired representations. Phonological errors may occur only when representations are impaired or in post-lexical deficits (conduction aphasia). We analysed the incidence of phonological naming errors of 30 individuals, 25 with anomic aphasia based on poor naming but good repetition and comprehension, and five with conduction aphasia based on poor naming and poor repetition. Individuals with anomic aphasia produced very few phonological errors compared to individuals with conduction aphasia (0-19.1% versus 42-66%). However, six individuals with anomia produced more than 11% phonological errors, suggesting two patterns of deficit: either impaired lexical representations or impaired access to them. The lack of phonological errors in most individuals with anomic aphasia suggests that access to the phonological output lexicon is semantically, not phonologically driven.

How trial-to-trial learning shapes mappings in the mental lexicon: Modelling lexical decision with linear discriminative learning.

Trial-to-trial effects have been found in a number of studies, indicating that processing a stimulus influences responses in subsequent trials. A special case are priming effects which have been modelled successfully with error-driven learning (Marsolek, 2008), implying that participants are continuously learning during experiments. This study investigates whether trial-to-trial learning can be detected in an unprimed lexical decision experiment. We used the Discriminative Lexicon Model (DLM; Baayen et al., 2019), a model of the mental lexicon with meaning representations from distributional semantics, which models error-driven incremental learning with the Widrow-Hoff rule. We used data from the British Lexicon Project (BLP; Keuleers et al., 2012) and simulated the lexical decision experiment with the DLM on a trial-by-trial basis for each subject individually. Then, reaction times were predicted with Generalized Additive Models (GAMs), using measures derived from the DLM simulations as predictors. We extracted measures from two simulations per subject (one with learning updates between trials and one without), and used them as input to two GAMs. Learning-based models showed better model fit than the non-learning ones for the majority of subjects. Our measures also provide insights into lexical processing and individual differences. This demonstrates the potential of the DLM to model behavioural data and leads to the conclusion that trial-to-trial learning can indeed be detected in unprimed lexical decision. Our results support the possibility that our lexical knowledge is subject to continuous changes.

Synoptic Reporting for Pretreatment CT Examination in Patients With Advanced Ovarian Cancer: Impact on Documentation of Disease Sites and Physician Satisfaction.

BACKGROUND. Imaging reports that consistently document all disease sites with a potential to increase surgical complexity or morbidity can facilitate ovarian cancer treatment planning. OBJECTIVE. The aims of this study were to compare simple structured reports and synoptic reports from pretreatment CT examinations in patients with advanced ovarian cancer in terms of completeness of documenting involvement of clinically relevant anatomic sites as well as to evaluate physician satisfaction with synoptic reports. METHODS. This retrospective study included 205 patients (median age, 65 years) who underwent contrast-enhanced abdominopelvic CT before primary treatment of advanced ovarian cancer from June 1, 2018, to January 31, 2022. A total of 128 reports generated on or before March 31, 2020, used a simple structured report (free text organized into sections); 77 reports generated on or after April 1, 2020, used a synoptic report (a list of 45 anatomic sites relevant to ovarian cancer management, each of which was classified in terms of disease absence versus presence). Reports were reviewed for completeness of documentation of involvement of the 45 sites. For patients who underwent neoadjuvant chemotherapy based on diagnostic laparoscopy findings or underwent primary debulking surgery with suboptimal resection, the EMR was reviewed to identify surgically established sites of disease that were unresectable or challenging to resect. Gynecologic oncology surgeons were electronically surveyed. RESULTS. The mean report turnaround time was 29.8 minutes for simple structured reports versus 54.5 minutes for synoptic reports (p < .001). A mean of 17.6 of 45 sites (range, four to 43 sites) were mentioned by simple structured reports versus 44.5 of 45 sites (range, 39-45) for synoptic reports (p < .001). Forty-three patients had surgically established unresectable or challenging-to-resect disease; involvement of anatomic site(s) with such disease was mentioned in 37% (11/30) of simple structured reports versus 100% (13/13) of synoptic reports (p < .001). All eight surveyed gynecologic oncology surgeons completed the survey. CONCLUSION. A synoptic report improved completeness of pretreatment CT reports in patients with advanced ovarian cancer, including for established sites of unresectable or challenging-to-resect disease. CLINICAL IMPACT. The findings indicate the role of disease-specific synoptic reports in facilitating referrer communication and potentially guiding clinical decision-making.

Recollecting What We Once Knew: My Life in Psycholinguistics.

The mid-twentieth century brought a radical change in how the linguistics community formulated its major goal, moving from a largely taxonomic science to Chomsky's revolution, which conceptualized language as a higher-order cognitive function. This article reviews the paths (not always direct) that brought Lila Gleitman into contact with that revolution, her contributions to it, and the evolution in her thinking about how language is learned by every child, regardless of extreme variation in the input received. To understand how that occurs, we need to discover what must be learned by the child and what is already there to guide that learning-what must be, in Plato's terms, "recollected." The growing picture shows a learner equipped with information-processing mechanisms that extract evidence about word meanings using various evidential sources. Chief among these are the observational and linguistic-syntactic contexts in which words occur. The former is supported by a mechanism Gleitman and her collaborators call "propose but verify," and the latter by a mechanism known as "syntactic boot-strapping."

Development of a patients' satisfaction analysis system using machine learning and lexicon-based methods.

BACKGROUND: Patients' rights are integral to medical ethics. This study aimed to perform sentiment analysis and opinion mining on patients' messages by a combination of lexicon-based and machine learning methods to identify positive or negative comments and to determine the different ward and staff names mentioned in patients' messages. METHODS: The level of satisfaction and observance of the rights of 250 service recipients of the hospital was evaluated through the related checklists by the evaluator. In total, 822 Persian messages, composed of 540 negative and 282 positive comments, were collected and labeled by the evaluator. Pre-processing was performed on the messages and followed by 2 feature vectors which were extracted from the messages, including the term frequency-inverse document frequency (TFIDF) vector and a combination of the multifeature (MF) (a lexicon-based method) and TFIDF (MF + TFIDF) vectors. Six feature selectors and 5 classifiers were used in this study. For the evaluations, 5-fold cross-validation with different metrics including area under the receiver operating characteristic curve (AUC), accuracy (ACC), F1 score, sensitivity (SEN), specificity (SPE) and Precision-Recall Curves (PRC) were reported. Message tag detection, which featured different hospital wards and identified staff names mentioned in the study patients' messages, was implemented by the lexicon-based method. RESULTS: The best classifier was Multinomial Naïve Bayes in combination with MF + TFIDF feature vector and SelectFromModel (SFM) feature selection (ACC = 0.89 ± 0.03, AUC = 0.87 ± 0.03, F1 = 0.92 ± 0.03, SEN = 0.93 ± 0.04, and SPE = 0.82 ± 0.02, PRC-AUC = 0.97). Two methods of assessment by the evaluator and artificial intelligence as well as survey systems were compared. CONCLUSION: Our results demonstrated that the lexicon-based method, in combination with machine learning classifiers, could extract sentiments in patients' comments and classify them into positive and negative categories. We also developed an online survey system to analyze patients' satisfaction in different wards and to remove conventional assessments by the evaluator.

Construction of an Emotional Lexicon of Patients With Breast Cancer: Development and Sentiment Analysis.

BACKGROUND: The innovative method of sentiment analysis based on an emotional lexicon shows prominent advantages in capturing emotional information, such as individual attitudes, experiences, and needs, which provides a new perspective and method for emotion recognition and management for patients with breast cancer (BC). However, at present, sentiment analysis in the field of BC is limited, and there is no emotional lexicon for this field. Therefore, it is necessary to construct an emotional lexicon that conforms to the characteristics of patients with BC so as to provide a new tool for accurate identification and analysis of the patients' emotions and a new method for their personalized emotion management. OBJECTIVE: This study aimed to construct an emotional lexicon of patients with BC. METHODS: Emotional words were obtained by merging the words in 2 general sentiment lexicons, the Chinese Linguistic Inquiry and Word Count (C-LIWC) and HowNet, and the words in text corpora acquired from patients with BC via Weibo, semistructured interviews, and expressive writing. The lexicon was constructed using manual annotation and classification under the guidance of Russell's valence-arousal space. Ekman's basic emotional categories, Lazarus' cognitive appraisal theory of emotion, and a qualitative text analysis based on the text corpora of patients with BC were combined to determine the fine-grained emotional categories of the lexicon we constructed. Precision, recall, and the F1-score were used to evaluate the lexicon's performance. RESULTS: The text corpora collected from patients in different stages of BC included 150 written materials, 17 interviews, and 6689 original posts and comments from Weibo, with a total of 1,923,593 Chinese characters. The emotional lexicon of patients with BC contained 9357 words and covered 8 fine-grained emotional categories: joy, anger, sadness, fear, disgust, surprise, somatic symptoms, and BC terminology. Experimental results showed that precision, recall, and the F1-score of positive emotional words were 98.42%, 99.73%, and 99.07%, respectively, and those of negative emotional words were 99.73%, 98.38%, and 99.05%, respectively, which all significantly outperformed the C-LIWC and HowNet. CONCLUSIONS: The emotional lexicon with fine-grained emotional categories conforms to the characteristics of patients with BC. Its performance related to identifying and classifying domain-specific emotional words in BC is better compared to the C-LIWC and HowNet. This lexicon not only provides a new tool for sentiment analysis in the field of BC but also provides a new perspective for recognizing the specific emotional state and needs of patients with BC and formulating tailored emotional management plans.

Sensing Psychological Well-being Using Social Media Language: Prediction Model Development Study.

BACKGROUND: Positive mental health is arguably increasingly important and can be revealed, to some extent, in terms of psychological well-being (PWB). However, PWB is difficult to assess in real time on a large scale. The popularity and proliferation of social media make it possible to sense and monitor online users' PWB in a nonintrusive way, and the objective of this study is to test the effectiveness of using social media language expression as a predictor of PWB. OBJECTIVE: This study aims to investigate the predictive power of social media corresponding to ground truth well-being data in a psychological way. METHODS: We recruited 1427 participants. Their well-being was evaluated using 6 dimensions of PWB. Their posts on social media were collected, and 6 psychological lexicons were used to extract linguistic features. A multiobjective prediction model was then built with the extracted linguistic features as input and PWB as the output. Further, the validity of the prediction model was confirmed by evaluating the model's discriminant validity, convergent validity, and criterion validity. The reliability of the model was also confirmed by evaluating the split-half reliability. RESULTS: The correlation coefficients between the predicted PWB scores of social media users and the actual scores obtained using the linguistic prediction model of this study were between 0.49 and 0.54 (P<.001), which means that the model had good criterion validity. In terms of the model's structural validity, it exhibited excellent convergent validity but less than satisfactory discriminant validity. The results also suggested that our model had good split-half reliability levels for every dimension (ranging from 0.65 to 0.85; P<.001). CONCLUSIONS: By confirming the availability and stability of the linguistic prediction model, this study verified the predictability of social media corresponding to ground truth well-being data from the perspective of PWB. Our study has positive implications for the use of social media to predict mental health in nonprofessional settings such as self-testing or a large-scale user study.

Applying core lexicon analysis in patients with anomic aphasia: Based on Mandarin AphasiaBank.

BACKGROUND: Patients with anomic aphasia experience difficulties in narrative processing. General discourse measures are time consuming and require necessary skills. Core lexicon analysis has been proposed as an effort-saving approach but has not been developed in Mandarin discourse. AIMS: This exploratory study was aimed (1) to apply core lexicon analysis in Mandarin patients with anomic aphasia at the discourse level and (2) to verify the problems with core words among people with anomic aphasia. METHODS & PROCEDURE: The core nouns and verbs were extracted from narrative language samples from 88 healthy participants. The production of core words for 12 anomic aphasia and 12 age- and education-matched controls were then calculated and compared. The correlation between the percentages and the Aphasia Quotients of the revised Western Aphasia Battery was analyzed as well. OUTCOMES & RESULTS: The core nouns and verbs were successfully extracted. Patients with anomic aphasia produced fewer core words than healthy people, and the percentages differed significantly in different tasks as well as word classes. There was no correlation between the core lexicon use and the severity of aphasia in patients with anomic aphasia. CONCLUSIONS & IMPLICATIONS: Core lexicon analysis may potentially serve as a clinician-friendly manner of quantifying core words produced at the discourse level in Mandarin patients with anomic aphasia. WHAT THIS PAPER ADDS: What is already known on the subject Discourse analyses in aphasia assessment and treatment have increasingly garnered attention. Core lexicon analysis based on English AphasiaBank has been reported in recent years. It is correlated with microlinguistic and macrolinguistic measures in aphasia narratives. Nevertheless, the application based on Mandarin AphasiaBank is still under development in healthy individuals and patients with anomic aphasia. What this paper adds to existing knowledge A Mandarin core lexicon set was developed for different tasks. The feasibility of core lexicon analysis to evaluate the corpus of patients with anomic aphasia was preliminarily discussed and the speech performance of patients and healthy people was then compared to provide a reference for the evaluation and treatment of clinical aphasia corpus. What are the potential or actual clinical implications of this work? The purpose of this exploratory study was to consider the potential use of core lexicon analysis to evaluate core word production in narrative discourse. Moreover, normative and aphasia data were provided for comparison to develop clinical use for Mandarin patients with anomic aphasia.

The Ovarian/Adnexal Reporting and Data System for Ultrasound: From Standardized Terminology to Optimal Risk Assessment and Management.

The American College of Radiology (ACR) Ovarian-Adnexal Reporting and Data System (O-RADS) lexicon and risk assessment tool for ultrasound (US) provides a framework for characterization of ovarian and adnexal pathology with the ultimate goal of harmonizing reporting and patient management strategies. Since the first O-RADS US publication in 2018, multiple validation studies have shown O-RADS US to have excellent diagnostic accuracy, with the majority of these studies using O-RADS 4 as the optimal cut-off for detecting ovarian cancer. Most of the existing validation studies include a dedicated training phase and confirm that ORADS US categories and lexicon descriptors are associated with high level inter-read agreement, regardless of radiologist training level or practice experience. O-RADS US has a similar inter-reader agreement when compared to Gynecologic Imaging Reporting and Data System (GIRADS), Assessment of Different Neoplasias in the adnexa (ADNEX), and International Tumor Analysis Group (IOTA) simple rules. System descriptors have been shown to correlate with expected malignancy rates and the O-RADS US risk stratification system has been shown to perform in the expected range of malignancy risk per category. Further directions will focus on clarifying governing concepts and lexicon terminology as well as further refining risk stratification categories based on data from published validation studies.

The Chinese lexicon of deaf readers: A database of character decisions and a comparison between deaf and hearing readers.

We present a psycholinguistic study investigating lexical effects on simplified Chinese character recognition by deaf readers. Prior research suggests that deaf readers exhibit efficient orthographic processing and decreased reliance on speech-based phonology in word recognition compared to hearing readers. In this large-scale character decision study (25 participants, each evaluating 2500 real characters and 2500 pseudo-characters), we analyzed various factors influencing character recognition accuracy and speed in deaf readers. Deaf participants demonstrated greater accuracy and faster recognition when characters were more frequent, were acquired earlier, had more strokes, displayed higher orthographic complexity, were more imageable in reference, or were less concrete in reference. Comparison with a previous study of hearing readers revealed that the facilitative effect of frequency on character decision accuracy was stronger for deaf readers than hearing readers. The effect of orthographic-phonological regularity differed significantly for the two groups, indicating that deaf readers rely more on orthographic structure and less on phonological information during character recognition. Notably, increased stroke counts (i.e., higher orthographic complexity) hindered hearing readers but facilitated recognition processes in deaf readers, suggesting that deaf readers excel at recognizing characters based on orthographic structure. The database generated from this large-scale character decision study offers a valuable resource for further research and practical applications in deaf education and literacy.

ARTICULATE: A European glossary of terms used in oral health professional education.

INTRODUCTION: The Erasmus+O-Health-EDU project aims to gain a comprehensive view of oral health professional (OHP) education in Europe, through the development of web-based surveys and online toolkits. A glossary to facilitate a common language through which academic teams could cooperate and communicate more accurately was identified as a key need within the project. The aim of ARTICULATE was thus to create a shared language, with a European focus, for terms and concepts used in the field of OHP education. METHODS: The methodology was developed from those published for construction of other glossaries with a circular and iterative process: the creation of content and definitions by a group of experts in OHP education, the testing of "fitness for purpose" of the content, and stakeholder consultation. All creation steps were followed by refinements based on testing results and stakeholder comments. The final glossary was then launched as an online resource including a built-in mechanism for user feedback. RESULTS: The scope and structure of the glossary were mapped out at a workshop with 12 dental education experts from 7 European countries. A total of 328 terms were identified, of which 171 were finally included in ARTICULATE. After piloting with a close group of other colleagues, the glossary was opened for external input. Thirty European Deans or Heads of Education assessed the definition of each term as "clear" or "not clear." A total of 86 definitions were described as "clear" by all individuals. Terms deemed unclear by at least one individual were revisited and changes made to 37 of the definitions. In conjunction with the launch of the glossary, a range of stakeholder organisations were informed and asked to participate in an open global consultation by providing feedback online. Since its launch in June 2021, the ARTICULATE website (https://o-health-edu.org/articulate) has had an average of 500 visits/month. To promote community ownership, forms embedded on the ARTICULATE webpage allow users to give feedback and suggest new terms. A standing taskforce will meet regularly to consider amendments and make changes to ensure that the glossary remains a relevant and up-to-date resource over time. CONCLUSION: ARTICULATE is a unique, evolving, online glossary of terms relating to OHP education, created as a resource for all interested OHP educators. The glossary is a key output of the O-Health-Edu project, which relies on a comprehensive vision of OHP education to address the future oral health needs of the European population.

Relationship between lexicon and grammar in children and youth who use augmentative and alternative communication.

The present study investigated the relationship between lexicon and grammar in individuals who use graphic symbol-based aided augmentative and alternative communication (AAC). Data came from 60 transcripts of generalization sessions that were part of two previous intervention studies, aimed at improving the expressive vocabulary and grammar of 12 children and youth who used graphic symbol-based AAC. The specific aims of the current study were to (a) describe vocabulary composition across different levels of expressive vocabulary and (b) analyze the relationship between global measures of expressive vocabulary and the use of grammar in individuals who use aided AAC. A series of multiple linear mixed effect regression analyses showed a positive predictive association between overall vocabulary size and the use of closed-class words, and a positive relationship between the use of verbs and the use of closed-class words. Additionally, the use of verbs had a significant positive association with the use of inflectional morphology, while the use of nouns did not. Theoretical and practical implications of these findings are discussed.

Frequency of word usage by Hebrew preschoolers: implications for AAC core vocabulary.

This study's goal was to inform the selection of the most frequently used words to serve as a reference for core vocabulary selection for Hebrew-speaking children who require AAC. The paper describes the vocabulary used by 12 Hebrew-speaking preschool children with typical development in two different conditions: peer talk, and peer talk with adult mediation. Language samples were audio-recorded, transcribed, and analyzed using the CHILDES (Child Language Data Exchange System) tools to identify the most frequently used words. The top 200 lexemes (all variations of a single word) in the peer talk and adult-mediated peer talk conditions accounted for 87.15% (n = 5008 tokens) and 86.4% (n = 5331 tokens) of the total tokens produced in each language sample (n = 5746, n = 6168), respectively. A substantially overlapping vocabulary of 337 lexemes accounted for up to 87% (n = 10411) of the tokens produced in the composite list (n = 11914). The results indicate that a relatively small set of words represent a large proportion of the words used by the preschoolers across two different conditions. General versus language-specific implications for core vocabulary selection for children in need of AAC devices are discussed.