Consolidation improves the learning of new meanings for known words but not necessarily their integration into semantic memory.

Consolidation is essential to the integration of novel words into the mental lexicon; however, its role in learning new meanings for known words remains unclear. This old-form-new-meaning learning is very common, as when one learns that "skate" is also a type of fish in addition to its familiar roller- or ice-skating meaning. To address consolidation effects for new meanings, we compared the behavioral and ERP measures on new and original meanings tested 24 hours after learning with words tested immediately after learning. Semantic judgments of both new and original meanings benefitted from the study-test interval. However, N400 amplitudes on studied words-indicators of meaning access from semantic memory-were unaffected by learning or consolidation. These results suggest that while sleep benefits memory for new meanings, the new meanings do not become integrated into the mental lexicon within that period. Instead, episodic retrieval remains functional in accessing new meanings even after 24 hours.

The effects of EFL wordlist and proficiency on vocabulary knowledge.

Introduction: The Gaokao Word List (GWL) in China serves as a guideline for learning L2 vocabulary, but there are few studies verifying its effect on university EFL learners' vocabulary knowledge. Method: This study investigated the effects of the GWL and EFL proficiency on 66 Chinese university EFL learners' vocabulary knowledge by administering word recognition tests. Results and discussion: The results showed that: (1) the GWL had significant effects on participants' receptive vocabulary knowledge; (2) EFL proficiency had significant effects on participants' word recognition, without interaction with the GWL. These findings were discussed through the lens of frequency of exposure, accounting for the overwhelming GWL effect on learners' vocabulary knowledge. We suggest EFL proficiency be taken into consideration when the GWL is revised in the future, to smoothen the transition in vocabulary learning from high school to university, and improve vocabulary learning efficiency.

Constraining current neuroanatomical models of reading: the view from Arabic.

There is a growing interest in imaging understudied orthographies to unravel their neuronal correlates and their implications for existing computational and neuroanatomical models. Here, we review current brain mapping literature about Arabic words. We first offer a succinct description of some unique linguistic features of Arabic that challenge current cognitive models of reading. We then appraise the existing functional neuroimaging studies that investigated written Arabic word processing. Our review revealed that (1) Arabic is still understudied, (2) the most investigated features concerned the effects of vowelling and diglossia in Arabic reading, (3) findings were not always discussed in the light of existing reading models such as the dual route cascaded, the triangle, and the connectionist dual process models, and (4) current evidence is unreliable when it comes to the exact neuronal pathways that sustain Arabic word processing. Overall, despite the fact that Arabic has some unique linguistic features that challenge and ultimately enrich current reading models, the existing functional neuroimaging literature falls short of offering a reliable evidence about brain networks of Arabic reading. We conclude by highlighting the need for more systematic studies of the linguistic features of Arabic to build theoretical and neuroanatomical models that are concurrently specific and general.

A comprehensive dataset for Arabic word sense disambiguation.

This data paper introduces a comprehensive dataset tailored for word sense disambiguation tasks, explicitly focusing on a hundred polysemous words frequently employed in Modern Standard Arabic. The dataset encompasses a diverse set of senses for each word, ranging from 3 to 8, resulting in 367 unique senses. Each word sense is accompanied by contextual sentences comprising ten sentence examples that feature the polysemous word in various contexts. The data collection resulted in a dataset of 3670 samples. Significantly, the dataset is in Arabic, which is known for its rich morphology, complex syntax, and extensive polysemy. The data was meticulously collected from various web sources, spanning news, medicine, finance, and more domains. This inclusivity ensures the dataset's applicability across diverse fields, positioning it as a pivotal resource for Arabic Natural Language Processing (NLP) applications. The data collection timeframe spans from the first of April 2023 to the first of May 2023. The dataset provides comprehensive model learning by including all senses for a frequently used Arabic polysemous term, even rare senses that are infrequently used in real-world contexts, thereby mitigating biases. The dataset comprises synthetic sentences generated by GPT3.5-turbo, addressing instances where rare senses lack sufficient real-world data. The dataset collection process involved initial web scraping, followed by manual sorting to distinguish word senses, supplemented by thorough searches by a human expert to fill in missing contextual sentences. Finally, in instances where online data for rare word senses was lacking or insufficient, synthetic samples were generated. Beyond its primary utility in word sense disambiguation, this dataset holds considerable value for scientists and researchers across various domains, extending its relevance to sentiment analysis applications.

Factors influencing the cruisers in recommending onshore excursions in Gran Canaria.

This paper investigates which elements are the ones that most influence the cruise passenger to recommend the onshore excursions, offering relevant information that could help all stakeholders (cruise firms, local operators, politicians, etc.) to better design/adapt shore excursions to the expectations of the cruise passengers, increasing the positive impact of this activity in the destination. The latter is essential for the existence of a good harmony between the cruise ships visiting a destination and the population living there. To this aim, an ordered logit model was estimated using a sample of 1,059 questionnaires obtained during the period 2018-2020. The results have shown that cruise passengers are more likely to recommend the onshore excursions if their expectations regarding the excursion have been covered, they have perceived a good quality/price ratio and have had a satisfactory tour guide service. Moreover, the fact that the cruisers aged between 45 and 55 are less likely to recommend excursions could suggest that the design of existing excursions could be improved by being tailored by demographic characteristics (i.e., aged-oriented) or even by offering different alternatives for each group during the shore excursion when possible. The results could be useful to all stakeholders when designing excursions to increase the positive impact of this activity on the destination.

Emotion processing in concrete and abstract words: evidence from eye fixations during reading.

We replicated and extended the findings of Yao et al. [(2018). Differential emotional processing in concrete and abstract words. Journal of Experimental Psychology: Learning, Memory, and Cognition, 44(7), 1064-1074] regarding the interaction of emotionality, concreteness, and imageability in word processing by measuring eye fixation times on target words during normal reading. A 3 (Emotion: negative, neutral, positive) × 2 (Concreteness: abstract, concrete) design was used with 22 items per condition, with each set of six target words matched across conditions in terms of word length and frequency. Abstract (e.g. shocking, reserved, fabulous) and concrete (e.g. massacre, calendar, treasure) target words appeared (separately) within contextually neutral, plausible sentences. Sixty-three participants each read all 132 experimental sentences while their eye movements were recorded. Analyses using Gamma generalised linear mixed models revealed significant effects of both Emotion and Concreteness on all fixation measures, indicating faster processing for emotional and concrete words. Additionally, there was a significant Emotion × Concreteness interaction which, critically, was modulated by Imageability in early fixation time measures. Emotion effects were significantly larger in higher-imageability abstract words than in lower-imageability ones, but remained unaffected by imageability in concrete words. These findings support the multimodal induction hypothesis and highlight the intricate interplay of these factors in the immediate stages of word processing during fluent reading.

Incorporating gendered analysis and flexibility in heavy work investment studies: a systematic literature review.

Introduction: Significant impacts of heavy work investment on employee well-being and organizational performance have prompted its increasing importance as a research topic. The findings about good or evil of these repercussions are nonetheless inconclusive. The intersection of Heavy Work Investment construct with gender has not been explicitly addressed by previous literature review and research. Besides, the relevance of flexibility for women, as one of the key factors for successful work-family balance management, still remains to be analyzed. Methods: A literature review on Heavy Work Investment was conducted using the SPAR-4-SLR protocol, wherein 83 articles were selected from a pool of 208 previously identified works. Bibliometric and content analysis techniques were employed, including co-word analysis, to evaluate research production, impact, and trends in the gender perspective within Heavy Work Investment. Results: As a result, a strategic diagram illustrates thematic topics, providing a clear understanding of the field's structure and evolution. Six thematic groups were identified, around work-family conflict as the central theme. Discussion: The explicit consideration of a gender perspective in literature involves nuanced differences regarding the conclusions of studies with a broader focus. First, the emerging prominence of studies on China and Japan becomes clear with gender as the specific focus of the review, aiming to clarify the experiences women face in more traditional societies with a more decisive division of roles. Second, there is a shift in interest regarding the analysis of Job Demands and Job Resources. Despite the apparent decline in interest in the former, the focus in gender literature clearly shifts toward the side of Job Resources, showing potential for the future. It could be understood that in a context of talent war and employee retention efforts, priority is given to better understanding of facilitating individual and organizational factors for work-life balance, especially for women. Future research areas are identified, including gender differences in organizational support and the impact of flexible work on the work-life balance, providing valuable insights for academia, practitioners, and organizations. The need for more comprehensive cross-cultural and gender research is also made clear.

Linking acoustic variability in the infants' input to their early word production.

Exposure to talker variability shapes how learning unfolds in the lab, and occurs in the everyday speech infants hear in daily life. Here, we asked whether aspects of talker variability in speech input are also linked to the onset of word production. We further asked whether these effects were redundant with effects of speech register (i.e., whether speech input was adult- vs. child-directed). To do so, we first extracted a set of highly common nouns from a longitudinal corpus of home recordings from North-American English-learning infants. We then used the acoustic variability in how these tokens were said to predict when the children first produced these same nouns. We found that in addition to frequency, variability in how words sound in 6-17 month's input predicted when children first said these words. Furthermore, while the proportion of child-directed speech also predicted the month of first production, it did so alongside measurements of acoustic variability in children's real-world input. Together, these results add to a growing body of literature suggesting that variability in how words sound in the input is linked to learning both in the lab and in daily life. RESEARCH HIGHLIGHTS: Talker variability shapes learning in the lab and exists in everyday speech; we asked whether it predicts word learning in the real world. Acoustic measurements of early words in infants' input (and their frequency) predicted when infants first said those same words. Speech register also predicted when infants said words, alongside effects of talker variability. Our results provide a deeper understanding of how sources of variability inherent to children's input connect to their learning and development.

Word learning is hands-on: Insights from studying natural behavior.

Infants' interactions with social partners are richly multimodal. Dyads respond to and coordinate their visual attention, gestures, vocalizations, speech, manual actions, and manipulations of objects. Although infants are typically described as active learners, previous experimental research has often focused on how infants learn from stimuli that is well-crafted by researchers. Recent research studying naturalistic, free-flowing interactions has explored the meaningful patterns in dyadic behavior that relate to language learning. Infants' manual engagement and exploration of objects supports their visual attention, creates salient and diverse views of objects, and elicits labeling utterances from parents. In this chapter, we discuss how the cascade of behaviors created by infant multimodal attention plays a fundamental role in shaping their learning environment, supporting real-time word learning and predicting later vocabulary size. We draw from recent at-home and cross-cultural research to test the validity of our mechanistic pathway and discuss why hands matter so much for learning. Our goal is to convey the critical need for developmental scientists to study natural behavior and move beyond our "tried-and-true" paradigms, like screen-based tasks. By studying natural behavior, the role of infants' hands in early language learning was revealed-though it was a behavior that was often uncoded, undiscussed, or not even allowed in decades of previous research. When we study infants in their natural environment, they can show us how they learn about and explore their world. Word learning is hands-on.

Exploring Social Sharing Value: Effects on Customer Attitudes and Behaviors in Restaurant Livestreaming.

In China, the integration of livestreaming into restaurant marketing has transitioned from mere entertainment to a vital business tool. This study examines the influence of social sharing value (SSV) on customer attitudes and behavioral intentions within the context of restaurant livestreams, applying the stimulus-organism-behavior-consequences (SOBC) model. Analyzing data from 1139 livestream viewers using partial least squares-path modeling (PLS-PM), the results reveal that SSV significantly enhances viewers' trust, satisfaction, word of mouth (WOM), and behavioral intentions. Trust was shown to elevate satisfaction, which, in turn, positively impacts WOM and purchase intentions. Among the SSV's components, brand intimacy emerged as highly influential. Notably, behavioral intention was found to significantly influence WOM activities, underscoring the critical role of proactive customer behaviors in promoting the brand. This study extends social exchange theory by quantifying relationship quality and adapting it to digital consumer interactions in the restaurant industry. The findings highlight the importance of cultivating SSV to bolster customer trust and satisfaction, thereby enhancing loyalty and advocacy. Effective engagement through livestreaming can amplify brand intimacy, establishing it as an indispensable strategy for maintaining competitiveness in the restaurant sector.

Word Forms Reflect Trade-Offs Between Speaker Effort and Robust Listener Recognition.

How do cognitive pressures shape the lexicons of natural languages? Here, we reframe George Kingsley Zipf's proposed "law of abbreviation" within a more general framework that relates it to cognitive pressures that affect speakers and listeners. In this new framework, speakers' drive to reduce effort (Zipf's proposal) is counteracted by the need for low-frequency words to have word forms that are sufficiently distinctive to allow for accurate recognition by listeners. To support this framework, we replicate and extend recent work using the prevalence of subword phonemic sequences (phonotactic probability) to measure speakers' production effort in place of Zipf's measure of length. Across languages and corpora, phonotactic probability is more strongly correlated with word frequency than word length. We also show this measure of ease of speech production (phonotactic probability) is strongly correlated with a measure of perceptual difficulty that indexes the degree of competition from alternative interpretations in word recognition. This is consistent with the claim that there must be trade-offs between these two factors, and is inconsistent with a recent proposal that phonotactic probability facilitates both perception and production. To our knowledge, this is the first work to offer an explanation why long, phonotactically improbable word forms remain in the lexicons of natural languages.

Modeling item-level variance of polysyllabic word reading in developing readers: Exploring semantically related child, word, and child-by-word predictors.

Across word reading development, there are important and evolving relationships between oral and written semantic knowledge. Recent research has focused on these relationships, with accumulating evidence supporting the role of word knowledge and related word characteristics as important factors influencing polysyllabic word reading abilities. The purpose of this study was to investigate how semantic-related effects across child-level skills (e.g., general vocabulary knowledge), word-level properties (e.g., age of acquisition), child-by-word-level familiarity (e.g., item-level familiarity), and interactions between key child attributes and word characteristics (e.g., word reading skill by age of acquisition) contribute to polysyllabic word reading. Specifically, we emphasize the semantic contributions of word-level features to word reading development, which have been relatively underexplored in the literature. A sample of elementary school students oversampled for word reading difficulty (N = 92) in Grades 3 to 5 read a set of polysyllabic words (J = 45) and completed a battery of reading and language-related measures. Using cross-classified random-effects models and accounting for various control variables, semantic-related variables representing item-level familiarity; child-level set for variability; and word-level age of acquisition and number of morphemes were significant predictors in the main-effects model. A significant interaction between sight word efficiency and age of acquisition indicated higher probabilities of correctly reading polysyllabic words at lower levels of acquisition for better readers. Results indicate important semantic-related influences on polysyllabic word reading at the child, word, and child-by-word levels, suggesting meaningful relationships between knowledge of the orthographic form of a word and semantic knowledge in developing readers.

Modelling individual differences in reading using an optimised MikeNet simulator: the impact of reading instruction.

Reading is vital for acquiring knowledge and studies have demonstrated that phonology-focused interventions generally yield greater improvements than meaning-focused interventions in English among children with reading disabilities. However, the effectiveness of reading instruction can vary among individuals. Among the various factors that impact reading skills like reading exposure and oral language skills, reading instruction is critical in facilitating children's development into skilled readers; it can significantly influence reading strategies, and contribute to individual differences in reading. To investigate this assumption, we developed a computational model of reading with an optimised MikeNet simulator. In keeping with educational practices, the model underwent training with three different instructional methods: phonology-focused training, meaning-focused training, and phonology-meaning balanced training. We used semantic reliance (SR), a measure of the relative reliance on print-to-sound and print-to-meaning mappings under the different training conditions in the model, as an indicator of individual differences in reading. The simulation results demonstrated a direct link between SR levels and the type of reading instruction. Additionally, the SR scores were able to predict model performance in reading-aloud tasks: higher SR scores were correlated with increased phonological errors and reduced phonological activation. These findings are consistent with data from both behavioral and neuroimaging studies and offer insights into the impact of instructional methods on reading behaviors, while revealing individual differences in reading and the importance of integrating OP and OS instruction approaches for beginning readers.

A Continuity in Logical Development: Domain-General Disjunctive Inference by Toddlers.

Children grow up surrounded by opportunities to learn (the language of their community, the movements of their body, other people's preferences and mental lives, games, social norms, etc.). Here, we find that toddlers (N = 36; age range 2.3-3.2 years) rely on a logical reasoning strategy, Disjunctive Inference (i.e., A OR B, A is ruled out, THEREFORE, B), across a variety of situations, all before they have any formal education or extensive experience with words for expressing logical meanings. In learning new words, learning new facts about a person, and finding the winner of a race, toddlers systematically consider and reject competitors before deciding who must be the winner. This suggests that toddlers may have a general-purpose logical reasoning tool that they can use in any situation.

Objectifying subjective memory complaints: VR-based Verbal Word Learning Test in chronic stroke patients.

Memory impairment imposes a great burden on stroke patients and can be divided into Objective Memory Problems (OMPs) and Subjective Memory Complaints (SMCs). Studies have shown that these do not always co-occur. Possibly, the gap between SMCs and OMPs can be bridged when using a more ecologically valid memory test and considering the impact of other common stroke symptoms such as sensory hypersensitivity (SHS) and fatigue. In the present study, we applied Virtual Reality (VR) to create a sensory-rich environment with real-life stimuli. Memory performance was tested with the 15-Verbal Word Learning Test (VLT). Furthermore, we assessed SMCs (Everyday Memory Questionnaire), and the levels of SHS (Multi-Modal Evaluation of Sensory Sensitivity) and fatigue in the previous month. 31 chronic stroke patients and 32 healthy controls participated. The results showed that participants' memory performance decreased in a sensory-rich compared to a neutral environment. This decrease did not significantly differ between the groups. Interestingly, fatigue and SHS are related to the level of SMC in stroke patients but no such evidence was found in healthy controls. Last, for stroke patients, we found a significant negative correlation between SMCs and memory performance in a sensory-rich environment, but not in a neutral environment. In conclusion, our study implicates that in stroke patients, fatigue and SHS are related to SMCs and that using a sensory-rich VR environment might be a more ecologically valid way to objectify SMCs. However, interpretative caution is warranted due to the absence of sex and age-matched controls and potential selection bias.

Sparking creativity: Encouraging creative idea generation through automatically generated word recommendations.

Creative block is a familiar foe to any who attempt to create and is especially related to "writers block". While significant effort has been focused on developing methods to break such blocks, it remains an active challenge. Here, we focus on the role of semantic memory structure in driving creative block, by having people get "stuck" in a certain part of their semantic memory network. We directly examine whether we can "pull out" a participant from where they got "stuck" in their semantic memory, breaking their creative impasse. Our Associative Creativity Sparker (ACS) is a cognitive network science-based online tool that aims to spark creative ideas and break creative impasse: Once a participant runs out of ideas in a creative idea generation task, word recommendations are suggested to prime new ideas. These word recommendations are either towards or away from previous ideas, as well as close or far from the target object, based on a conceptual space extracted from the participants responses using online text analysis. In Study 1, 121 participants use the ACS to generate creative alternative uses for five different objects and completed creativity and Gf tasks. In Study 2, we repeat the design of Study 1, but further examine the impact of writing experience on the ACS, by examining 120 novice and 120 experienced writers. Across both studies, our results indicate that the location of word recommendations affects the fluency and originality of one's ideas, and that novice and experienced writers differently benefit from these word recommendations.

Examining the influence of technological self-efficacy, perceived trust, security, and electronic word of mouth on ICT usage in the education sector.

The context of education has changed due to revolutionary developments in the information communication technology (ICT) industry in the post-COVID era. Innovative learning methods were introduced in the education sector to promote quality education. The students find it more convenient to use ICT tools to integrate their knowledge-seeking. China has recently paid more attention to developing and adopting electronic infrastructure. The study assesses the effect of technology self-efficacy (TSE) on ICT acceptance and implementation in China's education sector. It also analyzed the role of perceived trust, perceived security, and electronic word of mouth (eWOM) in integrating digital information sharing and interaction tools. Data is collected from 382 business students at Chinese universities. The results revealed that perceived trust mediates the relationship between TSE and the actual use of ICT tools, intention to use ICT tools for information, and intention to use ICT tools for interaction. Further, perceived security and eWOM significantly moderate the relationship between TSE and perceived trust. The findings indicate that it is essential to offer assistance and instruction to students in the educational sector so they can use ICT technology more frequently. It is also crucial for organizations to establish a supportive culture and provide the necessary technological resources to facilitate the use of ICT.

An efficient learning based approach for automatic record deduplication with benchmark datasets.

With technological innovations, enterprises in the real world are managing every iota of data as it can be mined to derive business intelligence (BI). However, when data comes from multiple sources, it may result in duplicate records. As data is given paramount importance, it is also significant to eliminate duplicate entities towards data integration, performance and resource optimization. To realize reliable systems for record deduplication, late, deep learning could offer exciting provisions with a learning-based approach. Deep ER is one of the deep learning-based methods used recently for dealing with the elimination of duplicates in structured data. Using it as a reference model, in this paper, we propose a framework known as Enhanced Deep Learning-based Record Deduplication (EDL-RD) for improving performance further. Towards this end, we exploited a variant of Long Short Term Memory (LSTM) along with various attribute compositions, similarity metrics, and numerical and null value resolution. We proposed an algorithm known as Efficient Learning based Record Deduplication (ELbRD). The algorithm extends the reference model with the aforementioned enhancements. An empirical study has revealed that the proposed framework with extensions outperforms existing methods.

Accuracy and Consistency of Confidence Limits for Monosyllable Identification Scores Derived Using Simulation, the Harrell-Davis Estimator, and Nonlinear Quantile Regression.

BACKGROUND: Audiological diagnosis and rehabilitation often involve the assessment of whether the maximum speech identification score (PBmax) is poorer than expected from the pure-tone average (PTA) threshold. This requires the estimation of the lower boundary of the PBmax values expected for a given PTA (one-tailed 95% confidence limit, CL). This study compares the accuracy and consistency of three methods for estimating the 95% CL. METHOD: The 95% CL values were estimated using a simulation method, the Harrell-Davis (HD) estimator, and non-linear quantile regression (nQR); the latter two are both distribution-free methods. The first two methods require the formation of sub-groups with different PTAs. Accuracy and consistency in the estimation of the 95% CL were assessed by applying each method to many random samples of 50% of the available data and using the fitted parameters to predict the data for the remaining 50%. STUDY SAMPLE: A total of 642 participants aged 17 to 84 years with sensorineural hearing loss were recruited from audiology clinics. Pure-tone audiograms were obtained and PBmax scores were measured using monosyllables at 40 dB above the speech recognition threshold or at the most comfortable level. RESULTS: For the simulation method, 6.7 to 8.2% of the PBmax values fell below the 95% CL for both ears, exceeding the target value of 5%. For the HD and nQR methods, the PBmax values fell below the estimated 95% CL for approximately 5% of the ears, indicating good accuracy. Consistency, estimated from the standard deviation of the deviations from the target value of 5%, was similar for all the methods. CONCLUSIONS: The nQR method is recommended because it has good accuracy and consistency, and it does not require the formation of arbitrary PTA sub-groups.

Optimizing word embeddings for small dataset: a case study on patient portal messages from breast cancer patients.

Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processing and, while word embedding models, such as word2vec, have the potential to extract meaningful signals from text, they are not readily applicable to patient portal messages. This is because embedding models typically require millions of training samples to sufficiently represent semantics, while the volume of patient portal messages associated with a particular clinical phenomenon is often relatively small. We introduce a novel adaptation of the word2vec model, PK-word2vec (where PK stands for prior knowledge), for small-scale messages. PK-word2vec incorporates the most similar terms for medical words (including problems, treatments, and tests) and non-medical words from two pre-trained embedding models as prior knowledge to improve the training process. We applied PK-word2vec in a case study of patient portal messages in the Vanderbilt University Medical Center electric health record system sent by patients diagnosed with breast cancer from December 2004 to November 2017. We evaluated the model through a set of 1000 tasks, each of which compared the relevance of a given word to a group of the five most similar words generated by PK-word2vec and a group of the five most similar words generated by the standard word2vec model. We recruited 200 Amazon Mechanical Turk (AMT) workers and 7 medical students to perform the tasks. The dataset was composed of 1389 patient records and included 137,554 messages with 10,683 unique words. Prior knowledge was available for 7981 non-medical and 1116 medical words. In over 90% of the tasks, both reviewers indicated PK-word2vec generated more similar words than standard word2vec (p = 0.01).The difference in the evaluation by AMT workers versus medical students was negligible for all comparisons of tasks' choices between the two groups of reviewers ( p = 0.774 under a paired t-test). PK-word2vec can effectively learn word representations from a small message corpus, marking a significant advancement in processing patient portal messages.