Language Processing Abilities Research Articles

Exploring the clinical diagnostic value of linguistic learning ability in patients with disorders of consciousness using electrooculography

For patients with disorders of consciousness (DoC), accurate assessment of residual consciousness levels and cognitive abilities is critical for developing appropriate rehabilitation interventions. In this study, we investigated the potential of electrooculography (EOG) in assessing language processing abilities and consciousness levels. Patients’ EOG data and related electrophysiological data were analysed before and after explicit language learning. The results showed distinct differences in vocabulary learning patterns among patients with varying levels of consciousness. While minimally conscious patients showed significant neural tracking of artificial words and notable learning effects similar to those observed in healthy controls, whereas patients with unresponsive wakefulness syndrome did not show such effects. Correlation analysis further indicated that EOG detected vocabulary learning effects with comparable validity to electroencephalography, reinforcing the credibility of EOG indicator as a diagnostic tool. Critically, EOG also revealed significant correlations between individual patients’ linguistic learning performance and their Oromotor/verbal function as assessed through behavioural scales. In conclusion, this study explored the differences in language processing abilities among patients with varying consciousness levels. By demonstrating the utility of EOG in evaluating consciousness and detecting vocabulary learning effects, as well as its potential to guide personalised rehabilitation, our findings indicate that EOG indicators show promise as a rapid, accurate and effective additional tool for diagnosing and managing patients with DoC.

The Influence of Music Training on Language Processing Skills in Adolescents in South Korea

Purpose: The aim of the study was to analyze the influence of music training on language processing skills in adolescents in South Korea. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study on the influence of music training on language processing skills in adolescents in South Korea revealed significant positive correlations between music education and language processing abilities. Adolescents who received music training demonstrated enhanced linguistic skills, including vocabulary acquisition and syntactic comprehension, compared to their peers without musical instruction. The findings suggest that musical training may contribute to cognitive advantages in language development during adolescence. Unique Contribution to Theory, Practice and Policy: Neurocognitive theory of music, transfer of learning theory & socio-cultural theory may be used to anchor future studies on the influence of music training on language processing skills in adolescents in South Korea. Advocate for the integration of structured music education programs in South Korean schools, emphasizing its potential benefits for language development. Advocate for policies that support the inclusion of music education as a core component of the curriculum, highlighting its role in fostering holistic cognitive development and academic achievement.

Exploring large language models for microstructure evolution in materials

There is a significant potential for coding skills to transition fully to natural language in the future. In this context, large language models (LLMs) have shown impressive natural language processing abilities to generate sophisticated computer code for research tasks in various domains. We report the first study on the applicability of LLMs to perform computer experiments on microstructure pattern formation in model materials. In particular, we exploit LLM’s ability to generate code for solving various types of phase-field-based partial differential equations (PDEs) that integrate additional physics to model material microstructures. The results indicate that LLMs have a remarkable capacity to generate multi-physics code and can effectively deal with materials microstructure problems up to a certain complexity. However, for complex multi-physics coupled PDEs for which a detailed understanding of the problem is required, LLMs fail to perform the task efficiently, since much more detailed instructions with many iterations of the same query are required to generate the desired output. Nonetheless, at their current stage of development and potential future advancements, LLMs offer a promising outlook for accelerating materials education and research by supporting beginners and experts in their physics-based methodology. We hope this paper will spur further interest to leverage LLMs as a supporting tool in the integrated computational materials engineering (ICME) approach to materials modeling and design.

Language Processing Abilities in a Multilingual Context: The Role of Linguistic Background among Students in Amman, Jordan

Aim. This quantitative experimental study aims to examine the impact of linguistic background on language processing abilities among learners at a language academy in Amman, Jordan. The purpose of the study is to investigate how different linguistic backgrounds influence language processing skills specifically in the areas of grammar, syntax, and semantics. 
 Participants. The participants consist of 100 learners aged 18-25 with Arabic, English, or German as their native or primary language.
 Methods. The study utilizes the Pinker Test as a measurement tool for assessing language processing abilities and collects data through a background questionnaire. Descriptive and inferential statistical analyses are conducted to analyze the data.
 Results. The results of the study indicate that linguistic background significantly affects language processing abilities (F(2, 97) = 4.72, p < 0.05). Post-hoc analyses reveal significant differences in grammar task performance between English-speaking individuals and Arabic-speaking participants (p < 0.05), while English and German speakers show similar scores. In the syntax task, English-speaking individuals outperform Arabic speakers (p < 0.05), with no significant difference between English and German speakers. In the semantics task, English speakers outperform both Arabic and German speakers (p < 0.05), while Arabic and German speakers differ significantly (p < 0.05). 
 Conclusions. Based on the findings, it can be concluded that linguistic background plays a significant role in language processing abilities, specifically in the areas of grammar, syntax, and semantics. These results support previous research and highlight the influence of language transfer effects and cognitive processing abilities influenced by linguistic background. The implications of this study's findings include the need for tailored teaching techniques that consider learners' linguistic backgrounds to enhance language instruction.

Effect of blended teaching on college students' EFL acquisition

The research into the efficacy of blended EFL (English as a Foreign Language) teaching at the collegiate level holds significant importance for comprehending and implementing this novel pedagogical approach on a larger scale within universities. Within this domain, scholars have primarily concentrated on feedback mechanisms and quality assurance, while comparatively neglecting the advancement of college students' foreign language proficiency and the individual variances in the acceptance and rewards of blended teaching across distinct language proficiency groups. In light of this, leveraging micro-data from a provincial normal university's blended college English teaching, this study employs R 3.6.1 and R Studio to implement multiple linear regression and conditional quantile models so as to assess the impact of blended teaching on different language proficiency groups across four dimensions: listening, reading, writing, and overall language proficiency. To mitigate endogenous system risk, students admitted to the same major are selected as samples and their data undergoes additional screening, excluding learners who failed the CET4 exam or did not participate in the CET6 exam. After employing purposive sampling techniques, a valid sample of 676 learners is established, comprising 363 learners in the experimental group for blended teaching intervention and 313 learners in the control group receiving traditional teaching. The study results indicates that the samples had random characteristics. The study findings suggest the following: (1) Blended teaching has a significant positive impact on enhancing the efficiency of English acquisition. (2) The effectiveness of blended teaching in improving learners' reading, listening, and writing skills follows a sequential decrease, exhibiting a downward trend as students' language ability increases. This indicates that blended teaching facilitates the acquisition of foundational language knowledge, however, its impact on more advanced language processing abilities is limited. (3) Blended teaching demonstrates a range effect, primarily benefiting learners at the intermediate level and below in terms of enhancing their language proficiency. Conversely, learners at the medium-high and high proficiency levels derive comparatively lesser benefits from this approach. This study introduces a new methodology by employing multiple linear regression and conditional quantile models to assess the impact of blended teaching. This methodology not only enables us to examine the overall impact of blended teaching, but also allows assessment of its effect on different proficiency groups, helping to identify its effectiveness on individual learners across four dimensions.

Role and challenges of ChatGPT, Gemini, and similar generative artificial intelligence in human resource management

The integration of generative artificial intelligence (AI) systems, such as ChatGPT, into Human Resource Management (HRM) has marked the beginning of a groundbreaking era in innovative workforce management and employee engagement. This research investigates the pivotal role played by ChatGPT and analogous generative AI technologies in HRM, underscoring their significance in the realms of recruitment, employee training, and organizational communication. Leveraging their natural language processing abilities, these AI systems streamline the recruitment process, ensuring unbiased candidate selection and enhancing the overall efficiency of HR departments. Moreover, in training and development initiatives, ChatGPT facilitates tailored learning experiences, adjusting content to meet individual employee needs, thereby fostering skill enhancement and professional growth. Nevertheless, the widespread adoption of ChatGPT in HRM is not without its challenges. Ethical concerns, such as data privacy and algorithmic bias, necessitate thorough examination to prevent discriminatory practices and guarantee equitable treatment of employees. Additionally, the requirement for continuous monitoring and refinement of AI algorithms to align with evolving organizational cultures and goals presents a significant hurdle. Moreover, striking a harmonious balance between AI-driven automation and human intervention is imperative to preserve the human touch in HRM processes, safeguarding the empathetic and intuitive elements that are essential for effective employee management. This research delves into these complex dynamics, shedding light on the transformative potential of ChatGPT and akin generative AI technologies in HRM, while also emphasizing the need for vigilance and strategic planning to address the associated challenges. Through an exhaustive analysis of real-world case studies and ethical frameworks, this study offers valuable insights for HR professionals, policymakers, and researchers endeavoring to navigate the intricate landscape of AI-powered HRM.

Deep learning-driven dyslexia detection model using multi-modality data.

Dyslexia is a neurological disorder that affects an individual's language processing abilities. Early care and intervention can help dyslexic individuals succeed academically and socially. Recent developments in deep learning (DL) approaches motivate researchers to build dyslexia detection models (DDMs). DL approaches facilitate the integration of multi-modality data. However, there are few multi-modality-based DDMs. In this study, the authors built a DL-based DDM using multi-modality data. A squeeze and excitation (SE) integrated MobileNet V3 model, self-attention mechanisms (SA) based EfficientNet B7 model, and early stopping and SA-based Bi-directional long short-term memory (Bi-LSTM) models were developed to extract features from magnetic resonance imaging (MRI), functional MRI, and electroencephalography (EEG) data. In addition, the authors fine-tuned the LightGBM model using the Hyperband optimization technique to detect dyslexia using the extracted features. Three datasets containing FMRI, MRI, and EEG data were used to evaluate the performance of the proposed DDM. The findings supported the significance of the proposed DDM in detecting dyslexia with limited computational resources. The proposed model outperformed the existing DDMs by producing an optimal accuracy of 98.9%, 98.6%, and 98.8% for the FMRI, MRI, and EEG datasets, respectively. Healthcare centers and educational institutions can benefit from the proposed model to identify dyslexia in the initial stages. The interpretability of the proposed model can be improved by integrating vision transformers-based feature extraction.

Investigation of the Impact of Dyslexia on Mathematics Achievements Among Secondary Level Students in Nepal

Dyslexia, a specific learning difficulty, has a profound impact on reading skills, language processing and mathematical abilities. It hampers word recognition, spelling, comprehension and decoding skills. The objective of this study was to ascertain the occurrence of dyslexia and its influence on mathematics achievement among secondary school students in Nepal. A mixed-method research design was employed for this purpose. The sample included 904 students from grade IX selected randomly. To identify dyslexic students, quantitative data was collected using adapted assessment tools such as the dyslexic student’s checklist for teachers (DSCT), dyslexic students’ checklist for parents (DSCP) and dyslexia identification self-assessment questionnaire (DISQ). These tools were utilized to screen for dyslexia. Qualitative data was gathered through an observation checklist and semi-structured interviews with selected students to verify the presence of dyslexia. The findings of the study revealed a prevalence rate of 5.53% for dyslexic students at the secondary level, with a slightly higher prevalence among male students (5.94%) compared to female students (5.22%). Moreover, the research demonstrated that dyslexic students exhibited significantly lower achievement scores in mathematics when compared to their non-dyslexic peers.

An investigation study on the interpretation of ultrasonic medical reports using OpenAI's GPT-3.5-turbo model.

Ultrasound medical reports are an important means of diagnosing diseases and assessing treatment effectiveness. However, their professional terms and complex sentences often make it difficult for ordinary people to understand. Therefore, this study explores the clinical value of using artificial intelligence systems based on ChatGPT to interpret ultrasound medical reports. In this study, a combination of online and offline questionnaires were used to survey both physicians and non-medical individuals. The questionnaires evaluated ChatGPT's interpretation of ultrasound reports from both professional and comprehensibility perspectives, and the results were analyzed using Excel spreadsheets. Additionally, a portion of the research content was evaluated using the Likert Scale 5-point method in the questionnaire. According to survey results, 67.4% of surveyed doctors believe that using ChatGPT for interpreting ultrasound medical reports can help improve work efficiency. At the same time, 69.72% of non-medical professionals believe it is necessary to enhance their understanding of medical ultrasound reports through ChatGPT interpretation, and 62.58% support the application of ChatGPT to ultrasound medical reports. The non-medical group's understanding of ultrasound medical reports significantly improved (p < 0.01) after implementing ChatGPT, However, 67.49% of the general public are concerned about ChatGPT's imperfect functionality, which may cause misleading information. This reflects that the public's trust in new technology is not high enough, and they are also worried about possible privacy leaks and security issues with ChatGPT technology. The higher acceptance and support of non-medical individuals for the interpretation of medical reports by ChatGPT might be due to the system's natural language processing abilities that allow them to better understand and evaluate report contents. However, the expertise and experience of physicians are still irreplaceable. This suggests that the ChatGPT-based ultrasound medical report interpretation system has certain clinical value and application prospects, but further optimization is necessary to address its shortcomings in data quality and professionalism. This study provides a reference and inspiration for promoting the application and development of ultrasound technology and artificial intelligence systems in the medical field.

Conversational artificial intelligence (chatGPT™) in the management of complex colorectal cancer patients: early experience.

In 2022 chatGPT™ (OpenAI, San Francisco) was introduced to the public. The complex reasoning and the natural language processing (NLP) ability of the AI platform has generated much excitement about the potential applications. This study conducted a preliminary analysis of the chatGPT™'s ability to formulate a management plan in accordance with oncological principles for patients with colorectal cancer. Colorectal cancer cases discussed in the multidisciplinary tumor (MDT) board at a single tertiary institution between September 2022 and January 2023 were prospectively collected. The treatment recommendations made by the chatGPT™ for Stage IV, recurrent, synchronous colorectal cancer were analysed for adherence to oncological principles. The recommendations by chatGPT™ were compared with the decision plans made by the MDT. In all cases, the chatGPT™ was able to adhere to oncological principles. The recommendations in all 30 cases factored in the patient's overall health and functional status. The oncological management recommendation concordance rate between chatGPT™ and the MDT was 86.7%. This study shows a high concordance rate of the chatGPT™'s recommendations with that given by the MDT in the management of complex colorectal patients. This will need to be verified in a larger prospective study.

Atypical Associations between Functional Connectivity during Pragmatic and Semantic Language Processing and Cognitive Abilities in Children with Autism.

Autism Spectrum Disorder (ASD) is characterized by both atypical functional brain connectivity and cognitive challenges across multiple cognitive domains. The relationship between task-dependent brain connectivity and cognitive abilities, however, remains poorly understood. In this study, children with ASD and their typically developing (TD) peers engaged in semantic and pragmatic language tasks while their task-dependent brain connectivity was mapped and compared. A multivariate statistical approach revealed associations between connectivity and psychometric assessments of relevant cognitive abilities. While both groups exhibited brain-behavior correlations, the nature of these associations diverged, particularly in the directionality of overall correlations across various psychometric categories. Specifically, greater disparities in functional connectivity between the groups were linked to larger differences in Autism Questionnaire, BRIEF, MSCS, and SRS-2 scores but smaller differences in WASI, pragmatic language, and Theory of Mind scores. Our findings suggest that children with ASD utilize distinct neural communication patterns for language processing. Although networks recruited by children with ASD may appear less efficient than those typically engaged, they could serve as compensatory mechanisms for potential disruptions in conventional brain networks.

Is there an influence of perceptual or cognitive impairment on complex sentence processing in hearing aid users?

Hearing-impaired listeners often have difficulty understanding complex sentences. It is not clear if perceptual or cognitive deficits have more impact on reduced language processing abilities, and how a hearing aid might compensate for that. In a prospective study with 5 hearing aid users and 5 normal hearing, age-matched participants, processing of complex sentences was investigated. Audiometric and working memory tests were performed. Subject- and object-initial sentences from the Oldenburg Corpus of Linguistically and audiologically controlled Sentences (OLACS) were presented to the participants during recording of an electroencephalogram (EEG). The perceptual difference between object and subject leading sentences does not lead to processing changes whereas the ambiguity in object leading sentences with feminine or neuter articles evokes a P600 potential. For hearing aid users, this P600 has a longer latency compared to normal hearing subjects. The EEG is a suitable method for investigating differences in complex speech processing for hearing aid users. Longer P600 latencies indicate higher cognitive effort for processing complex sentences in hearing aid users.

Healthcare: A Growing Role for Large Language Models and Generative AI

Abstract: Large language models and generative artificial intelligence (GAI) have recently demonstrated significant promise for revolutionizing a range of industries, including healthcare. The paper investigates how these cutting-edge AI developments are transforming healthcare applications. We focus on how big language models, like GPT-3 (generative pretrained transformer), Visual ChatGPT and generative AI, such Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), can be applied to solve important problems in the healthcare sector. Medical text analysis is one of the main uses of massive language models in the visual ChatGPT healthcare industry. These models have impressive natural language processing abilities that make it possible to effectively extract important information from electronic health records (EHRs), biomedical text data from large biobanks, scholarly articles and patient notes. The Biomedical Transformer Model represents a ground-breaking development in natural language processing for the biomedical field, exhibiting outstanding performance in comprehending and producing textual data. It opens up new avenues for biomedical research, diagnosis, and personalized therapy when combined with Multimodal Biomedical AI, which makes use of numerous data sources, including pictures, genomes, and clinical records. On the other side, generative AI has made great progress in medical picture analysis such as MRI scans and X-rays. The outstanding performance of GANs in medical picture synthesis and augmentation has helped to increase the precision and accuracy of diagnosis. Due to the issues with small and uneven medical datasets, VAEs have proven crucial in producing realistic medical images for training and research reasons. In addition to describing the various generative AI tools used in healthcare, this paper also provides an overview of multimodal medical LLMs and the biomedical transformer LLMs in the healthcare industry. Large language models and generative AI have great potential, but ethical issues and data privacy are still major problems in healthcare applications. Further, we investigate the potential role of multimodal medical LLMs as the foundation for novel assistive technologies in the fields of professional medicine, medical research, and consumer applications in the healthcare industry

Application of ChatGPT for an English Learning Platform

This paper aims to explore the potential of ChatGPT to serve as a comprehensive English learning platform for public school context by analyzing previous literature and current AI-based chatbots. To this end, the study investigated the potential of ChatGPT through four research questions: First, what are the educational possibilities of English learning chatbots?; Second, what are the possibilities and limitations of English learning chatbots when applied in classroom lessons?; Third, how can ChatGPT be utilized in classroom lessons?; Fourth, how can ChatGPT be utilized when applied to an intelligent adaptive learning model? The main conclusions of this discussion are as follows: First, the capabilities of AI-based English learning chatbots rely on natural language processing abilities, conversation modeling, providing dynamic feedback to learners, and offering personalized learning experiences which are lack in existing AI chatbots. Second, conventional English learning chatbots have been utilized in school education as free conversation apps, debate-specific bots, and integrated learning platforms. Third, ChatGPT has the potential to be integrated into a language learning platform and applied in formal learning environments. Finally, this paper proposes a plan to utilize ChatGPT in the context of an intelligent adaptive learning model.

Harnessing the Power of ChatGPT for Automating Systematic Review Process: Methodology, Case Study, Limitations, and Future Directions

Systematic reviews (SR) are crucial in synthesizing and analyzing existing scientific literature to inform evidence-based decision-making. However, traditional SR methods often have limitations, including a lack of automation and decision support, resulting in time-consuming and error-prone reviews. To address these limitations and drive the field forward, we harness the power of the revolutionary language model, ChatGPT, which has demonstrated remarkable capabilities in various scientific writing tasks. By utilizing ChatGPT’s natural language processing abilities, our objective is to automate and streamline the steps involved in traditional SR, explicitly focusing on literature search, screening, data extraction, and content analysis. Therefore, our methodology comprises four modules: (1) Preparation of Boolean research terms and article collection, (2) Abstract screening and articles categorization, (3) Full-text filtering and information extraction, and (4) Content analysis to identify trends, challenges, gaps, and proposed solutions. Throughout each step, our focus has been on providing quantitative analyses to strengthen the robustness of the review process. To illustrate the practical application of our method, we have chosen the topic of IoT applications in water and wastewater management and quality monitoring due to its critical importance and the dearth of comprehensive reviews in this field. The findings demonstrate the potential of ChatGPT in bridging the gap between traditional SR methods and AI language models, resulting in enhanced efficiency and reliability of SR processes. Notably, ChatGPT exhibits exceptional performance in filtering and categorizing relevant articles, leading to significant time and effort savings. Our quantitative assessment reveals the following: (1) the overall accuracy of ChatGPT for article discarding and classification is 88%, and (2) the F-1 scores of ChatGPT for article discarding and classification are 91% and 88%, respectively, compared to expert assessments. However, we identify limitations in its suitability for article extraction. Overall, this research contributes valuable insights to the field of SR, empowering researchers to conduct more comprehensive and reliable reviews while advancing knowledge and decision-making across various domains.

마음이론이 있는 ChatGPT: Level-1 자폐성장애인을 지원할 수 있을까?

Background &amp; Objective: Large Language Models (LLMs) like GPT, recognized for their natural language processing abilities, can generate human-like responses. This study explores using GPT-4, known for its advanced Theory of Mind, as an Augmentative and Alternative Communication (AAC) tool for individuals with Level-1 Autism Spectrum Disorder, who can converse but struggle with social communication. Results: GPT-4 can assist with 1) email composition, 2) online writing, and 3) participating in online discussions. Conclusion: GPT-4 holds potential to enhance existing AAC tools.

Relationships Between First and Second Language Phonological Awareness and Rapid Automatized Naming in Chinese Children with Dyslexia

Relationships Between First and Second Language Phonological Awareness and Rapid Automatized Naming in Chinese Children with Dyslexia

Based on natural language processing, human-computer dialogue, image recognition, and machine learning analysis whether artificial intelligence will surpass the human brain

With the popularization and development of the concept of artificial intelligence, the application of artificial intelligence has also begun to deepen into people's lives. While bringing convenience to people, it has also made some people worry about whether artificial intelligence will replace humans. Therefore, In order to make people understand the current development status and bottlenecks of artificial intelligence more intuitively, as well as the difference between artificial intelligence and human brain, this article will turn from speech recognition and natural language processing, human-computer dialogue, image recognition, and machine learning ability, that is, machine listening, reading, and thinking four aspects of research and discussion, and finally summarize why artificial intelligence cannot completely surpass humans.

Higher theta-beta ratio during screen-based vs. printed paper is related to lower attention in children: An EEG study.

Reading is considered a non-intuitive, cognitively demanding ability requiring synchronization between several neural networks supporting visual, language processing and higher-order abilities. With the involvement of technology in our everyday life, reading from a screen has become widely used. Several studies point to challenges in processing written materials from the screen due to changes in attention allocation when reading from a screen compared to reading from a printed paper. The current study examined the differences in brain activation when reading from a screen compared to reading from a printed paper focusing on spectral power related to attention in fifteen 6-8-year-old children. Using an electroencephalogram, children read two different age-appropriate texts, without illustrations, presented randomly on the screen and on a printed paper. Data were analyzed using spectral analyses in brain regions related to language, visual processing, and cognitive control, focusing on theta vs. beta waveforms. Results indicated that while reading from a printed paper was accompanied by higher energy in high-frequency bands (beta, gamma), reading from the screen was manifested by a higher power in the lower frequency bands (alpha, theta). Higher theta compared to the beta ratio, representing challenges in allocating attention to a given task, was found for the screen reading compared to the printed paper reading condition. Also, a significant negative correlation was found between differences in theta/beta ratio for screen vs paper reading and accuracy level in the age-normalized Sky-Search task measuring attention and a positive correlation with performance time. These results provide neurobiological support for the greater cognitive load and reduced focused attention during screen-based compared to print-based reading and suggest a different reliance on attention resources for the two conditions in children.

Modeling Bilingualism as a Dynamic Phenomenon in Healthy and Neurologically Affected Speakers Across the Lifespan: A Commentary on "Computational Modeling of Bilingual Language Learning: Current Models and Future Directions".

Modeling Bilingualism as a Dynamic Phenomenon in Healthy and Neurologically Affected Speakers Across the Lifespan: A Commentary on "Computational Modeling of Bilingual Language Learning: Current Models and Future Directions".