Task Description Research Articles

Aptitude, Anxiety, and Success in L2 Speech Development: A Longitudinal Study of Chinese EFL College-Level Learners.

This study examined the second language (L2) speech development of a group of Chinese English as a Foreign Language (EFL) college-level learners (N = 83) and the association between their aptitude, anxiety, and L2 speech development. The performance of the participants' L2 speech, including speech comprehensibility and accentedness, was evaluated using a sentence reading task and a pair of picture description tasks (pre- and post-test). In addition, students completed the LLAMA tests (language learning and aptitude measurement in adults) and the Second Language Speaking Anxiety Scale (SLSAS). The t-test revealed that after one semester of English speaking and pronunciation instruction, the EFL students in the study demonstrated significant gains in their L2 speech performance in terms of accentedness and comprehensibility in both controlled and impromptu tasks. Regression analyses revealed that phonemic coding ability positively predicted gains in comprehensibility but negatively predicted gains in accentedness during sentence reading. In addition, associative memory was positively associated with comprehensibility gains in the picture description task. Anxiety appeared to be a strong predictor of participants' gains in speech comprehensibility and accentedness during the picture description task.

Learning by Visualize a Nurse-Led CCOS Using the Functional Resonance Analysis Method.

Quality improvements (QIs) in dynamic and complex health care contexts require resilience and take variability into account in quality improvement. The Functional Resonance Analysis Method (FRAM) helps us understand resilience and gain insight into (un)desirable variability in the complex system of daily practice. We explored how using FRAM in the Deming cycle of a QI project can help professionals and researchers learn from, reflect upon, and improve complex processes. We used FRAM in a Dutch hospital to study a QI: Critical Care Outreach Service (CCOS). The aim was to use FRAM before and after implementation to create a FRAM model and reflect to health care professionals the mismatch between Work As Imagined (WAI) and Work As Done (WAD). The WAI FRAM model was co-created with professionals before the implementation of CCOS. We used descriptions of tasks and processes for ICU nurses and verified them in 30-minute semistructured interviews (N = 2). WAD was created by input of semistructured interviews with key professionals in CCOS (N = 21) and 3 nonparticipant observations of trained CCOS nurses. We validated WAD in 2 dialogue sessions with key professionals (N = 11). Data collection continued until saturation. Juxtaposing the WAI and WAD models showed that WAD contained additional functions and highlighted unexpectedly complex functions. Reflecting on the application of FRAM with health care professionals revealed opportunities and challenges, especially time investment. FRAM helps professionals outline processes and tasks (WAI), learn from, and reflect upon their daily practice (WAD). FRAM models help professionals identify variability proactively to improve practices that enhance resilient performance.

AI-assisted marking: Functionality and limitations of ChatGPT in written assessment evaluation

Generative artificial intelligence technologies, such as ChatGPT, bring an unprecedented change in education by leveraging the power of natural language processing and machine learning. Employing ChatGPT to assist with marking written assessment presents multiple advantages including scalability, improved consistency, eliminating biases associated with human subjectivity. This work aimed to evaluate the usefulness, reliability and accuracy of ChatGPT in marking written assessments of varied types and to identify its limitations and challenges. ChatGPT was instructed using a set of prompts to mark the assessment based on a rubric. ChatGPT was able to evaluate and assess both coding and reflective assessments and to distinguish between assignments of different quality, demonstrating high consistency and accuracy for higher quality assessments, comparable to a human marking. ChatGPT was also able to generate textual detailed justifications based on the rubric and assessment task description. There was a significant difference in the outcomes generated by different prompts. These preliminary findings suggest that utilising ChatGPT as a marking assistant can increase written assessment marking efficiency, reduce cost and potentially decrease the unfairness and bias by providing a moderating perspective. Implications for practice or policy: Assessment designers could reconsider the design, purpose and objectives of written assessments and leverage ChatGPT effectively for teaching and learning. Assessors might consider adapting the technology as a grading aid, to support a human-in-the-loop grading process, providing additional resources and time, moderating and refining individual feedback, to increase consistency and quality. Curriculum and programme leaders could develop guidelines around the ethical use of generative AI-assisted assessment practice, monitor and regulate the ongoing evaluation and refinement.

Variability in Vowel Space in Parkinson's Disease: Associations With Cognitive and Motor Impairment.

People with Parkinson's disease (PwP) typically experience impairments in vowel articulation; however, less is known about how this measure varies with speech task type and clinical characteristics such as cognitive impairment. We characterized vowel space in PwP with and without mild cognitive impairment (MCI) comparing performance across phonation, reading, and picture description tasks. We evaluated associations between vowel space and cognitive impairment, as well as motor symptom severity to elucidate the factors contributing to variability in this acoustic measure. PwP (n = 48) and age-matched controls (n = 15) performed sustained phonation of corner vowels, a reading passage, and a picture description task (Cookie Theft picture). PwP participants were classified as with normal cognition (PD-NC) or MCI (PD-MCI), and motor symptoms were assessed using the Movement Disorders Society Unified Parkingson's Disease Rating Scale Part III (MDS-UPDRS Part III). Vowel articulation index (VAI) for each task and mean difference in VAI between tasks was compared between the groups using linear mixed models adjusted for age, sex, and education. The impact of motor severity was assessed by additionally adjusting the model for MDS-UPDRS Part III score. In the adjusted mixed model, mean VAI was significantly lower in the PD-MCI group compared to the PD-NC group for all tasks. Within participants, adjusted mean differences demonstrated that all groups declined in VAI when sustained phonation was compared to either reading or picture description tasks. Adjustment for MDS-UPDRS Part III did not alter the results, suggesting no major association of motor impairment with vowel space variability within or between individuals or groups. Variability in vowel space is impacted by cognitive impairment and speech task in PwP. These findings are relevant to the further development of speech markers in PwP and other neurogenerative diseases that impact both cognitive and motor functions.

A Conceptual Model for Automation of Small Ports

Abstract For large ports and terminals, the loading and discharge operations have to a great extent been automated already. However, for the vast number of small ports, for instance all Norwegian ports, this large-scale automation is not possible due to the small size of the ports compared to the cost of introducing full automation. Still, these small ports are important logistics hubs in the transport system. They facilitate the shift from congested roads to the sea by allowing the goods to arrive closer to the final destination by using smaller, possibly autonomous, vessels. However, this puts pressure on the ports to facilitate reduced laytime in the port, to increase the port efficiency and to improve interoperability towards hinterland systems and terminals. For small ports, this means that manual and automated work processes need to exist side by side, and that it will be important to get an overview of which processes to automate and which will remain manual, in addition to the handling of the hand-over between humans and automation. This paper describes a conceptual model that can be used by small ports as a starting point on their way to introduce automation of their services supporting the cargo flow through the port. The model includes a description of port functions, tasks and responsibilities and use the concept of operational envelope to define the overall capabilities of the system, including both automation and human. This will be the starting point for how to structure the description of an operation centre that monitor and control the maritime terminal operations. The starting point for this work is a use case analysis performed in some small ports in Norway, and their need to optimize the utilization of their assets. The work is done as part of the AutoPort project which is a research project funded by the Norwegian Research Council (project number 337211). The goal of this work is to describe a methodology on how to identify the interface between automation and human operators when it comes to small ports and the movement of cargo through its terminals, and by this give a description on how different levels of automation can be implemented. The methodology consists of several steps, including definition of the context (big picture of the use case, actors and their roles, and communication), the scenario (port activities and processes), and the use cases including the control tasks performed by the automated systems.

The Relationship of Word-Finding Behaviors and Naming in Mild Cognitive Impairment and Dementia of Alzheimer’s Type

Objectives: Anomia, a fundamental language difficulty, presents in various forms of wordfinding behaviors in individuals with Mild Cognitive Impairment (MCI) and Dementia of Alzheimer’s Type (DAT). This study aims to compare the rates of word-finding behavior among these groups and determine the correlation between these rates and the results of naming tests. Methods: This study included 101 participants, consisting of forty-one healthy elderly adults, thirty individuals with MCI, and thirty individuals with DAT. Spontaneous speech data were collected using a picture description task. For the naming tests, we used the confrontation naming test (K-BNT) and the generative naming test (KCOWAT). Results: First, significant differences were observed in the rates of word-finding behavior among the three groups, especially in the global index, which indicates the overall rate of word-finding behaviors. Second, the correlation analysis results showed a significant negative correlation between the ratios of the global index, word reformulations, empty words, and performance on the confrontation naming test. Finally, there was a significant negative correlation between the ratios of the global index, word reformulations, repetitions, delays, insertions, and performance on the generative naming tests. Conclusion: These results suggest the necessity of developing a naming evaluation scale based on spontaneous speech to effectively assess anomia in individuals with MCI and DAT. The study found significant differences in the rates of word-finding behavior among healthy elderly adults, individuals with MCI, and individuals with DAT, and a correlation between the rates of word-finding behavior and naming tests.

Linguacodus: a synergistic framework for transformative code generation in machine learning pipelines

In the ever-evolving landscape of machine learning, seamless translation of natural language descriptions into executable code remains a formidable challenge. This article introduces Linguacodus, an innovative framework designed to tackle this challenge by deploying a dynamic pipeline that iteratively transforms natural language task descriptions into code through high-level data-shaping instructions. The core of Linguacodus is a fine-tuned large language model, empowered to evaluate diverse solutions for various problems and select the most fitting one for a given task. This article details the fine-tuning process and sheds light on how natural language descriptions can be translated into functional code. Linguacodus represents a substantial leap towards automated code generation, effectively bridging the gap between task descriptions and executable code. It holds great promise for advancing machine learning applications across diverse domains. Additionally, we propose an algorithm capable of transforming a natural description of an ML task into code with minimal human interaction. In extensive experiments on a vast machine learning code dataset originating from Kaggle, we showcase the effectiveness of Linguacodus. The investigations highlight its potential applications across diverse domains, emphasizing its impact on applied machine learning in various scientific fields.

Screening for early Alzheimer's disease: enhancing diagnosis with linguistic features and biomarkers.

Research has shown that speech analysis demonstrates sensitivity in detecting early Alzheimer's disease (AD), but the relation between linguistic features and cognitive tests or biomarkers remains unclear. This study aimed to investigate how linguistic features help identify cognitive impairments in patients in the early stages of AD. This study analyzed connected speech from 80 participants and categorized the participants into early-AD and normal control (NC) groups. The participants underwent amyloid-β positron emission tomography scans, brain magnetic resonance imaging, and comprehensive neuropsychological testing. Participants' speech data from a picture description task were examined. A total of 15 linguistic features were analyzed to classify groups and predict cognitive performance. We found notable linguistic differences between the early-AD and NC groups in lexical diversity, syntactic complexity, and language disfluency. Using machine learning classifiers (SVM, KNN, and RF), we achieved up to 88% accuracy in distinguishing early-AD patients from normal controls, with mean length of utterance (MLU) and long pauses ratio (LPR) serving as core linguistic indicators. Moreover, the integration of linguistic indicators with biomarkers significantly improved predictive accuracy for AD. Regression analysis also highlighted crucial linguistic features, such as MLU, LPR, Type-to-Token ratio (TTR), and passive construction ratio (PCR), which were sensitive to changes in cognitive function. Findings support the efficacy of linguistic analysis as a screening tool for the early detection of AD and the assessment of subtle cognitive decline. Integrating linguistic features with biomarkers significantly improved diagnostic accuracy.

An Analysis of Research Trends for Using Artificial Intelligence in Cultural Heritage

Artificial intelligence (AI) techniques have been increasingly applied in assisting various cultural heritage (CH)-related tasks. The aim of this study is to examine the research trends and current applications of AI in this vast domain. After obtaining a dataset from the Web of Science and Scopus databases, a scientometric analysis of research publications from 2019 to 2023 related to the use of AI in CH was conducted. The trending topics based on the author’s keywords were identified by using the ScientoPy v2.1.3 software. Through this approach, five main topics were identified: classification, computer vision, 3D reconstruction, recommender systems, and intangible cultural heritage. The analysis highlights the upward trend in publications in this field since 2019, indicating a growing interest in the application of AI techniques in CH. By analyzing the latest research in the field, it is observed that AI techniques are mostly applied to assist CH in the discovery, description, classification, and preservation tasks. The report gives insights into the main research areas and developing trends in the field of artificial intelligence and machine learning. The study offers important information about the key research areas and emerging trends related to using AI techniques in the CH field. This helps to recognize the potential, development, and increasing influence of these technologies within the CH domain. The findings of this study contribute to the future development of AI applications in CH, enabling professionals to use the advantages of these technologies.

Explicit information and practice type can affect the L2 acquisition of plural marking

Abstract Previous research has indicated that explicit information (EI) about either the first language (L1) or second language (L2) along with task-essential practice can facilitate L2 learning (e.g., Fernández, 2008; McManus, 2022). However, little research has examined L1–L2 contrastive EI with L1/L2 practice. Targeting plural-marking accuracy, the present study sought to fill this gap by exposing 127 Japanese intermediate learners of English to six online treatment sessions across four conditions: (1) non-contrastive EI (pluralization rule explanation) + L2 practice, (2) contrastive EI + L2 practice, (3) as per Condition 2 + additional L1 practice (application of L2 pluralization to L1 through the Japanese suffix –tachi), and (4) using prepositions as a control, non-contrastive EI (prepositional rule explanation) + L2 practice. Accuracy was tested at pre/post/delayed intervals using an acceptability judgement task (reading) and a picture description task (writing). Statistical analyses demonstrated greater gains from the contrastive variables in the immediate post-test, with L1 practice appearing especially beneficial, despite improvements diminishing in the delayed post-test. Supplementary data from retroactive interviews and questionnaire responses indicated favourable orientations towards the intervention. The study advances understanding of how EI and practice type can be used to maximize L2 learning through web-based practices tailored to the nature of the learning problem.

Adynamic spoken language in corpus callosum dysgenesis

Corpus callosum dysgenesis (CCD) is a congenital brain malformation that occurs when the development of the corpus callosum is disrupted, either partially or completely. The cognitive outcomes in individuals with CCD vary greatly, but generally the neuropsychological profile is characterised by slow processing speed, poor transfer of interhemispheric sensory-motor information, and impaired complex problem solving. Core language skills are often preserved in CCD, but there is some evidence that complex language may be impaired. Thus, the current study sought to examine whether spontaneous speech output was reduced in a cohort of individuals with CCD compared to age-matched controls. We further explored a series of factors that may be contributing to poor spontaneous speech in CCD, such as difficulties generating, selecting, and sequencing ideas for expression, as well as apathy and slowed processing speed. A cohort of 25 individuals with CCD and 39 neurotypical controls were enrolled in this study. Participants completed a picture description task to measure spontaneous speech output, alongside a series of cognitive and language baseline tests. Verbal and nonverbal fluency tasks gauged idea generation and sequencing, and sentence-level selection tasks measured idea selection. We found that, despite having largely intact core language skills, individuals with CCD produced significantly less spontaneous speech on the picture description task than controls. This language profile may be described as “adynamic”. Further, we found that poor spontaneous speech output in CCD was related to problems generating ideas for expression, as individuals with CCD performed below controls on the verbal and nonverbal fluency tasks. Exploratory analyses revealed that apathy and slowed processing speed may be contributing factors. Adynamia in CCD is a novel finding that may be an intervention target for improving communication skills in this population.

The potential impact of AI innovations on US occupations.

An occupation is comprised of interconnected tasks, and it is these tasks, not occupations themselves, that are affected by Artificial Intelligence (AI). To evaluate how tasks may be impacted, previous approaches utilized manual annotations or coarse-grained matching. Leveraging recent advancements in machine learning, we replace coarse-grained matching with more precise deep learning approaches. Introducing the AI Impact measure, we employ Deep Learning Natural Language Processing to automatically identify AI patents that may impact various occupational tasks at scale. Our methodology relies on a comprehensive dataset of 17,879 task descriptions and quantifies AI's potential impact through analysis of 24,758 AI patents filed with the United States Patent and Trademark Office between 2015 and 2022. Our results reveal that some occupations will potentially be impacted, and that impact is intricately linked to specific skills. These include not only routine tasks (codified as a series of steps), as previously thought but also nonroutine ones (e.g. diagnosing health conditions, programming computers, and tracking flight routes). However, AI's impact on labor is limited by the fact that some of the occupations affected are augmented rather than replaced (e.g. neurologists, software engineers, air traffic controllers), and the sectors affected are experiencing labor shortages (e.g. IT, Healthcare, Transport).

The artistic reality of Salman Rudshi's novel "The Florentine Enchantress"

The article presents a reconstruction and analysis of Salman Rushdie's famous book "The Florentine Enchantress". It is not very clear why she is so named, there are more significant figures in the novel, for example, Emperor Akbar. The question is raised about the integrity of artistic reality and the peculiarities of readers' understanding of the novel. In connection with this statement, the author recalls his distinction between two ways of constructing a modern work of art: in the first, the artist recreates the world opposed to him (including in some cases himself, but as alienated), in the second, he creates a reality reflecting the peculiarities of his own consciousness (this approach is called "private realism"). It is proposed to carry out a reconstruction that allows you to understand the work of art in terms of how it is constructed, what its author wanted to say, whether it is possible to reconstruct what he did, whether he succeeded in the idea, what his work was, etc. The implementation of this program results in an analysis of Rushdie's personality characteristics, as well as a description of the main tasks that he solves (reviving the story, entertaining readers, expressing one's own beliefs). Rushdie creates characters who live in two different realities ‒ historical and modern, as a result, history is partially distorted, and the characters acquire contradictory features. The techniques by which the artistic reality of the novel is created are analyzed in detail. The first technique is the identification of historical subjects with non‒historical ones, the second is the connection of storylines necessary for the author, the third is a technique widely used by Rushdie, not only to connect different lines and realities, but also for his other two purposes (entertaining readers and expressing his own thoughts) ‒ it's magic and fantasy. The question is considered why many readers do not understand the "Florentine Enchantress" well. There are two reasons for this. One is related to the very attitude of understanding: readers start reading Rushdie's works, believing that they are looking at an ordinary novel, but in fact we are talking about the genre of private realism. The second reason lies in the unpreparedness of readers to work with such complex material, which involves recreating a complete work and reality based on the images, storylines, themes and other expressive means offered by the author.

Understanding Musculoskeletal Risks in Retail Workers: An Ergonomic Assessment of Store 24H

Musculoskeletal disorders (MSDs) are prevalent in retail environments, often resulting from repetitive tasks, awkward postures, and prolonged standing. This study examined musculoskeletal discomfort and ergonomic risk factors within the retail environment of convenience stores, focusing on the prevalence of MSDs among employees of Store 24H, Dungun, Terengganu, Malaysia. The assessment began with a job task description, followed by workplace observation and worker input. Utilising a systematic approach, the research integrated two standardized assessment tools; the Cornell Musculoskeletal Questionnaire (CMQ) and the Initial Ergonomic Risk Assessment (ERA) Form Checklist, to identify the discomfort area and ergonomic risk factors associated with job tasks. Three job tasks were identified in the Store 24H environment; operating cash registers, customer service and restocking. The findings indicate a notable prevalence of MSDs associated with the identified high-risk tasks. Repetitive motions, awkward postures, prolonged standing, and forceful exertion were observed as primary ergonomic risk factors for these tasks which contribute to musculoskeletal discomfort in the neck, shoulder, back, hip, and knee. Additionally, forceful exertion and vibration exhibit high risks towards restocking tasks, resulting in restocking as the top contributor towards the MSDs. Hence, the findings highlight the need for advanced ERA and mitigation strategies, including workstation redesign, task rotation, ergonomic training and accessories, to promote employee well-being and reduce the incidence of MSDs among employees of Store 24H. Implementing these measures is essential for promoting the health and well-being of employees, decreasing the occurrence of musculoskeletal disorders, and improving overall safety and productivity in the workplace.

Data visualization and decision making in adults with acquired and developmental language disabilities: A scoping review.

Accessibility of data visualization has been explored for users with visual disabilities but the needs of users with language disabilities have seldom been considered. This scoping review synthesised what is known about data visualization for adults with language disabilities, specifically the acquired language disability, aphasia and Developmental Language Disorder. It sought to extract key findings and identify what practices support effective visualization for decision making for people with language disabilities. Papers were included if they investigated visualization of data, and the consumers of the data visualization were people with aphasia or developmental language disability. Seven databases were searched: CINAHL, Academic Search, Medline, PsychINFO, Ovid, ACM Digital Library and IEEE Xplore. Included studies were charted to extract title, author(s), year, country, paper type, scientific field, participant number(s), participant group(s), main topic, subtopic, method, task description, task category, data visualization, summary, key findings relevant to the review question, and guidelines or recommendations. Narrative synthesis was used to describe how people with language disability have interacted with data visualization from a range of literature. Six studies (seven publications) were included in the review. One study came from the field of health, one from a disability rights collaboration and four studies from computer science. No studies satisfying the review criteria explored data visualization for Developmental Language Disorder; however, five studies explored participants with cognitive disabilities that included impairments of language, so these were included. A range of visualization designs were found. Studies predominantly explored understanding of visualization (4/6). One study explored how to express data visually, and one explored the use of the visualization that is, for an action, choice, or decision. Cognitively accessible data visualization practices were described in four papers and synthesized. Supportive practices reported were reducing the cognitive load associated with processing a visualization and increasing personal relevance of data visualization. Accessible data visualization for adults with aphasia and Developmental Language Disorder has only minimally been explored. Practices to specifically support users with language disability are not yet apparent. As data use in making everyday decisions is widespread, future research should explore how people with language disabilities make use of data visualization. What is already known on this subject Visual resources are used widely to support people with language disabilities in understanding of language. That is, icons, maps timelines and so forth, are used to support auditory processing. However, data visualization is used routinely by people without a language disability to support everyday decisions for example, visualization of live traffic data is used to provide users with the best route to their destination. It is unclear whether any work has explored data visualization for people with language disabilities. What this paper adds to existing knowledge This paper brings together research on the use of data visualization by adults with either Developmental Language Disorder or aphasia, collectively people with language disabilities. It highlights a gap in the design of inclusive data visualization for language disabilities and the minimal research exploring the use of data visualization for decision making in these populations. What are the clinical implications of this work? Access to data can be empowering. It has potential to enable agency in decisions and increase social participation. The existing gap in knowledge about how to design inclusive data visualization for people with language disabilities thus poses a risk of exclusion and threats to informed decision making. Highlighting the current field of literature may drive research and clinical activity.

Typing Versus Handwriting: A Preliminary Investigation of Modality Effects in the Writing Output of People With Aphasia.

Handwriting and typing have different cognitive and motor demands; however, questions remain as to whether performance in people with aphasia varies based on modality. This study compares written discourse production across handwritten and onscreen typed modalities for a large sample of people with aphasia. We also aimed to explore potential variables that predict the number of written words generated by participants and determine if modality differences emerge when these variables are included as predictors. Writing samples, via handwriting and onscreen typing, elicited in a picture description task were collected from 52 participants with chronic aphasia and coded for number of words. Generalized linear mixed-effects models were used to model the data. Aphasia type, severity of aphasia, writing severity, and use of nondominant hand for writing or onscreen typing were included as predictor variables. There were no significant differences between the number of words generated in the typed modality versus handwritten modality for the sample. Of the predictor variables examined, Western Aphasia Battery-Revised writing scores significantly predicted the number of words produced (p < .001). However, the interaction of writing severity with modality was not significant. This preliminary study suggests that there was no effect of modality on one measure of written production, number of words. Future research is needed to evaluate if there are meaningful differences between modalities when additional measures, such as writing informativeness, are considered. https://doi.org/10.23641/asha.26506144.

Analysis of functional network asymmetry in major depressive disorder under four fNIRS tasks

BackgroundResearch in functional asymmetry of Major Depressive Disorder (MDD) under different tasks is crucial for clinical diagnose. MethodsFifty individuals with MDD and twenty healthy controls (HCS) were recruited for hemodynamic data collection under four fNIRS tasks (Emotional picture, Verbal fluency, Fingering and Negative emotional picture description task). Integral values and functional connectivity strength were employed to probe neural activation and functional connectivity in frontal and temporal lobes in MDD. Following, asymmetry characteristic of the frontal cortex between MDD and HCS under four tasks were carefully analyzed and compared. ResultsIndividuals with MDD demonstrated heightened connectivity between the frontal and right temporal lobes and reduced connectivity between the frontal and left temporal lobes compared to HCS in all tasks. Additionally, MDD exhibited attenuated activation in the left frontal lobes and exaggerated activation in the right frontal lobes, diverging from HCS. Furthermore, the disparities in left-right asymmetry characteristic of frontal cortex activation between MDD and HCS were more pronounced during the combined task. LimitationsFurther research is required to grasp the neurophysiological mechanisms governing left-right asymmetry across various tasks and the influence of task-induced brain fatigue on cerebral cortex hemodynamics in MDD. ConclusionThe left-right asymmetry feature provides valuable neurophysiological insights for diagnosing MDD clinically. Variations in activation patterns and functional connectivity features between MDD and HCS are closely tied to the task chosen. Thus, in clinical practice, carefully selecting appropriate fNIRS tasks and relevant features can significantly improve the diagnostic accuracy of MDD.

Dysfluency in primary progressive aphasia: Temporal speech parameters

ABSTRACT Analysing spontaneous speech in individuals experiencing fluency difficulties holds potential for diagnosing speech and language disorders, including Primary Progressive Aphasia (PPA). Dysfluency in the spontaneous speech of patients with PPA has mostly been described in terms of abnormal pausing behaviour, but the temporal features related to speech have drawn little attention. This study compares speech-related fluency parameters in the three main variants of PPA and in typical speech. Forty-three adults participated in this research, thirteen with the logopenic variant of PPA (lvPPA), ten with the non-fluent variant (nfvPPA), nine with the semantic variant (svPPA), and eleven who were healthy age-matched adults. Participants’ fluency was assessed through a picture description task from which 42 parameters were computed including syllable duration, speaking pace, the duration of speech chunks (i.e. interpausal units, IPU), and the number of linguistic units per IPU and per second. The results showed that each PPA variant exhibited abnormal speech characteristics reflecting various underlying factors, from motor speech deficits to higher-level issues. Out of the 42 parameters considered, 37 proved useful for characterising dysfluency in the three main PPA variants and 35 in distinguishing among them. Therefore, taking into account not only pausing behaviour but also temporal speech parameters can provide a fuller understanding of dysfluency in PPA. However, no single parameter by itself sufficed to distinguish one PPA group from the other two, further evidence that dysfluency is not dichotomous but rather multidimensional, and that complementary multiparametric analyses are needed.

PRF: A Program Reuse Framework for Automated Programming by Learning from Existing Robot Programs

This paper explores the problem of automated robot program generation from limited historical data when neither accurate geometric environmental models nor online vision feedback are available. The Program Reuse Framework (PRF) is developed, which uses expert-defined motion classes, a novel data structure introduced in this work, to learn affordances, workspaces, and skills from historical data. Historical data comprise raw robot joint trajectories and descriptions of the robot task being completed. Given new tasks, motion classes are then used again to formulate an optimization problem capable of generating new open-loop, skill-based programs to complete the tasks. To cope with a lack of geometric models, a technique to learn safe workspaces from demonstrations is developed, allowing the risk of new programs to be estimated before execution. A new learnable motion primitive for redundant manipulators is introduced, called a redundancy dynamical movement primitive, which enables new end-effector goals to be reached while mimicking the whole-arm behavior of a demonstration. A mobile manipulator part transportation task is used throughout to illustrate each step of the framework.

Analyzing Lexical Boost Effects in Priming of the Passive in Brazilian Portuguese

The lexical boost effect is an increase in the magnitude of structural priming effects when prime and target verbs are identical. This study investigates the connection of lexical boost to priming effects of the passive construction in Brazilian Portuguese (BP) in an image description task to contribute to the debate about whether lexical boost effects are a result of surface word repetition or a connection between the lexical item in the working memory and the structure in long-term memory. The task measured subjects’ working memory (WM) capacity and manipulated task protocol by adding a matching cover task to the picture description task. Data collected indicate that structural priming effects in the image description task were contingent on lexical identity, presenting a positive correlation with choice of structure in the description. WM capacity did not influence priming effects, nor did subjects’ performance in the cover task. Results provide evidence in favor of lexical boost effects being a result of residual activation of the connection between lexical item and structure. They also provide insight about the task and its applicability to priming effects of the passive construction in BP.