Context Of Interactions Research Articles

Balancing Standardization and Ecological Validity in the Measurement of Social Communication Intervention Outcomes

Purpose: Caregiver-mediated communication intervention outcomes are inconsistently measured, varying by assessment settings, materials, and activities. Standardized materials are often used for measuring outcomes, yet it remains unknown whether such standardized contexts equitably capture caregiver and child intervention outcomes representative of dyads' typical interactions. This within-subject study investigates how intervention outcomes differ between family-selected and standardized interactional contexts for autistic toddlers and their caregivers. Method: Following an 8-week caregiver-mediated telehealth intervention delivered to 22 dyads, caregiver outcomes (fidelity of using responsive communication facilitation strategies) and child outcomes (total spontaneous directed communicative acts) were measured during two interactional contexts using (a) family-selected activities and (b) a standardized toy set. A routines checklist surveyed the activities dyads value, enjoy, complete frequently, and/or find difficult with their child. Results: Caregiver outcomes and child outcomes did not significantly differ between the family-selected and standardized interactional contexts. Descriptive results suggest that the types of toys commonly included in standardized toy sets are representative of the materials many families choose when playing with their child at home. However, during the family-selected interactional context, the majority of dyads also chose materials or activities that were not available to them during the standardized context. Conclusion: It is necessary to carefully consider a more expansive approach to standardization in which intervention outcomes are measured in ecologically valid contexts, which meaningfully, accurately, and equitably capture caregiver and child functional outcomes, and the translation of interventions to families' everyday routines.

First-language acquisition in a multimodal language framework: Insights from speech, gesture, and sign

Children across the world acquire their first language(s) naturally, regardless of typology or modality (e.g. sign or spoken). Various attempts have been made to explain the puzzle of language acquisition using several approaches, trying to understand to what extent it can be explained by what children bring to language-learning situations as well as what they learn from the input and the interactive context. However, most of these approaches consider only speech development, thus ignoring the inherently multimodal nature of human language. As a multimodal view of language is becoming more widely adopted for the study of adult language, a multimodal approach to language acquisition is inevitable. Not only do children have the capacity to learn spoken and sign language equally easily, but spoken language acquisition consists of learning to coordinate linguistic expressions in both modalities, that is, in both speech and gesture. To provide a step forward in this direction, this article aims to synthesize findings from research studies that take a multimodal perspective on language acquisition in different sign and spoken languages, including the development of speech and accompanying gestures. Our review shows that while some aspects of language acquisition seem to be modality-independent, others might differ according to the affordances of each modality when used separately as well as together (either in sign, speech, and/or gesture). We argue that these findings need to be integrated into our understanding of language acquisition. We also identify which areas need future research for both spoken and sign language acquisition, taking into account not only multimodal but also cross-linguistic variation.

Semantic-guided attention and adaptive gating for document-level relation extraction

In natural language processing, document-level relation extraction is a complex task that aims to predict the relationships among entities by capturing contextual interactions from an unstructured document. Existing graph- and transformer-based models capture long-range relational facts across sentences. However, they still cannot fully exploit the semantic information from multiple interactive sentences, resulting in the exclusion of influential sentences for related entities. To address this problem, a novel Semantic-guided Attention and Adaptively Gated (SAAG) model is developed for document-level relation extraction. First, a semantic-guided attention module is designed to guide sentence representation by assigning different attention scores to different words. The multihead attention mechanism is then used to capture the attention of different subspaces further to generate a document context representation. Finally, the SAAG model exploits the semantic information by leveraging a gating mechanism that can dynamically distinguish between local and global contexts. The experimental results demonstrate that the SAAG model outperforms previous models on two public datasets.

Still Changing: Semantic Innovation and Change in Multicultural London English

We attempt to further our understanding of the beginnings of language change by analyzing a semantic innovation in Multicultural London English: the use of still as a discourse-pragmatic item in utterance-final position. We show that the change follows well-attested processes of semantic change, so it could have occurred at any time and amongst any group of speakers. Its emergence now, amongst young men in inner city London, can be explained by considering not only well-known external factors such as population change but also by analyzing the interactional contexts in which the new meaning emerges and by relating this to the local peer group culture. In this way we gain insights into how speakers can be motivated to use a form with a new meaning during interactional moves that are especially important for them in their everyday lives.

External Modifications of Requests among Jordanian University Students: A Socio-Pragmatic Analysis

The primary purpose of this study is to explore the external modifications of requests among Jordanian male and female university students in cross-gender settings and the factors affecting the choice of these strategies. The subjects of the study were 18 Jordanian undergraduate university students (9 males and 9 females) enrolled in different programs at Ajloun National University, Jordan. A mixed methodological approach was adopted for data collection. The data was retrieved from nine-hour audio recordings to gather naturally-occurring data and 18 DCTs to elicit organized data sets that can be analyzed based on patterns. Blum-Kulka et al.’s (1989) taxonomy of request strategies was espoused to analyze the obtained data. The data analysis revealed that the mitigating supportive moves (i.e., Imposition Minimizers, grounders, disarmers, preparators, getting a pre-commitment, promise of reward) were preferred by female students more than their male counterparts, whereas male students used Aggravating supportive moves (i.e., Threat, insult, Moralizing) more frequently than female students. Interestingly, other unique features that can be subsumed under Mitigating supportive moves manifested in Jordanian students’ requests, Namely, apology, self-introduction and gratitude. It was concluded that request strategies are not only used to fulfill certain needs, but also to manage social interactions and to strengthen relationships. Moreover, the way in which Jordanians use request strategies is strongly influenced by gender, cultural norms, and the context of interaction.

Integrating subject-specific workspace constraint and performance-based control strategy in robot-assisted rehabilitation

IntroductionThe robot-assistive technique has been widely developed in the field of neurorehabilitation for enhancement of neuroplasticity, muscle activity, and training positivity. To improve the reliability and feasibility in this patient–robot interactive context, motion constraint methods and adaptive assistance strategies have been developed to guarantee the movement safety and promote the training effectiveness based on the user’s movement information. Unfortunately, few works focus on customizing quantitative and appropriate workspace for each subject in passive/active training mode, and how to provide the precise assistance by considering movement constraints to improve human active participation should be further delved as well.MethodsThis study proposes an integrated framework for robot-assisted upper-limb training. A human kinematic upper-limb model is built to achieve a quantitative human–robot interactive workspace, and an iterative learning-based repulsive force field is developed to balance the compliant degrees of movement freedom and constraint. On this basis, a radial basis function neural network (RBFNN)-based control structure is further explored to obtain appropriate robotic assistance. The proposed strategy was preliminarily validated for bilateral upper-limb training with an end-effector-based robotic system.ResultsExperiments on healthy subjects are enrolled to validate the safety and feasibility of the proposed framework. The results show that the framework is capable of providing personalized movement workspace to guarantee safe and natural motion, and the RBFNN-based control structure can rapidly converge to the appropriate robotic assistance for individuals to efficiently complete various training tasks.DiscussionThe integrated framework has the potential to improve outcomes in personalized movement constraint and optimized robotic assistance. Future studies are necessary to involve clinical application with a larger sample size of patients.

Feature fusion and context interaction for RGB-D indoor semantic segmentation

Depth information in indoor scenes is crucial to understanding the spatial and occluding relationships of objects. Depth information contains rich geometric details and is unaffected by lighting, texture, and color, significantly enhancing traditional color images. RGB images and depth images have been widely applied in various image analysis. However, effectively leveraging the complementary information from both modalities remains a challenge. To address this issue, we propose a unified and effective feature fusion and context interaction network (FCINet). It includes three modules. Firstly, we construct a multi-modal feature fusion module (MFFM), which can achieve the aggregation of two modalities along spatial and channel dimensions. Secondly, we construct a global and local information interaction context module (GLCM) to encode rich semantic information. Finally, we construct a feature alignment and fusion module (FAFM), which integrates upsampled low-level features with high-level features to mitigate spatial information loss. Experimental results indicate that the model achieved favorable outcomes on both the NYU Depth v2 dataset and the more complex SUN RGB-D dataset.

Not Talking about Climate Change: Everyday Interactions, Relational Work and Climate Silences

Despite evidence of extensive and growing concern about climate change, citizens remain relatively unlikely to discuss it in everyday conversation, presenting a puzzle to commentators and researchers. Different explanations of climate silence have been suggested, most notably from social psychology and from political economy perspectives, which posit forms of cultural control. However, there is limited evidence about the relational contexts of everyday climate talk and the meanings that people themselves attach to it. In this article, we analyse data from new qualitative research and explore how climate talk is patterned, forms of self-silencing and the meanings attached to climate talk, with reference to its interactional and relational contexts. We argue that social interactional contexts, relational work and mundane forms of practical constraint play an under-investigated yet crucial role in limiting climate talk.

Using drama to enrich a genre-based pedagogy for teaching academic writing across content areas

Drama has the potential to enrich and enliven established approaches to literacy learning, engaging students and expanding their possibilities for meaning-making. Drama-rich pedagogy is well-established as an effective tool in supporting language and literacy development. However, research to date has not explored the possibilities of using drama to enrich established genre-based approaches to teaching writing. This article reports on findings from an interventionist case study in an Australian primary school examining how a Year Three/Four teacher used drama-rich pedagogy to support students’ academic writing of a scientific explanation text. The findings indicate three key pedagogical benefits of employing drama to build conceptual understandings of the field of study and relevant academic language as part of a genre-based pedagogy for teaching writing. Firstly, drama experiences can make visible students’ prior knowledge of a topic, allowing teachers to identify, interrogate and transform students’ misconceptions through reflective dialogue on embodied experience. These embodied and interactive experiences also support the development of shared understandings of key concepts and language. Finally, drama experiences provide embodied and interactive contexts in which students can move from common sense to more abstract/technical understandings of the topic of study which they can apply in writing their own texts. In sharing these findings, this article aims to illustrate how drama can be used to develop the conceptual and linguistic understandings required by genre pedagogy in engaging, embodied ways that draw productively on the diverse resources that students bring to their learning.

GestureGPT: Toward Zero-Shot Free-Form Hand Gesture Understanding with Large Language Model Agents

Existing gesture interfaces only work with a fixed set of gestures defined either by interface designers or by users themselves, which introduces learning or demonstration efforts that diminish their naturalness. Humans, on the other hand, understand free-form gestures by synthesizing the gesture, context, experience, and common sense. In this way, the user does not need to learn, demonstrate, or associate gestures. We introduce GestureGPT, a free-form hand gesture understanding framework that mimics human gesture understanding procedures to enable a natural free-form gestural interface. Our framework leverages multiple Large Language Model agents to manage and synthesize gesture and context information, then infers the interaction intent by associating the gesture with an interface function. More specifically, our triple-agent framework includes a Gesture Description Agent that automatically segments and formulates natural language descriptions of hand poses and movements based on hand landmark coordinates. The description is deciphered by a Gesture Inference Agent through self-reasoning and querying about the interaction context (e.g., interaction history, gaze data), which is managed by a Context Management Agent. Following iterative exchanges, the Gesture Inference Agent discerns the user’s intent by grounding it to an interactive function. We validated our framework offline under two real-world scenarios: smart home control and online video streaming. The average zero-shot Top-1/Top-5 grounding accuracies are 44.79%/83.59% for smart home tasks and 37.50%/73.44% for video streaming tasks. We also provide an extensive discussion that includes rationale for model selection, generalizability, and future research directions for a practical system etc.

THÖR-MAGNI: A large-scale indoor motion capture recording of human movement and robot interaction

We present a new large dataset of indoor human and robot navigation and interaction, called THÖR-MAGNI, that is designed to facilitate research on social human navigation: for example, modeling and predicting human motion, analyzing goal-oriented interactions between humans and robots, and investigating visual attention in a social interaction context. THÖR-MAGNI was created to fill a gap in available datasets for human motion analysis and HRI. This gap is characterized by a lack of comprehensive inclusion of exogenous factors and essential target agent cues, which hinders the development of robust models capable of capturing the relationship between contextual cues and human behavior in different scenarios. Unlike existing datasets, THÖR-MAGNI includes a broader set of contextual features and offers multiple scenario variations to facilitate factor isolation. The dataset includes many social human–human and human–robot interaction scenarios, rich context annotations, and multi-modal data, such as walking trajectories, gaze-tracking data, and lidar and camera streams recorded from a mobile robot. We also provide a set of tools for visualization and processing of the recorded data. THÖR-MAGNI is, to the best of our knowledge, unique in the amount and diversity of sensor data collected in a contextualized and socially dynamic environment, capturing natural human–robot interactions.

Early diagnosis of Alzheimer’s disease using a group self-calibrated coordinate attention network based on multimodal MRI

Convolutional neural networks (CNNs) for extracting structural information from structural magnetic resonance imaging (sMRI), combined with functional magnetic resonance imaging (fMRI) and neuropsychological features, has emerged as a pivotal tool for early diagnosis of Alzheimer’s disease (AD). However, the fixed-size convolutional kernels in CNNs have limitations in capturing global features, reducing the effectiveness of AD diagnosis. We introduced a group self-calibrated coordinate attention network (GSCANet) designed for the precise diagnosis of AD using multimodal data, including encompassing Haralick texture features, functional connectivity, and neuropsychological scores. GSCANet utilizes a parallel group self-calibrated module to enhance original spatial features, expanding the field of view and embedding spatial data into channel information through a coordinate attention module, which ensures long-term contextual interaction. In a four-classification comparison (AD vs. early MCI (EMCI) vs. late MCI (LMCI) vs. normal control (NC)), GSCANet demonstrated an accuracy of 78.70%. For the three-classification comparison (AD vs. MCI vs. NC), it achieved an accuracy of 83.33%. Moreover, our method exhibited impressive accuracies in the AD vs. NC (92.81%) and EMCI vs. LMCI (84.67%) classifications. GSCANet improves classification performance at different stages of AD by employing group self-calibrated to expand features receptive field and integrating coordinated attention to facilitate significant interactions among channels and spaces. Providing insights into AD mechanisms and showcasing scalability for various disease predictions.

Generalized contact matrices allow integrating socioeconomic variables into epidemic models.

Variables related to socioeconomic status (SES), including income, ethnicity, and education, shape contact structures and affect the spread of infectious diseases. However, these factors are often overlooked in epidemic models, which typically stratify social contacts by age and interaction contexts. Here, we introduce and study generalized contact matrices that stratify contacts across multiple dimensions. We demonstrate a lower-bound theorem proving that disregarding additional dimensions, besides age and context, might lead to an underestimation of the basic reproductive number. By using SES variables in both synthetic and empirical data, we illustrate how generalized contact matrices enhance epidemic models, capturing variations in behaviors such as heterogeneous levels of adherence to nonpharmaceutical interventions among demographic groups. Moreover, we highlight the importance of integrating SES traits into epidemic models, as neglecting them might lead to substantial misrepresentation of epidemic outcomes and dynamics. Our research contributes to the efforts aiming at incorporating socioeconomic and other dimensions into epidemic modeling.

PARALINGUISTIC FEATURES IN STUDENTS' STORYTELLING WITHIN MULTIMODAL COMMUNICATIONS

This study assessed paralinguistic features in Indonesian university students' contextual interaction during storytelling. Data collection was recorded from respondents' two video-based storytelling performances, while a self-rated questionnaire was distributed to 235 respondents out of 481 undergraduate English education students using simple random sampling. Data analysis used a mixed-methods approach to qualify students' paralinguistic features using the eduistic linguistics annotator (ELAN) and to quantify the paralinguistic features using statistical analyses through the significance of .05. The findings revealed that the ELAN analyzed the contextual interaction among freshmen, sophomores, juniors, and seniors. The students’ paralinguistic features corresponded with the lexical and semantic evidence, which approached the function of monosyllabic and bisyllabic words, nonverbal expressions, and interpretations. Bodily gesture quantitatively showed moderate category for 44.7% (t = 2.434; p = .016), articulation showed attributable category for 54.0% (t = 3.789; p = .000), facial expression showed moderate category for 61.7% (t = 2.472; p = .014), and voice loudness showed attributable category for 47.7% (t = 4.121; p = .000). Herein, positive and significant attribution were shown by these paralinguistic features towards students' contextual interactions in storytelling for 34.9% with the multiple regressions (F = 7.990, R² = .349, and p < .000). The paralinguistic features empirically address the multimodal communication modes to improve teaching and learning activities.

A theoretical exploration of the electronic structure and single photoionization of the many-electron system confined in Gaussian potential

This manuscript investigates the electronic structures, spectral properties, and photoionization processes of the confined atomic system. For this purpose, a relativistic methodology employing the Dirac–Coulomb Hamiltonian within the context of relativistic configuration interaction is suggested, utilizing independent particle basis wavefunctions. The key idea of this approach is to place the atom inside a Gaussian potential, which gives a realistic description of the spatial confinement in quantum dots due to a smooth change at the quantum dot boundaries and has a finite range and depth for the spatial confinement. As a result, the local central potential is modified, which is determined by a self-consistent process. The solutions to the Dirac equation, incorporating the aforementioned central potential, yield both the continuous and bound state wave functions. The photoionization process is determined through the application of the distorted wave approach within the context of relativistic Dirac theory. As an application, the electronic structures of the confined Li atom, including energies, ionization potentials, transition rates, and photoionization dynamical properties such as wave functions, cross sections, and photoelectron angular distributions, are systematically investigated within the dipole approximation for a wide range of potential depths and confining radii. A systematic comparison of the present outcomes is made with other available results. The present study is not only meaningful for fundamental research in atomic and molecular physics, but also has implications for a range of disciplines, such as nanochemistry, materials science, and other related fields.

Developing methods to identify resilience and improve communication about diagnosis in pediatric primary care.

Communication underlies every stage of the diagnostic process. The Dialog Study aims to characterize the pediatric diagnostic journey, focusing on communication as a source of resilience, in order to ultimately develop and test the efficacy of a structured patient-centered communication intervention in improving outpatient diagnostic safety. In this manuscript, we will describe protocols, data collection instruments, methods, analytic approaches, and theoretical frameworks to be used in to characterize the patient journey in the Dialog Study. Our approach to characterization of the patient journey will attend to patient and structural factors, like race and racism, and language and language access, before developing interventions. Our mixed-methods approach is informed by the Systems Engineering Initiative for Patient Safety (SEIPS) 3.0 framework (which describes the sociotechnical system underpinning diagnoses within the broader context of multiple interactions with different care settings over time) and the Safety II framework (which seeks to understand successful and unsuccessful adaptations to ongoing changes in demand and capacity within the healthcare system). We will assess the validity of different methods to detect diagnostic errors along the diagnostic journey. In doing so, we will emphasize the importance of viewing the diagnostic process as the product of communications situated in systems-of-work that are constantly adapting to everyday challenges.

Enhancing User Experience in VR Environments through AI-Driven Adaptive UI Design

This paper presents a new approach to improving user experience in virtual reality (VR) environments using AI-driven user interface (UI) design. The proposed system uses advanced machine learning techniques to update UI content based on user interaction and real-time context. A comprehensive literature review examines the current state of VR interfaces, AI applications in UI/UX design, and evolving UI technologies. Data is a multi-layered process combining data collection, processing, and editing over time. The design was developed and evaluated through a rigorous study involving 50 participants, comparing a modified UI against a static UI control. The results showed a significant improvement in performance, experience reduction, and overall user satisfaction. The modified UI group showed an 18.6% reduction in completion time, a 47.8% reduction in errors, and a 34.9% increase in user satisfaction scores compared to the static UI group. Physiological data analysis supports these findings, showing reduced stress and increased engagement. This research contributes to the field of VR interface design by providing empirical evidence for the effectiveness of AI-driven adaptive UIs in improving user experience and field performance. Virtual.

Influence of introducing a story stem in an interactive play context on maternal and their four-year-old children's use of mental state language.

Maternal and child mental state language is associated with improved socioemotional and cognitive child development. This study examined if introducing a story stem (a narrative playing out socioemotional conflicts) in a play situation facilitated maternal and child mental state language compared to a free-play (baseline) situation, and if mothers and children with low baseline mental state language profited more from the story stem situation. Participants were 101 four-year-old children and their mothers. Maternal and child mental state language correlated and providing the story stem increased both maternal and child mental state language. Providing a story stem increased mental state language more for mothers and children groups with lowest mental state language at baseline compared to a high mental state language group. The results indicate a validation of the use of story stems to increase maternal and child mental state language within a typical population.

Exploring Hedging in Spoken Discourse: Insights from Corpus Analysis

The present research deals with the phenomenon of hedging language, mainly focusing on the context of spontaneous verbal interactions. The main aim of the study is to analyze the occurrence of hedges in English spoken discourse as captured in the Spoken British National Corpus 2014. The research process deals with the differences in the usage of approximators and shields existing among men and women across different age groups and levels of education. The research methodology involves creating six sub-corpora, measuring hedge dispersion, examining selected hedges to ascertain their hedging function within the context and calculating frequencies within each sub-corpus. The research outcomes point to the prevalence of shields in spoken discourse. Plausibility shields are commonly used while attribution shields show lower usage. Adaptors contribute to mitigating certainty in utterances, whereas rounders play a less prominent role. These findings emphasize the nuanced interplay between linguistic strategies and speaker characteristics in spoken communication and highlight the importance of cooperative principles in discourse.

M2Tames: Interaction and Semantic Context Enhanced Pedestrian Trajectory Prediction

Autonomous driving pays considerable attention to pedestrian trajectory prediction as a crucial task. Constructing effective pedestrian trajectory prediction models depends heavily on utilizing the motion characteristics of pedestrians, along with their interactions among themselves and between themselves and their environment. However, traditional trajectory prediction models often fall short of capturing complex real-world scenarios. To address these challenges, this paper proposes an enhanced pedestrian trajectory prediction model, M2Tames, which incorporates comprehensive motion, interaction, and semantic context factors. M2Tames provides an interaction module (IM), which consists of an improved multi-head mask temporal attention mechanism (M2Tea) and an Interaction Inference Module (I2). M2Tea thoroughly characterizes the historical trajectories and potential interactions, while I2 determines the precise interaction types. Then, IM adaptively aggregates useful neighbor features to generate a more accurate interactive feature map and feeds it into the final layer of the U-Net encoder to fuse with the encoder’s output. Furthermore, by adopting the U-Net architecture, M2Tames can learn and interpret scene semantic information, enhancing its understanding of the spatial relationships between pedestrians and their surroundings. These innovations improve the accuracy and adaptability of the model for predicting pedestrian trajectories. Finally, M2Tames is evaluated on the ETH/UCY and SDD datasets for short- and long-term settings, respectively. The results demonstrate that M2Tames outperforms the state-of-the-art model MSRL by 2.49% (ADE) and 8.77% (FDE) in the short-term setting and surpasses the optimum Y-Net by 6.89% (ADE) and 1.12% (FDE) in the long-term prediction. Excellent performance is also shown on the ETH/UCY datasets.