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Non-scripted role-playing with heritage speakers and second language learners in the medical interpreting classroom

Abstract This article examines dialogue interpreting in unscripted role-plays in the community interpreting classroom. In 2019, faculty members from several departments at Viterbo University (La Crosse, Wisconsin) coordinated an interprofessional education collaboration via role-playing in the institution’s Clinical Simulation Learning Center. Nursing, social work and pre-medical students were given the health-professional roles of caring for community members with limited English proficiency (who acted as ‘patients’). Interpreting students, both heritage speakers of Spanish and second language learners (L2) of both English and Spanish, facilitated language access for all parties involved. Recordings of these dialogues were then transcribed, annotated, and analyzed via mixed methods. This study examines overall and comparative findings of how heritage speakers and second language learners interpret dialogue, focusing on the textual aspects of their exchanges. While no language profile seemed to perform particularly better overall, certain indicators were more problematic for L2 Spanish speakers and/or heritage speakers. The presentation of these results and conclusions intend to foster improved teaching interventions for classrooms with students of varying English <> Spanish language backgrounds.

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Adiposity and Insulin Resistance Moderate Brain Electrophysiology and Affect Regulation in Young Adults

AbstractBackgroundObesity and insulin resistance (IR) may negatively influence affect regulation in young adulthood. The paucity of studies that have investigated such associations have predominantly used functional magnetic resonance imaging. However, electroencephalography (EEG) can evaluate neural signatures in real‐time that may be complementary. Thus, this study investigated how adiposity and IR moderated brain activity and underlying affect regulation using EEG.MethodReal‐time EEG was recorded in 28 lean and obese young adults with and without IR (18‐39 years, 46.4% female). Two event‐related potential (ERP) components of affect regulation, early posterior negativity (EPN) and late positive potential (LPP), were quantified from EEG data. Participants completed the Interactional Picture Affective System task to measure affect regulation, from which mean valence ratings and stimulus‐to‐response‐onset reaction times were calculated. ERP components and affect regulation parameters were then contrasted in three conditions, as follows: 1) Negative – Neutral; 2) Positive – Neutral; and 3) Negative – Positive. Height, weight, body fat percentage (%BF), and serum proteomics were collected. Fasting glucose and insulin readings were obtained to quantify Homeostatic Model Assessment for Insulin Resistance (HOMA‐IR). Hierarchical moderated regression analysis was utilized to test the interrelationships between adiposity, IR, neural activity, and affect regulation.ResultAcross all contrasted conditions, HOMA‐IR and %BF were found to moderate the relationships between frontal and parietal LPP amplitudes during the late latency window and affect regulation. Specifically, higher late frontal LPP amplitudes were associated with less negative and more positive valence ratings to unpleasant and pleasant stimuli, respectively, among participants with low, but not high, IR and %BF levels. In the Negative – Neutral and Negative – Positive conditions, lower late parietal LPP amplitudes were also associated with less negative overall valence ratings in response to unpleasant stimuli, but only in lean, insulin sensitive participants. These results overall suggest that modulation of negative and positive affectivity only occurs in lean young adults without IR.ConclusionYoung adults with greater adiposity and IR showed worse affect regulation than those without obesity and IR. Furthermore, AD‐associated characteristics may start early in life, and EEG signatures may be a useful neuroimaging approach for tracking such events.

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Effect of Sound Source Location and Spatial Hearing on the Vestibulo-Ocular Reflex.

There have been conflicting results on the effect of auditory stimulation on the vestibulo-ocular reflex (VOR) with some studies indicating suppression, enhancement, or no effect. No studies to date have assessed the effect of sound source location on VOR gain or the relationship between spatial hearing ability and VOR gain. The purpose of the present study was to determine if VOR gain was affected by moving the location of the sound source within participants and to determine if these effects were related to spatial hearing ability. A between subjects repeated measures experimental design was utilized. Two groups of participants (adult and child) with normal otologic, vestibular, and neurologic function. 22 adults (20 female and 2 male; average age = 23 years) and 16 children (9 female and 7 male; average age = 7.5 years) were included in data analysis. VOR gain was measured using rotational chair stimulation in the following auditory conditions: silent, insert earphones, external loudspeaker at 0° azimuth rotating with participant, and external stationary speaker. Localization ability was measured using root mean square (RMS) error. Results indicated a significant effect for sound source location on VOR gain and VOR difference gain in both groups. RMS error was positively correlated for the moving and fixed sound source locations for both adults and children. VOR gain was significantly affected by location of the sound source. Findings suggest the presence and location of an auditory stimulus during rotational testing can alter results during the assessment.

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Transferring Procedural Knowledge Across Commonsense Tasks

Stories about everyday situations are an essential part of human communication, motivating the need to develop AI agents that can reliably understand these stories. Despite the long list of supervised methods for story completion and procedural understanding, current AI fails to generalize its procedural reasoning to unseen stories. This paper is based on the hypothesis that the generalization can be improved by associating downstream prediction with fine-grained modeling and the abstraction of procedural knowledge in stories. To test this hypothesis, we design LEAP: a comprehensive framework that reasons over stories by jointly considering their (1) overall plausibility, (2) conflict sentence pairs, and (3) participant physical states. LEAP integrates state-of-the-art modeling architectures, training regimes, and augmentation strategies based on natural and synthetic stories. To address the lack of densely annotated training data on participants and their physical states, we devise a robust automatic labeler based on semantic parsing and few-shot prompting with large language models. Our experiments with in- and out-of-domain tasks reveal insights into the interplay of architectures, training regimes, and augmentation strategies. LEAP’s labeler consistently improves performance on out-of-domain datasets, while our case studies show that the dense annotation supports explainability.

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