Gilts are motivated to exit a stall

Scientific Reports · 2025-02-26 · 1 citations

Stalls (or crates) are still a common type of housing in the swine industry, despite public concern and regional legislation restricting their use. In this study, we examined the motivation of gilts to exit a stall. Sixteen stall-naïve gilts (Large White x Landrace) were locked for 60 min in a gestation crate that had been mounted with a novel apparatus allowing continuous monitoring (2 Hz measuring frequency) of the force applied to its back gate by the animal. Raw force measurements were low-pass filtered and discrete pushing events identified via local maxima. All gilts displayed some level of motivation to exit the crate, ranging from 41 to 173 in the number of pushing events, as well as exerting a maximum force applied from 124 to 645 N. A hierarchical cluster analysis applied to the median and interquartile range (IQR) of force generated during individual pushing events yielded two behavioural profiles. One group of eight animals was more active than the other. This group exhibited a greater number of pushes, recorded a higher maximum, median force and its IQR, as well as a shorter time interval between two pushes (all t-tests with a P < 0.05). While all these naïve animals worked to leave the stall, gilts displayed different motivation profiles in trying to exit the stall consistent with a reactive/proactive framework. Taken together these findings provide further evidence to support stall confinement as aversive to swine but highlight the complexities in understanding and improving pig welfare.

The Minnesota Conference proposed guidelines for education and training in clinical neuropsychology

The Clinical Neuropsychologist · 2025-07-02 · 31 citations

OBJECTIVE: The Houston Conference Guidelines (Hannay et al., 1998) provided an initial framework for North American neuropsychology training that served the specialty well for several decades. Subsequent advances in technology, increased diversity of the U.S. and Canadian populations, and the adoption of competency-based training models within Health Service Psychology have created a need to update neuropsychology training guidelines. Therefore, in 2022, the Minnesota Conference to Update Education and Training Guidelines in Clinical Neuropsychology began a two-year drafting process leading to the currently proposed update. METHOD: A Steering Committee worked with content experts, consultants, and delegates representing North American neuropsychological organizations and specialists. The final version of the guidelines was developed after reviewing neuropsychological training literature, gathering feedback from specialists, and making iterative revisions of earlier drafts to reach consensus. CONCLUSION: The resulting "Minnesota Guidelines" include five foundational (Neuroscience and Brain Behavior Relationships; Integration of Science and Practice; Ethics, Standards, Laws, and Policies; Diversity; and Professional Relationships) and eight functional (Assessment; Intervention; Interdisciplinary Systems and Consultation; Research and Scholarship; Technology and Innovation; Teaching, Supervision, and Mentoring; Health and Professional Advocacy; and Administration, Management, and Business) areas of competency required for entry level specialty practice. While consensus was not achieved, a majority of voting delegates recommended the Guidelines for adoption and the Guidelines have been endorsed by six neuropsychology education and board certification organizations. The American Academy of Clinical Neuropsychology has not endorsed the Minnesota Guidelines and will not make an endorsement decision until three months after online publication.

Counteracting uncertainty: exploring the impact of anxiety on updating predictions about environmental states

Biological Cybernetics · 2025-02-20 · 3 citations

Anxious emotional states disrupt decision-making and control of dexterous motor actions. Computational work has shown that anxiety-induced uncertainty alters the rate at which we learn about the environment, but the subsequent impact on the predictive beliefs that drive action control remains to be understood. In the present work we tested whether anxiety alters predictive (oculo)motor control mechanisms. Thirty participants completed an experimental task that consisted of manual interception of a projectile performed in virtual reality. Participants were subjected to conditions designed to induce states of high or low anxiety using performance incentives and social-evaluative pressure. We measured subsequent effects on physiological arousal, self-reported state anxiety, and eye movements. Under high pressure conditions we observed visual sampling of the task environment characterised by higher variability and entropy of position prior to release of the projectile, consistent with an active attempt to reduce uncertainty. Computational modelling of predictive beliefs, using gaze data as inputs to a partially observable Markov decision process model, indicated that trial-to-trial updating of predictive beliefs was reduced during anxiety, suggesting that updates to priors were constrained. Additionally, state anxiety was related to a less deterministic mapping of beliefs to actions. These results support the idea that organisms may attempt to counter anxiety-related uncertainty by moving towards more familiar and certain sensorimotor patterns.

Immersive Extended Reality and Affective-Behavioral Outcomes in Nursing Students: A Systematic Review and Meta-Analysis of Randomized Studies

Cyberpsychology Behavior and Social Networking · 2025-12-22

Extended reality (XR) technologies, including virtual, augmented, and mixed reality, are increasingly used in nursing education to enhance experiential learning and emotional engagement. However, their effectiveness across learning domains remains uncertain. This systematic review and meta-analysis synthesized evidence from 32 randomized studies (n = 1,578) to evaluate XR’s impact on cognitive, psychomotor, and affective outcomes in nursing students. Five databases were searched (2012–2024). Meta-analyses were conducted using Review Manager 5.4 and R 4.3.3, and study quality was assessed with the Joanna Briggs Institute checklist. Certainty of evidence was rated using the Grading of Recommendations Assessment, Development and Evaluation approach, and subgroup sensitivity analyses explored heterogeneity. XR interventions produced significant effects on learner satisfaction (standardized mean difference [SMD] = 0.50, p = 0.02) and psychomotor performance (SMD = 0.46, p = 0.01). Effects on confidence (SMD = 0.45, p = 0.06) and cognitive outcomes (SMD = 0.24, p = 0.26) were smaller and inconsistent. Limited effects were found for attitudes and values. Subgroup analyses suggested that XR showed benefits for psychomotor outcomes relative to mannequin-based simulations and for satisfaction compared with video comparators, while value over lecture-based controls was limited. Removing outliers improved estimates and reduced heterogeneity. Findings suggest that XR may enhance skill acquisition and foster emotional engagement, although evidence for cognitive gains and attitudinal change remains limited. Results highlight XR’s potential to influence learner motivation, presence, and self-efficacy. Future studies should explore long-term outcomes, mixed reality, and adaptive XR systems. Broad integration should consider technical skills and affective and behavioral mechanisms essential to professional development.

Dating app use, psychological health, and psychological well-being: A systematic review and quantitative meta-analysis

Computers in Human Behavior · 2025-12-02

Virtual Reality Tennis Training: Performance Gains Derived from User Characteristics

Cyberpsychology Behavior and Social Networking · 2024-09-17 · 4 citations

There is growing interest in virtual reality (VR) training among competitive athletes and casual sports players alike as a tool to supplement real-life play within a highly controlled, intellectually stimulating environment. We examined data from a commercially available, recently released VR software for tennis for changes in and correlates of performance. Two most frequently used tasks were evaluated-Baseline Center and Quick Volley, which include Efficiency (both), Concentration (both), and Reaction Time (Quick Volley only) subtasks. In all, 1,124 (Baseline Center) and 745 (Quick Volley) users met inclusion criteria (completed more than four trials; active sometime between November 2022 and July 2023). We found that most users were male adults and were about evenly split between advanced/pro users and intermediate/beginner users. Two or three trajectories emerged across the subtasks. Performance gains were most pronounced on movement efficiency, especially early on. Adult users generally exhibited more improvement than junior users. Additionally, women and right-handed users improved more on Baseline Center subtasks, and advanced/pro users did better than intermediate/beginner users on Quick Volley subtasks. We discuss that, despite strong performance gains within VR environment, VR training may still reflect in better real-world performance, may increase confidence and accuracy of relevant movement, lower risk of injury, and present a welcome diversion from a potential monotony of performing sport-related tasks in purely real-world settings. Future research should explore the extent to which VR training transfers to real-world performance.

Intelligent Virtual Patients for Training Clinical Skills

Hochschule Düsseldorf · 2024-11-27 · 20 citations

The article presents the design process of intelligent virtual human patients that are used for the enhancement of clinical skills. The description covers the development from conceptualization and character creation to technical components and the application in clinical research and training. The aim is to create believable social interactions with virtual agents that help the clinician to develop skills in symptom and ability assessment, diagnosis, interview techniques and interpersonal communication. The virtual patient fulfills the requirements of a standardized patient producing consistent, reliable and valid interactions in portraying symptoms and behaviour related to a specific clinical condition.

Exploring Adaptive Virtual Reality Systems Used in Interventions for Children With Autism Spectrum Disorder: Systematic Review (Preprint)

2024-02-05 · 1 citations

<sec> <title>BACKGROUND</title> Adaptive systems serve to personalize interventions or training based on the user’s needs and performance. The adaptation techniques rely on an underlying engine responsible for processing incoming data and generating tailored responses. Adaptive virtual reality (VR) systems have proven to be efficient in data monitoring and manipulation, as well as in their ability to transfer learning outcomes to the real world. In recent years, there has been significant interest in applying these systems to improve deficits associated with autism spectrum disorder (ASD). This is driven by the heterogeneity of symptoms among the population affected, highlighting the need for early customized interventions that target each individual’s specific symptom configuration. </sec> <sec> <title>OBJECTIVE</title> Recognizing these technology-driven therapeutic tools as efficient solutions, this systematic review aims to explore the application of adaptive VR systems in interventions for young individuals with ASD. </sec> <sec> <title>METHODS</title> An extensive search was conducted across 3 different databases—PubMed Central, Scopus, and Web of Science—to identify relevant studies from approximately the past decade. Each author independently screened the included studies to assess the risk of bias. Studies satisfying the following inclusion criteria were selected: (1) the experimental tasks were delivered via a VR system, (2) system adaptation was automated, (3) the VR system was designed for intervention or training of ASD symptoms, (4) participants’ ages ranged from 6 to 19 years, (5) the sample included at least 1 group with ASD, and (6) the adaptation strategy was thoroughly explained. Relevant information extracted from the studies included the sample size and mean age, the study’s objectives, the skill trained, the implemented device, the adaptive strategy used, the engine techniques, and the signal used to adapt the systems. </sec> <sec> <title>RESULTS</title> Overall, a total of 10 articles were included, involving 129 participants, 76% of whom had ASD. The studies included level switching (7/10, 70%), adaptive feedback strategies (9/10, 90%), and weighing the choice between a machine learning (ML) adaptive engine (3/10, 30%) and a non-ML adaptive engine (8/10, 80%). Adaptation signals ranged from explicit behavioral indicators (6/10, 60%), such as task performance, to implicit biosignals, such as motor movements, eye gaze, speech, and peripheral physiological responses (7/10, 70%). </sec> <sec> <title>CONCLUSIONS</title> The findings reveal promising trends in the field, suggesting that automated VR systems leveraging real-time progression level switching and verbal feedback driven by non-ML techniques using explicit or, better yet, implicit signal processing have the potential to enhance interventions for young individuals with ASD. The limitations discussed mainly stem from the fact that no technological or automated tools were used to handle data, potentially introducing bias due to human error. </sec>

Complexity synchronization in emergent intelligence

Scientific Reports · 2024-03-21 · 9 citations

In this work, we use a simple multi-agent-based-model (MABM) of a social network, implementing selfish algorithm (SA) agents, to create an adaptive environment and show, using a modified diffusion entropy analysis (DEA), that the mutual-adaptive interaction between the parts of such a network manifests complexity synchronization (CS). CS has been shown to exist by processing simultaneously measured time series from among organ-networks (ONs) of the brain (neurophysiology), lungs (respiration), and heart (cardiovascular reactivity) and to be explained theoretically as a synchronization of the multifractal dimension (MFD) scaling parameters characterizing each time series. Herein, we find the same kind of CS in the emergent intelligence of groups formed in a self-organized social interaction without macroscopic control but with biased self-interest between two groups of agents playing an anti-coordination game. This computational result strongly suggests the existence of the same CS in real-world social phenomena and in human-machine interactions as that found empirically in ONs.

Feasibility study to identify machine learning predictors for a Virtual Environment Grocery Store

Virtual Reality · 2024-01-23 · 1 citations

Abstract Virtual reality-based assessment and training platforms proffer the potential for higher-dimensional stimulus presentations (dynamic; three dimensional) than those found with many low-dimensional stimulus presentations (static; two-dimensional) found in pen-and-paper measures of cognition. Studies have investigated the psychometric validity and reliability of a virtual reality-based multiple errands task called the Virtual Environment Grocery Store (VEGS). While advances in virtual reality-based assessments provide potential for increasing evaluation of cognitive processes, less has been done to develop these simulations into adaptive virtual environments for improved cognitive assessment. Adaptive assessments offer the potential for dynamically adjusting the difficulty level of tasks specific to the user’s knowledge or ability. Former iterations of the VEGS did not adapt to user performance. Therefore, this study aimed to develop performance classifiers from participants ( N = 75) using three classification techniques: Support Vector Machines (SVM), Naive Bayes (NB), and k-Nearest Neighbors (kNN). Participants were categorized as either high performing or low performing based upon the number items they were able to successfully find and add to their grocery cart. The predictors utilized for the classification focused on the times to complete tasks in the virtual environment. Results revealed that the SVM (88% correct classification) classifier was the most robust classifier for identifying cognitive performance followed closely by kNN (86.7%); however, NB tended to perform poorly (76%). Results suggest that participants’ task completion times in conjunction with SVM or kNN can be used to adjust the difficult level to best suit the user in the environment.