Older adults (OA) evaluate faces to be more trustworthy than do younger adults (YA), yet the processes supporting these more positive evaluations are unclear. This study identified neural mechanisms spontaneously engaged during face perception that differentially relate to OA' and YA' later trustworthiness evaluations. We examined two mechanisms: salience (reflected by amygdala activation) and reward (reflected by caudate activation) - both of which are implicated in evaluating trustworthiness. We emphasized the salience and reward value of specific faces by having OA and YA evaluate ingroup male White and outgroup Black and Asian faces. Participants perceived faces during fMRI and made trustworthiness evaluations after the scan. OA rated White and Black faces as more trustworthy than YA. OA had a stronger positive relationship between caudate activity and trustworthiness than YA when perceiving ingroup, but not outgroup, faces. Ingroup cues might intensify how trustworthiness is rewarding to OA, potentially reinforcing their overall positivity.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7895862 | PMC |
http://dx.doi.org/10.1080/13825585.2020.1809630 | DOI Listing |
Beijing Da Xue Xue Bao Yi Xue Ban
February 2025
Center for Digital Dentistry, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digi-tal Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Research Center of Engineering and Technology for Computerized Dentistry, Beijing 100081, China.
Objective: To develop an original-mirror alignment associated deep learning algorithm for intelligent registration of three-dimensional maxillofacial point cloud data, by utilizing a dynamic graph-based registration network model (maxillofacial dynamic graph registration network, MDGR-Net), and to provide a valuable reference for digital design and analysis in clinical dental applications.
Methods: Four hundred clinical patients without significant deformities were recruited from Peking University School of Stomatology from October 2018 to October 2022. Through data augmentation, a total of 2 000 three-dimensional maxillofacial datasets were generated for training and testing the MDGR-Net algorithm.
J Med Internet Res
January 2025
School of Business, Innovation and Sustainability, Halmstad University, Halmstad, Sweden.
Background: Recent advancements in artificial intelligence (AI) have changed the care processes in mental health, particularly in decision-making support for health care professionals and individuals with mental health problems. AI systems provide support in several domains of mental health, including early detection, diagnostics, treatment, and self-care. The use of AI systems in care flows faces several challenges in relation to decision-making support, stemming from technology, end-user, and organizational perspectives with the AI disruption of care processes.
View Article and Find Full Text PDFAm J Health Promot
January 2025
College of Social Work, University of South Carolina, Columbia, SC, USA.
Purpose: Artificially Intelligent (AI) chatbots have the potential to produce information to support shared prostate cancer (PrCA) decision-making. Therefore, our purpose was to evaluate and compare the accuracy, completeness, readability, and credibility of responses from standard and advanced versions of popular chatbots: ChatGPT-3.5, ChatGPT-4.
View Article and Find Full Text PDFAnesth Analg
January 2025
From the Department of Anesthesiology, University of Colorado School of Medicine, Aurora, Colorado.
This systematic review describes the available clinical practice guidelines (CPGs) for the anesthetic management of trauma and appraises the accessibility and quality of these resources. This review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A search was conducted across 8 databases (MEDLINE, Embase, Web of Science, CABI Digital Library, Global Index Medicus, SciELO, Google Scholar, and National Institute for Health and Care Excellence) for guidelines from 2010 to 2023.
View Article and Find Full Text PDFJMIR Hum Factors
January 2025
Institute of General Practice, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
Background: The internet is a key source of health information, but the quality of content from popular search engines varies, posing challenges for users-especially those with low health or digital health literacy. To address this, the "tala-med" search engine was developed in 2020 to provide access to high-quality, evidence-based content. It prioritizes German health websites based on trustworthiness, recency, user-friendliness, and comprehensibility, offering category-based filters while ensuring privacy by avoiding data collection and advertisements.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!