The present research investigated the nature of the inferences and decisions young children make about informants with a prior history of inaccuracies. Across three experiments, 3- and 4-year-olds (total N = 182) reacted to previously inaccurate informants who offered testimony in an object-labeling task. Of central interest was children's willingness to accept information provided by an inaccurate informant in different contexts of being alone, paired with an accurate informant, or paired with a novel (neutral) informant. Experiments 1 and 2 showed that when a previously inaccurate informant was alone and provided testimony that was not in conflict with the testimony of another informant, children systematically accepted the testimony of that informant. Experiment 3 showed that children accepted testimony from a neutral informant over an inaccurate informant when both provided information, but accepted testimony from an inaccurate informant rather than seeking information from an available neutral informant who did not automatically offer information. These results suggest that even though young children use prior history of accuracy to determine the relative reliability of informants, they are quite willing to trust the testimony of a single informant alone, regardless of whether that informant had previously been reliable.
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http://dx.doi.org/10.1111/desc.12134 | DOI Listing |
BMC Womens Health
January 2025
School of Nursing, Fudan University, 305 Fenglin Road, Shanghai, 200032, China.
Purpose: This scoping review aims to summarize online health information seeking (OHIS) behavior among breast cancer patients and survivors, identify research gaps, and offer insights for future studies.
Methods: Following Arksey and O'Malley's framework, we conducted a review across PubMed, Web of Science, CINAHL, MEDLINE, Cochrane, Embase, CNKI, Wanfang Data, and SinoMed, covering literature from 1 January 2014 to 13 August 2023. A total of 1,368 articles were identified, with 33 meeting the inclusion criteria.
J Imaging Inform Med
January 2025
College of Computer, Chongqing University, No. 55 Daxuecheng South Rd, Shapingba, 401331, Chongqing, China.
Convolutional neural networks (CNNs) have become indispensable to medical image diagnosis research, enabling the automated differentiation of diseased images from extensive medical image datasets. Due to their efficacy, these methods raise significant privacy concerns regarding patient images and diagnostic models. To address these issues, some researchers have explored privacy-preserving medical image diagnosis schemes using fully homomorphic encryption (FHE).
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Treeline Ecological Research, 21551 Twp Rd 520, Sherwood Park, Alberta, T8E 1E3, Canada.
Based on analysis of documents obtained in public databases and under freedom of information requests, this study assessed the Alberta Energy Regulator's (AER) monitoring and management of bitumen tailings spills. The AER's claims of no environmental impacts at any tailings spills lack corroborative environmental data. Claims of perfect spill recovery in 75% of tailings spills are not supported by credible evidence.
View Article and Find Full Text PDFPers Soc Psychol Bull
January 2025
Negotiation, Organizations & Markets Unit, Harvard Business School, Harvard University, Boston, Massachusetts.
Anonymization of job applicant resumes is a recommended strategy to increase diversity in organizations, but large-scale tests have shown mixed results. We consider decision-makers' social dominance orientation (SDO), a measure of anti-egalitarianism/endorsement of group-based hierarchy, to illustrate the limits of anonymization. Across four pre-registered studies ( = 3,150), we show that (a) lower SDO individuals are less likely to hire individuals from underrepresented groups when job materials are anonymized and (b) they are more likely to opt into using anonymization.
View Article and Find Full Text PDFSci Rep
January 2025
Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara, 630-0192, Japan.
Deep learning-based image segmentation has allowed for the fully automated, accurate, and rapid analysis of musculoskeletal (MSK) structures from medical images. However, current approaches were either applied only to 2D cross-sectional images, addressed few structures, or were validated on small datasets, which limit the application in large-scale databases. This study aimed to validate an improved deep learning model for volumetric MSK segmentation of the hip and thigh with uncertainty estimation from clinical computed tomography (CT) images.
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