The capacity to trust wisely is a critical facilitator of success and prosperity, and it has been conjectured that people of higher intelligence are better able to detect signs of untrustworthiness from potential partners. In contrast, this article reports five trust game studies suggesting that reading trustworthiness of the faces of strangers is a modular process. Trustworthiness detection from faces is independent of general intelligence (Study 1) and effortless (Study 2). Pictures that include nonfacial features such as hair and clothing impair trustworthiness detection (Study 3) by increasing reliance on conscious judgments (Study 4), but people largely prefer to make decisions from this sort of pictures (Study 5). In sum, trustworthiness detection in an economic interaction is a genuine and effortless ability, possessed in equal amount by people of all cognitive capacities, but whose impenetrability leads to inaccurate conscious judgments and inappropriate informational preferences.
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http://dx.doi.org/10.1037/a0028930 | DOI Listing |
Sci Rep
January 2025
Department of Computer Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi, 75660, Pakistan.
Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, in high-stakes and complex domains like healthcare, the opaque nature of these models makes it challenging to trust predictions, particularly in uncertain cases. This sort of uncertainty can be crucial in medical image analysis; diabetic retinopathy is an example where even slight errors without an indication of confidence can have adverse impacts.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI, 53706, USA.
Identifying transitional states is crucial for understanding protein conformational changes that underlie numerous biological processes. Markov state models (MSMs), built from Molecular Dynamics (MD) simulations, capture these dynamics through transitions among metastable conformational states, and have demonstrated success in studying protein conformational changes. However, MSMs face challenges in identifying transition states, as they partition MD conformations into discrete metastable states (or free energy minima), lacking description of transition states located at the free energy barriers.
View Article and Find Full Text PDFJ Med Internet Res
December 2024
Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé - LIMICS, Inserm, Université Sorbonne Paris-Nord, Sorbonne Université, Paris, France.
Background: Artificial intelligence (AI) applied to real-world data (RWD; eg, electronic health care records) has been identified as a potentially promising technical paradigm for the pharmacovigilance field. There are several instances of AI approaches applied to RWD; however, most studies focus on unstructured RWD (conducting natural language processing on various data sources, eg, clinical notes, social media, and blogs). Hence, it is essential to investigate how AI is currently applied to structured RWD in pharmacovigilance and how new approaches could enrich the existing methodology.
View Article and Find Full Text PDFPregnancy Hypertens
December 2024
Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia; Monash Women's, Monash Health, Clayton, Victoria, Australia.
Objectives: Over the last decades, there has been a rising number of randomised controlled trials (RCTs) on pre-eclampsia. We investigated pre-eclampsia RCTs between 1987 and 2021 and reported on trustworthiness, risk of biases, p-values, transparency, and usefulness.
Methods: We searched PubMed for RCTs containing "pre-eclampsia" or "hypertensive disorders of pregnancy" in the title between 1987 and 2021.
Anal Chem
December 2024
Department of Analytical Chemistry, Physical Chemistry and Chemical Engineering, University of Alcalá, E-28802 Madrid, Spain.
Here, we present three-dimensional-printed dual-channel flow-through miniaturized devices (3D) with dual electrochemical detection (ED) integrating two working electrodes each in an in-channel configuration (3D-ED). Prussian Blue (PB) shell-gold nanoparticles ((PB)AuNP) core-based electrochemistry was chosen for selective hydrogen peroxide determination. 3D-ED devices exhibited impress stability, identical intrachannel and interchannel electrochemical performances, and excellent interdevice precision with values under 9%, revealing the reliability of the design and fabrication of the devices.
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