Sexual minority individuals experience higher prevalence of major depression and more frequent depressive symptoms compared to heterosexual individuals. Although existing theories have suggested cognitive mechanisms that may explain these disparities, empirical tests are limited by a reliance on cross-sectional designs, self-reported measures, and nonprobability samples. We analyzed data from a longitudinal, population-based study of young adults ( = 1,065; = 497 sexual minority) who completed validated measures of depressive symptoms over a 3-year period; at Wave 2, participants completed the self-referent encoding task, a behavioral task assessing self-schemas and information processing biases. Self-schemas were measured with the drift rate, which was estimated via the composite of endorsement of positive or negative words as self-referential (or not) and the reaction time for these decisions. Information processing biases were operationalized as the total number of negative words that were both endorsed as self-referential and recalled after the task, divided by the total number of words endorsed and recalled. Compared to heterosexuals, sexual minorities displayed significantly higher negative self-schemas and recalled a significantly higher proportion of negative words endorsed as self-referential, relative to total number of words. In turn, these differences in self-schemas and information processing biases mediated the sexual orientation disparity in depressive symptoms. Moreover, among sexual minorities, perceived discrimination predicted greater negative self-schemas and information processing biases, which mediated the prospective association between discrimination and depressive symptoms. These findings provide the strongest evidence to date for cognitive risk factors that underlie sexual orientation disparities in depression, highlighting potential intervention targets. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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http://dx.doi.org/10.1037/abn0000823 | DOI Listing |
Nanotechnology
December 2024
IEMN, avenue Poincaré, Villeneuve-d'Ascq, Hauts-de-France, 59652, FRANCE.
We report here the reversibility and bistability of the switching behavior in an azobenzene derivative induced by the bias applied by a Scanning-Tunneling Microscopy (STM) tip, at low temperature and in ultra-high vacuum environment. This cis-to-trans and trans-to-cis switching were observed during STM imaging in either polarity at +2V or -2V, on a sub-second time scale. This results in a blinking effect visible on STM images, corresponding to the reversible switching of the azobenzene molecule under the applied STM bias through an electric field induced process.
View Article and Find Full Text PDFInverse design (ID) is a computational method that systematically explores a design space to find optimal device geometries based on specific performance criteria. In silicon photonics, ID often generates design features that degrade significantly due to the fabrication process, limiting the applicability of these devices in scalable fabrication. We demonstrate a solution to this performance degradation through fabrication-aware inverse design (FAID), integrating lithography models for deep-ultraviolet (DUV) lithography and electron-beam lithography (EBL) into the shape optimization approach of ID.
View Article and Find Full Text PDFAbstractTheoretical studies from diverse areas of population biology have shown that demographic stochasticity can substantially impact evolutionary dynamics in finite populations, including scenarios where traits that are disfavored by natural selection can nevertheless increase in frequency through the course of evolution. Here, we analytically describe the eco-evolutionary dynamics of finite populations from demographic first principles. We investigate how noise-induced effects can alter the evolutionary fate of populations in which total population size may vary stochastically over time.
View Article and Find Full Text PDFMem Cognit
December 2024
Faculty of Human Cultures and Sciences, Fukuyama University, Hiroshima, Japan.
This study examined informative and uninformative anchoring effects on judgments of learning (JOLs), focusing on two hypotheses: the optimistic/pessimistic and differential-scaling hypotheses. The optimistic/pessimistic hypothesis states that anchoring information changes subjective confidence in memory, whereas the differential-scaling hypothesis states that anchoring information elicits a scaling bias in the conversion process of subjective internal confidence into scale JOLs (i.e.
View Article and Find Full Text PDFFront Artif Intell
December 2024
Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
In response to the increasing significance of artificial intelligence (AI) in healthcare, there has been increased attention - including a Presidential executive order to create an AI Safety Institute - to the potential threats posed by AI. While much attention has been given to the conventional risks AI poses to cybersecurity, and critical infrastructure, here we provide an overview of some unique challenges of AI for the medical community. Above and beyond obvious concerns about vetting algorithms that impact patient care, there are additional subtle yet equally important things to consider: the potential harm AI poses to its own integrity and the broader medical information ecosystem.
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