Individuals with autism spectrum disorder (ASD) have been found to view social scenes differently compared to typically developing (TD) peers, but results can vary depending on context and age. We used eye-tracking in children and adults (age 6-63) to assess allocation of visual attention in a dynamic social orientation paradigm previously used only in younger children. The ASD group (n = 94) looked less at the actor's face compared to TD (n = 38) when they were engaged in activity (mean percentage of looking time, ASD = 30.7% vs TD = 34.9%; Cohen's d = 0.56; p value < 0.03) or looking at a moving toy (24.5% vs 33.2%; d = 0.65; p value < 0.001). Findings indicate that there are qualitative differences in allocation of visual attention to social stimuli across ages in ASD.ClinicalTrials.gov identifier: NCT02668991.
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http://dx.doi.org/10.1007/s10803-021-05279-z | DOI Listing |
Int Arch Occup Environ Health
December 2024
Social Science and Occupational Health Research Group, National Institute of Occupational Safety and Health, Nagao 6-21-1, Tama-Ku, Kawasaki, 214-8585, Japan.
Purpose: We estimated the static and dynamic effects of promotion to managerial positions on mental health and satisfaction using the recent estimation method; we addressed the gap in extant research by considering the heterogeneity in treatment timing to reconfirm evidence regarding the causal effects of promotion.
Methods: We used a modified estimation method of two-way fixed effects recommended by (Callaway and Sant'Anna, J Econom 225:200-230, 2021). To check for robustness, we compared the analyses using propensity sample matching, an alternative treatment timing heterogeneity-robust analysis, and a normal two-way fixed effects event study.
Eat Weight Disord
December 2024
Department of Dynamic and Clinical Psychology, and Health Studies, Faculty of Medicine and Psychology, Sapienza University of Rome, Rome, Italy.
Background: Eating disorders (EDs) are among the least studied mental disorders in individuals at clinical high risk for psychosis (CHR-P). The primary aim (a) of this systematic review and meta-analysis was to identify factors predicting ED diagnoses in CHR-P individuals. The secondary aim (b) was providing a comprehensive clinical description of individuals with both CHR-P and EDs/ED-related symptoms.
View Article and Find Full Text PDFCommunity Ment Health J
December 2024
School of Psychology, University of East London, London, UK.
The causal explanations voice-hearers have for their voice-hearing experiences may influence affective outcome and clinical decision making. Voice-hearers endorse a range of explanatory models, which do not consistently align with explanatory models held by healthcare professionals. Research has established that explanatory models for voice-hearing are dynamic rather than fixed, and are influenced by internal beliefs and motivations, culture, and contact with significant others.
View Article and Find Full Text PDFInfect Dis Rep
December 2024
Infectious Diseases Research Center of Laval University, CHU de Québec-Université Laval (CHUL Hospital), Quebec City, QC G1V 4G2, Canada.
During the pandemic, client-facing workers were perceived to be at greater risk of SARS-CoV-2 infection. This study investigated the risk factors for SARS-CoV-2 infection among a cohort of 304 retail workers in the Quebec City metropolitan area. After providing consent, participants were interviewed to gather information on demographic, socioeconomic, behavioural, and occupational variables.
View Article and Find Full Text PDFBiomimetics (Basel)
December 2024
Sussex AI, School of Engineering and Informatics, University of Sussex, Brighton BN1 9QJ, UK.
Visual navigation is a key capability for robots and animals. Inspired by the navigational prowess of social insects, a family of insect-inspired route navigation algorithms-familiarity-based algorithms-have been developed that use stored panoramic images collected during a training route to subsequently derive directional information during route recapitulation. However, unlike the ants that inspire them, these algorithms ignore the sequence in which the training images are acquired so that all temporal information/correlation is lost.
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