AI Article Synopsis
Article Abstract
Previous research has shown attentional biases in children with autism spectrum disorders (ASD) when processing distressing information. This study examined these attentional patterns as a function of the type of stimulus (scenes and faces) and the stimulus valence (happy, sad, threatening, neutral) using a within-subject design. A dot-probe was applied to ASD (n = 24) and typically developing (TD) children (n = 24). Results showed no differences between the groups for happy and sad stimuli. Critically, ASD children showed an attentional bias toward threatening scenes but away from threatening faces. Thus, the type of stimuli modulated the direction of attentional biases to distressing information in ASD children. These results are discussed in the framework of current theories on cognitive and emotional processing in ASD.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s10803-018-3847-8 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Attention-Based Interpretable Multiscale Graph Neural Network for MOFs.
J Chem Theory Comput
January 2025
The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
Metal-organic frameworks (MOFs) hold great potential in gas separation and storage. Graph neural networks (GNNs) have proven effective in exploring structure-property relationships and discovering new MOF structures. Unlike molecular graphs, crystal graphs must consider the periodicity and patterns.
View Article and Find Full Text PDFHumans excel at applying learned behavior to unlearned situations. A crucial component of this generalization behavior is our ability to compose/decompose a whole into reusable parts, an attribute known as compositionality. One of the fundamental questions in robotics concerns this characteristic: How can linguistic compositionality be developed concomitantly with sensorimotor skills through associative learning, particularly when individuals only learn partial linguistic compositions and their corresponding sensorimotor patterns? To address this question, we propose a brain-inspired neural network model that integrates vision, proprioception, and language into a framework of predictive coding and active inference on the basis of the free-energy principle.
View Article and Find Full Text PDFRevisiting the in-plane and in-channel diffusion of lithium ions in a solid-state electrolyte at room temperature through neural network-assisted molecular dynamics simulations.
Phys Chem Chem Phys
January 2025
Guizhou Provincial Key Laboratory of Computing and Network Convergence, School of Information, Guizhou University of Finance and Economics, Guiyang, Guizhou 550025, P. R. China.
Developing superionic conductor (SIC) materials offers a promising pathway to achieving high ionic conductivity in solid-state electrolytes (SSEs). The LiGePS (LGPS) family has received significant attention due to its remarkable ionic conductivity among various SIC materials. molecular dynamics (AIMD) simulations have been extensively used to explore the diffusion behavior of Li ions in LiGePS.
View Article and Find Full Text PDFPatterns of Preoperative Tumor Markers Can Predict Resectability and Prognosis of Peritoneal Metastases: A Clustering Analysis.
Ann Surg Oncol
January 2025
Department of Surgical Sciences, Colorectal Surgery, Uppsala University, Uppsala, Sweden.
Background: Prediction of open-close and long-term outcome is challenging in patients undergoing cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC). Prognostic scores often include factors not known at baseline. Therefore, we aimed to analyze whether patterns of preoperative tumor markers could aid in prediction of open-close surgery and outcome in patients with pseudomyxoma peritonei (PMP) or colorectal peritoneal metastases (PM).
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!