Previous research demonstrates that people with 22q11.2 deletion syndrome (22q11DS) have social and interpersonal skill deficits. However, the basis of this deficit is unknown. This study examined, for the first time, how people with 22q11DS process emotional face stimuli using visual scanpath technology. The visual scanpaths of 17 adolescents and age/gender matched healthy controls were recorded while they viewed face images depicting one of seven basic emotions (happy, sad, surprised, angry, fear, disgust and neutral). Recognition accuracy was measured concurrently. People with 22q11DS differed significantly from controls, displaying visual scanpath patterns that were characterised by fewer fixations and a shorter scanpath length. The 22q11DS group also spent significantly more time gazing at the mouth region and significantly less time looking at eye regions of the faces. Recognition accuracy was correspondingly impaired, with 22q11DS subjects displaying particular deficits for fear and disgust. These findings suggest that 22q11DS is associated with a maladaptive visual information processing strategy that may underlie affect recognition accuracy and social functioning deficits in this group.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.psychres.2009.06.007DOI Listing

Publication Analysis

Top Keywords

recognition accuracy
12
22q112 deletion
8
deletion syndrome
8
people 22q11ds
8
visual scanpath
8
fear disgust
8
22q11ds
6
visual
5
visual scanning
4
scanning faces
4

Similar Publications

Emotion recognition plays a crucial role in brain-computer interfaces (BCI) which helps to identify and classify human emotions as positive, negative, and neutral. Emotion analysis in BCI maintains a substantial perspective in distinct fields such as healthcare, education, gaming, and human-computer interaction. In healthcare, emotion analysis based on electroencephalography (EEG) signals is deployed to provide personalized support for patients with autism or mood disorders.

View Article and Find Full Text PDF

The increasing adoption of wearable technologies highlights the potential of electroencephalogram (EEG) signals for biometric recognition. However, the intrinsic variability in cross-session EEG data presents substantial challenges in maintaining model stability and reliability. Moreover, the diversity within single-task protocols complicates achieving consistent and generalized model performance.

View Article and Find Full Text PDF

Objective: Breast cancer stands as the most prevalent form of cancer among women globally. This heterogeneous disease exhibits varying clinical behaviors. The stratification of breast cancer patients into risk groups, determined by their metastasis and survival outcomes, is pivotal for tailoring personalized treatments and therapeutic interventions.

View Article and Find Full Text PDF

Background: Due to their young age and limited ability to communicate, pediatric patients in internal medicine wards are at risk of nursing assessment errors, which can lead to adverse events and disputes.

Objective: To explore the application effect of modified pediatric early warning score (PEWS) in the early identification of critically ill children in pediatric general wards.

Design: A single-blind, two-arm randomized controlled trial was conducted using a convenience sampling method.

View Article and Find Full Text PDF

Introduction: With the advent of technologies such as deep learning in agriculture, a novel approach to classifying wheat seed varieties has emerged. However, some existing deep learning models encounter challenges, including long processing times, high computational demands, and low classification accuracy when analyzing wheat seed images, which can hinder their ability to meet real-time requirements.

Methods: To address these challenges, we propose a lightweight wheat seed classification model called LWheatNet.

View Article and Find Full Text PDF

Want 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!