Previous studies have shown that mere words, particularly affective words, can dampen emotional responses. However, the effect of affective labels on emotional responding in the long term is unknown. The authors examined whether repeated exposure to aversive images would lead to more reduction in autonomic reactivity a week later if the images were exposed with single-word labels than without labels. In Experiment 1, healthy individuals were exposed to pictures of disturbing scenes with or without labels on Day 1. On Day 8, the same pictures from the previous week were exposed, this time without labels. In Experiment 2, participants were spider fearful and were exposed to pictures of spiders. In both experiments, although repeated exposure to aversive images (without labels) led to long-term attenuation of autonomic reactivity, exposure plus affective labels, but not nonaffective labels, led to more attenuation than exposure alone. Thus, affective labels may help dampen emotional reactivity in both the short and long terms. Implications for exposure therapy and translational studies are discussed.
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http://dx.doi.org/10.1037/1528-3542.8.3.307 | DOI Listing |
Cogn Neurodyn
December 2024
School of Computer Science and Engineering, Changchun University of Technology, Changchun, 130102 Jilin People's Republic of China.
The utilization of Electroencephalography (EEG) for emotion recognition has emerged as the primary tool in the field of affective computing. Traditional supervised learning methods are typically constrained by the availability of labeled data, which can result in weak generalizability of learned features. Additionally, EEG signals are highly correlated with human emotional states across temporal, spatial, and spectral dimensions.
View Article and Find Full Text PDFBipolar depression is commonly accompanied by cognitive impairments. Transcranial direct current stimulation (tDCS) is emerging as a novel non-invasive treatment for bipolar depression. Given the portability and safety of tDCS, we developed a home-based protocol with real-time supervision.
View Article and Find Full Text PDFBrain Inform
December 2024
Brain Cognition and Intelligent Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen, Fujian, China.
EEG-based emotion recognition uses high-level information from neural activities to predict emotional responses in subjects. However, this information is sparsely distributed in frequency, time, and spatial domains and varied across subjects. To address these challenges in emotion recognition, we propose a novel neural network model named Temporal-Spectral Graph Convolutional Network (TSGCN).
View Article and Find Full Text PDFJ Affect Disord
December 2024
Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom.
Behav Res Methods
December 2024
National Research Centre for Foreign Language Education, Beijing Foreign Studies University, No. 2 Xisanhuan North Road, Beijing, 100089, Haidian District, China.
The present study introduces affective norms for a set of 880 German words rated by learners of German as a second language (L2), i.e., the Affective Norms for German as a Second Language (ANGL2).
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