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Humans gain knowledge about threats not only from their own experiences but also from observing others' behavior. A neutral stimulus is associated with a threat stimulus for several times and the neutral stimulus will evoke fear responses, which is known as fear conditioning. When encountering a new event that is similar to one previously associated with a threat, one may feel afraid and produce fear responses. This is called fear generalization. Previous studies have mostly focused on fear conditioning and generalization based on direct learning, but few have explored how observational fear learning affects fear conditioning and generalization. To the best of our knowledge, no previous study has focused on the neural correlations of fear conditioning and generalization based on observational learning. In the present study, 58 participants performed a differential conditioning paradigm in which they learned the associations between neutral cues (i.e., geometric figures) and threat stimuli (i.e., electric shock). The learning occurred on their own (i.e., direct learning) and by observing other participant's responses (i.e., observational learning); the study used a within-subjects design. After each learning condition, a fear generalization paradigm was conducted by each participant independently while their behavioral responses (i.e., expectation of a shock) and electroencephalography (EEG) recordings or responses were recorded. The shock expectancy ratings showed that observational learning, compared to direct learning, reduced the differentiation between the conditioned threatening stimuli and safety stimuli and the increased shock expectancy to the generalization stimuli. The EEG indicated that in fear learning, threatening conditioned stimuli in observational and direct learning increased early discrimination (P1) and late motivated attention (late positive potential [LPP]), compared with safety conditioned stimuli. In fear generalization, early discrimination, late motivated attention, and orienting attention (alpha-event-related desynchronization [alpha-ERD]) to generalization stimuli were reduced in the observational learning condition. These findings suggest that compared to direct learning, observational learning reduces differential fear learning and increases the generalization of fear, and this might be associated with reduced discrimination and attentional function related to generalization stimuli.
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http://dx.doi.org/10.1016/j.pnpbp.2022.110650 | DOI Listing |
Nano Lett
September 2025
School of Materials and Chemistry, University of Shanghai for Science & Technology, Shanghai 200093, China.
Developing low-temperature gas sensors for parts per billion-level acetone detection in breath analysis remains challenging for non-invasive diabetes monitoring. We implement dual-defect engineering via one-pot synthesis of Al-doped WO nanorod arrays, establishing a W-O-Al catalytic mechanism. Al doping induces lattice strain to boost oxygen vacancy density by 31.
View Article and Find Full Text PDFPLoS One
September 2025
Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China.
MicroRNAs (miRNAs) are critical regulators of gene expression in cancer biology, yet their spatial dynamics within tumor microenvironments (TMEs) remain underexplored due to technical limitations in current spatial transcriptomics (ST) technologies. To address this gap, we present STmiR, a novel XGBoost-based framework for spatially resolved miRNA activity prediction. STmiR integrates bulk RNA-seq data (TCGA and CCLE) with spatial transcriptomics profiles to model nonlinear miRNA-mRNA interactions, achieving high predictive accuracy (Spearman's ρ > 0.
View Article and Find Full Text PDFPLoS One
September 2025
School of Psychology and Neuroscience, University of St Andrews, St Andrews, Fife, Scotland.
Many animal species live in multi-level societies regulated by complex patterns of dominance. Avoiding competition with dominant group-mates for resources such as food and mates is an important skill for subordinate individuals in these societies, if they wish to evade harassment and aggression. Chimpanzees (Pan troglodytes) are an example of such a species.
View Article and Find Full Text PDFBioinformatics
September 2025
Novo Nordisk Foundation Center for Protein Research, Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark.
Motivation: Representation learning has revolutionized sequence-based prediction of protein function and subcellular localization. Protein networks are an important source of information complementary to sequences, but the use of protein networks has proven to be challenging in the context of machine learning, especially in a cross-species setting.
Results: We leveraged the STRING database of protein networks and orthology relations for 1,322 eukaryotes to generate network-based cross-species protein embeddings.
IEEE Trans Neural Netw Learn Syst
September 2025
In industrial scenarios, semantic segmentation of surface defects is vital for identifying, localizing, and delineating defects. However, new defect types constantly emerge with product iterations or process updates. Existing defect segmentation models lack incremental learning capabilities, and direct fine-tuning (FT) often leads to catastrophic forgetting.
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