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Previous studies have suggested that emotional primes, presented as visual stimuli, influence face memory (e.g., encoding and recognition). However, due to stimulus-associated issues, whether emotional primes affect face encoding when the priming stimuli are presented in an auditory modality remains controversial. Moreover, no studies have investigated whether the effects of emotional auditory primes are maintained in later stages of face memory, such as face recognition. To address these issues, participants in the present study were asked to memorize angry and neutral faces. The faces were presented after a simple nonlinguistic interjection expressed with angry or neutral prosodies. Subsequently, participants completed an old/new recognition task in which only faces were presented. Event-related potential (ERP) results showed that during the encoding phase, all faces preceded by an angry vocal expression elicited larger N170 responses than faces preceded by a neutral vocal expression. Angry vocal expression also enhanced the late positive potential (LPP) responses specifically to angry faces. In the subsequent recognition phase, preceding angry vocal primes reduced early LPP responses to both angry and neutral faces and late LPP responses specifically to neutral faces. These findings suggest that the negative emotion of auditory primes influenced face encoding and recognition.
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http://dx.doi.org/10.1016/j.ijpsycho.2022.11.006 | DOI Listing |
Elife
September 2025
Center for Mind and Brain, University of California, Davis, Davis, United States.
Visual search relies on the ability to use information about the target in working memory to guide attention and make target-match decisions. The 'attentional' or 'target' template is thought to be encoded within an inferior frontal junction (IFJ)-visual attentional network. While this template typically contains veridical target features, behavioral studies have shown that target-associated information, such as statistically co-occurring object pairs, can also guide attention.
View Article and Find Full Text PDFPNAS Nexus
September 2025
Department of Chemical and Biomedical Engineering, University of Missouri, Columbia, MO 65211, USA.
DNA data storage is a promising alternative to conventional storage due to high density, low energy consumption, durability, and ease of replication. While information can be encoded into DNA via synthesis, high costs and the lack of rewriting capability limit its applications beyond archival storage. Emerging "hard drive" strategies seek to encode data onto universal DNA templates without de novo synthesis, using methods such as DNA nanostructures and base modifications.
View Article and Find Full Text PDFNeuropsychologia
September 2025
Icelandic Vision Lab, Department of Psychology, University of Iceland, Saemundargata 2, 102, Reykjavik, Iceland.
Developmental dyslexia is a disorder marked by difficulties in reading, spelling, and connecting sounds to written language. The high-level visual dysfunction hypothesis suggests these difficulties may partially arise from abnormalities in high-level visual cognition such as the ability to integrate visual input for higher-order cognitive functions such as reading. Here we examined adult (mean age = 35) dyslexic readers' neural functioning as they recognized identities of nonlinguistic visual objects, specifically houses and faces.
View Article and Find Full Text PDFEpilepsy Behav
September 2025
Department of Clinical and Experimental Epilepsy, University College London, London the United Kingdom of Great Britain and Northern Ireland; MRI Unit, Chalfont Centre for Epilepsy, Bucks, the United Kingdom of Great Britain and Northern Ireland. Electronic address:
Memory functional MRI (fMRI) has been used to explore cognitive processing in people with refractory temporal lobe epilepsy (TLE) to predict memory decline after anterior temporal lobe resection (ATLR). Traditional studies employed univariate analysis (UVA), focusing on isolated voxel activity in mesial temporal regions. By contrast, multivariate pattern analysis (MVPA), examines distributed activity patterns , offering deeper insight into neural networks supporting cognitive functions.
View Article and Find Full Text PDFBrief Bioinform
September 2025
Beijing Institute of Mathematical Sciences and Applications (BIMSA), Beijing 101408, P. R. China.
With the rapid development of genomic sequencing technologies, there is an increasing demand for efficient and accurate sequence analysis methods. However, existing methods face challenges in handling long, variable-length sequences and large-scale datasets. To address these issues, we propose a novel encoding method-Energy Entropy Vector (EEV).
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