Facial expressions of emotion constitute a critical portion of our non-verbal social interactions. In addition, the identity of the individual displaying this expression is critical to these interactions as they embody the context in which these expressions will be interpreted. To identify any overlapping and/or unique brain circuitry involved in the processing of these two information streams in a laboratory setting, participants performed a working memory (WM) task (i.e., n-back) in which they were instructed to monitor either the expression (EMO) or the identity (ID) of the same set of face stimuli. Consistent with previous work, during both the EMO and ID tasks, we found a significant increase in activity in dorsolateral prefrontal cortex (DLPFC) supporting its generalized role in WM. Further, individuals that showed greater DLPFC activity during both tasks also showed increased amygdala activity during the EMO task and increased lateral fusiform gyrus activity during the ID task. Importantly, the level of activity in these regions significantly correlated with performance on the respective tasks. These findings provide support for two separate neural circuitries, both involving the DLPFC, supporting working memory for the faces and expressions of others.
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http://dx.doi.org/10.1016/j.neuroimage.2011.02.051 | DOI Listing |
NPJ Digit Med
January 2025
School of Psychological Sciences, University of Haifa, Haifa, Israel.
Cognitive training is a promising intervention for psychological distress; however, its effectiveness has yielded inconsistent outcomes across studies. This research is a pre-registered individual-level meta-analysis to identify factors contributing to cognitive training efficacy for anxiety and depression symptoms. Machine learning methods, alongside traditional statistical approaches, were employed to analyze 22 datasets with 1544 participants who underwent working memory training, attention bias modification, interpretation bias modification, or inhibitory control training.
View Article and Find Full Text PDFSci Rep
January 2025
Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, 100081, China.
Aspect Category Sentiment Analysis (ACSA) is a fine-grained sentiment analysis task aimed at predicting the sentiment polarity associated with aspect categories within a sentence.Most existing ACSA methods are based on a given aspect category to locate sentiment words related to it. When irrelevant sentiment words have semantic meaning for the given aspect category, it may cause the problem that sentiment words cannot be matched with aspect categories.
View Article and Find Full Text PDFJ Neurosci
January 2025
Department of Psychology, New York University
How the prefrontal cortex contributes to working memory remains controversial, as theories differ in their emphasis on its role in storing memories versus controlling their content. To adjudicate between these competing ideas, we tested how perturbations to the human (both sexes) lateral prefrontal cortex impact the storage and control aspects of working memory during a task that requires human subjects to allocate resources to memory items based on their behavioral priority. Our computational model made a strong prediction that disruption of this control process would counterintuitively improve memory for low-priority items.
View Article and Find Full Text PDFPsychol Sci
January 2025
Department of Psychology, University of Massachusetts Boston.
Most work on working memory development has children remember a set of items as well as they can. However, this approach sidesteps the , the integration of external information with memory. Indeed, adults prefer to use external resources (e.
View Article and Find Full Text PDFJMIR Form Res
January 2025
Department of Computer Science, University of California, Irvine, Irvine, CA, United States.
Background: Acute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pain levels effectively. Current methods of pain assessment often rely on subjective patient reports or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems.
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