The present study examined whether individuals experienced the same cognitive advantage for online self-relevant information (nickname) as that experienced for information encountered in real life (real name) through two experiments at both the behavioural and neural levels (event-related potential, ERP). The results indicated that individuals showed the same cognitive advantage for nicknames and real names. At the behavioural level, a nickname was detected as quickly as the real name, and both were detected faster than a famous name; at the neural level, the P300 potential elicited by one's nickname was similar to that elicited by one's real name, and both the P300 amplitudes and latencies were larger and more prolonged than those elicited by other name stimuli. These results not only confirmed the cognitive advantage for one's own nickname and indicated that this self-advantage can be extended to online information, but also indicated that the virtual self could be integrated into the self and further expanded individuals' self-concept.
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http://dx.doi.org/10.1038/s41598-020-77538-5 | DOI Listing |
Trends Cogn Sci
January 2025
Department of Psychology, Humboldt University Berlin, Berlin, Germany; Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, USA.
Creative problem solving and memory are inherently intertwined: memory accesses existing knowledge while creativity enhances it. Recent studies show that insights often accompanying creative solutions enhance long-term memory. This insight memory advantage (IMA) is explained by the 'insight as prediction error (PE)' hypothesis which states that insights arise from PEs updating predictive solution models and thereby enhancing memory.
View Article and Find Full Text PDFBiomimetics (Basel)
December 2024
Institute of AI for Industries, Chinese Academy of Sciences Nanjing, 168, Tianquan Road, Nanjing 211135, China.
In this study, we designed a biomimetic artificial visual system (AVS) inspired by biological visual system that can process RGB images. Our approach begins by mimicking the photoreceptor cone cells to simulate the initial input processing followed by a learnable dendritic neuron model to replicate ganglion cells that integrate outputs from bipolar and horizontal cell simulations. To handle multi-channel integration, we utilize a nonlearnable dendritic neuron model to simulate the lateral geniculate nucleus (LGN), which consolidates outputs across color channels, an essential function in biological multi-channel processing.
View Article and Find Full Text PDFBioengineering (Basel)
December 2024
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, China.
With the aging population rising, the decline in spatial cognitive ability has become a critical issue affecting the quality of life among the elderly. Electroencephalogram (EEG) signal analysis presents substantial potential in spatial cognitive assessments. However, conventional methods struggle to effectively classify spatial cognitive states, particularly in tasks requiring multi-class discrimination of pre- and post-training cognitive states.
View Article and Find Full Text PDFR Soc Open Sci
January 2025
Department of General Psychology and Cognitive Neuroscience, Friedrich Schiller University, Jena, Germany.
Individuals can strongly vary in their ability to process face identity. Understanding the mechanisms driving these differences is important for theoretical development, and in clinical and applied contexts. Here we investigate the role of face-space properties in relation to individual face identity processing skills.
View Article and Find Full Text PDFHeliyon
January 2025
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China.
Objective And Rationale: Children's clinical pain phenotypes are complex, and there is a lack of objective biological diagnostic markers and cognitive patterns. Detecting physiological signals through wearable devices simplifies disease diagnosis and holds the potential for remote medical applications.
Method And Results: This research established a pain recognition model based on AI skin potential (SP) signal analysis.
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