Recall of a studied item and retrieval of its encoding context (source memory) both depend on recollection of qualitative information about the study episode. This study investigated whether recall and source memory engage overlapping neural regions. Participants (n = 18) studied a series of words, which were presented either to the left or right of fixation. fMRI data were collected during a subsequent test phase in which three-letter word-stems were presented, two thirds of which could be completed by a study item. Instructions were to use each stem as a cue to recall a studied word and, when recall was successful, to indicate the word's study location. When recall failed, the stem was to be completed with the first word to come to mind. Relative to stems for which recall failed, word-stems eliciting successful recall were associated with enhanced activity in a variety of cortical regions, including bilateral parietal, posterior midline, and parahippocampal cortex. Activity in these regions was enhanced when recall was accompanied by successful rather than unsuccessful source retrieval. It is proposed that the regions form part of a "recollection network" in which activity is graded according to the amount of information retrieved about a study episode.
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http://dx.doi.org/10.1162/jocn_a_00202 | DOI Listing |
J Exp Psychol Gen
January 2025
Institute for Mind and Biology, University of Chicago.
Individual differences in working memory predict a wide range of cognitive abilities. However, little research has been done on whether working memory continues to predict task performance after repetitive learning. Here, we tested whether working memory ability continued to predict long-term memory (LTM) performance for picture sequences even after participants showed massive learning.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Electrical Engineering, College of Engineering, Taif University, Taif, Saudi Arabia.
Modernizing power systems into smart grids has introduced numerous benefits, including enhanced efficiency, reliability, and integration of renewable energy sources. However, this advancement has also increased vulnerability to cyber threats, particularly False Data Injection Attacks (FDIAs). Traditional Intrusion Detection Systems (IDS) often fall short in identifying sophisticated FDIAs due to their reliance on predefined rules and signatures.
View Article and Find Full Text PDFWe have previously identified that infection induces a unique form of myeloid training that protects male but not female mice from high fat diet induced disease. Here we demonstrate that ovarian derived hormones account for this sex specific difference. Ovariectomy of females prior to infection permits metabolic reprogramming of the myeloid lineage, with BMDM exhibiting carbon source flexibility for cellular respiration, and mice protected from systemic metabolic disease.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Institute of Health and Rehabilitation Science, Xi'an Jiaotong University, Xi'an, 710049 Shaanxi China.
The locus coeruleus (LC), as the primary source of norepinephrine (NE) in the brain, is central to modulating cognitive and behavioral processes. This review synthesizes recent findings to provide a comprehensive understanding of the LC-NE system, highlighting its molecular diversity, neurophysiological properties, and role in various brain functions. We discuss the heterogeneity of LC neurons, their differential responses to sensory stimuli, and the impact of NE on cognitive processes such as attention and memory.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Artificial intelligence (AI), particularly through advanced large language model (LLM) technologies, is reshaping coal mine safety assessment methods with its powerful cognitive capabilities. Given the dynamic, multi-source, and heterogeneous characteristics of data in typical mining scenarios, traditional manual assessment methods are limited in their information processing capacity and cost-effectiveness. This study addresses these challenges by proposing an embodied intelligent system for mine safety assessment based on multi-level large language models (LLMs) for multi-source sensor data.
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