Judgments of learning are most accurate when made at a delay from the initial encoding of the assessed material. A wealth of evidence suggests that this is because a delay encourages participants to base their predictions on cues retrieved from long-term memory, which are generally the most diagnostic of later memory performance. We investigated the hypothesis that different types of study techniques affect delayed JOL accuracy by influencing the accessibility of cues stored in long-term memory. In two experiments, we measured the delayed-JOL accuracy of participants who encoded semantically unrelated and weakly related word pairs through one of three study techniques: reading the pairs twice (study practice), generating keywords (elaborative encoding), or taking a cued-recall test with feedback (retrieval practice). We also measured the accessibility, utilization, and diagnostic quality of two long-term memory cues at the time of the delayed JOL: (a) retrieval of the target, and (b) noncriterial cues (retrieval of contextual details pertaining to the encoding of the target). We found that the accessibility of targets was positively associated with delayed-JOL accuracy. Further, we provide evidence that when study techniques enhance the accessibility of targets, they likewise enhance delayed-JOL accuracy.
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http://dx.doi.org/10.3390/jintelligence10040101 | DOI Listing |
Sci 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 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.
View Article and Find Full Text PDFJ 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 Biol
January 2025
Department of Biochemistry and Molecular Biology, Dalhousie University, Halifax, Canada.
The role of epigenetics and chromatin in the maintenance of postmitotic neuronal cell identities is not well understood. Here, we show that the histone methyltransferase Trithorax (Trx) is required in postmitotic memory neurons of the Drosophila mushroom body (MB) to enable their capacity for long-term memory (LTM), but not short-term memory (STM). Using MB-specific RNA-, ChIP-, and ATAC-sequencing, we find that Trx maintains homeostatic expression of several non-canonical MB-enriched transcripts, including the orphan nuclear receptor Hr51, and the metabolic enzyme lactate dehydrogenase (Ldh).
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.
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