The hippocampus has been implicated in integrating information across separate events in support of mnemonic generalizations. These generalizations may be underpinned by processes at both encoding (linking similar information across events) and retrieval ("on-the-fly" generalization). However, the relative contribution of the hippocampus to encoding- and retrieval-based generalizations is poorly understood. Using fMRI in humans, we investigated the hippocampal role in gradually learning a set of spatial discriminations and subsequently generalizing them in an acquired equivalence task. We found a highly significant correlation between individuals' performance on a generalization test and hippocampal activity during the test, providing evidence that hippocampal processes support on-the-fly generalizations at retrieval. Within the same hippocampal region there was also a correlation between activity during the final stage of learning (when all associations had been learnt but no generalization was required) and subsequent generalization performance. We suggest that the hippocampus spontaneously retrieves prior events that share overlapping features with the current event. This process may also support the creation of generalized representations during encoding. These findings are supportive of the view that the hippocampus contributes to both encoding- and retrieval-based generalization via the same basic mechanism; retrieval of similar events sharing common features. © 2016 The Authors Hippocampus Published by Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/hipo.22688 | DOI Listing |
Comput Med Imaging Graph
January 2025
College of Medicine and Biological Information Engineering, Northeastern University, 110819, China. Electronic address:
With the increasing popularity of medical imaging and its expanding applications, posing significant challenges for radiologists. Radiologists need to spend substantial time and effort to review images and manually writing reports every day. To address these challenges and speed up the process of patient care, researchers have employed deep learning methods to automatically generate medical reports.
View Article and Find Full Text PDFQ J Exp Psychol (Hove)
December 2024
School of Psychology, University of Kent, Canterbury, UK.
There is evidence to suggest that variations in difficulty during learning can moderate long-term retention. However, the direction of this effect is under contention throughout the literature. According to both the Desirable Difficulties Framework (DDF) and the Retrieval Effort Hypothesis (REH), difficulty (thus relative effort) during retrieval-based learning can help achieve superior long-term retention.
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September 2024
Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
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June 2024
School of Medicine, University of Tübingen, Tübingen, Germany.
Background: Diagnostic errors pose significant health risks and contribute to patient mortality. With the growing accessibility of electronic health records, machine learning models offer a promising avenue for enhancing diagnosis quality. Current research has primarily focused on a limited set of diseases with ample training data, neglecting diagnostic scenarios with limited data availability.
View Article and Find Full Text PDFJ Exp Psychol Gen
July 2024
Department of Psychology, Western Kentucky University.
Practicing retrieval is a potent learning enhancer. Theoretical accounts of the testing effect generally suggest that the magnitude of the testing effect is dependent on retrieval practice performance, such that conditions that promote better retrieval practice performance should result in a greater testing effect. Empirical evidence, however, has been mixed.
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