The influence of Prediction Errors (PEs) on episodic memory has generated growing empirical and theoretical interest. This review explores how the relationship between PE and memory may evolve throughout lifespan. Drawing upon the predictive processing framework and the Predictive, Interactive Multiple Memory System (PIMMS) model in particular, the paper highlights the hierarchical organization of memory systems and the interaction between top-down predictions and bottom-up sensory input, proposing that PEs promote synaptic change and improve encoding and consolidation processes. We discuss the neuroscientific mechanisms underlying PE-driven memory enhancement, focusing on the involvement of the hippocampus, the entorhinal cortex-hippocampus pathway, and the noradrenergic sympathetic system. Recognizing the divergent trajectories of episodic and semantic memory across the lifespan is crucial when examining the effects of PEs on memory. This review underscores the heterogeneity of memory processes and neurocognitive mechanisms underlying PE-driven memory enhancement across age. Future research is suggested to directly compare neural networks involved in learning from PEs across different age groups and to contribute to a deeper understanding of PE-driven learning across age.
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http://dx.doi.org/10.1016/j.neubiorev.2023.105462 | DOI Listing |
Geroscience
January 2025
Department of Neurology, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea.
Background: Superagers, older adults with exceptional cognitive abilities, show preserved brain structure compared to typical older adults. We investigated whether superagers have biologically younger brains based on their structural integrity.
Methods: A cohort of 153 older adults (aged 61-93) was recruited, with 63 classified as superagers based on superior episodic memory and 90 as typical older adults, of whom 64 were followed up after two years.
Brain Inform
January 2025
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.
Cognitive resilience (CR) describes the phenomenon of individuals evading cognitive decline despite prominent Alzheimer's disease neuropathology. Operationalization and measurement of this latent construct is non-trivial as it cannot be directly observed. The residual approach has been widely applied to estimate CR, where the degree of resilience is estimated through a linear model's residuals.
View Article and Find Full Text PDFNat Mater
January 2025
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
Machine learning algorithms have proven to be effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is essential. Here we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a cryogenic in-memory computing scheme based on the coexistence of a chiral edge state and a topological surface state.
View Article and Find Full Text PDFBehav Res Methods
January 2025
Centre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Taipa, 999078, Macau, China.
The autobiographical implicit association test (aIAT) is an approach of memory detection that can be used to identify true autobiographical memories. This study incorporates mouse-tracking (MT) into aIAT, which offers a more robust technique of memory detection. Participants were assigned to mock crime and then performed the aIAT with MT.
View Article and Find Full Text PDFNPJ 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.
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