98%
921
2 minutes
20
The present study explored whether individual differences in implicit learning were related to the incorporation of waking events into dreams. Participants (N = 60) took part in a sequence learning task, a measure of implicit learning ability. They were then asked to keep a record of their waking experiences (personally significant events [PSEs]/major concerns), as well as their nightly dreams for a week. Of these, the responses of 51 participants were suitable for further analysis in which participants themselves and three independent judges rated the correlation between waking events and dreams of the same day. Implicit learning ability was found to significantly correlate with the incorporation of PSEs into dreams. The present results may lend support to the Horton and Malinowski autobiographical memory (AM) model, which accounts for the activation of memories in dreams as a reflection of sleep-dependent memory consolidation processes that focusses in particular on the hyperassociative nature of AM during sleep.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1111/jsr.13171 | DOI Listing |
Front Neural Circuits
September 2025
Neuroscience Institute, National Research Council (CNR), Pisa, Italy.
Neural circuits sculpt their structure and modify the strength of their connections to effectively adapt to the external stimuli throughout life. In response to practice and experience, the brain learns to distinguish previously undetectable stimulus features recurring in the external environment. The unconscious acquisition of improved perceptual abilities falls into a form of implicit learning known as perceptual learning.
View Article and Find Full Text PDFJ Neurophysiol
September 2025
School of Psychological and Cognitive Sciences, Peking University, Beijing, China.
Limiting cognitive resources negatively impacts motor learning, but its cognitive mechanism is still unclear. Previous studies failed to differentiate its effect on explicit (or cognitive) and implicit (or procedural) aspects of motor learning. Here, we designed a dual-task paradigm requiring participants to simultaneously perform a visual working memory task and a visuomotor rotation adaptation task to investigate how cognitive load differentially impacted explicit and implicit motor learning.
View Article and Find Full Text PDFIEEE Trans Biomed Eng
September 2025
Objective: Diffusion magnetic resonance imaging (dMRI) often suffers from low spatial and angular resolution due to inherent limitations in imaging hardware and system noise, adversely affecting the accurate estimation of microstructural parameters with fine anatomical details. Deep learning-based super-resolution techniques have shown promise in enhancing dMRI resolution without increasing acquisition time. However, most existing methods are confined to either spatial or angular super-resolution, disrupting the information exchange between the two domains and limiting their effectiveness in capturing detailed microstructural features.
View Article and Find Full Text PDFNEJM AI
September 2025
Department of Biomedical Informatics, Harvard Medical School, Boston.
Over the past two decades, network medicine (NM) has evolved to help define disease mechanisms, identify drug targets, and guide increasingly precise therapies. In recent years, the integration of NM with artificial intelligence (AI), particularly deep learning techniques, has evolved with increasing applications. AI techniques help elucidate complex disease mechanisms and define precise therapies.
View Article and Find Full Text PDFInt J Radiat Oncol Biol Phys
September 2025
Radiation Oncology, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143. Electronic address:
Purpose: Accelerating MR acquisition is essential for image guided therapeutic applications. Compressed sensing (CS) has been developed to minimize image artifacts in accelerated scans, but the required iterative reconstruction is computationally complex and difficult to generalize. Convolutional neural networks (CNNs)/Transformers-based deep learning (DL) methods emerged as a faster alternative but face challenges in modeling continuous k-space, a problem amplified with non-Cartesian sampling commonly used in accelerated acquisition.
View Article and Find Full Text PDF