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 |
Publication Analysis
Top Keywords
Similar Publications
External validation of AI-based scoring systems in the ICU: a systematic review and meta-analysis.
BMC Med Inform Decis Mak
January 2025
QUEST Center for Responsible Research, Berlin Institute of Health at Charité Universitätsmedizin Berlin, Berlin, Germany.
Background: Machine learning (ML) is increasingly used to predict clinical deterioration in intensive care unit (ICU) patients through scoring systems. Although promising, such algorithms often overfit their training cohort and perform worse at new hospitals. Thus, external validation is a critical - but frequently overlooked - step to establish the reliability of predicted risk scores to translate them into clinical practice.
View Article and Find Full Text PDFThe role of awareness and demand in evaluative learning.
J Pers Soc Psychol
January 2025
Department of Experimental-Clinical and Health Psychology, Ghent University.
Human likes and dislikes can be established or changed in numerous ways. Three of the most well-studied procedures involve exposing people to regularities in the environment (evaluative conditioning, approach-avoidance, mere exposure), to verbal information about upcoming regularities (evaluative conditioning, approach-avoidance, or mere exposure information), or to verbal information about the evaluative properties of an attitude object (persuasive messages). In the present study, we investigated the relation between, on the one hand, different types of experiment-related beliefs (regularity, influence, and hypothesis awareness) and demand reactions (demand compliance and reactance) and, on the other hand, evaluative learning about novel food brands (Experiments 1 and 2) and well-known food brands (Experiment 2) via persuasive messages, experienced regularities, and verbal information about regularities.
View Article and Find Full Text PDFHarnessing unlabeled data: Enhanced rare earth component content prediction based on BiLSTM-Deep autoencoder.
ISA Trans
January 2025
School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, 330013, Jiangxi, China; Key Laboratory of Advanced Control & Optimization of Jiangxi Province, Nanchang, 330013, Jiangxi, China. Electronic address:
Traditional data-driven models for predicting rare earth component content are primarily developed by relying on supervised learning methods, which suffer from limitations such as a lack of labeled data, lagging, and poor usage of a major amount of unlabeled data. This paper proposes a novel prediction approach based on the BiLSTM-Deep autoencoder enhanced traditional LSSVM algorithm, termed BiLSTM-DeepAE-LSSVM. This approach thoroughly exploits the implicit information contained in copious amounts of unlabeled data in the rare earth production process, thereby improving the traditional supervised prediction method and increasing the accuracy of component content predictions.
View Article and Find Full Text PDFWhat do coaches want? Exploring the preferences of youth rugby union coaches for education in the implementation of injury prevention programmes.
Phys Ther Sport
December 2024
Department of Physical Education and Sport Sciences, University of Limerick, Limerick, Ireland; Sport and Human Performance Research Centre, University of Limerick, Limerick, Ireland; Health Research Institute, University of Limerick, Limerick, Ireland; Lero, Irish Software Research Centre, University of Limerick, Ireland. Electronic address: https://twitter.com/IanCKenny.
Objective: To explore youth Rugby Union coaches' preferences for education and support in the implementation of injury prevention programmes (IPPs).
Methods: Twelve Rugby Union coaches involved with youth teams participated in five online focus groups. Conventional content analysis was used to determine preferences initially from transcripts, and thereafter main categories, generic categories and sub-categories.
Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents.
Sci Data
January 2025
IBM Research, Hursley, SO21 2JN, UK.
A significant challenge in computational chemistry is developing approximations that accelerate ab initio methods while preserving accuracy. Machine learning interatomic potentials (MLIPs) have emerged as a promising solution for constructing atomistic potentials that can be transferred across different molecular and crystalline systems. Most MLIPs are trained only on energies and forces in vacuum, while an improved description of the potential energy surface could be achieved by including the curvature of the potential energy surface.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!