The human brain spends 30-50% of its waking hours engaged in mind-wandering (MW), a common phenomenon in which individuals either spontaneously or deliberately shift their attention away from external tasks to task-unrelated internal thoughts. Despite the significant amount of time dedicated to MW, its underlying reasons remain unexplained. Our pre-registered study investigates the potential adaptive aspects of MW, particularly its role in predictive processes measured by statistical learning. We simultaneously assessed visuomotor task performance as well as the capability to extract probabilistic information from the environment while assessing task focus (on-task vs. MW). We found that MW was associated with enhanced extraction of hidden, but predictable patterns. This finding suggests that MW may have functional relevance in human cognition by shaping behavior and predictive processes. Overall, our results highlight the importance of considering the adaptive aspects of MW, and its potential to enhance certain fundamental cognitive abilities.
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http://dx.doi.org/10.1016/j.isci.2024.111703 | DOI Listing |
Chemosphere
March 2025
Herbert Wertheim College of Engineering, Engineering School of Sustainable Infrastructure and the Environment (ESSIE), Department of Environmental Engineering Sciences, University of Florida, 408 A.P. Black Hall, Gainesville, FL, 32611, United States. Electronic address:
Per- and polyfluoroalkyl substances (PFAS) are persistent environmental pollutants, and their presence in aquatic environments, especially coastal waters, poses significant ecological and human health risks. This study investigates the occurrence and behavior of four PFAS compounds in the Indian River Lagoon, a biodiverse estuarine ecosystem located in Florida USA, by evaluating how ecological and hydroclimatic factors influence PFAS occurrence. A Bayesian Logistic Regression Model (BLRM) was employed to quantify the relationships between environmental stressors such as salinity, precipitation, river discharge, water temperature, and pH, and the presence of these PFAS compounds.
View Article and Find Full Text PDFInt J Med Inform
March 2025
Department of Military Health Statistics, Naval Medical University, Shanghai, China. Electronic address:
Background: Timely and accurate outcome prediction is essential for clinical decision-making for ischemic stroke patients in the intensive care unit (ICU). However, the interpretation and translation of predictive models into clinical applications are equally crucial. This study aims to develop an interpretable machine learning (IML) model that effectively predicts in-hospital mortality for ischemic stroke patients.
View Article and Find Full Text PDFPsychiatry Res
March 2025
Department of Biomedical Informatics, College of Medicine, Konyang University, Daejeon, 35365, Republic of Korea; Konyang Medical data Research group-KYMERA, Konyang University Hospital, Daejeon, Republic of Korea; Myunggok Medical Research Center, Konyang University Hospital, Daejeon, Republic of Korea. Electronic address:
Various digital therapeutics (DTx), which utilize computerized cognitive training (CCT) to improve cognitive functioning, have been tested and released. However, the efficacy of these DTx approaches may be diverse. This study aims to meta-synthesize the associations between mobile applications and cognitive functioning outcomes in older adults with mild cognitive impairment (MCI) or dementia from randomized controlled trials (RCTs).
View Article and Find Full Text PDFBioinformatics
March 2025
Department of Statistics, Hunan University, Changsha, 410000, China.
Motivation: Inferring gene networks provides insights into biological pathways and functional relationships among genes. When gene expression samples exhibit heterogeneity, they may originate from unknown subtypes, prompting the utilization of mixture Gaussian graphical model for simultaneous subclassification and gene network inference. However, this method overlooks the heterogeneity of network relationships across subtypes and does not sufficiently emphasize shared relationships.
View Article and Find Full Text PDFElife
March 2025
Machine Learning Core, National Institute of Mental Health, Bethesda, United States.
Fiber photometry has become a popular technique to measure neural activity in vivo, but common analysis strategies can reduce the detection of effects because they condense signals into summary measures, and discard trial-level information by averaging . We propose a novel photometry statistical framework based on functional linear mixed modeling, which enables hypothesis testing of variable effects at , and uses trial-level signals without averaging. This makes it possible to compare the timing and magnitude of signals across conditions while accounting for between-animal differences.
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