Situation Model of Anticipated Response consequences in tactical decisions (SMART) describes the interaction of top-down and bottom-up processes in skill acquisition and thus the dynamic interaction of sensory and motor capacities in embodied cognition. The empirically validated, extended, and revised SMART-ER can now predict when specific dynamic interactions of top-down and bottom-up processes have a beneficial or detrimental effect on performance and learning depending on situational constraints. The model is empirically supported and proposes learning strategies for when situation complexity varies or time pressure is present. Experiments from expertise research in sports illustrate that neither bottom-up nor top-down processes are bad or good per se but their effects depend on personal and situational characteristics.
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http://dx.doi.org/10.3389/fpsyg.2014.01533 | DOI Listing |
G3 (Bethesda)
January 2025
Infectious Disease Epidemiology and Analytics G5 Unit, Institut Pasteur, Université Paris Cité, Paris 75015, France.
Genetic studies of Plasmodium parasites increasingly feature relatedness estimates. However, various aspects of malaria parasite relatedness estimation are not fully understood. For example, relatedness estimates based on whole-genome-sequence (WGS) data often exceed those based on sparser data types.
View Article and Find Full Text PDFHeliyon
January 2025
School of Digital Science, Universiti Brunei Darussalam, Gadong BE1410, Brunei Darussalam.
Microlearning has become increasingly popular not only in education sector but also in corporate sector in recent years. However, its definition and didactics conceptualization, integration into instruction design, and effects on learning outcomes remain largely underexplored in terms of synthesized findings. Consequently, challenges persist in clarifying microlearning definition, and didactics, and designing effective microlearning instruction to yield improved learning outcomes.
View Article and Find Full Text PDFFront Big Data
January 2025
Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland.
Atmospheric ozone chemistry involves various substances and reactions, which makes it a complex system. We analyzed data recorded by Switzerland's National Air Pollution Monitoring Network (NABEL) to showcase the capabilities of machine learning (ML) for the prediction of ozone concentrations (daily averages) and to document a general approach that can be followed by anyone facing similar problems. We evaluated various artificial neural networks and compared them to linear as well as non-linear models deduced with ML.
View Article and Find Full Text PDFInfect Control Hosp Epidemiol
January 2025
Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA, USA.
Objective: To describe the real-world clinical impact of a commercially available plasma cell-free DNA metagenomic next-generation sequencing assay, the Karius test (KT).
Methods: We retrospectively evaluated the clinical impact of KT by clinical panel adjudication. Descriptive statistics were used to study associations of diagnostic indications, host characteristics, and KT-generated microbiologic patterns with the clinical impact of KT.
Radiat Res
January 2025
Federal Office for Radiation Protection, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany.
Lifetime risk estimates play a key role in many areas of radiation research. Here, the focus is on the lifetime excess absolute risk (LEAR) for dying from lung cancer due to occupational radon exposure based on uranium miners cohort studies. The major components in estimating LEAR were systematically varied to investigate the variability and uncertainties of results.
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