Two experiments investigated the effect of prior knowledge on implicit and explicit learning. Implicit as opposed to explicit learning is sometimes characterized as unselective or purely statistical. During training, one group of participants was presented with category exemplars whose features could be tied together by integrative knowledge, whereas another group saw category exemplars with unrelated feature combinations. Half of the participants in each group learned these categories under a secondary-task condition (meant to discourage explicit learning), and the remaining half performed the categorization task under a single-task condition (meant to favour explicit learning). In a test phase, participants classified only the individual features of the training exemplars. Secondary- as opposed to single-task conditions impaired explicit but not implicit knowledge (as determined by subjective measures). Importantly, prior knowledge resulted in increased amounts of both implicit and explicit knowledge.
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http://dx.doi.org/10.1080/17470210701255374 | DOI Listing |
Matern Child Nutr
January 2025
ELEVATE Nutrition, Washington, District of Columbia, USA.
Alive & Thrive has been a major global nutrition initiative that aimed to learn how to improve maternal, infant, young child, and adolescent nutrition and health on a large scale. During 2009-2014, Alive & Thrive developed and implemented interventions to improve infant and young child feeding at scale in three countries. Subsequently, Alive & Thrive expanded its work to more than 15 geographies, including six country-specific and two regional programs, to additionally address maternal and adolescent nutrition while adding agriculture and social protection programs to improve maternal, infant, and young child nutrition.
View Article and Find Full Text PDFPrediction-powered inference (PPI) [1] and its subsequent development called PPI++ [2] provide a novel approach to standard statistical estimation leveraging machine learning systems to enhance unlabeled data with predictions. We use this paradigm in clinical trials. The predictions are provided by disease progression models, providing prognostic scores for all the participants as a function of baseline covariates.
View Article and Find Full Text PDFMagn Reson Imaging
January 2025
Department of Medical Imaging, Pingyin people's Hospital, Jinan 250400, China.
Magnetic Resonance Imaging is a cornerstone of medical diagnostics, providing high-quality soft tissue contrast through non-invasive methods. However, MRI technology faces critical limitations in imaging speed and resolution. Prolonged scan times not only increase patient discomfort but also contribute to motion artifacts, further compromising image quality.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Institute of Theoretical & Applied Informatics, Polish Academy of Sciences (IITiS-PAN), 44-100 Gliwice, Poland.
Edge computing systems must offer low latency at low cost and low power consumption for sensors and other applications, including the IoT, smart vehicles, smart homes, and 6G. Thus, substantial research has been conducted to identify optimum task allocation schemes in this context using non-linear optimization, machine learning, and market-based algorithms. Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Royal Danish Library, Special Collections, Søren Kierkegaards Plads. 1, 1221, Copenhagen K, Denmark.
Historical topographical maps contain valuable, spatially and thematically detailed information about past landscapes. Yet, for analyses of landscape dynamics through geographical information systems, it is necessary to "unlock" this information via map processing. For two study areas in northern and central Jutland, Denmark, we apply object-based image analysis, vector GIS, colour image segmentation, and machine learning processes to produce machine-readable layers for the land use and land cover categories forest, wetland, heath, dune sand, and water bodies from topographic maps from the late nineteenth century.
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