Recent findings suggest that textual constraint and reading goals influence inference generation. However, it is unclear how constraint and reading goals interact during predictive inference generation in the hemispheres. In the current divided visual field study, participants were given a study goal or not given a reading goal prior to reading text that was either strongly or weakly constrained toward a predictive inference. Participants then made lexical decisions to inference-related target words presented to either the left visual field-right hemisphere (LVF-RH) or the right visual field-left hemisphere (RVF-LH). When readers did not have a goal, strongly constrained inferences were processed similarly in the hemispheres, while a right hemisphere advantage was evident for weakly constrained inferences. However, when readers did have a goal, strongly and weakly constrained inferences were processed similarly in both hemispheres. Thus, goals of a reader seem to influence predictive inference generation in the hemispheres, particularly for weakly constrained text.
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http://dx.doi.org/10.1080/17588928.2016.1193482 | DOI Listing |
Phys Rev Lett
December 2024
Univ Coimbra, Faculdade de Ciências e Tecnologia da Universidade de Coimbra and CFisUC, Rua Larga, 3004-516 Coimbra, Portugal.
The search for primordial black holes (PBHs) with masses M≪M_{⊙} is motivated by natural early-Universe production mechanisms and that PBHs can be dark matter. For M≲10^{14} kg, the PBH density is constrained by null searches for their expected Hawking emission (HE), the characteristics of which are, however, sensitive to new states beyond the standard model. If there exists a large number of spin-0 particles in nature, PBHs can, through HE, develop and maintain non-negligible spins, modifying the visible HE.
View Article and Find Full Text PDFJ Pathol Inform
January 2025
Cincinnati Children's AI Imaging Research (CAIIR) Center, Cincinnati, OH, United States.
Background: Traditional liver fibrosis staging via percutaneous biopsy suffers from sampling bias and variable inter-pathologist agreement, highlighting the need for more objective techniques. Deep learning models for disease staging from medical images have shown potential to decrease diagnostic variability, with recent weakly supervised learning strategies showing promising results even with limited manual annotation.
Purpose: To study the clustering-constrained attention multiple instance learning (CLAM) approach for staging liver fibrosis on trichrome whole slide images (WSIs) of children and young adults.
Polymers (Basel)
November 2024
State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China.
Curdlan's application is constrained by high gelation concentration, poor water solubility, and incompatibility with other polysaccharides. To address these limitations, this study investigated the effects of different concentrations (0.05-0.
View Article and Find Full Text PDFObjective: Modeling dynamic neuronal activity within brain networks enables the precise tracking of rapid temporal fluctuations across different brain regions. However, current approaches in computational neuroscience fall short of capturing and representing the spatiotemporal dynamics within each brain network. We developed a novel weakly supervised spatiotemporal dense prediction model capable of generating personalized 4D dynamic brain networks from fMRI data, providing a more granular representation of brain activity over time.
View Article and Find Full Text PDFReg Environ Change
May 2024
Earth and Life Institute, University of Louvain, Louvain-la-Neuve, Belgium.
Unlabelled: Sustainable agricultural intensification aims at increasing yields on existing agricultural land without negative environmental impacts. Managing pests and diseases contributes to increasing yields. Without synthetic pesticides, this management is labour intensive.
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