According to classical theories, automatic processes are autonomous and independent of higher level cognitive influence. In contrast, the authors propose that automatic processing depends on attentional sensitization of task-congruent processing pathways. In 3 experiments, the authors tested this hypothesis with a modified masked semantic priming paradigm during a lexical decision task by measuring event-related potentials (ERPs): Before masked prime presentation, participants attended an induction task either to semantic or perceptual stimulus features designed to activate a semantic or perceptual task set, respectively. Semantic priming effects on the N400 ERP component, an electrophysiological index of semantic processing, were obtained when a semantic task set was induced immediately before subliminal prime presentation, whereas a previously induced perceptual task set attenuated N400 priming. Across experiments, comparable results were obtained regardless of the difficulty level and the verbal or nonverbal nature of the induction tasks. In line with the proposed attentional sensitization model, unconscious semantic processing is enhanced by a semantic and attenuated by a perceptual task set. Hence, automatic processing of unconscious stimuli is susceptible to top-down control for optimizing goal-related information processing.
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Database (Oxford)
January 2025
Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3, Copenhagen 2200, Denmark.
Lifestyle factors (LSFs) are increasingly recognized as instrumental in both the development and control of diseases. Despite their importance, there is a lack of methods to extract relations between LSFs and diseases from the literature, a step necessary to consolidate the currently available knowledge into a structured form. As simple co-occurrence-based relation extraction (RE) approaches are unable to distinguish between the different types of LSF-disease relations, context-aware models such as transformers are required to extract and classify these relations into specific relation types.
View Article and Find Full Text PDFLearn Mem
January 2025
Department of Cognitive Neuroscience, Radboud university medical center, 6500 HB Nijmegen, The Netherlands
Stressful and emotionally arousing experiences induce the release of noradrenergic and glucocorticoid hormones that synergistically strengthen memories but differentially regulate qualitative aspects of memory. This highlights the need for sophisticated behavioral tasks that allow for the assessment of memory quality. The dual-event inhibitory avoidance task for rats is such a behavioral task designed to evaluate both the strength and specificity of memory.
View Article and Find Full Text PDFJ Neural Eng
January 2025
Department of Neuroscience, Northwestern University, 303 East Chicago Ave, Chicago, Illinois, 60611, UNITED STATES.
Objective: Creating an intracortical brain-computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI decoder. We aimed to develop a method that differs from a globally optimized decoder to address this issue.
View Article and Find Full Text PDFEvol Comput
January 2025
Sorbonne Université, CNRS, ISIR., Paris, 75005, France
Quality-Diversity (QD) methods are algorithms that aim to generate a set of diverse and highperforming solutions to a given problem. Originally developed for evolutionary robotics, most QD studies are conducted on a limited set of domains'mainly applied to locomotion, where the fitness and the behavior signal are dense. Grasping is a crucial task for manipulation in robotics.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Chemistry, New York University, New York, New York 10003, United States.
Molecular Docking is a critical task in structure-based virtual screening. Recent advancements have showcased the efficacy of diffusion-based generative models for blind docking tasks. However, these models do not inherently estimate protein-ligand binding strength thus cannot be directly applied to virtual screening tasks.
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