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Perception is not exclusively determined by sensory input, being strongly shaped by expectations. Here, we manipulated target occurrence certainty-random (50%), probabilistic (63/75%), deterministic (100%)-to investigate how priors shape decision-making. Results revealed strong influence of expectations on decision-bias, with modulation increasing as priors attain predictive power. This influence was particularly evident in deterministic trials, where the prior's absolute validity heightened performance. Notably, individuals exhibited wide variability in predictive strategies: some exhibited strong prior-driven choice (), while others relied more on sensory input (). Relative to,exhibited reduced midfrontal theta rhythm in probabilistic trials, indicating less monitoring for actual target occurrence, and higher motor beta desynchronization in deterministic trials, suggesting a shift toward motor strategy implementing prior-congruent action. Crucially,prior-driven approach conferred an advantage in deterministic conditions. These findings highlight priors' impact on decision-making, emphasizing the interplay between monitoring and anticipatory mechanisms in leveraging expectations.
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http://dx.doi.org/10.1162/imag_a_00496 | DOI Listing |
Intractable Rare Dis Res
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Department of Dermatology, Saitama Medical Center, Saitama Medical University, Saitama, Japan.
Hereditary angioedema (HAE) is a rare, potentially life-threatening disorder characterized by recurrent, disabling episodes of subcutaneous or submucosal swelling. Lanadelumab, a monoclonal antibody targeting plasma kallikrein, is approved for long-term prophylaxis and has shown high efficacy in clinical trials. However, real-world data on its prolonged use, particularly from East Asia, remain scarce.
View Article and Find Full Text PDFDiscov Oncol
September 2025
Department of Biochemistry, Kampala International University, Ishaka, Uganda.
Neurophysiological alterations represent a growing concern in oncology, affecting both the central and peripheral nervous systems through diverse mechanisms. These include direct tumor infiltration, paraneoplastic immune responses, systemic inflammation, metabolic dysregulation, and treatment-induced neurotoxicity. Neurological complications range from cognitive impairment and peripheral neuropathy to motor deficits and autonomic dysfunction.
View Article and Find Full Text PDFAccount Res
August 2025
Institute of Ethics, School of Theology, Philosophy, and Music, Dublin City University, Dublin, Ireland.
Guided by Brey's Anticipatory Technology Ethics, we examined AI-based research mentors (AIRMs) through technology foresight as well as identification and evaluation of ethical issues. Scenario planning was employed to inform foresight, yielding four plausible future scenarios: 1) AIRMs are used solely for guidance, 2) AIRMs are used for guidance and monitoring, 3) AIRMs are banned, and 4) AIRMs are used solely for monitoring. Resnik's twelve principles informed the identification of ethical issues within these scenarios.
View Article and Find Full Text PDFCureus
July 2025
Anesthesiology and Perioperative Medicine, University of Miami, Miami, USA.
In a nonendemic setting, the confluence of malaria and pregnancy presents unique anesthetic challenges, particularly when the infection is undiagnosed at the time of an urgent cesarean section. This report involves a woman in her early 30s at 39 weeks of gestation with no prior health issues, who developed malarial symptoms upon returning from Haiti five months before. During labor, severe fetal heart rate decelerations necessitated immediate surgical intervention.
View Article and Find Full Text PDFSci Rep
August 2025
Civil Engineering Department, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia.
Effective forecasting of the Water Quality Index (WQI) considerably impacts water resource management as well as public health safety. This study proposes a new approach for WQI forecasting using stacked regression ensemble modeling integrated with SHAP (Shapley Additive explanations), a form of Explainable Artificial Intelligence (XAI). The model was developed using a dataset of 1,987 water quality samples from Indian rivers (2005-2014), processed through six optimized machine learning algorithms: XGBoost, CatBoost, Random Forest, Gradient Boosting, Extra Trees, and AdaBoost, combined using Linear Regression as the meta-learner.
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