Some decisions make a difference, but most are arbitrary and inconsequential, like which of several identical new pairs of socks should I wear? Healthy people swiftly make such decisions even with no rational reasons to rely on. In fact, arbitrary decisions have been suggested as demonstrating "free will". However, several clinical populations and some healthy individuals have significant difficulties in making such arbitrary decisions. Here, we investigate the mechanisms involved in arbitrary picking decisions. We show that these decisions, arguably based on a whim, are subject to similar control mechanisms as reasoned decisions. Specifically, error-related negativity (ERN) brain response is elicited in the EEG following change of intention, without an external definition of error, and motor activity in the non-responding hand resembles actual errors both by its muscle EMG temporal dynamics and by the lateralized readiness potential (LRP) pattern. This provides new directions in understanding decision-making and its deficits.
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http://dx.doi.org/10.1016/j.isci.2023.106373 | DOI Listing |
PLoS One
January 2025
Division of Cardiology, Department of Internal Medicine, Kaohsiung Medical University Gangshan Hospital, Kaohsiung, Taiwan.
Background/purpose: Dyslipidemia, a hallmark of metabolic syndrome (MetS), contributes to atherosclerotic and cardiometabolic disorders. Due to days-long analysis, current clinical procedures for cardiotoxic blood lipid monitoring are unmet. This study used AI-assisted attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy to identify MetS and precisely quantify multiple blood lipid levels with a blood sample of 0.
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 PDFJ Diabetes Sci Technol
January 2025
Profil, Neuss, Germany.
Background: Glucose is an essential molecule in energy metabolism. Dysregulated glucose metabolism, the defining feature of diabetes, requires active monitoring and treatment to prevent significant morbidity and mortality. Current technologies for intermittent and continuous glucose measurement are invasive.
View Article and Find Full Text PDFEur Radiol Exp
January 2025
IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
Background: Minimizing radiation exposure is crucial in monitoring adolescent idiopathic scoliosis (AIS). Generative adversarial networks (GANs) have emerged as valuable tools being able to generate high-quality synthetic images. This study explores the use of GANs to generate synthetic sagittal radiographs from coronal views in AIS patients.
View Article and Find Full Text PDFNeuroscience
January 2025
Departamento de Ciencias Médicas y de la Vida, Centro Universitario de la Ciénega, Universidad de Guadalajara, Ocotlán, Mexico; Laboratorio de Conducta Animal, Departamento de Psicología, Centro Universitario de la Ciénega, Universidad de Guadalajara, Ocotlán, Mexico.
Motor actions adapt dynamically to external changes through the brain's ability to predict sensory outcomes and adjust for discrepancies between anticipated and actual sensory inputs. In this study, we investigated how changes in target speed (v) and direction influenced visuomotor responses, focusing on gaze and manual joystick control during an interception task. Participants tracked a moving target with sinusoidal variations in v and directional changes, generating sensory prediction errors and requiring real-time adjustments.
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