Social learning is well established across species. While recent neuroimaging studies show that dorsomedial prefrontal cortex (DMPFC/preSMA) activation correlates with observational learning signals, the precise computations that are implemented by DMPFC/preSMA have remained unclear. To identify whether DMPFC/preSMA supports learning from observed outcomes or observed actions, or possibly encodes even a higher order factor (such as the reliability of the demonstrator), we downregulate DMPFC/preSMA excitability with continuous theta burst stimulation (cTBS) and assess different forms of observational learning. Relative to a vertex-cTBS control condition, DMPFC/preSMA downregulation decreases performance during action-based learning but has no effect on outcome-based learning. Computational modeling reveals that DMPFC/preSMA cTBS disrupts learning the predictability, a proxy of reliability, of the demonstrator and modulates the rate of learning from observed actions. Thus, our results suggest that the DMPFC is causally involved in observational action learning, mainly by adjusting the speed of learning about the predictability of the demonstrator.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11436984 | PMC |
http://dx.doi.org/10.1038/s41467-024-52559-0 | DOI Listing |
Environ Sci Technol
January 2025
State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
Air pollution is a leading contributor to the global disease burden. However, the complex nature of the chemicals to which humans are exposed through inhalation has obscured the identification of the key compounds responsible for diseases. Here, we develop a network topology-based framework to identify key toxic compounds in the airborne chemical exposome.
View Article and Find Full Text PDFBrain Inform
January 2025
Department of Computing, Glasgow Caledonian University, Glasgow, G4 0BA, Scotland.
A digital twin is a virtual model of a real-world system that updates in real-time. In healthcare, digital twins are gaining popularity for monitoring activities like diet, physical activity, and sleep. However, their application in predicting serious conditions such as heart attacks, brain strokes and cancers remains under investigation, with current research showing limited accuracy in such predictions.
View Article and Find Full Text PDFBreast Cancer
January 2025
Division of Breast and Endocrine Surgery, Department of Surgery, School of Medicine, Hyogo Medical University, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan.
Purpose: The aim of this study was to examine the clinical utility of tumor-infiltrating lymphocytes (TILs) evaluated by "average" and "hot-spot" methods in breast cancer patients.
Methods: We examined 367 breast cancer patients without neoadjuvant chemotherapy (NAC) by average and hot-spot methods to determine the consistency of TIL scores between biopsy and surgical specimens. TIL scores before NAC were also compared with the pathological complete response (pCR) rate and clinical outcomes in 144 breast cancer patients that received NAC.
J Med Syst
January 2025
Department of Computing, University of North Florida, 1 UNF Dr., Jacksonville, 32246, FL, USA.
The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into a multivariate analysis over a five-year period involving 21,969 patients. Our study introduces a predictive model framework that offers a holistic approach to managing the no-show problem in healthcare, incorporating elements into the objective function that address not only the accurate prediction of no-shows but also the management of service capacity, overbooking, and idle resource allocation resulting from mispredictions.
View Article and Find Full Text PDFJ Med Syst
January 2025
Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
Optimizing operating room (OR) utilization is critical for enhancing hospital management and operational efficiency. Accurate surgical case duration predictions are essential for achieving this optimization. Our study aimed to refine the accuracy of these predictions beyond traditional estimation methods by developing Random Forest models tailored to specific surgical departments.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!