How does the structure of a series of payments influence its recipient's satisfaction? A common hypothesis is that each payment will be compared with a single "standard" or "reference" payment (e.g., the average payment). Cognitive models of judgment such as range frequency theory predict in contrast that the entire payment distribution will influence evaluation of each individual payment. Two experiments examined satisfaction with a series of payments. In both experiments, most payments were either relatively high in the experienced distribution (the distribution was negatively skewed) or relatively low (positively skewed). The total and average payment was held constant. Experiment 1 found that average satisfaction with individual payments was higher when the payments were negatively skewed, consistent with range frequency theory, and earlier findings were extended by comparing range frequency theory with a range-based model, a rank-based model, and a reference point model at the individual level. Experiment 2 examined satisfaction with whole sequences of payments and found that receiving a negatively skewed sequence was more satisfying overall than receiving a positively skewed sequence. It is concluded that negatively skewed payment distributions are more satisfying, as predicted by cognitive models of judgment.
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http://dx.doi.org/10.3758/s13421-016-0604-0 | DOI Listing |
Materials (Basel)
December 2024
Tribology and Surfaces Group, Universidad Nacional de Colombia-Sede Medellín, Medellin 050034, Colombia.
Metal powders for additive manufacturing are expensive, and producing new ones from mined metals has a negative ecological impact. In this work, recycled and reused metal powders from MS1 steel for direct metal laser sintering (DMLS) 3D printing were evaluated in the laboratory. The powders were recycled by melting followed by gas atomizing.
View Article and Find Full Text PDFImmunology
January 2025
Anatomy, Dokkyo Medical University, Mibu, Tochigi, Japan.
Dendritic cells (DCs), the primary antigen-presenting cells, have traditionally been identified by CD103 molecules in rats, whereas mouse and human DCs are identified by CD11c molecules. However, this history does not preclude the existence of CD103 DCs in rats. To explore this possibility, we examined MHCII cells in rat spleen and thymus, identifying a novel population of CD103MHCIICD45RCD172a cells.
View Article and Find Full Text PDFBrain Behav
January 2025
Department of Radiology, Liuzhou Worker's Hospital, Guangxi, China.
Background: Adult glioblastomas (GBMs) are associated with high recurrence and mortality. Personalized treatment based on molecular markers may help improve the prognosis. We aimed to evaluate whether apparent diffusion coefficient (ADC) histogram analysis can better predict MGMT and TERT molecular characteristics and to determine the prognostic relevance of genetic profile in patients with GBM.
View Article and Find Full Text PDFSci Rep
December 2024
Decisions LAB, Department of Law, Economics and Human Sciences, University Mediterranea of Reggio Calabria, Via dei Bianchi, 2, 89131, Reggio Calabria, Italy.
Cervical cancer is one of the deadly diseases that affects women, which requires periodic examinations to identify and treat any cancerous tumors at a preliminary stage. The most prevalent examination tool for cervical cancer prompt identification is the cervical smear (Pap smear) testing; however, due to human negligence, this examination method has an elevated probability of negative findings. Cervical cancer classification using machine learning (ML) and deep learning (DL) has been extensively studied to enhance the conventional diagnostic process.
View Article and Find Full Text PDFJ Pathol Inform
January 2025
U.S. Food and Drug Administration, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, Silver Spring, MD, United States of America.
Objective: With the increasing energy surrounding the development of artificial intelligence and machine learning (AI/ML) models, the use of the same external validation dataset by various developers allows for a direct comparison of model performance. Through our High Throughput Truthing project, we are creating a validation dataset for AI/ML models trained in the assessment of stromal tumor-infiltrating lymphocytes (sTILs) in triple negative breast cancer (TNBC).
Materials And Methods: We obtained clinical metadata for hematoxylin and eosin-stained glass slides and corresponding scanned whole slide images (WSIs) of TNBC core biopsies from two US academic medical centers.
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