It has been repeatedly shown that in decisions under time constraints, individuals predominantly use noncompensatory strategies rather than complex compensatory ones. The authors argue that these findings might be due not to limitations of cognitive capacity but instead to limitations of information search imposed by the commonly used experimental tool Mouselab (J. W. Payne, J. R. Bettman, & E. J. Johnson, 1988). The authors tested this assumption in 3 experiments. In the 1st experiment, information was openly presented, whereas in the 2nd experiment, the standard Mouselab program was used under different time limits. The results indicate that individuals are able to compute weighted additive decision strategies extremely quickly if information search is not restricted by the experimental procedure. In a 3rd experiment, these results were replicated using more complex decision tasks, and the major alternative explanations that individuals use more complex heuristics or that they merely encode the constellation of cues were ruled out. In sum, the findings challenge the fundaments of bounded rationality and highlight the importance of automatic processes in decision making.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1037/0278-7393.34.5.1055 | DOI Listing |
Diagnosis (Berl)
January 2025
Department of Laboratory Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary.
Objectives: To examine factors impacting diagnostic evaluation of suspected deep vein thrombosis (DVT) by analyzing the test ordering patterns and provider decision-making within a universal health coverage system in Hungary.
Methods: We analyzed test orders for suspected DVT between 2007 and 2020, and the financial framework influencing diagnostic practices. An anonymous survey was also conducted among Emergency Department physicians to explore factors influencing diagnostic decision-making.
J Med Internet Res
January 2025
Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, China.
Background: Acute kidney injury (AKI) is a common complication in hospitalized older patients, associated with increased morbidity, mortality, and health care costs. Major adverse kidney events within 30 days (MAKE30), a composite of death, new renal replacement therapy, or persistent renal dysfunction, has been recommended as a patient-centered endpoint for clinical trials involving AKI.
Objective: This study aimed to develop and validate a machine learning-based model to predict MAKE30 in hospitalized older patients with AKI.
JCO Clin Cancer Inform
January 2025
Emory University School of Medicine, Atlanta, GA.
Purpose: Immune checkpoint inhibitors (ICIs) have demonstrated promise in the treatment of various cancers. Single-drug ICI therapy (immuno-oncology [IO] monotherapy) that targets PD-L1 is the standard of care in patients with advanced non-small cell lung cancer (NSCLC) with PD-L1 expression ≥50%. We sought to find out if a machine learning (ML) algorithm can perform better as a predictive biomarker than PD-L1 alone.
View Article and Find Full Text PDFJ Clin Neurophysiol
January 2025
Department of Neurology, Washington University in St Louis, St. Louis, MO.
Purpose: Continuous EEG (cEEG) monitoring is increasingly used in the management of neonates with seizures. There remains debate on what clinically relevant information can be gained from cEEG in neonates with suspected seizures, at high risk for seizures, or with definite seizures, as well as the use of cEEG for prognosis in a variety of conditions. In this guideline, we address these questions using American Clinical Neurophysiology Society structured methodology for clinical guideline development.
View Article and Find Full Text PDFSci Adv
January 2025
Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Retinomorphic systems that can see, recognize, and respond to real-time environmental information will extend the complexity and range of tasks that an exoskeleton robot can perform to better assist physically disabled people. However, the lack of ultrasensitive, reconfigurable, and large-scale integratable retinomorphic devices and advanced edge-processing algorithms makes it difficult to realize retinomorphic hardware. Here, we report the retinomorphic hardware prototype with a 4096-pixel perovskite image sensor array as core module to endow embodied intelligent vision functionalities.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!