Economic decision biases can reflect emotion and emotion dysfunction. Economic paradigms thus provide a solid framework for analysis of brain processes related to emotion and its disorders. Importantly for economic decisions, goal-conflict activates different negative motivational processes than pure loss; generating negative decision biases linked to anxiety and fear, respectively. Previously, right frontal goal-conflict specific EEG rhythmicity (GCSR) was shown to reflect anxiety processing. Here, we assessed GCSR in a forced-choice, economic decision-making task. Ninety participants were tested in three key conditions where gain:loss ratios of left mouse clicks were set to 75:25 (GAIN), 50:50 (CONFLICT) and 25:75 (LOSS). Right clicks produced no monetary consequences and skipped the current trial. The participants were not told the different conditions but could learn about them by associating the background stimulus color with the specific payoff. Goal-conflict was defined as the mathematical contrast of activity in CONFLICT minus the average of that in GAIN and LOSS. Replicating previous findings with somewhat different conditions, right frontal GCSR was detected. Importantly, greater right frontal GCSR significantly predicted a preference for economic safety in CONFLICT but not in GAIN or LOSS; but did not predict trait anxiety or neuroticism. We conclude that goal-conflict has unique neuroeconomics effects on choice biases; and that these reflect anxiety processing that is not effectively captured by trait anxiety or neuroticism.
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http://dx.doi.org/10.3389/fnins.2020.00342 | DOI Listing |
EClinicalMedicine
January 2025
School of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia.
Background: Discrete choice experiments (DCEs) are increasingly used to inform the design of health products and services. It is essential to understand the extent to which DCEs provide reliable predictions outside of experimental settings in real-world decision-making situations. We aimed to compare the prediction accuracy of stated preferences with real-world choices, as modelled from DCE data.
View Article and Find Full Text PDFClin Pharmacol Ther
January 2025
Boston Consulting Group, Zurich, Switzerland.
The value of a medicine is defined by its impact on patients, caregivers, health system, and society. A pharmaceutical company will generate evidence to demonstrate this value in various studies, including randomized clinical trials, non-interventional and observational studies, real-world data analyses, modeling, and simulation. The quality and strength of the evidence supporting a medicine's effectiveness, safety and product quality will drive decisions by healthcare system stakeholders for marketing authorization (regulatory authorities).
View Article and Find Full Text PDFJ Health Organ Manag
January 2025
The School of Business, Istanbul Medipol University, Istanbul, Turkey.
Purpose: Health technologies are an issue that directly affects the sustainability and quality of health services. Due to budget constraints, it is not financially possible for businesses to apply comprehensive improvement strategies to all these criteria. In this case, it is possible for businesses to implement more priority strategies.
View Article and Find Full Text PDFBMC Nurs
January 2025
Department of Nursing, Faculty of Health Sciences, Gaziantep Islam Science and Technology University, Gaziantep, Turkey.
Background: Brain drain refers to the migration of qualified professionals to developed countries in search of better living and working conditions, and has become a global concern, particularly in the healthcare sector. Migration of highly skilled nurses results in increased workload for the remaining nursing staff, limited access to quality healthcare services, and contributes to disparities in healthcare. Therefore, nursing students represent a critical demographic group for understanding the drivers of brain drain in the healthcare sector.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Department of Electrical Engineering, ESAT-STADIUS, KU Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.
Background: Waste and fraud are important problems for health insurers to deal with. With the advent of big data, these insurers are looking more and more towards data mining and machine learning methods to help in detecting waste and fraud. However, labeled data is costly and difficult to acquire as it requires expert investigators and known care providers with atypical behavior.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!