Emotions like regret and envy share a common origin: they are motivated by the counterfactual thinking of what would have happened had we made a different choice. When we contemplate the outcome of a choice we made, we may use the information on the outcome of a choice we did not make. Regret is the purely private comparison between two choices that we could have taken, envy adds to this the information on outcome of choices of others. However, envy has a distinct social component, in that it adds the change in the social ranking that follows a difference in the outcomes. We study the theoretical foundation and the experimental test of this view.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2827450 | PMC |
| http://dx.doi.org/10.1098/rstb.2009.0159 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Precision psychiatry: thinking beyond simple prediction models - enhancing causal predictions.
Br J Psychiatry
January 2025
Department of Psychiatry, University of Cambridge, Cambridge, UK.
Making informed clinical decisions based on individualised outcome predictions is the cornerstone of precision psychiatry. Prediction models currently employed in psychiatry rely on algorithms that map a statistical relationship between clinical features (predictors/risk factors) and subsequent clinical outcomes. They rely on associations that overlook the underlying causal structures within the data, including the presence of latent variables, and the evolution of predictors and outcomes over time.
View Article and Find Full Text PDFUp and down: counterfactual closeness is robust to direction of comparison.
Cogn Emot
December 2024
Department of Psychology, University of Waterloo, Waterloo, ON, Canada.
People often think about how things could have been better or worse. People make these upward and downward comparisons in different situations and with differing emotional consequences. We investigated whether the direction of counterfactual comparisons affects people's judgements of counterfactual closeness.
View Article and Find Full Text PDFRisk-Based Decision Making: Estimands for Sequential Prediction Under Interventions.
Biom J
December 2024
Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands.
Article Synopsis
- Prediction models help make medical decisions by estimating risks, advising high-risk individuals to undergo interventions while suggesting low-risk individuals avoid them.
- Traditional models may overlook the complexities of interventions since they often assess risk at just one point in time, while in reality, decisions are made repeatedly and may change over time.
- The article discusses how to formulate estimands for making better sequential predictions about interventions, using the example of choosing between vaginal delivery and cesarean section to inform future research and improve decision-making in clinical practice.
Unveiling the decision making process in Alzheimer's disease diagnosis: A case-based counterfactual methodology for explainable deep learning.
J Neurosci Methods
January 2025
National Institute of Technology, Tiruchirappalli, India. Electronic address:
Article Synopsis
- The study highlights how deep learning (DL) models are improving Alzheimer's disease diagnosis by using counterfactual inference to enhance explainability and clinical adoption.
- A new method is proposed that combines U-Net and GAN models to create counterfactual diagnostic maps, which help in understanding how changes in brain features affect diagnosis.
- Results show this new approach significantly outperforms existing methods, achieving 95% accuracy, and provides detailed visual explanations, making diagnostics more reliable and enhancing clinicians' understanding of disease progression.
Need Frustration and E-Cigarette Use and Dependence Among College Students: The Mediating Role of Ruminative Thinking.
Subst Use Misuse
January 2025
Department of Psychology, University of Wyoming, Laramie, WY, USA.
: The prevalence and dependence of e-cigarettes have increased among young adult college students in recent years. Though several independent risk factors for e-cigarette use and dependence have been identified, research employing theory-informed models to predict e-cigarette use and dependence is limited. : Using Self Determination Theory (SDT), e-cigarette use and dependence may be understood as stemming from need frustration that impairs autonomy, competence, and relatedness, which increases vulnerability for negative health outcomes, including e-cigarette use.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!