ArticleDecision (Washington, D.C.)2020
Hierarchies improve individual assessment of temporal discounting behavior.
Article in Decision (Washington, D.C.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Less systematic decision-making during delay discounting, but not discounting rate, is robustly linked to psychopathology.Journal of psychopathology and clinical science · 2026Article
- Comparing likelihood-based and likelihood-free approaches to fitting and comparing models of intertemporal choice.Behavior research methods · 2025Article
- A systematic investigation into the reliability of inter-temporal choice model parameters.Psychonomic bulletin & review · 2023Review
- The problems with delay discounting: a critical review of current practices and clinical applications.Psychological medicine · 2021Review
- Are You Sure: Preference and Ambivalence in Delay Discounting.Frontiers in behavioral neuroscience · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Delay discounting behavior has proven useful in assessing impulsivity across a wide range of populations. As such, accurate estimation of the shape of each individual's temporal discounting profile is paramount when drawing conclusions about how impulsivity relates to clinical and health outcomes such as gambling, addiction, and obesity. Here, we identify an estimation problem with current methods of assessing temporal discounting behavior, and propose a simple solution. First, through a simulation study we identify types of temporal discounting profiles that cannot reliably be estimated. Second, we show how imposing constraints through hierarchical modeling ameliorates these recovery problems. Finally, we apply our solution to a large data set from a temporal discounting task, and illustrate the importance of reliable estimation within patient populations. We conclude with a brief discussion on how hierarchical Bayesian methods can aid in model estimation, compensate for small samples, and improve predictions of externalizing psychopathology.
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Registered trials
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