ArticlePloS one2018
Risk preferences impose a hidden distortion on measures of choice impulsivity.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Thalamo-cortical white matter connectivity-defined thalamic subarea volumes predict individual delay discounting.Molecular psychiatry · 2026Article
- Article
- Sweet-taste liking is associated with preference for less risky and immediate rewards in economic decision-making.Frontiers in psychology · 2026Article
- Computational characterization of metacognitive ability in subjective decision-making.bioRxiv : the preprint server for biology · 2025Article
- Aberrant neural computation of social controllability in nicotine-dependent humans.Communications biology · 2024Article
- Temporal discounting predicts procrastination in the real world.Scientific reports · 2024Article
- Aberrant neural computation of social controllability in nicotine-dependent humans.Research square · 2024Article
- Article
- Environmental uncertainty and the advantage of impulsive choice strategies.PLoS computational biology · 2023Article
- Behavior of Rats in a Self-Paced Risky Decision-Making Task Based on Definite Probability.Brain sciences · 2022Article
- A Neuroeconomics Approach to Obesity.Biological psychiatry · 2022Review
- Decision neuroscience and neuroeconomics: Recent progress and ongoing challenges.Wiley interdisciplinary reviews. Cognitive science · 2022Review
- Comparing utility functions between risky and riskless choice in rhesus monkeys.Animal cognition · 2022Article
- A Model Guided Approach to Evoke Homogeneous Behavior During Temporal Reward and Loss Discounting.Frontiers in psychiatry · 2022Article
- A Proof-of-Concept Ecological Momentary Assessment Study of Day-Level Dynamics in Value-Based Decision-Making in Opioid Addiction.Frontiers in psychiatry · 2022Article
- Individual Differences in Intertemporal Choice.Frontiers in psychology · 2021Review
- Live fast, die young and sleep later: Life history strategy and human sleep behavior.Evolution, medicine, and public health · 2021Article
- Neural and behavioral correlates of episodic memory are associated with temporal discounting in older adults.Neuropsychologia · 2020Article
- Neural correlates of reward magnitude and delay during a probabilistic delay discounting task in alcohol use disorder.Psychopharmacology · 2020Article
- Sensitivity of reaction time to the magnitude of rewards reveals the cost-structure of time.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
Measuring temporal discounting through the use of intertemporal choice tasks is now the gold standard method for quantifying human choice impulsivity (impatience) in neuroscience, psychology, behavioral economics, public health and computational psychiatry. A recent area of growing interest is individual differences in discounting levels, as these may predispose to (or protect from) mental health disorders, addictive behaviors, and other diseases. At the same time, more and more studies have been dedicated to the quantification of individual attitudes towards risk, which have been measured in many clinical and non-clinical populations using closely related techniques. Economists have pointed to interactions between measurements of time preferences and risk preferences that may distort estimations of the discount rate. However, although becoming standard practice in economics, discount rates and risk preferences are rarely measured simultaneously in the same subjects in other fields, and the magnitude of the imposed distortion is unknown in the assessment of individual differences. Here, we show that standard models of temporal discounting -such as a hyperbolic discounting model widely present in the literature which fails to account for risk attitudes in the estimation of discount rates- result in a large and systematic pattern of bias in estimated discounting parameters. This can lead to the spurious attribution of differences in impulsivity between individuals when in fact differences in risk attitudes account for observed behavioral differences. We advance a model which, when applied to standard choice tasks typically used in psychology and neuroscience, provides both a better fit to the data and successfully de-correlates risk and impulsivity parameters. This results in measures that are more accurate and thus of greater utility to the many fields interested in individual differences in impulsivity.
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