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Updated: Jan 22, 2026

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The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
Published on: August 25, 2023
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What the Future Holds and When: A Description-Experience Gap in Intertemporal Choice.
Junyi Dai1,2, Thorsten Pachur2, Timothy J Pleskac2,3
11 Department of Psychology and Behavioral Sciences, Zhejiang University.
Psychological Science
|July 19, 2019
Summary
People
Area of Science:
- Behavioral Economics
- Decision Science
- Cognitive Psychology
Background:
- Intertemporal choice involves uncertainty about waiting times for outcomes.
- Understanding how people perceive and react to temporal uncertainty is crucial for economic and psychological models.
Purpose of the Study:
- To investigate how the method of learning about temporal uncertainty (description vs. experience) influences time preferences.
- To examine the description-experience gap in intertemporal choice under temporal uncertainty.
Main Methods:
- Two studies were conducted involving choices between sure delays and lotteries with probabilistic delays.
- Temporal uncertainty was manipulated through explicit description (timing risk) or experiential sampling (timing uncertainty).
- Modeling analyses were used to understand probability weighting and the description-experience gap.
Main Results:
- Preference for lotteries over sure delays differed based on whether temporal uncertainty was described or experienced.
- When the shorter delay was rare, lotteries were preferred more in the described condition than the experienced condition.
- When the longer delay was rare, the preference pattern reversed, showing a description-experience gap.
Conclusions:
- The way temporal uncertainty is learned significantly impacts intertemporal choices and time preferences.
- Findings suggest that underexperiencing rare delays and distinct probability weighting patterns contribute to the observed description-experience gap.
- Results challenge existing models of intertemporal choice and the universality of inverse-S-shaped probability weighting.
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