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Intertemporal Salience Theory
Last registered on February 26, 2021


Trial Information
General Information
Intertemporal Salience Theory
Initial registration date
February 25, 2021
Last updated
February 26, 2021 12:54 PM EST
Primary Investigator
Other Primary Investigator(s)
PI Affiliation
Additional Trial Information
In development
Start date
End date
Secondary IDs
Economic decisions which implicate both risk and time are frequent. While experimental evidence demonstrates robust deviations from the canonical model, Discounted Expected Utility (DEU), debate persists on what non-DEU models are appropriate for rationalizing choice. We proposes an extension of atemporal salience theory (Bordalo et al., 2012, 2013b) for the treatment of intertemporal lotteries. The elaborated model rationalizes prominent DEU deviations and delivers additional testable predictions. The model’s predictions are explored in three existing data sets, Roughly 80% of prior experimental deviations from DEU are consistent with intertemporal salience, demonstrating the value of the theory. Here we propose a novel experiment to further distinguish intertemporal salience theory from competing theories.
External Link(s)
Registration Citation
He, Songyu and Charles Sprenger. 2021. "Intertemporal Salience Theory." AEA RCT Registry. February 26. https://doi.org/10.1257/rct.7253-1.0.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
We are interested in how preferences over risky intertemporal lotteries are affected by salience level of different states.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The proposed experiment has a within-subjects design aimed at testing our model. Subjects will face a total of 6 tasks. Tasks 1-3 try to differentiate our theory from others and test the accuracy of its special predictions. Tasks 4 and 5 are meant to investigate whether salience theory can contaminate the robustness of earlier results from relevant study. In addition, we add a question to serve as attention check.
Tasks 1-3: for these tasks, subjects make decisions from two options, option A and B. Each option provides two possible payments to subjects. Were all payments to be made, option A provides $18 in one week and $2 in four weeks while option B provides $10 both in one week and four weeks. Except for one question in which the all corresponding payments from every option are promised, each payment in rest tasks has 50% chance to be made. In every task, We introduce four equiprobable events, and for each option we manipulate under which two of these events the payments will be made. As a result, we generate different predictions for different cases.
Tasks 4 and 5: for each task, subjects make one decision from two options, option A and B. Each option provides one possible payment to subjects one week after the experiment. Option A provides $5 with 75% chance and $15 with 25% chance while option B provides $0 with 25% chance and $10 with 75% chance. Same as before, we manipulate the events under which the payment from each option will be made.
Experimental Design Details
Randomization Method
We use python to realize randomization.
Randomization Unit
Task randomization: At session level, each of task 1-3 and attention check is completed first by exactly 25% of total subjects. The subsequent randomization is randomized at individual level. The appearance order of option A and B is randomized under each task at individual level.
Payment randomization: one out of the six tasks will be chosen by using a random number from 1 - 6 generated by program. The actual payment relevant event will be chosen by two digital coins generate by program.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
About 100 subjects
Sample size: planned number of observations
About 100
Sample size (or number of clusters) by treatment arms
The primary goal of our experiment is to observe within-subject preference variations among different tasks, so all subjects are in treatment group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
Caltech Committee for the Protection of Human Subjects
IRB Approval Date
IRB Approval Number
IRB Name
University of California, San Diego Human Research Protections Program
IRB Approval Date
IRB Approval Number
Analysis Plan
Analysis Plan Documents
Experiment Pre-analysis.pdf

MD5: 6409a7d619d8982bef447bc35c0076d9

SHA1: 8317fecf39bd6b9b65f0bb398ba5fc8e21827813

Uploaded At: February 25, 2021

Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)