Intertemporal Salience Theory

Last registered on February 26, 2021


Trial Information

General Information

Intertemporal Salience Theory
Initial registration date
February 25, 2021

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
February 26, 2021, 12:54 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.


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.
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)

Institutional Review Boards (IRBs)

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


MD5: 6409a7d619d8982bef447bc35c0076d9

SHA1: 8317fecf39bd6b9b65f0bb398ba5fc8e21827813

Uploaded At: February 25, 2021


Post Trial Information

Study Withdrawal

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information


Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials