Beliefs from Cues

Last registered on March 07, 2025
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Pre-Trial

Trial Information

General Information

Title
Beliefs from Cues
RCT ID
AEARCTR-0015484
Initial registration date
March 04, 2025

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
March 07, 2025, 8:02 AM EST

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

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Primary Investigator

Name
John Conlon
Affiliation
Carnegie Mellon University
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Other Primary Investigator(s)

PI Name
Spencer Kwon
PI Affiliation
Brown University
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Additional Trial Information

Status
In development
Start date
2025-03-05
End date
2025-04-30
Keywords
Behavior
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We study the effect of cues---normatively irrelevant messages that may nonetheless bring certain possibilities to min---on beliefs in a simple controlled experiment conducted on Prolific.
External Link(s)

Registration Citation

Citation
Conlon, John and Spencer Kwon. 2025. "Beliefs from Cues." AEA RCT Registry. March 07. https://doi.org/10.1257/rct.15484-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2025-03-05
Intervention End Date
2025-03-14

Primary Outcomes

Primary Outcomes (end points)
Our primary variable of interest is participants' beliefs about how often the process ends with a positive score. We are also collecting data on how often they believe it ends with an even/odd score. In addition, we collect similarity data to generate predictions from our model.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
We also elicit from participants one outcome that they felt came to mind for them while they were forming their beliefs. Finally we collect a measure of cognitive uncertainty for each belief question, though our model does not generate predictions about cognitive uncertainty.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Treatment variation consists in randomizing whether and what types of cues---example outcomes from the data-generating process---that participants see while they form their beliefs.
Experimental Design Details
Not available
Randomization Method
Done within qualtrics using javascript.
Randomization Unit
Individual-level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
We plan to collect data from 3190 participants.
Sample size: planned number of observations
3190 participants.
Sample size (or number of clusters) by treatment arms
200 each in the following treatments:
1. Control
2. One baseline positive cue
3. One baseline negative cue
4. One extreme positive cue
5. One extreme negative cue
6. One close positive cue
7. One close negative cue
8. Five baseline positive cues
9. Five baseline negative cues
10. Ten baseline positive cues
11. Ten baseline negative cues
12. One impossible positive cue
13. One impossible negative cue

In addition, we will recruit 10 participants to see one cue with a final score equal to each of the 59 possible values not covered by any of the other treatments.

This leads to 13*200 + 10*59 = 3,190 participants.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Carneie Mellon University
IRB Approval Date
2025-02-21
IRB Approval Number
STUDY2025_00000052
Analysis Plan