Narrative Belief Updating

Last registered on October 31, 2025
View Trial History

Pre-Trial

Trial Information

General Information

Title
Narrative Belief Updating
RCT ID
AEARCTR-0015672
Initial registration date
April 01, 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
April 04, 2025, 10:01 AM EDT

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

Last updated
October 31, 2025, 10:03 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country
Italy
Region

Primary Investigator

Name
Andrea Albertazzi
Affiliation
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Paolo Pin
PI Affiliation
University of Siena
Contact Investigator
PI Name
Marco Stimolo
PI Affiliation
University of Siena
Contact Investigator
PI Name
Alessandro Stringhi
PI Affiliation
Prague University of Economics and Business
Contact Investigator

Additional Trial Information

Status
In development
Start date
2025-04-02
End date
2026-04-09
Keywords
Behavior, Lab
Additional Keywords
belief updating, Bayesian, experiment, narratives, belief formation, information processing, uninformative signals, Bayes’ theorem
JEL code(s)
C91; D81; D83; D91
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In this project, we examine how narratives influence the incorporation of new information into individuals’ beliefs. Specifically, we will replicate a classic Bayesian problem in a controlled experiment, where individuals face uncertainty and use new information to update their beliefs about the true state of the world. To this end, we will randomly assign participants to one of two conditions. In the baseline treatment, participants will engage with an abstract statistical urn problem: participants must identify from which of two urns, with different compositions, the colored balls are drawn. In the narrative treatment, participants will instead receive a fictional story—framed around identifying a culprit—that mirrors the same statistical problem. The only difference from the baseline is a more contextually rich framing.
By comparing how participants update their beliefs in these two conditions, we aim to assess whether the presence of a narrative systematically alters the way new informative and uninformative evidence is interpreted.
External Link(s)

Registration Citation

Citation
Albertazzi, Andrea et al. 2025. "Narrative Belief Updating." AEA RCT Registry. October 31. https://doi.org/10.1257/rct.15672-2.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Participants will complete a belief‐updating task based on a classic Bayesian inference problem. Each participant is randomly assigned to one of two between‐subjects conditions. In the Control (Abstract) condition, the task is presented as a neutral statistical problem involving two urns with different color compositions. Participants observe sequential draws of colored balls and must update their beliefs about which urn the draws come from. In the Narrative condition, the same statistical structure is embedded in a short fictional story in which participants must infer the identity of a culprit based on successive pieces of evidence. The only difference between treatments lies in the framing: while the underlying probabilistic structure and evidence are identical, the narrative condition provides a contextually rich storyline that may alter how information is interpreted and integrated into beliefs.

The information-generating process is the following: participants will receive a signal in favour of the true state (positive) with a 45% chance, in favour of the incorrect state (negative) with 30%, and will receive nondiagnostic information with a 25% chance.
Intervention Start Date
2025-04-02
Intervention End Date
2026-04-09

Primary Outcomes

Primary Outcomes (end points)
Original experiment: We are interested in if and how a narrative changes the way people update new information. For this reason, we will primarily focus on deviations of beliefs from their Bayesian benchmark. We will do this for both experimental treatments.
We will also look at whether uninformative signals affect belief updating.

Follow-up: In the original experiment, we observed that participants exposed to narrative framing tended to misinterpret non-diagnostic signals, treating them as if they carried positive or negative information. In this follow-up, we introduce a treatment where the same narrative is presented, but the non-diagnostic signals are replaced with blank (missing) information. The primary outcome remains participants’ reported beliefs and their deviations from the Bayesian benchmark.
Primary Outcomes (explanation)
The primary outcome is the comparison of participants’ reported beliefs from the Bayesian posterior implied by observed signals.

In the follow-up, we look at these comparisons across two narrative conditions—with and without non-diagnostic signals.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Original experiment: In both treatments, participants will be presented with an initial "story". In the baseline, the story concerns two urns containing balls of three different colours. In the narrative treatment, the story is about two possible culprits of a series of thefts. Subjects are asked to state their prior belief about which one between the two urns/culprits is the correct one. Then, participants play 10 rounds in which they receive new information every time. The new information can be in favour of one of the two alternatives or uninformative. The ex-ante probabilities of receiving each piece of new information are public knowledge. Subjects are asked to state their posterior belief in every round upon receiving the new information.

Follow-up: The structure of this experiment is the same as the Narrative treatment in the original experiment. The only difference is that instead of receiving a nondiagnostic signal with a probability of 0.25, subjects will receive no information (a blank signal) with the same probability.
Experimental Design Details
Not available
Randomization Method
Original experiment: At the beginning of each experimental session, a computer will randomly assign each participant to one of the two treatments.

Follow-up: There is no treatment randomization as there is only one treatment.
Randomization Unit
Original experiment: The randomization is at the individual level.

Follow-up: There is no treatment randomization as there is only one treatment.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Original experiment: We plan to run 24 lab sessions of 15 subjects each. We will continue collecting data until subjects show up. We plan to collect at least 90 observations per treatment. This means that at least 180 participants must complete the experiment.
In case of funding availability, we will also run an online version of the experiment to increase the robustness of the lab results. In this case, we plan to collect at least 300 observations.

Follow-up: as in the original lab experiment, we aim to recruit at least 90 participants in this treatment.
Sample size: planned number of observations
We plan to run 24 lab sessions of 15 subjects each. We will continue collecting data until subjects show up. We plan to collect at least 90 observations per treatment. This means that at least 180 participants must complete the experiment. In case of funding availability, we will also run an online version of the experiment to increase the robustness of the lab results. In this case, we plan to collect at least 300 observations. Follow-up: as in the original lab experiment, we aim to recruit at least 90 participants in this treatment.
Sample size (or number of clusters) by treatment arms
We plan to run 24 lab sessions of 15 subjects each. We will continue collecting data until subjects show up. We plan to collect at least 90 observations per treatment. This means that at least 180 participants must complete the experiment.
In case of funding availability, we will also run an online version of the experiment to increase the robustness of the lab results. In this case, we plan to collect at least 300 observations.


Follow-up: as in the original lab experiment, we aim to recruit at least 90 participants in this treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

Documents

Document Name
Translated Instructions
Document Type
survey_instrument
Document Description
Intructions translated from Italian to English.
File
Translated Instructions

MD5: ae4773d015564af6754129559060a851

SHA1: cab01c5cf010e03ff92c959d629827cef62e5f73

Uploaded At: March 29, 2025

Document Name
Original Instructions
Document Type
survey_instrument
Document Description
Original instructions in Italian.
File
Original Instructions

MD5: 4d7f6e672acd8a12529270e5846ee41c

SHA1: 2c2dc266cf5dab451c3dce2c00424bc337ec02fa

Uploaded At: April 01, 2025

Document Name
Original Instructions - Follow up
Document Type
survey_instrument
Document Description
Original instructions in Italian - follow up treatment.
File
Original Instructions - Follow up

MD5: 5e03bc6754a08c5dc7a57d776a0b0baa

SHA1: 39037143683d7fad21ccae4aa494ca846cb627a0

Uploaded At: October 28, 2025

IRB

Institutional Review Boards (IRBs)

IRB Name
Comitato per la RicercA Etica nelle scienze Umane e Sociali – CAREUS
IRB Approval Date
2024-03-04
IRB Approval Number
n. 17/2024
IRB Name
Comitato per la RicercA Etica nelle scienze Umane e Sociali – CAREUS
IRB Approval Date
2025-10-07
IRB Approval Number
5/2025
Analysis Plan

Analysis Plan Documents

Analysis plan

MD5: 73ed563530565fdd2de7a15afb6de4cc

SHA1: ade6235eaa7b0b62e61b13138b81c5afd1d702c4

Uploaded At: March 31, 2025

Updated Pre Analysis Plan

MD5: 425ba82340cfc83beb59102a32f7f46d

SHA1: eb41a9007b9f68c9a3943bda1b88b09b2f32c440

Uploaded At: October 31, 2025