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Field
Abstract
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Before
In this project, we aim to explore 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 world. To do this, we will randomly assign participants to one of two conditions. In the baseline treatment, participants will engage with an abstract problem of identifying which urn a series of drawn balls comes from. In the narrative treatment, participants will instead receive a made-up story—framed around identifying a culprit—which mirrors the same uncertainty and signals as the baseline but embeds them in a more emotionally and contextually rich framework. 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.
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After
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.
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Last Published
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Before
April 04, 2025 10:01 AM
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After
October 31, 2025 10:03 AM
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Intervention (Public)
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Before
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After
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.
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Primary Outcomes (End Points)
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Before
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.
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After
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.
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Primary Outcomes (Explanation)
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Before
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After
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.
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Experimental Design (Public)
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Before
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. 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..
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After
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.
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Randomization Method
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Before
At the beginning of each experimental session, a computer will randomly assign each participant to one of the two treatments.
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After
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.
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Randomization Unit
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Before
The randomization is at the individual level.
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After
Original experiment: The randomization is at the individual level.
Follow-up: There is no treatment randomization as there is only one treatment.
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Planned Number of Clusters
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Before
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.
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After
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.
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Planned Number of Observations
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Before
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.
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After
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.
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Sample size (or number of clusters) by treatment arms
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Before
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.
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After
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.
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Intervention (Hidden)
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Before
In the experimental treatment, participants will be exposed to a fictional story that incorporates contextual details.
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After
Follow-up: After conducting the experiment originally preregistered, we decided to implement a follow-up study with an additional treatment. In this new condition, participants will be exposed to the same fictional story (narrative) used in the previous experiment; however, they will receive no information (a blank line) instead of non-diagnostic signals.
This additional treatment aims to isolate the effect of non-diagnostic signals within the narrative condition from the effect of the narrative itself.
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Public analysis plan
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No
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After
Yes
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Public locations
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No
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After
Yes
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