How Does Ego-Relevance Affect Nuisance Neglect? The Role of Motivated Skepticism

Last registered on August 27, 2026

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Pre-Trial

Trial Information

General Information

Title

How Does Ego-Relevance Affect Nuisance Neglect? The Role of Motivated Skepticism

RCT ID

AEARCTR-0017553

Initial registration date

August 27, 2026

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

August 27, 2026, 1:00 PM EDT

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

Locations

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Primary Investigator

Name

Shuyu Wang

Affiliation

Southern University of Science and Technology

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Other Primary Investigator(s)

PI Name

Maoliang Ye

PI Affiliation

Southern University of Science and Technology

Contact Investigator

Additional Trial Information

Status

In development

Start date

2026-09-07

End date

2027-08-01

Keywords

Behavior, Lab

Additional Keywords

belief updating, motivated skepticism, ego relevance, cognitive bias, nuisance neglect

JEL code(s)

C91, D83, D91

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

Do people account for payoff-irrelevant but signal-relevant variables (i.e., nuisance variables) when interpreting information, and does ego-relevance influence this inference process?

We study nuisance neglect in a transparent two-component signal-generating structure in which a payoff-relevant rank state (X) and a payoff-irrelevant but signal-relevant nuisance variable (Y) jointly determine the observed signal (S).

In the experiment, each subject completes two parallel tasks in randomized order: an IQ-style task, which is ego-relevant, and a temperature-guessing task, which is ego-irrelevant. In each task, we elicit subjects’ prior beliefs about their rank state, provide a signal generated by a known rule that combines the true state and the nuisance variable, and then elicit posterior beliefs.

We randomize the posterior elicitation condition between subjects. In the Narrow condition, subjects report posterior beliefs about the state only. In the Broad condition, subjects first report which distributions of the nuisance variable are objectively compatible with the observed signal, and then report posterior beliefs about the state. This condition makes the nuisance variable salient and encourages subjects to consider how it enters the signal-generating process.

We measure nuisance neglect using the relative distance between subjects’ posterior beliefs and the Bayesian and fully nuisance-neglect benchmarks. Our main analysis tests whether ego-relevance affects nuisance neglect differently across signal valence. We hypothesize that ego-relevance increases nuisance neglect for good signals but decreases nuisance neglect for bad signals.

External Link(s)

Registration Citation

Citation

Wang, Shuyu and Maoliang Ye. 2026. "How Does Ego-Relevance Affect Nuisance Neglect? The Role of Motivated Skepticism." AEA RCT Registry. August 27. https://doi.org/10.1257/rct.17553-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

First, ego relevance is varied through the task. Each subject completes two tasks in randomized order: an IQ-style task, which is ego-relevant, and a temperature-guessing task, which is ego-irrelevant. In each task, subjects report beliefs about their performance rank in that task. This structure allows us to compare belief updating across ego-relevant and ego-irrelevant settings while using all task-level observations. Task order is randomized to help address possible order effects.

Second, posterior elicitation condition is randomized between subjects through the elicitation format. In the Narrow condition, subjects report posterior beliefs about the rank state only. In the Broad condition, subjects first report which distributions of the nuisance variable are objectively compatible with the observed signal under the known signal-generating rule. They then report posterior beliefs about the rank state.

Intervention Start Date

2026-09-07

Intervention End Date

2027-08-01

Primary Outcomes

Primary Outcomes (end points)

Prior beliefs, posterior beliefs, compatible nuisance-variable distributions, nuisance neglect index

Primary Outcomes (explanation)

Prior beliefs (by task): For each task, the prior outcome is the subject’s reported probability distribution over the discrete rank states before observing the signal.

Posterior beliefs (by task): For each task, the posterior outcome is the subject’s reported probability distribution over the same rank states after observing the signal.

Compatible nuisance-variable distributions (Broad treatment): In the Broad condition, subjects report the nuisance-variable distributions that are objectively compatible with the observed signal.

Nuisance neglect index (by task): For each task, we compare the subject’s reported posterior with two benchmarks. The first is the Bayesian posterior, calculated using the subject’s reported prior beliefs and the known signal-generating rule. The second is a naïve posterior that treats the signal as directly revealing the rank state. The index compares how close the subject’s reported posterior is to these two benchmarks. We use Wasserstein-1 distance as the main distance measure and normalize the resulting index so that larger values indicate stronger nuisance neglect.

Secondary Outcomes

Secondary Outcomes (end points)

Realized rank, gender, age, education level, major, statistics coursework exposure, intensity of ego relevance, self-reported consideration of the nuisance variable, response time in posterior belief elicitation, willingness to pay for an additional ranking signal and extra posterior belief after receiving the additional signal.

Secondary Outcomes (explanation)

Realized rank (by task): Each subject’s realized performance rank in the IQ task and in the temperature-guessing task, constructed from objective task performance.

Statistics coursework exposure: Indicator for whether the subject has taken any coursework related to probability, statistics, econometrics, or data science (self-reported).

Intensity of ego relevance (by task): Self-reported intensity of ego relevance associated with performance in the IQ task and the temperature-guessing task, measured using Likert-scale measures.

Self-reported consideration of the nuisance variable (by task): Self-reported extent to which subjects considered the effect of the nuisance variable on the ranking signal in the IQ task and in the temperature-guessing task, measured using Likert-scale measures.

Response time in posterior belief elicitation (by task): Time spent on the posterior belief elicitation page in the IQ task and in the temperature-guessing task.

Willingness to pay for an additional ranking signal: Maximum amount the subject is willing to pay at the end of the second task module to obtain an additional ranking signal generated using an independent draw of the nuisance variable.

Extra posterior belief after an additional independent signal: Posterior belief distribution reported after receiving the additional independent ranking signal at the end of the second task module. This variable is defined only for subjects who obtain the additional signal.

Experimental Design

Each subject completes two task modules in randomized order: an IQ-style task module and a temperature-guessing task module.

In each module, subjects first complete the task and are then assigned to a newly formed four-person comparison group. Group assignment is conducted separately in the two modules. They then report a prior belief distribution over their own performance rank within that group. There are four possible performance ranks. Next, subjects observe a rank-related signal. The signal is determined jointly by the subject’s realized rank and a payoff-irrelevant but signal-relevant nuisance variable. Subjects then report beliefs using one of two posterior elicitation conditions.

Each subject is randomly assigned to one posterior elicitation condition, either Narrow or Broad, and uses that same condition in both task modules. In the Narrow condition, subjects report posterior beliefs about their performance rank only. In the Broad condition, subjects report which distributions of the nuisance variable are objectively compatible with the observed signal under the known signal-generating rule, in addition to reporting posterior beliefs about their performance rank.

At the end of the second module, subjects are given an opportunity to purchase an additional ranking signal generated using an independent draw of the nuisance variable and, if they obtain it, submit an additional posterior belief report.

At the end of the experiment, subjects complete a short questionnaire collecting demographics and background characteristics, task-specific intensity of ego relevance, and self-reported consideration of the nuisance variable.

Experimental Design Details

Not available

Randomization Method

Computer-generated randomization implemented by the experimental software.

Randomization Unit

Individual.

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

N/A

Sample size: planned number of observations

About 400 subjects (university students).

Sample size (or number of clusters) by treatment arms

We plan to recruit 400 subjects in total. Subjects are assigned to one of four experimental cells defined by posterior elicitation condition and task order:

Narrow condition, IQ task first, temperature-guessing task second: approximately 152 subjects.
Narrow condition, temperature-guessing task first, IQ task second: approximately 152 subjects.
Broad condition, IQ task first, temperature-guessing task second: approximately 48 subjects.
Broad condition, temperature-guessing task first, IQ task second: approximately 48 subjects.

The allocation prioritizes the Narrow condition, which is the focus of the primary analysis examining the effect of ego-relevance on nuisance neglect across signal valence. The Broad condition receives a smaller allocation and is used for the mechanism analysis examining whether making the nuisance variable salient reduces nuisance neglect.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

IRB

Institutional Review Boards (IRBs)

IRB Name

Southern University of Science and Technology Institutional Review Board

IRB Approval Date

2026-01-04

IRB Approval Number

2025PES444

Analysis Plan

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