Nudging with Attribution Bias: Promoting Healthy Over Unhealthy Food Preferences

Last registered on February 18, 2026

Pre-Trial

Trial Information

General Information

Title
Nudging with Attribution Bias: Promoting Healthy Over Unhealthy Food Preferences
RCT ID
AEARCTR-0016293
Initial registration date
February 11, 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
February 18, 2026, 9:15 AM EST

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

Locations

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Primary Investigator

Affiliation
University of Florida

Other Primary Investigator(s)

PI Affiliation
University of Florida
PI Affiliation
Texas A&M University

Additional Trial Information

Status
In development
Start date
2026-04-01
End date
2026-07-01
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Diet-related diseases are a leading contributor to global morbidity, mortality, and rising health expenditures. Beyond direct health impacts, poor diets also impose large social and economic costs through lost productivity, increased healthcare demand, and long-term fiscal pressures on public systems. These challenges raise the importance of identifying behavioral mechanisms that systematically bias food choice and exploring how they can be leveraged to improve consumer diets. One such mechanism is attribution bias—the tendency for individuals to misattribute state-driven experiences to intrinsic product properties. While widely observed in behavioral studies, little is known about the internal mechanisms through which attribution bias shapes preferences, and whether this bias can be leveraged in behavioral nudges to steer individual choices away from unhealthy foods towards healthier alternatives. This study fills this gap by integrating behavioral and neuroeconomic methods to examine how hunger-induced attribution bias influences choice between healthy and unhealthy snack alternatives.
External Link(s)

Registration Citation

Citation
Adhikari, Prabin, Bachir Kassas and Rodolfo Nayga. 2026. "Nudging with Attribution Bias: Promoting Healthy Over Unhealthy Food Preferences." AEA RCT Registry. February 18. https://doi.org/10.1257/rct.16293-1.0
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2026-04-01
Intervention End Date
2026-07-01

Primary Outcomes

Primary Outcomes (end points)
1. Subjects' choices between the healthy and equivalent, unhealthy snack in the choice task will be compared between control and treatment.
2. Event-Related Potentials (ERPs) will be measured during the cue exposure task and key components (e.g., P1, N1, N2, P3, LPP, RewP) will be compared between control and treatment.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Age, gender, ethnicity, income, education, exercise frequency and intensity, duration since last meal, current appetite, number of meals and snacks per day, expected time until next meal, consumption experience.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment is conducted across two days. On the first day, subjects will consume a healthy snack under a control or treatment condition. On day two, they will make choices between the healthy snack and an equivalent, unhealthy alternative in a choice task. Neurophysiological indicators of preferences for the healthy and unhealthy snacks will also be collected during a cue exposure task.
Experimental Design Details
Not available
Randomization Method
Participants will be assigned equally to Control and Treatment groups (60 in each). First, each of the 120 participants is labeled with a unique identifier. A random number is then generated for each ID from a uniform distribution. The numbers are ranked from lowest to highest, and the 60 IDs associated with the lowest numbers are placed in the Control group, while the remaining 60 are placed in the Treatment group. This ensures an exact 50–50 split while preserving random assignment.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Not clustered
Sample size: planned number of observations
120
Sample size (or number of clusters) by treatment arms
60
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Choice outcome minimum detectable effect size: 0.230 (m1=0.568, sd1=0.502, sd2=0.381)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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