Can Attribution Bias Improve Preferences for Healthy Snacks? Evidence from a Neuroeconomic Study

Last registered on February 18, 2026

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Pre-Trial

Trial Information

General Information

Title

Can Attribution Bias Improve Preferences for Healthy Snacks? Evidence from a Neuroeconomic Study

RCT ID

AEARCTR-0013120

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, 6:00 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

Name

Prabin Adhikari

Affiliation

University of Florida

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Other Primary Investigator(s)

PI Name

Bachir Kassas

PI Affiliation

University of Florida

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PI Name

Rodolfo Nayga

PI Affiliation

Texas A & M University

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Additional Trial Information

Status

In development

Start date

2026-03-01

End date

2026-06-01

Keywords

Behavior, Health, Lab

Additional Keywords

Neuroeconomics, Electroencephalography, Willingness to Pay

JEL code(s)

C91, D87, D91

Secondary IDs

Prior work

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

Abstract

Diet-related chronic diseases impose substantial health and economic costs worldwide, making it critical to understand the mechanisms that drive persistent unhealthy food consumption. Traditional approaches to addressing this challenge focused on hard-end (e.g., taxes, subsidies, bans) and soft-end (e.g., nudges) measures to influence consumer decisions. However, these measures fail to account for a critical root cause of unhealthy food consumption: the indulgent and rewarding experience gained from consuming unhealthy foods. It is therefore important to account for consumption experience and memory when designing nudges or interventions to steer consumers to healthy food choices. This study fills this gap by utilizing attribution bias to examine how induced positive consumption experience with a healthy food influences future preferences. A neuroeconomic approach is adopted to investigate both neurophysiological and behavioral indicators of preference.

External Link(s)

Registration Citation

Citation

Adhikari, Prabin, Bachir Kassas and Rodolfo Nayga. 2026. "Can Attribution Bias Improve Preferences for Healthy Snacks? Evidence from a Neuroeconomic Study." AEA RCT Registry. February 18. https://doi.org/10.1257/rct.13120-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention Start Date

2026-03-01

Intervention End Date

2026-06-01

Primary Outcomes

Primary Outcomes (end points)

1. Subjects' bids in a Becker-DeGroot-Marschak (BDM) task will be compared across control and treatment.
2. Frontal Asymmetry Index (FAI) will be calculated from EEG data and compared between control and treatment as subjects view the product in the BDM.
3. Event-Related Potentials (ERPs) will be measured during a 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

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 report their preferences by answering survey questions and completing a BDM task. Neurophysiological indicators of preference will also be collected as subjects evaluate the healthy food product in the BDM task and during their participation in 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?