Exploring the Link Between Visual Attention to Familiar or Novel Food Stimuli and Food Choice Using Integrated Electroencephalography and Eye Tracking: Protocol for Nonrandomized Pilot Study
Bibliographic record
Abstract
BACKGROUND: Understanding the factors influencing food choice is critical for developing effective strategies to promote healthier eating habits and creating policies that support public health. Attentional bias, the inclination to focus attention on specific stimuli, plays a significant role in shaping food preferences by affecting how individuals perceive and react to various food-related elements. Various methodologies exist to examine attentional bias, including the dot-probe task, which measures reaction times to probes appearing after paired stimuli (eg, novel vs familiar food images); eye-tracking, which tracks gaze patterns and fixations to determine visual attention; and electroencephalography, which records brain activity, capturing early and late neural responses (eg, N100, P300) linked to attention processing; however, integrated approaches combining these methods to assess bias toward familiar versus novel foods remain underexplored. OBJECTIVE: This study aims to examine differences in attention toward familiar versus novel food stimuli using integrated eye-tracking, dot-probe, and electroencephalography methods, and to explore associations with self-reported food choice. METHODS: A total of 40 healthy adult participants will be recruited. Participants will be presented with pairs of familiar or novel food images, while their visual attention and brain activity are recorded concurrently. Eye-tracking metrics, including time to first fixation and total fixation duration, will be used to assess visual attention. Electroencephalography data will be collected to measure the amplitude of event-related potential components, such as P300 and N100, associated with attentional processing. Reaction times will also be recorded as a behavioral measure of attentional engagement with familiar versus novel food items. Data analysis will involve repeated measures ANOVA to examine the effects of food familiarity and novelty on attentional bias metrics. Correlation analyses will also be conducted to explore the relationships between eye-tracking, electroencephalography, and dot-probe measures. RESULTS: This study was approved by the Ethics Committee of the Iran University of Medical Sciences in February 2021 and funded in January 2022. Data collection began in November 2022 and is expected to be completed in July 2025. As of the submission of this study, 36 individuals have been recruited. Data analysis has not yet commenced, but it is planned to begin upon the completion of data collection. The results are anticipated to be published by December 2025. The protocol was registered with the Open Science Framework in September 2024. CONCLUSIONS: The main outcome of this study is identifying differences in attentional bias metrics toward familiar versus novel food stimuli at different presentation times. These findings will provide preliminary data on the application of an integrated approach for capturing attentional bias to food-based stimuli based on their familiarity or novelty, and how these biases may be linked to food choice behaviors. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/69541.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".