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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,023 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».