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ORIGINAL RESEARCH article

Front. Psychol.

Sec. Eating Behavior

Volume 16 - 2025 | doi: 10.3389/fpsyg.2025.1652861

Algorithms on the Tip of the Tongue: A Study on the Influence of AI Recommendations on the Acceptance of High-Calorie Food—An ERP Study

Provisionally accepted
  • 1Kunming Medical University Haiyuan College, Kunming, China
  • 2Huaqiao University, Quanzhou, China
  • 3Jianghan University, Wuhan, China

The final, formatted version of the article will be published soon.

The rapid advancement of artificial intelligence (AI) has instigated transformative alterations in food marketing. Nevertheless, studies on the efficacy of AI advice about high-calorie foods, which often provoke consumer ambivalence, are inadequate. Moreover, prior research on food marketing have not included a comprehensive examination of consumers' conscious and unconscious sentiments. This research, grounded on the Associative-Propositional Evaluation (APE) model, examines the influence of various recommendation sources (AI recommendation vs human recommendation) on consumers' acceptance of high-calorie food. Study 1 investigates consumers' mostly propositional evaluative attitudes using a scenario-based experiment. Study 2 utilizes event-related potentials (ERPs) technology to investigate consumers' fast, subconscious, and mostly associative evaluative views. Study 1 revealed that consumers exhibit a greater propensity to accept high-calorie food based on human suggestions compared to AI recommendations, whereas they show a higher willingness to accept low-calorie food from AI recommendations rather than from human recommendations. Study 2 demonstrated that human suggestions produced a higher P2 amplitude compared to AI recommendations, and high-calorie food resulted in a bigger P2 amplitude than low-calorie food. The high-calorie food recommended by humans elicited a greater LPP amplitude than that recommended by AI. This research explores the neural basis of the influence of different recommendation sources on the acceptance of high-calorie food, providing a scientific basis and new perspectives for enterprises and public health agencies to optimize the design of food recommendation systems, support the formulation of public health policies, and jointly promote healthy dietary behaviors.

Keywords: artificial intelligence, High-calorie food, recommendations, Associative-propositional evaluation model, Event-related potentials

Received: 27 Jun 2025; Accepted: 20 Oct 2025.

Copyright: © 2025 Yang, Lv and Wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Dong Lv, ld15798002728@163.com
Qiang Wei, weiqiang@jhun.edu.cn

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