EDITORIAL article

Front. Hum. Neurosci.

Sec. Cognitive Neuroscience

Volume 19 - 2025 | doi: 10.3389/fnhum.2025.1638225

This article is part of the Research TopicMachine-Learning/Deep-Learning methods in Neuromarketing and Consumer NeuroscienceView all 10 articles

Editorial: Machine-Learning/Deep-Learning methods in Neuromarketing and Consumer Neuroscience

Provisionally accepted
Marco BilucagliaMarco Bilucaglia1,2*Luca MainardiLuca Mainardi3,4Thomas Zoëga RamsøyThomas Zoëga Ramsøy5,6Paul Joseph ZakPaul Joseph Zak7,8Margherita ZitoMargherita Zito1,2Vincenzo RussoVincenzo Russo1,2
  • 1Department of Business, Law, Economics and Consumer Behaviour "Carlo A. Ricciardi", Università IULM, Milan, Italy
  • 2Università IULM, Behavior and Brain Lab IULM - Neuromarketing Research Center, Milan, Italy
  • 3Politecnico di Milano Dipartimento di Elettronica Informazione e Bioingegneria, Milan, Italy
  • 4Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, SpinLabs, Milan, Italy
  • 5Neurons Inc., Høje Taastrup, Denmark
  • 6International Center for Applied Neuroscience, Rørvig, Denmark
  • 7Claremont Graduate University Center for Neuroeconomics Studies, Claremont, United States
  • 8Immersion Neuroscience, Henderson, NV, United States

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This Editorial introduces the Research Topic "Machine-Learning/Deep-Learning Methods in Neuromarketing and Consumer Neuroscience," which aims to promote data-driven approaches as solutions to two persistent challenges in the field: low ecological validity and the problem of reverse inference. Despite their promise, ML/DL methods remain underutilized in Neuromarketing and Consumer Neuroscience research. The Research Topic comprises eight contributions—six experimental studies, one dataset paper, and one meta-analysis—exploring mental states such as emotion, engagement, preference, and willingness to pay. These studies employ a range of classifiers, from traditional and ensemble models to shallow and deep neural networks, applied to data from both peripheral and central nervous systems.

Keywords: consumer neuroscience and neuromarketing, machine learning, deep learning, Consumer behaivior, XAI and explainable artificial intelligence, artificial intelligence - AI, Consumer behaviour and decision-making theory, neuroscience methods

Received: 30 May 2025; Accepted: 03 Jun 2025.

Copyright: © 2025 Bilucaglia, Mainardi, Ramsøy, Zak, Zito and Russo. 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: Marco Bilucaglia, Department of Business, Law, Economics and Consumer Behaviour "Carlo A. Ricciardi", Università IULM, Milan, Italy

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Supplementary Material