Machine-learning/Deep-learning methods in neuromarketing and consumer neuroscience: Volume III

The fields of Consumer Neuroscience and Neuromarketing represent the frontier of understanding human behavior, merging advanced neuroscientific methodologies with behavioral science to decode the cognitive and emotional drivers of consumption. While Consumer Neuroscience provides the academic bedrock for understanding decision-making and choice preferences, Neuromarketing translates these insights into actionable business strategies, such as optimizing brand engagement and product design.Traditional market research—relying on self-reporting and questionnaires—is increasingly being augmented by the objective measurement of physiological modalities. By capturing central nervous system activity (e.g., EEG, fMRI, fNIRS) alongside peripheral responses (e.g., HRV, Skin Conductance, EMG) and biometric data (e.g., Eye-tracking, Facial Action Coding, Speech Analysis), researchers can access the "hidden" subconscious drivers of consumer choice.The first wave of this field relied on knowledge-driven indices, which often struggled with stability, scalability, and predictive power. This new volume focuses on the paradigm shift toward Data-Driven modeling. By leveraging Machine Learning (ML) and Deep Learning (DL), we can now transform multi-modal physiological data into highly accurate predictive tools. This evolution allows us to move beyond mere correlation, enabling the prediction of complex, high-level consumer behaviors in real-time.As we enter this next phase, we must also address the "black box" nature of AI. This collection takes a particular interest in Explainable AI (XAI)—ensuring that neuroscientific models are not only predictive but also interpretable. Furthermore, with the rise of wearable neurotechnology and Generative AI, we are moving from controlled lab settings to "in-the-wild" consumer environments, raising critical questions regarding data privacy and Neuroethics.This Research Topic seeks original research, reviews, and methodological papers that push the boundaries of ML and DL within Consumer Neuroscience. We particularly welcome submissions that explore high-dimensional data integration and the transition from laboratory findings to real-world applications.Potential topics include, but are not limited to:• Advanced ML/DL Architectures: Novel neural networks (CNNs, RNNs, Transformers) for emotion recognition and mental state estimation in consumer contexts.• Explainable AI (XAI) in Neuroscience: Frameworks that provide neurobiological interpretations of "black-box" predictive models.• Real-world & Mobile Sensing: Utilizing wearables and portable EEG/fNIRS for "in-the-wild" consumer behavior tracking (e.g., in-store navigation, live event engagement).• Cross-Modal Data Fusion: Innovative techniques for integrating disparate data streams (e.g., combining EEG with eye-tracking and heart rate) to increase predictive accuracy.• Generative AI & Synthetic Data: The use of AI to augment small neuroscientific datasets or to create personalized consumer stimuli.• Neuroethics & Data Privacy: Addressing the ethical implications of "brain-reading" in commercial sectors and the development of frameworks for responsible Neuromarketing.• Large-scale Benchmarking: Original open-access datasets and meta-analyses that evaluate the robustness of existing ML models across diverse populations. Topic editors: Paul Joseph Zak, Marco Bilucaglia, Thomas Zoëga Ramsøy, Margherita Zito, Vincenzo Russo Source: https://www.frontiersin.org/research-topics/80492/machine-learningdeep-learning-methods-in-neuromarketing-and-consumer-neuroscience-volume-iii