Predictive Neuromarketing: A Bayesian and Predictive-Coding Framework for Consumer Neuroscience

Abstract

Neuromarketing has emerged as a rapidly expanding field aimed at understanding the neural mechanisms that shape consumer behaviour. Yet despite its empirical success, the field lacks a unifying computational theory. In contrast, cognitive neuroscience increasingly converges on the Bayesian Brain and predictive-coding frameworks, which conceptualise perception, learning, and decision-making as hierarchical predictive processes driven by minimisation of precision-weighted prediction errors. This paper introduces Predictive Neuromarketing, a hybrid neuroscience–marketing paradigm that integrates predictive coding with consumer neuroscience findings. We develop a mathematical framework that formalises consumer expectations, brand priors, price cues, and prediction errors, providing a computational explanation for phenomena such as price placebo effects, brand-identity modulation, electroencephalography (EEG)-based preference prediction, and neuroforecasting of advertising success. We then reinterpret the empirical neuromarketing literature through this lens and propose experimental paradigms to test predictive-coding principles in consumer contexts. By embedding neuromarketing within a rigorous predictive framework, we offer a mechanistic account of how marketing stimuli shape consumer beliefs, valuation, and behaviour. The paper concludes with ethical considerations and a research agenda for advancing Predictive Neuromarketing. The contribution of this worki s the formal integration of consumer neuroscience findings into a cohesive Bayesian and predictive-coding generative model, producing clear, testable computational predictions without introducing additional empirical data.