Abstract. This paper explores the growing intersection between machine learning systems and human neuroeconomic processes, examining how AI-driven environments influence decisionmaking at the neural level. Drawing on insights from computational neuroscience, cognitive psychology, and neuroeconomics, the study outlines how reinforcement learning architectures employed in AI align structurally with the brain’s valuation systems. It highlights the modulation of neural circuits – such as the ventral striatum, prefrontal cortex, and anterior cingulate cortex – through algorithmic feedback, personalization, and reward optimization mechanisms. The paper argues that prolonged engagement with predictive technologies can shape cognitive autonomy, reward sensitivity, and exploratory behaviour, with potential long-term implications for cognitive sovereignty. To address these concerns, the authors propose neuroadaptive and ethically aligned AI design principles that preserve decision-making agency, cognitive flexibility, and mental wellbeing. The study contributes to the emerging field of neuroeconomic design and suggests a paradigm shift toward human-compatible AI systems.

Keywords: neuroeconomics, machine learning, reinforcement learning, cognitive autonomy, decision-making, AI ethics, predictive systems.