Neuroplasticity in Economic Decision Making Under Active Choice

Neuroeconomics, an emerging field blending neurobiology and economic theory, investigates decision-making and reinforcement learning. Grounded in early Russian and Soviet research, this field examines how dopaminergic circuits assess and choose favorable options based on sensory, motivational, and cognitive inputs. Traditional decision-making models often overlook perceptual changes linked to reinforcement learning. Modern theories suggest that sensory cortex adaptability, influenced by reinforcement, plays a critical role in decision-making. Our study aims to integrate sensory plasticity into neuroeconomic models, providing a comprehensive understanding of how experiences and repeated behaviors affect sensory cortex reorganization and decision-making. We developed an audio version of a lottery-like one-armed bandit task to explore the dynamics of reinforcement learning and its relationship with neuroplasticity markers (MMN, P3a, and brain oscillatory activity). The study involved 29 participants who chose between two audio-encoded options with different reward probabilities. EEG data were recorded and analyzed using custom Python scripts and the MNE library. Artifact removal and segmentation were performed, focusing on the Cz electrode due to its prominent amplitude expression. A repeated measures ANOVA revealed significant differences in responses to standard and deviant sounds before and after the game, and between sounds encoding large and small losses. These findings highlight the role of sensory neuroplasticity in decision-making and underscore the importance of incorporating sensory changes in reinforcement learning models. This study enhances our understanding of the interaction between sensory input and reinforcement learning in shaping behavior.