Dottorato in NEUROSCIENZE DEL COMPORTAMENTO

Titolo della tesi: Behavioral And Neurophysiological Investigations of Rewarded Decision-Making

This thesis investigates the complex relationship between reward and decision-making, focusing on how reward modulates cognitive control, motor planning, and neural network dynamics during decision-making tasks. The research is structured into three studies that address these mechanisms from behavioral, physiological, and neural perspectives. The first study explores how reward influences decision-making strategies using a modified stop-signal task (SST), with three reward contexts cued by different symbols: Neutral (equal reward for go and stop trials), Go+ (higher reward for go trials), and Stop+ (higher reward for stop trials), by employing the Drift Diffusion Model (DDM). Results indicate that reward expectations promote proactive strategies, , leading to faster reaction times and a bias toward initiating action rather than relying on reactive control mechanisms. This suggests that reward primarily modulate motor preparation, leading to quicker decision selection. The second study investigates the physiological underpinnings of reward-driven decision-making by analyzing pupil dilation, a marker of cognitive effort. Results show that reward modulates cognitive effort, with greater pupil dilation observed in conditions requiring more complex decision strategies. Notably, the Neutral reward condition, where rewards are equally distributed across both trial types, elicits the highest cognitive effort, aligning with the Expected Value of Control model. The third study shifts focus to the neural level, invetigating how reward modulates network dynamics in the dorsal premotor cortex (PMd) during decision-making. Using graph theory and multifractal analysis, we demonstrate that reward influences the topological organization of neural networks, with higher-reward conditions requiring greater inhibition (Stop plus condition) exhibiting more random network configurations topologies, suggesting less efficient information transfer. Together, these studies provide a multi-layered understanding of how reward modulates decision-making across behavioral, physiological, and neural domains. Our findings suggest that reward expectations not only shape cognitive strategies and motor responses but also influence the dynamic organization of PMd networks, offering valuable insights into the mechanisms underlying adaptive decision-making in complex environments.