In this study, we investigated individual differences in neurocomputational brain activation within a healthy population using a Pavlovian task in an fMRI setting. Our goal was to determine the extent to which individuals relied on model-free versus model-based processes during learning and to explore the relationship between these processes and maladaptive behaviors. Drawing on the instrumental conditioning literature, we hypothesized that participants with higher activation related to reward prediction errors, relative to state prediction errors, would be more vulnerable to maladaptive behaviors. Reward prediction errors, associated with model-free processes, and state prediction errors, linked to model-based processes, were derived from BOLD responses in specific regions of interest and computed as the difference to create a beta index. We conducted an exploratory factor analysis to extract transdiagnostic scores from validated psychological questionnaires, followed by correlational analyses between the beta index and the transdiagnostic factors. The results revealed a positive correlation between the beta index and a factor associated with impulsivity, as well as a negative correlation between the beta index and a factor linked to depression. These findings provide preliminary insight into the neurocomputational mechanisms of pavlovian learning and their relationship to maladaptive behavior. However, they should be interpreted with caution, given the study's limitations.