Reinforcement Learning is a framework from behavioural science that looks at how agents learn from reward outcomes through trial and error. This project aims to investigate the relationship between RL-related behaviour and structural variation within target brain regions, traditionally associated with reward-based learning and decision-making. Behavioural and structural 7T MRI data of 37 participants was used for the analyses. Behavioural data from a probabilistic learning task was analysed using the RL-Accumulator Decision Model (RL-ARD) (Miletić et al., 2021). Grey matter volume was extracted and computed from chosen frontostriatal, limbic, and dopaminergic regions. Brain-behaviour relationships were examined using Bayesian regression analyses on the behavioural parameters and grey matter volumes. The findings give insight into the structural basis of individual differences in learning and decision behaviour in an RL context.