Dissociable Microstructural Correlates of Learning Rate and Learning Noise in Gamified Reward-Based Decision-Making

Description: Humans learn which actions yield the highest rewards through trial and error, gradually forming expectations about outcomes. Yet, people differ substantially in how quickly and precisely they learn. Such individual variability may partly be explained by differences in the brain’s microstructural organisation. In this large-scale study, 248 participants completed a gamified reward-learning task and underwent quantitative MRI to assess whole-brain microstructural indices of myelination (R1) and cortical iron (R2*). Using computational modelling, we quantified participants’ learning rates and learning noise, reflecting variability in how reward information is updated over time. Whole-brain voxel-based quantification analyses revealed that increased myelination in the cerebellum was associated with a higher learning rate, whereas learning noise was linked to increased myelination and iron concentration in the precentral gyrus. Together, these findings show that reward learning is not a unitary process but is instead shaped by distinct neurobiological pathways that support learning precision and noise. This work highlights how microstructural variation in sensorimotor and associative cortices contributes to stable versus variable reward learning behaviour across individuals.

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