The aim of my research is to gain a better understanding of statistical learning from a psychological and neuroscientific perspective. Statistical learning is a fundamental learning mechanism that extracts repeating patterns from the environment. It plays an important role in perception, predictive processing, memory and the acquisition of skills; it is therefore an important aspect of life from infancy to old age.

In the past decades, psychology and neuroscience has focused extensively on the role of cerebral cortex in cognition, at the expense of the subcortical structures. However, emerging evidence shows that subcortical structures play important roles in cognition as well. My research aims to contribute to this field by highlighting that multiple cortical and subcortical structures contribute to a given cognitive domain, the same structures contribute to multiple domains, and their roles vary dynamically according to the properties of the task at hand (many structures to many functions - MaMa - model).