Encoding new memories not only takes place simultaneously with occurring events but also against the backdrop of a rich library of information acquired through ones life. Previous studies show that prior knowledge (e.g. schemas) strengthens the encoding and accelerates the recall of new memories that are both in agreement with (congruent), or in opposition (incongruent) to that previous knowledge. The contradictions between these two lines of research have not been resolved yet. A third suggestion of how prior knowledge influences memory comes from a recent framework, SLIMM (van Kesteren et al., 2012), which postulates that learning shows a non-linear, U-shaped function with degrees of congruency to prior information. However, the SLIMM model remains under scrutiny as not all of its hypotheses have been successfully tested yet. Furthermore, the neural underpinnings of such learning processes remain unknown. While the SLIMM model predicts a trade-off between the mPFC and MTL structures for congruent and incongruent effects respectively, other models predict an essential role of MTL structures in encoding congruent information. In this PhD project, we aim to use behavioural methods as well as neuroimaging techniques (fMRI) in order to understand whether and how prior knowledge structures enhance the encoding and retrieval of new events. Using a novel spatial schema paradigm, we will compare three conditions with varying degrees of congruency to previous knowledge in order to test the three seemingly contradictory patterns of findings in the literature. Additionally, we will use fMRI to directly compare learning systems in the brain that support learning under certain (congruent) and uncertain (incongruent) conditions. This will allow us to offer a refined neuroscientific model of how brain networks interact to successfully integrate new information with previous knowledge schemas. Importantly, to the best of our knowledge, we will be the first to use a machine learning classifier to decode schemas in the brain by investigating which brain networks are responsible for representing knowledge structures and integrating new learning into them. Understanding how learning is influenced by previous knowledge bear implications for improving clinical conditions, educational methods and machine learning techniques.
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