Abstract

Complex decisions require an adequate model of the world that needs to be updated in response to changes in the environment. In the first part of the talk, I present data addressing how humans form models of other people’s preferences and integrate this information into an own mPFC value computation. By combining computational and representational fMRI techniques I show that prediction errors caused by learning about the preferences of another individual drive changes in local cortical representations, resulting in a change in subjects´ own preference. In the second part of the talk, I show that a newly learnt model of the relationships between objects is stored as a map in the entorhinal cortex. This demonstrates that complex map-like organisations can be reconstructed from entorhinal fMRI responses when relationships are non-spatial rather than spatial, discrete rather than continuous and unavailable to conscious awareness. The representation of abstract knowledge in map-like structures suggests that inferences do not need to rely on experiences but can be computed anew from mapped knowledge.