  Animals flexibly change their behaviour depending on context. In theory, such
context-dependent behaviour can be explained by model-based reinforcement
learning models. However, existing models lack structure underlying
context-dependent model selection and thus, a correspondence to neural activity
and brain regions. Here, we employ interacting sequential and context-inference
modules to drive model-based learning as a means to better understand
experimental neuronal activity data, lesion studies, and clinical research. We
propose a neural circuit implementation of the sequential module by the
hippocampus and the context-inference module by the cortex that together enable
flexible behaviour. Our model explains a variety of experimental findings,
including impairments in model-based reasoning reported in lesion studies.
Furthermore, our model predicts the relationship between deficits in
model-based learning and sensory processing, which often co-occur in psychoses
such as schizophrenia (SZ) or autism spectrum disorder (ASD).
