One of the fundaments of associative learning theories is that surprising events drive
learning by signalling the need to update one’s beliefs. It has long been suggested
that plasticity of connection strengths between neurons underlies the learning of
predictive associations: Neural units encoding associated entities change their
connectivity to encode the learned associative strength. Surprisingly, previous
imaging studies have focused on correlations between regional brain activity and
variables of learning models, but neglected how these variables changes in interregional
connectivity. Dynamic Causal Models (DCMs) of neuronal populations and
their effective connectivity form a novel technique to investigate such learning
dependent changes in connection strengths.
In the work presented here, I embedded computational learning models into DCMs to
investigate how computational processes are reflected by changes in connectivity.
These novel models were then used to explain fMRI data from three associative
learning studies. The first study integrated a Rescorla-Wagner model into a DCM
using an incidental learning paradigm where auditory cues predicted the
presence/absence of visual stimuli. Results showed that even for behaviourally
irrelevant probabilistic associations, prediction errors drove the consolidation of
connection strengths between the auditory and visual areas. In the second study I
combined a Bayesian observer model and a nonlinear DCM, using an fMRI
paradigm where auditory cues differentially predicted visual stimuli, to investigate
how predictions about sensory stimuli influence motor responses. Here, the degree of
striatal prediction error activity controlled the plasticity of visuo-motor connections.
In a third study, I used a nonlinear DCM and data from a fear learning study to
demonstrate that prediction error activity in the amygdala exerts a modulatory
influence on visuo-striatal connections.
Though postulated by many models and theories about learning, to our knowledge
the work presented in this thesis constitutes the first direct report that prediction
errors can modulate connection strength
