  Recent research revealed a considerable lack of reliability for user feedback
when interacting with adaptive systems, often denoted as user noise or human
uncertainty. Moreover, this lack of reliability holds striking impacts for the
assessment of adaptive systems and personalisation approaches. Whenever
research on this topic is done, there is a very strong system-centric view in
which user variation is something undesirable and should be modelled with the
eye to eliminate. However, the possibilities of extracting additional
information were only insufficiently considered so far.
  In this contribution we consider the neuroscientific theory of the Bayesian
brain in order to develop novel user models with the power of turning the
variability of user behaviour into additional information for improving
recommendation and personalisation. To this end, we first introduce an adaptive
model in which populations of neurons provide an estimation for a feedback to
be submitted. Subsequently, we present various decoder functions with which
neuronal activity can be translated into quantitative decisions. The interplay
of cognition model and decoder functions lead to different model-based
properties of decision-making. This will help to associate users to different
clusters on the basis of their individual neural characteristics and thinking
patterns. By means of user experiments and simulations, we show that this
information can be used to improve the standard collaborative filtering.
