  In this paper we consider the neuroscientific theory of the Bayesian brain in
the light of adaptive web systems and content personalisation. In particular,
we elaborate on neural mechanisms of human decision-making and the origin of
lacking reliability of user feedback, often denoted as noise or human
uncertainty. To this end, we first introduce an adaptive model of cognitive
agency in which populations of neurons provide an estimation for states of the
world. Subsequently, we present various so-called decoder functions with which
neuronal activity can be translated into quantitative decisions. The interplay
of the underlying cognition model and the chosen decoder function leads to
different model-based properties of decision processes. The goal of this paper
is to promote novel user models and exploit them to naturally associate users
to different clusters on the basis of their individual neural characteristics
and thinking patterns. These user models might be able to turn the variability
of user behaviour into additional information for improving web personalisation
and its experience.
