  In recent years, research unveiled more and more evidence for the so-called
Bayesian Brain Paradigm, i.e. the human brain is interpreted as a probabilistic
inference machine and Bayesian modelling approaches are hence used
successfully. One of the many theories is that of Probabilistic Population
Codes (PPC). Although this model has so far only been considered as meaningful
and useful for sensory perception as well as motor control, it has always been
suggested that this mechanism also underlies higher cognition and
decision-making. However, the adequacy of PPC for this regard cannot be
confirmed by means of neurological standard measurement procedures.
  In this article we combine the parallel research branches of recommender
systems and predictive data mining with theoretical neuroscience. The nexus of
both fields is given by behavioural variability and resulting internal
distributions. We adopt latest experimental settings and measurement approaches
from predictive data mining to obtain these internal distributions, to inform
the theoretical PPC approach and to deduce medical correlates which can indeed
be measured in vivo. This is a strong hint for the applicability of the PPC
approach and the Bayesian Brain Paradigm for higher cognition and human
decision-making.
