Computational Approaches to Access Probabilistic Population Codes for Higher Cognition an Decision-Making

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.

Neuroscientific User Models: The Source of Uncertain User Feedback and Potentials for Improving Web Personalisation

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.

Human Uncertainty and Ranking Error -- The Secret of Successful Evaluation in Predictive Data Mining

One of the most crucial issues in data mining is to model human behaviour in order to provide personalisation, adaptation and recommendation. This usually involves implicit or explicit knowledge, either by observing user interactions, or by asking users directly. But these sources of information are always subject to the volatility of human decisions, making utilised data uncertain to a particular extent. In this contribution, we elaborate on the impact of this human uncertainty when it comes to comparative assessments of different data mining approaches. In particular, we reveal two problems: (1) biasing effects on various metrics of model-based prediction and (2) the propagation of uncertainty and its thus induced error probabilities for algorithm rankings. For this purpose, we introduce a probabilistic view and prove the existence of those problems mathematically, as well as provide possible solution strategies. We exemplify our theory mainly in the context of recommender systems along with the metric RMSE as a prominent example of precision quality measures.