Neuroscientific User Models
نویسندگان
چکیده
In this paper we consider the neuroscientic 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, oen denoted as noise or human uncertainty. To this end, we rst 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. e interplay of the underlying cognition model and the chosen decoder function leads to dierent model-based properties of decision processes. e goal of this paper is to promote novel user models and exploit them to naturally associate users to dierent clusters on the basis of their individual neural characteristics and thinking paerns. ese user models might be able to turn the variability of user behaviour into additional information for improving web personalisation and its experience.
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