2018/02/16 by Kevin Jasberg, Jasberg, Kevin, Sergej Sizov +1
Neuroscience · #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Neural and Evolutionary Computing (cs.NE) #Neural dynamics and brain function
paper · pdf · doi:10.48550/arxiv.1802.05892
openalex publication_date 2018/02/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper we consider the neuroscientific theory of the Bayesian brain in\nthe light of adaptive web systems and content personalisation. In particular,\nwe elaborate on neural mechanisms of human decision-making and the origin of\nlacking reliability of user feedback, often denoted as noise or human\nuncertainty. To this end, we first introduce an adaptive model of cognitive\nagency in which populations of neurons provide an estimation for states of the\nworld. Subsequently, we present various so-called decoder functions with which\nneuronal activity can be translated into quantitative decisions. The interplay\nof the underlying cognition model and the chosen decoder function leads to\ndifferent model-based properties of decision processes. The goal of this paper\nis to promote novel user models and exploit them to naturally associate users\nto different clusters on the basis of their individual neural characteristics\nand thinking patterns. These user models might be able to turn the variability\nof user behaviour into additional information for improving web personalisation\nand its experience.\n