Information theory has influenced a large number of scientific fields since its first introduction in 1948. Apart from Fitts' law and Hick's law, which came out when experimental psychologists were still enthusiastic about applying information theory to various areas of psychology, the relation between information theory and human-computer interaction (HCI) has rarely been explored.
This thesis strives to bridge the gap between information theory and HCI by taking the stance that human-computer interaction can be considered as a communication process and therefore can be characterized using information-theoretic concepts. The three main contributions are: (1) a detailed historical perspective on how information theory influenced psychology and HCI, particularly an in-depth discussion and analysis of how relevant Hick's law is for HCI; (2) a Bayesian Information Gain (BIG) framework that quantifies the information sent by the user to the computer to express her intention; and (3) a further illustration of the advantages of using information-theoretic measures to evaluate input performance and to characterize the rich aspects of an interaction task. This thesis demonstrates that information theory can be used as a unified tool to understand and design human-computer communication & interaction.