Information theory as a unifying tool for understanding and designing human-computer interaction
Résumé
Information theory, particularly in a Bayesian context, has recently regained interest as a unified tool to understand and design human-computer communication and interaction. Like in everyday life, by continuously making predictions using previous experiences, the human-computer interaction should somehow aim at reducing the level of uncertainty (entropy), reinforcing true predictions and correcting wrong ones, via some feedback interaction loop. Taking the stance that human-computer interaction can be considered as a communication process, where uncertainty and information are described using information-theoretic terms, we can design interaction in such a way to optimize decisions under uncertainty. In my presentation I will illustrate these concepts in the context of a Bayesian experimental design using an expected utility function, where the computer can "runs experiments" on the user by sending feedback that maximizes the expected gain of information by the computer, and exploits the users’ subsequent input to update its knowledge as interaction progresses. Applications to Fitts' law, fast file retrieval and multiscale navigation are given to illustrate the concepts.