analyzing human gaze path during an interactive optimization task
Interactive Evolutionary Computation (IEC) community aims at reducing user's fatigue during an optimization task involving subjective criteria: a set of graphic potential solutions are simultaneously shown to a user which task is to identify most interesting solutions to the problem he had to solve. Evolutionary operators are applied to user choices expecting to produce better solutions. As traditional IEC ask the user to give a mark to each solution or to explicitly choose bests solutions with a mouse, we propose a new framework that uses in real time gaze information to predict which parts of a screen is more significant for a user. We can therefore avoid the user to explicitly choose which solutions are interesting for him. In this paper, we mainly focus on automatically ordering solutions shown on a screen given a gaze path obtained by an eye-tracker. We applied several supervised learning methods (SVM, neural networks...) on two different experiments. We obtain a formula that predict with 85\% user choices. We demonstrate that decisive criterion is time spent on one solution and we show the independency between this formula and the experiment.
interactive evolutionary computation supervised learning human gaze behaviour implicit choice eye-tracker