Probing of human implicit intent based on eye movement and pupillary analysis for augmented cognition

Abstract

The ultimate purpose of augmented cognition is to enhance human cognitive abilities, which are intrinsically limited. To enhance limited human cognitive abilities, we developed a human augmented cognition system that can offer appropriate information or services by actively responding to the user's intention. This article mainly describes a framework for probing human implicit intentions for the purpose of augmented cognition. The type of user intention, either task‐free human implicit intention or task‐oriented human implicit intention, can be predicted based on fixation count, fixation length, and pupil size variation induced by eye response. Further, these features are used to detect the transition point between task‐free human implicit intention and task‐oriented human implicit intention. Maximum a Posteriori in Naïve Bayes classification model is used for selecting relevant query keywords to search and retrieve specific information from a personalized knowledge database. The experimental results show that the proposed human intention recognition and probing models are suitable for achieving the goal of augmented cognition. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 114–126, 2013