Using a linear support-vector machine, we achieve an overall accuracy over 88% for the article-genre detection by using a leave-one-subject-out cross-validation evaluation. The statistics-based features focus on modeling the overall gaze-typing behaviors during the process and the sequence-based features focus on the transition of the gaze-typing behaviors as the piece of writing progresses. Our method deploys statistics- and sequence-based features to infer the mental state of the author during the writing process. Our study involves 46 native Chinese speakers of varying ages from children to elderly. Our study focuses on Chinese typing, particularly via the Pinyin input method, which generates text via a two step method, and requires additional cognitive processes compared to typing in phonographic languages such as English. In this paper, we study the gaze-typing behaviors, specifically, the coordination between eye gaze and typing dynamics, of writers who are producing original articles in different genres: reminiscent, logical and creative. However, only a few studies have explored this relationship. Given that writing is an intensively cognitive process, it makes sense that the type of writing that is being produced would have an effect on the writer’s gaze and typing behaviors. Writing is one of the most common activities undertaken on a computer, and the activity of writing has been widely studied.
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