Inverse Eye Tracking – Inferring Eye Movements from Keypress Data
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School of Science |
Master's thesis
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en
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40
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Abstract
We propose a model that predicts where users direct their gaze during interactions using only keypress data. Starting from a log of keypresses, the model generates a scanpath that details the user’s eye movements over time as they type. This model serves as a substitute for human data in scenarios where collecting eye-tracking data is costly or impractical. Our approach is built on three key insights: first, we design an inference architecture that captures individual user characteristics as a low-dimensional parameter vector; second, we introduce a novel loss function to align inferred eye movements with the timing of keypresses; third, we adopt a hybrid training strategy combining human data with synthetically generated data. This method is applicable to interactive systems equipped with predictive models of user behavior. In our evaluation, focused on the demanding task of touchscreen typing, the model demonstrated high accuracy in replicating real eye movements.Description
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Oulasvirta, AnttiOther note
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The videos for Individual-level analysis
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supplementary_videos.zip