Abstract
This paper presents a threefold model of information seeking. A visual, a semantic and a memory map are dynamically computed in order to predict the location of the next fixation. This model is applied to a task in which the goal is to find among 40 words the one which best corresponds to a definition. Words have visual features and they are semantically organized. The model predicts scanpaths which are compared to human scanpaths on 3 high-level variables (number of fixations, average angle between saccades, rate of progression saccades). The best fit to human data is obtained when the memory map is given a strong weight and the semantic component a low weight.
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Chanceaux, M., Guérin-Dugué, A., Lemaire, B., Baccino, T. (2008). Towards a Model of Information Seeking by Integrating Visual, Semantic and Memory Maps. In: Caputo, B., Vincze, M. (eds) Cognitive Vision. ICVW 2008. Lecture Notes in Computer Science, vol 5329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92781-5_6
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DOI: https://doi.org/10.1007/978-3-540-92781-5_6
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