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An evaluation of scanpath-comparison and machine-learning classification algorithms used to study the dynamics of analogy making

Abstract : In recent years, eyetracking has begun to be used to study the dynamics of analogy making. Numerous scanpath-comparison algorithms and machine-learning techniques are available that can be applied to the raw eyetracking data. We show how scanpath-comparison algorithms, combined with multidimensional scaling and a classification algorithm, can be used to resolve an outstanding question in analogy making-namely, whether or not children's and adults' strategies in solving analogy problems are different. (They are.) We show which of these scanpath-comparison algorithms is best suited to the kinds of analogy problems that have formed the basis of much analogy-making research over the years. Furthermore, we use machine-learning classification algorithms to examine the item-to-item saccade vectors making up these scanpaths. We show which of these algorithms best predicts, from very early on in a trial, on the basis of the frequency of various item-to-item saccades, whether a child or an adult is doing the problem. This type of analysis can also be used to predict, on the basis of the item-to-item saccade dynamics in the first third of a trial, whether or not a problem will be solved correctly.
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01416271
Contributeur : Lead - Université de Bourgogne <>
Soumis le : mercredi 14 décembre 2016 - 11:42:03
Dernière modification le : vendredi 8 juin 2018 - 14:50:07

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Robert M. French, Yannick Glady, Jean-Pierre Thibaut. An evaluation of scanpath-comparison and machine-learning classification algorithms used to study the dynamics of analogy making. Behavior Research Methods, Psychonomic Society, Inc, 2016, pp.1-12. ⟨10.3758/s13428-016-0788-z⟩. ⟨hal-01416271⟩

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