An evaluation of scanpath-comparison and machine-learning classification algorithms used to study the dynamics of analogy making.
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.
LEAD-CNRS UMR 5022, Université de Bourgogne-Franche-Comté, Pôle AAFE, 11 Esplanade Erasme, 21000, Dijon, France. robert.french@u-bourgogne.fr.
Robert M
RM
French
LEAD-CNRS UMR 5022, Université de Bourgogne-Franche-Comté, Pôle AAFE, 11 Esplanade Erasme, 21000, Dijon, France.
Yannick
Y
Glady
LEAD-CNRS UMR 5022, Université de Bourgogne-Franche-Comté, Pôle AAFE, 11 Esplanade Erasme, 21000, Dijon, France.
Jean-Pierre
JP
Thibaut
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D002648:Child; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D011340:Problem Solving; D012438:Saccades; D055815:Young Adult
Analogy strategies; Eyetracking algorithms; Jarodzka algorithm; LDA; SVM