An evaluation of scanpath-comparison and machine-learning classification algorithms used to study the dynamics of analogy making
Category
Journal Article
Authors
French, R. M., Glady, Y., Thibaut, J.-P.
Year
2017
Title
An evaluation of scanpath-comparison and machine-learning classification algorithms used to study the dynamics of analogy making
Journal / book / conference
Behavior Research Methods, doi:10.3758/s13428-016-0788-z
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.
Keywords
eyetracking algorithm, Jarodzka algorithm, LDA, SVM, analogy strategies