Task Decoding using Recurrent Quantification Analysis of Eye Movements

In recent years, there has been a surge of interest in the possibility of using machine-learning
techniques to decode generating properties of eye-movement data. Here we explore a relatively new
approach to eye movement quantification, Recurrence Quantification Analysis RQA— which allows
analysis of spatio-temporal fixation patterns — and assess its diagnostic power with respect to task
decoding. Fifty participants completed both aesthetic-judgment and visual-search tasks over natural
images of indoor scenes. Six different sets of features were extracted from the eye movement data,
including aggregate, fixation-map, and RQA measures. These feature vectors were then used to train
six separate support vector machines using an n-fold cross validation procedure in order to classify a
scanpath as being generated under either an aesthetic-judgment or visual- search task. Analyses
indicated that all classifiers decoded task significantly better than chance. Pairwise comparisons
revealed that all RQA feature sets afforded significantly greater decoding accuracy than the aggregate
features. The superior performance of RQA features compared to the others may be that they are
relatively invariant to changes in observer or stimulus; although RQA features significantly decoded
observer- and stimulus-identity, analyses indicated that spatial distribution of fixations were most
informative about stimulus-identity whereas aggregate measures were most informative about
observer-identity. Therefore, changes in RQA values could be more confidently attributed to changes in
task, rather than observer or stimulus, relative to the other feature sets. The findings of this research
have significant implications for the application of RQA in studying eye-movement dynamics in topdown
attention.