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Task Decoding using Recurrent Quantification Analysis of Eye Movements

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Date Issued:
2015
Summary:
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.
Title: Task Decoding using Recurrent Quantification Analysis of Eye Movements.
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Name(s): LaCombe, Daniel C. Jr.
Barenholtz, Elan
Graduate College
Type of Resource: text
Genre: Poster
Date Created: 2015
Date Issued: 2015
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 1 p.
Language(s): English
Summary: 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.
Identifier: FA00005892 (IID)
Collection: FAU Student Research Digital Collection
Note(s): The Sixth Annual Graduate Research Day was organized by Florida Atlantic University’s Graduate Student Association. Graduate students from FAU Colleges present abstracts of original research and posters in a competition for monetary prizes, awards, and recognition.
Held by: Florida Atlantic University Libraries
Sublocation: Digital Library
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00005892
Use and Reproduction: Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.