Bilateral Netherlands: Scanpaths when viewing faces

Lead Research Organisation: University of Bristol
Department Name: Experimental Psychology


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Cristino F (2010) ScanMatch: a novel method for comparing fixation sequences. in Behavior research methods

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Pellicano E (2011) Children with autism are neither systematic nor optimal foragers. in Proceedings of the National Academy of Sciences of the United States of America

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Sebastiaan Mathôt (Author) (2012) A simple way to estimate similarity between pairs of eye movement sequences in Journal of eye movement research

Description The primary output of this grant was the development of a novel approach to comparing saccadic eye movement sequences based on the Needleman-Wunsch algorithm used in bioinformatics to compare DNA sequences. In the proposed method, the saccade sequence is spatially and temporally binned and then recoded to create a sequence of letters that retains fixation location, time, and order information. The comparison of two letter sequences is made by maximizing the similarity score computed from a substitution matrix that provides the score for all letter pair substitutions and a penalty gap. The substitution matrix provides a meaningful link between each location coded by the individual letters. This link could be distance but could also encode any useful dimension, including perceptual or semantic space. We showed, by using synthetic and behavioral data, the benefits of this method over existing methods. The ScanMatch toolbox for MATLAB is freely available online ( and this work was reported in Cristino, Mathot, Theeuwes & Gilchrist (2010).

In a subsequent paper (Mathôt, Cristino, Gilchrist & Theeuwes, 2012) we proposed an alternative algorithm to estimate the similarity between a pair of eye movement sequences. The proposed algorithm relies on a straight-forward geometric representation of eye movement data. The algorithm is considerably simpler to implement and complements are previous similarity measure and is particularly suited for exploratory analyses. To validate the algorithm, we conducted a benchmark experiment using realistic artificial eye movement data. Based on similarity ratings obtained from the proposed algorithm, we defined two clusters in an unlabelled set of eye movement sequences. As a measure of the algorithm's sensitivity, we quantified the extent to which these data-driven clusters matched two pre-defined groups (i.e., the 'real' clusters). The same analysis was performed using two other, commonly used similarity measures. The results show that the proposed algorithm is a viable similarity measure.

Cristino, F., Mathot, S., Theeuwes, J. & Gilchrist, I. D. (2010). ScanMatch: A Novel Method for Comparing Fixation Sequences. Behaviour Research Methods, 42, 692-700.

Mathôt, S., Cristino, F., Gilchrist, I. D., & Theeuwes, J. (2012). A simple way to estimate similarity between pairs of eye movement sequences. Journal of Eye Movement Research 5(1):4, 115.
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