Widely applicable MATLAB routines for automated analysis of saccadic reaction times
2015 (English)In: Behavior Research Methods, ISSN 1554-351X, E-ISSN 1554-3528, Vol. 47, no 2, 538-548 p.Article in journal (Refereed) Published
Saccadic reaction time (SRT) is a widely used dependent variable in eye-tracking studies of human cognition and its disorders. SRTs are also frequently measured in studies with special populations, such as infants and young children, who are limited in their ability to follow verbal instructions and remain in a stable position over time. In this article, we describe a library of MATLAB routines (Mathworks, Natick, MA) that are designed to (1) enable completely automated implementation of SRT analysis for multiple data sets and (2) cope with the unique challenges of analyzing SRTs from eye-tracking data collected from poorly cooperating participants. The library includes preprocessing and SRT analysis routines. The preprocessing routines (i.e., moving median filter and interpolation) are designed to remove technical artifacts and missing samples from raw eye-tracking data. The SRTs are detected by a simple algorithm that identifies the last point of gaze in the area of interest, but, critically, the extracted SRTs are further subjected to a number of postanalysis verification checks to exclude values contaminated by artifacts. Example analyses of data from 5- to 11-month-old infants demonstrated that SRTs extracted with the proposed routines were in high agreement with SRTs obtained manually from video records, robust against potential sources of artifact, and exhibited moderate to high test-retest stability. We propose that the present library has wide utility in standardizing and automating SRT-based cognitive testing in various populations. The MATLAB routines are open source and can be downloaded from http://www.uta.fi/med/icl/methods.html .
Place, publisher, year, edition, pages
2015. Vol. 47, no 2, 538-548 p.
IdentifiersURN: urn:nbn:se:uu:diva-275922DOI: 10.3758/s13428-014-0473-zPubMedID: 24788324OAI: oai:DiVA.org:uu-275922DiVA: diva2:901643