Widely applicable MATLAB routines for automated analysis of saccadic reaction times

Abstract

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.Electronic supplementary materialThe online version of this article (doi:10.3758/s13428-014-0473-z) contains supplementary material, which is available to authorized users.

Asymmetrical modulation of time perception by increase versus decrease in coherence of motion

“…Preprocessing of eye tracking data was done by replacing each bad value with the last good value before it, as described in Leppänen, Forssman, Kaatiala, Yrttiaho, and Wass (2014). Bad values were determined based on the most conservative (exclusive) measure of gaze data validity as suggested by Tobii, where the eye-tracking system successfully recorded both eyes and was sure as to which eye the detected gaze data came from (invalid and uncertain frames during encoding: M = 25.01 %, SD = 19.28 %; invalid and uncertain frames during reproduction: M = 26.58 %, SD = 20.28 %; see Nevalainen & Sajaniemi, 2004, for reference values).…”

Evidence for spared attention to faces in 7-month-old infants after prenatal exposure to antiepileptic drugs

“…To determine the duration of infants' gaze fixations to nonface patterns and faces, we used a library of MATLAB (Mathworks, Natick, MA) functions, designed for automated extraction of the fixation duration parameters from raw eye-tracking data (x-y gaze position coordinates [33]). Briefly, the analysis consisted of the following stages: First, a 15-sample median filter was applied for removing abrupt spikes in the gaze data (attributable to technical artifacts).…”

“…Third, invalid trials were removed from the analyses (i.e., trials with N 200 ms of missing eye position data, b 70% of gaze fixation at the first stimulus by the end of the analysis period, anticipatory eye movements with a latency b 150 ms, or computer timing errors). Finally, the duration of gaze fixation at the first stimulus (a nonface pattern or pictures of faces) was calculated for each stimulus condition by using the same criteria as those used in previous studies [33]. The duration index was calculated by measuring the time from the onset of the lateral stimulus to the onset of a gaze shift to the lateral stimulus or a 1000-ms time limit.…”

“…Recent developments in eye-tracking-based testing of infant cognition [28][29][30] have opened new possibilities for automated testing of infants' visual acuity, visuospatial orienting (gaze shifts to a novel visual stimulus), and attention to salient social stimuli, such as faces [31][32][33][34][35]. These processes provide the key "building blocks" of later emerging advanced cognitive, academic, and social skills [36,37].…”

“…Similarly, tests assessing infants' attentional bias for faces at 7 months predict socioemotional development at the age of 14 months [40], and reduced face preference is associated with later autism spectrum disorders [41][42][43][44][45][46] and atypical development [47]. Compared with traditional observational methods, eye-tracking-based testing of infants allows for completed automatization of the test procedure, as well as accurate, transparent physiological metrics of the sensory and cognitive processes in infants [33,47,48].…”