GazeR: A Package for Processing Gaze Position and Pupil Size Data
- PDF / 8,374,140 Bytes
- 24 Pages / 595.276 x 790.866 pts Page_size
- 90 Downloads / 160 Views
GazeR: A Package for Processing Gaze Position and Pupil Size Data Jason Geller 1 & Matthew B. Winn 2 & Tristian Mahr 3 & Daniel Mirman 4
# The Psychonomic Society, Inc. 2020
Abstract Eye-tracking is widely used throughout the scientific community, from vision science and psycholinguistics to marketing and human-computer interaction. Surprisingly, there is little consistency and transparency in preprocessing steps, making replicability and reproducibility difficult. To increase replicability, reproducibility, and transparency, a package in R (a free and widely used statistical programming environment) called gazeR was created to read and preprocess two types of data: gaze position and pupil size. For gaze position data, gazeR has functions for reading in raw eye-tracking data, formatting it for analysis, converting from gaze coordinates to areas of interest, and binning and aggregating data. For data from pupillometry studies, the gazeR package has functions for reading in and merging multiple raw pupil data files, removing observations with too much missing data, eliminating artifacts, blink identification and interpolation, subtractive baseline correction, and binning and aggregating data. The package is open-source and freely available for download and installation: https://github.com/dmirman/gazer. We provide stepby-step analyses of data from two tasks exemplifying the package’s capabilities. Keywords eye-tracking . open science . pupillometry . visual world paradigm . R
Introduction Recent advances in eye-tracking technology make it a highly powerful and relatively inexpensive tool to gather fine-grained measures of the temporal dynamics of cognitive processing. Because of this, a growing number of fields, from vision science and psycholinguistics to marketing and human-computer interaction, have adopted this methodology. Despite its growing presence, there is considerable variability in how eye-tracking data are processed. With increased attention on replicability, reproducibility, and transparency, there is a need for a cross-platform, Electronic supplementary material The online version of this article (https://doi.org/10.3758/s13428-020-01374-8) contains supplementary material, which is available to authorized users. * Jason Geller [email protected] 1
Department of Psychological & Brain Sciences, The University of Iowa, Iowa City, IA 52242, USA
2
Department of Speech-Language-Hearing Sciences, University of Minnesota, 164 Pillsbury Dr. SE, Minneapolis, MN 55455, USA
3
Waisman Center, University of Wisconsin-Madison, 1500 Highland Ave, Madison, WI 53705, USA
4
Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, UK
fully free implementation of standard practices in eye-tracking data processing. R (R Core Team, 2019) is a widely-used, free, cross-platform, and open-source statistical programming language that provides the tools needed to meet those needs. In R, there are few established pipelines for handling pupil and fixation data from the visual world parad
Data Loading...
