- #EYE TRACKING DATA ANALYSIS HOW TO#
- #EYE TRACKING DATA ANALYSIS DOWNLOAD#
- #EYE TRACKING DATA ANALYSIS FREE#
Decades of research have shown that the time course of fixation proportions – that is, the probability of fixating a particular object at a particular time – reflects the activation of that object’s mental representation. In a typical instantiation of the Visual World Paradigm (VWP), participants hear spoken instructions to manipulate or select one of several images on a computer screen or objects in the real world (Cooper, 1974 Tanenhaus, Spivey-Knowlton, Eberhard, & Sedivy, 1995). In the remainder of this report, we provide a step-by-step walk through of the installation and core functionality of the gazeR package. A secondary aim is to facilitate reproducible and transparent preprocessing of these types of data, using conventional practices in eye-tracking data processing, and smoothing the transition from data preprocessing to data analysis and visualization. While there exist various packages and online resources to get started with eye-tracking, such materials are typically limited to the analysis of a single participant and do not represent what researchers typically want to do with their data.
#EYE TRACKING DATA ANALYSIS HOW TO#
The main aim of the present paper is to illustrate and explain how to analyze gaze and pupil data in a more standardized way using gazeR, such that it may be used by researchers to analyze their own data. There are several conceptual or theoretical discussions on best practices when analyzing pupil and gaze data available elsewhere (see Mathôt et al., 2018 Winn, Wendt, Koelewijn, and Kuchinsky, 2018 Salverda & Tanenhaus, 2018). In this paper, we provide a step-by-step walk-through of how to use the gazeR package to analyze data from experiments in which the primary outcome measure is gaze position or pupil size.
The gazeR package is also designed to be as familiar as possible for the regular R user, thus handling data in formats and functions that will be accessible for most users. The gazeR package is meant to facilitate the end-to-end handling of eye-tracking data within a single programming environment (R) – from reading in raw data files to statistical analysis and generating figures. To meet this need, we created the gazeR package.
In R, there are few established pipelines for handling pupil and fixation data from the visual world paradigm and pupillometry, especially contained in one package (see Tables 1 and 2). 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.
#EYE TRACKING DATA ANALYSIS FREE#
With increased attention on replicability, reproducibility, and transparency, there is a need for a cross-platform, fully free implementation of standard practices in eye-tracking data processing. Despite its growing presence, there is considerable variability in how eye-tracking data are processed.
Because of this, a growing number of fields, from vision science and psycholinguistics to marketing and human-computer interaction, have adopted this methodology. 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.
We provide step-by-step analyses of data from two tasks exemplifying the package’s capabilities.
#EYE TRACKING DATA ANALYSIS DOWNLOAD#
The package is open-source and freely available for download and installation. 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. 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. 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. Surprisingly, there is little consistency and transparency in preprocessing steps, making replicability and reproducibility difficult. Eye-tracking is widely used throughout the scientific community, from vision science and psycholinguistics to marketing and human-computer interaction.