This package is designed to make dealing with eye-tracking data easier. It addresses tasks along the pipeline from raw data to analysis and visualization.
Eye-tracking is an increasingly popular method within cognitive science. However, researchers lack a standardized framework and set of tools that are (a) easy to use and (b) based on free and open-source software. We offer a framework and accompanying package in R that addresses this gap. Our tools address tasks along the pipeline from raw data to analysis and visualization. We offer several popular types of analyses, including linear and growth curve time analyses, onset-contingent reaction time analyses, and cluster mass analyses. We also offer novel non-parametric techniques that address limitations in current analyses, with a particular emphasis on statistically estimating when looking behavior diverges across experimental conditions (rather than just whether it has diverged at some time-point). Finally, our R package is optimized for processing very large datasets extremely quickly. Our paper will outline the proposed workflow, discuss the logic and limitations of each analysis, and provide a walkthrough tutorial.
brockf
commented
9 years ago
Abstract:
Eye-tracking is an increasingly popular method within cognitive science. However, researchers lack a standardized framework and set of tools that are (a) easy to use and (b) based on free and open-source software. We offer a framework and accompanying package in R that addresses this gap. Our tools address tasks along the pipeline from raw data to analysis and visualization. We offer several popular types of analyses, including linear and growth curve time analyses, onset-contingent reaction time analyses, and cluster mass analyses. We also offer novel non-parametric techniques that address limitations in current analyses, with a particular emphasis on statistically estimating when looking behavior diverges across experimental conditions (rather than just whether it has diverged at some time-point). Finally, our R package is optimized for processing very large datasets extremely quickly. Our paper will outline the proposed workflow, discuss the logic and limitations of each analysis, and provide a walkthrough tutorial.