Eye tracking data analysis4/1/2024 ![]() ![]() 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. 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. ![]()
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