gazeR

gazeR processes and standardizes eye-tracking gaze position and pupil size data for preprocessing and analysis.


Key Features:

  • Data import and formatting: Imports raw eye-tracking and pupil size files into R and formats them for analysis.
  • Gaze-to-AOI conversion: Converts gaze coordinates to predefined areas of interest (AOIs).
  • Pupillometry file merging: Reads and merges multiple raw pupil size files for integrated pupillometry datasets.
  • Missing data handling: Removes observations that exceed a defined threshold of incompleteness.
  • Blink artifact identification and interpolation: Identifies blink artifacts and interpolates missing values across blinks.
  • Baseline correction: Applies subtractive baseline correction to pupil size data relative to a predefined baseline period.
  • Binning and aggregation: Bins and aggregates gaze position and pupillometry data over time intervals or experimental conditions.

Scientific Applications:

  • Vision science: Supports preprocessing and analysis of eye-tracking datasets in vision science studies.
  • Psycholinguistics: Supports analysis of eye movement and pupillometry data in psycholinguistic experiments.
  • Marketing research: Enables standardized preprocessing of gaze and pupil data for marketing research.
  • Human-computer interaction: Facilitates preprocessing and aggregation of gaze and pupil measures in human-computer interaction studies.

Methodology:

Computational steps include importing and formatting raw gaze and pupil size files, reading and merging multiple pupil files, converting gaze coordinates to AOIs, identifying and interpolating blink artifacts, removing observations exceeding a missingness threshold, applying subtractive baseline correction, and binning and aggregating data.

Topics

Details

Tool Type:
library
Programming Languages:
R
Added:
1/18/2021
Last Updated:
1/22/2021

Operations

Publications

Geller J, Winn MB, Mahr T, Mirman D. GazeR: A Package for Processing Gaze Position and Pupil Size Data. Behavior Research Methods. 2020;52(5):2232-2255. doi:10.3758/s13428-020-01374-8. PMID:32291732. PMCID:PMC7544668.