iMap4: An open source toolbox for the statistical fixation mapping of eye movement data with linear mixed modeling

Area of Science:

• Vision Science
• Cognitive Neuroscience
• Computational Neuroscience

Background:

• Statistical mapping of eye movements is crucial but challenging due to sparse data and high variability.
• Conventional linear modeling approaches yield unstable estimations and underpowered results for 2D fixation distributions.
• Existing methods fall short in addressing the complexities of eye movement data analysis.

Purpose of the Study:

• To present iMap4, an enhanced MATLAB toolbox for robust statistical fixation mapping of eye movement data.
• To implement advanced statistical frameworks comparable to neuroimaging processing toolboxes.
• To provide a user-friendly tool for analyzing fixation distributions and identifying significant differences.

Main Methods:

• Utilizes univariate, pixel-wise linear mixed models on smoothed fixation data.
• Incorporates flexibility for multiple between- and within-subjects comparisons and linear contrasts.
• Introduces novel nonparametric tests based on resampling for statistical significance assessment.

Main Results:

• iMap4 provides stable and powerful statistical estimations for eye movement fixation data.
• The toolbox successfully validates the approach using experimental and simulation data.
• Offers straightforward, easy-to-interpret statistical graphical outputs.

Conclusions:

• iMap4 represents a significant advancement in the data-driven processing of eye movement fixation data.
• It aligns eye movement analysis with the robust standards of statistical neuroimaging methods.
• Facilitates more reliable and interpretable insights into visual attention and behavior.