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

Junpeng Lao, Sébastien Miellet, Cyril Pernet, Nayla Sokhn, Roberto Caldara

A major challenge in modern eye movement research is to statistically map where observers are looking, by isolating the significant differences between groups and conditions. As compared to the signals from contemporary neuroscience measures, such as magneto/electroencephalography and functional magnetic resonance imaging, eye movement data are sparser, with much larger variations in space across trials and participants. As a result, the implementation of a conventional linear modeling approach on two-dimensional fixation distributions often returns unstable estimations and underpowered results, leaving this statistical problem unresolved (Liversedge, Gilchrist, & Everling, 2011). Here, we present a new version of the iMap toolbox (Caldara & Miellet, 2011) that tackles this issue by implementing a statistical framework comparable to those developed in state-of-the-art neuroimaging data-processing toolboxes. iMap4 uses univariate, pixel-wise linear mixed models on smoothed fixation data, with the flexibility of coding for multiple between- and within-subjects comparisons and performing all possible linear contrasts for the fixed effects (main effects, interactions, etc.). Importantly, we also introduced novel nonparametric tests based on resampling, to assess statistical significance. Finally, we validated this approach by using both experimental and Monte Carlo simulation data. iMap4 is a freely available MATLAB open source toolbox for the statistical fixation mapping of eye movement data, with a user-friendly interface providing straightforward, easy-to-interpret statistical graphical outputs. iMap4 matches the standards of robust statistical neuroimaging methods and represents an important step in the data-driven processing of eye movement fixation data, an important field of vision sciences.