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. Compared to signals of contemporary neuroscience measures, such as M/EEG and fMRI, 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 and Miellet, 2011) which tackles this issue by implementing a statistical framework comparable to those developped in state-of the- art neuroimaging data processing toolboxes. iMap4 uses univariate, pixel-wise Linear Mixed Models (LMM) on the smoothed fixation data, with the flexibility of coding for multiple between- and within- subject comparisons and performing all the 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.
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