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

Abstract

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-ofthe-art neuroimaging data-processing toolboxes. iMap4 uses univariate, pixel-wise linear mixed models on smoothed fixation data, with the flexibility of coding for multiple betweenand 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.Keywords Eye movement analysis . Statistical mapping . Linear mixed models Human beings constantly move the eyes to sample visual information of interest from the environment. Eye fixations deliver inputs with the highest resolution to the human visual cortex from the fovea, as well as blurry, low-spatial-frequency information from peripheral vision (Rayner, 1998). Thus, isolating statistically where and how long fixations are deployed to process visual information is of particular interest to behavioral researchers, psychologists, and neuroscientists. Moreover, fixation mapping has a wide range of practical applications in determining marketing strategies and the understanding of consumer behaviour (Duchowski, 2002).Conventional eye movement data analyses rely on the estimation of probabilities of occurrence of fixations and saccades (or their characteristics, such as duration or length) within predefined regions of interest (ROIs), which are at best defined a priori-but often also defined a posteriori, on the basis of data exploration, which inflates the Type I error rate.

The peripheral preview effect with faces: Combined EEG and eye-tracking suggests multiple stages of trans-saccadic predictive and non-predictive processing

“…In theory, a difference in fixation location might also have influenced the FRP, because differences in fixation locations imply differences in low-level visual input that affect visual ERP responses (De Lissa et al, 2014). To rule out this confound, we analyzed the distribution of target fixations with iMap4 (Lao et al, 2017). This toolbox models fixation location and duration by creating a heat map and by fitting a linear mixed model with predictors according the experimental design to each pixel of the heat map.…”

Neural Representations of Faces Are Tuned to Eye Movements

“…Using the iMAP4 toolbox (Lao et al, 2017) we computed a linear regression to explore the relationship between the fixation bias (the z-scored fixation duration) displayed during the recognition phase and neural face discrimination (i.e., the FPVS response amplitude). To this aim we performed a linear mixed-effects model with random effect for intercept and Fixation duration grouped by subject.…”

Out of mind, out of sight? Investigating abnormal face scanning in autism spectrum disorder using gaze‐contingent paradigm

“…Such analyses could detect the presence of an effect but do not take full advantage of the wealth of information contained in the eye‐tracking data, therefore it can only provide relatively crude information as to ‘when’ and ‘where’ an effect occurs. Recently, a data‐driven method—iMap (Lao et al, ), also used by the current study, was developed to allow for statistical testing of condition differences on any part of a stimulus without the restriction of the AOIs in the pixel level, which solved the ‘where’ problem. Here, we proposed a data‐driven method based on a moving‐average approach with a cluster‐based permutation test to control the family‐wise error rate to solve the ‘when’ problem.…”

“…The AOI approach is based on the prior hypothesis, that is, the group difference of the eye‐looking time. For full presentation of difference on any part of the face (in pixel space) without the restriction of the AOI between group and condition, we used a data‐driven approach based on iMap4 (Lao, Miellet, Pernet, Sokhn, & Caldara, ). See detailed method in the supplementary material.…”