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.

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BackgroundIndividuals with neurodevelopmental disorders often have atypical emotion profiles, but little is known about how they regulate their emotions. While several studies have examined emotion regulation strategy use in autism spectrum disorder (ASD), only a few have included individuals with intellectual disability (ID) or focused on specific syndromes such as Williams syndrome (WS).MethodsA parent-reported survey launched during the first months of the COVID-19 pandemic allowed to exploratorily study emotion regulation strategy use and its link to anxiety in individuals with ASD with (N=785) and without ID (N=596), WS (N=261), and Intellectual Disability not otherwise specified (N=649).ResultsUsing multilevel analyses, besides revealing specific group differences in emotion regulation strategy use, a variety of strategies (e.g., rumination, avoiding information, repetitive behaviors) were found to be linked to elevated levels of anxiety, while focusing on the positive was linked to lower anxiety levels in all groups. Moreover, only autistic people without ID used humor more frequently while experiencing lower anxiety levels.ConclusionThis study sheds light on an underexplored area of emotion regulation strategy use in different neurodevelopmental disorders. It also paves the way to further examine emotion regulation in more rigorous ways to better understand emotion regulation in different neurodevelopmental disorders as well as the impact on outcome measures such as anxiety. This exploratory study may help to develop and validate adequate measures to study a broad array of ER strategies used by individuals with neurodevelopmental disorders.

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Determining the familiarity and identity of a face have been considered as independent processes. Covert face recognition in cases of acquired prosopagnosia, as well as rapid detection of familiarity have been taken to support this view. We tested P.S. a well-described case of acquired prosopagnosia, and two healthy controls (her sister and daughter) in two saccadic reaction time (SRT) experiments. Stimuli depicted their family members and well-matched unfamiliar distractors in the context of binary gender, or familiarity decisions. Observers' minimum SRTs were estimated with Bayesian approaches. For gender decisions, P.S. and her daughter achieved sufficient performance, but displayed different SRT distributions. For familiarity decisions, her daughter exhibited above chance level performance and minimum SRTs corresponding to those reported previously in healthy observers, while P.S. performed at chance. These findings extend previous observations, indicating that decisional space determines performance in both the intact and impaired face processing system.

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