iMap 4: An Open Source Toolbox for the Statistical Fixation Mapping of Eye Movement data with Linear Mixed Modeling

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

doi:10.1167/15.12.793

Cited by 6 publications

(5 citation statements)

References 30 publications

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“…To further explore the spatial distribution of the fixation pattern, we performed in a second complementary analysis a spatial mapping analysis of the fixation duration using iMap4 (Caldara & Miellet, 2011; Lao, Miellet, Pernet, Sokhn, & Caldara, 2015). iMap4 is a data-driven analysis framework for statistical fixation mapping using linear mixed model (LMM) and nonparametric statistics based on resampling (Lao et al., 2015). The fixation duration vector of each single trial was projected into a two-dimensional space according to the x - and y -coordinates of the fixation using iMap4.…”

Section: Resultsmentioning

confidence: 99%

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Visual and Hedonic Perception of Food Stimuli in Children with Autism Spectrum Disorders and their Relationship to Food Neophobia

Luisier

Petitpierre

Bérod³

et al. 2019

Perception

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The present study examined whether children with autism spectrum disorder (ASD) and typically developing (TD) children differed in visual perception of food stimuli at both sensorimotor and affective levels. A potential link between visual perception and food neophobia was also investigated. To these aims, 11 children with ASD and 11 TD children were tested. Visual pictures of food were used, and food neophobia was assessed by the parents. Results revealed that children with ASD explored visually longer food stimuli than TD children. Complementary analyses revealed that whereas TD children explored more multiple-item dishes (vs. simple-item dishes), children with ASD explored all the dishes in a similar way. In addition, children with ASD gave more negative appreciation in general. Moreover, hedonic rating was negatively correlated with food neophobia scores in children with ASD, but not in TD children. In sum, we show here that children with ASD have more difficulty than TD children in liking a food when presented visually. Our findings also suggest that a prominent factor that needs to be considered is time management during the food choice process. They also provide new ways of measuring and understanding food neophobia in children with ASD.

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Section: Resultsmentioning

confidence: 99%

“…The linear mixed models were fitted using Maximal Likelihood with the default iMap4 settings. Linear contrast of the model coefficients was performed as hypothesis testing, with a bootstrap spatial clustering procedure threshold on the cluster mass as multiple comparison corrections (Lao et al., 2015).…”

Section: Resultsmentioning

confidence: 99%

Visual and Hedonic Perception of Food Stimuli in Children with Autism Spectrum Disorders and their Relationship to Food Neophobia

Luisier

Petitpierre

Bérod³

et al. 2019

Perception

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“…Eye movement data were analyzed using the new version of iMap 15 17 , which implements a robust data-driven approach based on a Linear Mixed Model (LMM) and a bootstrap clustering method for hypothesis testing. Fixations and saccades were extracted from the raw data by using the default settings in the Eyelink software.…”

Section: Methodsmentioning

confidence: 99%

“…To address this question, we recorded the eye movements of male observers during attractiveness judgments by combining a data-driven analysis (i.e., i Map4 15 17 ) and a gaze-contingent design 18 . i Map4 implements a Linear Mixed-Model (LMM – see methods) with a robust statistical approach to correct for the multiple comparison problem driven by repetitive testing in pixel space.…”

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confidence: 99%

Mapping female bodily features of attractiveness

Bovet

Lao

Bartholomée

et al. 2016

Sci Rep

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“Beauty is bought by judgment of the eye” (Shakespeare, Love’s Labour’s Lost), but the bodily features governing this critical biological choice are still debated. Eye movement studies have demonstrated that males sample coarse body regions expanding from the face, the breasts and the midriff, while making female attractiveness judgements with natural vision. However, the visual system ubiquitously extracts diagnostic extra-foveal information in natural conditions, thus the visual information actually used by men is still unknown. We thus used a parametric gaze-contingent design while males rated attractiveness of female front- and back-view bodies. Males used extra-foveal information when available. Critically, when bodily features were only visible through restricted apertures, fixations strongly shifted to the hips, to potentially extract hip-width and curvature, then the breast and face. Our hierarchical mapping suggests that the visual system primary uses hip information to compute the waist-to-hip ratio and the body mass index, the crucial factors in determining sexual attractiveness and mate selection.

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“…Since fixation maps are usually smoothed by a Gaussian filter, neighboring pixels are not independent of each other, which has further implications for the statistical analysis. Later versions of iMap (Lao et al, 2015) use therefore different statistics, such as pixel-wise linear mixed effect models and a bootstrapping approach. This leads to an increase in statistical power so that even more subtle effects can be detected.…”

Section: Scanpath Comparison Based On Fixation Mapsmentioning

confidence: 99%

SubsMatch 2.0: Scanpath comparison and classification based on subsequence frequencies

et al. 2016

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Our eye movements are driven by a continuous trade-off between the need for detailed examination of objects of interest and the necessity to keep an overview of our surrounding. In consequence, behavioral patterns that are characteristic for our actions and their planning are typically manifested in the way we move our eyes to interact with our environment. Identifying such patterns from individual eye movement measurements is however highly challenging. In this work, we tackle the challenge of quantifying the influence of experimental factors on eye movement sequences. We introduce an algorithm for extracting sequence-sensitive features from eye movements and for the classification of eye movements based on the frequencies of small subsequences. Our approach is evaluated against the state-of-the art on a novel and a very rich collection of eye movements data derived from four experimental settings, from static viewing tasks to highly dynamic outdoor settings. Our results show that the proposed method is able to classify eye movement sequences over a variety of experimental designs. The choice of parameters is discussed in detail with special focus on highlighting different aspects of general scanpath shape. Algorithms and evaluation data are available at: www.ti.uni-tuebingen.de/scanpathcomparison.html.

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iMap 4: An Open Source Toolbox for the Statistical Fixation Mapping of Eye Movement data with Linear Mixed Modeling

Cited by 6 publications

References 30 publications

Visual and Hedonic Perception of Food Stimuli in Children with Autism Spectrum Disorders and their Relationship to Food Neophobia

Visual and Hedonic Perception of Food Stimuli in Children with Autism Spectrum Disorders and their Relationship to Food Neophobia

Mapping female bodily features of attractiveness

SubsMatch 2.0: Scanpath comparison and classification based on subsequence frequencies

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