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iMap: A novel method for statistical fixation mapping of eye movement data. Behavior Research Methods.
Journal article

iMap: A novel method for statistical fixation mapping of eye movement data.

  • Caldara, Roberto Department of Psychology, University of Fribourg, Switzerland
  • Miellet, Sebastien Centre for Cognitive Neuroimaging, Institute of Neuroscience and Psychology, University of Glasgow, Scotland
    2011
Published in:
  • Behavior Research Methods. - 2011, vol. 43, no. 3, p. 864-878
Eye movements
Statistical fixation maps
Data-driven analyses
Random field theory
Matlab toolbox
English Eye movement data analyses are commonly based on the probability of occurrence of saccades and fixations (and their characteristics) in given regions of interest (ROIs). In this article, we introduce an alternative method for computing statistical fixation maps of eye movements--iMap--based on an approach inspired by methods used in functional magnetic resonance imaging. Importantly, iMap does not require the a priori segmentation of the experimental images into ROIs. With iMap, fixation data are first smoothed by convolving Gaussian kernels to generate three- dimensional fixation maps. This procedure embodies eyetracker accuracy, but the Gaussian kernel can also be flexibly set to represent acuity or attentional constraints. In addition, the smoothed fixation data generated by iMap conform to the assumptions of the robust statistical random field theory (RFT) approach, which is applied thereafter to assess significant fixation spots and differences across the three-dimensional fixation maps. The RFT corrects for the multiple statistical comparisons generated by the numerous pixels constituting the digital images. To illustrate the processing steps of iMap, we provide sample analyses of real eye movement data from face, visual scene, and memory processing. The iMap MATLAB toolbox is editable and freely available for download online (www.unifr.ch/psycho/ibmlab/).
Faculty
Faculté des lettres et des sciences humaines
Department
Département de Psychologie
Language
  • English
Classification
Psychology
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/301912
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