Different champs de recherche

De l'analyse des mouvements oculaires aux neurosciences culturelles, affectives ou sociales en passant par la vision active, l'attention ou la cognition visuelle, notre laboratoire s'intéresse à différents champs de recherche. Pour plus d'information veuillez consulter les pages personnelles des membres de notre équipe.



20TH OF JUNE 2013


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). We implemented an alternative data-driven method to compute statistical fixation maps of eye movements - iMap - based on an approach inspired by methods used in functional Magnetic Resonance Imaging.



iMap 3 implements a new statistical engine compared to the previous versions. 

Therefore, we recommend to carefully read the new manual, as well as the paper below, before using it:

Caldara, R., & Miellet, S. (2011). iMap: A Novel Method for Statistical Fixation Mapping of Eye Movement data, Behavior Research Methods, 43(3), 864-78 [PDF]






We have received feedback and queries from different users. Following our discussions we have implemented significant changes from the original Version 1 code.

If you are interested to access a previous version of the iMap toolbox, please contact us by email. Please note that we strongly discourage users to use them for a scientific purpose, as they might contain bugs and statistical approaches we do not encourage to use anymore.

We wish to sincerely thank all the users that sent us comments.


Version 2.1

1. Solve potential problems related with when the dimensions (x,y) of the search space, in case they do not have an even numbers of pixels.

Version 2

1. The setting of the parameters is now done via a configuration structure (see examples), which allows more flexibility in calling iMap, but gives also to the user flexibility in inserting his own parameters.
2. New parameters have been added for: setting the colorbar scaling, setting the sigma (kernel for the statistical smoothing), setting the significancy of the threshold.
3. The "clicking step" used to generate the maps is no longer necessary. Many thanks to Junpeng Lao who wrote the ‘indtorgb’ function.
4. The one-tailed and two-tailed critical values (found in Zcrit.txt or displayed in the Matlab command window) are defined from the value of the significancy threshold set as one of the parameters.
6. The contours for significant areas are now displayed in white for all the fixation maps. It should improve the view of the significant areas.
7. A mistake in the data preparation code for the scenes example has been fixed.

Version 1.1

1- Fixes potential problems with floating point in the calculation of the search-space size
2- Creates a Zcrit.txt file indicating the size of the search-space, the critical value of a one-tailed Z for alpha = .05 (significancy threshold for the individual maps), the critical value of a two-tailed Z for alpha = .05 (significancy threshold for the difference map). This information is also displayed in the Matlab command window.
3- The CiVol and STAT_THRESHOLD functions have been modified to avoid the display of confusing information in the Matlab command window.

Feel free to contact us to provide comments, as we aim at continuing the development of the toolbox in the future, as well as implementing new plug-ins... just stay tuned.

Département de Psychologie - R. Faucigny 2 - 1700 Fribourg - Tel +41 26 / 300 7620 - psychologie [at] unifr.ch   -   Swiss University