Investigating cultural diversity for extrafoveal information use in visual scenes

Abstract

Culture shapes how people gather information from the visual world. We recently showed that Western observers focus on the eyes region during face recognition, whereas Eastern observers fixate predominantly the center of faces, suggesting a more effective use of extrafoveal information for Easterners compared to Westerners. However, the cultural variation in eye movements during scene perception is a highly debated topic. Additionally, the extent to which those perceptual differences across observers from different cultures rely on modulations of extrafoveal information use remains to be clarified. We used a gaze-contingent technique designed to dynamically mask central vision, the Blindspot, during a visual search task of animals in natural scenes. We parametrically controlled the Blindspots and target animal sizes (0°, 2°, 5°, or 8°). We processed eye-tracking data using an unbiased data-driven approach based on fixation maps and we introduced novel spatiotemporal analyses in order to finely characterize the dynamics of scene exploration. Both groups of observers, Eastern and Western, showed comparable animal identification performance, which decreased as a function of the Blindspot sizes. Importantly, dynamic analysis of the exploration pathways revealed identical oculomotor strategies for both groups of observers during animal search in scenes. Culture does not impact extrafoveal information use during the ecologically valid visual search of animals in natural scenes.

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

“…These analyses showed significant fixation hotspots on the search target. Additional analyses revealed fixations on the targets despite large Blindspots and similar fixation patterns for EA versus WC participants (see Miellet et al, 2010), along with the flexibility of using a subset of fixations for generating the statistical maps. Finally, in the last dataset, we compared high-and low-checking observers in a memory task.…”

“…In our previous work, we were very careful not to "oversmooth" our data. We used Gaussian kernels with a standard deviation covering approximately 0.5°of visual angle (Blais et al, 2008;Caldara et al, 2010;Jack et al, 2009;Kelly et al, 2010;Miellet et al, 2011;Miellet et al, 2010;, which is roughly the size of a fourth of the fovea (Hood & Finkelstein, 1986). We thought that this was a sensible choice for the question we aimed to address: investigating cultural diversity in face processing.…”

“…Therefore, to meet this requirement, the faces we used in our previous studies (Blais et al, 2008;Caldara et al, 2010;Jack et al, 2009;Kelly et al, 2010;Rodger et al, 2010), and in the present examples, were normalized for their eye and mouth positions. In the example we provided for visual scenes, we demonstrated that is also possible to normalize natural scenes, by arbitrarily centering the object of interest in the middle of the scene (see Miellet et al, 2010); note that the task used here was to find and identify an animal. However, iMap does not prevent the analysis of a unique input space, as long as many eye movement samples are collected for a particular input space to ensure the statistical validity of the analysis.…”

“…The RFT approach is based on two main processing stages: First, it relies on the estimation of the smoothness (spatial correlation) of the statistical maps, and then these smoothness values are used to determine Fig. 1 (a) Example extracted from a trial of the animal visual search task used in Miellet et al's (2010) study. The red contour represents an ROI based on the edges of the koala, a rule routinely used in the eye movement literature relying on ROIs.…”

“…iMap has already been used and successfully validated in a series of eye movement studies (Blais, Jack, Scheepers, Fiset, & Caldara, 2008;Jack, Blais, Scheepers, Schyns, & Caldara, 2009;Kelly, Miellet, & Caldara, 2010;Miellet, Caldara, & Schyns, 2011;Miellet, Zhou, He, Rodger, & Caldara, 2010;Rodger, Kelly, Blais, & Caldara, 2010). To illustrate the functionality and flexibility of iMap, we provide examples below from real eye movement data and diverse statistical comparisons (i.e., across different groups of observers, in the same observers but with different tasks, etc.).…”