scholarly journals Correction: Cultural variations in global and local attention and eye-movement patterns during the perception of complex visual scenes: Comparison of Czech and Taiwanese university students

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247219
Author(s):  
Jiří Čeněk ◽  
Jie-Li Tsai ◽  
Čeněk Šašinka
PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242501
Author(s):  
Jiří Čeněk ◽  
Jie-Li Tsai ◽  
Čeněk Šašinka

Previous research on cross-cultural differences in visual attention has been inconclusive. Some studies have suggested the existence of systematic differences in global and local attention and context sensitivity, while others have produced negative or mixed results. The objective in this study was to examine the similarities and differences in holistic and analytic cognitive styles in a sample of Czech and Taiwanese university students. Two cognitive tasks were conducted: a Compound Figures Test and a free-viewing scene perception task which manipulated several focal objects and measured eye-movement patterns. An analysis of the reaction times in the Compound Figures Test showed no clear differences between either sample. An analysis of eye-movement metrics showed certain differences between the samples. While Czechs tended to focus relatively more on the focal objects measured by the number of fixations, the Taiwanese subjects spent more time fixating on the background. The results were consistent for scenes with one or two focal objects. The results of a correlation analysis of both tasks showed that they were unrelated. These results showed certain differences between the samples in visual perception but were not as systematic as the theory of holistic and analytic cognitive styles would suggest. An alternative model of cross-cultural differences in cognition and perception is discussed.


2020 ◽  
Vol 10 (16) ◽  
pp. 5552 ◽  
Author(s):  
Xiaoying Guo ◽  
Liang Li ◽  
Akira Asano ◽  
Chie Muraki Asano

Global and local features are essential for visual-similarity texture perception. Therefore, understanding how people allocate their visual attention when viewing textures with global or local similarity is important. In this work, we investigate the influences of global and local features of a texture on eye-movement patterns and analyze the relationship between eye-movement patterns and visual-similarity selection. First, we synthesized textures by separately controlling global and local textural features through the primitive, grain, and point configuration (PGPC) texture model, a mathematical morphology-based texture model. Second, we conducted an experiment to acquire eye-movement data where participants identified the texture that was highly similar to the standard texture. Experiment data were obtained through an eye-tracker from 60 participants. The collected eye-tracking data were analyzed in terms of three metrics, including total fixation duration in each region of interest (ROI), fixation-point variance in each ROI, and fixation-transfer counts between different ROIs. Analysis results indicated the following. (1) The global and local features of a texture influenced eye-movement patterns. In particular, the texture image that was globally similar to the standard texture contained dispersed fixation points. By contrast, the texture image that was locally similar to the standard texture contained concentrated fixation points. The domination of global and local features influenced the viewers’ similarity choice. (2) The final visual-similarity selection was related to the fixation-transfer count between different ROIs, but not to the fixation time in each ROI. This research also extends the applicability of the mathematical morphology-based texture model to human visual perception.


2021 ◽  
Author(s):  
David St Clair ◽  
Graeme MacLennan ◽  
Sara A. Beedie ◽  
Eva Nouzová ◽  
Helen Lemmon ◽  
...  

2015 ◽  
Vol 160 ◽  
pp. 23-34 ◽  
Author(s):  
Emanuela Bricolo ◽  
Carola Salvi ◽  
Marialuisa Martelli ◽  
Lisa S. Arduino ◽  
Roberta Daini

2021 ◽  
pp. 145-168
Author(s):  
A. G. Gale ◽  
J. M. Findlay

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