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Qiufang Fu, Yong-Jin Liu, Wenfeng Chen, Xiaolan Fu; The time course of natural scene categorization in human brain: simple line-drawings vs. color photographs. Journal of Vision 2013;13(9):1060. doi: 10.1167/13.9.1060.
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Human beings are very quick and efficient at categorizing nature scenes. Recent functional MRI studies found that the neural activation in response to line-drawings was similar to color photographs (e.g., Walter et al., 2011). However, it remains unclear whether the time course of braining activity was similar to the different images. The present study was aimed to investigate how the nature scene categorization of line-drawings and color photographs was reflected in event-related potentials (ERPs). Color photographs and line-drawings of six natural scene categories (beaches, city streets, forests, highways, mountains and offices) were adopted as stimuli, which were first used by Walter et al. (2011). All the images were resized to a resolution of 320 * 240 pixels. Each of them was flashed for 13 ms, and then followed by 100 ms of mask stimuli and 500 ms of a blank. The stimulus onset asynchrony (SOA) between the image and the mask was 13, 26, 40 or 213 ms. On each trial, participants were asked to report the scene category by pressing a key after the blank. Each participant was tested for 10 blocks of 96 trials. ERPs were recorded during the experiment. The behavioral results showed that the accuracy was higher for line-drawings than color photographs when SOA was 13 ms, but was lower when SOA was greater than 13 ms. Importantly, the ERP results revealed that a more negative N170 in occipital electrodes and a more positive P2 in frontocentral electrodes were elicited by incorrect than correct categorization of color photographs, whereas a more positive P1 in occipital electrodes and a more negative N2 in frontocentral electrodes were led to by incorrect than correct categorization of line-drawings. These findings help to understand the role of color in rapid scene categorization and how human brain categorizes different visual stimuli.
Meeting abstract presented at VSS 2013
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