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Attentional blink (AB) is a biological effect, showing that for 200 to 500msafter paying attention to one visual target, it is difficult to notice anothertarget that appears next, and attentional blink magnitude (ABM) is a indicatingparameter to measure the degree of this effect. Researchers have shown thatdifferent categories of images can access the consciousness of human minddifferently, and produce different ranges of ABM values. So in this paper, wecompare two different types of images, categorized as animal and object, bypredicting ABM values directly from image features extracted from convolutionalneural network (CNN), and indirectly from functional magnetic resonance imaging(fMRI) data. First, for two sets of images, we separately extract their averagefeatures from layers of Alexnet, a classic model of CNN, then input thefeatures into a trained linear regression model to predict ABM values, and wefind higher-level instead of lower-level image features determine thecategorical difference in AB effect, and mid-level image features predict ABMvalues more correctly than low-level and high-level image features. Then weemploy fMRI data from different brain regions collected when the subjectsviewed 50 test images to predict ABM values, and conclude that brain regionscovering relatively broader areas, like LVC, HVC and VC, perform better thanother smaller brain regions, which means AB effect is more related to syntheticimpact of several visual brain regions than only one particular visual regions.

Categorical Difference and Related Brain Regions of the Attentional Blink Effect