Categorical Difference and Related Brain Regions of the Attentional Blink Effect
Renzhou Gui, Xiaohong Ji

TL;DR
This study investigates how different image categories and brain regions influence the attentional blink effect by predicting AB magnitude from CNN features and fMRI data, revealing the importance of mid-level features and broader brain areas.
Contribution
It demonstrates that mid-level CNN features and larger brain regions better predict attentional blink magnitude, highlighting the categorical and neural basis of the effect.
Findings
Mid-level CNN features predict ABM more accurately.
Broader brain regions like LVC, HVC, and VC are more involved in AB.
Categorical differences in images affect AB magnitude.
Abstract
Attentional blink (AB) is a biological effect, showing that for 200 to 500ms after paying attention to one visual target, it is difficult to notice another target that appears next, and attentional blink magnitude (ABM) is a indicating parameter to measure the degree of this effect. Researchers have shown that different categories of images can access the consciousness of human mind differently, and produce different ranges of ABM values. So in this paper, we compare two different types of images, categorized as animal and object, by predicting ABM values directly from image features extracted from convolutional neural network (CNN), and indirectly from functional magnetic resonance imaging (fMRI) data. First, for two sets of images, we separately extract their average features from layers of Alexnet, a classic model of CNN, then input the features into a trained linear regression model…
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Taxonomy
TopicsVisual perception and processing mechanisms · Neural dynamics and brain function · Retinal Development and Disorders
MethodsLinear Regression
