Convolutional Neural Networkを用いた知覚交替の認識モデル

抄録

When humans try to recognize ambiguous images, that perception is unambiguous yet changes over time. This phenomenon called perceptual change. It has been considered that perceptual change is caused by top-down attention which selectively promotes/suppresses reactivity to features depending on the expected recognition result. However, modeling research of perceptual change which take the object recognition process into account has not progressed. Therefore, we modeled the perceptual change phenomenon taking the object recognition process into account by using Convolutional Neural Networks (CNN). I simulate the perceptual change phenomenon using this model, and I visualize features which are promoted/suppressed by top-down attention. I show that these features are important to unambiguous perception.