  Convolutional neural network (CNN), as an important model in artificial
intelligence, has been widely used and studied in different disciplines. The
computational mechanisms of CNNs are still not fully revealed due to the their
complex nature. In this study, we focused on 4 extensively studied CNNs
(AlexNet, VGG11, VGG13, and VGG16) which has been analyzed as human-like models
by neuroscientists with ample evidence. We trained these CNNs to emotion
valence classification task by transfer learning. Comparing their performance
with human data, the data unveiled that these CNNs would partly perform as
human does. We then update the object-based AlexNet using self-attention
mechanism based on neuroscience and behavioral data. The updated FE-AlexNet
outperformed all the other tested CNNs and closely resembles human perception.
The results further unveil the computational mechanisms of these CNNs.
Moreover, this study offers a new paradigm to better understand and improve CNN
performance via human data.
