  The complex information processing system of humans generates a lot of
objective and subjective evaluations, making the exploration of human cognitive
products of great cutting-edge theoretical value. In recent years, deep
learning technologies, which are inspired by biological brain mechanisms, have
made significant strides in the application of psychological or cognitive
scientific research, particularly in the memorization and recognition of facial
data. This paper investigates through experimental research how neural networks
process and store facial expression data and associate these data with a range
of psychological attributes produced by humans. Researchers utilized deep
learning model VGG16, demonstrating that neural networks can learn and
reproduce key features of facial data, thereby storing image memories.
Moreover, the experimental results reveal the potential of deep learning models
in understanding human emotions and cognitive processes and establish a
manifold visualization interpretation of cognitive products or psychological
attributes from a non-Euclidean space perspective, offering new insights into
enhancing the explainability of AI. This study not only advances the
application of AI technology in the field of psychology but also provides a new
psychological theoretical understanding the information processing of the AI.
The code is available in here: https://github.com/NKUShaw/Psychoinformatics.
