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.

翻译：人类复杂的信息处理系统产生了大量客观与主观评价，使得对人类认知产品的探索具有重要的前沿理论价值。近年来，受生物大脑机制启发的深度学习技术，在心理或认知科学研究中的应用取得了显著进展，尤其在面部数据的记忆与识别方面。本文通过实验研究探讨了神经网络如何处理和存储面部表情数据，并将这些数据与人类产生的一系列心理属性相关联。研究人员利用深度学习模型VGG16，证明了神经网络能够学习并再现面部数据的关键特征，从而存储图像记忆。此外，实验结果揭示了深度学习模型在理解人类情感和认知过程中的潜力，并从非欧几里得空间视角建立了认知产品或心理属性的流形可视化解释，为增强人工智能的可解释性提供了新见解。本研究不仅推动了人工智能技术在心理学领域的应用，还为理解人工智能的信息处理提供了新的心理学理论视角。代码可在此处获取：https://github.com/NKUShaw/Psychoinformatics。