Abstract
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: this https URL.
Abstract (translated)
人类的复杂信息处理系统产生了很多客观和主观的评价，这使得探索人类认知产品在很大程度上具有前沿理论价值。近年来，受到生物大脑机制启发的人工智能技术在应用心理学或认知科学研究方面取得了显著进展，特别是在面部数据记忆和识别方面。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，以及将这些数据与人类产生的各种心理属性相关联。研究者使用了深度学习模型VGG16，证明了神经网络可以学习和复制面部数据的 key features，从而存储图像记忆。此外，实验结果揭示了人工智能模型在理解人类情感和认知过程方面的潜力，并从非欧氏空间角度建立了一个多维可视化解释，为提高AI的透明度提供了新的见解。这项研究不仅在心理学领域推动了AI技术的应用，还提供了关于AI信息处理的新心理理论理解。代码可在此处查看：https://www. this URL.
URL
https://arxiv.org/abs/2404.18352
