Beltzung Benjamin, Pelé Marie, Renoult Julien P, Sueur Cédric
CNRS, IPHC UMR, Université de Strasbourg, Strasbourg, France.
ANTHROPO LAB - ETHICS EA 7446, Université Catholique de Lille, Lille, France.
Front Psychol. 2023 Feb 8;14:992541. doi: 10.3389/fpsyg.2023.992541. eCollection 2023.
In recent years, computer science has made major advances in understanding drawing behavior. Artificial intelligence, and more precisely deep learning, has displayed unprecedented performance in the automatic recognition and classification of large databases of sketches and drawings collected through touchpad devices. Although deep learning can perform these tasks with high accuracy, the way they are performed by the algorithms remains largely unexplored. Improving the interpretability of deep neural networks is a very active research area, with promising recent advances in understanding human cognition. Deep learning thus offers a powerful framework to study drawing behavior and the underlying cognitive processes, particularly in children and non-human animals, on whom knowledge is incomplete. In this literature review, we first explore the history of deep learning as applied to the study of drawing along with the main discoveries in this area, while proposing open challenges. Second, multiple ideas are discussed to understand the inherent structure of deep learning models. A non-exhaustive list of drawing datasets relevant to deep learning approaches is further provided. Finally, the potential benefits of coupling deep learning with comparative cultural analyses are discussed.
近年来，计算机科学在理解绘图行为方面取得了重大进展。人工智能，更确切地说是深度学习，在通过触摸板设备收集的大量草图和绘图数据库的自动识别和分类中展现出了前所未有的性能。尽管深度学习能够高精度地执行这些任务，但算法执行这些任务的方式在很大程度上仍未得到探索。提高深度神经网络的可解释性是一个非常活跃的研究领域，在理解人类认知方面最近取得了有前景的进展。因此，深度学习提供了一个强大的框架来研究绘图行为和潜在的认知过程，特别是在知识尚不完整的儿童和非人类动物身上。在这篇文献综述中，我们首先探讨深度学习应用于绘图研究的历史以及该领域的主要发现，同时提出开放性挑战。其次，讨论了多种理解深度学习模型固有结构的思路。还进一步提供了与深度学习方法相关的绘图数据集的非详尽列表。最后，讨论了将深度学习与比较文化分析相结合的潜在益处。
