ReviewFrontiers in psychology2023
Deep learning for studying drawing behavior: A review.
Review in Frontiers in psychology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Research on concept generation design of automobile seats based on human-machine co-creation.Scientific reports · 2025Article
- Discovery of novel potential 11β-HSD1 inhibitors through combining deep learning, molecular modeling, and bio-evaluation.Molecular diversity · 2025Article
- Beyond human perception: challenges in AI interpretability of orangutan artwork.Primates; journal of primatology · 2025Article
- Constructing a norm for children's scientific drawing: Distribution features based on semantic similarity of large language models.Biology methods & protocols · 2025Article
- Deep learning for automatic facial detection and recognition in Japanese macaques: illuminating social networks.Primates; journal of primatology · 2024Article
- Enhancing behavior classification of children in dynamic interaction scenes through improved DCNN model.PeerJ. Computer science · 2024Article
- Editorial: Recent Advances in Animal Cognition and Ethology.Animals : an open access journal from MDPI · 2023Article
- Using Artificial Intelligence to Analyze Non-Human Drawings: A First Step with Orangutan Productions.Animals : an open access journal from MDPI · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.