Beltzung Benjamin, Pelé Marie, Renoult Julien P, Shimada Masaki, Sueur Cédric
IPHC, University of Strasbourg, CNRS, UMR 7178, 67000 Strasbourg, France.
ANTHROPO-LAB, ETHICS EA 7446, Université Catholique de Lille, 59000 Lille, France.
Animals (Basel). 2022 Oct 14;12(20):2761. doi: 10.3390/ani12202761.
Drawings have been widely used as a window to the mind; as such, they can reveal some aspects of the cognitive and emotional worlds of other animals that can produce them. The study of non-human drawings, however, is limited by human perception, which can bias the methodology and interpretation of the results. Artificial intelligence can circumvent this issue by allowing automated, objective selection of features used to analyze drawings. In this study, we use artificial intelligence to investigate seasonal variations in drawings made by Molly, a female orangutan who produced more than 1299 drawings between 2006 and 2011 at the Tama Zoological Park in Japan. We train the VGG19 model to first classify the drawings according to the season in which they are produced. The results show that deep learning is able to identify subtle but significant seasonal variations in Molly's drawings, with a classification accuracy of 41.6%. We use VGG19 to investigate the features that influence this seasonal variation. We analyze separate features, both simple and complex, related to color and patterning, and to drawing content and style. Content and style classification show maximum performance for moderately complex, highly complex, and holistic features, respectively. We also show that both color and patterning drive seasonal variation, with the latter being more important than the former. This study demonstrates how deep learning can be used to objectively analyze non-figurative drawings and calls for applications to non-primate species and scribbles made by human toddlers.
绘画已被广泛用作洞察思维的窗口；因此，它们可以揭示能够创作绘画的其他动物认知和情感世界的某些方面。然而，对非人类绘画的研究受到人类感知的限制，这可能会使研究方法和结果解释产生偏差。人工智能可以通过对用于分析绘画的特征进行自动、客观的选择来规避这个问题。在本研究中，我们利用人工智能来调查一只名为莫莉的雌性猩猩所绘图画中的季节性变化。莫莉于2006年至2011年期间在日本多摩动物园创作了1299多幅画作。我们训练VGG19模型首先根据画作的创作季节对其进行分类。结果表明，深度学习能够识别莫莉画作中细微但显著的季节性变化，分类准确率为41.6%。我们使用VGG19来研究影响这种季节性变化的特征。我们分析了与颜色和图案以及绘画内容和风格相关的单独特征，包括简单特征和复杂特征。内容分类和风格分类分别在中等复杂、高度复杂和整体特征方面表现出最佳性能。我们还表明，颜色和图案都推动了季节性变化，其中图案比颜色更重要。这项研究展示了深度学习如何用于客观分析抽象绘画，并呼吁将其应用于非灵长类物种以及人类幼儿的涂鸦。
