Using Artificial Intelligence to Analyze Non-Human Drawings: A First Step with Orangutan Productions

Abstract

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.

Fractals and artificial intelligence to decrypt ideography and understand the evolution of language

“…For instance, the subject used red extensively after the birth of an orangutan baby and blue and yellow after the visit of schoolchildren who were wearing yellow hats and blue coats. This study's findings were verified using deep-learning models (Beltzung, Pelé, Renoult, Shimada, & Sueur, 2022b). The convolutional neural networks used in orangutan drawings showed a seasonal effect on the style and content of the productions.…”

The puzzle of ideography

“…For instance, the subject used red extensively after the birth of an orangutan baby and blue and yellow after the visit of schoolchildren who were wearing yellow hats and blue coats. This study's findings were verified using deep-learning models (Beltzung, Pelé, Renoult, Shimada, & Sueur, 2022b). The convolutional neural networks used in orangutan drawings showed a seasonal effect on the style and content of the productions.…”

Editorial: Recent Advances in Animal Cognition and Ethology

“…This innovative tool promises to revolutionise the study of honeybee behaviour without disrupting hive activities. Similarly, researchers used artificial intelligence to analyse non-figurative drawings created by an orangutan [ 19 ], revealing subtle seasonal variations and emphasising the potential of AI in objectively interpreting animal-produced art. Other apes, chimpanzees, were the focus of one study, which delved into their numerical abilities [ 20 ].…”