Genty, E. | Search bioRxiv

bioRxiv2023

Chimpanzees and bonobos reinstate an interrupted triadic game

When humans engage in joint action, they seem to so with an underlying sense of joint commitment, a feeling of mutual obligation towards their partner and a shared goal. Whether our closest living relatives, bonobos and chimpanzees, experience and understand joint commitment in the same way is subject to debate. Crucial evidence concerns how participants respond to interruptions of joint actions, particularly if they protest or attempt to reengage their reluctant or distracted partners. During dyadic interactions, bonobos and chimpanzees appear to have some sense of joint commitment, according to recent studies. Yet, data are inconsistent for triadic games with objects. We addressed this issue by engaging N=23 apes (5 adult chimpanzees, 5 infant bonobos, 13 adult bonobos) in a "tug-of-war" game with a human experimenter who abruptly stopped playing. Adult apes readily attempted to reengage the experimenter (>60% of subjects on first trial), with no group differences in the way of reengagement. Infant bonobos rarely reengaged and never did so on their first trial. Importantly, when infants reengaged passive partners, they mostly deployed (tactile) signals, yet rarely game-related behaviours (GRBs) as commonly observed in adults. These findings might explain negative results of earlier research. Bonobos and chimpanzees may thus have motivational foundations for joint commitment, although this capacity might develop over lifetime. We discuss this finding in relation to evolutionary and developmental theories on joint commitment.

animal behavior and cognition↗

bioRxiv2023

ASBAR: an Animal Skeleton-Based Action Recognition framework. Recognizing great ape behaviors in the wild using pose estimation with domain adaptation.

The study and classification of animal behaviors have traditionally relied on direct human observation or video analysis, processes that are labor-intensive, time-consuming, and prone to human bias. Advances in machine learning for computer vision, particularly in pose estimation and action recognition, offer transformative potential to enhance the understanding of animal behaviors. However, the integration of these technologies for behavior recognition remains underexplored, particularly in natural settings. We introduce ASBAR (Animal Skeleton-Based Action Recognition), a novel framework that integrates pose estimation and behavior recognition into a cohesive pipeline. To demonstrate its utility, we tackled the challenging task of classifying natural behaviors of great apes in the wild. Our approach leverages the OpenMonkeyChallenge dataset, one of the largest open-source primate pose datasets, to train a robust pose estimation model using DeepLabCut. Subsequently, we extracted skeletal motion data from the PanAf500 dataset, a collection of in-the-wild videos of gorillas and chimpanzees annotated with nine behavior categories. Using PoseConv3D from MMAction2, we trained a skeleton-based action recognition model, achieving a Top-1 accuracy of 75.3%. This performance is comparable to previous video-based methods while reducing input data size by approximately 20-fold, offering significant advantages in computational efficiency and storage. To support further research, we provide an open-source, terminal-based GUI for training and evaluation, along with a dataset of 5,440 annotated keypoints for replication and extension to other species and behaviors. All models, code, and data are publicly available at: https://github.com/MitchFuchs/asbar

animal behavior and cognition↗