Traditional methods of data analysis in animal behavior research are usually based on measuring behavior by manually coding a set of chosen behavioral parameters, which is naturally prone to human bias and error, and is also a tedious labor-intensive task. Machine learning techniques are increasingly applied to support researchers in this field, mostly in a supervised manner: for tracking animals, detecting land marks or recognizing actions. Unsupervised methods are increasingly used, but are under-explored in the context of behavior studies and applied contexts such as behavioral testing of dogs. This study explores the potential of unsupervised approaches such as clustering for the automated discovery of patterns in data which have potential behavioral meaning. We aim to demonstrate that such patterns can be useful at exploratory stages of data analysis before forming specific hypotheses. To this end, we propose a concrete method for grouping video trials of behavioral testing of animal individuals into clusters using a set of potentially relevant features. Using an example of protocol for testing in a “Stranger Test”, we compare the discovered clusters against the C-BARQ owner-based questionnaire, which is commonly used for dog behavioral trait assessment, showing that our method separated well between dogs with higher C-BARQ scores for stranger fear, and those with lower scores. This demonstrates potential use of such clustering approach for exploration prior to hypothesis forming and testing in behavioral research.
Bibliographical noteFunding Information:
The research was partially supported by the Ministry of Science and Technology of Israel and RFBR according to the research project no. 19-57-06007, the Israel Ministry of Agriculture, the Animal Welfare division of the Flemish department of Environment and an internal research project from VIVES University College (PWO development and testing of an observation and scoringstool for social skills of pups toward humans). We thank Nareed Farhat for her help with data analysis. Many thanks go to the dog owners and their dogs for their participation in the study.
Copyright © 2022 Menaker, Monteny, de Beeck and Zamansky.
- animal behavior
- behavioral testing
- Data Science
- machine learning
ASJC Scopus subject areas
- Veterinary (all)