Digitally-Enhanced Dog Behavioral Testing: Getting Help from the Machine
Nareed Farhat, Teddy Lazebnik, Joke Monteny, Christel Palmyre Henri, Moons, Eline Wydooghe, Dirk van der Linden, Anna Zamansky

TL;DR
This study introduces an automated, machine learning-based approach to objectively assess dog behavioral traits, reducing subjectivity and effort compared to traditional questionnaire and observation methods.
Contribution
It presents a novel digitally-enhanced testing paradigm that uses clustering and predictive models to evaluate dog coping styles and behaviors with high accuracy.
Findings
Unsupervised clustering distinguished dog coping styles with significant behavioral differences.
Machine learning classifier achieved 78% accuracy in expert-level coping style prediction.
Regression models accurately predicted specific behavioral trait scores.
Abstract
The assessment of behavioral traits in dogs is a well-studied challenge due to its many practical applications such as selection for breeding, prediction of working aptitude, chances of being adopted, etc. Most methods for assessing behavioral traits are questionnaire or observation-based, which require a significant amount of time, effort and expertise. In addition, these methods are also susceptible to subjectivity and bias, making them less reliable. In this study, we proposed an automated computational approach that may provide a more objective, robust and resource-efficient alternative to current solutions. Using part of a Stranger Test protocol, we tested n=53 dogs for their response to the presence and benign actions of a stranger. Dog coping styles were scored by three experts. Moreover, data were collected from their handlers using the Canine Behavioral Assessment and Research…
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Taxonomy
TopicsHuman-Animal Interaction Studies · Neuroendocrine regulation and behavior · Primate Behavior and Ecology
