Digitally-enhanced dog behavioral testing.

Behavioral traits in dogs are assessed for a wide range of purposes such as determining selection for breeding, chance of being adopted or prediction of working aptitude. Most methods for assessing behavioral traits are questionnaire or observation-based, requiring significant amounts of time, effort and expertise. In addition, these methods might be also susceptible to subjectivity and bias, negatively impacting their reliability. 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 neutral actions of a stranger. Dog coping styles were scored by three dog behavior experts. Moreover, data were collected from their owners/trainers using the Canine Behavioral Assessment and Research Questionnaire (C-BARQ). An unsupervised clustering of the dogs' trajectories revealed two main clusters showing a significant difference in the stranger-directed fear C-BARQ category, as well as a good separation between (sufficiently) relaxed dogs and dogs with excessive behaviors towards strangers based on expert scoring. Based on the clustering, we obtained a machine learning classifier for expert scoring of coping styles towards strangers, which reached an accuracy of 78%. We also obtained a regression model predicting C-BARQ scores with varying performance, the best being Owner-Directed Aggression (with a mean average error of 0.108) and Excitability (with a mean square error of 0.032). This case study demonstrates a novel paradigm of 'machine-based' dog behavioral assessment, highlighting the value and great promise of AI in this context.

Clustering for Automated Exploratory Pattern Discovery in Animal Behavioral Data.

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.

A Treatment Plan for Dogs (Canis familiaris) That Show Impaired Social Functioning towards Their Owners.

Many domestic dogs are uncomfortable when humans perform trivial and benign actions that the animals perceive as threatening. A common technique for addressing canine emotional discomfort involves desensitization, where the intensity of a problematic stimulus is gradually increased while the dog remains relaxed. Desensitization requires a skillful owner and is complicated when actions of the owner are the stimuli to be desensitised. This paper introduces a behaviour modification programme for dogs with impaired social functioning in relation to the (inter)actions by their owners, consisting of (1) increasing owner knowledge and awareness regarding dog body language and perception of owner actions, (2) management of the daily life of the dog through general stress reduction and avoidance of stressful situations, and (3) behaviour modification through training. The latter component entails a non-threatening, predictable exercise in which the dog has control over any perceived threats, the introduction of the safety cue with subsequent desensitization, and engaging activities with the owner that the dog finds enjoyable. We also present a case series report to examine a selection of dogs with impaired social functioning, from signalment to outcome, when treated with the proposed behaviour modification and examine which adaptations were made to the plan according to individual dogs. Finally, we avenues for future research.