Automated behavioral scoring: Do we even need humans?

Abstract

The development of automated behavior scoring technology has been a tremendous boon to the study of social behavior. However, completely outsourcing behavioral analysis to a computer runs the risk of overlooking important nuances, and researchers risk distancing themselves from their very object of study. Here, I make the case that while automating analysis has been valuable, and overautomating analysis is risky, more effort should be spent automating the collection of behavioral data. Continuous automated behavioral observations conducted in situ have the promise to reduce confounding elements of social behavior research, such as handling stress, novel environments, one‐time “snapshot” measures, and experimenter presence. Now that we have the capability to automatically process behavioral observations thanks to machine vision and machine learning, we would do well to leverage the same open‐source ethos to increase the throughput of behavioral observation and collection. Fortunately, several such platforms have recently been developed. Repeated testing in the home environment will produce higher qualities and quantities of data, bringing us closer to realizing the ethological goals of studying animal behavior in a naturalistic context.