Abstract
<p>Behavioral observation has a longstanding tradition in psychology. However, its reliance on manual coding makes observational methods costly and prone to biases that arise from the inherent limits of coding schemes and from the subjectivity of human raters. Behavioral observation research therefore has drawbacks in terms of applicability, accessibility, reproducibility, and equitability. Computer vision (CV) methods have promise in this regard. In the context of quantifying human movement, CV approaches to pose estimation are becoming increasingly precise and have the benefit of being non-intrusive, reproducible, and low-cost. With such methods, human movement in videos can be processed as multivariate time-series data, offering a basis for a more comprehensive analysis of individual, interpersonal, and social dynamics, opening up the possibility for the widespread study of diverse populations in out-of-lab real-world contexts. Fulfilling such a promise goes beyond technical progress, it requires a theory-driven implementation of new accessible observational methods made equitable to learn. To showcase this promise, we present a CV pipeline with analyses of the temporal dynamics of dyadic interactions, including a cross-cultural investigation of movement fluidity in sibling cooperation during task-focused interactions and longitudinal assessments of infant-caregiver coordination during free-play sessions. The pipeline incorporates a pedagogical computational notebook, pose estimation (YOLOv8), theory-driven linear and non-linear time series analysis, and a CV method for more privacy-aware video data archiving. Together, this pipeline showcases a promising advancement in behavioral observation methods, expanding both who can conduct research and who can benefit from it.</p>