Proceedings of the Annual Meeting of the Cognitive Science Society

The `chattr` R package enables users to easily detect and describe temporal contingencies in pre-annotated interactional data. Temporal contingency analysis is ubiquitous across signal system research, including human and non-human animal communication. Current approaches require manual evaluation (i.e., do not scale up), are proprietary/over-specialized (i.e., have limited utility), or are constructed ad-hoc per study (i.e., are variable in construct). `Chattr`'s theoretically motivated, customizable, and open source code provides a set of core functions that allow users to quickly and automatically extract contingency information in data already annotated for interactant activity (via manual or automated annotation). We demonstrate the use of `chattr` by testing predictions about turn-taking behavior in three language development corpora. We find that the package effectively recovers documented variation in linguistic input given both manual and automatically created speech annotations and note future directions for package development key to its use across multiple research domains.