Measuring predictability in the home environment using daylong audio recordings*

Naturalistic day-long recordings offer researchers the opportunity to study the physical and social environments surrounding infants and children. These recordings allow for analyses across modalities, over time, and at multiple hierarchical levels. In this paper, we focus on audio data to present automated methods to measure patterns ranging from low-level acoustic features to high-level semantic complexity and surprisal. Our goal is to illustrate potential untapped applications of wearable devices, particularly audio recordings, in developmental research. Using environmental predictability as an example, we demonstrate how complex constructs that unfold over different timescales can be meaningfully analysed. We also discuss the key considerations and limitations associated with this approach, highlighting both its utility and the practical challenges it entails.