Taking a bottom–up, data-driven approach to characterizing variability across early-life environments.

Children's long-term outcomes correlate with the physical and social features of the homes, schools, and neighborhoods in which they grow up. Yet, current techniques for establishing these correlations are limited. This article (a) presents five main issues linked to the currently used approaches. For example, most research relies on questionnaires or coding brief videoed interactions, which focus on a limited set of dimensions selected by researchers ("top-down" approach). As a result, these methods are limited because they provide only a static snapshot, which often conflates conceptually diverse levels, including the physical (e.g., noise, light) and social environments (e.g., caregiver mood, responsiveness). New technologies offer alternative approaches to these issues. This article (b) highlights how continuous multifeature observations, processed using machine learning tools, can improve our description of early development and inform theorizing of the mechanisms through which environments shape outcomes. Devices worn by children and those around them provide long-form recordings, allowing researchers to track how naturalistic environments vary over time and how children respond to them in real time. This shift supports a more dynamic, bottom-up, data-driven, and potentially culturally neutral approach to characterizing environmental variability. Finally, the article (c) discusses key limitations, notably the need to ensure that machine learning methods used to analyze early environments are trained using culturally diverse data, evaluating remaining risks of bias, and practical and ethical considerations. (PsycInfo Database Record (c) 2026 APA, all rights reserved).