Abstract
Everyday sights, sounds, and actions are the experiences available to shape experience-dependent change. Recent efforts to quantify this everyday input – using wearable sensors in order to capture experiences that are neither scripted by theorists nor perturbed by the presence of an outsider recording – have revealed striking heterogeneity. There is no meaningfully “representative” hour of a day, instance of a category, interaction context, or infant. Such heterogeneity raises questions about how to optimize everyday sampling schemes in order to yield data that advance theories of experience-dependent change. Here, I review lessons from recent research sampling infants’ everyday language, music, action, and vision at multiple timescales, with specific attention to needed next steps. I suggest that most extant evidence about everyday ecologies arises from Opportunistic Sampling and that we must collectively focus our ambitions on a next wave of Distributionally Informed Sampling. In particular, we must center (1) activity distributions with their correlated opportunities to encounter particular inputs, (2) content distributions of commonly and rarely encountered instances, (3) temporal distributions of input that comes and goes, and (4) input trajectories that change over developmental time, as we model everyday experiences and their consequences. Throughout, I highlight practical constraints (e.g., sensor battery life and fussy infants) and payoffs (e.g., annotation protocols that yield multi-timescale dividends) in these efforts. Grappling with the fact that people do not re-live the same hour all life long is a necessary and exciting next step as we build theories of everyday experience-dependent change.