Quantifying everyday ecologies: Principles for manual annotation of many hours of infants' lives

Abstract

Everyday experiences are the experiences available to shape developmental change. Remarkable advances in devices used to record infants’ and toddlers’ everyday experiences, as well as in repositories to aggregate and share such recordings across teams of theorists, have yielded a potential gold mine of insights to spur next-generation theories of experience-dependent change. Making full use of these advances, however, currently requires manual annotation. Manually annotating many hours of everyday life is a dedicated pursuit requiring significant time and resources, and in many domains is an endeavor currently lacking foundational facts to guide potentially consequential implementation decisions. These realities make manual annotation a frequent ‘off ramp’ en route to discoveries, as theorists instead opt for narrower scoped activities. Here, we provide theorists with a framework for manually annotating many hours of everyday life designed to reduce both theoretical and practical overwhelm. We share insights based on our team’s recent adventures in the previously uncharted territory of everyday music. We identify principles, and share implementation examples and tools, to help theorists achieve scalable solutions to challenges that are especially fierce when annotating extended timescales. These principles for quantifying everyday ecologies will help theorists collectively maximize return on investment in databases of everyday recordings and will enable a broad community of scholars – across institutions, skillsets, experiences, and working environments – to make discoveries about the experiences upon which development may depend.

Everyday Parameters for Episode‐to‐Episode Dynamics in the Daily Music of Infancy

“…These details can be annotated together with details of nonmusical episodes like linguistic exchanges and naps in order to advance theories of experience‐dependent change that respect the rich nature of infants’ everyday soundscapes (Warlaumont, Sobowale, & Fausey, 2022). We encourage theorists to collectively make progress by annotating the shared everyday recordings (Mendoza & Fausey, 2018; 2021a; 2021b).…”

“…Prompts for greater fidelity to properties of everyday time include favoring continuous time instead of “trial” units (Huk, Bonnen, & He, 2018), instantiating slower rates of switching between instances (Duncan & Schlichting, 2018), investigating extended timescale dynamics (Creel, 2019; Halpern & Bartlett, 2010; Naselaris, Allen, & Kay, 2021; Spencer, Perone, & Buss, 2011; Vlach, 2019), and connecting multiple scales of integrating information over time (Hasson et al., 2015; Thelen & Smith, 1994). Sampling extended timescales of everyday life is a foundational step en route to better aligning empirical protocols with everyday temporal realities in part because it affords discoveries about multiple timescales (Mendoza & Fausey, 2021b; Ritwika et al., 2020). The everyday parameters discovered here propel theorists toward testing hypotheses in which learners encounter many opportunities to integrate musical episodes over seconds mixed with some opportunities to integrate over hours.…”

Using a developmental-ecological approach to understand the relation between language and music

“…In the past two decades, there has been a surge in naturalistic work in developmental psychology (Adolph et al, 2016;Bulgarelli & Bergelson, 2020;Sullivan et al, 2020;Yoshida & Smith, 2008) which has yielded several insights on the design and interpretation of naturalistic data (see Mendoza and Fausey, 2021, for an introduction to thinking through decisions surrounding who, what, where, and how to build and code large, naturalistic corpora). Here, we give recommendations specific to studying the early language and music environment in tandem, from a developmental-ecological perspective.…”

Learning language in vivo

“…Some heroic efforts to annotate daylong data in more detail have demonstrated both the richness of what could be studied were it manually annotated (e.g., Bergelson, Amatuni, et al, 2019; Montag, 2020) and the potential drawbacks of relying exclusively on automated output (e.g., Ferjan Ramírez et al, 2021). Encouragingly, protocols and guides for manually annotating daylong data have started to emerge, some with an eye toward maximizing efficiency (Casillas, Bergelson, et al, 2017; Cychosz et al, 2021; Mendoza & Fausey, 2021). Researchers choosing this path can expect large investments of time and money (see Figure 1).…”

“…LM, lab manager; PI, primary investigator; RA, undergraduate research assistant. See also, e.g., Ferjan Ramírez et al (2021) on annotation of automatically selected segments; Cychosz et al (2021) on clip classification; Bergelson, Amatuni, et al (2019) on noun annotation; and Mendoza and Fausey (2021) for an in‐depth discussion of workflow…”