Quantifying Everyday Ecologies: Principles for Manual Annotation of Many Hours of Infants' Lives

Automatic assessment of infant carrying and holding using at-home wearable recordings

Assessing infant carrying and holding (C/H), or physical infant-caregiver interaction, is important for a wide range of contexts in development research. An automated detection and quantification of infant C/H is particularly needed in long term at-home studies where development of infants' neurobehavior is measured using wearable devices. Here, we first developed a phenomenological categorization for physical infant-caregiver interactions to support five different definitions of C/H behaviors. Then, we trained and assessed deep learning-based classifiers for their automatic detection from multi-sensor wearable recordings that were originally used for mobile assessment of infants' motor development. Our results show that an automated C/H detection is feasible at few-second temporal accuracy. With the best C/H definition, the automated detector shows 96% accuracy and 0.56 kappa, which is slightly less than the video-based inter-rater agreement between trained human experts (98% accuracy, 0.77 kappa). The classifier performance varies with C/H definition reflecting the extent to which infants' movements are present in each C/H variant. A systematic benchmarking experiment shows that the widely used actigraphy-based method ignores the normally occurring C/H behaviors. Finally, we show proof-of-concept for the utility of the novel classifier in studying C/H behavior across infant development. Particularly, we show that matching the C/H detections to individuals' gross motor ability discloses novel insights to infant-parent interaction.

Open Science Considerations for Descriptive Research in Developmental Science

Descriptive developmental research seeks to document, describe, and analyze the conditions under which infants and children live and learn. Here, we articulate how open-science practices can be incorporated into descriptive research to increase its transparency, reliability, and replicability. To date, most open-science practices have been oriented toward experimental rather than descriptive studies, and it can be confusing to figure out how to translate open-science practices (e.g., preregistration) for research that is more descriptive in nature. We discuss a number of unique considerations for descriptive developmental research, taking inspiration from existing open-science practices and providing examples from recent and ongoing studies. By embracing a scientific culture where descriptive research and open science coexist productively, developmental science will be better positioned to generate comprehensive theories of development and understand variability in development across communities and cultures.

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

Experience‐dependent change pervades early human development. Though trajectories of developmental change have been well charted in many domains, the episode‐to‐episode schedules of experiences on which they are hypothesized to depend have not. Here, we took up this issue in a domain known to be governed in part by early experiences: music. Using a corpus of longform audio recordings, we parameterized the daily schedules of music encountered by 35 infants ages 6–12 months. We discovered that everyday music episodes, as well as the interstices between episodes, typically persisted less than a minute, with most daily schedules also including some very extended episodes and interstices. We also discovered that infants encountered music episodes in a bursty rhythm, rather than a periodic or random rhythm, over the day. These findings join a suite of recent discoveries from everyday vision, motor, and language that expand our imaginations beyond artificial learning schedules and enable theorists to model the history‐dependence of developmental process in ways that respect everyday sensory histories. Future theories about how infants build knowledge across multiple episodes can now be parameterized using these insights from infants’ everyday lives.

Using a Developmental-Ecological Approach to Understand the Relation Between Language and Music

Neurocognitive and genetic approaches have made progress in understanding language-music interaction in the adult brain. Although there is broad agreement that learning processes affect how we represent, comprehend, and produce language and music, there is little understanding of the content and dynamics of the early language-music environment in the first years of life. A developmental-ecological approach sees learning and development as fundamentally embedded in a child’s environment, and thus requires researchers to move outside of the lab to understand what children are seeing, hearing, and doing in their daily lives. In this paper, after first reviewing the limitations of traditional developmental approaches to understanding language-music interaction, we describe how a developmental-ecological approach can not only inform developmental theories of language-music learning, but also address challenges inherent to neurocognitive and genetic approaches. We then make suggestions for how researchers can best use the developmental-ecological approach to understand the similarities, differences, and co-occurrences in early music and language input.