RESUMEN
There has been significant progress in understanding the effects of childhood poverty on neurocognitive development. This progress has captured the attention of policymakers and promoted progressive policy reform. However, the prevailing emphasis on the harms associated with childhood poverty may have inadvertently perpetuated a deficit-based narrative, focused on the presumed shortcomings of children and families in poverty. This focus can have unintended consequences for policy (e.g., overlooking strengths) as well as public discourse (e.g., focusing on individual rather than systemic factors). Here, we join scientists across disciplines in arguing for a more well-rounded, "strength-based" approach, which incorporates the positive and/or adaptive developmental responses to experiences of social disadvantage. Specifically, we first show the value of this approach in understanding normative brain development across diverse human environments. We then highlight its application to educational and social policy, explore pitfalls and ethical considerations, and offer practical solutions to conducting strength-based research responsibly. Our paper re-ignites old and recent calls for a strength-based paradigm shift, with a focus on its application to developmental cognitive neuroscience. We also offer a unique perspective from a new generation of early-career researchers engaged in this work, several of whom themselves have grown up in conditions of poverty. Ultimately, we argue that a balanced strength-based scientific approach will be essential to building more effective policies.
RESUMEN
Technological advances in psychological research have enabled large-scale studies of human behavior and streamlined pipelines for automatic processing of data. However, studies of infants and children have not fully reaped these benefits because the behaviors of interest, such as gaze duration and direction, still have to be extracted from video through a laborious process of manual annotation, even when these data are collected online. Recent advances in computer vision raise the possibility of automated annotation of these video data. In this article, we built on a system for automatic gaze annotation in young children, iCatcher, by engineering improvements and then training and testing the system (referred to hereafter as iCatcher+) on three data sets with substantial video and participant variability (214 videos collected in U.S. lab and field sites, 143 videos collected in Senegal field sites, and 265 videos collected via webcams in homes; participant age range = 4 months-3.5 years). When trained on each of these data sets, iCatcher+ performed with near human-level accuracy on held-out videos on distinguishing "LEFT" versus "RIGHT" and "ON" versus "OFF" looking behavior across all data sets. This high performance was achieved at the level of individual frames, experimental trials, and study videos; held across participant demographics (e.g., age, race/ethnicity), participant behavior (e.g., movement, head position), and video characteristics (e.g., luminance); and generalized to a fourth, entirely held-out online data set. We close by discussing next steps required to fully automate the life cycle of online infant and child behavioral studies, representing a key step toward enabling robust and high-throughput developmental research.