The current study examines the organization of attention skills across the preschool year before kindergarten, and tests how distinct attention subcomponents predict early academic skills in a sample of low‐income children (n = 99). Children completed well‐validated attention tasks in fall at 4.5 years old and spring at 5 years old, capturing the abilities to selectively focus, sustain attention, and employ executive control. Exploratory factor analyses at both time points support a 2‐factor model differentiating selective and sustained attention from attention processing speed and executive attention, suggesting that attention in low‐income preschoolers may have a simpler organization than the 3‐factor structure found in adulthood. Multiple regression models find children's ability to selectively focus and sustain attention serves as a robust concurrent and longitudinal predictor of academic skills. These results highlight the role of selective and sustained attention processes in supporting school readiness for economically vulnerable children.

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.

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