iCatcher+: Robust and Automated Annotation of Infants’ and Young Children’s Gaze Behavior From Videos Collected in Laboratory, Field, and Online Studies

Yotam Erel*, Katherine Adams Shannon, Junyi Chu, Kim Scott, Melissa Kline Struhl, Peng Cao, Xincheng Tan, Peter Hart, Gal Raz, Sabrina Piccolo, Catherine Mei, Christine Potter, Sagi Jaffe-Dax, Casey Lew-Williams, Joshua Tenenbaum, Katherine Fairchild, Amit Bermano, Shari Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
JournalAdvances in Methods and Practices in Psychological Science
Issue number2
StatePublished - 1 Apr 2023


FundersFunder number
Defense Advanced Research Projects Agency Machine Common Sense ProgramCW3013552
Siegel Family EndowmentS4881
Yandex Foundation
National Institutes of HealthF32HD103363, F32HD105705, R01HD095912
Simons Foundation
Blavatnik Family Foundation
Quest for Intelligence, Massachusetts Institute of Technology


    • cognitive development
    • deep learning
    • eye tracking
    • online data collection
    • open source


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