skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Online looking-time research for infants using the Japanese version of Lookit
Behavioral experiments with infants are generally costly, and developmental scientists often struggle with recruiting participants. Online experiments are an effective approach to address these issues by offering alternative routes to expand sample sizes and access more diverse populations. However, data collection procedures in online experiments have not been sufficiently established. Differences in procedures between laboratory and online experiments can lead to other issues such as decreased data quality and the need for preprocessing. Moreover, data collection platforms for non-English speaking participants remain scarce. This article introduces the Japanese version of Lookit, a platform dedicated to online looking-time experiments for infants. Lookit is integrated into Children Helping Science, a broader platform for online developmental studies operated by the Massachusetts Institute of Technology (Cambridge, MA, USA). In addition, we review the state-of-the-art of automated gaze coding algorithms for infant studies and provide methodological considerations that researchers should consider when conducting online experiments. We hope this article will serve as a starting point for promoting online experiments with young children in Japan and contribute to creating a more robust developmental science.  more » « less
Award ID(s):
2209756
PAR ID:
10540743
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
JAPANESE PSYCHOLOGICAL REVIEW
Date Published:
Journal Name:
Japanese Psychological Review
Volume:
67
Issue:
1
ISSN:
2433-4650
Page Range / eLocation ID:
16-30
Subject(s) / Keyword(s):
developmental research asynchronous online testing infancy research method looking measures
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Remote data collection procedures can strengthen developmental science by addressing current limitations to in-person data collection and helping recruit more diverse and larger samples of participants. Thus, remote data collection opens an opportunity for more equitable and more replicable developmental science. However, it remains an open question whether remote data collection procedures with children participants produce results comparable to those obtained using in-person data collection. This knowledge is critical to integrate results across studies using different data collection procedures. We developed novel web-based versions of two tasks that have been used in prior work with 4-6-year-old children and recruited children who were participating in a virtual enrichment program. We report the first successful remote replication of two key experimental effects that speak to the emergence of structured semantic representations ( N = 52) and their role in inferential reasoning ( N = 40). We discuss the implications of these findings for using remote data collection with children participants, for maintaining research collaborations with community settings, and for strengthening methodological practices in developmental science. 
    more » « less
  2. Online developmental psychology studies are still in their infancy, but their role is newly urgent in the light of the COVID-19 pandemic and the suspension of in-person research. Are online studies with infants a suitable stand-in for laboratory-based studies? Across two unmonitored online experiments using a change-detection looking-time paradigm with 96 7-month-old infants, we found that infants did not exhibit measurable sensitivities to the basic shape information that distinguishes between 2D geometric forms, as had been observed in previous laboratory experiments. Moreover, while infants were distracted in our online experiments, such distraction was nevertheless not a reliable predictor of their ability to discriminate shape information. Our findings suggest that the change-detection paradigm may not elicit infants’ shape discrimination abilities when stimuli are presented on small, personal computer screens because infants may not perceive two discrete events with only one event displaying uniquely changing information that draws their attention. Some developmental paradigms used with infants, even those that seem well-suited to the constraints and goals of online data collection, may thus not yield results consistent with the laboratory results that rely on highly controlled settings and specialized equipment, such as large screens. As developmental researchers continue to adapt laboratory-based methods to online contexts, testing those methods online is a necessary first step in creating robust tools and expanding the space of inquiry for developmental science conducted online. 
    more » « less
  3. 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. 
    more » « less
  4. As improvements in medicine lower infant mortality rates, more infants with neuromotor challenges survive past birth. The motor, social, and cognitive development of these infants are closely interrelated, and challenges in any of these areas can lead to developmental differences. Thus, analyzing one of these domains - the motion of young infants - can yield insights on developmental progress to help identify individuals who would benefit most from early interventions. In the presented data collection, we gathered day-long inertial motion recordings from N = 12 typically developing (TD) infants and N = 24 infants who were classified as at risk for developmental delays (AR) due to complications at or before birth. As a first research step, we used simple machine learning methods (decision trees, k-nearest neighbors, and support vector machines) to classify infants as TD or AR based on their movement recordings and demographic data. Our next aim was to predict future outcomes for the AR infants using the same simple classifiers trained from the same movement recordings and demographic data. We achieved a 94.4% overall accuracy in classifying infants as TD or AR, and an 89.5% overall accuracy predicting future outcomes for the AR infants. The addition of inertial data was much more important to producing accurate future predictions than identification of current status. This work is an important step toward helping stakeholders to monitor the developmental progress of AR infants and identify infants who may be at the greatest risk for ongoing developmental challenges. 
    more » « less
  5. This project explores how children and youth below the age of 18 sought to help others during the COVID-19 pandemic. We used the data included in this publication to answer research questions such as “How did children in the U.S. help others and themselves during the COVID-19 pandemic?” and “What issues were children in the U.S. concerned about during the COVID-19 pandemic?” This project includes a data dictionary and a dataset that summarizes a unique collection of 115 news articles focused on the helping behaviors and key concerns of children in the U.S. during the pandemic. The articles appeared in print or online news sources between 2020 and 2023. We searched for media coverage using terms such as “kids,” “help,” “volunteer,” “actions,” “pandemic,” and “COVID-19.” Over time we refined and added additional search terms based on emergent themes such as “raising money,” “making personal protective equipment,” and “helping with homework.” We limited our searches by language (English), geography (the United States), and time (an article had to be published between January 2020, when the virus was first detected in the U.S., and November 2023, when we ended our searches for the dataset). When we identified news coverage that fit our definition of helping behaviors, we saved a PDF of the article (all PDFs are available upon request from the PI). Information included in this dataset is summarized as follows: (1) article citation and link; (2) article synopsis; (3) information on the child or children featured in the article; (4) summary of key helping behaviors or other actions taken by children during the pandemic; (5) information on who children were trying to help or what type of change they were attempting to influence; (6) quotes from children or youth; and (7) notations of photos, videos, or links to additional resources. The envisioned audience for this data includes social science and public health researchers, journalists, and policy makers with an interest in children and the pandemic, specifically, or disasters and altruism, more broadly. 
    more » « less