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.
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Bringing Home Baby Euclid: Testing Infants’ Basic Shape Discrimination Online
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.
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- Award ID(s):
- 1845924
- PAR ID:
- 10316357
- Date Published:
- Journal Name:
- Frontiers in Psychology
- Volume:
- 12
- ISSN:
- 1664-1078
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Research in developmental cognitive science reveals that human infants perceive shape changes in 2D visual forms that are repeatedly presented over long durations. Nevertheless, infants’ sensitivity to shape under the brief conditions of natural viewing has been little studied. Three experiments tested for this sensitivity by presenting 128 seven‐month‐old infants with shapes for the briefer durations under which they might see them in dynamic scenes. The experiments probed infants’ sensitivity to two fundamental geometric properties of scale‐ and orientation‐invariant shape:
relative length andangle. Infants detected shape changes in closed figures, which presented changes in both geometric properties. Infants also detected shape changes in open figures differing in angle when figures were presented at limited orientations. In contrast, when open figures were presented at unlimited orientations, infants detected changes in relative length but not in angle. The present research therefore suggests that, as infants look around at the cluttered and changing visual world, relative length is the primary geometric property by which they perceive scale‐ and orientation‐invariant shape. -
How can researchers best measure infants' motor experiences in the home? Body position—whether infants are held, supine, prone, sitting, or upright—is an important developmental experience. However, the standard way of measuring infant body position, video recording by an experimenter in the home, can only capture short instances, may bias measurements, and conflicts with physical distancing guidelines resulting from the COVID-19 pandemic. Here, we introduce and validate an alternative method that uses machine learning algorithms to classify infants' body position from a set of wearable inertial sensors. A laboratory study of 15 infants demonstrated that the method was sufficiently accurate to measure individual differences in the time that infants spent in each body position. Two case studies showed the feasibility of applying this method to testing infants in the home using a contactless equipment drop-off procedure.more » « less
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Abstract How does one assess developmental change when the measures themselves change with development? Most developmental studies of word learning use
either looking (infants) or pointing (preschoolers and older). With little empirical evidence of the relationship between the two measures, developmental change is difficult to assess. This paper analyzes 914 pointing, looking children (451 female, varied ethnicities, 2.5–6.5 years, dates: 2009–2019) in 36 word‐ or sound‐learning experiments with two‐alternative test trials. Looking proportions and pointing accuracy correlated strongly (r = .7). Counter to the “looks first” hypothesis, looks were not sensitive to incipient knowledge that pointing missed: when pointing is at chance, looking proportions are also. Results suggest one possible path forward for assessing continuous developmental change. Methodological best practices are discussed. -
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
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fpsyg.2021.734592
