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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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Key considerations for child and adolescent MRI data collection
Cognitive neuroimaging researchers' ability to infer accurate statistical conclusions from neuroimaging depends greatly on the quality of the data analyzed. This need for quality control is never more evident than when conducting neuroimaging studies with children and adolescents. Developmental neuroimaging requires patience, flexibility, adaptability, extra time, and effort. It also provides us a unique, non-invasive way to understand the development of cognitive processes, individual differences, and the changing relations between brain and behavior over the lifespan. In this discussion, we focus on collecting magnetic resonance imaging (MRI) data, as it is one of the more complex protocols used with children and youth. Through our extensive experience collecting MRI datasets with children and families, as well as a review of current best practices, we will cover three main topics to help neuroimaging researchers collect high-quality datasets. First, we review key recruitment and retention techniques, and note the importance for consistency and inclusion across groups. Second, we discuss ways to reduce scan anxiety for families and ways to increase scan success by describing the pre-screening process, use of a scanner simulator, and the need to focus on participant and family comfort. Finally, we outline several important design considerations in developmental neuroimaging such as asking a developmentally appropriate question, minimizing data loss, and the applicability of public datasets. Altogether, we hope this article serves as a useful tool for those wishing to enter or learn more about developmental cognitive neuroscience.
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- Award ID(s):
- 1941193
- PAR ID:
- 10386285
- Date Published:
- Journal Name:
- Frontiers in Neuroimaging
- Volume:
- 1
- ISSN:
- 2813-1193
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fnimg.2022.981947
