Online Developmental Science to Foster Innovation, Access, and Impact

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Abstract

We propose that developmental cognitive science should invest in an online CRADLE, a Collaboration for Reproducible and Distributed Large-Scale Experiments that crowdsources data from families participating on the internet. Here, we discuss how the field can work together to further expand and unify current prototypes for the benefit of researchers, science, and society.

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Most cited references 7

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Sampling in Developmental Science: Situations, Shortcomings, Solutions, and Standards.

Marc H. Bornstein, Justin Jager, Diane Putnick (2013)

Sampling is a key feature of every study in developmental science. Although sampling has far-reaching implications, too little attention is paid to sampling. Here, we describe, discuss, and evaluate four prominent sampling strategies in developmental science: population-based probability sampling, convenience sampling, quota sampling, and homogeneous sampling. We then judge these sampling strategies by five criteria: whether they yield representative and generalizable estimates of a study's target population, whether they yield representative and generalizable estimates of subsamples within a study's target population, the recruitment efforts and costs they entail, whether they yield sufficient power to detect subsample differences, and whether they introduce "noise" related to variation in subsamples and whether that "noise" can be accounted for statistically. We use sample composition of gender, ethnicity, and socioeconomic status to illustrate and assess the four sampling strategies. Finally, we tally the use of the four sampling strategies in five prominent developmental science journals and make recommendations about best practices for sample selection and reporting.

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Lookit (Part 1): A New Online Platform for Developmental Research

Kimberly Scott, Laura Schulz (2017)

Many important questions about children’s early abilities and learning mechanisms remain unanswered not because of their inherent scientific difficulty but because of practical challenges: recruiting an adequate number of children, reaching special populations, or scheduling repeated sessions. Additionally, small participant pools create barriers to replication while differing laboratory environments make it difficult to share protocols with precision, limiting the reproducibility of developmental research. Here we introduce a new platform, “Lookit,” that addresses these constraints by allowing families to participate in behavioral studies online via webcam. We show that this platform can be used to test infants (11–18 months), toddlers (24–36 months), and preschoolers (36–60 months) and reliably code looking time, preferential looking, and verbal responses, respectively; empirical results of these studies are presented in Scott, Chu, and Schulz ( 2017 ). In contrast to most laboratory-based studies, participants were roughly representative of the American population with regards to income, race, and parental education. We discuss broad technical and methodological aspects of the platform, its strengths and limitations, recommendations for researchers interested in conducting developmental studies online, and issues that remain before online testing can fulfill its promise.

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No Participant Left Behind: Conducting Science During COVID-19

Stella Lourenco, Arber Tasimi (2020)

Cognitive scientists have ramped up online testing in response to the coronavirus pandemic. Although research conducted online solves the problem of data collection, a lack of internet access among low-income and minority communities may reduce the diversity of study samples and, thus, impact the generalizability of scientific findings.

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Author and article information

Contributors

Mark Sheskin

Journal

Journal ID (nlm-ta): Trends Cogn Sci

Journal ID (iso-abbrev): Trends Cogn. Sci. (Regul. Ed.)

Title: Trends in Cognitive Sciences

Publisher: The Authors. Published by Elsevier Ltd.

ISSN (Print): 1364-6613

ISSN (Electronic): 1879-307X

Publication date PMC-release: 2 July 2020

Publication date (Electronic): 2 July 2020

Affiliations

[1 ]Department of Psychology, Yale University, New Haven, CT, USA

[2 ]Social Sciences, Minerva Schools at KGI, San Francisco, CA, USA

[3 ]Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

[4 ]School of Behavioral and Brain Sciences, The University of Texas at Dallas, Dallas, TX, USA

[5 ]Department of Psychology and Neuroscience, Duke University, Durham, NC, USA

[6 ]Center for Cognitive Neuroscience, Duke University, Durham, NC, USA

[7 ]Department of Psychology, Rutgers University - Newark, Newark, NJ, USA

[8 ]Department of Psychology, Harvard University, Cambridge, MA, USA

[9 ]Department of Computer Science, Stanford University, Stanford, CA, USA

[10 ]Department of Psychology, Stanford University, Stanford, CA, USA

[11 ]Department of Psychology, New York University, New York, NY, USA

[12 ]Data Science Initiative, Harvard University, Cambridge, MA, USA

Author notes

[* ]Correspondence: msheskin@ 123456gmail.com

[13]

These authors contributed equally

Article

Publisher ID: S1364-6613(20)30145-5

DOI: 10.1016/j.tics.2020.06.004

PMC ID: 7331515

PubMed ID: 32624386

SO-VID: f4ac13e4-ead6-4902-b637-fa41749f8568

License:

Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

Online Developmental Science to Foster Innovation, Access, and Impact

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Abstract

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Good practices in (cog-neuro) science and science communication

Most cited references 7

Sampling in Developmental Science: Situations, Shortcomings, Solutions, and Standards.

Lookit (Part 1): A New Online Platform for Developmental Research

No Participant Left Behind: Conducting Science During COVID-19

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