Elsevier

Infant Behavior and Development

Volume 54, February 2019, Pages 156-165
Infant Behavior and Development

Review
Reproducibility and a unifying explanation: Lessons from the shape bias

https://doi.org/10.1016/j.infbeh.2018.09.011Get rights and content

Highlights

  • Individual experiments and replications form the basis of scientific inquiry, but limits inherent in a single result only advance science so far.

  • We advance science through deep, unified understanding of phenomena, gained through integrating across multiple studies.

  • Here, we highlight four lessons for science, using the shape bias as an example.

  • Valid conclusions require attention to, and unified explanations of, all the data.

Abstract

The goal of science is to advance our understanding of particular phenomena. However, in the field of development, the phenomena of interest are complex, multifaceted, and change over time. Here, we use three decades of research on the shape bias to argue that while replication is clearly an important part of the scientific process, integration across the findings of many studies that include variations in procedure is also critical to create a coherent understanding of the thoughts and behaviors of young children. The “shape bias,” or the tendency to generalize a novel label to novel objects of the same shape, is a reliable and robust behavioral finding and has been shown to predict future vocabulary growth and possible language disorders. Despite the robustness of the phenomenon, the way in which the shape bias is defined and tested has varied across studies and laboratories. The current review argues that differences in performance that come from even seemingly minor changes to the participants or task can offer critical insight to underlying mechanisms, and that working to incorporate data from multiple labs is an important way to reveal how task variation and a child’s individual pathway creates behavior—a key issue for understanding developmental phenomena.

Section snippets

The goal of science

What is the goal of science? Science is not simply about experiments; it is about gaining knowledge. It is about building deep, coherent, and unified explanations of multiple phenomena. Such explanations allow control and prediction of outcomes in new experiments; they provide new understanding of old findings and support the mining, and comprehension, of old data. The best explanations, however, do more than this—they make connections to new domains, allowing control and prediction in

Examining “best practices” through 30 years of research on the shape bias

Experiments often begin with a question or hypothesis. For example, one might propose (as was once suggested, MacNamara, 1972, 1982) that very young children know from the start of word learning that nouns refer to objects. What experiment would test that idea? There are lots of choices, and many critical gaps in our expertise. As we all learned in Experimental Design 101, we need to operationally define our terms—“know”, “noun”, “object”. We need to determine which experimental factor can be

Conclusions

Our goal in science is to advance the field through valid conclusions that can do real work. Experimental approaches are always a work in progress, always needing to be revised and sometimes to be changed in major ways. Alas, scientists are people with all the strengths, weaknesses, ambitions, and honest (and sometimes, but quite rarely, dishonest) aspirations, and these factors can lead to non-replicable studies. The current crisis has put much needed attention on the issue of whether

Funding

Funding provided by R01HD045713 to LKS, R01HD067315 to EC and R01HD007475 to LBS.

Declarations of interest

None.

Consent

The content is solely the responsibility of the authors.

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