ReviewReproducibility and a unifying explanation: Lessons from the shape bias
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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Children's generalization of novel names in comparison settings: The role of semantic distance during learning and at test
2023, Journal of Experimental Child PsychologyOver-reliance on English hinders cognitive science
2022, Trends in Cognitive SciencesCitation Excerpt :Speakers of different languages are ‘not equivalent as observers’ (as famously noted by Whorf [46]). These biases seem to take hold early in life: for instance, shape has been postulated to be a critical semantic dimension readily available to all humans starting from as early as 2 years of age [47]. However, during language learning, both vocabulary and language structure influence attention to shape, such that the well-established English ‘shape-bias’ may not arise in other languages, such as Tsimane (Bolivia) [48].
The signature-testing approach to mapping biological and artificial intelligences
2022, Trends in Cognitive SciencesCitation Excerpt :In recent years, convolutional neural networks (CNNs) have shown similar accuracy to humans when identifying images within specific datasets, leading AI researchers to ask whether CNNs also show the same information-processing patterns during image identification. Humans show a shape information-processing bias and, thus, tend to classify images via their shape [70,71]. By contrast, CNNs tend to classify images via their texture (a texture information-processing bias) [72–76].
How do children with developmental language disorder extend novel nouns?
2021, Journal of Experimental Child PsychologyCitation Excerpt :We compared extensions of novel nouns for five object classes: solid objects, animate objects, nonsolid substances, functional role categories, and spatial configuration categories. These object classes have already been examined separately (Christie & Gentner, 2010; Gentner et al., 2011; Jones & Smith, 1998; Smith et al., 2002; Soja et al., 1991) or in comparison (e.g., solid and nonsolid objects; see Kucker et al., 2019, for a recent review) in various studies in TD children, but to our knowledge these five classes have never been compared in the same study in children with DLD or in TD children. Second, we have seen that these abilities are related to the size of the lexicon (Smith et al., 2002; Thom & Sandhofer, 2009).
Towards a more robust and replicable science of infant development
2019, Infant Behavior and DevelopmentFor human-like models, train on human-like tasks
2023, Behavioral and Brain Sciences