Word learning is 'smart': evidence that conceptual information affects preschoolers' extension of novel words

Booth, Amy E.; Waxman, Sandra R.

doi:10.1016/s0010-0277(02)00015-x

Cited by 162 publications

(139 citation statements)

References 35 publications

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“…Given a nonsolid substance, children extend the name by material. These are highly reliable and replicable results-obtained by many researchers-and in their broad outline characteristic of children learning a variety of languages (e.g., Booth & Waxman, 2002;Gathercole & Min, 1997;Kobayashi, 1998;Landau, Smith, & Jones, 1988Yoshida & Smith, 2001; see also Keil, 1994).…”

Section: Children's Novel Word Generalizationssupporting

confidence: 69%

“…The features and co-occurring words are thus potential context cues that could shift attention in systematic ways to the relevant properties for the particular kind of category-to multiple properties for animals to shape for artifacts, and to material for substances. And, indeed, the literature is filled with experimental demonstrations of these effects (Booth & Waxman, 2002;Colunga, 2006;Colunga & Smith, 2004;Gathercole, Cramer, Somerville, & Jansen op de Haar, 1995;McPherson, 1991;Samuelson, Horst, Schutte, & Dobbertin, 2008;Soja, 1994;Ward, Becker, Hass, & Vela, 1991;Yoshida & Smith, 2005;Yoshida, Swanson, Drake, & Gudel, 2001).…”

Section: Children's Novel Word Generalizationsmentioning

confidence: 99%

“…This-taking well-documented mechanisms from one area of research and asking whether they might apply in another, moving the field toward a more unified understanding of what might seem at first unrelated domains-should not be contentious. However, the literature on early word learning is highly contentious (Booth & Waxman, 2002;Cimpian & Markman, 2005;Smith & Samuelson, 2006) and not in a productive way, as the explanations pitted against each other are not empirically resolvable in a straightforward way.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mechanisms of Cognitive Development: Domain-General Learning or Domain-Specific Constraints?

Sloutsky¹

2007

PsycEXTRA Dataset

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Section: Children's Novel Word Generalizationssupporting

confidence: 69%

Section: Children's Novel Word Generalizationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mechanisms of Cognitive Development: Domain-General Learning or Domain-Specific Constraints?

Sloutsky¹

2007

PsycEXTRA Dataset

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“…Booth & Waxman (2002, 2005 revealed that children behaved differently depending on whether shown a target object in an animate or an artifact context. They suggested that children generalize novel nouns based not only on similarity in SHAPE but on other conceptual information such as ANIMACY.…”

Section: Relationship To Counterargumentsmentioning

confidence: 99%

Children construct novel word meaning ad-hoc based on known words: Computational model of shape and material biases

Kurosaki

Omori

2007

Cognitive Systems Research

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Taking the stance that two well-known word learning biases (shape and material bias) are formed through learning (learned bias account, LBA), we illustrated a concrete computational mechanism with "ad-hoc meaning substitution (AMS)" hypothesis, and verified it by two computer simulations. AMS represents that when given a novel word and a corresponding instance, children create novel word meaning by using the known word meaning and the instance as an ad-hoc template. The AMS function enables fast mapping and vocabulary spurt. To describe the AMS process computationally, we introduced "word distributional prototype (WDP)," which is the explicit representation of word meaning with an inductive learning function. Simulation 1 revealed that when a network with WDP and AMS was given a biased vocabulary reflecting young children, it demonstrated shape, material, and overgeneralized shape biases. This result suggested that a triad of word meaning induction, ad-hoc meaning substitution, and early biased vocabulary is essential for the emergence of biases. Simulation 2 introduced the notion of maturity that denoted a degree of learning convergence for each word meaning, and then the network showed neither shape nor material bias during an early small vocabulary. This result indicated that the period at which each bias emerges is decided by maturity. Though AMS consists of simpler and valider mechanisms than those proposed in previous studies, it could reproduce behavior of shape and material biases and explain their emergence process clearly. These results suggest that phenomena concerning shape and material biases are explicable with a simple ad-hoc learning instead of meta-learning among LBA or innate language-specific ones.

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“…Studies with preschool children show that the properties they rely on for judging category membership vary with the category. For example, internal properties determine category membership for animals, but not for artifacts (Diesendruck et al 1998), and for the very same entities, different physical features determine categorization depending on whether they are described as animals as opposed to artifacts (Booth & Waxman 2002;Keil 1995). Calculations of similarityand of category membership -depend on children's beliefs about what is being categorized.…”

Section: Jules Davidoffmentioning

confidence: 99%

Two types of thought: Evidence from aphasia

Davidoff

2005

Behav Brain Sci

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Abstract:The distinction between rules and similarity is central to our understanding of much of cognitive psychology. Two aspects of existing research have motivated the present work. First, in different cognitive psychology areas we typically see different conceptions of rules and similarity; for example, rules in language appear to be of a different kind compared to rules in categorization. Second, rules processes are typically modeled as separate from similarity ones; for example, in a learning experiment, rules and similarity influences would be described on the basis of separate models. In the present article, I assume that the rules versus similarity distinction can be understood in the same way in learning, reasoning, categorization, and language, and that a unified model for rules and similarity is appropriate. A rules process is considered to be a similarity one where only a single or a small subset of an object's properties are involved. Hence, rules and overall similarity operations are extremes in a single continuum of similarity operations. It is argued that this viewpoint allows adequate coverage of theory and empirical findings in learning, reasoning, categorization, and language, and also a reassessment of the objectives in research on rules versus similarity.Keywords: categorization; cognitive explanation; language; learning; reasoning; rules; similarity Emmanuel Pothos received his D.Phil. in Experimental Psychology from the University of Oxford in 1998. Since then he has been a lecturer of psychology at the University of Wales at Bangor and at the University of Edinburgh; he is currently at the University of Crete. His research activity includes computational models in unsupervised categorization and statistical methods for disambiguating rules and similarity in learning. Additionally, he is looking at applications of learning models in the study of addictive behavior. A focal point of his work is the a priori comparability of theoretical accounts and explanatory concepts in cognitive psychology.partly because they uniquely have in common a particular set of properties or features, as research in basic level categorization and spontaneous classification shows (Pothos & Chater 2002;Rosch & Mervis 1975). I postulate that the object properties relevant in deciding how to categorize it amongst a number of candidate categories are the properties uniquely common to the instances of each category (cf. Aha & Goldstone 1992). For example, in order to decide whether my car keys are a member of the category 'things to take out of my house when it is on fire," I will have to consider only whether car keys match the uniquely common properties of the other members of that category ("credit cards," "my cat," "my university diploma") (Barsalou 1991). Note that the present proposal does not involve any commitment about the form of features or properties (cf. Marr 1982). We simply require that at some level it is possible to represent objects in terms of discrete entities (perceptual features, abstract proper...

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Word learning is 'smart': evidence that conceptual information affects preschoolers' extension of novel words

Cited by 162 publications

References 35 publications

Mechanisms of Cognitive Development: Domain-General Learning or Domain-Specific Constraints?

Mechanisms of Cognitive Development: Domain-General Learning or Domain-Specific Constraints?

Children construct novel word meaning ad-hoc based on known words: Computational model of shape and material biases

Two types of thought: Evidence from aphasia

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