Abstract not found

Previous research has shown that children go through a stage in which they know that the number words each refer to a distinct numerosity, yet do not know which numerosity each number word picks out (Wynn, 1992). How do children attain this level of knowledge? We explore the possibility that particular properties of how number words are used within sentences inform children of the semantic class to which they belong. An analysis of transcripts of the spontaneous speech of three one- and two-year-old children and their parents (from the CHILDES database; MacWhinney & Snow, 1990) suggests that the relevant cues are available as input in parents ’ speech to children, and that children generally honour these properties of number words in their own speech. Implications of this proposal for word learning more generally are discussed.

It is often asserted that young children’s word learning is guided by constraints or internal biases. Constraints are broadly described as ‘‘any factor that favors some possibilities over others’ ’ (Medin et al., 1990). Researchers have argued that specialized lexical constraints cause children to make some inferences about word meanings before others. An analysis shows that the concept constraint is not informative because it does not differentiate a circumscribed set of word learning behaviors. Defining constraints as innate and domain-specific does not remedy this problem. We cannot separate the effects of so-called constraints or biases from a wide range of cognitive and contextual influences on children’s inferences about novel word meanings. This conclusion is supported by a selective review of these influences. The summary highlights our need for an explanatory framework that is sufficiently rich to capture the flexibility and diversity of children’s word learning. The core of such a framework is summarized as a set of general characteristics of human word learning. These characteristics must serve as a starting point for any viable theory of word learning. Prescriptions for future development of a viable framework are suggested. © 2000 Academic Press Word learning 1 is a complex and intractable problem for which researchers have offered a seemingly simple and powerful solution. The problem is that preschoolers ’ prolific acquisition of new words (averaging a half dozen per day; Carey, 1978) seems impossible given the radical indeterminacy of word meanings. A novel word has an indefinite number of possible meanings, and it is unlikely that children regularly receive information that unambiguously specifies a single meaning. Yet children often infer new words ’ correct or Preparation of the manuscript was supported by a postdoctoral fellowship from the Spencer

cognitive architecture?

Abstract not found

A recent trend in the cognitive sciences is the development of models of language acquisition in which word meaning is grounded in the learner’s perceptions and actions. Such physical descriptions of meaning are inadequate for many verbs, however, because of the ambiguous nature of intentional action. We describe a model that addresses such ambiguities by explicitly representing the role of intention recognition in word learning. By augmenting this model with phrase boundary information, we show improvement in learning compared to the original syntax-free model. Greater relative improvement is found in learning verbs than nouns. Evaluations are performed using data collected in a virtual environment. Results highlight the importance of representing intentions in cognitive models and suggest a greater role for the representation of intentions in applied areas of Artificial Intelligence.

Running head: vocabulary learning