The authors present a Bayesian framework for understanding how adults and children learn the meanings of words. The theory explains how learners can generalize meaningfully from just one or a few positive examples of a novel word’s referents, by making rational inductive inferences that integrate prior knowledge about plausible word meanings with the statistical structure of the observed examples. The theory addresses shortcomings of the two best known approaches to modeling word learning, based on deductive hypothesis elimination and associative learning. Three experiments with adults and children test the Bayesian account’s predictions in the context of learning words for object categories at multiple levels of a taxonomic hierarchy. Results provide strong support for the Bayesian account over competing accounts, in terms of both quantitative model fits and the ability to explain important qualitative phenomena. Several extensions of the basic theory are discussed, illustrating the broader potential for Bayesian models of word learning.

Human concept learning presents a version of the classic problem of induction, which is made particularly difficult by the combination of two requirements: the need to learn from a rich (i.e. nested and overlapping) vocabulary of possible concepts and the need to be able to generalize concepts reasonably from only a few positive examples. I begin this thesis by considering a simple number concept game as a concrete illustration of this ability. On this task, human learners can with reasonable confidence lock in on one out of a billion billion billion logically possible concepts, after seeing only four positive examples of the concept, and can generalize informatively after seeing just a single example. Neither of the two classic approaches to inductive inference -- hypothesis testing in a constrained space of possible rules and computing similarity to the observed examples -- can provide a complete picture of how people generalize concepts in even this simple setting. This thesis prop...

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

Flexible induction is the adaptation of probabilistic inferences to changing problems. Young children’s flexibility was tested in a word-learning task. Children 3 to 6 years old were told 3 novel words for each of several novel objects. Children generalized each word to other objects with the same body shape, the same material, or the same part as the first object. Each word was preceded by a different predicate (i.e., “looks like a …, ” “is made of …, ” or “has a …”) that implies a different attribute (shape, material, or part, respectively). Three-year-olds showed limited use of predicates to infer word meanings, and they used predicates from previous trials to infer the meanings of later words. 4- to 6-year-olds used predicate cues more consistently and made inferences that were implied by the most recent predicate cue. Notably, 3-year-olds performed near ceiling in a control task that eliminated the need to use probabilistic inductive cues (Experiment 3). The results suggest that flexibility develops as a function of (a) sensitivity to between-problem variability and indeterminacy and (b) ability to decontextualize the most recent verbal cue to guide of inductive inferences. The epitome of human reasoning is the ability to solve variable, novel problems while ignoring irrelevant information. This is difficult for adults (Dominowski,

To investigate how young children learn categorical semantic relations between words, 4- to 7-year-olds were taught four labels for novel categories in an “alien ” microworld. After two play sessions, where each label was given, with defining information, at least 20 times, comprehension and production were tested. Results of two experiments show that 6-7-yearolds learned more words and correct semantic relations than 4-5-year-olds. The exclusion relation between contrasting category labels was easy to learn, and some findings suggested that hierarchical words are more easily learned than overlapping ones. Both studies showed no advantage to explicitly telling children semantic relations between words (e.g., “All fegs are wuddles.”). The results qualify a common assumption that preschool children have precocious abilities