In the novel noun generalization task, 2 1/2-year-old children display generalized expectations about how solid and nonsolid things are named, extending names for never-before-encountered solids by shape and for never-before-encountered nonsolids by material.This distinction between solids and nonsolids has been interpreted in terms of an ontological distinction between objects and substances.Nine simulations and behavioral experiments tested the hypothesis that these expectations arise from the correlations characterizing early learned noun categories.In the simulation studies, connectionist networks were trained on noun vocabularies modeled after those of children.These networks formed generalized expectations about solids and nonsolids that match children’s performances in the novel noun generalization task in the very different languages of English and Japanese.The simulations also generate new predictions supported by new experiments with children.Implications are discussed in terms of children’s development of distinctions between kinds of categories and in terms of the nature of this knowledge. Concepts are hypothetical constructs, theoretical devices hypothesized to explain data, what people do, and what people say. The question of whether a particular theory can explain children’s concepts is therefore semantically strange because strictly speaking this question asks about an explanation of an explanation.We begin with this reminder because the goal of the research reported here is to understand the role of associative processes in children’s systematic attention to the shape of solid things and to the material of nonsolid things in the task of forming new lexical categories. These attentional biases have been interpreted in terms of children’s concepts about the ontological kinds of object and substance

Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models help explain how the rest can be acquired. To illustrate this claim, we develop models that acquire two kinds of overhypotheses — overhypotheses about feature variability (e.g. the shape bias in word learning) and overhypotheses about the grouping of categories into ontological kinds like objects and substances.

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,

What is abstraction? In our view, abstraction is generalization. Specifically, we propose that abstract concepts emerge as the natural product of associative learning and generalization by similarity. We support this proposal by presenting evidence for two ideas: first, that children’s knowledge about how categories are organized and how words refer to them can be explained as learned generalizations over specific experiences of words referring to categories; and second, that the path of concepts from concrete to more abstract can be observed throughout development and that even in their more abstract form, concepts retain some of their original sensory basis. We illustrate these two facts by examining, in two kinds of learners—networks and young children—the development of three abstract ideas: (i) the idea of word; (ii) the idea of object; and (iii) the idea of substance.

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This thesis aims to develop a psychologically plausible account of concepts by integrating key insights from philosophy (on the metaphysical basis for concept possession) and psychology (on the mechanisms underlying concept acquisition). I adopt an approach known as informational atomism, developed by Jerry Fodor. Informational atomism is the conjunction of two theses: (i) informational semantics, according to which conceptual content is constituted exhaustively by nomological mind–world relations; and (ii) conceptual atomism, according to which (lexical) concepts have no internal structure. I argue that informational semantics needs to be supplemented by allowing content-constitutive rules of inference (“meaning postulates”). This is because the content of one important class of concepts, the logical terms, is not plausibly informational. And since, it is argued, no principled distinction can be drawn between logical concepts and the rest, the problem that this raises is a general one.

The question of how meaning might be acquired by young children and represented by adult speakers of a language is one of the most debated topics in cognitive science. Existing semantic representation models are primarily amodal based on information provided by the linguistic input despite ample evidence indicating that the cognitive system is also sensitive to perceptual information. In this work we exploit the vast resource of images and associated documents available on the web and develop a model of multimodal meaning representation which is based on the linguistic and visual context. Experimental results show that a closer correspondence to human data can be obtained by taking the visual modality into account. 1

Young children often exhibit flexible behaviors relying on different kinds of information in different situations. This flexibility has been traditionally attributed to conceptual knowledge. Reported research demonstrates that flexibility can be acquired implicitly and it does not require conceptual knowledge. In Experiment 1, 4- to 5-yearolds successfully learned different context-predictor contingencies and subsequently flexibly relied on different predictors in different contexts. Experiments 2A and 2B indicated that flexible generalization stems from implicit attentional learning rather than from rule discovery, and Experiment 3 pointed to very limited strategic control over generalization behaviors in 4- to 5-year-olds. These findings indicate that mundane mechanisms grounded in associative and attentional learning may give rise to smart flexible behaviors. Even early in development, people’s generalization is remarkably flexible—depending on a situation, people may rely on different kinds of information. This flexibility has been found in a variety of generalization tasks, including lexical extension, categorization, and property induction. For example, in a lexical extension task (Jones, Smith, & Landau, 1991), 2- to 3-year-olds were presented with a target, which was named (i.e., ‘‘this is a dax’’), and asked to find another dax among test items. Children extended the label by shape alone when the target and test objects were presented without eyes. However, they extended the label by shape and texture when the objects were presented with eyes. Children exhibit similar flexibility in categorization and induction tasks. For example, in a categorization task, 3- to 4-year-olds were more likely to group items on the basis of color if the items were introduced as food, but on the basis of shape if the items were introduced as toys (Macario, 1991). In another task, 4- to 5-year-olds were presented with a target and two test items, such that one test item shared the label with the target and the other looked similar to the target. Participants were then told that the target had a particular property and asked which

In typical development, word learning goes from slow and laborious to fast and seemingly effortless. Typically developing 2-year-olds are so skilled at learning noun categories that they seem to intuit the whole range of things in the category from hearing a single instance named – they are biased learners. This is not the case for children below the 20th percentile on productive vocabulary (late talkers). This paper looks at the vocabulary composition of age-matched 18-30-month-old late- and early-talking children. The results of Experiment 1 show that late talkers ’ vocabularies are more variable than early talker’s vocabularies. Crucially, Experiment 2 shows that neural networks trained on the vocabularies of individual late talkers learn qualitatively different biases than those trained on early talker vocabularies. These simulations make testable predictions for world learning biases of late- vs. early-talking children. The implications for diagnosis and intervention are discussed.