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This paper explores the hypothesis that language communication in its very first stage is bootstrapped in a social learning process under the strong influence of culture. A concrete framework for social learning has been developed based on the notion of a language game. Autonomous robots have been programmed to behave according to this framework. We show experiments that demonstrate why there has to be a causal role of language on category acquisition; partly by showing that it leads effectively to the bootstrapping of communication and partly by showing that other forms of learning do not generate categories usable in communication or make information assumptions which cannot be satisfied.

We present statistical analyses of the large-scale structure of three types of semantic networks: word associations, WordNet, and Roget's thesaurus. We show that they have a small-world structure, characterized by sparse connectivity, short average path-lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale-free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities have also been found in certain other complex natural networks, such as the world wide web, but they are not consistent with many conventional models of semantic organization, based on inheritance hierarchies, arbitrarily structured networks, or high-dimensional vector spaces. We propose that these structures reflect the mechanisms by which semantic networks grow. We describe a simple model for semantic growth, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node. This model generates appropriate small-world statistics and power-law connectivity distributions, and also suggests one possible mechanistic basis for the effects of learning history variables (age-ofacquisition, usage frequency) on behavioral performance in semantic processing tasks.

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Address correspondence to

xi 1 Introduction 1 1.1 Intuitions and Evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Previous Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.1 The Classical Dual Route Model . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.2 Seidenberg and McClelland 1989 . . . . . . . . . . . . . . . . . . . . . . 10 1.2.3 Plaut and Shallice 1993 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.2.4 Plaut et al. 1996: Naming . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.2.5 Bullinaria 1996 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.2.6 Plaut 1997: Lexical Decision . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.2.7 Harm and Seidenberg 1998: Naming . . . . . . . . . . . . . . . . . . . . 16 1.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2 A New Computational Model 18 2.1 Principles . . . . . . . . . . . . . . . . . . . . . . . . ...

Theories of categorization have typically focused on the internal structure of categories. This paper is concerned with the external structure of categories. In particular , it is suggested that many categories specify the relational role that is played by category members. To support this claim, the paper distinguishes between traditional feature-based categories, relational categories (which specify a relational structure) and role-governed categories (which specify that an item plays a particular role within a relational structure). After discussing the relationship among these types of categories, the implications of this view for the study of category learning and category use are discussed.

According to an "external grounding" theory of meaning, a concept's meaning depends on its connection to the external world. By a "conceptual web" account, a concept's meaning depends on its relations to other concepts within the same system. We explore one aspect of meaning, the identification of matching concepts across systems (e.g. people, theories, or cultures). We present a computational algorithm called ABSURDIST (Aligning Between Systems Using Relations Derived Inside Systems for Translation) that uses only within-system similarity relations to find between-system translations. While illustrating the sufficiency of a conceptual web account for translating between systems, simulations of ABSURDIST also indicate powerful synergistic interactions between intrinsic, within-system information and extrinsic information. q 2002 Elsevier Science B.V. All rights reserved.

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 widely accepted that there is a distinction to be made between the explicit content and the implicit import of an utterance. There is much less agreement about the precise nature of this distinction, how it is to be drawn, and whether any such two-way distinction can do justice to the levels and kinds of meaning involved in utterance interpretation. Grice's distinction between what is said by an utterance and what is implicated is probably the best known instantiation of the explicit/implicit distinction. His distinction, along with many of its post-Gricean heirs, is closely entwined with another distinction: that between semantics and pragmatics. Indeed, on some construals they are seen as essentially one and the same; "what is said" is equated with the truthconditional content of the utterance which in turn is equated with (context-relative) sentence meaning, leaving implicatures (conventional and conversational) as the sole domain of pragmatics. This is emphatica