The evolution of language implies the parallel evolution of an ability to respond appropriately to signals (language understanding) and an ability to produce the appropriate signals in the appropriate circumstances (language production). When linguistic signals are produced to inform other individuals, individuals that respond appropriately to these signals may increase their reproductive chances but it is less clear what is the reproductive advantage for the languages producers. We present simulations in which populations of neural networks living in an environment evolve a simple language with an informative function. Signals are produced to help other individuals to categorize edible and poisonous mushrooms in order to decide whether to approach or avoid encountered mushrooms. Language production, while not under direct evolutionary pressure, evolves as a by-product of the independently evolving perceptual ability to categorize mushrooms. Keywords: Language evolution, Ge...

this paper. We also thank the following for permission to reproduce images of their products: Consumer Value Stores, Disney Enterprises, Inc., International Home Foods, Inc., Johnson & Johnson, Lehigh Valley Farms, Mott's Consumer Services, Neutrogena Corporation, Playtex Products Inc., The Procter & Gamble Company, Rite Aid Corporation, Rubber Maid Incorporated, Spring Tree Corporation, and Unilever United States, Inc. Address correspondence and reprint requests to either Barbara Malt, Department of Psychology, 17 Memorial Drive East, Lehigh University, Bethlehem, PA 18015 (e-mail: bcm@lehigh.edu) or Steven Sloman, Department of Cognitive and Linguistic Sciences, Box 1978, Brown University, Providence, RI 02912 (e-mail: Steven_Sloman@brown.edu)

This paper provides a tutorial introduction to computational studies of how children learn their native languages. Its aim is to make recent advances accessible to the broader research community. and to place them in the context of current theoretical issues. The first section locates computational studies and behavioral studies within a common theoretical framework. The next two sections review two papers that appear in this volume: one on learning the meanings of words and one on learning the sounds of words. The following section highlights an idea which emerges independently in these two papers and which I have dubbed autonomous bootstrapping. Classical bootstrapping hypotheses propose that children begin to get a toe-hold in a particular linguistic domain, such as syntax, by exploiting information from another domain, such as semantics. Autonomous bootstrapping complements the cross-domain acquisition strategies of classical bootstrapping with strategies that apply within a single domain. Autonomous bootstrapping strategies work by representing partial and/or uncertain linguistic knowledge and using it to analyze the input. The next two sections review two more more contributions to this special issue: one on learning word meanings via selectional preferences and one on algorithms for setting grammatical parameters. The final section suggests directions for future research.

Abstract This article investigates the problem of how language learners decipher what words mean. In many recent models of language evolution, agents are provided with innate meanings a priori and explicitly transfer them to each other as part of the communication process. By contrast, I investigate how successful communication systems can emerge without innate or transferable meanings, and show that this is dependent on the agents developing highly synchronized conceptual systems. I present experiments with various cognitive, communicative, and environmental factors which affect the likelihood of agents achieving meaning synchronization and demonstrate that an intelligent meaning creation strategy in a clumpy world leads to the highest level

This paper views lexical acquisition OS a problem of induction: Children must figure out the meaning of a given term, given the large number of possible mean-ings any term could have. If children had to consider, evaluate, and rule out an unlimited number of hypotheses obout each word in order to figure out its mean-ing, learning word meanings would be hopeless. Children must, therefore, be limited in the kinds of hypotheses they consider as possible word meanings. This paper considers three possible constraints on word meanings: (1) The whole object assumption which leads children to interpret navel terms as labels for objects-not parts, substances, or other properties of objects: (2) The taxonomic assumption which leads children to consider labels as referring to objects of like kind, rather than to objects that are thematically related: and (3) The mutual exclusivity assumption which leads children to expect each object to hove only one label. Some of the evidence for these constraints is reviewed. Children acquire the vocabulary of natural languages at remarkable speed. In a carefully documented study of an individual child’s vocabulary acquisi-tion, Dromi (1987) reports a point at which her child began acquiring new vocabulary at the rate of 45 words a week. This fits with calculations re-ported by Carey (1978): by age six children have learned 9,000-14,000 words which works out to roughly nine new words a day from about 18 months on. It is still largely a mystery as to how children acquire language at this astonishing rate. A traditional explanation for how children form categories and acquire category terms was to assume a kind of general, all-purpose, inductive mechanism. Inhelder and Piaget (1964) and Bruner, Olver, and Greenfield (1966) implicitly held some form of this model. This view about how cate-gories are acquired contains many implicit assumptions about the nature of categories, about the way in which they are learned, and about how chil-dren’s abilities to categorize change with development (for a discussion of

Abstract. The embodiment hypothesis is the idea that intelligence emerges in the interaction of an agent with an environment and as a result of sensorimotor activity. In this paper we offer six lessons for developing embodied intelligent agents suggested by research in developmental psychology. We argue that starting as a baby grounded in a physical, social and linguistic world is crucial to the development of the flexible and inventive intelligence that characterizes humankind.

Word learning is a ‘‘chicken and egg’’ problem. If a child could understand speakers ’ utterances, it would be easy to learn the meanings of individual words, and once a child knows what many words mean, it is easy to infer speakers’ intended meanings. To the beginning learner, however, both individual word meanings and speakers ’ intentions are unknown. We describe a computational model of word learning that solves these two inference problems in parallel, rather than relying exclusively on either the inferred meanings of utterances or cross-situational word-meaning associations. We tested our model using annotated corpus data and found that it inferred pairings between words and object concepts with higher precision than comparison models. Moreover, as the result of making probabilistic inferences about speakers’ intentions, our model explains a variety of behavioral phenomena described in the word-learning literature. These phenomena include mutual exclusivity, one-trial learning, cross-situational learning, the role of words in object individuation, and the use of inferred intentions to disambiguate reference.

Human language is unique among the communication systems of the natural world. The vocabulary of human language is unique in being both culturally-transmitted and symbolic. In this paper I present an investigation into the factors involved in the evolution of such vocabulary systems. I investigate both the cultural evolution of vocabulary systems and the biological evolution of learning rules for vocabulary acquisition.

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

Traditional explanatory accounts of the evolution of language frequently appeal to a “conventional neo-Darwinian process ” (Pinker & Bloom 1990: 707), assuming that humans have evolved an innate, genetically-encoded language acquisition device,