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.

For infants, early word learning is a chicken-and-egg problem. One way to learn a word is to observe that it co-occurs with a particular referent across different situations. Another way is to use the social context of an utterance to infer the intended referent of a word. Here we present a Bayesian model of cross-situational word learning, and an extension of this model that also learns which social cues are relevant to determining reference. We test our model on a small corpus of mother-infant interaction and find it performs better than competing models. Finally, we show that our model accounts for experimental phenomena including mutual exclusivity, fast-mapping, and generalization from social cues. To understand the difficulty of an infant word-learner, imagine walking down the street with a friend who suddenly says “dax blicket philbin na fivy! ” while at the same time wagging her elbow. If you knew any of these words you might infer from the syntax of her sentence that blicket is a novel noun, and hence the name of a novel object. At the same time, if you knew that this friend indicated her attention by wagging her elbow at objects, you might infer that she intends to refer to an object in a

This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: (i) how agents learn and represent compositional actions; (ii) how agents learn and represent compositional lexicons; (iii) the dynamics of social interaction and learning; and (iv) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test-scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.

The authors asked whether there is evidence to support the existence of the vocabulary spurt, an increase in the rate of word learning that is thought to occur during the 2nd year of life. Using longitudinal data from 38 children, they modeled the rate of word learning with two functions, one with an inflection point (logistic), which would indicate a spurt, and one without an inflection point (quadratic). Comparing the fits of these two functions using likelihood ratios, they found that just 5 children had a better logistic fit, which indicated that these children underwent a spurt. The implications for theories of cognitive and language development are considered. Typically developing children utter their first words between 8 and 14 months of age. At this time, they add words to their repertoire at a slow rate. As they get older and their vocabulary increases, their rate of learning new words also increases—it has to if they are to reach an average vocabulary level of 300 words by 24 months (Fenson, Dale, Reznick, Bates, & Thal, 1994) and 60,000 words by 18 years (Aitchinson, 1994). In addition, it is widely held that children’s rate of vocabulary acquisition does not simply increase but undergoes a discrete transition at approximately 50 words. At this time, children putatively switch from an initial stage of slow vocabulary growth to a subsequent stage of faster growth. This transition has been referred to as the vocabulary spurt, the vocabulary burst, or the naming explosion (L.

Words, kinds and causal powers: A theory theory perspective on early naming and categorization. For some twenty-five years, the prevailing theories of categorization in philosophy have invoked the idea of “kinds ” (Putnam, 1975; Kripke, 1972). When we look at how adults use words to refer to categories of things we find that they only rarely categorize objects on the basis of their common properties. Instead, adults seem to categorize objects together when they believe that they belong to the same “kind”; that is, that they share some common, abstract “essence.” Psychological investigations of adults have largely confirmed these philosophical intuitions, adults do seem to group objects together based on “kinds ” rather than properties (Murphy &

journal homepage: www.elsevier.com/locate/jecp What’s in the name? Or how rocks and stones

There is currently rather little agreement about the existence of, and explanation for, a vocabulary spurt in children during the second year. Here we apply a Functional Data Analysisbased technique called Automatic Maxima Detection to the problem of finding vocabulary spurts in a sample of 20 children. Even with considerable smoothing of the data, children were found to exhibit multiple vocabulary spurts of varying intensity and location. These results should provide a clearer target for researchers interested in detecting and explaining these deviations from linear growth.

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of young children with Williams syndrome*

The right thing at the right time: why ostensive naming