Abstract not found

Abstract not found

Language is about symbols, and those symbols must be grounded in the physical world. Children learn to associate language with sensorimotor experiences during their development. In light of this, we first provide a computational account of how words are mapped to their perceptually grounded meanings. Moreover, the main part of this work proposes and implements a computational model of how word learning influences the formation of object categories to which those words refer. This model simulates the bi-directional relationship between word and object category learning: (1) object categorization provides mental representations of meanings that are mapped to words to form lexical items; (2) linguistic labels help object categorization by providing additional teaching signals; and (3) these two learning processes interplay with each other and form a developmental feedback loop. Compared with the method that performs these two tasks separately, our model shows promising improvements in both word-to-world mapping and perceptual categorization, suggesting a unified view of lexical and category learning in an integrative framework. Most importantly, this work provides a cognitively plausible explanation of the mechanistic nature of early word learning and object learning from co-occurring multisensory data.

Previous research has shown that, for learners to fully acquire a miniature phrase structure language, the language must contain cues to the phrases—for example, prosodic grouping or morphological agreement of the words within a phrase (Morgan, Meier, & Newport, 1987, 1989). Research on word segmentation has shown that learners can use transitional probabilities between syllables to segment speech into word-like units (Saffran, Aslin, & Newport, 1996). In the present research, we combine and extend these two sets of findings, asking whether learners can use transitional probabilities between words (or word classes) to segment sentences into phrases, and use this phrasal information to fully acquire the syntax of a miniature language. Adult subjects were exposed to sentences from a miniature language. A pattern in the transitional probabilities between words—high within phrases, low at phrase boundaries—was created by adding syntactic properties that are widespread in natural languages: optional phrases, repeated phrases, moved phrases, different-sized form classes, or all four properties combined. All conditions outperformed controls in learning the language. The best learning occurred with all properties combined, despite the fact that this language was the most complex. These data address the important question of how language learning is successful in the face of the massive complexity of natural languages. In our experiments, learning got better, not worse, when properly structured complexity was added to a language. The results also show that the same type of statistical computation useful in word segmentation might be used as well in learning syntax, suggesting that the range of statistics needed for acquiring various types of structure in natural languages might be suitably small. Correspondence should be addressed to Susan P. Thompson, Department of Psychology, 205

This ESSLLI workshop is devoted to connecting the linguistic use of resource logics and categorial grammar to minimalist grammars and related generative grammars. Minimalist grammars are relatively recent, and although they stem from a long tradition of work in transformational grammar, they are largely informal apart from a few research papers. The study of resource logics, on the other hand, is formal and stems naturally from a long logical tradition. So although there appear to be promising connections between these traditions, there is at this point a rather thin intersection between them. The papers in this workshop are consequently rather diverse, some addressing general similarities between the two traditions, and others concentrating on a thorough study of a particular point. Nevertheless they succeed in convincing us of the continuing interest of studying and developing the relationship between the minimalist program and resource logics. This introduction reviews some of the basic issues and prior literature. 1 The interest of a convergence What would be the interest of a convergence between resource logical investigations of

We extend the in-situ Hebbian-linked SOMs network by Miikkulainen to come up with two unsupervised neural networks that learn the mapping between the individual modes of a multimodal dataset. The first network, the single-pass Hebbian linked SOMs network, extends the in-situ Hebbian-linked SOMs network by enabling the Hebbian link weights to be computed through one-shot learning. The second network, a modified counterpropagation network, extends the unsupervised learning of crossmodal mappings by making it possible for only one self-organising map to implement the crossmodal mapping. The two proposed networks each have a smaller computation time and achieve lower crossmodal mean squared errors than the in-situ Hebbian-linked SOMs network when assessed on two bimodal datasets, an audio-acoustic speech utterance dataset and a phonological-semantics child utterance dataset. Of the three network architectures, the modified counterpropagation network achieves the highest percentage of correct classifications comparable to that of the LVQ-2 algorithm by Kohonen and the neural network for category learning by de Sa and Ballard in classification tasks using the audio-acoustic speech utterance dataset.

This paper presents a graph-theoretic model of the acquisition of lexical syntactic representations. The representations the model learns are non-categorical or graded. We propose a new evaluation methodology of syntactic acquisition in the framework of exemplar theory. When applied to the CHILDES corpus, the evaluation shows that the model’s graded syntactic representations perform better than previously proposed categorical representations. 1

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Language development is characterized by predictable shifts in the words that children learn and the complexity of their utterances. But language development typically occurs simultaneously with cognitive development and maturation, making it difficult to determine the causes of these shifts. We explored how acquisition precedes in the absence of possible cognitive or maturational roadblocks, by examining the acquisition of English in internationally-adopted preschoolers. Like infants these children acquire a language from child-directed speech in the home, but they are older and more cognitively sophisticated. We collected parental reports (CDI-2) and speech samples from 14 preschoolers, 3 to 18 months after they were adopted from China. These children made rapid progress in acquiring English and showed the same developmental patterns as monolingual infants (matched for vocabulary size). Early on, their vocabularies were dominated by nouns, their utterances were short, and function morphemes were almost entirely absent. Children at later stages of development had more diverse lexicons and produced longer utterances with more closed-class morphemes. Keywords: language development, international adoption, word learning, syntax, language production, nouns, verbs Language development is marked by a series a qualitative shifts. Infants speak in singleword utterances for several months before beginning to combine words. Young children learn a disproportionate number of nouns before acquiring a balanced complement of verbs, adjectives and prepositions. Young English speakers typically omit function morphemes from their early word combinations, and then gradually begin to add them in. A central question in language acquisition is what causes children to move through these phas...