Does knowledge of language consist of mentally-represented rules? Rumelhart and McClelland have described a connectionist (parallel distributed processing) model of the acquisition of the past tense in English which successfully maps many stems onto their past tense forms, both regular (walk/walked) and irregular (go/went), and which mimics some of the errors and sequences of development of children. Yet the model contains no explicit rules, only a set of neuron-style units which stand for trigrams of phonetic features of the stem, a set of units which stand for trigrams of phonetic features of the past form, and an array of connections between the two sets of units whose strengths are modified during learning. Rumelhart and McClelland conclude that linguistic rules may be merely convenient approximate fictions and that the real causal processes in language use and acquisition must be characterized as the transfer of activation levels among units and the modification of the weights of their connections. We analyze both the linguistic and the developmental assumptions of the model in detail and discover that (1) it cannot represent certain words, (2) it cannot learn many rules, (3) it can learn rules found in no human language, (4) it cannot explain morphological and phonological regularities, (5) it cannot explain the differences between irregular and regular forms, (6) it fails at its assigned task of mastering the past tense of English, (7) it gives an incorrect explanation for two developmental phenomena: stages of overregularization of irregular forms such as bringed, and the appearance of doubly-marked forms such as ated, and (8) it gives accounts of two others (infrequent overregularization of verbs ending in t/d, and the order of acquisition of different irregula...

Quartz, S. & Sejnowski, T.J. (1997). The neural basis of cognitive development: A constructivist manifesto.

Abstract not found

Children learn a variety of verbs for hand actions starting in their second year of life. The semantic distinctions can be subtle, and they vary across languages, yet they are learned quickly. Howis this possible? This dissertation explores the hypothesis that to explain the acquisition and use of action verbs, motor control must be taken into account. It presents a model of embodied semantics|based on the principles of neural computation in general and on the human motor system in particular|which takes a set of labelled actions and learns both to label novel actions and to obey verbal commands. Akey feature of the model is the executing schema, anactivecontroller mechanism which, by actually driving behavior, allows the model to carry out verbal commands. A hard-wired mechanism links the activity of executing schemas to a set of linguistically important features including hand posture, joint motions, force, aspect and goals. The feature set is relatively small and is xed, helping to make learning tractable. Moreover, the use of traditional feature structures facilitates the use of model merging, a Bayesian probabilistic learning algorithm which rapidly learns plausible word meanings, automatically determines an appropriate number of senses for each verb, and can plausibly be mapped to a connectionist recruitment

Abstract not found

It is perhaps misleading to use the word theory to describe the view of language acquisition and cognitive development, which is the subject of this chapter. This word is used as a matter of convenience; it applies here to what is best characterized as a partially completed program of research—a jigsaw puzzle in which certain pieces have been positioned with

We present a data-driven variant of the LR algorithm for dependency parsing, and extend it with a best-first search for probabilistic generalized LR dependency parsing. Parser actions are determined by a classifier, based on features that represent the current state of the parser. We apply this parsing framework to both tracks of the CoNLL 2007 shared task, in each case taking advantage of multiple models trained with different learners. In the multilingual track, we train three LR models for each of the ten languages, and combine the analyses obtained with each individual model with a maximum spanning tree voting scheme. In the domain adaptation track, we use two models to parse unlabeled data in the target domain to supplement the labeled out-ofdomain training set, in a scheme similar to one iteration of co-training. 1

Abstract not found

A large amount of recent research has shown that two-year-olds know much of the syntax of their language, particularly the system of inflection and verb

MacWhinney (1978) presented a computational model of the acquisition of morphophonology. The present chapter attempts to extend the model presented in that earlier paper to the acquisition of word-order patterns. This extension is supported by an examination of the previous research on syntactic acquisition. In the final section of the chapter, further possible extensions to phonology and semantics are considered. The crucial claim underlying the basic approach to both morphophonology and syntax is that use of a given rule system is governed by a system of alternative strategies. Within such a multileveled model, alternative strategies can be compared in terms of their relative complexity. In the present chapter, these alternative strategies are evaluated through application of the following analytic technique: 1. A relatively simple strategy that can account for at least some of the observed data is presented. 2. It is shown that there are at least some data that are best explained by this strategy. 3. It is shown that, at some point in development, the child produces forms that cannot be explained by this simple strategy alone. 4. A strategy of somewhat greater complexity and power is introduced and it is shown that this strategy can account for at least some of the data not explained by the simpler (and weaker) strategy. This line of argumentation proceeds until evidence has been presented for six alternative strategies in word-order processing.