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...

haracterized by descriptions of each module and how each serves in those tasks for which it is best suited. However, these theories often do not emphasize how modules interact in producing responses and in learning. In this article we will develop a modular theory of categorization that follows from two distinct accounts of this behavior. The first account is that of rule-based theories of categorization. These theories emerge from a philosophical tradition in which concepts and categorization are described in terms of definitional rules. For example, if a living thing has a wide, flat tail and constructs dams by cutting down trees with its This work was supported by Indiana University Cognitive Science Program Fellowships and by NIMH ResearchTraining Grant PHS-T32-MH19879-03 to Erickson, and in part by NIMH FIRST Award 1-R29-MH51572-01 to Kruschke. This research was reported as a poster at the 1996 Cognitive Science Society Conference in San Diego, CA. We than

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The representation of regular morphological processes has been the subject of much controversy, particularly in the debate between single and dual route models of morphology.

John Maynard Smith and Eörs Szathmáry argued that human language signified the eighth major transition in evolution: human language marked a new form of information transmission from one generation to another [Maynard Smith J, Szathmáry E. The major transitions in evolution. Oxford: Oxford Univ. Press; 1995]. According to this view language codes cultural information and as such forms the basis for the evolution of complexity in human culture. In this article we develop the theory that language also codes information in another sense: languages code information on their own structure. As a result, languages themselves provide information that influences their own survival. To understand the consequences of this theory we discuss recent computational models of linguistic evolution. Linguistic evolution is the process by which languages themselves evolve. This article draws together this recent work on linguistic evolution and highlights the significance of this process in understanding the evolution of linguistic complexity. Our conclusions are that: (1) the process of linguistic transmission constitutes the basis for an evolutionary system, and (2), that this evolutionary system is only superficially comparable to the process of

Approaches to morphology typically account for regular, completely productive affixation, while ignoring subregular and semiproductive schemata. The alternative approach to derivational morphology presented here relates exceptions and subregularities to productive rules. It accounts for the contribution lexicalized words make to the rule, and for the fact that not all new formations follow the `rules'. It also captures linguistically relevant generalizations that cannot be expressed in other theories. The approach is formalized in terms of complex recursive schemata structured in a multiple inheritance hierarchy, without positing lexical rules or lexical entries for affixes. These schemata structure the existing lexicon, reducing redundancy, and at the same time serve as the basis for productive word formation. The approach handles zero-derivation and other nonconcatenative morphology straightforwardly.

Nonword pronunciation is a form of generalization behavior that has been at the center of debates about models of word recognition, the role of rules in explaining behavior, and the adequacy of the parallel distributed processing approach. An experiment yielded data concerning the pronunciation of a large corpus of nonwords. The data were then used to assess 2 models of naming: a model developed by D. C. Plaut and J. L. McClelland (1993), which is similar to the one described by M. S. Seidenberg and J. L. McClelland (1989) but uses improved orthographic and phonological representations, and the grapheme-phoneme correspondence rules of M. Coltheart, B. Curtis, P. Atkins, and M. Haller's (1993) dual-route model. Both models generate plausible nonword pronunciations and match subjects' responses accurately. The dual-route model does so by using rules that generate correct output for most words but mispronounce a significant number of exceptions. The parallel distributed processing model does so by finding a set of weights that allow it to generate correct output for both "rule-governed" items and exceptions. Some ways in which the two approaches differ and other issues facing them are also discussed.

An increasing number of connectionist models have been proposed to explain behavioral deficits in developmental disorders. These simulations motivate serious consideration of the theoretical implications of the claim that a developmental disorder fits within the parameter space of a particular computational model of normal development. The authors examine these issues in depth with respect to a series of new simulations investigating past-tense formation in Williams syndrome. This syndrome and the past-tense domain are highly relevant because both have been used to make strong theoretical claims about the processes underlying normal language acquisition. The authors conclude that computational models have great potential to advance psychologists ’ understanding of developmental deficits because they focus on the developmental process itself as a pivotal causal factor in producing atypical phenotypic outcomes. Computational models have become an increasingly prevalent tool for investigating mechanisms of change within cognitive development (e.g., Simon & Halford, 1995). Much of this research has used connectionist learning systems (i.e., computer models loosely based on principles of neural information processing) to construct cognitive-level explanations of behavior (Elman et al.,

To be published in Behavioral and Brain Sciences (in press)

To have learned the morphology of a natural language is to have the capacity both to recognize and to produce words consisting of novel combinations of familiar morphemes. Most recent work on the acquisition of morphology takes the perspective of production, but it is receptive morphology which comes first in the child. This paper presents a connectionist model of the acquisition of the capacity to recognize morphologically complex words. The model takes sequences of phonetic segments as inputs and maps them onto output units representing the meanings of lexical and grammatical morphemes. It consists of a simple recurrent network with separate hidden-layer modules for the tasks of recognizing the root and the grammatical morphemes of the input word. Experiments with artificial language stimuli demonstrate that the model generalizes to novel words for morphological rules of all but one of the major types found in natural languages and that a version of the network with unassig...