In the novel noun generalization task, 2 1/2-year-old children display generalized expectations about how solid and nonsolid things are named, extending names for never-before-encountered solids by shape and for never-before-encountered nonsolids by material.This distinction between solids and nonsolids has been interpreted in terms of an ontological distinction between objects and substances.Nine simulations and behavioral experiments tested the hypothesis that these expectations arise from the correlations characterizing early learned noun categories.In the simulation studies, connectionist networks were trained on noun vocabularies modeled after those of children.These networks formed generalized expectations about solids and nonsolids that match children’s performances in the novel noun generalization task in the very different languages of English and Japanese.The simulations also generate new predictions supported by new experiments with children.Implications are discussed in terms of children’s development of distinctions between kinds of categories and in terms of the nature of this knowledge. Concepts are hypothetical constructs, theoretical devices hypothesized to explain data, what people do, and what people say. The question of whether a particular theory can explain children’s concepts is therefore semantically strange because strictly speaking this question asks about an explanation of an explanation.We begin with this reminder because the goal of the research reported here is to understand the role of associative processes in children’s systematic attention to the shape of solid things and to the material of nonsolid things in the task of forming new lexical categories. These attentional biases have been interpreted in terms of children’s concepts about the ontological kinds of object and substance

Neuropsychological dissociations between regular and irregular past tense verb processing have been explained in two ways: (a) separate mechanisms of a rule-governed process for regular verbs and a lexical-associative process for irregular verbs; (b) a single system drawing on phonological and semantic knowledge. The latter account invokes phonological impairment as the basis of poorer performance for regular than irregular past tense forms, due to greater phonological complexity of the regular past. In 10 nonfluent aphasic patients, the apparent disadvantage for the production of regular past tense forms disappeared when phonological complexity was controlled. In a same-different judgment task on spoken words, all patients were impaired at judging regular stem and past-tense verbs like man/ manned to be different, but equally poor at phonologically matched non-morphological discriminations like men/mend. These results indicate a central phonological deficit that is not limited to speech output nor to morphological processing; under such a deficit, distinctions lacking phonological salience, as typified by regular past tense English verbs, become especially vulnerable.

I consider the role of cognitive modeling in cognitive science. Modeling, and the computers that enable it, are central to the field, but the role of modeling is often misunderstood. Models are not intended to capture fully the processes they attempt to elucidate. Rather, they are explorations of ideas about the nature of cognitive processes. In these explorations, simplification is essential—through simplification, the implications of the central ideas become more transparent. This is not to say that simplification has no downsides; it does, and these are discussed. I then consider several contemporary frameworks for cognitive modeling, stressing the idea that each framework is useful in its own particular ways. Increases in computer power (by a factor of about 4 million) since 1958 have enabled new modeling paradigms to emerge, but these also depend on new ways of thinking. Will new paradigms emerge again with the next 1,000-fold increase?

One of the great challenges in linguistics and cognitive science is to understand the nature of the mental representation of language. The precise mechanisms of the mind are unknown, but can be modeled through observation and experimentation. By viewing the mind as a computational device that receives input (primary linguistic data) and produces output (the development of grammatical speech) during language acquisition, one can reason about what representations and algorithms must be internal to the learner. In this thesis, I investigate the acquisition of morphology. The principal challenges are how to learn a theory in the presence of sparse data, and in a manner that can provide explanations for the developmental processes in child language acquisition. The main idea underlying this work is that a consideration of the different aspects of language acquisition places strong constraints on cognitively plausible representations and algorithms that are internal to the learner. To develop a model of morphology acquisition, I pursue three lines of work: iv First, I formulate a cognitively-oriented computational framework for studying language acquisition that consists of four components: the linguistic representation, the

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Four pairs of connectionist simulations are presented in which quasi-regular mappings are computed using localist and distributed representations. In each simulation, a control parameter termed input gain was modulated over the only level of representation that mapped inputs to outputs. Input gain caused both localist and distributed models to shift between regularity-based and item-based modes of processing. Performance on irregular items was selectively impaired in the regularity-based modes, whereas performance on novel items was selectively impaired in the item-based modes. Thus, the models exhibited double dissociations without separable processing components. These results are discussed in the context of analogous dissociations found in language domains such as word reading and inflectional morphology.

Cognitive science is, more than anything else, a pursuit of cognitive mechanisms. To make headway towards a mechanistic account of any particular cognitive phenomenon, a researcher must choose among the many architectures available to guide and constrain the account. It is thus fitting that this volume on contemporary debates in cognitive science includes two issues of architecture, each articulated in the 1980s but still unresolved: • Just how modular is the mind? (section 1) – a debate initially pitting encapsulated mechanisms (Fodorian modules that feed their ultimate outputs to a nonmodular central cognition) against highly interactive ones (e.g., connectionist networks that continuously feed streams of output to one another). • Does the mind process language-like representations according to formal rules? (this section) – a debate initially pitting symbolic architectures (such as Chomsky’s generative grammar or Fodor’s language of thought) against less language-like architectures (such as connectionist or dynamical ones). Our project here is to consider the second issue within the broader context of where cognitive science has been and where it is headed. The notion that cognition in general—not just language

In handwriting the drawing or copying of an individual letter involves a process of linearising whereby the form of the letter is broken down into a temporal sequence of strokes for production. In experienced writers, letters are produced consistently using the same production methods that are economic in terms of movement. This regularity permits a rule-based description of such production processes, which can be used in the teaching of handwriting skills. In this paper, the outstanding question from rule-based descriptions as to how consistent and stable letter production behaviour emerges as a product of practice and experience is addressed through the implementation of a connectionist model of sequential letter production. This model: (1) examines the emergence of letter production behaviour, namely- the linearising process, (2) explores how letters may be internally represented across both spatial and temporal dimensions, and (3) investigates the impact of learning certain letter production methods when generalising to produce novel letterforms. In conclusion, the connectionist model offers an emergent account of letter production behaviour, which addresses the co-representation of spatial and temporal dimensions of letters, and the impact of learning experiences upon behaviour. 2 1.

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The study of human intelligence was once dominated by symbolic approaches, but over the last 30 years an alternative approach has arisen. Symbols and processes that operate on them are often seen today as approximate characterizations of the emergent consequences of sub- or nonsymbolic processes, and a wide range of constructs in cognitive science can be understood as emergents. These include representational constructs (units, structures, rules), architectural constructs (central executive, declarative memory), and developmental processes and outcomes (stages, sensitive periods, neurocognitive modules, developmental disorders). The greatest achievements of human cognition may be largely emergent phenomena. It remains a challenge for the future to learn more about how these greatest achievements arise and to emulate them in artificial systems.