Abstract. Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances. Instance-based learning algorithms do not maintain a set of abstractions derived from specific instances. This approach extends the nearest neighbor algorithm, which has large storage requirements. We describe how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy. While the storage-reducing algorithm performs well on several realworld databases, its performance degrades rapidly with the level of attribute noise in training instances. Therefore, we extended it with a significance test to distinguish noisy instances. This extended algorithm's performance degrades gracefully with increasing noise levels and compares favorably with a noise-tolerant decision tree algorithm.

We develop a connectionist approach to processing in quasi-regular domains, as exemplified by English word reading. A consideration of the shortcomings of a previous implementation (Seidenberg & McClelland, 1989, Psych. Rev.) in reading nonwords leads to the development of orthographic and phonological representations that capture better the relevant structure among the written and spoken forms of words. In a number of simulation experiments, networks using the new representations learn to read both regular and exception words, including low-frequency exception words, and yet are still able to read pronounceable nonwords as well as skilled readers. A mathematical analysis of the effects of word frequency and spelling-sound consistency in a related but simpler system serves to clarify the close relationship of these factors in influencing naming latencies. These insights are verified in subsequent simulations, including an attractor network that reproduces the naming latency data directly in its time to settle on a response. Further analyses of the network's ability to reproduce data on impaired reading in surface dyslexia support a view of the reading system that incorporates a graded division-of-labor between semantic and phonological processes. Such a view is consistent with the more general Seidenberg and McClelland framework and has some similarities with---but also important differences from---the standard dual-route account.

The dynamical hypothesis is the claim that cognitive agents are dynamical systems. It stands opposed to the dominant computational hypothesis, the claim that cognitive agents are digital computers. This target article articulates the dynamical hypothesis and defends it as an open empirical alternative to the computational hypothesis. Carrying out these objectives requires extensive clarification of the conceptual terrain, with particular focus on the relation of dynamical systems to computers. Key words cognition, systems, dynamical systems, computers, computational systems, computability, modeling, time. Long Abstract The heart of the dominant computational approach in cognitive science is the hypothesis that cognitive agents are digital computers; the heart of the alternative dynamical approach is the hypothesis that cognitive agents are dynamical systems. This target article attempts to articulate the dynamical hypothesis and to defend it as an empirical alternative to the compu...

We previously introduced an exemplar model, named GCM-ISW, that exploits a highly flexible weighting scheme. Our simulations showed that it records faster learning rates and higher asymptotic accuracies on several artificial categorization tasks than models with more limited abilities to warp input spaces. This paper extends our previous work; it describes experimental results that suggest human subjects also invoke such highly flexible schemes. In particular, our model provides significantly better fits than models with less flexibility, and we hypothesize that humans selectively weight attributes depending on an item's location in the input space. We need more flexible models of concept learning Many theories of human concept learning posit that concepts are represented by prototypes (Reed, 1972) or exemplars (Medin & Schaffer, 1978). Prototype models represent concepts by the "best example" or "central tendency" of the concept. 1 A new item belongs in a category C if it is relat...

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the properties of distributed network models, and support this account by demonstrating that an implemented simulation closely approximates the empirical findings despite the absence of expectancy-based processes and postlexical semantic matching. The results suggest that distributed network models can provide a viable single-mechanism account of lexical processing. Introduction It is well-established that people are faster and more accurate to read a word (e.g., BUTTER) when it is preceded by a related word (e.g., BREAD) compared with when it is preceded by an unrelated word (e.g., DOCTOR; The research was supported by an NIMH FIRST award (MH55628) to the first author and by NIMH Training Grant 5T32MH19102 and NICHD Grant 80258. The computational simulation was run using customized software written within the Xerion simulator (version 3.1) developed by Drew van Camp, Tony Plate, and Geoff Hinton at the Univers

this article. PRINCIPLES UNDERLYING THE CONNECTIONIST APPROACH The connectionist approach instantiates a number of computational principles that are relevant to morphological processing (see Figure 2). We discuss #ve central ones in some detail because they are important for understanding the conditions under which the approach predicts morphological effects in the absence of semantic and/or phonological similarity (for additional background on principles of connectionist modelling, see Chauvin &Rumelhart, 1995; Hertz, Krogh, &Palmer, 1991; McClelland et al., 1986; Rumelhart, Hinton, & Williams, 1986a; Smolensky, Mozer, & Rumelhart, 1996)

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Connectionist modeling offers a useful computational framework for exploring the nature of normal and impaired cognitive processes. The current work extends the relevance of connectionist modeling in neuropsychology to address issues in cognitive rehabilitation: the degree and speed of recovery through retraining, the extent to which improvement on treated items generalizes to untreated items, and how treated items are selected to maximize this generalization. A network previously used to model impairments in mapping orthography to semantics is retrained after damage. The degree of relearning and generalization varies considerably for different lesion locations, and has interesting implications for understanding the nature and variability of recovery in patients. In a second simulation, retraining on words whose semantics are atypical of their category yields more generalization than retraining on more typical words, suggesting a counterintuitive strategy for selecting items in patient therapy to maximize recovery. In a final simulation, changes in the pattern of errors produced by the network over the course of recovery is used to constrain explanations of the nature of recovery of analogous brain-damaged patients. Taken together, the findings demonstrate that the nature of relearning in damaged connectionist networks can make important contributions to a theory of rehabilitation in patients.

Evolutionary simulation modelling is presented as a methodology involving the application of modelling techniques developed within the artificial sciences to evolutionary problems. Although modelling work employing this methodology has a long and interesting history, it has remained, until recently, a relatively underdeveloped practice, lacking a unifying theoretical framework.