The German plural system has become a focal point for conflicting theories of language, both linguistic and cognitive. We present simulation results with three simple classifiers - an ordinary nearest neighbour algorithm. Nosofsky's 'Generalized Context Model' (GCM) and a standard, three-layer backprop network - predicting the plural class from a phonological representation of the singular in German. Though these are absolutely 'minimal' models, in terms of architecture and input information, they nevertheless do remarkably well. The nearest neighbour predicts the correct plural class with an accuracy of 72% for a set of 24,640 nouns from the CELEX database. With a subset of 8,598 (non-compound) nouns, the nearest neighbour, the GCM and the network score 71.0%, 75.0% and 83.5%, respectively, on novel items. Furthermore, they outperform a hybrid, 'pattem-associator + default rule', model, as proposed by Marcus et al. (1995), on this data set.

Newly bom infants are able to finely discriminate almost all human speech contrasts and their phonemic category boundaries are initially identical, even for phonemes outside their target language, A connectionist model is described which accounts for this ability. The approach taken has been to develop a model of innately guided learning in which an artificial neural network (ANN) is stored in a "genome" which encodes its architecture and learning rules. The space of possible ANNs is searched with a genetic algorithm for networks that can learn to discriminate human speech sounds. These networks perform equally well having been trained on speech spectra from any human language so far tested (English, Cantonese, Swahih, Farsi, Czech, Hindi, Hungarian, Korean, Polish, Russian, Slovak. Spanish, Ukranian and Urdu). Training the feature detectors requires exposure to just one minute of speech in any of these languages. Categorisation of speech sounds based on the network representations showed the hallmarks of categorical perception, as found in human infants and adults.

In a major theoretical paper, Gigerenzer and Goldstein (1996a) argue that classical rationality should be rejected as a norm of good reasoning, and that this thesis undermines both rational models of human thought and the alternative heuristics-and-biases program. They illustrate their argument by proposing that a specific cognitive estimation problem may be carried out by the "Take the Best" algorithm, which is "fast and frugal," but not rational. We argue: (1) that "fast and frugal" cognitive algorithms may approximate rational norms, and only in this way can their success be explained; and (2) that new computer simulations, and considerations of speed and generality, suggest that other algorithms are at least as psychologically plausible as Take the Best.

Inflectional morphology has figured prominently not only in debate about the nature of linguistic knowledge, but also in the foundational debate between proponents of symbolic and of connectionist accounts of cognition. We present two experiments designed to test predictions of Tinker's (1991) dual-route account of inflection, the central component of which is a symbolic rule. Contrary to the predictions of the dual-route account, we find evidence of both frequency and similarity effects on the regularization of novel items (i.e., pseudo words).