We present a neural network model of learning and processing the English past tense that is based on the notion that experience-dependent cortical development is a core aspect of cognitive development. During learning the model adds and removes units and connections to develop a task-specific final architecture. The model provides an integrated…

The authors present a computational model that builds a holographic lexicon representing both word meaning and word order from unsupervised experience with natural language. The model uses simple convolution and superposition mechanisms to learn distributed holographic representations for words. The structure of the resulting lexicon can account…

Processing language requires the retrieval of concepts from memory in response to an ongoing stream of information. This retrieval is facilitated if one can infer the gist of a sentence, conversation, or document and use that gist to predict related concepts and disambiguate words. This article analyzes the abstract computational problem…

Plaut and Booth developed a distributed connectionist model of written word comprehension and evaluated it against empirical findings on individual and developmental differences in semantic priming in visual lexical decision. Borowsky and Besner raised a number of challenges for this model. First, the model was not shown to be capable of…

D. C. Plaut and J. R. Booth presented a parallel distributed processing model that purports to simulate human lexical decision performance. This model (and D. C. Plaut, 1995) offers a single mechanism account of the pattern of factor effects on reaction time (RT) between semantic priming, word frequency, and stimulus quality without requiring a…