In the course of the discussion, Professor Quine pinpoints the difficulties involved in translation, brings to light the anomalies and conflicts implicit in our ...

Chomsky proposes a reformulation of the theory of transformational generative grammar that takes recent developments in the descriptive analysis of particular ...

Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a (...) single novel stimulus, and for stimuli that can be represented as points in a continuous metric psychological space. Here we recast Shepard's theory in a more general Bayesian framework and show how this naturally extends his approach to the more realistic situation of generalizing from multiple consequential stimuli with arbitrary representational structure. Our framework also subsumes a version of Tversky's set-theoretic model of similarity, which is conventionally thought of as the primary alternative to Shepard's continuous metric space model of similarity and generalization. This unification allows us not only to draw deep parallels between the set-theoretic and spatial approaches, but also to significantly advance the explanatory power of set-theoretic models. Key Words: additive clustering; Bayesian inference; categorization; concept learning; contrast model; features; generalization; psychological space; similarity. (shrink)

Linguists intuitions about language change can be captured by adynamical systems model derived from the dynamics of language acquisition.Rather than having to posit a separate model for diachronic change, as hassometimes been done by drawing on assumptions from population biology (cf.Cavalli-Sforza and Feldman, 1973; 1981; Kroch, 1990), this new modeldispenses with these independent assumptions by showing how the behavior ofindividual language learners leads to emergent, global populationcharacteristics of linguistic communities over several generations. As thesimplest case, we formalize the example of (...) two grammars and show that eventhis situation leads directly to a nonlinear (quadratic) dynamical system.We study this one parameter model in a variety of situations for differentkinds of acquisition algorithms and maturational times, showing howdifferent learning theories can have very different evolutionaryconsequences. This allows us to formulate an evolutionary criterion for theadequacy of grammatical and learning theories. An application of thecomputational model to the historical loss of Verb Second from Old French toModern French is described showing how otherwise adequate grammaticaltheories might fail the evolutionary criterion. (shrink)

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable (...) and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists. (shrink)

This book explores issues at the core of modern linguistics and cognitive science. Why are all languages similar in some ways and in others utterly different? Why do languages change and change variably? How did the human capacity for language evolve, and how far did it do so as an innate ability? Simon Kirby looks at these questions from a broad perspective, arguing that they can be studied together. The author begins by examining how far the universal properties of language (...) may be explained by examining the way it is used, and how far by the way it is structured. He then considers what insights may be gained by combining functional and formal approaches. In doing so he develops a way of treating language as an adaptive system, in which its communicative and formal roles are both crucial and complementary. In order to test the effectiveness of competing theories and explanations, Simon Kirby develops computational models to show what universals emerge given a particular theory of language use or acquisition. He presents here both the methodology and the results. Function, Selection, and Innateness is important for its argument, its methodology, and its conclusions. It is a powerful demonstration of the value of looking at language as an adaptive system and goes to the heart of current debates on the evolution and nature of language. (shrink)

This book explores a new approach to understanding the human mind - rational analysis - that regards thinking as a facility adapted to the structure of the world. This approach is most closely associated with the work of John R Anderson, who published the original book on rational analysis in 1990. Since then, a great deal of work has been carried out in a number of laboratories around the world, and the aim of this book is to bring this work (...) together for the benefit of the general psychological audience. The book contains chapters by some of the world's leading researchers in memory, categorisation, reasoning, and search, who show how the power of rational analysis can be applied to the central question of how humans think. It will be of interest to students and researchers in cognitive psychology, cognitive science, and animal behaviour. (shrink)

Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, (...) and other applications. (shrink)