Children’s Concept Learning as Rule-Sampling Systems with Markovian Properties

  • Charles J. Brainerd
Part of the Springer Series in Cognitive Development book series (SSCOG)

Abstract

One of the principal routes to generality in any science is the formulation and testing of mathematical models of the events that one studies. Mathematical models offer investigators a number of technical advantages in the treatment and reporting of data, with the most obvious ones being elegance, precision, and predictive power. In addition, however, the vigorous application of mathematical models to well- defined data spaces often produces more than mere technical advantages. Models that have especially simple and comprehensible forms may precipitate advances in theoretical understanding by focusing our attention on abstract communalities between seemingly disparate phenomena. The classic example of this effect in psychology is the remarkable degree of theoretical unification that was achieved in Bush and Mosteller’s (1955) application of linear difference equations to conditioning paradigms. More recently, the impetus for many hypotheses about the mechanics of adult memory has come from simple stochastic models (see Greeno, 1974; Greeno, James, DaPolito, & Poison, 1978). The distinction between short-term and long- term storage, for example, was motivated in large measure by the application of finite Markov chains to paired-associate data (e.g., Atkinson & Crothers, 1964; Greeno, 1967).

Keywords

Eter Triad Shoe Estima Cardi 

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© Springer-Verlag New York, Inc. 1982

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  • Charles J. Brainerd

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