A general framework for the analysis of concept identification tasks

Abstract

A general framework is presented for concept identification based on hypothesis-testing theory. It is a modification of the duoprocess theory presented by Chumbley (1972). It is shown how Markov models for various complex concept identification tasks may be derived from this framework and how such models may be analyzed by making use of probability generating functions. Two experiments are described. In experiment 1 three tasks were used: two simple tasks, where the subject either only had to select the relevant dimension in order to solve the problem or only had to learn a short list of paired-associates, and a more complex task, where both processes were needed to reach the solution. The results were in general favorable to the theory. Experiment 2 was designed to test the application of the theory to the four-choice concept problem. The predictions of the theory are compared to those of the subproblem learning theory of trabasso and Bower (1964), modified to include a ‘learning-on-errors’ assumption. The fit of the duoprocess theory was reasonably good and superior to that of the subproblem learning theory.