Statistical methods for a general theory of all-or-none learning

Greeno and Steiner have shown that a three state Markov Chain with a single absorbing state is equivalent to many of the current formalizations of All-or-None learning theories. Distribution statistics and other summary statistics are derived from the general model. Expressions for the maximum likelihood estimators of its parameters and the sampling variances of the estimates are presented. Likelihood ratio tests for several different null hypotheses are derived. These tests permit one to evaluate the usual null hypotheses in terms of the parameters of a process model.This research was supported in part by a USPHS Predoctoral Research Fellowship, 1-F1-MH-31-289-01, by a grant from the Graduate School of the University of Texas, and by grant HD-02212-01 from the National Institute of Child Health and Human Development to Dr. John Theios.     

Three common factor models for groups of variables

For the case of two or more groups of variables (batteries), generalizations on three common factor models, the Equal Residual Variances model, the Image model of Jöreskog, and the Canonical Factor Analysis model, are described and illustrated with empirical examples.The author is indebted to Dr. E. J. Burr for his valuable criticisms.