Some determinants of inhibitory stimulus control

Interspersed reinforcement and extinction during discrimination learning generate a U-shaped gradient of inhibition about the stimulus correlated with extinction. The present work showed that extinction is not a necessary determinant of inhibitory stimulus control. In Exp. I, a reduction in the rate of reinforcement, through a shift from a multiple variable-interval 1-min variable-interval 1-min schedule to a multiple variable-interval 1-min variable-interval 5-min schedule, resulted in a post-discrimination line orientation gradient of inhibition about the stimulus correlated with the variable-interval 5-min schedule. In Exp. II, the rates of reinforcement, correlated with a pair of stimuli, were held constant during a shift from a multiple variable-interval 1-min variable-interval 1-min schedule to a multiple variable-interval 1-min differential-reinforcement-of-low-rate schedule. Inhibitory stimulus control about the stimulus correlated with the differential reinforcement of low rate was obtained. In both experiments, a reduction in the rate of responding during one stimulus and behavioral contrast during the other stimulus preceded the observation of inhibitory stimulus control.     

A general approach to confirmatory maximum likelihood factor analysis

We describe a general procedure by which any number of parameters of the factor analytic model can be held fixed at any values and the remaining free parameters estimated by the maximum likelihood method. The generality of the approach makes it possible to deal with all kinds of solutions: orthogonal, oblique and various mixtures of these. By choosing the fixed parameters appropriately, factors can be defined to have desired properties and make subsequent rotation unnecessary. The goodness of fit of the maximum likelihood solution under the hypothesis represented by the fixed parameters is tested by a large samplex ² test based on the likelihood ratio technique. A by-product of the procedure is an estimate of the variance-covariance matrix of the estimated parameters. From this, approximate confidence intervals for the parameters can be obtained. Several examples illustrating the usefulness of the procedure are given.This work was supported by a grant (NSF-GB 1985) from the National Science Foundation to Educational Testing Service.