The asymptotic distribution of response probabilities in the Audley-Jonckheere learning model

The asymptotic distributions of response probabilities in the Audley-Jonckheere learning model, as applied to the two-choice learning situation, are described for (1) the experimenter-controlled events model, (2) the subject-controlled events model, and (3) the experimenter-subject-controlled events model.     

Response latencies in human matching-to-sample

Optimum characterization of individual information-processing skills requires isolation of assessable components. In matching-to-sample, three elementary processes might be separated if a proposed model of serial, selfterminating search were supported: registration of the stimulus in short-term memory, comparison of the two registered stimuli, and execution of the identifying response. Support for the model was obtained when response latencies were examined as a function of left-to-right position of the target stimulus in a display, but quantitative estimates of components could not be made because of interactions between tasks and the linearity of the scanning process. A second experiment, which varied the number of comparison stimuli, yielded highly linear functions whose slopes and intercepts violated certain aspects of the model. Data on eye movements obtained in a third experiment again supported the basic model but indicated that median response latencies represent a confounding of optimal and suboptimal performances.     

Psychophysical methodology IV: Phi gamma hypothesis and the method of limits

Assuming the phi-gamma hypothesis, deductions concerning the method of limits (ML) are derived. For a given step size, the selection of the initial stimulus for ascending series or for descending series has little effect on the summary statistical measures of the ML. Estimates of ML statistics are derived for different step sizes, and these estimates are used to estimate the mean and standard deviation of the phi-gamma hypothesis. Also considered are how summary statistical measures of the ML are influenced by extremely large and small step sizes and by the definition of a ML threshold.