Models for Classification and Memory

Indiana University, Bloomington IN

Investigators

Abstract

This work involves the continued development and application of models for recognition memory. The first phase included formulation of the architecture and basic processing modules of a recognition model based on general theories of classification and memory; the model has been formulated with special attention to usefulness in increasing the informativeness of measures extracted from recognition data. A major published application reported a study of how performance on a test of recognition of an item or event depends on past frequencies of encounters with the same or similar stimuli and on presence or absence of positive or negative payoffs for correct responses on previous tests. The major result reported was that performance in recognition is not simply a matter of matching perceived with remembered stimulus patterns, but is influenced by many of the same conditions that control goal-directed behavior. At this juncture, a new set of benchmark experiments has been completed and is ready for model-based analysis. Several of the experiments were addressed to the problem of how prior familiarity with stimuli such as words or faces affects their recognition in a new situation; others deal with ways of enhancing the precision of measurement of the way factors such as stimulus frequency, stimulus exposure duration, and retention interval affect performance in situations that simulate eyewitness identification. Results from this series of experiments are ready for model-based analyses, which will be very computationally demanding, because all models and model versions examined will be fitted to the data of individual subjects, typically 30 to 80 in number per experiment. In all of this work, there will be major attention to advancing methodology for dealing with artifacts of averaging data and to communicating theoretical results in forms available to potential users in education, medicine, and technology.