INTERVENING CONCEPTS IN MULTIVARIATE ENVIRONMENT

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We propose to investigate and model how humans learn intervening concepts. Intervening concepts, such as for example the psychological concept of hunger, seem to be an important and natural part of human cognition. In our paradigm, subjects will be exposed to multivariate input-output relations related by hidden intervening concepts. Subjects' global and trial-by-trial learning processes will be examined as subjects attempt to learn how to predict the outputs given a particular set of input values. In a series of nine experiments, we plan to determine (a) the conditions under which a hidden intervening concept can be learned solely on the basis of experience with the input-output relations, and (b) the mechanisms used to learn a hidden intervening concept. The proposed experiments are designed to obtain converging evidence from a wide range and variety of experimental manipulations and analytical methods. The experiments include manipulations of (a) training and transfer across different types of causal structures, (b) various instructional conditions, (b) various types of training sequences, (c) various types of stimulus conditions, and (d) the use of selection versus reception concept learning training procedures. Two new and different types of analyses will be used to analyze the results: (a) a model-free global (principle component) measure of intervening concept learning which will be used to examine the global effects of the experimental manipulations, and (b) micro-level analyses of the trial by trial learning process. One important feature of both methods of analyses is that they can be performed at the individual subject level, thus permitting an investigation of the implications of individual differences in learning of intervening concepts for successful transfer performance. Importantly, the results will be used to compare and contrast a passive adaptive network learning model versus an active hypothesis testing learning model.