In this conference presentation, a macro level, or systems, model for driving while intoxicated (DWI) and fatal crashes is discussed. The discussion begins with background information on how the model was developed and why it contains only four variables. The variables are: (1) Norms, in terms of drinking, driving, riding, DWI or RWID (riding with an impaired driver); (2) Availability of alcohol, cars, drivers, places to go fast; (3) Formal social controls of drinking, cars and driving; and (4) Environmental pressure (which can be either risk-enhancing or risk-reducing).

Functional measurement methodology was applied to test theMotive×Expectancy×Value (MEV) model of human motivation. Subjects judged the value of hypothetical “games of chance” in which hunger, chance of winning a sandwich, and sandwich preference were varied along with similar information concerning a drink. Graphical tests showed excellent agreement between the data and the theoretical properties of parallelism and linear fan shape. Exact statistical tests of goodness of fit confirmed these graphical tests. Despite the complexity of the task, which required integration of six pieces of information, subjects' judgments obeyed a simple cognitive algebra. Applications of functional measurement were suggested for approach-avoidance conflict, level of aspiration, work motivation, and achievement motivation. These methods can provide exact tests of the behavior models in terms of the subjective values at the level of the individual. They thus provide a unified nomothetic-ideographic approach to motivation theory.

A simple adding model was tested for the integration of the hedonic component of taste stimuli. Subjects tasted mixtures of quinine sulfate and apple juice and rated the pleasantness of the composite taste, The rating data showed significant nonadditivity, which could reflect failure of the model itself or merely a nonlinear response output function. Since the rating data could be transformed to additivity, justification for such transformation was sought through a second task based on the logic of two-stage integration. In this task, subjects rated the difference between taste mixtures. The subtracting model underlying this judgment provided functional scale values for the taste mixtures on validated interval scales. These functional scale values were consistent with the predictions of the simple adding model, and thus justified the transformation of the single rating data to additivity. The application of functional measurement to obtain the psychophysical functions of the two taste components is also illustrated.