Representations of perceptions of risks.

Abstract

Abstract : The perceptions of risks (e.g., diseases, accidents, natural hazards) is investigated using a multi-task, multi-model approach. We studied the proximities among 18 risks induced by three tasks: judgment of similarity, conditional prediction and dimensional evaluation. The comparative judgments (similarity and prediction) were reasonably close but the dimensional evaluation did not correlate highly with either similarity or prediction. Similarity judgments and conditional predictions appear to be represented best by tree models, which are based on discrete features, while the dimensional evaluations are better explained by spatial models, such as multidimensional scaling and factor analysis. We discuss the implications of these results for the study of mental representation and for the analysis of risk perception. (Author)