Peat: An Integrative Model of Attribution Processes

Publisher This chapter discusses a core model of attribution processes, PEAT, stated in probabilities. Two mechanisms are primarily responsible for the ability of PEAT to integrate attribution theory. PEAT has been able to provide some improvement in the clarity of the concepts used in attribution theory because it has reduced those concepts to the precise vocabulary of probabilities. The most important clarification was the distinction between contrast-based and prediction-based definitions of covariation information. A second clarification was the distinction, expressed in probabilities, between causal and characteristic attributions. PEAT is also promising as a generator of empirical research. Some of that research may arise from its comments on other theories, for example, PEAT states that when observers are operating under correspondent inference theory (CIT), they will spontaneously make assumptions about consistency even though CIT has no theoretical role for consistency. The chapter concludes that further theoretical work needs to be done to extend the basics of PEAT described in new areas. PEAT promises to generate both theoretical and empirical work.