A Feature-Based Inference Model of Numerical Estimation : The Split Seed Effect

Prior research has identified two modes of quantitative estimation: numerical-retrieval and ordinal-conversion. In this paper we introduce a third mode, which operates by a feature-based inference process. In contrast to prior research, the results of 3 experiments demonstrate that people estimate automobile prices by combining metric information associated with two critical features: product class and brand status . In addition, Experiments 2 and 3 demonstrated that when participants are seeded with the actual current base price of one of the to-be-estimated vehicles, they respond by revising the general metric and splitting the information carried by the seed between the two critical features. As a result, the degree of post-seeding revision is directly related to the number of these features that the seed and the transfer items have in common. The paper concludes with a general discussion of the practical and theoretical implications of our findings