title: A feature-based inference model of numerical estimation: the split-seed effect.
abstract: 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 three 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.
authors: University of Alberta, Edmonton, AB, Canada. Kyle.Murray@ualberta.ca
authors: Kyle B
authors: KB
authors: Murray
authors: Norman R
authors: NR
authors: Brown
mesh_terms: D000328:Adult; D001245:Association Learning; D001288:Attention; D001336:Automobiles / Q000191:economics; D003132:Commerce / Q000191:economics; D003469:Culture; D003657:Decision Making; D005260:Female; D006801:Humans; D008297:Male; D008433:Mathematics; D011340:Problem Solving; D014163:Transfer, Psychology; D055815:Young Adult