A feature-based inference model of numerical estimation: the split-seed effect.

Full 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.

Author information

Author/s: Murray, Kyle B (KB); Brown, Norman R (NR);

Affiliation: University of Alberta, Edmonton, AB, Canada. Kyle.Murray(-atsign-)ualberta.ca

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: Acta psychologica (Acta Psychol (Amst)), published in Netherlands. (Language: eng)

Reference: 2009-Jul; vol 131 (issue 3) : pp 221-34

Dates: Created 2009/07/06; Completed 2009/10/13;

PMID: 19560107, status: MEDLINE (last retrieval date: 10/13/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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