JOURNAL ARTICLE

Exploring the Impact of Decoys on Decision‐Making by Young Children.

  • Published In: Journal of Behavioral Decision Making, 2024, v. 37, n. 3. P. 1 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Parrish, Audrey E.; Dawes, Jillian; Thompson, Hannah L. 3 of 3

Abstract

The asymmetric dominance effect (or decoy effect) is a decision‐making phenomenon that occurs when preference for a target alternative shifts with the addition of a similar, yet inferior alternative dubbed the decoy. Despite the considerable number of studies examining the decoy effect with adult humans and animals, there is comparatively less research on context effects within the developmental domain. In this study, we explored the impact of a decoy on choice behavior by young children (3–9 years old) using a preferential choice task as well as a perceptual discrimination task. Introduction of an inferior decoy impacted choice behavior across 2‐alternative (binary) versus 3‐alternative (trinary) sets, such that inclusion of the dominated decoy in expanded sets decreased selection of the superior target alternative. This pattern of results indicates a reversal of the standard attraction effect, also known as the repulsion effect. We discuss these findings in light of the adult and comparative literatures on decoy effects as well as call for additional developmental studies exploring the impact of inferior alternatives in multialternative decision‐making. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Behavioral Decision Making. 2024/07, Vol. 37, Issue 3, p1
  • Document Type:Article
  • Subject Area:Psychology
  • Publication Date:2024
  • ISSN:0894-3257
  • DOI:10.1002/bdm.2385
  • Accession Number:178646786
  • Copyright Statement:Copyright of Journal of Behavioral Decision Making is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.