Valuations of target items are drawn towards unavailable decoy items due to prior expectations

Liz Izakson, Minhee Yoo, Adam Hakim, Ian Krajbich, Ryan Webb, Dino J. Levy*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

When people make choices, the items they consider are often embedded in a context (of other items). How this context affects the valuation of the specific item is an important question. High-value context might make items appear less attractive because of contrast-the tendency to normalize perception of an object relative to its background-or more attractive because of assimilation-the tendency to group objects together. Alternatively, a high-value context might increase prior expectations about the item's value. Here, we investigated these possibilities. We examined how unavailable context items affect choices between two target items, as well as the willingness-To-pay for single targets. Participants viewed sets of three items for several seconds before the target(s) were highlighted. In both tasks, we found a significant assimilation-like effect where participants were more likely to choose or place a higher value on a target when it was surrounded by higher-value context. However, these context effects were only significant for participants' fastest choices. Using variants of a drift-diffusion model, we established that the unavailable context shifted participants' prior expectations towards the average values of the sets but had an inconclusive effect on their evaluations of the targets during the decision (i.e. drift rates). In summary, we find that people use context to inform their initial valuations. This can improve efficiency by allowing people to get a head start on their decision. However, it also means that the valuation of an item can change depending on the context.

Original languageEnglish
Article numberpgae232
JournalPNAS Nexus
Volume3
Issue number7
DOIs
StatePublished - 1 Jul 2024

Keywords

  • assimilation
  • context
  • decision making
  • drift-diffusion model
  • sequential sampling model

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