Order matters: How covert value updating during sequential option sampling shapes economic preference.
PLoS Comput Biol
; 16(8): e1007920, 2020 08.
Article
em En
| MEDLINE
| ID: mdl-32780741
Standard neuroeconomic decision theory assumes that choice is based on a value comparison process, independent from how information about alternative options is collected. Here, we investigate the opposite intuition that preferences are dynamically shaped as options are sampled, through iterative covert pairwise comparisons. Our model builds on two lines of research, one suggesting that a natural frame of comparison for the brain is between default and alternative options, the other suggesting that comparisons spread preferences between options. We therefore assumed that during sequential option sampling, people would 1) covertly compare every new alternative to the current best and 2) update their values such that the winning (losing) option receives a positive (negative) bonus. We confronted this "covert pairwise comparison" model to models derived from standard decision theory and from known memory effects. Our model provided the best account of human choice behavior in a novel task where participants (n = 92 in total) had to browse through a sequence of items (food, music or movie) of variable length and ultimately select their favorite option. Consistently, the order of option presentation, which was manipulated by design, had a significant influence on the eventual choice: the best option was more likely to be chosen when it came earlier in the sequence, because it won more covert comparisons (hence a greater total bonus). Our study provides a mechanistic understanding of how the option sampling process shapes economic preference, which should be integrated into decision theory.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Comportamento de Escolha
/
Modelos Psicológicos
Tipo de estudo:
Health_economic_evaluation
/
Prognostic_studies
Limite:
Adult
/
Female
/
Humans
/
Male
Idioma:
En
Revista:
PLoS Comput Biol
Assunto da revista:
BIOLOGIA
/
INFORMATICA MEDICA
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
França