Information acquisition and decision strategies in intertemporal choice.

Attention Computational modeling Decision making Intertemporal choice Process tracing

Journal

Cognitive psychology
ISSN: 1095-5623
Titre abrégé: Cogn Psychol
Pays: Netherlands
ID NLM: 0241111

Informations de publication

Date de publication:
05 2023
Historique:
received: 20 01 2022
revised: 16 03 2023
accepted: 21 03 2023
medline: 8 5 2023
pubmed: 31 3 2023
entrez: 30 3 2023
Statut: ppublish

Résumé

Intertemporal decision models describe choices between outcomes with different delays. While these models mainly focus on predicting choices, they make implicit assumptions about how people acquire and process information. A link between information processing and choice model predictions is necessary for a complete mechanistic account of decision making. We establish this link by fitting 18 intertemporal choice models to experimental datasets with both choice and information acquisition data. First, we show that choice models have highly correlated fits: people that behave according to one model also behave according to other models that make similar information processing assumptions. Second, we develop and fit an attention model to information acquisition data. Critically, the attention model parameters predict which type of intertemporal choice models best describes a participant's choices. Overall, our results relate attentional processes to models of intertemporal choice, providing a stepping stone towards a complete mechanistic account of intertemporal decision making.

Identifiants

pubmed: 36996641
pii: S0010-0285(23)00020-8
doi: 10.1016/j.cogpsych.2023.101562
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

101562

Informations de copyright

Copyright © 2023 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Lisheng He (L)

SILC Business School, Shanghai University, China. Electronic address: hlisheng@shu.edu.cn.

Daniel Wall (D)

Department of Psychology, University of Pennsylvania, United States.

Crystal Reeck (C)

Fox School of Business, Temple University, United States.

Sudeep Bhatia (S)

Department of Psychology, University of Pennsylvania, United States.

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Classifications MeSH