Publication

A hierarchical Bayesian modeling approach to searching and stopping in multi-attribute judgment

van Ravenzwaaij, D., Moore, C. P., Lee, M. D. & Newell, B. R., 2014, In : Cognitive Science. 38, 7, p. 1384-1405 22 p.

Research output: Contribution to journalArticleAcademicpeer-review

APA

van Ravenzwaaij, D., Moore, C. P., Lee, M. D., & Newell, B. R. (2014). A hierarchical Bayesian modeling approach to searching and stopping in multi-attribute judgment. Cognitive Science, 38(7), 1384-1405. https://doi.org/10.1111/cogs.12119

Author

van Ravenzwaaij, D. ; Moore, C. P. ; Lee, Michael D ; Newell, B. R. / A hierarchical Bayesian modeling approach to searching and stopping in multi-attribute judgment. In: Cognitive Science. 2014 ; Vol. 38, No. 7. pp. 1384-1405.

Harvard

van Ravenzwaaij, D, Moore, CP, Lee, MD & Newell, BR 2014, 'A hierarchical Bayesian modeling approach to searching and stopping in multi-attribute judgment', Cognitive Science, vol. 38, no. 7, pp. 1384-1405. https://doi.org/10.1111/cogs.12119

Standard

A hierarchical Bayesian modeling approach to searching and stopping in multi-attribute judgment. / van Ravenzwaaij, D.; Moore, C. P.; Lee, Michael D; Newell, B. R.

In: Cognitive Science, Vol. 38, No. 7, 2014, p. 1384-1405.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

van Ravenzwaaij D, Moore CP, Lee MD, Newell BR. A hierarchical Bayesian modeling approach to searching and stopping in multi-attribute judgment. Cognitive Science. 2014;38(7):1384-1405. https://doi.org/10.1111/cogs.12119


BibTeX

@article{b48b175080e249a491ba9f0d6f341e63,
title = "A hierarchical Bayesian modeling approach to searching and stopping in multi-attribute judgment",
abstract = "In most decision-making situations, there is a plethora of information potentially available to people. Deciding what information to gather and what to ignore is no small feat. How do decision makers determine in what sequence to collect information and when to stop? In two experiments, we administered a version of the German cities task developed by Gigerenzer and Goldstein (1996), in which participants had to decide which of two cities had the larger population. Decision makers were not provided with the names of the cities, but they were able to collect different kinds of cues for both response alternatives (e. g., {"}Does this city have a university?{"}) before making a decision. Our experiments differed in whether participants were free to determine the number of cues they examined. We demonstrate that a novel model, using hierarchical latent mixtures and Bayesian inference (Lee & Newell, 2011) provides a more complete description of the data from both experiments than simple conventional strategies, such as the take-the-best or the Weighted Additive heuristics.",
keywords = "REASON DECISION-MAKING, THE-BEST, RATIONALITY, INFERENCES, STRATEGIES, FRUGAL",
author = "{van Ravenzwaaij}, D. and Moore, {C. P.} and Lee, {Michael D} and Newell, {B. R.}",
year = "2014",
doi = "10.1111/cogs.12119",
language = "English",
volume = "38",
pages = "1384--1405",
journal = "Cognitive Science",
issn = "0364-0213",
publisher = "Wiley",
number = "7",

}

RIS

TY - JOUR

T1 - A hierarchical Bayesian modeling approach to searching and stopping in multi-attribute judgment

AU - van Ravenzwaaij, D.

AU - Moore, C. P.

AU - Lee, Michael D

AU - Newell, B. R.

PY - 2014

Y1 - 2014

N2 - In most decision-making situations, there is a plethora of information potentially available to people. Deciding what information to gather and what to ignore is no small feat. How do decision makers determine in what sequence to collect information and when to stop? In two experiments, we administered a version of the German cities task developed by Gigerenzer and Goldstein (1996), in which participants had to decide which of two cities had the larger population. Decision makers were not provided with the names of the cities, but they were able to collect different kinds of cues for both response alternatives (e. g., "Does this city have a university?") before making a decision. Our experiments differed in whether participants were free to determine the number of cues they examined. We demonstrate that a novel model, using hierarchical latent mixtures and Bayesian inference (Lee & Newell, 2011) provides a more complete description of the data from both experiments than simple conventional strategies, such as the take-the-best or the Weighted Additive heuristics.

AB - In most decision-making situations, there is a plethora of information potentially available to people. Deciding what information to gather and what to ignore is no small feat. How do decision makers determine in what sequence to collect information and when to stop? In two experiments, we administered a version of the German cities task developed by Gigerenzer and Goldstein (1996), in which participants had to decide which of two cities had the larger population. Decision makers were not provided with the names of the cities, but they were able to collect different kinds of cues for both response alternatives (e. g., "Does this city have a university?") before making a decision. Our experiments differed in whether participants were free to determine the number of cues they examined. We demonstrate that a novel model, using hierarchical latent mixtures and Bayesian inference (Lee & Newell, 2011) provides a more complete description of the data from both experiments than simple conventional strategies, such as the take-the-best or the Weighted Additive heuristics.

KW - REASON DECISION-MAKING

KW - THE-BEST

KW - RATIONALITY

KW - INFERENCES

KW - STRATEGIES

KW - FRUGAL

U2 - 10.1111/cogs.12119

DO - 10.1111/cogs.12119

M3 - Article

C2 - 24646326

VL - 38

SP - 1384

EP - 1405

JO - Cognitive Science

JF - Cognitive Science

SN - 0364-0213

IS - 7

ER -

ID: 27953228