Hierarchical Bayesian estimation and hypothesis testing for delay discounting tasks

Benjamin T. Vincent (Lead / Corresponding author)

Research output: Contribution to journalArticle

16 Citations (Scopus)
112 Downloads (Pure)

Abstract

A state-of-the-art data analysis procedure is presented to conduct hierarchical Bayesian inference and hypothesis testing on delay discounting data. The delay discounting task is a key experimental paradigm used across a wide range of disciplines from economics, cognitive science, and neuroscience, all of which seek to understand how humans or animals trade off the immediacy verses the magnitude of a reward. Bayesian estimation allows rich inferences to be drawn, along with measures of confidence, based upon limited and noisy behavioural data. Hierarchical modelling allows more precise inferences to be made, thus using sometimes expensive or difficult to obtain data in the most efficient way. The proposed probabilistic generative model describes how participants compare the present subjective value of reward choices on a trial-to-trial basis, estimates participant- and group-level parameters. We infer discount rate as a function of reward size, allowing the magnitude effect to be measured. Demonstrations are provided to show how this analysis approach can aid hypothesis testing. The analysis is demonstrated on data from the popular 27-item monetary choice questionnaire (Kirby, Psychonomic Bulletin & Review, 16(3), 457–462 2009), but will accept data from a range of protocols, including adaptive procedures. The software is made freely available to researchers.
Original languageEnglish
Pages (from-to)1608-1620
Number of pages13
JournalBehavior Research Methods
Volume48
Issue number4
Early online date5 Nov 2015
DOIs
Publication statusPublished - Dec 2016

Fingerprint

Reward
Cognitive Science
Statistical Models
Software
Economics
Research Personnel
Delay Discounting
Hypothesis Testing
Inference

Keywords

  • Decision Making
  • Delay discounting
  • Inter-temporal choice
  • Magnitude effect
  • Time preference
  • Bayesian estimation
  • MCMC
  • Financial psychphysics

Cite this

@article{cae0c8c396f747a783bd14123f95e1f7,
title = "Hierarchical Bayesian estimation and hypothesis testing for delay discounting tasks",
abstract = "A state-of-the-art data analysis procedure is presented to conduct hierarchical Bayesian inference and hypothesis testing on delay discounting data. The delay discounting task is a key experimental paradigm used across a wide range of disciplines from economics, cognitive science, and neuroscience, all of which seek to understand how humans or animals trade off the immediacy verses the magnitude of a reward. Bayesian estimation allows rich inferences to be drawn, along with measures of confidence, based upon limited and noisy behavioural data. Hierarchical modelling allows more precise inferences to be made, thus using sometimes expensive or difficult to obtain data in the most efficient way. The proposed probabilistic generative model describes how participants compare the present subjective value of reward choices on a trial-to-trial basis, estimates participant- and group-level parameters. We infer discount rate as a function of reward size, allowing the magnitude effect to be measured. Demonstrations are provided to show how this analysis approach can aid hypothesis testing. The analysis is demonstrated on data from the popular 27-item monetary choice questionnaire (Kirby, Psychonomic Bulletin & Review, 16(3), 457–462 2009), but will accept data from a range of protocols, including adaptive procedures. The software is made freely available to researchers.",
keywords = "Decision Making, Delay discounting, Inter-temporal choice, Magnitude effect, Time preference, Bayesian estimation, MCMC, Financial psychphysics",
author = "Vincent, {Benjamin T.}",
year = "2016",
month = "12",
doi = "10.3758/s13428-015-0672-2",
language = "English",
volume = "48",
pages = "1608--1620",
journal = "Behavior Research Methods",
issn = "1554-351X",
publisher = "Springer Verlag",
number = "4",

}

TY - JOUR

T1 - Hierarchical Bayesian estimation and hypothesis testing for delay discounting tasks

AU - Vincent, Benjamin T.

PY - 2016/12

Y1 - 2016/12

N2 - A state-of-the-art data analysis procedure is presented to conduct hierarchical Bayesian inference and hypothesis testing on delay discounting data. The delay discounting task is a key experimental paradigm used across a wide range of disciplines from economics, cognitive science, and neuroscience, all of which seek to understand how humans or animals trade off the immediacy verses the magnitude of a reward. Bayesian estimation allows rich inferences to be drawn, along with measures of confidence, based upon limited and noisy behavioural data. Hierarchical modelling allows more precise inferences to be made, thus using sometimes expensive or difficult to obtain data in the most efficient way. The proposed probabilistic generative model describes how participants compare the present subjective value of reward choices on a trial-to-trial basis, estimates participant- and group-level parameters. We infer discount rate as a function of reward size, allowing the magnitude effect to be measured. Demonstrations are provided to show how this analysis approach can aid hypothesis testing. The analysis is demonstrated on data from the popular 27-item monetary choice questionnaire (Kirby, Psychonomic Bulletin & Review, 16(3), 457–462 2009), but will accept data from a range of protocols, including adaptive procedures. The software is made freely available to researchers.

AB - A state-of-the-art data analysis procedure is presented to conduct hierarchical Bayesian inference and hypothesis testing on delay discounting data. The delay discounting task is a key experimental paradigm used across a wide range of disciplines from economics, cognitive science, and neuroscience, all of which seek to understand how humans or animals trade off the immediacy verses the magnitude of a reward. Bayesian estimation allows rich inferences to be drawn, along with measures of confidence, based upon limited and noisy behavioural data. Hierarchical modelling allows more precise inferences to be made, thus using sometimes expensive or difficult to obtain data in the most efficient way. The proposed probabilistic generative model describes how participants compare the present subjective value of reward choices on a trial-to-trial basis, estimates participant- and group-level parameters. We infer discount rate as a function of reward size, allowing the magnitude effect to be measured. Demonstrations are provided to show how this analysis approach can aid hypothesis testing. The analysis is demonstrated on data from the popular 27-item monetary choice questionnaire (Kirby, Psychonomic Bulletin & Review, 16(3), 457–462 2009), but will accept data from a range of protocols, including adaptive procedures. The software is made freely available to researchers.

KW - Decision Making

KW - Delay discounting

KW - Inter-temporal choice

KW - Magnitude effect

KW - Time preference

KW - Bayesian estimation

KW - MCMC

KW - Financial psychphysics

U2 - 10.3758/s13428-015-0672-2

DO - 10.3758/s13428-015-0672-2

M3 - Article

C2 - 26542975

VL - 48

SP - 1608

EP - 1620

JO - Behavior Research Methods

JF - Behavior Research Methods

SN - 1554-351X

IS - 4

ER -