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Related Concept Videos

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Decision Making: P-value Method

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Related Experiment Video

Updated: Jul 4, 2026

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

An algorithm for identifying nonsystematic delay-discounting data.

Matthew W Johnson1, Warren K Bickel

  • 1Behavioral Pharmacology Research Unit, John Hopkins University School of Medicine, Baltimore, MD 21224-6823, USA. mwj@jhu.edu

Experimental and Clinical Psychopharmacology
|June 11, 2008
PubMed
Summary
This summary is machine-generated.

The R2 measure is problematic for identifying nonsystematic data in discounting studies. A new model-free algorithm, based on a decreasing discounting function, offers a better alternative for identifying outliers.

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Area of Science:

  • Behavioral Economics
  • Decision Science
  • Psychology

Background:

  • Discounting studies often use the R2 measure to exclude data with poor model fits.
  • Previous research has identified issues with using R2 for assessing discounting data fits.

Purpose of the Study:

  • To demonstrate the limitations of using the R2 measure in delay-discounting studies.
  • To introduce and validate a model-free algorithm for identifying nonsystematic data.

Main Methods:

  • Reanalyzed data from three previous delay-discounting studies (161 individuals).
  • Investigated the correlation between discounting rate parameter and R2.
  • Developed and applied a novel algorithm based on a monotonically decreasing discounting function.

Main Results:

  • A significant positive correlation between R2 and discounting rate was observed, indicating R2's bias towards lower discounting rates.
  • The R2 measure is more stringent for low discounting rates than high ones.
  • The new algorithm identified 13 nonsystematic cases out of 161 datasets.

Conclusions:

  • R2 is an unreliable metric for assessing discounting data fits and identifying nonsystematic data.
  • A model-free algorithm based on expected discounting function monotonicity is a viable alternative.
  • This algorithm can improve discounting methodology and outlier identification in various discounting studies.