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Assessing the distortions introduced when calculating d': A simulation approach.

Yiyang Chen1, Heather R Daly2, Mark A Pitt2

  • 1Department of Psychology, University of Kansas, Lawrence, Kansas, USA. chenyiyang@ku.edu.

Behavior Research Methods
|July 3, 2024
PubMed
Summary

Estimating discriminability (d-prime) can be distorted by experimental design choices and correction methods. Simulation studies reveal potential biases, leading to inaccurate statistical inference.

Keywords:
Correction methodsEstimation distortionSensitivityShiny applicationSignal detection theoryType I and Type II errors

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Area of Science:

  • Psychology
  • Cognitive Science
  • Psychophysics

Background:

  • The discriminability measure (d-prime) is crucial for estimating sensitivity in psychology, independent of response bias.
  • Conventional d-prime estimation relies on hit and false-alarm rates, requiring corrections for perfect performance.
  • These corrections, along with experimental design factors, can introduce significant distortion in d-prime estimates.

Purpose of the Study:

  • To investigate how experimental design properties and correction methods distort d-prime estimation.
  • To identify conditions under which d-prime distortion can mislead statistical inference, including Type I and Type II errors.
  • To propose a simulation-based approach for researchers to assess and mitigate d-prime estimation distortion.

Main Methods:

  • Three simulation studies were conducted to examine the impact of various experimental design parameters (e.g., number of trials, sample size, task difficulty) on d-prime estimation.
  • The simulations incorporated the application of correction methods used for perfect performance scenarios.
  • An R Shiny application was developed to estimate d-prime distortion based on simulated or observed data.

Main Results:

  • Distortion in d-prime estimation is not solely due to perfect performance corrections but is complexly influenced by interactions with experimental design factors.
  • Specific combinations of design choices and correction methods can lead to substantial d-prime distortion.
  • The simulations demonstrated that such distortions can result in erroneous statistical inferences.

Conclusions:

  • Researchers should be aware that standard d-prime estimation can be biased by experimental design and correction methods.
  • Simulating d-prime estimation is recommended to proactively identify and address potential distortions.
  • The developed R Shiny application offers a practical tool for researchers to evaluate and minimize d-prime estimation biases, improving the reliability of sensitivity measures.