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Accuracy and Precision01:52

Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.  Highly accurate measurements...
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Getting more from accuracy and response time data: methods for fitting the linear ballistic accumulator.

Chris Donkin1, Lee Averell, Scott Brown

  • 1University of Newcastle, Callaghan, New South Wales, Australia. chris.donkin@newcastle.edu.au

Behavior Research Methods
|November 10, 2009
PubMed
Summary

This study introduces the linear ballistic accumulator (LBA) model for decision-making, offering a computationally tractable alternative to complex cognitive models. It provides accessible tools in Excel, R, and WinBUGS for parameter estimation and data visualization.

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Area of Science:

  • Cognitive psychology
  • Computational modeling
  • Decision science

Background:

  • Standard ANOVA techniques offer limited insight into decision processes.
  • Cognitive models provide deeper understanding but are often computationally complex.
  • The linear ballistic accumulator (LBA) offers a computationally tractable decision model.

Purpose of the Study:

  • To provide accessible methods for estimating LBA model parameters.
  • To offer practical tools for researchers studying decision-making.
  • To facilitate the application of the LBA model in various research settings.

Main Methods:

  • Development of an Excel worksheet for LBA parameter estimation.
  • Creation of R scripts for LBA analysis and data visualization.
  • Implementation of LBA into WinBUGS for Bayesian analysis.

Main Results:

  • Three distinct methods (Excel, R, WinBUGS) for LBA parameter estimation are presented.
  • R scripts enable graphical summaries of data and model predictions.
  • A simulation study assessed the impact of sample size on parameter recovery for each method.

Conclusions:

  • The provided tools enhance the accessibility and application of the LBA model.
  • Researchers can now more easily utilize the LBA for analyzing response time and accuracy data.
  • The study offers practical resources for advancing cognitive decision-making research.