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Polarimetry finds application in chemical kinetics to measure the concentration and reaction kinetics of optically active substances during a chemical reaction. Optically active substances have the capability of rotating the plane of polarization of linearly polarized light passing through them—a feature called optical rotation. Optical activity is attributed to the molecular structure of substances. Normal monochromatic light is unpolarized and possesses oscillations of the electrical...
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Applying the LATER model to reaction time data: an open-source toolkit.

Andrew J Anderson1, Damien J Mannion2, Maria Del Mar Quiroga2

  • 1Department of Optometry and Vision Sciences, The University of Melbourne, Parkville, Victoria, Australia.

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|December 24, 2024
PubMed
Summary
This summary is machine-generated.

We introduce LATERmodel, an R package simplifying the Linear Approach to Threshold with Ergodic Rate (LATER) model for analyzing reaction time distributions. This tool makes complex decision-making analysis accessible to researchers without programming skills.

Keywords:
decision-makinglatencymodelingreaction timesaccade

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Psychology

Background:

  • Reaction time distributions offer insights into neural decision-making.
  • The Linear Approach to Threshold with Ergodic Rate (LATER) model is a simple yet powerful tool for analyzing these distributions.
  • Current LATER model application is hindered by complex visualization and fitting procedures.

Purpose of the Study:

  • To develop an accessible open-source R package, LATERmodel, for visualizing and fitting reaction time distributions using the LATER model.
  • To provide a user-friendly tool for researchers without programming backgrounds to apply the LATER model.

Main Methods:

  • Development of an open-source R package, LATERmodel.
  • Implementation of visualization and fitting procedures for the LATER model.
  • Inclusion of a RShiny graphical user interface for enhanced accessibility.

Main Results:

  • LATERmodel successfully replicates key features of previous LATER analysis tools.
  • The package offers more robust fitting procedures and improved methods for analyzing early responses.
  • The RShiny interface allows non-programmers to utilize the LATER model.

Conclusions:

  • LATERmodel democratizes the application of the LATER model for reaction time analysis.
  • The package facilitates deeper understanding of decision-making processes across various research fields.
  • This tool empowers researchers, including clinicians, to analyze complex cognitive data more effectively.