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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.

Journal of Neurophysiology

|December 24, 2024

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-making latency modeling reaction time saccade

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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.