rtdists: Response Time Distributions

Singmann, Henrik; Brown, Scott; Gretton, Matthew; Heathcote, Andrew · 2014 · Unknown

DOI: 10.32614/cran.package.rtdists

archive: archived pipeline: cataloged verified

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Summary

The provided text is documentation for the `rtdists` R package, not a research paper reporting original empirical findings or theoretical derivations. It describes software tools for computing response time distributions based on two established cognitive models: the Ratcliff diffusion model and the Linear Ballistic Accumulator (LBA). The package addresses the need for accessible, computationally efficient methods to calculate probability density functions (PDF), cumulative distribution functions (CDF), quantile functions, and random number generation for these models. The diffusion model implementation relies on C code by Andreas and Jochen Voss, while the LBA implementation supports various underlying distributions for drift rates, including normal, gamma, Frechet, and log-normal. The documentation details specific parameterizations for both models, noting deviations from standard literature conventions, such as defining the non-decision time ($t_0$) as the lower bound of a uniform distribution rather than its midpoint, and setting the default diffusion constant ($s$) to 1 instead of 0.1. The methods described involve fully vectorized functions that allow for trial-wise parameter variations. For the diffusion model, parameters include threshold separation ($a$), starting point ($z$), drift rate ($v$), and non-decision time ($t_0$), along with inter-trial variability parameters for drift ($sv$), starting point ($sz$), and non-decision time ($st_0$). The LBA functions similarly accommodate parameters for start point interval ($A$), response threshold ($b$), and drift rate distributions. The documentation provides examples of fitting these models to data using maximum likelihood estimation via the `nlminb` optimizer, demonstrating how to recover parameters from simulated data. It also highlights computational considerations, such as the impact of non-zero variability parameters on calculation speed and the handling of defective CDFs in quantile calculations. The significance of this work lies in providing a standardized, open-source tool for researchers in cognitive psychology and neuroscience to analyze response time data. By implementing these complex mathematical models in R, the package facilitates the application of diffusion and LBA models to experimental data, enabling detailed inference about decision-making processes, including speed-accuracy trade-offs and evidence accumulation dynamics. The documentation serves as a practical guide for users to implement these models, ensuring correct parameterization and efficient computation.

Key finding

The rtdists package provides a comprehensive suite of functions for computing and simulating response time distributions using the Ratcliff diffusion model and the linear ballistic accumulator model.

Methodology

other

Provenance

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Topics

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• perception reaction time