Another Approximation of the First-Passage Time Densities for the Ratcliff Diffusion Decision Model
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We present a novel method for approximating the probability density function (PDF) of the first-passage times in the Ratcliff diffusion decision model (DDM). We implemented this approximation method in using the package to utilize the faster language while maintaining the language interface. In addition to our novel approximation method, we also compiled all known approximation methods for the DDM density function (with fixed and variable drift rate), including previously unused combinations of techniques found in the relevant literature. We ported these approximation methods to and optimized them to run in this new language. Given an acceptable error tolerance in the value of the PDF approximation, we benchmarked all of these approximation methods to compare their speed against each other and also against commonly used functions from the literature. The results of these tests show that our novel approximation method is not only orders of magnitude faster than the current standards, but it is also faster than all of the other approximation methods available even after translation and optimization to the faster language. All of these approximation methods are bundled in the package for the statistical computing language; this package is available via CRAN, and the source code is available on GitHub.
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