Overview

Documentation Under Construction

SSMPlots.jl provides convience functions for generating plots related to sequential sampling models (SSMs). SSMs are models of human decision making in which evidence for each option accumulates dynamically until the evidence for one option hits a decision threshold. For more information, see the references below. Please visit SequentialSamplingModels.jl to find information about a Julia package for using SSMs.

Installation

In the REPL, type ] to enter package mode and type

add SSMPlots

to add SSPlots.jl to your environment.

Quick Example

In this quick example, we will plot the histogram for the Racing Diffusion Model (RDM). Note that you will need to install SequentialSamplingModels in order for the example to work.

using SequentialSamplingModels
using SSMPlots

dist = RDM(;ν = [2.0,1.50], k = 0.50, A = 1.0, τ = 0.30)
histogram(dist; xlims=(0,1.5))

References

Evans, N. J. & Wagenmakers, E.-J. Evidence accumulation models: Current limitations and future directions. Quantitative Methods for Psychololgy 16, 73–90 (2020).

Forstmann, B. U., Ratcliff, R., & Wagenmakers, E. J. (2016). Sequential sampling models in cognitive neuroscience: Advantages, applications, and extensions. Annual Review of Psychology, 67, 641-666.

Jones, M., & Dzhafarov, E. N. (2014). Unfalsifiability and mutual translatability of major modeling schemes for choice reaction time. Psychological Review, 121(1), 1.