SequentialSamplingModels.SequentialSamplingModels
— ModuleSequentialSamplingModels.jl
A collection of sequential sampling models based on the Distributions.jl API.
Currently Supported models
LBA
: Linear Ballistic AccumulatorLNR
: Lognormal Race ModelWald
: a shifted Wald represented a single boundary diffusion processWaldMixture
: a shifted Wald represented a single boundary diffusion process with across-trial variability in the drift rate
SequentialSamplingModels.DiffusionRace
— TypeRacing Diffusion Constructor
ν
: a vector of drift ratesk
: k = b - A where b is the decision threshold, and A is the maximum starting pointA
: the maximum starting point diffusion process, sampled from Uniform distributionθ
: a encoding-motor time offset
Usage
using SequentialSamplingModels
dist = DiffusionRace(;ν=[1,2], k=.3, A=.7, θ=.2)
data = rand(dist, 10)
like = pdf.(dist, data)
loglike = logpdf.(dist, data)
References
Tillman, G., Van Zandt, T., & Logan, G. D. (2020). Sequential sampling models without random between-trial variability: The racing diffusion model of speeded decision making. Psychonomic Bulletin & Review, 27, 911-936.
SequentialSamplingModels.LBA
— TypeLinear Ballistic Accumulator Constructor
ν
: a vector of drift ratesA
: max start pointk
: A + k = b, where b is the decision thresholdσ
: drift rate standard deviation (default=1)τ
: a encoding-response offset
Usage
using SequentialSamplingModels
dist = LBA(ν=[3.0,2.0], A = .8, k = .2, τ = .3)
choice,rt = rand(dist, 10)
like = pdf.(dist, choice, rt)
loglike = logpdf.(dist, choice, rt)
References
SequentialSamplingModels.LNR
— TypeLognormal Race Model Constructor
μ
: a vector of means in log-spaceσ
: a standard deviation parameter in log-spaceϕ
: a encoding-response offset
Usage
using SequentialSamplingModels
dist = LNR(μ=[-2,-3], σ=1.0, ϕ=.3)
data = rand(dist, 10)
like = pdf.(dist, data)
loglike = logpdf.(dist, data)
References
Rouder, J. N., Province, J. M., Morey, R. D., Gomez, P., & Heathcote, A. (2015). The lognormal race: A cognitive-process model of choice and latency with desirable psychometric properties. Psychometrika, 80(2), 491-513.
SequentialSamplingModels.Wald
— TypeWald Constructor
υ
: drift rateα
: decision thresholdθ
: a encoding-response offset
Usage
using SequentialSamplingModels
dist = Wald(υ=3.0, α=.5, θ=.130)
rt = rand(dist, 10)
like = pdf.(dist, rt)
loglike = logpdf.(dist, rt)
SequentialSamplingModels.WaldA
— TypeWaldA Constructor
ν
: drift ratek
: k = b - A where b is the decision threshold, and A is the maximum starting pointA
: the maximum starting point diffusion process, sampled from Uniform distributionθ
: a encoding-motor time offset
Usage
using SequentialSamplingModels
dist = WaldA(ν=.5, σ=1.0, ϕ=.3)
data = rand(dist, 10)
like = pdf.(dist, data)
loglike = logpdf.(dist, data)
References
Tillman, G., Van Zandt, T., & Logan, G. D. (2020). Sequential sampling models without random between-trial variability: The racing diffusion model of speeded decision making. Psychonomic Bulletin & Review, 27, 911-936.
SequentialSamplingModels.WaldMixture
— TypeWaldMixture Constructor
υ
: drift rateσ
: standard deviation of drift rateα
: decision thresholdθ
: a encoding-response offset
Usage
using SequentialSamplingModels
dist = WaldMixture(υ=3.0, σ=.2, α=.5, θ=.130)
rt = rand(dist, 10)
like = pdf.(dist, rt)
loglike = logpdf.(dist, rt)
References
Steingroever, H., Wabersich, D., & Wagenmakers, E. J. (2020). Modeling across-trial variability in the Wald drift rate parameter. Behavior Research Methods, 1-17.