specify bounds of parameters

Author: itsdfish

Project.toml

@@ -1,7 +1,7 @@
 name = "SequentialSamplingModels"
 uuid = "0e71a2a6-2b30-4447-8742-d083a85e82d1"
 authors = ["itsdfish"]
-version = "0.11.8"
+version = "0.11.9"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"

src/AttentionalDiffusion.jl

@@ -5,13 +5,13 @@ An object for the attentional diffusion model.
 
 # Parameters 
 
-- `ν`: relative decision values (i.e., drift rates)
-- `σ`: standard deviation of noise in evidence accumulation
-- `Δ`: constant of evidence accumulation speed (evidence per ms)
-- `α`: evidence threshold 
-- `z`: initial evidence 
-- `θ`: bias towards attended alternative (lower indicates more bias)
-- `τ`: non-decision time
+- `ν::Vector{T}`: relative decision values (i.e., drift rates). ν ∈ ℝⁿ.
+- `σ::T`: standard deviation of noise in evidence accumulation. σ ∈ ℝ.
+- `Δ::T`: constant of evidence accumulation speed (evidence per ms). Δ ∈ ℝ.
+- `α::T`: evidence threshold. α ∈ ℝ.  
+- `z::T`: initial evidence. z ∈ [0, α] 
+- `θ::T`: bias towards attended alternative (lower indicates more bias)
+- `τ::T`: non-decision time. τ ∈ [0, min_rt]
 
 # Constructors
 

src/CircularDDM.jl

@@ -6,12 +6,12 @@ A circular drift diffusion model (CDDM) for continous responding. CCDM is typica
 working memory tasks. Currently supports the 2D case. 
 
 # Parameters 
-- `ν`: a vector drift rates. ν₁ is the mean drift rate along the x-axis; ν₂ is the mean drift rate along the y-axis.
-- `σ`: intra-trial drift rate variability 
-- `η`: a vector across-trial standard deviations of  drift rates. η₁ is the standard deviation of drift rate along the x-axis; 
-    η₂ is the standard deviation of drift rate along the y-axis
-- `α`: response boundary as measured by the radious of a circle 
-- `τ`: mean non-decision time 
+- `ν::T`: a vector drift rates. ν₁ is the mean drift rate along the x-axis; ν₂ is the mean drift rate along the y-axis. ν ∈ ℝⁿ.
+- `σ::T`: intra-trial drift rate variability. σ ∈ ℝ.
+- `η::T`: a vector across-trial standard deviations of  drift rates. η₁ is the standard deviation of drift rate along the x-axis; 
+    η₂ is the standard deviation of drift rate along the y-axis. η ∈ ℝⁿ⁺.
+- `α::T`: response boundary as measured by the radius of a circle. α ∈ ℝ⁺.
+- `τ::T`: mean non-decision time. τ ∈ [0, min_rt]. 
 
 # Constructors
 

src/ClassicMDFT.jl

@@ -5,10 +5,10 @@
 A model type for Multiattribute Decision Field Theory. 
     
 # Parameters 
-- `σ = 1.0`: diffusion noise 
-- `α = 15.0`: evidence threshold 
-- `τ = .30`: non-decision time
-- `w::Vector{T}`: attention weights vector where each element corresponds to the attention given to the corresponding dimension
+- `σ::T = 1.0`: diffusion noise. σ ∈ ℝ⁺. 
+- `α::T = 15.0`: evidence threshold. α  ∈ ℝ⁺.
+- `τ::T = .30`: non-decision time. τ ∈ [0, min_rt].
+- `w::Vector{T}`: attention weights vector where each element corresponds to the attention given to the corresponding dimension. wᵢ ∈ [0,1], ∑wᵢ = 1.
 - `S::Array{T, 2}`: feedback matrix allowing self-connections and interconnections between alternatives. Self-connections range from zero to 1, where s_ij < 1 represents decay. Interconnections 
      between options i and j  where i ≠ j are inhibatory if s_ij < 0.
 - `C::Array{T, 2}`: contrast weight matrix where c_ij is the contrast weight when comparing options i and j.

src/DDM.jl

@@ -5,10 +5,10 @@ Model object for the standard Drift Diffusion Model.
 
 # Parameters
 
-- `ν`: drift rate. Average slope of the information accumulation process. The drift gives information about the speed and direction of the accumulation of information. Typical range: -5 < ν < 5
-- `α`: boundary threshold separation. The amount of information that is considered for a decision. Typical range: 0.5 < α < 2
-- `z`: starting point. Indicator of an an initial bias towards a decision. The z parameter is relative to a (i.e. it ranges from 0 to 1).
-- `τ`: non-decision time. The duration for a non-decisional processes (encoding and response execution). Typical range: 0.1 < τ < 0.5 
+- `ν::T`: drift rate. Average slope of the information accumulation process. The drift gives information about the speed and direction of the accumulation of information. ν ∈ ℝ. Typical range: ν ∈ [-5,5].
+- `α::T`: boundary threshold separation. The amount of information that is considered for a decision. α ∈ ℝ⁺. Typical range: α ∈ [.5, 2].
+- `z::T`: starting point. Indicator of an an initial bias towards a decision. The z ∈ [0,1] parameter is relative to α.
+- `τ::T`: non-decision time. The duration for a non-decisional processes (encoding and response execution). τ ∈ [0, min_rt]. Typical range: τ ∈ [.1,.5].
 
 # Constructors 
 

src/LBA.jl

@@ -5,11 +5,11 @@ A model object for the linear ballistic accumulator.
 
 # Parameters
 
-- `ν::Vector{T}`: a vector of drift rates
-- `σ::T1`: a scalar or vector of drift rate standard deviation
-- `A::T`: max start point
-- `k::T`: A + k = b, where b is the decision threshold
-- `τ::T`: an encoding-response offset
+- `ν::Vector{T}`: a vector of drift rates. α ∈ ℝ⁺.
+- `σ::T1`: a scalar or vector of drift rate standard deviation. σ ∈ ℝ⁺.
+- `A::T`: max start point. A ∈ ℝ⁺.
+- `k::T`: A + k = b, where b is the decision threshold. k ∈ ℝ⁺.
+- `τ::T`: an encoding-response offset. τ ∈ [0, min_rt].
 
 # Constructors 
 

src/LCA.jl

@@ -5,12 +5,12 @@ A model type for the Leaky Competing Accumulator.
     
 # Parameters 
 
-- `ν`: drift rates 
-- `σ`: diffusion noise 
-- `β`: lateral inhabition 
-- `λ`: leak rate
-- `α`: evidence threshold 
-- `τ`: non-decision time 
+- `ν::Vector{T}`: drift rates 
+- `σ::T`: diffusion noise 
+- `β::T`: lateral inhabition 
+- `λ::T`: leak rate
+- `α::T`: evidence threshold 
+- `τ::T`: non-decision time. τ ∈ [0, min_rt] 
 
 # Constructors 
 

src/LNR.jl

@@ -1,11 +1,11 @@
 """
-    LNR{T<:Real} <: AbstractLNR
+    LNR{T <: Real, T1 <: Union{<:T, Vector{<:T}}} <: AbstractLNR{T, T1}
 
 # Parameters 
 
-- `ν`: a vector of means in log-space
-- `σ`: a scalar or vector of standard deviation parameter in log-space
-- `τ`: a encoding-response offset
+- `ν::Vector{T}`: a vector of means in log-space. ν ∈ ℝⁿ.
+- `σ::T1`: a scalar or vector of standard deviation parameter in log-space. σ ∈ ℝ⁺.
+- `τ::T`: a encoding-response offset. τ ∈ [0, min_rt].
 
 # Constructors
 

src/MDFT.jl

@@ -4,15 +4,15 @@
 A model type for simulating Multi-attribute Decision Field Theory (MDFT) as an Stochastic Differential Equation (SDE). 
     
 # Parameters 
-- `σ = 1.0`: diffusion noise 
-- `α = 15.0`: evidence threshold 
-- `τ = .30`: non-decision time
-- `γ::T`: scales the valance, `CMW`, functioning like a drift rate
+- `σ::T = 1.0`: diffusion noise. σ ∈ ℝ⁺. 
+- `α::T = 15.0`: evidence threshold. α ∈ ℝ⁺.
+- `τ::T = .30`: non-decision time. τ ∈ [0, min_rt].
+- `γ::T`: scales the valance, `CMW`, functioning like a drift rate. γ ∈ ℝ⁺.
 - `κ::Vector{T}`: exponential rate parameters for switching attention between attributes. Currently, limited to two 
-    attributes 
-- `ϕ1`: controls the sensitivity of lateral inhibition to distance in the distance function for creating the feedback matrix, `S`
-- `ϕ2`: controls evidence decay and maximum inhibition in the distance function for creating the feedback matrix, `S`
-- `β`: controls the weight of the dominance dimension in the feedback matrix distance function. If `β` < 0, the indifference dimension 
+    attributes. κ ∈ ℝ⁺.
+- `ϕ1::T`: controls the sensitivity of lateral inhibition to distance in the distance function for creating the feedback matrix, `S`. ϕ1 ∈ ℝ⁺.
+- `ϕ2::T`: controls evidence decay and maximum inhibition in the distance function for creating the feedback matrix, `S`. ϕ2 ∈ ℝ⁺.
+- `β::T`: controls the weight of the dominance dimension in the feedback matrix distance function. If `β` < 0, the indifference dimension 
     recieves more where. If `β` > 0, the dominance dimension recieves more weight
 - `S::Array{T, 2}`: feedback matrix allowing self-connections and interconnections between alternatives. Self-connections range from zero to 1, where s_ij < 1 represents decay. Interconnections 
      between options i and j where i ≠ j are inhibitory if s_ij < 0.

src/MLBA.jl

@@ -3,15 +3,15 @@
 
 # Fields 
 
-- `ν::Vector{T}`: a vector of drift rates, which is a function of β₀, λₚ, λₙ, γ
-- `β₀::T`: baseline input for drift rate 
-- `λₚ::T`: decay constant for attention weights of positive differences
-- `λₙ::T`: decay constant for attention weights of negative differences  
-- `γ::T`: risk aversion exponent for subjective values
-- `σ::T1`: a scalar or vector of drift rate standard deviation
-- `A::T`: max start point
-- `k::T`: A + k = b, where b is the decision threshold
-- `τ::T`: an encoding-response offset
+- `ν::Vector{T}`: a vector of drift rates, which is a function of β₀, λₚ, λₙ, γ. ν ∈ ℝⁿ.
+- `β₀::T`: baseline input for drift rate. β₀ ∈ ℝ. 
+- `λₚ::T`: decay constant for attention weights of positive differences. λₚ ∈ ℝ⁺.
+- `λₙ::T`: decay constant for attention weights of negative differences. λₙ ∈ ℝ⁺.
+- `γ::T`: risk aversion exponent for subjective values. γ ∈ ℝ⁺.
+- `σ::T1`: a scalar or vector of drift rate standard deviation. σ ∈ ℝ⁺.
+- `A::T`: max start point. A ∈ ℝ⁺.
+- `k::T`: A + k = b, where b is the decision threshold. k ∈ ℝ⁺.
+- `τ::T`: an encoding-response offset. τ ∈ [0, min_rt].
 
 # Constructors