QOEM{T<:Real,V<:AbstractVector{T}} <: AbstractQOEM

A model object for a quantum model for medical diagnosis.

Basis vectors

A person's cognitive state Ψ is represented in a 4D belief space where basis vectors correspond to hypotheses and evidence states:

1. disease present and positive evidence for disease
2. disease present and negative evidence for disease
3. disease absent and positive evidence for disease
4. disease absent and negative evidence for disease

Fields

• Ψ::V: initial state vector (superposition)
• γₕ::T: rotation for positive evidence from medical history
• γₗ::T: rotation for negative evidence from laboratory test
• σ::T: the standard deviation of probability judgments

Example

Ψ = [√(.676 / 2),√(.676 / 2),√(.324 / 2),√(.324 / 2)]
γₕ = 4.4045 / √(.5)
γₗ = 0.3306 / √(.5)
σ = .10
model = QOEM(;Ψ, γₕ, γₗ, σ)
predict(model)

References

Trueblood, J. S., & Busemeyer, J. R. (2011). A quantum probability account of order effects in inference. Cognitive science, 35(8), 1518-1552.

rand(model::AbstractQOEM, n::Int; t = 1, r = .05)

Generates simulated data for the following conditions:

Order 1

1. Pr(disease)
2. Pr(disease | lab test)
3. Pr(disease | lab test, medical history)

Order 2

1. Pr(disease)
2. Pr(disease | medical history)
3. Pr(disease | medical history, lab test)

Arguments

• model::AbstractQOEM: a model object for a quantum order effect model
• n: the number of trials per condition

Keywords

• t = 1: time of decision

Example

using QuantumOrderEffectModels
Ψ = @. √([.35,.35,.15,.15])
γₕ = 2
γₗ = .5
σ = .05
n_trials = 100
model = QOEM(;Ψ, γₕ, γₗ, σ)
data = rand(model, n_trials)

logpdf(model::AbstractQOEM, n::Int, n_d::Vector{Int}; t = 1, r = .05)

Returns the joint log density given data for the following conditions:

Order 1

1. Pr(disease)
2. Pr(disease | lab test)
3. Pr(disease | lab test, medical history)

Order 2

1. Pr(disease)
2. Pr(disease | medical history)
3. Pr(disease | medical history, lab test)

Arguments

• model::AbstractQOEM: a model object for a quantum order effect model
• n: the number of trials per condition
• n_d: the number of defections in each condition

Keywords

• `t = 1: time of decision

Example

Ψ = @. √([.35,.35,.15,.15])
γₕ = 2
γₗ = .5
σ = .05
n_trials = 100
model = QOEM(;Ψ, γₕ, γₗ, σ)
data = rand(model, n_trials)
logpdf(model, data)

logpdf(model::AbstractQOEM, n::Int, n_d::Vector{Int}; t = 1, r = .05)

Returns the joint log density given data for the following conditions:

Order 1

1. Pr(disease)
2. Pr(disease | lab test)
3. Pr(disease | lab test, medical history)

Order 2

1. Pr(disease)
2. Pr(disease | medical history)
3. Pr(disease | medical history, lab test)

Arguments

• model::AbstractQOEM: a model object for a quantum order effect model
• n: the number of trials per condition
• n_d: the number of defections in each condition

Keywords

• `t = 1: time of decision

Example

using QuantumOrderEffectModels
Ψ = @. √([.35,.35,.15,.15])
γₕ = 2
γₗ = .5
σ = .05
n_trials = 100
model = QOEM(;Ψ, γₕ, γₗ, σ)
data = rand(model, n_trials)
logpdf(model, data)

predict(model::AbstractQOEM; t = 1)

Returns predicted response probability for the following conditions:

Arguments

• model::AbstractQOEM

Keywords

• t = 1: time of decision

Output

The output is a vector of predictions corresponding to the following conditions:

Order 1

1. Pr(disease)
2. Pr(disease | lab test)
3. Pr(disease | lab test, medical history)

Order 2

1. Pr(disease)
2. Pr(disease | medical history)
3. Pr(disease | medical history, lab test)

Example

using QuantumOrderEffectModels
Ψ = @. √([.35,.35,.15,.15])
γₕ = 2
γₗ = .5
σ = .05
model = QOEM(;Ψ, γₕ, γₗ, σ)
predict(model)