Feature sequential sampling models #139

Zooaal · 2025-02-19T15:25:52Z

New Features:

New Component "Drift_Component" which is used for simulating evidence accumulation process with different Models.
3 Models for the usage in "Drift_Component": LBA, DDM and Kelly Model.
Functions to simulate the models traces and response times.
New Onset "SequenceOnset" which can be used with SequenceDesign to define a Onset for each Component in a Sequence.
New Onset "DriftOnset" special onset for the Drift_Component.
simulate_interonset_distances Functions for the "DriftOnset" used to return the response times of the models used.
simulate_onsets Function for the "SequenceOnset" specifies how the onsets for the sequences are calculated defined by the Onset for each Component. Important here: Onsets are defined for the next Component. For Example "SCR"=> S->C, C->R and R->S
New File "sequentialSamplingModelSimulation.jl": Defines the Kelly Model and its simulation. Furthermore the simulation of the LBA and DDM are designed. In the Future more Models can be defined here.

Important Changes:

Added using of SepuentialSamplingModels Package

On Top I added unittests and docstrings for all new features

For better usability I have created two HowTo in the documentation:

One explains how to simulate an entire evidence accumulation process as a basis.
Another one explains how to use the KellyModel, how to define parameters in the design and how to simulate an overlap of components.

behinger

This is an intermediate review, I'm running out of time, but I didnt check everything yet

src/component.jl

src/onset.jl

src/component.jl

Wording DocString Co-authored-by: Benedikt Ehinger <[email protected]>

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JuliaFormatter

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UnfoldSim.jl/src/onset.jl

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end

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UnfoldSim.jl/src/sequentialSamplingModelSimulation.jl

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           drift_rate::Union{Real, String} # drift rate 
        
           event_onset::Union{Real, String} # onset(sensory evidence) 
        
           sensor_encoding_delay::Union{Real, String} # var(sensory encoding delay) 
        
           accumulative_level_noise::Union{Real, String} # accum level noise 
        
           boundary::Union{Real, String} # boundaryary height 
        
           motor_onset::Union{Real, String} # onset(motor) 
        
           motor_delay::Union{Real, String} # var(motor) 
        
           post_accumulation_duration_mean::Union{Real, String} # mean(post decision) 
        
           post_accumulation_duration_variability::Union{Real, String} # var(post decision) 
        
           CPPrampdownDur::Union{Real, String} # CPPrampdown duration

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UnfoldSim.jl/src/sequentialSamplingModelSimulation.jl

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CPPrampdownDur = 0.1

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UnfoldSim.jl/src/sequentialSamplingModelSimulation.jl

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           return new(drift_rate, event_onset, sensor_encoding_delay, accumulative_level_noise, boundary, motor_onset, 
        
               motor_delay, post_accumulation_duration_mean, post_accumulation_duration_variability, 
        
               CPPrampdownDur)

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UnfoldSim.jl/src/sequentialSamplingModelSimulation.jl

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           return Dict( 
        
               name => getfield(model, name) for name in fieldnames(typeof(model)) 
        
           )

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UnfoldSim.jl/src/sequentialSamplingModelSimulation.jl

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           evidence = zeros(length(time_vec)); 
        
           evidence[time_vec .>= (model.event_onset+(rand(rng) -.5)*model.sensor_encoding_delay)] .= 1;  
        
           startAccT = time_vec[findfirst(evidence .== 1)]; 
        
           noise = vcat(zeros( 
        
               sum(time_vec .< startAccT)), 
        
               randn(rng,sum(time_vec .>=  startAccT)) .*  model.accumulative_level_noise .*sqrt(Δt)); 
        
           ev=evidence; 
        
           ev[time_vec .< startAccT] .= 0; # set to zero all of the evidence before accT 
        
           cum_evidence = cumsum(ev .* model.drift_rate .* Δt .+ noise,dims=1); # This is the cumulative differential evidence, just as in a 1d DDM.

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UnfoldSim.jl/src/sequentialSamplingModelSimulation.jl

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           cum_evidence = abs.(cum_evidence); 
        
           dti = findfirst(cum_evidence .> model.boundary); # finding the sample point of threshold crossing of each, then will pick the earlier as the winner

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UnfoldSim.jl/src/sequentialSamplingModelSimulation.jl

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	rt = time_vec[dti] + model.motor_onset + (rand(rng)-.5) * model.motor_delay;

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UnfoldSim.jl/src/sequentialSamplingModelSimulation.jl

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           post_acc_duration = model.post_accumulation_duration_mean .+ model.post_accumulation_duration_variability .* rand(rng);

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UnfoldSim.jl/src/sequentialSamplingModelSimulation.jl

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acc_stop_index = dti + (post_acc_duration÷Δt)|>Int;

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UnfoldSim.jl/src/sequentialSamplingModelSimulation.jl

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           cum_evidence = abs.(cum_evidence); 
        
           if acc_stop_index<length(time_vec)

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UnfoldSim.jl/src/sequentialSamplingModelSimulation.jl

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           tmp = cum_evidence[acc_stop_index] .- (1:(nT-acc_stop_index)) .*cum_evidence[acc_stop_index] .* (Δt ./ model.CPPrampdownDur) 
        
           cum_evidence[(acc_stop_index+1):end] .= max.(Ref(0), tmp);

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UnfoldSim.jl/src/sequentialSamplingModelSimulation.jl

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return rt/Δt, cum_evidence[1:end]

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UnfoldSim.jl/src/sequentialSamplingModelSimulation.jl

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           function trace_sequential_sampling_model(rng, component::Drift_Component, design::AbstractDesign)

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UnfoldSim.jl/src/sequentialSamplingModelSimulation.jl

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           model = component.model_type(; (key => parameters[key] for key in keys(parameters))...)

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UnfoldSim.jl/src/sequentialSamplingModelSimulation.jl

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rt = (time_steps[end] + model.τ)/Δt

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UnfoldSim.jl/src/sequentialSamplingModelSimulation.jl

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rt = (time_vec[end] + model.τ)/Δt

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UnfoldSim.jl/src/sequentialSamplingModelSimulation.jl

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rt = (time_steps[end] + model.τ)/Δt

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UnfoldSim.jl/src/sequentialSamplingModelSimulation.jl

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end

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UnfoldSim.jl/test/component.jl

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           # Test UnfoldSim.simulate_component(rng, c::Drift_Component, design::AbstractDesign) 
        
           boundary = 1.0 
        
           model_parameter = UnfoldSim.create_kelly_parameters_dict(UnfoldSim.KellyModel(boundary=boundary)); 
        
           c = Drift_Component(simulate_component, 0:1/500:1.0, 1/500, KellyModel, model_parameter); 
        
           design_single = UnfoldSim.SingleSubjectDesign(conditions = Dict(:condition => [1])); 
        
           design_seq = UnfoldSim.SequenceDesign(design_single,"SCR_"); 
        
           result_traces = UnfoldSim.simulate_component(StableRNG(1),c,design_seq) 
        
           @test size(result_traces) == (501, 3) 
        
           @test any(result_traces .== 0) 
        
           @test any(result_traces .>= boundary)

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UnfoldSim.jl/test/component.jl

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           # Test UnfoldSim.simulate_component(rng, c::Drift_Component, design::AbstractDesign) 
        
           boundary = 1.0 
        
           model_parameter = UnfoldSim.create_kelly_parameters_dict(UnfoldSim.KellyModel(boundary=boundary)); 
        
           c = Drift_Component(simulate_component, 0:1/500:1.0, 1/500, KellyModel, model_parameter); 
        
           design_single = UnfoldSim.SingleSubjectDesign(conditions = Dict(:drift_rate => [0.5, 0.8], :condition => [1])); 
        
           design_seq = UnfoldSim.SequenceDesign(design_single,"SCR_"); 
        
           result_traces = UnfoldSim.simulate_component(StableRNG(1),c,design_seq)

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UnfoldSim.jl/test/component.jl

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           @test size(result_traces) == (501, 6) 
        
           @test any(result_traces .== 0) 
        
           @test any(result_traces .>= boundary)

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UnfoldSim.jl/test/component.jl

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           # Test calculate_response_times_for_ssm(rng, component::Drift_Component, design::AbstractDesign) 
        
           model_parameter = UnfoldSim.create_kelly_parameters_dict(UnfoldSim.KellyModel()); 
        
           c = Drift_Component(simulate_component, 0:1/500:1.0, 1/500, KellyModel, model_parameter); 
        
           design_single = UnfoldSim.SingleSubjectDesign(conditions = Dict(:drift_rate => [0.5, 0.8], :condition => [1])); 
        
           design_seq = UnfoldSim.SequenceDesign(design_single,"SCR_"); 
        
           sub_design = UnfoldSim.SubselectDesign(design_seq, 'C') 
        
           result_rts = UnfoldSim.calculate_response_times_for_ssm(StableRNG(1),c,sub_design) 
        
           @test size(result_rts) == (2,) 
        
           @test isapprox(result_rts, [399.6903067274333, 388.89617910657597], atol=1e-8)

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UnfoldSim.jl/test/component.jl

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           # Test get_model_parameter(rng, evt, d::Dict) 
        
           rng = StableRNG(1) 
        
           model_parameter = UnfoldSim.create_kelly_parameters_dict(UnfoldSim.KellyModel(drift_rate="drift_rate")); 
        
           c = Drift_Component(simulate_component, 0:1/500:1.0, 1/500, KellyModel, model_parameter); 
        
           drift_rates = [0.5, 0.8] 
        
           design_single = UnfoldSim.SingleSubjectDesign(conditions = Dict(:drift_rate => drift_rates, :condition => [1])); 
        
           events = UnfoldSim.generate_events(rng, design_single) 
        
           for (i, evt) in enumerate(eachrow(events))

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UnfoldSim.jl/test/component.jl

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end

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UnfoldSim.jl/test/onset.jl

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fs=500

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UnfoldSim.jl/test/onset.jl

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               Dict('S'=>UniformOnset(width=0,offset=80), 
        
                   'C'=>DriftOnset(), 
        
                   'R'=>UniformOnset(width=0,offset=120))) 
        
           model_parameter = UnfoldSim.create_kelly_parameters_dict(UnfoldSim.KellyModel(drift_rate="drift_rate")); 
        
           c = UnfoldSim.Drift_Component(simulate_component, 0:1/500:1.0, 1/500, KellyModel, model_parameter);

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UnfoldSim.jl/test/onset.jl

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           design_single = UnfoldSim.SingleSubjectDesign(conditions = Dict(:drift_rate => [0.5, 0.8], :condition => [1])); 
        
           design_seq = UnfoldSim.SequenceDesign(design_single,"SCR_");

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UnfoldSim.jl/test/onset.jl

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           simulation = UnfoldSim.Simulation(design_rep, components, sequence_onset, UnfoldSim.NoNoise())

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UnfoldSim.jl/test/onset.jl

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               Dict('S'=>UniformOnset(width=0,offset=80), 
        
                   'C'=>(DriftOnset(), UniformOnset(width=0, offset=140)), 
        
                   'R'=>UniformOnset(width=0,offset=120))) 
        
           simulation = UnfoldSim.Simulation(design_rep, components, sequence_onset, UnfoldSim.NoNoise())

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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Δt = 1/fs; # time step

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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time_vec = 0:Δt:tEnd; # time base

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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	km = KellyModel(event_onset=assert_event_onset, drift_rate=assert_drift_rate);

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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           result_rt, result_trace = UnfoldSim.KellyModel_simulate_cpp(rng, KellyModel(boundary=boundary), time_vec, Δt)

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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@test isapprox(result_rt, 399.6903067274333, atol=1e-8)

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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           result_sim_rt, result_sim_trace = UnfoldSim.SSM_Simulate(rng, KellyModel(), Δt, time_vec)

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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           model_parameter = UnfoldSim.create_kelly_parameters_dict(KellyModel(boundary=boundary)); 
        
           c = UnfoldSim.Drift_Component(simulate_component, time_vec, Δt, KellyModel, model_parameter); 
        
           design_single = UnfoldSim.SingleSubjectDesign(conditions = Dict(:drift_rate => [0.5, 0.8], :condition => [1])); 
        
           design_seq = UnfoldSim.SequenceDesign(design_single,"SCR_");

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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           result_rts, result_traces = UnfoldSim.trace_sequential_sampling_model(rng, c, design_seq)

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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           result_rt, result_trace = UnfoldSim.SSM_Simulate(deepcopy(rng), KellyModel(), Δt, time_vec)

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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@test isapprox(result_rt, 399.6903067274333, atol=1e-8)

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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@test isapprox(result_rt, 223.00000000000003, atol=1e-8)

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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@test isapprox(result_rt, 397.0, atol=1e-8)

src/onset.jl

Co-authored-by: Benedikt Ehinger <[email protected]>

github-actions

Remaining comments which cannot be posted as a review comment to avoid GitHub Rate Limit

JuliaFormatter

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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Δt = 1/fs; # time step

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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time_vec = 0:Δt:tEnd; # time base

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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	km = KellyModel(event_onset=assert_event_onset, drift_rate=assert_drift_rate);

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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           result_rt, result_trace = UnfoldSim.KellyModel_simulate_cpp(rng, KellyModel(boundary=boundary), time_vec, Δt)

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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@test isapprox(result_rt, 399.6903067274333, atol=1e-8)

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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           result_sim_rt, result_sim_trace = UnfoldSim.SSM_Simulate(rng, KellyModel(), Δt, time_vec)

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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           model_parameter = UnfoldSim.create_kelly_parameters_dict(KellyModel(boundary=boundary)); 
        
           c = UnfoldSim.Drift_Component(simulate_component, time_vec, Δt, KellyModel, model_parameter); 
        
           design_single = UnfoldSim.SingleSubjectDesign(conditions = Dict(:drift_rate => [0.5, 0.8], :condition => [1])); 
        
           design_seq = UnfoldSim.SequenceDesign(design_single,"SCR_");

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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           result_rts, result_traces = UnfoldSim.trace_sequential_sampling_model(rng, c, design_seq)

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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           result_rt, result_trace = UnfoldSim.SSM_Simulate(deepcopy(rng), KellyModel(), Δt, time_vec)

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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@test isapprox(result_rt, 399.6903067274333, atol=1e-8)

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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@test isapprox(result_rt, 223.00000000000003, atol=1e-8)

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UnfoldSim.jl/test/sequentialSamplingModelSimulation.jl

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@test isapprox(result_rt, 397.0, atol=1e-8)

src/onset.jl

Formatting Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

src/sequentialSamplingModelSimulation.jl

src/component.jl

src/sequentialSamplingModelSimulation.jl

test/component.jl

github-actions · 2025-02-26T13:42:04Z

docs/literate/HowTo/driftComponent.jl

+    data_epochs,
+);
+results = coeftable(m)
+plot_erp(results; mapping = (; color = :eventname))


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Suggested change

plot_erp(results; mapping = (; color = :eventname))

plot_erp(results; mapping = (; color = :eventname))

github-actions · 2025-02-26T13:42:04Z

docs/literate/HowTo/simulateDriftOverlap.jl

+    data,
+);
+results = coeftable(m)
+plot_erp(results; mapping = (; color = :eventname))


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Suggested change

plot_erp(results; mapping = (; color = :eventname))

plot_erp(results; mapping = (; color = :eventname))

github-actions · 2025-02-26T13:42:05Z

src/sequentialSamplingModelSimulation.jl

+```
+"""
+function SSM_Simulate(rng, model::KellyModel, sfreq, max_length)
+    Δt = 1/sfreq


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Suggested change

Δt = 1/sfreq

Δt = 1 / sfreq

github-actions · 2025-02-26T13:42:05Z

src/sequentialSamplingModelSimulation.jl

+        evidence = evidence[1:max_length]
+    end
+    return rt, evidence
+end


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Suggested change

end

end

github-actions · 2025-02-26T13:42:05Z

test/component.jl

+      # Test UnfoldSim.simulate_component(rng, c::DriftComponent, design::AbstractDesign)
+      boundary = 1.0
+      model_parameter = Dict(:boundary => boundary);
+      c = DriftComponent(500, 500, KellyModel, model_parameter);
+      design_single = UnfoldSim.SingleSubjectDesign(conditions = Dict(:condition => [1]));
+      design_seq = UnfoldSim.SequenceDesign(design_single,"SCR_");
+      result_traces = UnfoldSim.simulate_component(StableRNG(1),c,design_seq)
+
+      @test size(result_traces) == (501, 3)
+      @test any(result_traces .== 0)
+      @test any(result_traces .>= boundary)


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Suggested change

# Test UnfoldSim.simulate_component(rng, c::DriftComponent, design::AbstractDesign)

boundary = 1.0

model_parameter = Dict(:boundary => boundary);

c = DriftComponent(500, 500, KellyModel, model_parameter);

design_single = UnfoldSim.SingleSubjectDesign(conditions = Dict(:condition => [1]));

design_seq = UnfoldSim.SequenceDesign(design_single,"SCR_");

result_traces = UnfoldSim.simulate_component(StableRNG(1),c,design_seq)

@test size(result_traces) == (501, 3)

@test any(result_traces .== 0)

@test any(result_traces .>= boundary)

# Test UnfoldSim.simulate_component(rng, c::DriftComponent, design::AbstractDesign)

boundary = 1.0

model_parameter = Dict(:boundary => boundary)

c = DriftComponent(500, 500, KellyModel, model_parameter)

design_single = UnfoldSim.SingleSubjectDesign(conditions = Dict(:condition => [1]))

design_seq = UnfoldSim.SequenceDesign(design_single, "SCR_")

result_traces = UnfoldSim.simulate_component(StableRNG(1), c, design_seq)

github-actions · 2025-02-26T13:42:06Z

test/sequentialSamplingModelSimulation.jl

+        model_parameter = Dict(:boundary => boundary);
+        c = UnfoldSim.DriftComponent(
+            max_length,
+            fs,
+            KellyModel,
+            model_parameter,
+        )


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Suggested change

model_parameter = Dict(:boundary => boundary);

c = UnfoldSim.DriftComponent(

max_length,

fs,

KellyModel,

model_parameter,

)

model_parameter = Dict(:boundary => boundary)

c = UnfoldSim.DriftComponent(max_length, fs, KellyModel, model_parameter)

github-actions · 2025-02-26T13:42:06Z

test/sequentialSamplingModelSimulation.jl

+        )
+        design_seq = UnfoldSim.SequenceDesign(design_single, "SCR_")
+
+        result_rts, result_traces = UnfoldSim.trace_sequential_sampling_model(rng, c, design_seq)


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Suggested change

result_rts, result_traces = UnfoldSim.trace_sequential_sampling_model(rng, c, design_seq)

result_rts, result_traces =

UnfoldSim.trace_sequential_sampling_model(rng, c, design_seq)

github-actions · 2025-02-26T13:42:06Z

test/sequentialSamplingModelSimulation.jl

+    end
+
+    @testset "SSM_Simulate" begin
+        result_rt, result_trace = UnfoldSim.SSM_Simulate(deepcopy(rng), KellyModel(), fs, max_length)


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Suggested change

result_rt, result_trace = UnfoldSim.SSM_Simulate(deepcopy(rng), KellyModel(), fs, max_length)

result_rt, result_trace =

UnfoldSim.SSM_Simulate(deepcopy(rng), KellyModel(), fs, max_length)

github-actions · 2025-02-26T13:42:06Z

test/sequentialSamplingModelSimulation.jl

+        @test any(result_trace .== 0)
+        @test any(result_trace .>= boundary)
+
+        result_rt, result_trace = UnfoldSim.SSM_Simulate(deepcopy(rng), DDM(), fs, max_length)


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Suggested change

result_rt, result_trace = UnfoldSim.SSM_Simulate(deepcopy(rng), DDM(), fs, max_length)

result_rt, result_trace =

UnfoldSim.SSM_Simulate(deepcopy(rng), DDM(), fs, max_length)

github-actions · 2025-02-26T13:42:06Z

test/sequentialSamplingModelSimulation.jl

+        @test isapprox(result_rt, 223.00000000000003, atol = 1e-8)
+        @test any(result_trace .== 0)
+
+        result_rt, result_trace = UnfoldSim.SSM_Simulate(deepcopy(rng), LBA(), fs, max_length)


[JuliaFormatter] _{reported by reviewdog 🐶}

Suggested change

result_rt, result_trace = UnfoldSim.SSM_Simulate(deepcopy(rng), LBA(), fs, max_length)

result_rt, result_trace =

UnfoldSim.SSM_Simulate(deepcopy(rng), LBA(), fs, max_length)

github-actions · 2025-02-28T17:21:40Z

src/sequentialSamplingModelSimulation.jl

+        return new(drift_rate, event_onset, sensor_encoding_delay, accumulative_level_noise, boundary, motor_onset,
+            motor_delay, post_accumulation_duration, post_accumulation_duration_variability,
+            ramp_down_duration)


[JuliaFormatter] _{reported by reviewdog 🐶}

Suggested change

return new(drift_rate, event_onset, sensor_encoding_delay, accumulative_level_noise, boundary, motor_onset,

motor_delay, post_accumulation_duration, post_accumulation_duration_variability,

ramp_down_duration)

return new(

drift_rate,

event_onset,

sensor_encoding_delay,

accumulative_level_noise,

boundary,

motor_onset,

motor_delay,

post_accumulation_duration,

post_accumulation_duration_variability,

ramp_down_duration,

)

github-actions · 2025-02-28T17:21:40Z

src/sequentialSamplingModelSimulation.jl

+    rt = time_vec[dti] + model.motor_onset + (rand(rng) - 0.5) * model.motor_delay
+
+    # now make the CPP peak and go down linearly after a certain amount of post-dec accum time for this trial:
+    post_acc_duration = model.post_accumulation_duration .+ model.post_accumulation_duration_variability .* rand(rng);


[JuliaFormatter] _{reported by reviewdog 🐶}

Suggested change

post_acc_duration = model.post_accumulation_duration .+ model.post_accumulation_duration_variability .* rand(rng);

post_acc_duration =

model.post_accumulation_duration .+

model.post_accumulation_duration_variability .* rand(rng)

github-actions · 2025-02-28T17:21:40Z

src/sequentialSamplingModelSimulation.jl

+        tmp = cum_evidence[acc_stop_index] .- (1:(nT-acc_stop_index)) .*cum_evidence[acc_stop_index] .* (Δt ./ model.ramp_down_duration)
+        cum_evidence[(acc_stop_index+1):end] .= max.(Ref(0), tmp);


[JuliaFormatter] _{reported by reviewdog 🐶}

Suggested change

tmp = cum_evidence[acc_stop_index] .- (1:(nT-acc_stop_index)) .*cum_evidence[acc_stop_index] .* (Δt ./ model.ramp_down_duration)

cum_evidence[(acc_stop_index+1):end] .= max.(Ref(0), tmp);

tmp =

cum_evidence[acc_stop_index] .-

(1:(nT-acc_stop_index)) .* cum_evidence[acc_stop_index] .*

(Δt ./ model.ramp_down_duration)

cum_evidence[(acc_stop_index+1):end] .= max.(Ref(0), tmp)

Zooaal added 2 commits February 13, 2025 13:15

add: Sequential Sampling Model, Evidence Accumulation Simulation

ffc7094

update: major fixes

0324d96

Zooaal requested review from behinger and jschepers February 19, 2025 15:25

behinger changed the base branch from main to exponential-noise February 21, 2025 11:05

behinger marked this pull request as ready for review February 21, 2025 11:05

behinger changed the base branch from exponential-noise to fix#124 February 21, 2025 11:06

behinger reviewed Feb 21, 2025

View reviewed changes

Update src/component.jl

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Wording DocString Co-authored-by: Benedikt Ehinger <[email protected]>

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Feature sequential sampling models #139

Feature sequential sampling models #139

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	drift_rate::Union{Real, String} # drift rate
	event_onset::Union{Real, String} # onset(sensory evidence)
	sensor_encoding_delay::Union{Real, String} # var(sensory encoding delay)
	accumulative_level_noise::Union{Real, String} # accum level noise
	boundary::Union{Real, String} # boundaryary height
	motor_onset::Union{Real, String} # onset(motor)
	motor_delay::Union{Real, String} # var(motor)
	post_accumulation_duration_mean::Union{Real, String} # mean(post decision)
	post_accumulation_duration_variability::Union{Real, String} # var(post decision)
	CPPrampdownDur::Union{Real, String} # CPPrampdown duration

	return new(drift_rate, event_onset, sensor_encoding_delay, accumulative_level_noise, boundary, motor_onset,
	motor_delay, post_accumulation_duration_mean, post_accumulation_duration_variability,
	CPPrampdownDur)

	return Dict(
	name => getfield(model, name) for name in fieldnames(typeof(model))
	)

	evidence = zeros(length(time_vec));
	evidence[time_vec .>= (model.event_onset+(rand(rng) -.5)*model.sensor_encoding_delay)] .= 1;
	startAccT = time_vec[findfirst(evidence .== 1)];

	noise = vcat(zeros(
	sum(time_vec .< startAccT)),
	randn(rng,sum(time_vec .>= startAccT)) .* model.accumulative_level_noise .*sqrt(Δt));
	ev=evidence;
	ev[time_vec .< startAccT] .= 0; # set to zero all of the evidence before accT
	cum_evidence = cumsum(ev .* model.drift_rate .* Δt .+ noise,dims=1); # This is the cumulative differential evidence, just as in a 1d DDM.

	cum_evidence = abs.(cum_evidence);
	dti = findfirst(cum_evidence .> model.boundary); # finding the sample point of threshold crossing of each, then will pick the earlier as the winner

	cum_evidence = abs.(cum_evidence);
	if acc_stop_index<length(time_vec)

	tmp = cum_evidence[acc_stop_index] .- (1:(nT-acc_stop_index)) .cum_evidence[acc_stop_index] . (Δt ./ model.CPPrampdownDur)
	cum_evidence[(acc_stop_index+1):end] .= max.(Ref(0), tmp);

	# Test UnfoldSim.simulate_component(rng, c::Drift_Component, design::AbstractDesign)
	boundary = 1.0
	model_parameter = UnfoldSim.create_kelly_parameters_dict(UnfoldSim.KellyModel(boundary=boundary));
	c = Drift_Component(simulate_component, 0:1/500:1.0, 1/500, KellyModel, model_parameter);
	design_single = UnfoldSim.SingleSubjectDesign(conditions = Dict(:condition => [1]));
	design_seq = UnfoldSim.SequenceDesign(design_single,"SCR_");
	result_traces = UnfoldSim.simulate_component(StableRNG(1),c,design_seq)

	@test size(result_traces) == (501, 3)
	@test any(result_traces .== 0)
	@test any(result_traces .>= boundary)

	@test size(result_traces) == (501, 6)
	@test any(result_traces .== 0)
	@test any(result_traces .>= boundary)

	# Test calculate_response_times_for_ssm(rng, component::Drift_Component, design::AbstractDesign)
	model_parameter = UnfoldSim.create_kelly_parameters_dict(UnfoldSim.KellyModel());
	c = Drift_Component(simulate_component, 0:1/500:1.0, 1/500, KellyModel, model_parameter);
	design_single = UnfoldSim.SingleSubjectDesign(conditions = Dict(:drift_rate => [0.5, 0.8], :condition => [1]));
	design_seq = UnfoldSim.SequenceDesign(design_single,"SCR_");
	sub_design = UnfoldSim.SubselectDesign(design_seq, 'C')
	result_rts = UnfoldSim.calculate_response_times_for_ssm(StableRNG(1),c,sub_design)
	@test size(result_rts) == (2,)
	@test isapprox(result_rts, [399.6903067274333, 388.89617910657597], atol=1e-8)

	# Test get_model_parameter(rng, evt, d::Dict)
	rng = StableRNG(1)
	model_parameter = UnfoldSim.create_kelly_parameters_dict(UnfoldSim.KellyModel(drift_rate="drift_rate"));
	c = Drift_Component(simulate_component, 0:1/500:1.0, 1/500, KellyModel, model_parameter);
	drift_rates = [0.5, 0.8]
	design_single = UnfoldSim.SingleSubjectDesign(conditions = Dict(:drift_rate => drift_rates, :condition => [1]));
	events = UnfoldSim.generate_events(rng, design_single)
	for (i, evt) in enumerate(eachrow(events))

	Dict('S'=>UniformOnset(width=0,offset=80),
	'C'=>DriftOnset(),
	'R'=>UniformOnset(width=0,offset=120)))
	model_parameter = UnfoldSim.create_kelly_parameters_dict(UnfoldSim.KellyModel(drift_rate="drift_rate"));
	c = UnfoldSim.Drift_Component(simulate_component, 0:1/500:1.0, 1/500, KellyModel, model_parameter);

	Dict('S'=>UniformOnset(width=0,offset=80),
	'C'=>(DriftOnset(), UniformOnset(width=0, offset=140)),
	'R'=>UniformOnset(width=0,offset=120)))
	simulation = UnfoldSim.Simulation(design_rep, components, sequence_onset, UnfoldSim.NoNoise())

	plot_erp(results; mapping = (; color = :eventname))
	plot_erp(results; mapping = (; color = :eventname))

	result_rts, result_traces = UnfoldSim.trace_sequential_sampling_model(rng, c, design_seq)
	result_rts, result_traces =
	UnfoldSim.trace_sequential_sampling_model(rng, c, design_seq)

	result_rt, result_trace = UnfoldSim.SSM_Simulate(deepcopy(rng), KellyModel(), fs, max_length)
	result_rt, result_trace =
	UnfoldSim.SSM_Simulate(deepcopy(rng), KellyModel(), fs, max_length)

	result_rt, result_trace = UnfoldSim.SSM_Simulate(deepcopy(rng), DDM(), fs, max_length)
	result_rt, result_trace =
	UnfoldSim.SSM_Simulate(deepcopy(rng), DDM(), fs, max_length)

	result_rt, result_trace = UnfoldSim.SSM_Simulate(deepcopy(rng), LBA(), fs, max_length)
	result_rt, result_trace =
	UnfoldSim.SSM_Simulate(deepcopy(rng), LBA(), fs, max_length)

Feature sequential sampling models #139

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