import psyneulink as pnl
comp = pnl.Composition(name='comp')
fn = pnl.IntegratorMechanism(name='fn',
function=pnl.FitzHughNagumoIntegrator(
name='FitzHughNagumoIntegrator Function-0',
d_v=1,
initial_v=-1,
initializer=[[0]],
default_variable=[[0]]))
im = pnl.IntegratorMechanism(name='im',
function=pnl.AdaptiveIntegrator(
initializer=[[0]],
rate=0.5,
default_variable=[[0]]))
comp.add_node(fn)
comp.add_node(im)
comp.add_projection(projection=pnl.MappingProjection(
name='MappingProjection from fn[OutputPort-0] to im[InputPort-0]',
function=pnl.LinearMatrix(matrix=[[1.0]], default_variable=[-1.0])),
sender=fn,
receiver=im)
comp.scheduler.add_condition(fn, pnl.Always())
comp.scheduler.add_condition(
im, pnl.All(pnl.EveryNCalls(fn, 20.0), pnl.AfterNCalls(fn, 1600.0)))
comp.scheduler.termination_conds = {
variable=1.0,
intensity_cost_function=pnl.Exponential(rate=0.8046),
allocation_samples=signalSearchRange)
objective_mech = pnl.ObjectiveMechanism(
function=pnl.LinearCombination(operation=pnl.PRODUCT),
monitor=[
reward, (Decision.output_ports[pnl.PROBABILITY_UPPER_THRESHOLD], 1, -1)
])
# Model Based OCM (formerly controller)
evc_gratton.add_controller(controller=pnl.OptimizationControlMechanism(
agent_rep=evc_gratton,
features=[
target_stim.input_port, flanker_stim.input_port, reward.input_port
],
feature_function=pnl.AdaptiveIntegrator(rate=1.0),
objective_mechanism=objective_mech,
function=pnl.GridSearch(),
control_signals=[target_rep_control_signal, flanker_rep_control_signal]))
evc_gratton.show_graph(show_controller=True)
evc_gratton.enable_controller = True
targetFeatures = [1, 1, 1]
flankerFeatures = [1, -1, 1]
rewardValues = [100, 100, 100]
stim_list_dict = {
target_stim: targetFeatures,
flanker_stim: flankerFeatures,
def get_stroop_model(unit_noise_std=.01, dec_noise_std=.1):
# model params
integration_rate = 1
hidden_func = pnl.Logistic(gain=1.0, x_0=4.0)
# input layer, color and word
reward = pnl.TransferMechanism(name='reward')
punish = pnl.TransferMechanism(name='punish')
inp_clr = pnl.TransferMechanism(
size=N_UNITS, function=pnl.Linear, name='COLOR INPUT'
)
inp_wrd = pnl.TransferMechanism(
size=N_UNITS, function=pnl.Linear, name='WORD INPUT'
)
# task layer, represent the task instruction; color naming / word reading
inp_task = pnl.TransferMechanism(
size=N_UNITS, function=pnl.Linear, name='TASK'
)
# hidden layer for color and word
hid_clr = pnl.TransferMechanism(
size=N_UNITS,
function=hidden_func,
integrator_mode=True,
integration_rate=integration_rate,
# noise=pnl.NormalDist(standard_deviation=unit_noise_std).function,
noise=pnl.NormalDist(standard_deviation=unit_noise_std),
name='COLORS HIDDEN'
)
hid_wrd = pnl.TransferMechanism(
size=N_UNITS,
function=hidden_func,
integrator_mode=True,
integration_rate=integration_rate,
# noise=pnl.NormalDist(standard_deviation=unit_noise_std).function,
noise=pnl.NormalDist(standard_deviation=unit_noise_std),
name='WORDS HIDDEN'
)
# output layer
output = pnl.TransferMechanism(
size=N_UNITS,
function=pnl.Logistic,
integrator_mode=True,
integration_rate=integration_rate,
# noise=pnl.NormalDist(standard_deviation=unit_noise_std).function,
noise=pnl.NormalDist(standard_deviation=unit_noise_std),
name='OUTPUT'
)
# decision layer, some accumulator
signalSearchRange = pnl.SampleSpec(start=0.05, stop=5, step=0.05)
decision = pnl.DDM(name='Decision',
input_format=pnl.ARRAY,
function=pnl.DriftDiffusionAnalytical(drift_rate=1,
threshold =1,
noise=1,
starting_point=0,
t0=0.35),
output_ports=[pnl.RESPONSE_TIME,
pnl.PROBABILITY_UPPER_THRESHOLD,
pnl.PROBABILITY_LOWER_THRESHOLD]
)
driftrate_control_signal = pnl.ControlSignal(projections=[(pnl.SLOPE, inp_clr)],
variable=1.0,
intensity_cost_function=pnl.Exponential(rate=1),#pnl.Exponential(rate=0.8),#pnl.Exponential(rate=1),
allocation_samples=signalSearchRange)
threshold_control_signal = pnl.ControlSignal(projections=[(pnl.THRESHOLD, decision)],
variable=1.0,
intensity_cost_function=pnl.Linear(slope=0),
allocation_samples=signalSearchRange)
reward_rate = pnl.ObjectiveMechanism(function=pnl.LinearCombination(operation=pnl.PRODUCT,
exponents=[[1],[1],[-1]]),
monitor=[reward,
decision.output_ports[pnl.PROBABILITY_UPPER_THRESHOLD],
decision.output_ports[pnl.RESPONSE_TIME]])
punish_rate = pnl.ObjectiveMechanism(function=pnl.LinearCombination(operation=pnl.PRODUCT,
exponents=[[1],[1],[-1]]),
monitor=[punish,
decision.output_ports[pnl.PROBABILITY_LOWER_THRESHOLD],
decision.output_ports[pnl.RESPONSE_TIME]])
objective_mech = pnl.ObjectiveMechanism(function=pnl.LinearCombination(operation=pnl.SUM,
weights=[[1],[-1]]),
monitor=[reward_rate, punish_rate])
# objective_mech = pnl.ObjectiveMechanism(function=object_function,
# monitor=[reward,
# punish,
# decision.output_ports[pnl.PROBABILITY_UPPER_THRESHOLD],
# decision.output_ports[pnl.PROBABILITY_LOWER_THRESHOLD],
# (decision.output_ports[pnl.RESPONSE_TIME])])
# PROJECTIONS, weights copied from cohen et al (1990)
wts_clr_ih = pnl.MappingProjection(
matrix=[[2.2, -2.2], [-2.2, 2.2]], name='COLOR INPUT TO HIDDEN')
wts_wrd_ih = pnl.MappingProjection(
matrix=[[2.6, -2.6], [-2.6, 2.6]], name='WORD INPUT TO HIDDEN')
wts_clr_ho = pnl.MappingProjection(
matrix=[[1.3, -1.3], [-1.3, 1.3]], name='COLOR HIDDEN TO OUTPUT')
wts_wrd_ho = pnl.MappingProjection(
matrix=[[2.5, -2.5], [-2.5, 2.5]], name='WORD HIDDEN TO OUTPUT')
wts_tc = pnl.MappingProjection(
matrix=[[4.0, 4.0], [0, 0]], name='COLOR NAMING')
wts_tw = pnl.MappingProjection(
matrix=[[0, 0], [4.0, 4.0]], name='WORD READING')
# build the model
model = pnl.Composition(name='STROOP model')
model.add_node(decision, required_roles=pnl.NodeRole.OUTPUT)
model.add_node(reward, required_roles=pnl.NodeRole.OUTPUT)
model.add_node(punish, required_roles=pnl.NodeRole.OUTPUT)
model.add_linear_processing_pathway([inp_clr, wts_clr_ih, hid_clr])
model.add_linear_processing_pathway([inp_wrd, wts_wrd_ih, hid_wrd])
model.add_linear_processing_pathway([hid_clr, wts_clr_ho, output])
model.add_linear_processing_pathway([hid_wrd, wts_wrd_ho, output])
model.add_linear_processing_pathway([inp_task, wts_tc, hid_clr])
model.add_linear_processing_pathway([inp_task, wts_tw, hid_wrd])
model.add_linear_processing_pathway([output, pnl.IDENTITY_MATRIX, decision]) # 3/15/20
# model.add_linear_processing_pathway([output, [[1,-1]], (decision, pnl.NodeRole.OUTPUT)]) # 3/15/20
# model.add_linear_processing_pathway([output, [[1],[-1]], decision]) # 3/15/20
model.add_nodes([reward_rate, punish_rate])
controller = pnl.OptimizationControlMechanism(agent_rep=model,
features=[inp_clr.input_port,
inp_wrd.input_port,
inp_task.input_port,
reward.input_port,
punish.input_port],
feature_function=pnl.AdaptiveIntegrator(rate=0.1),
objective_mechanism=objective_mech,
function=pnl.GridSearch(),
control_signals=[driftrate_control_signal,
threshold_control_signal])
model.add_controller(controller=controller)
# collect the node handles
nodes = [inp_clr, inp_wrd, inp_task, hid_clr, hid_wrd, output, decision, reward, punish,controller]
metadata = [integration_rate, dec_noise_std, unit_noise_std]
return model, nodes, metadata
inputLayer = pnl.TransferMechanism( #default_variable=[[0.0, 0.0]],
size=2,
function=pnl.Linear(slope=1, intercept=0),
output_ports=[pnl.RESULT],
name='Input')
inputLayer.set_log_conditions([pnl.RESULT])
# Recurrent Transfer Mechanism that models the recurrence in the activation between the two stimulus and action
# dimensions. Positive self excitation and negative opposite inhibition with an integrator rate = tau
# Modulated variable in simulations is the GAIN variable of this mechanism
activation = pnl.RecurrentTransferMechanism(
default_variable=[[0.0, 0.0]],
function=pnl.Logistic(gain=1.0),
matrix=[[1.0, -1.0], [-1.0, 1.0]],
integrator_mode=True,
integrator_function=pnl.AdaptiveIntegrator(rate=(tau)),
initial_value=np.array([[0.0, 0.0]]),
output_ports=[pnl.RESULT],
name='Activity')
activation.set_log_conditions([pnl.RESULT, "mod_gain"])
stimulusInfo = pnl.TransferMechanism(default_variable=[[0.0, 0.0]],
size=2,
function=pnl.Linear(slope=1, intercept=0),
output_ports=[pnl.RESULT],
name="Stimulus Info")
stimulusInfo.set_log_conditions([pnl.RESULT])
controlledElement = pnl.TransferMechanism(
def test_stability_flexibility_susan_and_sebastian(self):
# computeAccuracy(trialInformation)
# Inputs: trialInformation[0, 1, 2, 3]
# trialInformation[0] - Task Dimension : [0, 1] or [1, 0]
# trialInformation[1] - Stimulus Dimension: Congruent {[1, 1] or [-1, -1]} // Incongruent {[-1, 1] or [1, -1]}
# trialInformation[2] - Upper Threshold: Probability of DDM choosing upper bound
# trialInformation[3] - Lower Threshold: Probability of DDM choosing lower bound
def computeAccuracy(trialInformation):
# Unload contents of trialInformation
# Origin Node Inputs
taskInputs = trialInformation[0]
stimulusInputs = trialInformation[1]
# DDM Outputs
upperThreshold = trialInformation[2]
lowerThreshold = trialInformation[3]
# Keep Track of Accuracy
accuracy = []
# Beginning of Accuracy Calculation
colorTrial = (taskInputs[0] == 1)
motionTrial = (taskInputs[1] == 1)
# Based on the task dimension information, decide which response is "correct"
# Obtain accuracy probability from DDM thresholds in "correct" direction
if colorTrial:
if stimulusInputs[0] == 1:
accuracy.append(upperThreshold)
elif stimulusInputs[0] == -1:
accuracy.append(lowerThreshold)
if motionTrial:
if stimulusInputs[1] == 1:
accuracy.append(upperThreshold)
elif stimulusInputs[1] == -1:
accuracy.append(lowerThreshold)
# Accounts for initialization runs that have no variable input
if len(accuracy) == 0:
accuracy = [0]
# print("Accuracy: ", accuracy[0])
# print()
return [accuracy]
# BEGIN: Composition Construction
# Constants as defined in Musslick et al. 2018
tau = 0.9 # Time Constant
DRIFT = 1 # Drift Rate
STARTING_POINT = 0.0 # Starting Point
THRESHOLD = 0.0475 # Threshold
NOISE = 0.04 # Noise
T0 = 0.2 # T0
# Task Layer: [Color, Motion] {0, 1} Mutually Exclusive
# Origin Node
taskLayer = pnl.TransferMechanism(default_variable=[[0.0, 0.0]],
size=2,
function=pnl.Linear(slope=1,
intercept=0),
output_states=[pnl.RESULT],
name='Task Input [I1, I2]')
# Stimulus Layer: [Color Stimulus, Motion Stimulus]
# Origin Node
stimulusInfo = pnl.TransferMechanism(default_variable=[[0.0, 0.0]],
size=2,
function=pnl.Linear(slope=1,
intercept=0),
output_states=[pnl.RESULT],
name="Stimulus Input [S1, S2]")
# Activation Layer: [Color Activation, Motion Activation]
# Recurrent: Self Excitation, Mutual Inhibition
# Controlled: Gain Parameter
activation = pnl.RecurrentTransferMechanism(
default_variable=[[0.0, 0.0]],
function=pnl.Logistic(gain=1.0),
matrix=[[1.0, -1.0], [-1.0, 1.0]],
integrator_mode=True,
integrator_function=pnl.AdaptiveIntegrator(rate=(tau)),
initial_value=np.array([[0.0, 0.0]]),
output_states=[pnl.RESULT],
name='Task Activations [Act 1, Act 2]')
# Hadamard product of Activation and Stimulus Information
nonAutomaticComponent = pnl.TransferMechanism(
default_variable=[[0.0, 0.0]],
size=2,
function=pnl.Linear(slope=1, intercept=0),
input_states=pnl.InputState(combine=pnl.PRODUCT),
output_states=[pnl.RESULT],
name='Non-Automatic Component [S1*Activity1, S2*Activity2]')
# Summation of nonAutomatic and Automatic Components
ddmCombination = pnl.TransferMechanism(
size=1,
function=pnl.Linear(slope=1, intercept=0),
input_states=pnl.InputState(combine=pnl.SUM),
output_states=[pnl.RESULT],
name="Drift = (S1 + S2) + (S1*Activity1 + S2*Activity2)")
decisionMaker = pnl.DDM(function=pnl.DriftDiffusionAnalytical(
drift_rate=DRIFT,
starting_point=STARTING_POINT,
threshold=THRESHOLD,
noise=NOISE,
t0=T0),
output_states=[
pnl.DECISION_VARIABLE, pnl.RESPONSE_TIME,
pnl.PROBABILITY_UPPER_THRESHOLD,
pnl.PROBABILITY_LOWER_THRESHOLD
],
name='DDM')
taskLayer.set_log_conditions([pnl.RESULT])
stimulusInfo.set_log_conditions([pnl.RESULT])
activation.set_log_conditions([pnl.RESULT, "mod_gain"])
nonAutomaticComponent.set_log_conditions([pnl.RESULT])
ddmCombination.set_log_conditions([pnl.RESULT])
decisionMaker.set_log_conditions([
pnl.PROBABILITY_UPPER_THRESHOLD, pnl.PROBABILITY_LOWER_THRESHOLD,
pnl.DECISION_VARIABLE, pnl.RESPONSE_TIME
])
# Composition Creation
stabilityFlexibility = pnl.Composition(controller_mode=pnl.BEFORE)
# Node Creation
stabilityFlexibility.add_node(taskLayer)
stabilityFlexibility.add_node(activation)
stabilityFlexibility.add_node(nonAutomaticComponent)
stabilityFlexibility.add_node(stimulusInfo)
stabilityFlexibility.add_node(ddmCombination)
stabilityFlexibility.add_node(decisionMaker)
# Projection Creation
stabilityFlexibility.add_projection(sender=taskLayer,
receiver=activation)
stabilityFlexibility.add_projection(sender=activation,
receiver=nonAutomaticComponent)
stabilityFlexibility.add_projection(sender=stimulusInfo,
receiver=nonAutomaticComponent)
stabilityFlexibility.add_projection(sender=stimulusInfo,
receiver=ddmCombination)
stabilityFlexibility.add_projection(sender=nonAutomaticComponent,
receiver=ddmCombination)
stabilityFlexibility.add_projection(sender=ddmCombination,
receiver=decisionMaker)
# Beginning of Controller
# Grid Search Range
searchRange = pnl.SampleSpec(start=1.0, stop=1.9, num=10)
# Modulate the GAIN parameter from activation layer
# Initalize cost function as 0
signal = pnl.ControlSignal(
projections=[(pnl.GAIN, activation)],
function=pnl.Linear,
variable=1.0,
intensity_cost_function=pnl.Linear(slope=0.0),
allocation_samples=searchRange)
# Use the computeAccuracy function to obtain selection values
# Pass in 4 arguments whenever computeRewardRate is called
objectiveMechanism = pnl.ObjectiveMechanism(
monitor=[
taskLayer, stimulusInfo,
(pnl.PROBABILITY_UPPER_THRESHOLD, decisionMaker),
(pnl.PROBABILITY_LOWER_THRESHOLD, decisionMaker)
],
function=computeAccuracy,
name="Controller Objective Mechanism")
# Sets trial history for simulations over specified signal search parameters
metaController = pnl.OptimizationControlMechanism(
agent_rep=stabilityFlexibility,
features=[taskLayer.input_state, stimulusInfo.input_state],
feature_function=pnl.Buffer(history=10),
name="Controller",
objective_mechanism=objectiveMechanism,
function=pnl.GridSearch(),
control_signals=[signal])
stabilityFlexibility.add_controller(metaController)
stabilityFlexibility.enable_controller = True
# stabilityFlexibility.model_based_optimizer_mode = pnl.BEFORE
for i in range(1, len(stabilityFlexibility.controller.input_states)):
stabilityFlexibility.controller.input_states[
i].function.reinitialize()
# Origin Node Inputs
taskTrain = [[1, 0], [0, 1], [1, 0], [0, 1]]
stimulusTrain = [[1, -1], [-1, 1], [1, -1], [-1, 1]]
inputs = {taskLayer: taskTrain, stimulusInfo: stimulusTrain}
stabilityFlexibility.run(inputs)
output_ports=[
pnl.DECISION_VARIABLE, pnl.RESPONSE_TIME,
pnl.PROBABILITY_UPPER_THRESHOLD
],
name='Decision',
)
comp = pnl.Composition(name="evc")
comp.add_node(Reward, required_roles=[pnl.NodeRole.TERMINAL])
comp.add_node(Decision, required_roles=[pnl.NodeRole.TERMINAL])
task_execution_pathway = [Input, pnl.IDENTITY_MATRIX, Decision]
comp.add_linear_processing_pathway(task_execution_pathway)
ocm = pnl.OptimizationControlMechanism(
state_features=[Input, Reward],
state_feature_function=pnl.AdaptiveIntegrator(rate=0.5),
agent_rep=comp,
# function=pnl.GaussianProcessOptimization,
function=pnl.GridSearch,
control_signals=[("drift_rate", Decision), ("threshold", Decision)],
objective_mechanism=pnl.ObjectiveMechanism(monitor_for_control=[
Reward, Decision.PROBABILITY_UPPER_THRESHOLD,
(Decision.RESPONSE_TIME, -1, 1)
]))
comp.add_controller(controller=ocm)
comp.enable_controller = True
# Stimuli
comp._analyze_graph()
def test_evc_gratton(self):
# Stimulus Mechanisms
target_stim = pnl.TransferMechanism(name='Target Stimulus',
function=pnl.Linear(slope=0.3324))
flanker_stim = pnl.TransferMechanism(
name='Flanker Stimulus', function=pnl.Linear(slope=0.3545221843))
# Processing Mechanisms (Control)
Target_Rep = pnl.TransferMechanism(name='Target Representation')
Flanker_Rep = pnl.TransferMechanism(name='Flanker Representation')
# Processing Mechanism (Automatic)
Automatic_Component = pnl.TransferMechanism(name='Automatic Component')
# Decision Mechanism
Decision = pnl.DDM(name='Decision',
function=pnl.DriftDiffusionAnalytical(
drift_rate=(1.0),
threshold=(0.2645),
noise=(0.5),
starting_point=(0),
t0=0.15),
output_states=[
pnl.DECISION_VARIABLE, pnl.RESPONSE_TIME,
pnl.PROBABILITY_UPPER_THRESHOLD
])
# Outcome Mechanism
reward = pnl.TransferMechanism(name='reward')
# Pathways
target_control_pathway = [target_stim, Target_Rep, Decision]
flanker_control_pathway = [flanker_stim, Flanker_Rep, Decision]
target_automatic_pathway = [target_stim, Automatic_Component, Decision]
flanker_automatic_pathway = [
flanker_stim, Automatic_Component, Decision
]
pathways = [
target_control_pathway, flanker_control_pathway,
target_automatic_pathway, flanker_automatic_pathway
]
# Composition
evc_gratton = pnl.Composition(name="EVCGratton")
evc_gratton.add_node(Decision, required_roles=pnl.NodeRole.OUTPUT)
for path in pathways:
evc_gratton.add_linear_processing_pathway(path)
evc_gratton.add_node(reward, required_roles=pnl.NodeRole.OUTPUT)
# Control Signals
signalSearchRange = pnl.SampleSpec(start=1.0, stop=1.8, step=0.2)
target_rep_control_signal = pnl.ControlSignal(
projections=[(pnl.SLOPE, Target_Rep)],
function=pnl.Linear,
variable=1.0,
intensity_cost_function=pnl.Exponential(rate=0.8046),
allocation_samples=signalSearchRange)
flanker_rep_control_signal = pnl.ControlSignal(
projections=[(pnl.SLOPE, Flanker_Rep)],
function=pnl.Linear,
variable=1.0,
intensity_cost_function=pnl.Exponential(rate=0.8046),
allocation_samples=signalSearchRange)
objective_mech = pnl.ObjectiveMechanism(
function=pnl.LinearCombination(operation=pnl.PRODUCT),
monitor=[
reward,
(Decision.output_states[pnl.PROBABILITY_UPPER_THRESHOLD], 1,
-1)
])
# Model Based OCM (formerly controller)
evc_gratton.add_controller(controller=pnl.OptimizationControlMechanism(
agent_rep=evc_gratton,
features=[
target_stim.input_state, flanker_stim.input_state,
reward.input_state
],
feature_function=pnl.AdaptiveIntegrator(rate=1.0),
objective_mechanism=objective_mech,
function=pnl.GridSearch(),
control_signals=[
target_rep_control_signal, flanker_rep_control_signal
]))
evc_gratton.enable_controller = True
targetFeatures = [1, 1, 1]
flankerFeatures = [1, -1, 1]
rewardValues = [100, 100, 100]
stim_list_dict = {
target_stim: targetFeatures,
flanker_stim: flankerFeatures,
reward: rewardValues
}
evc_gratton.run(inputs=stim_list_dict)
expected_results_array = [[[0.32257752863413636], [0.9481940753514433],
[100.]],
[[0.42963678062444666],
[0.47661180945923376], [100.]],
[[0.300291026852769], [0.97089165101931],
[100.]]]
expected_sim_results_array = [
[[0.32257753], [0.94819408], [100.]],
[[0.31663196], [0.95508757], [100.]],
[[0.31093566], [0.96110142], [100.]],
[[0.30548947], [0.96633839], [100.]],
[[0.30029103], [0.97089165], [100.]],
[[0.3169957], [0.95468427], [100.]],
[[0.31128378], [0.9607499], [100.]],
[[0.30582202], [0.96603252], [100.]],
[[0.30060824], [0.9706259], [100.]],
[[0.29563774], [0.97461444], [100.]],
[[0.31163288], [0.96039533], [100.]],
[[0.30615555], [0.96572397], [100.]],
[[0.30092641], [0.97035779], [100.]],
[[0.2959409], [0.97438178], [100.]],
[[0.29119255], [0.97787196], [100.]],
[[0.30649004], [0.96541272], [100.]],
[[0.30124552], [0.97008732], [100.]],
[[0.29624499], [0.97414704], [100.]],
[[0.29148205], [0.97766847], [100.]],
[[0.28694892], [0.98071974], [100.]],
[[0.30156558], [0.96981445], [100.]],
[[0.29654999], [0.97391021], [100.]],
[[0.29177245], [0.97746315], [100.]],
[[0.28722523], [0.98054192], [100.]],
[[0.28289958], [0.98320731], [100.]],
[[0.42963678], [0.47661181], [100.]],
[[0.42846471], [0.43938586], [100.]],
[[0.42628176], [0.40282965], [100.]],
[[0.42314468], [0.36732207], [100.]],
[[0.41913221], [0.333198], [100.]],
[[0.42978939], [0.51176048], [100.]],
[[0.42959394], [0.47427693], [100.]],
[[0.4283576], [0.43708106], [100.]],
[[0.4261132], [0.40057958], [100.]],
[[0.422919], [0.36514906], [100.]],
[[0.42902209], [0.54679323], [100.]],
[[0.42980788], [0.50942101], [100.]],
[[0.42954704], [0.47194318], [100.]],
[[0.42824656], [0.43477897], [100.]],
[[0.42594094], [0.3983337], [100.]],
[[0.42735293], [0.58136855], [100.]],
[[0.42910149], [0.54447221], [100.]],
[[0.42982229], [0.50708112], [100.]],
[[0.42949608], [0.46961065], [100.]],
[[0.42813159], [0.43247968], [100.]],
[[0.42482049], [0.61516258], [100.]],
[[0.42749136], [0.57908829], [100.]],
[[0.42917687], [0.54214925], [100.]],
[[0.42983261], [0.50474093], [100.]],
[[0.42944107], [0.46727945], [100.]],
[[0.32257753], [0.94819408], [100.]],
[[0.31663196], [0.95508757], [100.]],
[[0.31093566], [0.96110142], [100.]],
[[0.30548947], [0.96633839], [100.]],
[[0.30029103], [0.97089165], [100.]],
[[0.3169957], [0.95468427], [100.]],
[[0.31128378], [0.9607499], [100.]],
[[0.30582202], [0.96603252], [100.]],
[[0.30060824], [0.9706259], [100.]],
[[0.29563774], [0.97461444], [100.]],
[[0.31163288], [0.96039533], [100.]],
[[0.30615555], [0.96572397], [100.]],
[[0.30092641], [0.97035779], [100.]],
[[0.2959409], [0.97438178], [100.]],
[[0.29119255], [0.97787196], [100.]],
[[0.30649004], [0.96541272], [100.]],
[[0.30124552], [0.97008732], [100.]],
[[0.29624499], [0.97414704], [100.]],
[[0.29148205], [0.97766847], [100.]],
[[0.28694892], [0.98071974], [100.]],
[[0.30156558], [0.96981445], [100.]],
[[0.29654999], [0.97391021], [100.]],
[[0.29177245], [0.97746315], [100.]],
[[0.28722523], [0.98054192], [100.]],
[[0.28289958], [0.98320731], [100.]],
]
for trial in range(len(evc_gratton.results)):
assert np.allclose(
expected_results_array[trial],
# Note: Skip decision variable OutputState
evc_gratton.results[trial][1:])
for simulation in range(len(evc_gratton.simulation_results)):
assert np.allclose(
expected_sim_results_array[simulation],
# Note: Skip decision variable OutputState
evc_gratton.simulation_results[simulation][1:])
def test_evc(self):
# Mechanisms
Input = pnl.TransferMechanism(name='Input')
reward = pnl.TransferMechanism(
output_states=[pnl.RESULT, pnl.OUTPUT_MEAN, pnl.OUTPUT_VARIANCE],
name='reward')
Decision = pnl.DDM(function=pnl.DriftDiffusionAnalytical(
drift_rate=(1.0,
pnl.ControlProjection(function=pnl.Linear,
control_signal_params={
pnl.ALLOCATION_SAMPLES:
np.arange(0.1, 1.01, 0.3)
})),
threshold=(1.0,
pnl.ControlProjection(function=pnl.Linear,
control_signal_params={
pnl.ALLOCATION_SAMPLES:
np.arange(0.1, 1.01, 0.3)
})),
noise=0.5,
starting_point=0,
t0=0.45),
output_states=[
pnl.DECISION_VARIABLE, pnl.RESPONSE_TIME,
pnl.PROBABILITY_UPPER_THRESHOLD
],
name='Decision')
comp = pnl.Composition(name="evc")
comp.add_node(reward, required_roles=[pnl.NodeRole.OUTPUT])
comp.add_node(Decision, required_roles=[pnl.NodeRole.OUTPUT])
task_execution_pathway = [Input, pnl.IDENTITY_MATRIX, Decision]
comp.add_linear_processing_pathway(task_execution_pathway)
comp.add_controller(controller=pnl.OptimizationControlMechanism(
agent_rep=comp,
features=[Input.input_state, reward.input_state],
feature_function=pnl.AdaptiveIntegrator(rate=0.5),
objective_mechanism=pnl.ObjectiveMechanism(
function=pnl.LinearCombination(operation=pnl.PRODUCT),
monitor=[
reward, Decision.output_states[
pnl.PROBABILITY_UPPER_THRESHOLD],
(Decision.output_states[pnl.RESPONSE_TIME], -1, 1)
]),
function=pnl.GridSearch(),
control_signals=[("drift_rate", Decision), ("threshold",
Decision)]))
comp.enable_controller = True
comp._analyze_graph()
stim_list_dict = {Input: [0.5, 0.123], reward: [20, 20]}
comp.run(inputs=stim_list_dict, retain_old_simulation_data=True)
# Note: Removed decision variable OutputState from simulation results because sign is chosen randomly
expected_sim_results_array = [[[10.], [10.0], [0.0], [0.48999867],
[0.50499983]],
[[10.], [10.0], [0.0], [1.08965888],
[0.51998934]],
[[10.], [10.0], [0.0], [2.40680493],
[0.53494295]],
[[10.], [10.0], [0.0], [4.43671978],
[0.549834]],
[[10.], [10.0], [0.0], [0.48997868],
[0.51998934]],
[[10.], [10.0], [0.0], [1.08459402],
[0.57932425]],
[[10.], [10.0], [0.0], [2.36033556],
[0.63645254]],
[[10.], [10.0], [0.0], [4.24948962],
[0.68997448]],
[[10.], [10.0], [0.0], [0.48993479],
[0.53494295]],
[[10.], [10.0], [0.0], [1.07378304],
[0.63645254]],
[[10.], [10.0], [0.0], [2.26686573],
[0.72710822]],
[[10.], [10.0], [0.0], [3.90353015],
[0.80218389]],
[[10.], [10.0], [0.0], [0.4898672],
[0.549834]],
[[10.], [10.0], [0.0], [1.05791834],
[0.68997448]],
[[10.], [10.0], [0.0], [2.14222978],
[0.80218389]],
[[10.], [10.0], [0.0], [3.49637662],
[0.88079708]],
[[15.], [15.0], [0.0], [0.48999926],
[0.50372993]],
[[15.], [15.0], [0.0], [1.08981011],
[0.51491557]],
[[15.], [15.0], [0.0], [2.40822035],
[0.52608629]],
[[15.], [15.0], [0.0], [4.44259627],
[0.53723096]],
[[15.], [15.0], [0.0], [0.48998813],
[0.51491557]],
[[15.], [15.0], [0.0], [1.0869779],
[0.55939819]],
[[15.], [15.0], [0.0], [2.38198336],
[0.60294711]],
[[15.], [15.0], [0.0], [4.33535807],
[0.64492386]],
[[15.], [15.0], [0.0], [0.48996368],
[0.52608629]],
[[15.], [15.0], [0.0], [1.08085171],
[0.60294711]],
[[15.], [15.0], [0.0], [2.32712843],
[0.67504223]],
[[15.], [15.0], [0.0], [4.1221271],
[0.7396981]],
[[15.], [15.0], [0.0], [0.48992596],
[0.53723096]],
[[15.], [15.0], [0.0], [1.07165729],
[0.64492386]],
[[15.], [15.0], [0.0], [2.24934228],
[0.7396981]],
[[15.], [15.0], [0.0], [3.84279648],
[0.81637827]]]
for simulation in range(len(expected_sim_results_array)):
assert np.allclose(
expected_sim_results_array[simulation],
# Note: Skip decision variable OutputState
comp.simulation_results[simulation][0:3] +
comp.simulation_results[simulation][4:6])
expected_results_array = [[[20.0], [20.0], [0.0], [1.0],
[2.378055160151634], [0.9820137900379085]],
[[20.0], [20.0], [0.0], [0.1],
[0.48999967725112503],
[0.5024599801509442]]]
for trial in range(len(expected_results_array)):
np.testing.assert_allclose(
comp.results[trial],
expected_results_array[trial],
atol=1e-08,
err_msg='Failed on expected_output[{0}]'.format(trial))
def runStabilityFlexibility(tasks, stimuli, gain):
integrationConstant = 0.8 # time constant
DRIFT = 0.25 # Drift Rate
STARTING_POINT = 0.0 # Starting Point
THRESHOLD = 0.05 # Threshold
NOISE = 0.1 # Noise
T0 = 0.2 # T0
wa = 0.2
g = gain
# first element is color task attendance, second element is motion task attendance
inputLayer = pnl.TransferMechanism( #default_variable=[[0.0, 0.0]],
size=2,
function=pnl.Linear(slope=1, intercept=0),
output_states=[pnl.RESULT],
name='Input')
inputLayer.set_log_conditions([pnl.RESULT])
# Recurrent Transfer Mechanism that models the recurrence in the activation between the two stimulus and action
# dimensions. Positive self excitation and negative opposite inhibition with an integrator rate = tau
# Modulated variable in simulations is the GAIN variable of this mechanism
activation = pnl.RecurrentTransferMechanism(
default_variable=[[0.0, 0.0]],
function=pnl.Logistic(gain=g),
matrix=[[1.0, -1.0], [-1.0, 1.0]],
integrator_mode=True,
integrator_function=pnl.AdaptiveIntegrator(rate=integrationConstant),
initial_value=np.array([[0.0, 0.0]]),
output_states=[pnl.RESULT],
name='Activity')
activation.set_log_conditions([pnl.RESULT, "mod_gain"])
stimulusInfo = pnl.TransferMechanism(default_variable=[[0.0, 0.0]],
size=2,
function=pnl.Linear(slope=1,
intercept=0),
output_states=[pnl.RESULT],
name="Stimulus Info")
stimulusInfo.set_log_conditions([pnl.RESULT])
congruenceWeighting = pnl.TransferMechanism(
default_variable=[[0.0, 0.0]],
size=2,
function=pnl.Linear(slope=wa, intercept=0),
name='Congruence * Automatic Component')
controlledElement = pnl.TransferMechanism(
default_variable=[[0.0, 0.0]],
size=2,
function=pnl.Linear(slope=1, intercept=0),
input_states=pnl.InputState(combine=pnl.PRODUCT),
output_states=[pnl.RESULT],
name='Stimulus Info * Activity')
controlledElement.set_log_conditions([pnl.RESULT])
ddmCombination = pnl.TransferMechanism(size=1,
function=pnl.Linear(slope=1,
intercept=0),
output_states=[pnl.RESULT],
name="DDM Integrator")
ddmCombination.set_log_conditions([pnl.RESULT])
decisionMaker = pnl.DDM(
function=pnl.DriftDiffusionAnalytical(drift_rate=DRIFT,
starting_point=STARTING_POINT,
threshold=THRESHOLD,
noise=NOISE,
t0=T0),
output_states=[
pnl.DECISION_VARIABLE, pnl.RESPONSE_TIME,
pnl.PROBABILITY_UPPER_THRESHOLD, pnl.PROBABILITY_LOWER_THRESHOLD
],
name='DDM')
decisionMaker.set_log_conditions([
pnl.PROBABILITY_UPPER_THRESHOLD, pnl.PROBABILITY_LOWER_THRESHOLD,
pnl.DECISION_VARIABLE, pnl.RESPONSE_TIME
])
########### Composition
stabilityFlexibility = pnl.Composition()
### NODE CREATION
stabilityFlexibility.add_node(inputLayer)
stabilityFlexibility.add_node(activation)
stabilityFlexibility.add_node(congruenceWeighting)
stabilityFlexibility.add_node(controlledElement)
stabilityFlexibility.add_node(stimulusInfo)
stabilityFlexibility.add_node(ddmCombination)
stabilityFlexibility.add_node(decisionMaker)
stabilityFlexibility.add_projection(sender=inputLayer, receiver=activation)
stabilityFlexibility.add_projection(sender=activation,
receiver=controlledElement)
stabilityFlexibility.add_projection(sender=stimulusInfo,
receiver=congruenceWeighting)
stabilityFlexibility.add_projection(sender=stimulusInfo,
receiver=controlledElement)
stabilityFlexibility.add_projection(sender=congruenceWeighting,
receiver=ddmCombination)
stabilityFlexibility.add_projection(sender=controlledElement,
receiver=ddmCombination)
stabilityFlexibility.add_projection(sender=ddmCombination,
receiver=decisionMaker)
runs = len(tasks)
inputs = {inputLayer: tasks, stimulusInfo: stimuli}
stabilityFlexibility.run(inputs)
decisions = decisionMaker.log.nparray()
upper, lower = extractValues(decisions)
modelResults = [tasks, stimuli, upper, lower]
accuracies = computeAccuracy(modelResults)
activations = activation.log.nparray()
activity1 = []
activity2 = []
for i in range(0, runs):
activity1.append(activations[1][1][4][i + 1][0])
activity2.append(activations[1][1][4][i + 1][1])
return accuracies, activity1, activity2
function=pnl.SoftMax(output=pnl.ALL, gain=1.0),
output_states=[
{
pnl.NAME:
'SELECTED ACTION',
pnl.VARIABLE: [(pnl.INPUT_STATE_VARIABLES, 0),
(pnl.OWNER_VALUE, 0)],
# pnl.VARIABLE: [(pnl.OWNER_VALUE, 0)],
pnl.FUNCTION:
pnl.OneHot(mode=pnl.PROB_INDICATOR).function
},
{
pnl.NAME: 'REWARD RATE',
# pnl.VARIABLE: [pnl.OWNER_VALUE],
pnl.VARIABLE: [(pnl.OWNER_VALUE, 0)],
pnl.FUNCTION: pnl.AdaptiveIntegrator(rate=0.2)
},
{
pnl.NAME:
'CONFLICT K',
# pnl.VARIABLE: [pnl.OWNER_VALUE],
pnl.VARIABLE: [(pnl.OWNER_VALUE, 0)],
#Jon said this should also work and would be safer: [(pnl.OWNER_VALUE, 0),
#(pnl.OWNER_VALUE, 1)], but it doesn't work (maybe I did sth wrong)
pnl.FUNCTION:
pnl.Stability(default_variable=[0, 0],
metric=pnl.ENERGY,
normalize=True)
},
],
#as stated in the paper 'Response conflict was calculated as a normalized measure of the energy in the response units during the trial'
congruenceWeighting = pnl.TransferMechanism(
default_variable=[[0.0, 0.0]],
size=2,
function=pnl.Linear(slope=congruentWeight, intercept=0),
name='Congruence * Automatic Component')
# Activation Layer: [Color Activation, Motion Activation]
# Recurrent: Self Excitation, Mutual Inhibition
# Controlled: Gain Parameter
activation = pnl.RecurrentTransferMechanism(
default_variable=[[0.0, 0.0]],
function=pnl.Logistic(gain=1.0),
matrix=[[1.0, -1.0], [-1.0, 1.0]],
integrator_mode=True,
integrator_function=pnl.AdaptiveIntegrator(rate=integrationConstant),
initial_value=np.array([[0.0, 0.0]]),
output_states=[pnl.RESULT],
name='Task Activations [Act 1, Act 2]')
# Hadamard product of Activation and Stimulus Information
nonAutomaticComponent = pnl.TransferMechanism(
default_variable=[[0.0, 0.0]],
size=2,
function=pnl.Linear(slope=1, intercept=0),
input_states=pnl.InputState(combine=pnl.PRODUCT),
output_states=[pnl.RESULT],
name='Non-Automatic Component [S1*Activity1, S2*Activity2]')
# Summation of nonAutomatic and Automatic Components
ddmCombination = pnl.TransferMechanism(
