def test_EVC_gratton():
def test_search_function(controller=None, **kwargs):
result = np.array(controller.allocationPolicy).reshape(
len(controller.allocationPolicy), -1)
return result
def test_outcome_function(**kwargs):
result = np.array([0])
return result
# Preferences:
mechanism_prefs = ComponentPreferenceSet(
prefs={
kpVerbosePref: PreferenceEntry(False, PreferenceLevel.INSTANCE),
kpReportOutputPref: PreferenceEntry(False,
PreferenceLevel.INSTANCE)
})
process_prefs = ComponentPreferenceSet(
reportOutput_pref=PreferenceEntry(False, PreferenceLevel.INSTANCE),
verbose_pref=PreferenceEntry(True, PreferenceLevel.INSTANCE))
# Control Parameters
signalSearchRange = np.arange(1.0, 2.0, 0.2)
# Stimulus Mechanisms
Target_Stim = TransferMechanism(name='Target Stimulus',
function=Linear(slope=0.3324))
Flanker_Stim = TransferMechanism(name='Flanker Stimulus',
function=Linear(slope=0.3545221843))
# Processing Mechanisms (Control)
Target_Rep = TransferMechanism(
name='Target Representation',
function=Linear(
slope=(1.0,
ControlProjection(function=Linear,
control_signal_params={
ALLOCATION_SAMPLES: signalSearchRange
}))),
prefs=mechanism_prefs)
Flanker_Rep = TransferMechanism(
name='Flanker Representation',
function=Linear(
slope=(1.0,
ControlProjection(function=Linear,
control_signal_params={
ALLOCATION_SAMPLES: signalSearchRange
}))),
prefs=mechanism_prefs)
# Processing Mechanism (Automatic)
Automatic_Component = TransferMechanism(name='Automatic Component',
function=Linear(slope=(1.0)),
prefs=mechanism_prefs)
# Decision Mechanisms
Decision = DDM(
function=BogaczEtAl(drift_rate=(1.0),
threshold=(0.2645),
noise=(0.5),
starting_point=(0),
t0=0.15),
prefs=mechanism_prefs,
name='Decision',
output_states=[
DECISION_VARIABLE, RESPONSE_TIME, PROBABILITY_UPPER_THRESHOLD
],
)
# Outcome Mechanisms:
Reward = TransferMechanism(name='Reward')
# Processes:
TargetControlProcess = Process(default_variable=[0],
pathway=[Target_Stim, Target_Rep, Decision],
prefs=process_prefs,
name='Target Control Process')
FlankerControlProcess = Process(
default_variable=[0],
pathway=[Flanker_Stim, Flanker_Rep, Decision],
prefs=process_prefs,
name='Flanker Control Process')
TargetAutomaticProcess = Process(
default_variable=[0],
pathway=[Target_Stim, Automatic_Component, Decision],
prefs=process_prefs,
name='Target Automatic Process')
FlankerAutomaticProcess = Process(
default_variable=[0],
pathway=[Flanker_Stim, Automatic_Component, Decision],
prefs=process_prefs,
name='Flanker1 Automatic Process')
RewardProcess = Process(default_variable=[0],
pathway=[Reward],
prefs=process_prefs,
name='RewardProcess')
# System:
mySystem = System(
processes=[
TargetControlProcess, FlankerControlProcess,
TargetAutomaticProcess, FlankerAutomaticProcess, RewardProcess
],
controller=EVCControlMechanism,
enable_controller=True,
monitor_for_control=[
Reward, (Decision.PROBABILITY_UPPER_THRESHOLD, 1, -1)
],
# monitor_for_control=[Reward, DDM_PROBABILITY_UPPER_THRESHOLD, (DDM_RESPONSE_TIME, -1, 1)],
name='EVC Gratton System')
# Show characteristics of system:
mySystem.show()
mySystem.controller.show()
# mySystem.show_graph(show_control=True)
# configure EVC components
mySystem.controller.control_signals[
0].intensity_cost_function = Exponential(rate=0.8046).function
mySystem.controller.control_signals[
1].intensity_cost_function = Exponential(rate=0.8046).function
for mech in mySystem.controller.prediction_mechanisms.mechanisms:
if mech.name == 'Flanker Stimulus Prediction Mechanism' or mech.name == 'Target Stimulus Prediction Mechanism':
# when you find a key mechanism (transfer mechanism) with the correct name, print its name
print(mech.name)
mech.function_object.rate = 1.0
if 'Reward' in mech.name:
print(mech.name)
mech.function_object.rate = 1.0
# mySystem.controller.prediction_mechanisms[mech].parameterStates['rate'].base_value = 1.0
print('new rate of integration mechanisms before System execution:')
# for mech in mySystem.controller.prediction_mechanisms.keys():
for mech in mySystem.controller.prediction_mechanisms.mechanisms:
print(mech.name)
print(mech.function_object.rate)
print('----')
# generate stimulus environment
nTrials = 3
targetFeatures = [1, 1, 1]
flankerFeatures = [1, -1,
1] # for full simulation: flankerFeatures = [-1,1]
reward = [100, 100, 100]
targetInputList = targetFeatures
flankerInputList = flankerFeatures
rewardList = reward
# targetInputList = np.random.choice(targetFeatures, nTrials).tolist()
# flankerInputList = np.random.choice(flankerFeatures, nTrials).tolist()
# rewardList = (np.ones(nTrials) * reward).tolist() #np.random.choice(reward, nTrials).tolist()
stim_list_dict = {
Target_Stim: targetInputList,
Flanker_Stim: flankerInputList,
Reward: rewardList
}
mySystem.controller.reportOutputPref = True
expected_results_array = [
0.2645,
0.32257753,
0.94819408,
100.,
0.2645,
0.31663196,
0.95508757,
100.,
0.2645,
0.31093566,
0.96110142,
100.,
0.2645,
0.30548947,
0.96633839,
100.,
0.2645,
0.30029103,
0.97089165,
100.,
0.2645,
0.3169957,
0.95468427,
100.,
0.2645,
0.31128378,
0.9607499,
100.,
0.2645,
0.30582202,
0.96603252,
100.,
0.2645,
0.30060824,
0.9706259,
100.,
0.2645,
0.29563774,
0.97461444,
100.,
0.2645,
0.31163288,
0.96039533,
100.,
0.2645,
0.30615555,
0.96572397,
100.,
0.2645,
0.30092641,
0.97035779,
100.,
0.2645,
0.2959409,
0.97438178,
100.,
0.2645,
0.29119255,
0.97787196,
100.,
0.2645,
0.30649004,
0.96541272,
100.,
0.2645,
0.30124552,
0.97008732,
100.,
0.2645,
0.29624499,
0.97414704,
100.,
0.2645,
0.29148205,
0.97766847,
100.,
0.2645,
0.28694892,
0.98071974,
100.,
0.2645,
0.30156558,
0.96981445,
100.,
0.2645,
0.29654999,
0.97391021,
100.,
0.2645,
0.29177245,
0.97746315,
100.,
0.2645,
0.28722523,
0.98054192,
100.,
0.2645,
0.28289958,
0.98320731,
100.,
0.2645,
0.28289958,
0.98320731,
100.,
0.2645,
0.42963678,
0.47661181,
100.,
0.2645,
0.42846471,
0.43938586,
100.,
-0.2645,
0.42628176,
0.40282965,
100.,
0.2645,
0.42314468,
0.36732207,
100.,
-0.2645,
0.41913221,
0.333198,
100.,
0.2645,
0.42978939,
0.51176048,
100.,
0.2645,
0.42959394,
0.47427693,
100.,
-0.2645,
0.4283576,
0.43708106,
100.,
0.2645,
0.4261132,
0.40057958,
100.,
-0.2645,
0.422919,
0.36514906,
100.,
0.2645,
0.42902209,
0.54679323,
100.,
0.2645,
0.42980788,
0.50942101,
100.,
-0.2645,
0.42954704,
0.47194318,
100.,
-0.2645,
0.42824656,
0.43477897,
100.,
0.2645,
0.42594094,
0.3983337,
100.,
-0.2645,
0.42735293,
0.58136855,
100.,
-0.2645,
0.42910149,
0.54447221,
100.,
0.2645,
0.42982229,
0.50708112,
100.,
-0.2645,
0.42949608,
0.46961065,
100.,
-0.2645,
0.42813159,
0.43247968,
100.,
-0.2645,
0.42482049,
0.61516258,
100.,
0.2645,
0.42749136,
0.57908829,
100.,
0.2645,
0.42917687,
0.54214925,
100.,
-0.2645,
0.42983261,
0.50474093,
100.,
-0.2645,
0.42944107,
0.46727945,
100.,
-0.2645,
0.42944107,
0.46727945,
100.,
0.2645,
0.32257753,
0.94819408,
100.,
0.2645,
0.31663196,
0.95508757,
100.,
0.2645,
0.31093566,
0.96110142,
100.,
0.2645,
0.30548947,
0.96633839,
100.,
0.2645,
0.30029103,
0.97089165,
100.,
0.2645,
0.3169957,
0.95468427,
100.,
0.2645,
0.31128378,
0.9607499,
100.,
0.2645,
0.30582202,
0.96603252,
100.,
0.2645,
0.30060824,
0.9706259,
100.,
0.2645,
0.29563774,
0.97461444,
100.,
0.2645,
0.31163288,
0.96039533,
100.,
0.2645,
0.30615555,
0.96572397,
100.,
0.2645,
0.30092641,
0.97035779,
100.,
0.2645,
0.2959409,
0.97438178,
100.,
0.2645,
0.29119255,
0.97787196,
100.,
0.2645,
0.30649004,
0.96541272,
100.,
0.2645,
0.30124552,
0.97008732,
100.,
0.2645,
0.29624499,
0.97414704,
100.,
0.2645,
0.29148205,
0.97766847,
100.,
0.2645,
0.28694892,
0.98071974,
100.,
0.2645,
0.30156558,
0.96981445,
100.,
0.2645,
0.29654999,
0.97391021,
100.,
0.2645,
0.29177245,
0.97746315,
100.,
0.2645,
0.28722523,
0.98054192,
100.,
0.2645,
0.28289958,
0.98320731,
100.,
0.2645,
0.28289958,
0.98320731,
100.,
]
Flanker_Rep.set_log_conditions((SLOPE, CONTROL))
mySystem.run(
num_trials=nTrials,
inputs=stim_list_dict,
)
np.testing.assert_allclose(
pytest.helpers.expand_np_ndarray(mySystem.results),
expected_results_array,
atol=1e-08,
verbose=True,
)
