cs1_score = expr.behav_score(stage = 'test',
trial_pos = ['cs1 -> nothing'],
resp_pos = ['us'])
cs2_score = expr.behav_score(stage = 'test',
trial_pos = ['cs2 -> nothing'],
resp_pos = ['us'])
##### DEFINE OATS AND EXPERIMENTS #####
# basic conditioning, i.e. acquistion of a conditioned response
conditioning = expr.experiment(schedules = {'control': no_cond, 'experimental': cond},
oats = {'acquistion': expr.oat(schedule_pos = ['experimental'],
schedule_neg = ['control'],
behav_score_pos = cs_score,
behav_score_neg = cs_score)
})
# basic extinction of a conditioned response
extinction = expr.experiment(schedules = {'control': cond, 'experimental': extn},
oats = {'extinction': expr.oat(schedule_pos = ['control'],
schedule_neg = ['experimental'],
behav_score_pos = cs_score,
behav_score_neg = cs_score)
})
# ABA renewal
aba_renewal = expr.experiment(schedules = {'experimental': extn_aba, 'control': extn_aaa},
oats = {'renewal': expr.oat(schedule_pos = ['experimental'],
schedule_neg = ['control'],
# learned predictiveness
design = expr.schedule(resp_type = 'choice',
stages = {'relevance': expr.stage(x_pn = [['a', 'x'], ['a', 'y'], ['b', 'x'], ['b', 'y']],
y = [['cat1'], ['cat1'], ['cat2'], ['cat2']],
n_rep = 10),
'transfer': expr.stage(x_pn = [['a', 'x'], ['b', 'y']],
y = [['cat3'], ['cat4']],
n_rep = 10),
'test': expr.stage(x_pn = [['a', 'y'], ['b', 'x']],
y_psb = ['cat3', 'cat4'],
lrn = False,
n_rep = 1)})
rel_irl_oat = expr.oat(schedule_pos = ['design'],
behav_score_pos = expr.behav_score(stage = 'test',
trial_pos = ['a.y -> nothing', 'b.x -> nothing'],
trial_neg = ['a.y -> nothing', 'b.x -> nothing'],
resp_pos = ['cat3', 'cat4'],
resp_neg = ['cat4', 'cat3']))
learned_predictiveness = expr.experiment(schedules = {'design': design},
oats = {'rel_irl': rel_irl_oat})
del design; del rel_irl_oat
# inattention after blocking
design = expr.schedule(resp_type = 'choice',
stages = {'single_cue': expr.stage(x_pn = [['a'], ['b']], y = [['cat1'], ['cat2']], n_rep = 5),
'double_cue': expr.stage(x_pn = [['a', 'x'], ['b', 'y'], ['e', 'f'], ['g', 'h']], y = 2*[['cat1'], ['cat2']], n_rep = 5),
'transfer': expr.stage(x_pn = [['e', 'y'], ['g', 'x']], y = [['cat3'], ['cat4']], n_rep = 10),
'inattention_test': expr.stage(x_pn = [['e', 'x'], ['g', 'y']], y_psb = ['cat3', 'cat4'], lrn = False, n_rep = 1)})
cs2_score = expr.behav_score(stage='test',
trial_pos=['cs2 -> nothing'],
resp_pos=['us'])
##### DEFINE OATS AND EXPERIMENTS #####
# basic conditioning, i.e. acquistion of a conditioned response
conditioning = expr.experiment(schedules={
'control': no_cond,
'experimental': cond
},
oats={
'acquistion':
expr.oat(schedule_pos=['experimental'],
schedule_neg=['control'],
behav_score_pos=cs_score,
behav_score_neg=cs_score)
})
# basic extinction of a conditioned response
extinction = expr.experiment(schedules={
'control': cond,
'experimental': extn
},
oats={
'extinction':
expr.oat(schedule_pos=['control'],
schedule_neg=['experimental'],
behav_score_pos=cs_score,
behav_score_neg=cs_score)
})
'test': test_same
})
# group "different"
different = expr.schedule(resp_type='exct',
stages={
'cond': training,
'ex_cs1': extinction_cs1,
'ex_cs2': extinction_cs2,
'ex_cs2': extinction_cs2,
'ex_cs1': extinction_cs1,
'test': test_different
})
# behavioral score
cs1_score = expr.behav_score(stage='test',
trial_pos=['cs1 -> nothing'],
resp_pos=2 * ['us'])
# experiment object
oc_renewal = expr.experiment(schedules={
'same': same,
'different': different
},
oats={
'renewal':
expr.oat(schedule_pos=['different'],
schedule_neg=['same'],
behav_score_pos=cs1_score,
behav_score_neg=cs1_score)
})
y_psb = ['cat3', 'cat4'],
n_rep = 8),
'test': expr.stage(
x_pn = [['t5', 'b6'], ['t6', 'b5'], ['t1', 'b2'], ['t2', 'b1'], ['t3', 'b4'], ['t4', 'b3']],
y_psb = ['cat3', 'cat4'],
lrn = False,
n_rep = 2)},
x_dims = {'fruits': ['alpha', 'beta', 'theta', 'phi'], 'benign_faces': ['b1', 'b2', 'b3', 'b4', 'b5', 'b6'], 'angry_faces': ['t1', 't2', 't3', 't4', 't5', 't6']})
rel_irl = expr.oat(schedule_pos = ['design'],
behav_score_pos = expr.behav_score(stage = 'test',
trial_pos = ['t1.b2 -> nothing',
't2.b1 -> nothing',
't3.b4 -> nothing',
't4.b3 -> nothing'],
trial_neg = ['t1.b2 -> nothing',
't2.b1 -> nothing',
't3.b4 -> nothing',
't4.b3 -> nothing'],
resp_pos = ['cat4', 'cat3', 'cat3', 'cat4'],
resp_neg = ['cat3', 'cat4', 'cat4', 'cat3'])
)
threat_benign_os = expr.oat(schedule_pos = ['design'],
behav_score_pos = expr.behav_score(stage = 'test',
trial_pos = ['t5.b6 -> nothing', 't6.b5 -> nothing'],
trial_neg = ['t5.b6 -> nothing', 't6.b5 -> nothing'],
resp_pos = ['cat3', 'cat4'],
resp_neg = ['cat4', 'cat3'])
)
['i2', 'pc2'], ['i2', 'pr2']],
y=[['c1'], ['r1'], ['c2'], ['r2']],
y_psb=['c1', 'r1', 'c2', 'r2'],
n_rep=15),
'test':
expr.stage(x_pn=[['pc1'], ['pr1'], ['pc2'], ['pr2'],
['pc1', 'pr1'], ['pc2', 'pr2']],
y_psb=['c1', 'r1', 'c2', 'r2'],
lrn=False,
n_rep=2)
})
pc_pr = expr.oat(schedule_pos=['design'],
behav_score_pos=expr.behav_score(
stage='test',
trial_pos=['pc1.pr1 -> nothing', 'pc2.pr2 -> nothing'],
trial_neg=['pc1.pr1 -> nothing', 'pc2.pr2 -> nothing'],
resp_pos=['r1', 'r2'],
resp_neg=['c1', 'c2']))
basic_ibre = expr.experiment(schedules={'design': design},
oats={'pc_pr': pc_pr})
del design
del pc_pr
##### NOVEL TEST CUES IN THE INVERSE BASE RATE EFFECT DESIGN #####
# Juslin, Wennerholm and Winman (2001), Experiment 1
# This is a basic inverse base rate effect design with added novel cue test trials.
# Participants preferred the rare outcomes on the novel test trials, which