# extinction with extra US (for reinstatement)
extn_extra_us = expr.schedule(resp_type = 'exct', stages = {'cond': training_stage, 'extinction': extn_stage, 'extra_us': extra_us_stage, 'test': test_stage})
# control condition for extinction with US (for reinstatement)
extn_extra_us_ctl = expr.schedule(resp_type = 'exct', stages = {'cond': training_stage, 'extinction': extn_stage, 'extra_us': extra_us_ctl_stage, 'test': test_stage})
# extinction with delay before test (explicit)
extn_delay_explicit = expr.schedule(resp_type = 'exct', stages = {'cond': training_stage, 'extinction': extn_stage, 'delay': delay_stage, 'test': test_stage})
# extinction with delay before test (implicit)
extn_delay_implicit = expr.schedule(resp_type = 'exct', stages = {'cond': training_stage, 'extinction': extn_stage, 'test': test_stage}, delays = [0, 100])
##### DEFINE BEHAVIORAL SCORES #####
cs_score = expr.behav_score(stage = 'test',
trial_pos = ['cs -> nothing'],
resp_pos = ['us'])
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},
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'])
)
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)})
['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
# Winman et al took this as evidence for their eliminative inference explanation of the IBRE.