This version includes inter-trial intervals (ITIs).
'''
iti = 5
n_rep_train = 5
n_rep_extn = 5
n_rep_test = 2
n_rep_pre_exp = 5
n_rep_no_stim = 10
##### DEFINE STAGES #####
no_stim_stage = expr.stage(x_pn = [[]],
x_bg = ['ctx'],
y_psb = ['us'],
order_fixed = True,
iti = 0,
n_rep = n_rep_no_stim)
no_stim_ctx_a_stage = expr.stage(x_pn = [[]],
x_bg = ['ctx_a'],
y_psb = ['us'],
order_fixed = True,
iti = 0,
n_rep = n_rep_no_stim)
test_stage = expr.stage(x_pn = [['cs']],
x_bg = ['ctx'],
y_psb = ['us'],
order_fixed = True,
iti = iti,
import pandas as pd
from statsrat import expr
'''
Simplified category learning tasks (smaller versions of the tasks like those in 'cat' -> 'kitten').
'''
# 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
The ITI was 2 minutes (4 times the CS length).
The test took place on day 4, and consisted of 4 trials. The ITI was 2 minutes (4 times the CS length).
"""
from statsrat import expr
iti = 5
n_rep_train = 5
n_rep_extn = 10
n_rep_test = 5
training = expr.stage(x_pn=[['cs1'], ['cs2']],
x_bg=['ctx_a'],
y=2 * [['us']],
y_psb=['us'],
order_fixed=True,
iti=iti,
n_rep=n_rep_train)
extinction_cs1 = expr.stage(x_pn=[['cs1']],
x_bg=['ctx_b1'],
y_psb=['us'],
order_fixed=True,
iti=iti,
n_rep=n_rep_extn)
extinction_cs2 = expr.stage(x_pn=[['cs2']],
x_bg=['ctx_b2'],
y_psb=['us'],
order_fixed=True,
from statsrat import expr
# Category learning tasks.
# FAST (Joel Stoddard collab)
design = expr.schedule(resp_type = 'choice',
stages = {
'tutorial_0a': expr.stage(
x_pn = [['alpha'], ['beta']],
y = [['cati'], ['catii']],
y_psb = ['cati', 'catii'],
n_rep = 6),
'tutorial_0b': expr.stage(
x_pn = [['alpha'], ['beta']],
y = [['cati'], ['catii']],
y_psb = ['cati', 'catii'],
n_rep = 6),
'tutorial_0c': expr.stage(
x_pn = [['alpha', 'theta'], ['beta', 'theta'], ['alpha', 'phi'], ['beta', 'phi']],
y = 2*[['cati'], ['catii']],
y_psb = ['cati', 'catii'],
n_rep = 6),
'relevance': expr.stage(
x_pn = [['b1', 't1'], ['b1', 't2'], ['b2', 't1'], ['b2', 't2'], ['t3', 'b3'], ['t3', 'b4'], ['t4', 'b3'], ['t4', 'b4']],
y = 2*[['cat1'], ['cat1'], ['cat2'], ['cat2']],
y_psb = ['cat1', 'cat2'],
n_rep = 12),
'transfer': expr.stage(
x_pn = [['t5', 'b5'], ['t6', 'b6'], ['t1', 'b1'], ['t2', 'b2'], ['t3', 'b3'], ['t4', 'b4']],
y = 3*[['cat3'], ['cat4']],
y_psb = ['cat3', 'cat4'],
from statsrat import expr
'''
Inverse base rate effect (IBRE) and related experiments.
'''
##### BASIC INVERSE BASE RATE EFFECT DESIGN #####
# Kruschke (1996), Experiment 1 (inverse base rate effect)
# I'm missing some of the test trial types.
design = expr.schedule(resp_type='choice',
stages={
'training':
expr.stage(freq=[3, 1, 3, 1],
x_pn=[['i1', 'pc1'], ['i1', 'pr1'],
['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'],
iti = 5
n_rep_train = 5
n_rep_extn = 5
n_rep_test = 2
n_rep_pre_exp = 5
n_rep_no_stim = 10
##### DEFINE STAGES #####
no_stim_stage_t0 = expr.stage(x_pn=[[]],
x_bg=['ctx', 'time'],
x_value=pd.Series({
'cs': 1.0,
'ctx': 1.0,
'time': 0.0
}),
y_psb=['us'],
order_fixed=True,
iti=0,
n_rep=n_rep_no_stim)
no_stim_ctx_a_stage_t0 = expr.stage(x_pn=[[]],
x_bg=['ctx_a', 'time'],
x_value=pd.Series({
'cs': 1.0,
'ctx_a': 1.0,
'time': 0.0
}),
y_psb=['us'],
order_fixed=True,