def get_cond_diff(cond_1, cond_2, voi):
""" This function computes the differences in perseveration and estimation errors between
two task conditions in the follow-up experiment
:param cond_1: First condition of interest
:param cond_2: Second condition of interest
:param voi: Variable of interes
:return: desc, stat, zero_stat: descriptive, inferential statistics and inferential statistics against zero
"""
# Identify variable of interest
if voi == 1:
voi_name = 'e_t'
print_name = 'Estimation error'
elif voi == 2:
voi_name = 'pers'
print_name = 'Perseveration'
else:
voi_name = 'motor_pers'
print_name = 'Motor perseveration'
# Compute mean of variable of interest
voi_cond_1 = get_mean_voi(cond_1, voi)
voi_cond_2 = get_mean_voi(cond_2, voi)
# Compute difference between conditions
cond_diff = voi_cond_2.copy()
cond_diff[voi_name] = cond_diff[voi_name] - voi_cond_1[voi_name]
print('\n\n' + print_name + ' difference\n')
median_diff, q1_diff, q3_diff, p_values_diff, stat_diff = get_stats(cond_diff, 2, voi_name)
stat = pd.DataFrame()
stat['p'] = p_values_diff
stat['stat'] = stat_diff
stat.index.name = 'test'
stat = stat.rename({0: 'kw', 1: 'ch_ad', 2: 'ch_ya', 3: 'ch_oa', 4: 'ad_ya', 5: 'ad_oa', 6: 'ya_oa'}, axis='index')
desc = pd.DataFrame()
desc['median'] = round(median_diff, 3)
desc['q1'] = round(q1_diff, 3)
desc['q3'] = round(q3_diff, 3)
desc.index.name = 'age_group'
desc = desc.rename({1: 'ch', 2: 'ad', 3: 'ya', 4: 'oa'}, axis='index')
print('\n\n' + print_name + ' difference test against zero\n')
_, _, _, p_values_diff, stat_diff = get_stats(cond_diff, 2, voi_name, test=2)
zero_stat = pd.DataFrame()
zero_stat['p'] = p_values_diff
zero_stat['stat'] = stat_diff
zero_stat.index.name = 'age_group'
zero_stat = zero_stat.rename({0: 'ch', 1: 'ya', 2: 'oa'}, axis='index')
return cond_diff, desc, stat, zero_stat
# Save statistics for Latex manuscript
fig_5_ef_desc.to_csv('~/Dropbox/Apps/Overleaf/al_manuscript/al_dataframes/fig_5_ef_desc.csv')
fig_5_ef_stat.to_csv('~/Dropbox/Apps/Overleaf/al_manuscript/al_dataframes/fig_5_ef_stat.csv')
# ---------------------------------------------------------------------------------------
# 3. Differences of perseveration frequency between shifting- and stable bucket condition
# ---------------------------------------------------------------------------------------
# Read out push and noPush trials
df_noPush = df_exp2[df_exp2['cond'] == 'main_noPush']
df_push = df_exp2[df_exp2['cond'] == 'main_push']
# Perseveration in stable-bucket condition
voi = 2
pers_noPush = get_mean_voi(df_noPush, voi)
# Compute differences
fig_5_c_cond_diff, fig_5_c_desc, fig_5_c_stat, fig_5_c_zero_stat = get_cond_diff(df_noPush, df_push, voi)
fig_5_c_desc.to_csv('~/Dropbox/Apps/Overleaf/al_manuscript/al_dataframes/fig_5_c_desc.csv')
fig_5_c_stat.to_csv('~/Dropbox/Apps/Overleaf/al_manuscript/al_dataframes/fig_5_c_stat.csv')
fig_5_c_zero_stat.to_csv('~/Dropbox/Apps/Overleaf/al_manuscript/al_dataframes/fig_5_c_zero_stat.csv')
# ----------------------------------------------------------------------------------
# 4. Differences of estimation errors between shifting- and stable bucket condition
# ----------------------------------------------------------------------------------
voi = 1
fig_5_d_cond_diff, fig_5_d_desc, fig_5_d_stat, fig_5_d_zero_stat = get_cond_diff(df_noPush, df_push, voi)
fig_5_d_desc.to_csv('~/Dropbox/Apps/Overleaf/al_manuscript/al_dataframes/fig_5_d_desc.csv')
fig_5_d_stat.to_csv('~/Dropbox/Apps/Overleaf/al_manuscript/al_dataframes/fig_5_d_stat.csv')
# Perseveration-frequency differences between high- and low-reward condition
voi = 2
_, fig_SM_1_a_desc, _, fig_SM_1_a_zero_stat = get_cond_diff(low, high, voi)
fig_SM_1_a_zero_stat.to_csv('~/Dropbox/Apps/Overleaf/al_manuscript/al_dataframes/fig_SM_1_a_zero_stat.csv')
fig_SM_1_a_desc.to_csv('~/Dropbox/Apps/Overleaf/al_manuscript/al_dataframes/fig_SM_1_a_desc.csv')
# Estimation-error differences between high- and low-reward condition
voi = 1
_, fig_SM_1_b_desc, _, fig_SM_1_b_zero_stat = get_cond_diff(low, high, voi)
fig_SM_1_b_zero_stat.to_csv('~/Dropbox/Apps/Overleaf/al_manuscript/al_dataframes/fig_SM_1_b_zero_stat.csv')
fig_SM_1_b_desc.to_csv('~/Dropbox/Apps/Overleaf/al_manuscript/al_dataframes/fig_SM_1_b_desc.csv')
# Perseveration in high- and low reward condition
voi = 2
pers_high = get_mean_voi(high, voi)
pers_low = get_mean_voi(low, voi)
pers_high['Condition'] = 1
pers_low['Condition'] = 2
# Perseveration in stable- and shifting-bucket condition
voi = 2
pers_noPush = get_mean_voi(df_noPush, voi)
pers_push = get_mean_voi(df_push, voi)
pers_noPush['Condition'] = 1
pers_push['Condition'] = 2
# Estimation errors in high- and low-reward condition
voi = 1
est_err_high = get_mean_voi(high, voi)
est_err_low = get_mean_voi(low, voi)
path = paths.split(os.path.sep)
home_dir = path[1]
# ------------
# 1. Load data
# ------------
# Load data from first experiment
df_exp1 = pd.read_pickle('al_data/data_prepr_1.pkl')
# Parameter estimates
model_results = pd.read_pickle('al_data/estimates_first_exp_25_sp.pkl')
# Compute perseveration frequency
voi = 2
pers = get_mean_voi(df_exp1, voi)
# Print out perseveration statistics for paper
print('\n\nPerseveration\n')
median_pers, q1_pers, q3_pers, p_pers, stat_pers = get_stats(pers, 1, 'pers')
# Create data frame for descriptive results
fig_3_a_desc = pd.DataFrame()
# Median perseveration
fig_3_a_desc['median'] = round(median_pers, 3)
# First quartile
fig_3_a_desc['q1'] = round(q1_pers, 3)
# Third quartile
# Get home directory
paths = os.getcwd()
path = paths.split(os.path.sep)
home_dir = path[1]
# ------------------------------------
# 1. Load data and compute performance
# ------------------------------------
# Load data
df_exp1 = pd.read_pickle('al_data/data_prepr_1.pkl')
n_subj = len(np.unique(df_exp1['subj_num']))
# Compute estimation errors
voi = 1
e_t = get_mean_voi(df_exp1, voi)
# ---------------------------------
# 2. Compute average learning rates
# ---------------------------------
# Initialize learning rate and age_group variables
alpha = np.full(n_subj, np.nan)
age_group = np.full(n_subj, np.nan)
# Cycle over participants
for i in range(0, n_subj):
# Extract data of current participant
df_subj = df_exp1[(df_exp1['subj_num'] == i + 1)].copy()
x = np.linspace(0, len(df_subj) - 1, len(df_subj))