def _step(self, action):
"""
_step receives an action and returns:
a new observation, obs
reward associated with the action, reward
a boolean variable indicating whether the experiment has end, done
a dictionary with extra information:
ground truth correct response, info['gt']
boolean indicating the end of the trial, info['new_trial']
"""
# ---------------------------------------------------------------------
# Reward and observations
# ---------------------------------------------------------------------
trial = self.trial
info = {'new_trial': False}
info['gt'] = np.zeros((3, ))
# rewards
reward = 0
# observations
obs = np.zeros((3, ))
if self.in_epoch(self.t, 'fixation'):
info['gt'][0] = 1
obs[0] = 1
if self.actions[action] != 0:
info['new_trial'] = self.abort
reward = self.R_ABORTED
elif self.in_epoch(self.t, 'decision'):
info['gt'][int((trial['ground_truth'] / 2 + 1.5))] = 1
gt_sign = np.sign(trial['ground_truth'])
action_sign = np.sign(self.actions[action])
if gt_sign == action_sign:
reward = self.R_CORRECT
elif gt_sign == -action_sign:
reward = self.R_FAIL
info['new_trial'] = self.actions[action] != 0
else:
info['gt'][0] = 1
# this is an 'if' to allow the stimulus and fixation periods to overlap
if self.in_epoch(self.t, 'stimulus'):
obs[0] = 1
high = (trial['ground_truth'] > 0) + 1
low = (trial['ground_truth'] < 0) + 1
obs[high] = self.scale(trial['coh']) +\
self.rng.gauss(mu=0, sigma=self.sigma)/np.sqrt(self.dt)
obs[low] = self.scale(-trial['coh']) +\
self.rng.gauss(mu=0, sigma=self.sigma)/np.sqrt(self.dt)
# ---------------------------------------------------------------------
# new trial?
reward, info['new_trial'] = tasktools.new_trial(
self.t, self.tmax, self.dt, info['new_trial'], self.R_MISS, reward)
if info['new_trial']:
self.t = 0
self.num_tr += 1
else:
self.t += self.dt
done = self.num_tr > self.num_tr_exp
return obs, reward, done, info
def _step(self, action):
trial = self.trial
info = {'new_trial': False, 'gt': np.zeros((3, ))}
reward = 0
obs = np.zeros((3, ))
if self.t == 0: # at stage 1, if action==fixate, abort
if action == 0:
reward = self.R_ABORTED
info['new_trial'] = True
else:
state = trial['transition'][action]
obs[int(state)] = 1
reward = trial['reward'][int(state - 1)]
elif self.t == self.dt:
obs[0] = 1
if action != 0:
reward = self.R_ABORTED
info['new_trial'] = True
else:
raise ValueError('t is not 0 or 1')
# ---------------------------------------------------------------------
# new trial?
reward, info['new_trial'] = tasktools.new_trial(
self.t, self.tmax, self.dt, info['new_trial'], self.R_MISS, reward)
if info['new_trial']:
self.t = 0
self.num_tr += 1
else:
self.t += self.dt
done = self.num_tr > self.num_tr_exp
return obs, reward, done, info
def _step(self, action):
trial = self.trial
# ---------------------------------------------------------------------
# Reward and inputs
# ---------------------------------------------------------------------
trial = self.trial
info = {'new_trial': False}
# ground truth signal is not well defined in this task
info['gt'] = np.zeros((4, ))
# rewards
reward = 0
# observations
obs = np.zeros((4, ))
if self.in_epoch(self.t, 'fixation'):
obs[0] = 1
if self.actions[action] != 0:
info['new_trial'] = self.abort
reward = self.R_ABORTED
elif self.in_epoch(self.t, 'decision'):
if self.actions[action] == 2:
if trial['wager']:
reward = self.R_SURE
else:
reward = self.R_ABORTED
else:
gt_sign = np.sign(trial['ground_truth'])
action_sign = np.sign(self.actions[action])
if gt_sign == action_sign:
reward = self.R_CORRECT
elif gt_sign == -action_sign:
reward = self.R_FAIL
info['new_trial'] = self.actions[action] != 0
if self.in_epoch(self.t, 'delay'):
obs[0] = 1
elif self.in_epoch(self.t, 'stimulus'):
high = (trial['ground_truth'] > 0) + 1
low = (trial['ground_truth'] < 0) + 1
obs[high] = self.scale(+trial['coh']) +\
self.rng.gauss(mu=0, sigma=self.sigma)/np.sqrt(self.dt)
obs[low] = self.scale(-trial['coh']) +\
self.rng.gauss(mu=0, sigma=self.sigma)/np.sqrt(self.dt)
if trial['wager'] and self.in_epoch(self.t, 'sure'):
obs[3] = 1
# ---------------------------------------------------------------------
# new trial?
reward, info['new_trial'] = tasktools.new_trial(
self.t, self.tmax, self.dt, info['new_trial'], self.R_MISS, reward)
if info['new_trial']:
self.t = 0
self.num_tr += 1
else:
self.t += self.dt
done = self.num_tr > self.num_tr_exp
return obs, reward, done, info
def _step(self, action):
trial = self.trial
# ---------------------------------------------------------------------
# Reward and inputs
# ---------------------------------------------------------------------
# epochs = trial['epochs']
info = {'new_trial': False}
info['gt'] = np.zeros((2,))
reward = 0
obs = np.zeros((5,))
if self.in_epoch(self.t, 'fixation'):
info['gt'][0] = 1
obs[0] = 1 # TODO: fixation cue only during fixation period?
if self.actions[action] != -1:
info['new_trial'] = self.abort
reward = self.R_ABORTED
elif self.in_epoch(self.t, 'decision'):
info['gt'][int((trial['ground_truth']/2+.5))] = 1
gt_sign = np.sign(trial['ground_truth'])
action_sign = np.sign(self.actions[action])
if (action_sign > 0):
info['new_trial'] = True
if (gt_sign > 0):
reward = self.R_CORRECT
else:
reward = self.R_INCORRECT
else:
info['gt'][0] = 1
if self.in_epoch(self.t, 'dpa1'):
dpa1, _ = trial['pair']
obs[dpa1] = 1
if self.in_epoch(self.t, 'dpa2'):
_, dpa2 = trial['pair']
obs[dpa2] = 1
# ---------------------------------------------------------------------
# new trial?
reward, info['new_trial'] = tasktools.new_trial(self.t, self.tmax,
self.dt,
info['new_trial'],
self.R_MISS, reward)
if info['new_trial']:
info['new_trial'] = True
self.t = 0
self.num_tr += 1
else:
self.t += self.dt
done = self.num_tr > self.num_tr_exp
return obs, reward, done, info
def _step(self, action):
# ---------------------------------------------------------------------
# Reward and inputs
# ---------------------------------------------------------------------
trial = self.trial
info = {'new_trial': False, 'gt': np.zeros((2, ))}
reward = 0
obs = np.zeros((3, ))
if self.in_epoch(self.t, 'fixation'):
obs[0] = 1
info['gt'][0] = 1
if self.actions[action] != -1:
info['new_trial'] = self.abort
reward = self.R_ABORTED
if self.in_epoch(self.t, 'production'):
t_prod = self.t - trial['durations']['measure'][1]
eps = abs(t_prod - trial['production'])
if eps < self.dt / 2 + 1:
info['gt'][1] = 1
else:
info['gt'][0] = 1
if action == 1:
info['new_trial'] = True # terminate
# actual production time
eps_threshold = 0.2 * trial['production'] + 25
if eps > eps_threshold:
reward = self.R_FAIL
else:
reward = (1. - eps / eps_threshold)**1.5
reward = min(reward, 0.1)
reward *= self.R_CORRECT
else:
info['gt'][0] = 1
if self.in_epoch(self.t, 'ready'):
obs[1] = 1
if self.in_epoch(self.t, 'set'):
obs[2] = 1
# ---------------------------------------------------------------------
# new trial?
reward, info['new_trial'] = tasktools.new_trial(
self.t, self.tmax, self.dt, info['new_trial'], self.R_MISS, reward)
if info['new_trial']:
self.t = 0
self.num_tr += 1
else:
self.t += self.dt
done = self.num_tr > self.num_tr_exp
return obs, reward, done, info
def _step(self, action):
# ---------------------------------------------------------------------
# Reward
# ---------------------------------------------------------------------
trial = self.trial
info = {'new_trial': False, 'gt': np.zeros((3, ))}
reward = 0
obs = np.zeros((3, ))
if self.in_epoch(self.t, 'fixation'):
info['gt'][0] = 1
obs[0] = 1
if self.actions[action] != 0:
info['new_trial'] = self.abort
reward = self.R_ABORTED
elif self.in_epoch(self.t, 'decision'):
info['gt'][int((trial['ground_truth'] / 2 + 1.5))] = 1
gt_sign = np.sign(trial['ground_truth'])
action_sign = np.sign(self.actions[action])
if gt_sign == action_sign:
reward = self.R_CORRECT
elif gt_sign == -action_sign:
reward = self.R_FAIL
info['new_trial'] = self.actions[action] != 0
else:
info['gt'][0] = 1
# ---------------------------------------------------------------------
# Inputs
# ---------------------------------------------------------------------
if self.in_epoch(self.t, 'sample'):
obs[trial['sample'] + 1] = 1
if self.in_epoch(self.t, 'test'):
obs[trial['test'] + 1] = 1
# ---------------------------------------------------------------------
# new trial?
reward, info['new_trial'] = tasktools.new_trial(
self.t, self.tmax, self.dt, info['new_trial'], self.R_MISS, reward)
if info['new_trial']:
self.t = 0
self.num_tr += 1
else:
self.t += self.dt
done = self.num_tr > self.num_tr_exp
return obs, reward, done, info
def _step(self, action):
# ---------------------------------------------------------------------
# Reward and inputs
# ---------------------------------------------------------------------
trial = self.trial
info = {'new_trial': False}
# rewards
reward = 0
# observations
obs = np.zeros((2, ))
info['gt'] = np.zeros((3, ))
if self.in_epoch(self.t, 'fixation'):
info['gt'][0] = 1
obs[0] = 1
if self.actions[action] != 0:
info['new_trial'] = self.abort
reward = self.R_ABORTED
elif self.in_epoch(self.t, 'decision'):
info['gt'][int((trial['ground_truth'] / 2 + 1.5))] = 1
gt_sign = np.sign(trial['ground_truth'])
action_sign = np.sign(self.actions[action])
if gt_sign == action_sign:
reward = self.R_CORRECT
elif gt_sign == -action_sign:
reward = self.R_FAIL
info['new_trial'] = self.actions[action] != 0
else:
info['gt'][0] = 1
if self.in_epoch(self.t, 'f1'):
obs[1] = self.scale_p(trial['f1']) +\
self.rng.gauss(mu=0, sigma=self.sigma)/np.sqrt(self.dt)
elif self.in_epoch(self.t, 'f2'):
obs[1] = self.scale_p(trial['f2']) +\
self.rng.gauss(mu=0, sigma=self.sigma)/np.sqrt(self.dt)
# ---------------------------------------------------------------------
# new trial?
reward, info['new_trial'] = tasktools.new_trial(
self.t, self.tmax, self.dt, info['new_trial'], self.R_MISS, reward)
if info['new_trial']:
self.t = 0
self.num_tr += 1
else:
self.t += self.dt
done = self.num_tr > self.num_tr_exp
return obs, reward, done, info
def _step(self, action):
# ---------------------------------------------------------------------
# Reward and inputs
# ---------------------------------------------------------------------
trial = self.trial
info = {'new_trial': False}
info['gt'] = np.zeros((2, ))
reward = 0
obs = np.zeros((3, ))
if self.in_epoch(self.t, 'fixation'):
info['gt'][0] = 1
obs[0] = 1 # fixation cue only during fixation period
if self.actions[action] != -1:
info['new_trial'] = self.abort
reward = self.R_ABORTED
elif self.in_epoch(self.t, 'decision'):
info['gt'][int((trial['ground_truth'] / 2 + .5))] = 1
gt_sign = np.sign(trial['ground_truth'])
action_sign = np.sign(self.actions[action])
if (action_sign > 0):
info['new_trial'] = True
if (gt_sign > 0):
reward = self.R_CORRECT
else:
reward = self.R_INCORRECT
else:
info['gt'][0] = 1
if self.in_epoch(self.t, 'stimulus'):
# observation
stim = (trial['ground_truth'] > 0) + 1
obs[stim] = 1
# ---------------------------------------------------------------------
# new trial?
reward, info['new_trial'] = tasktools.new_trial(
self.t, self.tmax, self.dt, info['new_trial'], self.R_MISS, reward)
if info['new_trial']:
self.t = 0
self.num_tr += 1
else:
self.t += self.dt
done = self.num_tr > self.num_tr_exp
return obs, reward, done, info
def _step(self, action):
trial = self.trial
# ---------------------------------------------------------------------
# Reward
# ---------------------------------------------------------------------
# epochs = trial['epochs']
info = {'new_trial': False}
info['gt'] = np.zeros((3, ))
reward = 0
if (self.in_epoch(self.t, 'fixation')
or self.in_epoch(self.t, 'offer-on')):
if (action != self.actions['FIXATE']):
info['new_trial'] = self.abort
reward = self.R_ABORTED
elif self.in_epoch(self.t, 'decision'):
if action in [
self.actions['CHOOSE-LEFT'], self.actions['CHOOSE-RIGHT']
]:
info['new_trial'] = True
juiceL, juiceR = trial['juice']
nB, nA = trial['offer']
rA = nA * self.R_A
rB = nB * self.R_B
if juiceL == 'A':
rL, rR = rA, rB
else:
rL, rR = rB, rA
if action == self.actions['CHOOSE-LEFT']:
reward = rL
elif action == self.actions['CHOOSE-RIGHT']:
reward = rR
# ---------------------------------------------------------------------
# Inputs
# ---------------------------------------------------------------------
obs = np.zeros(len(self.inputs))
if not self.in_epoch(self.t, 'decision'):
obs[self.inputs['FIXATION']] = 1
if self.in_epoch(self.t, 'offer-on'):
juiceL, juiceR = trial['juice']
obs[self.inputs['L-' + juiceL]] = 1
obs[self.inputs['R-' + juiceR]] = 1
obs[self.inputs['N-L']] = self.scale(trial['nL']) +\
self.rng.gauss(mu=0, sigma=self.sigma)/np.sqrt(self.dt)
obs[self.inputs['N-R']] = self.scale(trial['nR']) +\
self.rng.gauss(mu=0, sigma=self.sigma)/np.sqrt(self.dt)
# ---------------------------------------------------------------------
# new trial?
reward, info['new_trial'] = tasktools.new_trial(
self.t, self.tmax, self.dt, info['new_trial'], self.R_MISS, reward)
if info['new_trial']:
self.t = 0
self.num_tr += 1
else:
self.t += self.dt
done = self.num_tr > self.num_tr_exp
return obs, reward, done, info
def _step(self, action):
# -----------------------------------------------------------------
# Reward
# -----------------------------------------------------------------
trial = self.trial
dt = self.dt
rng = self.rng
# epochs = trial['epochs']
info = {'new_trial': False}
info['gt'] = np.zeros((3, ))
reward = 0
if self.in_epoch(self.t, 'fixation'):
info['gt'][0] = 1
if (action != self.actions['FIXATE']):
info['new_trial'] = self.abort
reward = self.R_ABORTED
elif self.in_epoch(self.t, 'decision'):
info['gt'][int((trial['ground_truth'] / 2 + 1.5))] = 1
if action == self.actions['left']:
info['new_trial'] = True
if trial['context'] == 'm':
correct = (trial['left_right_m'] < 0)
else:
correct = (trial['left_right_c'] < 0)
if correct:
reward = self.R_CORRECT
elif action == self.actions['right']:
info['new_trial'] = True
if trial['context'] == 'm':
correct = (trial['left_right_m'] > 0)
else:
correct = (trial['left_right_c'] > 0)
if correct:
reward = self.R_CORRECT
else:
info['gt'][0] = 1
# -------------------------------------------------------------------------------------
# Inputs
# -------------------------------------------------------------------------------------
if trial['context'] == 'm':
context = self.inputs['motion']
else:
context = self.inputs['color']
if trial['left_right_m'] < 0:
high_m = self.inputs['m-left']
low_m = self.inputs['m-right']
else:
high_m = self.inputs['m-right']
low_m = self.inputs['m-left']
if trial['left_right_c'] < 0:
high_c = self.inputs['c-left']
low_c = self.inputs['c-right']
else:
high_c = self.inputs['c-right']
low_c = self.inputs['c-left']
obs = np.zeros(len(self.inputs))
if (self.in_epoch(self.t, 'fixation')
or self.in_epoch(self.t, 'stimulus')
or self.in_epoch(self.t, 'delay')):
obs[context] = 1
if self.in_epoch(self.t, 'stimulus'):
obs[high_m] = self.scale(+trial['coh_m']) +\
rng.gauss(mu=0, sigma=self.sigma) / np.sqrt(dt)
obs[low_m] = self.scale(-trial['coh_m']) +\
rng.gauss(mu=0, sigma=self.sigma) / np.sqrt(dt)
obs[high_c] = self.scale(+trial['coh_c']) +\
rng.gauss(mu=0, sigma=self.sigma) / np.sqrt(dt)
obs[low_c] = self.scale(-trial['coh_c']) +\
rng.gauss(mu=0, sigma=self.sigma) / np.sqrt(dt)
# ---------------------------------------------------------------------
# new trial?
reward, info['new_trial'] = tasktools.new_trial(
self.t, self.tmax, self.dt, info['new_trial'], self.R_MISS, reward)
if info['new_trial']:
self.t = 0
self.num_tr += 1
else:
self.t += self.dt
done = self.num_tr > self.num_tr_exp
return obs, reward, done, info
