def set_autorun(self, exp_plan):
def rt(mean, trialno):
add = np.random.normal(mean,scale=.2) / self.rp['autorun']
return self.trial[0].dur + add
invert_resp = exp.invert_dict(self.computer.valid_responses)
for trialno, trial in enumerate(exp_plan):
if trial['context'] == 'parts':
acc = [.1,.1,.1,.1]
acc[trial['pos']] = .7
resp = exp.weighted_choice(choices=invert_resp, weights=acc)
trial['autoresp'] = resp # poor accuracy
trial['autort'] = rt(1., trialno) # slow responses
elif trial['context'] == 'whole': # lower accuracy for morphed
acc = [.05,.05,.05,.05]
acc[trial['pos']] = .85
resp = exp.weighted_choice(choices=invert_resp, weights=acc)
trial['autoresp'] = resp # good accuracy
trial['autort'] = rt(.8, trialno) # fast responses
return exp_plan
def set_autorun(self, exp_plan):
def rt(mean, trialno):
add = np.random.normal(mean, scale=.2) / self.rp['autorun']
return self.trial[0].dur + add
invert_resp = exp.invert_dict(self.computer.valid_responses)
for trialno, trial in enumerate(exp_plan):
if trial['context'] == 'parts':
acc = [.1, .1, .1, .1]
acc[trial['pos']] = .7
resp = exp.weighted_choice(choices=invert_resp, weights=acc)
trial['autoresp'] = resp # poor accuracy
trial['autort'] = rt(1., trialno) # slow responses
elif trial['context'] == 'whole': # lower accuracy for morphed
acc = [.05, .05, .05, .05]
acc[trial['pos']] = .85
resp = exp.weighted_choice(choices=invert_resp, weights=acc)
trial['autoresp'] = resp # good accuracy
trial['autort'] = rt(.8, trialno) # fast responses
return exp_plan
def get_autorun_vals(self, pos, this_intensity, corr_resp):
"""If autorun, simulate user responses
"""
def rt(mean):
add = np.random.normal(mean,scale=.2)/self.rp['autorun']
return trial_dur + add
invert_resp = exp.invert_dict(self.computer.valid_responses)
trial_dur = sum([ev.dur for ev in self.trial])
thres = np.random.normal(self.convergeto[pos], scale=1)
thres = max(0, thres)
invert= {'right': 'left', 'left': 'right'}
if this_intensity < thres:
fake_resp = invert[corr_resp] # wrong response
else:
fake_resp = corr_resp # correct response
auto = [('autoresp', invert_resp[fake_resp]),
('autort', rt(0.500))]
return auto
def get_autorun_vals(self, posno, this_intensity, corr_resp):
"""If autorun, simulate user responses
"""
def rt(mean):
add = np.random.normal(mean, scale=.2) / self.rp['autorun']
return trial_dur + add
invert_resp = exp.invert_dict(self.computer.valid_responses)
trial_dur = sum([ev.dur for ev in self.trial])
if corr_resp == 'same':
fake_resp = 'same' # participants should be perfect here
else:
thres = np.random.normal(self.convergeto[posno], scale=1)
thres = max(0, thres)
if this_intensity < thres:
fake_resp = 'same'
else:
fake_resp = 'diff'
auto = [('autoresp', invert_resp[fake_resp]), ('autort', rt(0.500))]
return auto
def set_autorun(self, exp_plan):
def rt(mean):
add = np.random.normal(mean,scale=.2) / self.rp['autorun']
return trial_dur + add
invert_resp = exp.invert_dict(self.computer.valid_responses)
trial_dur = sum([ev.dur for ev in self.trial])
for trial in exp_plan:
if trial['pos'] == 'attended':
acc = .9
elif trial['pos'] == 'unattended':
acc = .6
if trial['corr_resp'] == 'same':
resp_ind = exp.weighted_choice(choices=invert_resp.keys(),
weights=[1-acc, acc])
else:
resp_ind = exp.weighted_choice(choices=invert_resp.keys(),
weights=[acc, 1-acc])
trial['autoresp'] = invert_resp[resp_ind]
trial['autort'] = rt(.8)
return exp_plan
def set_autorun(self, exp_plan):
def rt(mean):
add = np.random.normal(mean, scale=.2) / self.rp['autorun']
return trial_dur + add
invert_resp = exp.invert_dict(self.computer.valid_responses)
trial_dur = sum([ev.dur for ev in self.trial])
for trial in exp_plan:
if trial['pos'] == 'attended':
acc = .9
elif trial['pos'] == 'unattended':
acc = .6
if trial['corr_resp'] == 'same':
resp_ind = exp.weighted_choice(choices=invert_resp.keys(),
weights=[1 - acc, acc])
else:
resp_ind = exp.weighted_choice(choices=invert_resp.keys(),
weights=[acc, 1 - acc])
trial['autoresp'] = invert_resp[resp_ind]
trial['autort'] = rt(.8)
return exp_plan