def load_decks():
global decka_counter
global deckb_counter
global deckc_counter
global deckd_counter
if decka_counter < 40:
decka = "yes"
else:
decka = "no"
if deckb_counter < 40:
deckb = "yes"
else:
deckb = "no"
if deckc_counter < 40:
deckc = "yes"
else:
deckc = "no"
if deckd_counter < 40:
deckd = "yes"
else:
deckd = "no"
deck_chunk = actr.define_chunks([
'isa', 'decks', 'deckA', decka, 'deckB', deckb, 'deckC', deckc,
'deckD', deckd
])
actr.set_buffer_chunk("visual", deck_chunk[0])
def train_once(letter):
global chunk_names;
for chunk in chunk_names[letter]:
actr.set_buffer_chunk('imaginal', chunk[0])
actr.clear_buffer('imaginal')
return
def add_past_tense_to_memory ():
word = random_word()
actr.set_buffer_chunk('imaginal',
actr.define_chunks(['verb',word[0],
'stem',word[1],
'suffix',word[2]])[0])
actr.clear_buffer('imaginal')
def make_one_goal():
word = random_word()
actr.set_buffer_chunk('imaginal',actr.define_chunks(['verb',word[0]])[0])
actr.goal_focus('starting-goal')
return(word)
def present_new_situation(self, buffer="imaginal"):
"""Creates a new situation for the model and presents to the WHERE buffer"""
if actr.mp_time() == self.PTET:
newdef = self.generate_random_memory(traumatic=True)
else:
newdef = self.generate_random_memory(traumatic=False)
newchunk = actr.define_chunks(newdef[0])
actr.set_buffer_chunk(buffer, newchunk[0])
def present_stim():
global chunks
global stims
global i
chunks = actr.define_chunks(['isa', 'stimulus', 'picture', stims[i]])
actr.set_buffer_chunk('visual', chunks[0])
print('Presented: ', stims[i])
print('correct response: ', cor_resps[i])
def run_trial(self, trial):
"""A trial"""
sen = trial.sentence
pic = trial.picture
chunk_s = actr.define_chunks(sen.chunk_definition)[0]
actr.set_buffer_chunk('visual',
chunk_s)
actr.run(time = 10)
chunk_p = actr.define_chunks(pic.chunk_definition)[0]
actr.schedule_set_buffer_chunk('visual',
chunk_p,
actr.mp_time() + 0.05)
actr.run(time = 10)
def task1(prime_stimulus):
"""
This function simulates the prime sentence verification task. The model parses in the prime sentence,
and attempts to comprehend it.
:param prime_stimulus: list, for simplification,
assumes only syntax and syntax-corr changes based on condition
:return:
"""
prime_sentence = actr.define_chunks(prime_stimulus)[0]
actr.set_buffer_chunk('visual', prime_sentence) # prime sentence
# set init goal
# actr.record_history('buffer-trace', 'vocal')
actr.goal_focus('wait-for-screen')
actr.run(10)
def task2(target_stimulus=None):
"""
This function simulates the picture description task. The model observes the picture stimuli
and attempts to describe the picture using one of potential syntactic structure.
:param target_stimulus: None, for simplification,
assume the picture stimuli uses the same verb as prime sentence
:return:
"""
target_stimulus = ['isa', 'picture',
'agent', 'n3',
'patient', 'n4',
'action', 'v']
target_picture = actr.define_chunks(target_stimulus)[0]
# set second goal
actr.goal_focus('wait-for-next-screen')
actr.set_buffer_chunk('visual', target_picture) # target picture
actr.run(10)
def present_feedback():
global i
global current_response
global accuracy
feedback = 'no'
# check if response matches the appropriate key for the current stimulus in cue
#need list of correct responses
if current_response[i] == cor_resps[i]:
feedback = 'yes'
accuracy[i] = 1
chunks = actr.define_chunks(['isa', 'feedback', 'feedback', feedback])
actr.set_buffer_chunk('visual', chunks[0])
print("Feedback given: ", feedback)
#increase index for next stimulus
i = i + 1
actr.schedule_event_relative(1, 'present_stim')
def show_result(choice):
actr.mod_chunk('response', 'answer', choice)
actr.set_buffer_chunk('imaginal', 'response')
actr.schedule_event_relative(2,
'utility-learning-issues-choose',
output='medium')
def load_reward():
global reward
reward_chunk = actr.define_chunks(
['isa', 'reward-amount', 'amount', reward])
actr.set_buffer_chunk("visual", reward_chunk[0])