def runblock(task,block):
if block=="try":
# A short try-out block that contains only 4 trials,
# two at the smallest stimulus, two at the biggest one.
# We also give feedback.
runtest(task,["min","max","min","max"])
if block=="train":
# A short try-out block that contains 10 trials, but
# follows the MLP procedure (i.e. dynamic selection
# of the stimuli). We don't give feedback.
# Make sure that we have only 10 trials in total
task.NTRIALS_A = 4
task.N_CATCH_TRIALS_A = 1
task.NTRIALS_B = 4
task.N_CATCH_TRIALS_B = 1
# Then start the MLP
mlp = MLP()
mlp.run( task,
participant="%s-anisochrony-%s"%(participant,
block),
evaluate=evaluateGUI,
preKeypress=False,
)
if block in ["1","2","3"]:
# The "real" experimental blocks
# Then start the MLP
mlp = MLP()
mlp.run( task,
participant="%s-anisochrony-%s"%(participant,
block),
evaluate=evaluateGUI,
preKeypress=False,
)
""" This is an example script that shows how to run the python implementation of MLP. First you have to create a task (here just a null task that asks you for a response). You then feed this to the run() method of an MLP object, and it will run the given task. """ from mlpcore.task import * from mlpcore.mlp import MLP task = Task() mlp = MLP() mlp.run(task)
""" This is an example script that shows how to run the python implementation of MLP. First you have to create a task (here just a null task that asks you for a response). You then feed this to the run() method of an MLP object, and it will run the given task. """ from mlpcore.task import * from mlpcore.mlp import MLP task = Task() mlp = MLP() mlp.run(task)
