How to specify conditions in the sample object for modeling RT within a factorial design ?

[prank24]

Hi,

I am trying to model reaction time data for a behavioral experiment which has a 2 (stimulus_1) x2 (stimulus_2) design. I need separate drift rates for the different conditions. The example in the documentation creates the sample from the solution itself, hence I am not able to find an appropriate example for a factorial design.

In my case, the RT is modelled for stimulus_1 (emotion discrimination - Positive or Negative) which is paired with stimulus_2 valence (compatible or incompatible). Here's how I have approached the solution:

I've created a custom Drift class to handle the varying drift rates based on the "compatibility" condition:

class CustomDrift(Drift):
name = "Drift rate varies by compatibility condition"
required_parameters = ["d_compatible", "d_incompatible"]
required_conditions = ["compatibility"]
def get_drift(self, conditions, **kwargs):
if conditions["compatibility"] == "compatible":
return self.d_compatible
elif conditions["compatibility"] == "incompatible":
return self.d_incompatible

And defined my model as follows:

model_1 = Model(name='PvsN',
drift=CustomDrift(d_compatible=Fittable(minval=-10, maxval=10),
d_incompatible=Fittable(minval=-10, maxval=10)),
noise=NoiseConstant(noise=1.0),
bound=BoundConstant(B=Fittable(minval=0.1, maxval=5)),
overlay=OverlayNonDecision(nondectime=Fittable(minval=0.05, maxval=2)),
choice_names=('P', 'N'),
dx=0.001, dt=0.001, T_dur=2.0
)

I am struggling to understand how to specify the conditions in the sample object. With my half-baked understanding I would write:
sample1 = sample(choice_upper=positive_rts, choice_lower=negative_rts, undecided=0, choice_names=('P','N'))

The documentation states: " To specify conditions, pass a keyword argument to the constructor. The name should be the name of the property, and the value should be a tuple of length two or three. The first element of the tuple should be a list of length equal to the number of correct trials, and the second should be equal to the number of error trials."

It is unclear to me what data do I have to put in the tuple as a list of length equal to the number of correct trials.
I want to model the choice as positive/negative instead of correct/incorrect.
It would be immensely helpful if an example could be shared.

[mwshinn]

You don't need to use the Sample constructor directly, you can make it from a pandas dataframe. Also, you probably don't want to be using the object oriented interface anymore. Take a look at the quickstart guide which describes the new way of interfacing with PyDDM - I think this guide will address all of your questions.