I am working with a model which predicts RT, choice and the path of fixations that subjects make. I am able to run test simulations with

model = bf.simulation.GenerativeModel(prior=prior, simulator=simulator, name=“M”)

if I just use RT and choice, but if I add in the fixations, I get an error in line 660 of simulation.py. Essentially, when running a full experiment with multiple trials, the fixation path matrix has 2 dimensions (100 x nTrials) while the RT and choice are vectors (with length = nTrials) and when simulation.py tries to turn this into a numpy array to save to the sim_data dictionary, an error occurs.

Is there any way that I can specify a 2d matrix as part of the model output, in addition to RT and choice? To clarify, if I run one trial of the model, the output is one reaction time, one choice (1 or 2) and a vector of fixations (e.g. 1, 4 ,4, 5…).

To ensure the output is always the same size, I preallocate a vector of nans with 100 elements (because the model can run for maximum 100 timesteps) and then add the outputted fixation paths to it.

How can I specify to the GenerativeModel or Simulator that the model output is 2 scalars and a vector? Hope this makes sense, thank you!