Hi. So I am soon (a few weeks?) about to do an alpha release of a Python based package which will allow people to run Bayesian Adaptive Design on their experiments from within PsychoPy. Think along the lines of staircases, but Bayesian, which takes you to QUEST / Psi-Marginal etc, but better, and then add the ability to simultaneously generate multiple design variables (aka stimulus properties) so as to maximis information gain per experimental trial. The primary set of experiments will be based around delayed and risky choice tasks, but the approach can be extended to any 2-choice tasks such as yes/no, 2AFC.
I was hoping to get some advice or pointers on whether my general approach was good, or needed some tweaks. The plan is to make it as easy as possible, and to work on mac, pc, ms surface, etc…
- install Anaconda Python 3
- Install PsychoPy
- Download my toolbox code containing PsychoPy experiments and my Python code
- run one of the psychopy experiments.
Question 1: Am I right in going for Python 3 here? I’ve managed to get PsychoPy 1.90.1 and Python 3.6 working on both Mac and PC, but I have had some issues making it work on a Mac with Python 3.7. So as far as I’m concerned, it works, but I’m not clear if I’m making life harder for myself going for Python 3?
Question 2: My objective of simplicity for the experimenter. I want to maximise probability that this will work first time with no headaches. But if I wanted to use other Python packages is it best to
- Figure out some way of pip or conda installing these packages then figuring out how to make PsychoPy aware of those packages
- Go old school and just copy/paste other people’s packages into mine. This seems easiest, but potentially it’s flawed in a way that I’m not aware of now.
- or… Just really don’t. There is no reliable method