PsychoPy: "Conditional" but random selection from list across two dimensions

Question

In my experiment, I am presenting images (faces) that are different across 2 dimensions: face identity and emotion.

There are 5 faces displaying 5 different emotional expressions; making 25 unique stimuli in total. These only need to be presented once (so 25 trials).

After I present one of the faces, the next face has to be different on only the emotion OR the identity, but the same on the other.

Example:

Face 1, emotion 1 -> face 3, emotion 1 -> face 3, emotion 4 -> ... etc.

1: is psychopy up to this task? I have mostly worked with the builder so far, except for some data-logging code, but I'd be happy to get more experienced with the coder.

My hunch is that I would need to add two columns to the list of trials, one for identity and one for emotion. Then use the getEarlierTrial call somehow, but I pretty much get lost at this point.

2: Would anyone be willing to point me in the right direction please?

Many thanks in advance.

Answer 1

This is difficult to implement in Builder's normal mode of operation, which is to drive trials from a fixed list of conditions. Although the order of rows can be randomised across subjects, the pairings of values across columns remains constant.

The standard answer to this is what you allude to in your comment above: in code, shuffle the conditions file at the beginning of each experiment, so each subject is in essence having their trials driven by a unique conditions file.

You seem happy to do this in Matlab. That would work fine, as this stuff can be done before PsychoPy even launches. But it could also very easily be implemented in Python code. That way you could do everything in PsychoPy, and in this case there would be no need to abandon Builder. You'd just insert a code component with some code to run at the beginning of the experiment that customises a conditions file.

You'll need to create three lists, not two, i.e. you also need a list of pseudo-random choices to alternate between preserving either face or emotion from trial to trial: if you do this fully randomly, you'll get unbalanced, and exhaust one of the attributes before the other.

from numpy.random import shuffle

# make a list of 25 dictionaries of unique face/emotion pairs:
pairsList = []
for face in ['1', '2', '3', '4', '5']:
    for emotion in ['1', '2', '3', '4', '5']:
        pairsList.append({'faceNum': face, 'emotionNum': emotion})
shuffle(pairsList)

# a list of whether to alternate between preserving face or emotion across trials:
attributes = ['faceNum', 'emotionNum'] * 12 # length 24
shuffle(attributes) 

# need to create an initial selection before cycling though the 
# next 24 randomised but balanced choices:
pair = pairsList.pop()
currentFace = pair['faceNum']
currentEmotion = pair['emotionNum']
images = ['face_' + currentFace + '_emotion_' + currentEmotion + '.jpg']

for attribute in attributes:
    if attribute == 'faceNum':
        selection = currentFace
    else:
        selection = currentEmotion

    # find another pair with the same selected attribute:
    for pair in pairsList:

        if pair[attribute] == selection:
            # store the combination for this trial:
            currentFace = pair['faceNum']
            currentEmotion = pair['emotionNum']
            images.append('face_' + currentFace + '_emotion_' + currentEmotion + '.jpg')

            # remove this combination so it can't be used again
            pairsList.remove(pair)

images.reverse()
print(images)

Then just write the images list to a single column .csv file to use as a conditions file.

Remember to set the loop in Builder to be in a fixed order, not randomised, as the list itself has the randomisation built in.