Now that you have a working version of the basic expression-categorization study (a fixed debugThis2.py), let's split up the trial-generation part from the rest of the experimental script:
generateTrials.py. trials, it writes the trial info to a CSV file called trials.csv which contains in each row all the information needed for the current trial, separated by commas. This first row of this file should contain a column header:isMatch,emotionPrompt,shownActor,shownCategory,targetFaceImage
from generateTrials import *
generateTrials() This should have the effect of creating trials.csv. You should now have a trialList that you can access like so:
trialList = importTrials('trialList.txt')
for curTrial in trialList:
curTrial['isMatch'] #contains 1/0 depending on whether the current trial is a match or mismatch
Why did we go through the trouble of writing to a reading from a file? To have an extra record of the trial list to which a particular subject was exposed and to double-check that the distributions of different conditions are what we want.
Let's add the capability to collect the subject's code. Pop up a box (as you did in Exercise 2 prompting the experimenter to provide a subject code. Pass this values to generateTrials() and have generateTrials use it to create not a generically named trials.csv, but a trials file specific to this participant, e.g., if the subject code is ec_101, the trials-file should be ec_101_trials.csv
Now let's create an output file containing our data! Have your main script write to a subjCode_data.csv file. Each line should correspond to a trial and contain the following information, in this order:
Independent Variables:
Dependent Variables:
Here's a more interesting version of the expressions categorization experiment. The code below will show a prompt (Happy, Angry, or Sad) as before, and then show you three faces (one of which displayes the prompted expression). The user should respond with the the 1 key the prompted expression is on the left, the 2 key if it's in the middle, and the 3 key if on the right.
import random
import random
import sys
import numpy as np
from psychopy import visual, core, event
categories = {'Happy':'F', 'Angry':'W', 'Sad':'T'}
actors = ['001m', '001w', '002m', '002w', '003m', '003w', '004m', '004w', '005m', '005w']
suffix = '_90_60.jpg'
positions = {'left':(-190,0), 'middle':(0,0), 'right':(190,0)}
responseMapping = {'left':'1','middle':'2','right':'3'}
def randomButNot(l,toExclude,num):
chosen = random.sample(l,num)
while toExclude in chosen:
chosen = random.sample(l,num)
return chosen
def generateTrials(numTrials):
trials=[]
for i in range(numTrials):
targetCategory = random.choice(categories.keys())
distractorCategories = randomButNot(categories.keys(),targetCategory,2)
actorsToShow = np.random.choice(actors,3)
#this is the random.choice() function from the numpy library which samples
#with replacement. cf. random.sample() samples WITHOUT replacement
targetLocation = random.choice(positions.keys())
trials.append({
'emotionPrompt':targetCategory,
'targetImage':actorsToShow[0]+categories[targetCategory]+suffix,
'distractorImage1': actorsToShow[1]+categories[distractorCategories[0]]+suffix,
'distractorImage2': actorsToShow[2]+categories[distractorCategories[1]]+suffix,
'targetLocation': targetLocation
})
return trials
trials = generateTrials(40)
win = visual.Window([1024,700],color="black", units='pix')
prompt = visual.TextStim(win=win,text='',color="white",height=60)
correctFeedback = visual.TextStim(win=win,text='CORRECT',color="green",height=60)
incorrectFeedback = visual.TextStim(win=win,text='ERROR',color="red",height=60)
pic1 = visual.ImageStim(win=win, mask=None,interpolate=True)
pic2 = visual.ImageStim(win=win, mask=None,interpolate=True)
pic3 = visual.ImageStim(win=win, mask=None,interpolate=True)
for curTrial in trials:
win.flip()
core.wait(.25)
prompt.setText(curTrial['emotionPrompt'])
prompt.draw()
win.flip()
core.wait(.5)
win.flip()
core.wait(.1)
pic1.setImage('faces/'+curTrial['targetImage'])
pic2.setImage('faces/'+curTrial['distractorImage1'])
pic3.setImage('faces/'+curTrial['distractorImage2'])
pic1.setPos(positions[curTrial['targetLocation']])
distractorPositions = randomButNot(positions.keys(),curTrial['targetLocation'],2)
pic2.setPos(positions[distractorPositions[0]])
pic3.setPos(positions[distractorPositions[1]])
pic1.draw()
pic2.draw()
pic3.draw()
win.flip()
response = event.waitKeys(keyList=responseMapping.values())[0]
print response,responseMapping[curTrial['targetLocation']]
if response==responseMapping[curTrial['targetLocation']]:
correctFeedback.draw()
else:
incorrectFeedback.draw()
core.wait(.5)
Begin by modularizing the generateTrials() code as in Exercise 5a.
Notice how we're displaying the faces in a horizontal orientation. This allows for having the mouths and eyes nicely aligned which may help comparing faces. Let's see if there's an effect of this by intermixing trials with the three faces horizontally oriented as in the code above, and trials that are vertically oriented. To get you started, you'll want to do is update your positions dictionary to this:
positions = {
'vertical': {'bottom':(0,-190), 'middle':(0,0), 'top':(0,190)},
'horizontal': {'left':(-190,0), 'middle':(0,0), 'right':(190,0)}
}
You'll then want to introduce a position factor positionType which is 'vertical' or 'horizontal' (positions.keys()) and based on whether a given trial is 'vertical' or 'horizontal' you'll want to:
curPositionType, use positions[curPositionType] to access the dictionary containing the possible positions.random.choice(positions[curPositionType].keys())Now let's create an output file containing our data! Have your main script write to a subjCode_data.csv file. Each line should correspond to a trial and contain the following information, precisely in this order:
Independent Variables:
Dependent Variables:
You may run into some trouble figuring out the emotion of the face that the participant clicked on.
If you set your generateTrials() function correctly, the following code will work:
isRight = int(pic1.contains(response))
if isRight:
correctFeedback.draw()
emotionChosen = curTrial['emotionPrompt']
else:
incorrectFeedback.draw()
if pic2.contains(response):
emotionChosen = curTrial['distractorEmotion1']
elif pic3.contains(response):
emotionChosen = curTrial['distractorEmotion2']
Please run yourself on this task to produce 100 trials of data. Should take <10 mins. Please take care to have your output file be precisely in the above-mentioned format so that we can combine data from everyone in the class. Here is a sample output file. To check that your data is in the correct format, do the following:
At the terminal, inside
cat sample.csv your_data.csv > data_format_test.csv
(replacing your_data.csv with the name of your output file, and including the appropriate path to sample_data.csv if it's not in the same directory as your data)
Load the data into R:
In R: dat <- read.csv('data_format_test.csv')
Look at the summary: summary(dat)
Let's cross the actor and positionType to see if e.g., having faces lined up in horizontally helps especially when they're all of same gender. You want to have the following trial distribution
horizontal (50%). Of these:
same-gender(Male) - 25%
same-gender(Female) - 25%
different-gender - 50%
vertical (50%). Of these:
same-gender(Male) - 25%
same-gender(Female) - 25%
different-gender - 50%To cross factors, you can use for-loops, but a more compact way is to use the itertools package. So you might want to do something like this:
from itertools import product
positions = {
'vertical': {'bottom':(-190,0), 'middle':(0,0), 'top':(190,0)},
'horiontal': {'left':(0,-190), 'middle':(0,0), 'right':(0,190)}
}
genderMix = {'same-gender':['male','female'], 'diff-gender':[]}
trialTypes = list(product(positions.keys(), genderMix.keys()))
print trialTypes