Coding a full experiment¶

How do we even start?¶

Don’t be tempted to try and write a script from beginning to end in one go! Break it down into chunks that you can manage. e.g.:

• create a trial

  • create window

  • create fixation, cue and probe stimuli

  • set up the timing of the trial

• set up conditions

  • need to alter probe location

  • need to alter cues

• make sure data are being saved

Create a window¶

First we need to import the necessary libraries. For an experiment we nearly always need to import the visual, event, data and core modules from PsychoPy:

from psychopy import visual, event, core, data, gui

then creating a window is another single line. We’ll use units of pixels for the window for simplicity. Then all our stimulus dimensions will be in pixels:

win = visual.Window([1024,768], fullscr=False, units='pix')

Save your experiment and run it to make sure a window flashes up.

Our trial starts with a fixation¶

Immediately after creating our window we usually initialise objects like stimuli and clocks:

#initialise some stimuli
fixation = visual.Circle(win, size = 5,
    lineColor = 'white', fillColor = 'lightGrey')

Later (on each trial) we’ll need to draw the fixation point and then flip the screen so that the drawing becomes visible:

#run one trial
fixation.draw()
win.flip()

Create your probe stimulus¶

Just for variety, let’s create a Gaussian spot for the probe. You need this code where your stimuli are being initialised (doesn’t matter if it’s before or after probe):

probe = visual.GratingStim(win, size = 80, # 'size' is 3xSD for gauss,
    pos = [300, 0], #we'll change this later
    tex = None, mask = 'gauss',
    color = 'green')

After drawing the fixation point and flipping, we need to do the same for the probe:

probe.draw()
win.flip()

We also need a cue¶

We could use some image of an arrow for this. Or we could create some shape of our own with custom vertices:

cue = visual.ShapeStim(win,
    vertices = [[-30,-20], [-30,20], [30,0]],
    lineColor = 'red', fillColor = 'salmon')

Also add draw() code like the other objects. Again, it doesn’t matter the order we initialise it, but the drawing needs to be between the fixation and the probe.

Understanding Window.flip()¶

• All the draw() commands operate on a memory buffer called the ‘back buffer’ on the graphics card.

• When you flip() the window it causes everything in that ‘back buffer’ to become visible on the physical screen.

• The flip() command waits until the next screen refresh to present your stimuli (every 1/60s, so about 16.6ms)

• It will then wait until the physical screen refresh occurs (if possible with your graphics card settings)

This has various knock-on effects:

• That means your screen flips (and intervening code) are tied to a fixed rate of 1/60s

• It is physically impossible to draw your stimulus for partial frames (e.g. 25ms) on a 60Hz screen

• Also, if Python/PsychoPy has to run too much code between flips you might ‘drop’ a frame (fail to get it drawn by the time of the screen refresh)

• If you don’t call flip() for a while, or if you drop a frame, the screen will stay as it is for another frame

Set some timing parameters¶

If you run now the objects will be presented for a single frame each (1/60th of sec). That’s too short for us to see. We need to set times for our objects. we can achieve that with the core.wait() function.

Possible: “hard code” the values by typing them where needed.

Better: store them as variables at the top of the script

Even better: store them in a dictionary that we can save easily in the data files:

info = {} #a dictionary
info['fixTime'] = 0.5 # seconds
info['cueTime'] = 0.2
info['probeTime'] = 0.2

Pause after flipping the window for each object¶

Add a line to wait after each flip of the window:

# run one trial
fixation.draw()
win.flip()
core.wait(info['fixTime'])

cue.draw()
win.flip()
core.wait(info['cueTime'])

probe.draw()
win.flip()
core.wait(info['probeTime'])

This is not actually a very precise way to control timing, but it’s very easy!

Drawing two objects at the same time¶

If you draw() two stimuli before a win.flip() then they both appear on the same frame. For the probe presentation let’s have the fixation as well:

fixation.draw()
probe.draw()
win.flip()
core.wait(info['probeTime'])

If the stimuli overlap in space then the later draw() will occlude the earlier one. You can also set the degree of opacity of stimuli so that they are partially visible.

Let’s run two trials¶

We could copy and paste the trial code to run repeated trials.

Try doing that so that you get two repeats of the trial.

That’s very inefficient though, so undo it.

Exercise: Why not create a loop to run as many trials as you like? That would be more efficient. Add a for… loop and indent your trial code so that it is ‘contained’ in the loop. Set the loop to run for 5 ‘repeats’.

Solution:

for trial in range(5):
    fixation.draw()
    win.flip()
    core.wait(info['fixTime'])

    cue.draw()
    win.flip()
    core.wait(info['cueTime'])

    probe.draw()
    win.flip()
    info['probeTime']

Remember us mentioning makign an experiment was a good way to learn to code? Well, this is a useful point for us to practice different methods for Loops in Python.

Controlling conditions¶

We need the stimuli to differ on each trial, which TrialHandler can also help us with. It expects to receive conditions (aka trialTypes) as a list of dictionaries, where one dictionary specifies the parameters for one condition. We could write that by code using a for…loop, but it might be easier this time to use a spreadsheet.

You could have achieved exactly the same as this using code to create a list of dictionaries with one dictionary for each type of trial in your conditions.

Create a conditions file¶

We can import conditions from either .xlsx or .csv files (the same way we do in builder).

Create a file with:

• headings that specify dictionary fields

• (headings better with no spaces or punctuation, although that won’t actually matter today)

• one row per condition/trial-type

• no missing columns or rows (e.g. don’t leave a row between header and trials)

For the Posner task we need control of:

• direction of cue (we could rotate the stimulus 180 deg to point the other way)

• location of probe

For analysis it’s handy also to store:

• something to signal whether this trial is ‘valid cue’

• a description of this trial?

So we might have a sheet like this:

Save the file in xlsx or csv format. e.g. “conditions.csv”

Import that file and put it to use¶

The data module in PsychoPy has a function to import such files. It gives a list of dicts that can be used directly in the TrialHandler:

conditions = data.importConditions('conditions.csv')
trials = data.TrialHandler(trialList=conditions, nReps=5)
for thisTrial in trials:
    #code to run one trial here
    ...

This will run 5 repeats of our 10 trial types randomly. The way we’ve set this up we’ll get 50 trials with 80% valid probes.

Updating stimuli¶

Each time through the loop the value thisTrial is a dictionary for one trial, with keys that have the column names:

for thisTrial in trials:
    #code to run one trial here
    probe.setPos( [thisTrial['probeX'], 0] )
    cue.setOri( thisTrial['cueOri'] )

You can see the code changes here through looking at the version history on the gitlab project page.

Create a base filename¶

Let’s create a filename using the participant name and the date. OK, so we’ll need to get those!

For the username, we can easily create a dialog box that uses our info dictionary to store information (top of our script):

info = {} #a dictionary
#present dialog to collect info
info['participant'] = ''
dlg = gui.DlgFromDict(info) #(and from psychopy import gui at top of script)
if not dlg.OK:
    core.quit()
#add additional info after the dialog has gone
info['fixTime'] = 0.5 # seconds
info['cueTime'] = 0.2
info['probeTime'] = 0.2
info['dateStr'] = data.getDateStr() #will create str of current date/time

Now we’ve collected the information there are various ways to create our filename string. All of these achieve the same thing, e.g. data/jwp_2014_Apr_13_1406

filename = "data/" + info['participant'] + "_" + info['dateStr']
filename = "data/%s_%s"%(info['participant'], info['dateStr'])
filename = "data/{0}_{1}".format(info['participant'], info['dateStr'])
filename = "data/{0['participant']}_{0['dateStr']}".format(info)
filename = "data/{participant}_{dateStr}".format(**info)

You can see them looking increasingly obscure, but increasingly brief.

Create ExperimentHandler¶

After your code to create the TrialHandler loop:

#add trials to the experiment handler to store data
thisExp = data.ExperimentHandler(
        name='Posner', version='1.0', #not needed, just handy
        extraInfo = info, #the info we created earlier
        dataFileName = filename, # using our string with data/name_date
        )
thisExp.addLoop(trials) #there could be other loops (like practice loop)

AND at the end of the response collection we need to inform the experiment handler that it’s time to consider the trial complete:

...
trials.addData('rt', rt)
trials.addData('corr', corr)
thisExp.nextEntry()

Exercise¶

You can use the Coder “demos” to help find relevant code snippets.

In code:

1. Store if the participant was correct or incorrect to the data file.

2. Add some feedback text to tell the participant if they were correct or incorrect.

3. Make this feedback green/red for correct/incorrect responses.

4. Add instructions to the start of the experiment.