Writing your own Experiments

First Steps

pychoacoustics can be easily extended with new experiments written by users. User-written experiments need to reside in a Python package called labexp, and this package needs to be in your Python path. No worries if you’re not familiar with packaging Python software, we’ll go through the process of adding a new experiment step by step.

First of all, you need to create a directory called pychoacoustics_exp inside your home directory, and a sub-directory called labexp inside the pychoacoustics_exp directory. If you don’t know where your home directory is located you can find out from a Python shell with the following commands:

import os
os.path.expanduser('~')

You can create the pychoacoustics_exp and labexp directories from a Python shell as shown below:

import os
dirPath = os.path.expanduser('~/pychoacoustics_exp/labexp/')
os.makedirs(dirPath)

Each user experiment will be written in a single file contained in the labexp directory. Let’s imagine we want to create an experiment for a frequency discrimination task. We create a file named freq.py in the labexp directory. In addition to the experiment file we need an additional file that lists all the experiments contained in the labexp directory. This file must be named __init__.py, and in our case it will have the following content:

__all__ = ["freq"]

here the variable __all__ is simply a Python list with the name of the experiment files. So, if one day we decide to write a new experiment on, let’s say, level discrimination, in a file called lev.py we would simply add it to the list in __init__.py:

__all__ = ["freq",
           "lev"]

For people familiar with packaging Python modules it should be clear by now that the labexp folder is a Python package containing various modules (the experiment files). If at some point we want to remove an experiment from pychoacoustics, for example because it contains a bug that does not allow the program to start, we can simply remove it from the list in __init__.py. Let’s go back to the freq.py file. Here we need to define three functions. For our example the names of these functions would be:

initialize_freq()
select_default_parameters_freq()
doTrial_freq()

basically the function names consist of a fixed prefix, followed by the name of the experiment file. So, in the case of the level experiment example, written in the file lev.py, the three functions would be called:

initialize_lev()
select_default_parameters_lev()
doTrial_lev()

we’ll look at each function in detail in the next section. Briefly, the initialize_ function is used to set some general parameters and options for our experiment; the select_default_parameters_ function lists all the widgets (text fields and choosers) of our experiment and their default values; finally, the doTrial_ function contains the code that generates the sounds and plays them during the experiment.

Anatomy of a `pychoacoustics` experiment file

The `initialize_` function

The initialize_ function of our frequency discrimination experiment is shown below:

 def initialize_freq(prm):
   exp_name = "Frequency Discrimination Demo"
   prm["experimentsChoices"].append(exp_name)
   prm[exp_name] = {}
   prm[exp_name]["paradigmChoices"] = ["Transformed Up-Down",
                                       "Weighted Up-Down",
                                       "UML",
                                       "PSI"]

   prm[exp_name]["opts"] = ["hasISIBox", "hasAlternativesChooser",
                            "hasFeedback"]

   prm[exp_name]['defaultAdaptiveType'] = "Geometric"
   prm[exp_name]['defaultNIntervals'] = 2
   prm[exp_name]['defaultNAlternatives'] = 2
   prm[exp_name]["execString"] = "freq"
   prm[exp_name]["version"] = "1"

   return prm

When the function is called, it is passed a dictionary containing various parameters through the prm argument. The function modifies this dictionary by adding the parameters of the experiment, and returns the dictionary back to the main routine.

Let’s analyze the function for our experiment. On line 2 we give a label to the experiment, this can be anything we want, except the label of an experiment already existing. On line 3 we add this experiment label to the list of “experimentsChoices”. On line 4 we create a new sub-dictionary that has as a key the experiment label. Next we list the paradigms that our experiment supports by creating a paradigmChoices key and giving the names of the supported paradigms as a list. The paradigms listed here must be within the set of paradigms supported by pychoacoustics (see Section Available Paradigms for a description of the paradigms currently supported). In the next line we set an opts key containing a list of options. The full list of options that can be set here is described in details in Section The Experiment “opts”. In brief, for our experiment we want to have a widget to set the silent interval (ISI) between presentation intervals (hasISIBox), a widget to choose the number of response alternatives (hasAlternativesChooser), and a widget to set the feedback on or off for a given block of trials (hasFeedback).

In the next line we specify defaultAdaptiveType, the default type of adaptive track that will be selected when the experiment is loaded, this could be either “Geometric”, or “Arithmetic”. Specifying a “defaultAdaptiveType” is optional. The type of the adaptive procedure can in any case be changed later by the experimenter in the control window. In the next two lines we specify the default number of intervals, and the default number of alternatives that will be used when the experiment is loaded. Since we have inserted the “hasAlternativesChooser” option, the number of intervals and alternatives can be later changed by the experimenter using the appropriate choosers in the control window. The next line of the initialize_ function sets the execString of our experiment. This must be the name of our experiment file, so in our case freq. Finally, we give our experiment a version label. This is optional, but it can be very useful as this version label will be stored in the result files when the experiment is run. This makes it possible to track which version of the experiment was used in a given session.

Before we proceed, a note on the use of a function called QApplication.translate is necessary. You may occasionally see this function in pychoacoustics experiment files and in this manual. This function serves to translate strings from one language to another. For the moment it doesn’t really do much in pychoacoustics because string translation is not currently functional for the control window, it is only functional for the response box. This function takes three string arguments, and the text to be translated is the middle argument. For example, in the initialize_ function above, we could have written QApplication.translate("", "Transformed Up-Down", "") instead of Transformed Up-Down. You don’t need to use this function in your experiments. If you do, you need to import the QApplication. How to do this depends on which version of PyQt you’re using, as shown below:

from PyQt4.QtGui import QApplication #if you're using PyQt4
from PySide.QtGui import QApplication #if you're using PySide
from PyQt5.QtWidgets import QApplication #if you're using PyQt5

The `select_default_parameters_` function

All the widgets (text fields and choosers) needed for an experiment are defined in the select_default_parameters_ function. For our frequency discrimination experiment, the function looks as follows:

 def select_default_parameters_freq(parent, paradigm, par):

   field = []
   fieldLabel = []
   chooser = []
   chooserLabel = []
   chooserOptions = []

   fieldLabel.append("Frequency (Hz)")
   field.append(1000)

   fieldLabel.append("Difference (%)")
   field.append(20)

   fieldLabel.append("Level (dB SPL)")
   field.append(50)

   fieldLabel.append("Duration (ms)")
   field.append(180)

   fieldLabel.append("Ramps (ms)")
   field.append(10)


   chooserOptions.append(["Right",
                          "Left",
                          "Both"])
   chooserLabel.append("Ear:")
   chooser.append("Right")

   prm = {}
   prm['field'] = field
   prm['fieldLabel'] = fieldLabel
   prm['chooser'] = chooser
   prm['chooserLabel'] = chooserLabel
   prm['chooserOptions'] =  chooserOptions

   return prm

The select_default_parameters_ function accepts three arguments, “parent” is simply a reference to the pychoacoustics application, “paradigm” is the paradigm with which the function has been called, while “par” is a variable that can hold some special values for initializing the function. The use of the “par” argument will be discussed later on when procedures with interleaved tracks will be described. For the time being you should just know that the select_default_parameters_ should always have this argument. From line three to line seven, we create a series of empty lists. The field and fieldLabel lists will hold the default values of our text field widgets, and their labels, respectively. The chooser and chooserLabel lists will likewise hold the default values of our chooser widgets, and their labels, while the chooserOptions list will hold the possible values that our choosers can take. On lines 9 to 29 we populate these lists for our frequency discrimination experiment. From line 31 to line 36 we insert in a dictionary the field, fieldLabel, chooser, chooserLabel and chooserOptions lists that we previously creaetd and populated. Finally, on line 38, the function returns this dictionary.

The `doTrial_` function

The doTrial_ function is called each time a trial is started, and is responsible for generating the sounds and presenting them to the listener. The doTrial_ function for our frequency discrimination experiment is shown below:

def doTrial_freq(parent):

   currBlock = 'b'+ str(parent.prm['currentBlock'])
    if parent.prm['startOfBlock'] == True:
        parent.prm['adaptiveParam'] = \
          parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Difference (%)")]
        parent.writeResultsHeader('log')

    frequency = \
      parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Frequency (Hz)")]
    level = \
      parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Level (dB SPL)")]
    duration = \
      parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Duration (ms)")]
    ramps = \
      parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Ramps (ms)")]
    channel = \
      parent.prm[currBlock]['chooser'][parent.prm['chooserLabel'].index("Ear:")]
    phase = 0

    correctFrequency = frequency + (frequency*parent.prm['adaptiveParam'])/100
    stimulusCorrect = pureTone(correctFrequency, phase, level, duration,
                               ramps, channel, parent.prm['sampRate'],
                               parent.prm['maxLevel'])

    stimulusIncorrect = []
    for i in range((parent.prm['nIntervals']-1)):
        thisSnd = pureTone(frequency, phase, level, duration, ramps, channel,
                           parent.prm['sampRate'], parent.prm['maxLevel'])
        stimulusIncorrect.append(thisSnd)

    parent.playRandomisedIntervals(stimulusCorrect, stimulusIncorrect)

As you can see on the first line, the doTrial_ function is passed as an argument its parent. This is important because the parent contains a dictionary with the parameters for the current experiment (parent.prm). The parameters for each stored block of the experiment are stored in the parent.prm dictionary with keys starting with b followed by the block number. For example parent.prm['b3'] contains the parameters for the third stored block. The current block number is stored in parent.prm['currentBlock'], and on line 3 we retrieve the dictionary key for the current block. On line 4 we start an if block that is executed only at the first trial of each block. In this block we retrieve the % frequency difference between the standard and the comparison stimuli for the first trial, and we store it in the parent.prm['adaptiveParam'] variable. Since we’re using an adaptive procedure, this variable will be automatically increased or decreased by pychoacoustics on successive trials on the bases of the responses given by the listener. On line 7 we tell pychoacoustics to write the header of the ‘log’ result files (see Log Results Files).

On lines 9-16 we read off the values of the text field widgets for the current block of trials. The values of these field widgets are stored in the list parent.prm[currBlock]['field'], and we exploit the label of each text field widget to retrieve its index in the list. For example parent.prm['fieldLabel'].index("Frequency (Hz)") retrieves the index of the text widget that stores the frequency of the standard tone for the current block of trials. On line 18 we read off the value of the only chooser widget for the current block of trials. The values of chooser widgets are stored in the list parent.prm[currBlock]['chooser'], and we exploit the label of each chooser widget to retrieve its index in the list as we did for text field widgets.

Our next step will be to generate the stimuli for the trial. In a X-Intervals task we have to generate X stimuli. In our case, the standard stimuli will have always the same frequency, we retrieved its value on lines 9-10 of our doTrial_ function. If a listener presses the button corresponding to one of the the standard stimuli his response will be incorrect. For this reason we will store the standard stimuli in a list called stimulusIncorrect = []. The comparison stimulus will be instead stored in a variable called stimulusCorrect. The frequency of the comparison stimulus, which can vary from trial to trial, depending on the current value of parent.prm['adaptiveParam'] is computed on line 21. On lines 22-24 we generate the stimulus using the pureTone function that is available in the sndlib module. Note that in order to access this function you need to import it by adding the following line at the top of the freq.py file where the experiment is stored:

from pychoacoustics.sndlib import pureTone

Note also that we need to pass the current samplig rate and the current maximum output level of our headphones (see Edit Phones Dialog) to the pureTone function. Their values are stored respectively in the parent.prm['sampRate'] and parent.prm['maxLevel'] variables. On lines 26-30 we generate and store the standard stimuli in the stimulusIncorrect list. The number of standard stimuli to generate will be equal to the number of intervals minus one. The number of intervals is stored in the parent.prm['nIntervals'] variable. Finally on line 32 we call the parent.playRandomisedIntervals function to play the stimuli. This function requires two arguments, the correct stimulus, and a list containing the incorrect stimuli. That’s it, our frequency discrimination experiment is ready and we can test it on pychoacoustics.

Adding support for the Constant Paradigm

So far our frequency discrimination experiment supports only adaptive paradigms.

Adding support for the constant paradigm, in which the frequency difference between the standard and comparison stimuli is fixed across a block of trials is easy. All we need to do is add “Constant m-Intervals n-Alternatives” to the list of paradigms supported paradims in the initialize_ function:

prm[exp_name]["paradigmChoices"] = ["Transformed Up-Down",
                                    "Weighted Up-Down",
                                    "UML",
                                    "PSI"
                                    "Constant m-Intervals n-Alternatives"]

Now our frequency discrimination task supports also the constant paradigm.

Showing/Hiding Widgets Dynamically

Often you may want to write a single experiment that can handle a number of different experimental conditions. This usually leads to a growing number of widgets in the control window that can be distracting. To address this issue, in pychoacoustics it is possible to dinamically show or hide widgets depending on the value taken by chooser widgets. To do this, you need to write a function called get_fields_to_hide_ that specifies the conditions upon which certain widgets are shown or hidden.

For a practical example, let’s extend the frequency discrimination experiment described in the sections above so that it can handle not only conditions in which the standard frequency is fixed, but also conditions in which the standard frequency is roved from trial to trial within a specified frequency range. In the select_default_parameters_ function of our frequency discrimination experiment we had a text field for setting the standard frequency:

fieldLabel.append("Frequency (Hz)")
field.append(1000)

now we’ll add two additional text fields to set the frequency range for the roved-frequency case:

fieldLabel.append("Frequency (Hz)")
field.append(1000)

fieldLabel.append("Min. Frequency (Hz)")
field.append(250)

fieldLabel.append("Max. Frequency (Hz)")
field.append(4000)

we also add a chooser to control whether for the current block the standard frequency should be fixed or roved:

chooserOptions.append(["Fixed",
                       "Roved"])
chooserLabel.append("Standard Frequency:")
chooser.append("Fixed")

The get_fields_to_hide_ for this experiment is shown below:

def get_fields_to_hide_freq(parent):
   if parent.chooser[parent.prm['chooserLabel'].index("Standard Frequency:")].currentText() == "Fixed":
      parent.fieldsToHide = [parent.prm['fieldLabel'].index("Min. Frequency (Hz)"),
                             parent.prm['fieldLabel'].index("Max. Frequency (Hz)")]
      parent.fieldsToShow = [parent.prm['fieldLabel'].index("Frequency (Hz)")]
   elif parent.chooser[parent.prm['chooserLabel'].index("Standard Frequency:")].currentText() == "Roved":
      parent.fieldsToHide = [parent.prm['fieldLabel'].index("Frequency (Hz)")]
      parent.fieldsToShow = [parent.prm['fieldLabel'].index("Min. Frequency (Hz)"),
                             parent.prm['fieldLabel'].index("Max. Frequency (Hz)")]

As for the other experiment functions that we have discussed before, the actual name is the concatenation of a prefix, in this case get_fields_to_hide_, and the name of the experiment file, in this case freq. As you can see on line 1, this function takes as an argument parent, which contains the lists of widgets for the current experiment. We need to tell the get_fields_to_hide_ function that if the standard frequency is fixed, it should show only the Frequency (Hz) text field, and hide the Min. Frequency (Hz) and Max. Frequency (Hz) text fields. Vice-versa, if the standard frequency is roved, it should show only the Min. Frequency (Hz) and Max. Frequency (Hz) text fields, and hide the Frequency (Hz) text field. On line 2 we start an if block which will be executed if the value of the Standard Frequency chooser (retrieved by the currentText attribute), is set to Fixed. Note how we exploit once again the chooserLabel to find the index of the chooser we want with parent.prm['chooserLabel'].index("Standard Frequency:"). Next, we define two lists, one containing the indexes of the fields to hide parent.fieldsToHide, and one containing the indexes of the fields to show parent.fieldsToShow. Once more we exploit the fieldLabel to retrieve the indexes of the fields we want to get (e.g. parent.prm['fieldLabel'].index("Min. Frequency (Hz)")). From line 6 to line 9 we handle the case in which the standard frequency is roved. The logic of the code is the same as for the fixed standard frequency case.

To complete the experiment we need to add a couple of lines to the doTrial_ function to handle the case in which the standard frequency is roved. The new function is shown below:

def doTrial_freq2(parent):
   currBlock = 'b'+ str(parent.prm['currentBlock'])
   if parent.prm['startOfBlock'] == True:
      parent.prm['adaptiveParam'] = \
        parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Difference (%)")]
      parent.writeResultsHeader('log')

   frequency = \
     parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Frequency (Hz)")]
   minFrequency = \
     parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Min. Frequency (Hz)")]
   maxFrequency = \
     parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Max. Frequency (Hz)")]
   level = \
     parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Level (dB SPL)")]
   duration = \
     parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Duration (ms)")]
   ramps = \
     parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Ramps (ms)")]
   phase = 0
   channel = \
     parent.prm[currBlock]['chooser'][parent.prm['chooserLabel'].index("Ear:")]
   stdFreq = \
      parent.prm[currBlock]['chooser'][parent.prm['chooserLabel'].index("Standard Frequency:")]

   if stdFreq == "Roved":
      frequency = random.uniform(minFrequency, maxFrequency)
   correctFrequency = frequency + (frequency*parent.prm['adaptiveParam'])/100
   stimulusCorrect = pureTone(correctFrequency, phase, level, duration,
                              ramps, channel, parent.prm['sampRate'],
                              parent.prm['maxLevel'])

   stimulusIncorrect = []
   for i in range((parent.prm['nIntervals']-1)):
      thisSnd = pureTone(frequency, phase, level, duration, ramps, channel,
                         parent.prm['sampRate'], parent.prm['maxLevel'])
      stimulusIncorrect.append(thisSnd)
   parent.playRandomisedIntervals(stimulusCorrect, stimulusIncorrect)

On lines 10-13 we read off the minimum and maximum frequency values for the roved-standard case. On line 23-24 we retrieve the value of the Standard Frequency: chooser. On lines 26-27 we state that if the value of the standard frequency chooser is equal to Roved, then the standard frequency for that trial should be drawn from a uniform distribution ranging from minFrequency to maxFrequency. The rest of the function is unchanged. Note that we’re using the a Python module called random on line 27, so we need to add import random at the top of our freq.py file.

It is also possible to show/hide choosers. Let’s extend the frequency-discrimination experiment by allowing for the possibility that the standard frequency is roved on a log scale (which in fact would be a better choice given that frequency scaling in the auditory system is approximately logarithmic). To do this, we first add a new chooser to set the roving scale:

chooserOptions.append(["Linear",
                       "Log"])
chooserLabel.append("Roving Scale:")
chooser.append("Linear")

Because this chooser is useful only when the standard frequency is roved, we’ll tell the get_fields_to_hide_ function to show/hide it depending on the value of the Standard Frequency chooser. The new get_fields_to_hide_ function is shown below:

def get_fields_to_hide_freq(parent):
   if parent.chooser[parent.prm['chooserLabel'].index("Standard Frequency:")].currentText() == "Fixed":
      parent.fieldsToHide = [parent.prm['fieldLabel'].index("Min. Frequency (Hz)"),
                             parent.prm['fieldLabel'].index("Max. Frequency (Hz)")]
      parent.fieldsToShow = [parent.prm['fieldLabel'].index("Frequency (Hz)")]
      parent.choosersToHide = [parent.prm['chooserLabel'].index("Roving Scale:")]
   elif parent.chooser[parent.prm['chooserLabel'].index("Standard Frequency:")].currentText() == "Roved":
      parent.fieldsToHide = [parent.prm['fieldLabel'].index("Frequency (Hz)")]
      parent.fieldsToShow = [parent.prm['fieldLabel'].index("Min. Frequency (Hz)"),
                             parent.prm['fieldLabel'].index("Max. Frequency (Hz)")]
      parent.choosersToShow = [parent.prm['chooserLabel'].index("Roving Scale:")]

We’ve just added two lines. Line 6 gets executed if the Standard Frequency chooser is set to Fixed, and adds the Roving Scale chooser to the parent.choosersToHide list. Line 11 gets executed if the Standard Frequency chooser is set to Roved, and adds the Roving Scale chooser to the parent.choosersToShow list.

Finally, we need to add/modify a couple of lines of the doTrial_ function. First of all we need to read off the value of the new Roving Scale chooser:

rovingScale = \
  parent.prm[currBlock]['chooser'][parent.prm['chooserLabel'].index("Roving Scale:")]

second, we need to set the standard frequency depending on whether it is drawn from a linear or a logarithmic distribution:

if stdFreq == "Roved":
   if rovingScale == "Linear":
      frequency = random.uniform(minFrequency, maxFrequency)
   elif rovingScale == "Log":
      frequency = 10**(random.uniform(log10(minFrequency), log10(maxFrequency)))

Note that we’re using the log10 function from numpy here, so we need to add from numpy import log10 at the top of our freq.py file.

Writing a “Constant 1-Interval 2-Alternatives” Paradigm Experiment

In the next paragraphs we’ll see an example of an experiment using the “Constant 1-Interval 2-Alternatives” paradigm. The experiment a is simple “Yes/No” signal detection task. On each trial the listener is presented with a single interval which may or may not contain a pure tone, and s/he has to tell if the tone was present or not.

The initialize_ function for the signal detection experiment is shown below, since the general framework for writing an experiment is the same as for the adaptive paradigm, only the differences from an adaptive-paradigm experiment will be highlited.

def initialize_sig_detect(prm):
   exp_name = "Signal Detection Demo"
   prm["experimentsChoices"].append(exp_name)
   prm[exp_name] = {}
   prm[exp_name]["paradigmChoices"] = ["Constant 1-Interval 2-Alternatives"]
   prm[exp_name]["opts"] = ["hasFeedback"]
   prm[exp_name]["buttonLabels"] = ["Yes", "No"]
   prm[exp_name]['defaultNIntervals'] = 1
   prm[exp_name]['defaultNAlternatives'] = 2

   prm[exp_name]["execString"] = "sig_detect"
   return prm

On line 5 we list the available paradigms for the experiment, in this case the only paradigm possible is Constant 1-Interval 2-Alternatives. On line 7 we insert hasFeedback to the list of experiment options, so that feedback can be provided at the end of each trial. Since we’ll have a single observation interval we don’t add the hasISIBox option, because we don’t need to have a silent inteval between observation intervals. On line 7, we set the labels for the buttons, which represent the two response alternatives: “Yes” or “No”. On line 8 and line 9 we set the number of intervals and the number of response alternatives.

The select_default_parameters_ function for the signal detection experiment is shown below:

def select_default_parameters_sig_detect(parent, par):

   field = []
   fieldLabel = []
   chooser = []
   chooserLabel = []
   chooserOptions = []

   fieldLabel.append(parent.tr("Frequency (Hz)"))
   field.append(1000)

   fieldLabel.append(parent.tr("Duration (ms)"))
   field.append(2)

   fieldLabel.append(parent.tr("Ramps (ms)"))
   field.append(4)

   fieldLabel.append(parent.tr("Level (dB SPL)"))
   field.append(30)

   chooserOptions.append([parent.tr("Right"), parent.tr("Left"), parent.tr("Both")])
   chooserLabel.append(parent.tr("Channel:"))
   chooser.append(parent.tr("Both"))

   prm = {}
   prm['field'] = field
   prm['fieldLabel'] = fieldLabel
   prm['chooser'] = chooser
   prm['chooserLabel'] = chooserLabel
   prm['chooserOptions'] =  chooserOptions

   return prm

there is nothing really new here compared to experiments with adaptive paradigms that we have seen before. We initialize the text fields that we need in order to set the frequency duration and level of the signal. We also initialize a chooser to set the channels on which the signal should be presented.

The doTrial_ function for the signal detection task is shown below:

def doTrial_sig_detect(parent):

   currBlock = 'b'+ str(parent.prm['currentBlock'])
   if parent.prm['startOfBlock'] == True:
       parent.writeResultsHeader('log')
       parent.prm['conditions'] = ["Yes","No"]

   parent.currentCondition = random.choice(parent.prm['conditions'])
   if parent.currentCondition == 'Yes':
       parent.correctButton = 1
   elif parent.currentCondition == 'No':
       parent.correctButton = 2

   freq    = parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Frequency (Hz)")]
   dur     = parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Duration (ms)")]
   ramps   = parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Ramps (ms)")]
   lev     = parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Level (dB SPL)")]
   phase   = 0
   channel = parent.prm[currBlock]['chooser'][parent.prm['chooserLabel'].index(parent.tr("Channel:"))]

   if parent.currentCondition == 'No':
       lev = -200
   sig = pureTone(freq, phase, lev, dur, ramps, channel, parent.prm['sampRate'], parent.prm['maxLevel'])


   parent.playSequentialIntervals([sig])

For experiments using the “Constant 1-Interval 2-Alternatives” paradigm it is necessary to list the experimental conditions in the doTrial_ function. We do this on line 6. On line 8, we bind the response buttons to the correct response. Since the button number 1 is the “Yes” button, we say that in the case of a signal trial (parent.currentCondition == "Yes") the correct button to press is the button number 1, otherwise the correct button to press is the button number 2.

On lines 14-23 we read off the values of the text fields and generate the sound to play (signal or silence) according to the experimental condition. Finally, on line 25 we use the parent.playSequentialIntervals function to present the sound to the listener. This function accepts as an argument a list of sounds to play sequentially. In our case we have only a single sound to insert in the list. More details on the playSequentialIntervals function are provided in Section The Play Sound Functions.

Writing an experiment for the Transformed Up-Down Interleaved, Weighted Up-Down Interleaved, and Multiple Constants m-Intervals n-Alternatives Paradigms

This section will walk you through an example of an experiment that can be used with the transformed up-down interleaved and weighted up-down interleaved paradigms. These paradigms are simple extensions of the transformed up-down and weighted up-down paradigms in which multiple independent adaptive tracks are run simultaneously and are randomly interleaved in a single block of trials.

Because experiments that support the transformed up-down interleaved and weighted up-down interleaved paradigms can be easily modified to support also the multiple constants m-intervals n-alternatives paradigm, this paradigm will be also added in our example experiment. This paradigm is a simple extension of the constant m-intervals n-alternatives paradigm, in which rather than having a single constant difference between the standard and comparison tones, multiple constant differences are tested in a single block of trials.

The example experiment that we’ll look at is a simple signal detection in quiet experiment, that could be used to measure an audiogram. For this reason it is called “Demo Audiogram Multiple Frequencies” (it can be found in the file audiogram_mf.py in the default_experiments folder). The experiment can be used to setup a virtually unlimited number of adaptive tracks, and each track can be used to track the signal-detection threshold for a specific frequency.

As for the multiple constants procedure, the experiment could be similarly used to measure percent correct performance for tones of different frequencies presented at the same level. However, a more interesting possibility is to use the experiment to measure percent correct performance for the same frequency at different fixed levels. This could then be used to derive a psychometric function relating percent correct performance to signal level.

The initialize_ function of the experiment is shown below:

def initialize_audiogram_mf(prm):
   exp_name = QApplication.translate("","Demo Audiogram Multiple Frequencies","")
   prm["experimentsChoices"].append(exp_name)
   prm[exp_name] = {}
   prm[exp_name]["paradigmChoices"] = [QApplication.translate("","Transformed Up-Down Interleaved",""),
                                     QApplication.translate("","Weighted Up-Down Interleaved",""),
                                     QApplication.translate("","Multiple Constants m-Intervals n-Alternatives","")]


   prm[exp_name]["opts"] = ["hasISIBox", "hasAlternativesChooser", "hasFeedback",
                          "hasNTracksChooser"]
   prm[exp_name]['defaultAdaptiveType'] = QApplication.translate("","Arithmetic","")
   prm[exp_name]['defaultNIntervals'] = 2
   prm[exp_name]['defaultNAlternatives'] = 2
   prm[exp_name]['defaultNTracks'] = 4

   prm[exp_name]["execString"] = "audiogram_mf"
   prm[exp_name]["version"] = "1"

 return prm

the first part of the function doesn’t need much explanation if you’ve follwed the previous examples. The experiments opts has a new item hasNTracksChooser. This option allows the user to dynamically change the number of adaptive tracks to be used (or the number of constant differences to measure for the multiple constants paradigm). Besides this, the only new thing compared to previous examples is that we also specify the default number of tracks with prm[exp_name]['defaultNTracks'] = 4.

The select_default_parameters_ for the “Demo Audiogram Multiple Frequencies” experiment is shown below:

def select_default_parameters_audiogram_mf(parent, par):

   nDifferences = par['nDifferences']

   field = []
   fieldLabel = []
   chooser = []
   chooserLabel = []
   chooserOptions = []

   for i in range(nDifferences):
      fieldLabel.append(parent.tr("Frequency (Hz) " + str(i+1)))
      field.append(1000+1000*i)
      fieldLabel.append(QApplication.translate("","Level (dB SPL) " + str(i+1),""))
      field.append(50)

   fieldLabel.append(QApplication.translate("","Bandwidth (Hz)",""))
   field.append(10)

   fieldLabel.append(QApplication.translate("","Duration (ms)",""))
   field.append(180)

   fieldLabel.append(QApplication.translate("","Ramps (ms)",""))
   field.append(10)


   chooserOptions.append([QApplication.translate("","Right",""),
                        QApplication.translate("","Left",""),
                        QApplication.translate("","Both","")])
   chooserLabel.append(QApplication.translate("","Ear:",""))
   chooser.append(QApplication.translate("","Right",""))
   chooserOptions.append([QApplication.translate("","Sinusoid",""),
                        QApplication.translate("","Narrowband Noise","")])
   chooserLabel.append(QApplication.translate("","Type:",""))
   chooser.append(QApplication.translate("","Sinusoid",""))

   prm = {}
   prm['field'] = field
   prm['fieldLabel'] = fieldLabel
   prm['chooser'] = chooser
   prm['chooserLabel'] = chooserLabel
   prm['chooserOptions'] =  chooserOptions

   return prm

The transformed/weighted up-down interleaved paradigms can be run with any number of adaptive tracks. Similarly, the multiple constants m-intervals n-alternatives procedure can be run with any number of constant differences between the standard and comparison intervals. All the user has to do is select the desired number of adaptive tracks, or constant differences from the appropriate chooser in the pychoacoustics control window. select_default_parameters_ function, however, needs to know how many tracks or how many constant differences are being run in order to set up the necessary fields storing the experimental variables. The par argument that is always passed to the select_default_parameters_ function has the purpose of passing additional parameters to dinamycally modify the behavior of the function in cases like this.

In the case of paradigms with interleaved tracks, or multiple constant differences the par argument has a key called nDifferences that specifies the number of tracks or constant differences. For the current experiment we retieve this value on line 3. Then, on lines 11-15 we set up a for loop in which we add a field to store the frequency and level of the tones for each adaptive track. The rest of the function is similar to previous examples, so it will not be discussed further.

The get_fields_to_hide_ function for the “Demo Audiogram Multiple Frequencies” experiment is shown in the code block below. Again, nothing new here.

def get_fields_to_hide_audiogram_mf(parent):
   if parent.chooser[parent.prm['chooserLabel'].index(QApplication.translate("","Type:",""))].currentText() == QApplication.translate("","Sinusoid",""):
      parent.fieldsToHide = [parent.prm['fieldLabel'].index(QApplication.translate("","Bandwidth (Hz)",""))]
   else:
      parent.fieldsToShow = [parent.prm['fieldLabel'].index(QApplication.translate("","Bandwidth (Hz)",""))]

The doTrial_ function for the “Demo Audiogram Multiple Frequencies” experiment is shown below:

def doTrial_audiogram_mf(parent):
   currBlock = 'b'+ str(parent.prm['currentBlock'])
   nDifferences = parent.prm['nDifferences']
   if parent.prm['startOfBlock'] == True:
      parent.prm['additional_parameters_to_write'] = {}
      parent.prm['conditions'] = []
      parent.prm['adaptiveParam'] = []
      for i in range(nDifferences):
         parent.prm['conditions'].append(str(parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index(QApplication.translate("","Frequency (Hz) " + str(i+1),""))]))
         parent.prm['adaptiveParam'].append(parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index(QApplication.translate("","Level (dB SPL) " + str(i+1),""))])
      parent.writeResultsHeader('log')

   frequency = []
   for i in range(nDifferences):
      frequency.append(parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index(QApplication.translate("","Frequency (Hz) " + str(i+1),""))])

   parent.currentCondition = parent.prm['conditions'][parent.prm['currentDifference']] #this is necessary for counting correct/total trials
   correctLevel = parent.prm['adaptiveParam'][parent.prm['currentDifference']]

   currentFrequency = frequency[parent.prm['currentDifference']]
   bandwidth = parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index(QApplication.translate("","Bandwidth (Hz)",""))]
   phase = 0

   incorrectLevel = -200
   duration = parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index(QApplication.translate("","Duration (ms)",""))]
   ramps = parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index(QApplication.translate("","Ramps (ms)",""))]
   channel = parent.prm[currBlock]['chooser'][parent.prm['chooserLabel'].index(QApplication.translate("","Ear:",""))]
   sndType = parent.prm[currBlock]['chooser'][parent.prm['chooserLabel'].index(QApplication.translate("","Type:",""))]

   if sndType == QApplication.translate("","Narrowband Noise",""):
      if bandwidth > 0:
         parent.stimulusCorrect = steepNoise(currentFrequency-(bandwidth/2), currentFrequency+(bandwidth/2), correctLevel - (10*log10(bandwidth)),
                                             duration, ramps, channel, parent.prm['sampRate'], parent.prm['maxLevel'])
      else:
         parent.stimulusCorrect = pureTone(currentFrequency, phase, correctLevel, duration, ramps, channel, parent.prm['sampRate'], parent.prm['maxLevel'])
   elif sndType == QApplication.translate("","Sinusoid",""):
      parent.stimulusCorrect = pureTone(currentFrequency, phase, correctLevel, duration, ramps, channel, parent.prm['sampRate'], parent.prm['maxLevel'])


   parent.stimulusIncorrect = []
   for i in range((parent.prm['nIntervals']-1)):
      thisSnd = pureTone(currentFrequency, phase, incorrectLevel, duration, ramps, channel, parent.prm['sampRate'], parent.prm['maxLevel'])
      parent.stimulusIncorrect.append(thisSnd)
   parent.playRandomisedIntervals(parent.stimulusCorrect, parent.stimulusIncorrect)

note that on line 3 we retrieve the number of adaptive tracks (for adaptive interleaved paradigms), or the number of constant differences (for multiple constant paradigms) that we’re currently running. This parameter is stored in the parent.prm dictionary.

At the start of a block of trials (cfr. line 4) we set up a number of parameters. Among these there are two in particular that need some explanation. The ``parent.prm[‘adaptiveParam’] on line 7 is a list that is populated in the for loop starting on line 9 with the initial values of the parameter that is adaptively varying for each track. The ``parent.prm[‘conditions’] on the other hand is a parameter that is used only when the experiment is run with the multiple constants m-intervals n-alternatives paradigm. It’s a list of labels for each “condition” that is being run in the experiment, that is for each constant difference that is being tested.

On lines 13-15 we retrieve the frequencies of the tones used for each track or constant difference.

On line 17 we retrieve the label of the current condition and store it in the parent.currentCondition variable. Thisvariable will be used by pychoacoustics for keeping count of the correct and total number of trials for each constant difference when using the multiple constants paradigm. Note how the parent.prm['currentDifference'] variable is used for this purpose. This variable is the index to the current track or current cosnatnt difference that is being currently tested. This variable is set outside of the doTrial_ function, (a random track or constant difference is chosen for each trial) but we can retrieve its value through the parent handle.

On line 18 we make use of the parent.prm['currentDifference'] variable again, this time to retrieve the level of the comparison stimulus for the track or constant difference that is run on the current trial. The rest of the function is not different from the doTrial_ functions used in transformed/weighted up-down paradigms with non-interleaved tracks, and should be easy to follow if you’ve followed the previous examples.

Writing a matching experiment using interleaved adaptive tracks

The transformed up-down and weighted up-down interleaved procedures can be used to write matching experiments. As described by [Jesteadt1980], two interleaved adaptive tracks can be used to target points on the psychometric function that are symmetric around the 50% point (e.g. 71% and 29%), and then average the threshold in each track in order to estimate the point of subjective equality. For example, in a level-matching experiment one track could target the point at which the listener judges the comparison tone to be louder than the standard tone 71% of the time, while the other track targets the point at which the listener judges the comparison tone to be louder than the standard 29% of the time (or equivalently, softer than the standard 71% of the time).

In this section we’ll show how to write in pychoacoustics a level-matching experiment similar to the one described by [Jesteadt1980]. This experiment is one of the default experiments available in pychoacoustics, and is called Demo Level Matching.

The initialize_ function of the experiment is shown in the code block below.

def initialize_lev_match(prm):
   exp_name = "Demo Level Matching"
   prm["experimentsChoices"].append(exp_name)
   prm[exp_name] = {}
   prm[exp_name]["paradigmChoices"] = ["Transformed Up-Down Interleaved",
                                       "Weighted Up-Down Interleaved"]

   prm[exp_name]["opts"] = ["hasISIBox", "hasAlternativesChooser"]
   prm[exp_name]['defaultAdaptiveType'] = QApplication.translate("","Arithmetic","")
   prm[exp_name]['defaultNIntervals'] = 2
   prm[exp_name]['defaultNAlternatives'] = 2
   prm[exp_name]['defaultNTracks'] = 2
   prm[exp_name]["execString"] = "lev_match"

among the paradigmChoices we include the “Transformed Up-Down Interleaved”, and the “Weighted Up-Down Interleaved”. The experiment has just two experiment opts: one to add an ISI box, the other one to add an alternatives chooser (we’ll probably want to run this experiment only with two intervals, and two alternatives, so in principle we could do without the alternative chooser, but currently, for technical reasons the hasAlternativesChooser option has to be added with the “Transformed Up-Down Interleaved”, and the “Weighted Up-Down Interleaved” paradigms). Besides specifying the default number of intervals and alternatives, we also specify the default number of interleaved tracks using the defaultNTracks key. Because we have not added a hasNTracksChooser in the experiment the default number of tracks specified here will be the default and only possible number of tracks in the experiment.

The select_default_parameters_ function is shown below:

def select_default_parameters_lev_match(parent, par):

   field = []
   fieldLabel = []
   chooser = []
   chooserLabel = []
   chooserOptions = []

   fieldLabel.append("Starting Level Track 1 (dB SPL)")
   field.append(75)

   fieldLabel.append("Starting Level Track 2 (dB SPL)")
   field.append(55)

   fieldLabel.append(parent.tr("Frequency Standard Tone (Hz)"))
   field.append(1000)

   fieldLabel.append(parent.tr("Frequency Comparison Tone (Hz)"))
   field.append(250)

   fieldLabel.append(parent.tr("Level Standard Tone (dB SPL)"))
   field.append(65)

   fieldLabel.append(parent.tr("Duration (ms)"))
   field.append(180)

   fieldLabel.append(parent.tr("Ramps (ms)"))
   field.append(10)

   chooserOptions.append(["Right", "Left", "Both"])
   chooserLabel.append(QApplication.translate("","Ear:",""))
   chooser.append(QApplication.translate("","Both",""))


   prm = {}
   prm['field'] = field
   prm['fieldLabel'] = fieldLabel
   prm['chooser'] = chooser
   prm['chooserLabel'] = chooserLabel
   prm['chooserOptions'] =  chooserOptions

   return prm

the first two fields will be used to set the starting level of the comparison tone in each track. The next two fields will be used to set the frequencies of the standard and comparison tone. The next field will be used to set the level of the standard tone which will be fixed throughout a block of trials. The last two fields will be used to set the duration of the tone (excluding the ramps), and the duration of its onset and offset ramps. The only chooser will be used to set the ear to which the tones will be presented.

The doTrial_ function for the level matching experiment is shown below:

def doTrial_lev_match(parent):
   currBlock = 'b'+ str(parent.prm['currentBlock'])
   if parent.prm['startOfBlock'] == True:
      parent.prm['adaptiveParam'] = []
      parent.prm['adaptiveParam'].append(parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Starting Level Track 1 (dB SPL)")])
      parent.prm['adaptiveParam'].append(parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Starting Level Track 2 (dB SPL)")])
      parent.writeResultsHeader('log')



  standardFrequency = parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Frequency Standard Tone (Hz)")]
  comparisonFrequency = parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Frequency Comparison Tone (Hz)")]
  standardLevel = parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Level Standard Tone (dB SPL)")]
  duration = parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Duration (ms)")]
  ramps = parent.prm[currBlock]['field'][parent.prm['fieldLabel'].index("Ramps (ms)")]
  phase = 0
  channel = parent.prm[currBlock]['chooser'][parent.prm['chooserLabel'].index("Ear:")]

  comparisonLevel = parent.prm['adaptiveParam'][parent.prm['currentDifference']]

  comparisonTone = pureTone(comparisonFrequency, phase, comparisonLevel, duration, ramps,
                            channel, parent.prm['sampRate'], parent.prm['maxLevel'])

  standardToneList = []
  for i in range((parent.prm['nIntervals']-1)):
     thisSnd = pureTone(standardFrequency, phase, standardLevel, duration, ramps, channel,
                        parent.prm['sampRate'], parent.prm['maxLevel'])
     standardToneList.append(thisSnd)
  parent.playRandomisedIntervals(comparisonTone, standardToneList)

The adaptive parameter for an experiment with interleaved tracks is not a single number, but a list containing the values of the adaptive parameter for each track. Therefore, on line 4 we create the list, and on lines 5 and 6 we populate this list with the initial values of each of the adaptive tracks.

From lines 11 to 17 we retrieve the values of all the fields and choosers. Nothing new here. On line 19 we retrieve the value of the adaptive parameter (which in this case is the level of the comparison tone) for the current trial. To do this, we refer to a key in the parent.prm dictionary called currentDifference. This key holds the index of the track which has been randomly selected by pychoacoustics for the current trial.

From line 21 to 28 we prepare the stimuli to be presented in the standard and comparison intervals. We then pass these stimuli as arguments to the playRandomisedIntervals functions. This experiment is ready to be run.

The up-down rules of the two adaptive tracks need to be set up appropriately to run the matching experiment. Let’s, take as an example the experiment described in [Jesteadt1980] in which we wish to determine the intensity of a 250-Hz tone required to match the loudness of a 1000-Hz tone presented at 40 dB SPL. In the pychoacoustics control window, after having selected the Demo Level Matching experiment, we set the frequency of the standard tone to 1000 Hz, and the frequency of the comparison tone to 250 Hz. We also set the level of the standard tone to 40 dB SPL. We then set the upper, and lower tracks to 60 and 30 dB SPL, two values that should bracket the point of subjective equality.

The task for the listener is an objective one: s/he will have to tell on each trial which tone was louder. For track 1, we set the rule down to 2, and the rule up to 1. For track 2 instead, we set the rule down to 1, and the rule up to 2. In this way, track 1 will target the point in the psychometric function at which the listener judges the comparison tone to be louder than the standard 70.7% of the time. Track 2 will target instead the point in the psychometric function at which the listener judges the comparison tone to be louder than the standard 29.3% of the time. For track 1, when the listener chooses the comparison interval twice in a row the level of the 250-Hz tone (the comparison tone) is decreased, while each time s/he chooses the standard interval the level of the 250-Hz tone is increased. For track 2, when the listener chooses the standard interval twice in a row the level of the 250-Hz tone is increased, while each time the listener chooses the level of the 250-Hz tone is decreased. For both tracks “correct” responses move the track down. There are no correct or incorrect responses in a subjective task like this. The Corr. Resp. Move Track X (down or up) choosers are not named appropriately for this task. They should be named something like “when the comparison interval is chosen track X moves” down or up. However, since the underlying code for adaptive interleaved paradigms is the same for objective and subjective tasks, for simplicity and ease of maintenance of the underlying code they are called Corr. Resp. Move Track X (down or up). .

Writing a “Constant 1-Pair Same/Different” Paradigm Experiment

Todo

Describe of to write experiments for the “Constant 1-Pair Same/Different” paradigm.

Writing an “Odd One Out” Paradigm Experiment

Todo

Describe of to write experiments for the “Odd One Out” paradigm.

The Experiment “opts”

``hasAlternativesChooser`` This option adds two chooser widgets, one to dynamically change the number of observation intervals (labelled “Intervals”), and one to dinamically change the number of response alternatives (labelled “Alternatives). This option is generally used in adaptive paradigms (“Transformed Up-Down”, “Weighted Up-Down”, as well as their interleaved versions). The number of response alternatives that can be choosen from the widget can be either equal to the number of observation intervals, or to the number of observation intervals minus one. In the latter case the standard stimulus is presented in the first interval, as a reference, with no corresponding response alternative, see [GrimaultEtAl2002] for an example of this \(n\)-intervals, \(n-1\) alternatives presentation mode. The selected number of intervals and alternatives can be accessed in the experiment file through the parent.prm['nIntervals'], and parent.prm['nAlternatives'] variables respectively.
``hasAltReps`` This option can be used to change the way in which the stimuli are presented in the “Transformed Up-Down” paradigm or other adaptive paradigms. In these paradigms, normally there is an observation interval containing the target stimulus (comparison interval), and one or more other intervals containing the non-target stimuli (standard intervals). An alternative way to present the stimuli is to have an alternation of the target and non-target stimuli (e.g. ABAB) in the comparison interval, and a repetition of the non-target stimulus in the standard interval (AAAA) [KingEtAl2013]. If the hasAltReps option is enabled, there will be two additional text boxes, Alternated (AB) Reps. and Alternated (AB) Reps. ISI (ms). The first text box controls the number of times the alternated target and non-target stimuli should be repeated, a value of zero corresponds to no alternation, that is only a single stimulus (either the target, or the non target) is presented in each interval. If the value is one, a single alternation will occur (AB), if the value is two, two alternations occur (ABAB), and so on. The second text box controls the ISI between the stimuli presented within an interval. The selected number of alternated repetitions, and the ISI between alternating stimuli can be accessed in the experiment file through the parent.prm['altReps'], and parent.prm['altRepsISI'] variables respectively. The setup of the alternated repetitions must be done within each experiment file.
``hasFeedback`` This option controls whether the “Response Light” chooser has a “Feedback” option or not. You may want to enable this option for all “objective” experiments that have a clear “correct” response. You may want to disable this option for “subjective” experiments, such as matching experiments, in which there is no “correct” response.
``hasISIBox`` If this option is enabled, a box labelled ISI (ms) is added. This is generally used to set the silent period between observation intervals in the “Transformed Up-Down” and similar adaptive procedures. Its value can be accessed in the experiment file through the parent.prm['isi'] variable. However, normally this should not be necessary because the playRandomisedIntervals function automatically uses this value to set the silent period between observation intervals.
``hasNDifferencesChooser`` This option is useful in the “Multiple Constants 1-Interval 2-Alternatives Paradigm” to dinamically change the number of experimental conditions. For example, if you have a signal detection experiment in which a fixed number of signals (with a constant amplitude) can occur, this option allows to choose the number of conditions dinamically. If this option is enabled, a chooser labelled No. Alternatives is added. The value selected can be accessed through the par['nDifferences'] variable in the select_default_parameters_ function, and through the parent.prm['nDifferences'] variable in the doTrial function.
``hasNTracksChooser`` This option can be used to dinamically change the number of tracks in interleaved adaptive paradigms (e.g. “Transformed Up-Down Interleaved). If enabled, a No. Tracks chooser is added. The value selected can be accessed through the par['nDifferences'] variable in the select_default_parameters_ function, and through the parent.prm['nDifferences'] variable in the doTrial function.
``hasPrecursorInterval`` If this option is enabled, a chooser controlling whether a precursor interval should be presented or not is added. This chooser is labelled Precursor Interval. If this option is enabled, and the chooser is set to “Yes”, then a precursorStim sound needs to be passed to the playRandomisedIntervals function. This sound will be presented before each observation interval.
``hasPostcursorInterval`` If this option is enabled, a chooser controlling whether a postcursor interval should be presented or not is added. This chooser is labelled Postcursor Interval. If this option is enabled, and the chooser is set to “Yes”, then a postcursorStim sound needs to be passed to the playRandomisedIntervals function. This sound will be presented after each observation interval.
``hasPreTrialInterval`` If this option is enabled, a chooser controlling whether a pre-trial interval should be presented or not is added. This chooser is labelled Pre-Trial Interval. If this option is enabled, and the chooser is set to “Yes”, then a preTrialStim sound needs to be passed to the playRandomisedIntervals function. This sound will be presented at the beginning of each trial.

The Play Sound Functions

Todo

Illustrate the functions to play sounds

Simulations

pychoacoustics is not designed to run simulations in itself, however it provides a hook to redirect the control flow to an auditory model that you need to specify yourself in the experiment file. You can retrieve the current response mode from the experiment file with:

 parent.prm['allBlocks']['responseMode']

so, in the experiment file, after the creation of the stimuli for the trial you can redirect the control flow of the program depending on the response mode:

 if parent.prm['allBlocks']['responseMode'] != "Simulated Listener":
    #we are not in simulation mode, play the stimuli for the listener
    parent.playSoundSequence(sndSeq, ISIs)
 if parent.prm['allBlocks']['responseMode'] == "Simulated Listener":
    #we are in simulation mode
    #pass the stimuli to an auditory model and decision device
    #---
    #Here you specify your model, pychoacoustics doesn't do it for you!
    # at the end your simulated listener arrives to a response that is
    # either correct or incorrect
    #---
    parent.prm['trialRunning'] = False
    #this is needed for technical reasons (if the 'trialRunning'
    #flag were set to 'True' pychoacoustics would not process
    #the response.
    #
    #let's suppose that at the end of the simulation you store the
    #response in a variable called 'resp', that can take as values
    #either the string 'Correct' or the string 'Incorrect'.
    #You can then proceed to let pychoacoustics process the response:
    #
    if resp == 'Correct':
       parent.sortResponse(parent.correctButton)
    elif resp == 'Incorrect':
       #list all the possible 'incorrect' buttons
       inc_buttons = numpy.delete(numpy.arange(
                                  self.prm['nAlternatives'])+1,
                                  self.correctButton-1))
       #choose one of the incorrect buttons
       parent.sortResponse(random.choice(inc_buttons))

Writing your own Experiments

First Steps

Anatomy of a pychoacoustics experiment file

The initialize_ function

The select_default_parameters_ function

The doTrial_ function

Adding support for the Constant Paradigm

Showing/Hiding Widgets Dynamically

Writing a “Constant 1-Interval 2-Alternatives” Paradigm Experiment

Writing an experiment for the Transformed Up-Down Interleaved, Weighted Up-Down Interleaved, and Multiple Constants m-Intervals n-Alternatives Paradigms

Writing a matching experiment using interleaved adaptive tracks

Writing a “Constant 1-Pair Same/Different” Paradigm Experiment

Writing an “Odd One Out” Paradigm Experiment

The Experiment “opts”

The Play Sound Functions

Simulations

Anatomy of a `pychoacoustics` experiment file

The `initialize_` function

The `select_default_parameters_` function

The `doTrial_` function