ABX discrimination test
ABX discrimination is a term that is used for three stimuli presented on an ABX trial. The third is the focus. The first two stimuli (A and B) are standard, S1 and S2 in a randomly chosen order, and the subjects' task is to choose which of the two is matched by the final stimulus (X). (Glottopedia)
This package contains the operations necessary to initialize, calculate and analyse the results of an ABX discrimination task.
Check out the full documentation at readthedocs.
It is composed of 3 main modules and other submodules.
- task module is used for creating a new task and preprocessing.
- distances package is used for calculating the distances necessary for the score calculation.
- score module is used for computing the score of a task.
- analyze module is used for analysing the results.
The features can be calculated in numpy via external tools, and made compatible with this package with the h5features module, or directly calculated with one of our tools like the feature_extraction module.
See Files Format for a description of the files used as input and output.
According to what you want to study, it is important to characterise the ABX triplets. You can characterise your task along 3 axes: on, across and by a certain label.
An example of ABX triplet:
A and X share the same 'on' attribute; A and B share the same 'across' attribute; A,B and X share the same 'by' attribute.
Example of use
See examples/complete_run.sh for a command line run and examples/complete_run.py for a python utilisation.
The package installation requires cython, and it must be installed by hand for the moment.
pip install cython
conda install cython
You can then install the package using pip:
pip install git+https://github.com/bootphon/ABXpy
The module should work with the anaconda distribution of python. However, you may get some (unrelevant) warnings while running task.py.
Run the tests
Generate the documentation
Note that you will get warnings if you don't have the h5features module in your path.
cd docs make html
(you can also generate the doc in several other formats, see the Makefile)
- If you use this software in your research, please cite:
- ABX discriminability, Schatz T., Bach F. and Dupoux E., in preparation.