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remove lexdec from running-exp chapter
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chrplr committed Feb 27, 2024
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18 changes: 18 additions & 0 deletions docs/lexical-decision.rst
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Expand Up @@ -179,6 +179,24 @@ Check a solution :download:`analyze_RT.py <../experiments/xpy_lexical_decision/a



Auditory Lexical Decision
-------------------------

Transform ``lexdec_v1.py`` into an auditory lexical decision script using the sound files
from the `lexical decision folder <../experiments/xpy_lexical_decision/>`::

bonjour.wav
chien.wav
président.wav
clapour.wav
lopadol.wav
mirance.wav


Solution at :download:`lexdec_audio.py <../experiments/xpy_lexical_decision/lexdec_audio.py>`



Finally
-------

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198 changes: 1 addition & 197 deletions docs/running-experiments.rst
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Expand Up @@ -122,209 +122,13 @@ Do you replicate the distance effect reported by Dehaene, S., Dupoux, E., & Mehl
Experimental Psychology: Human Perception and Performance*, 16, 626–641.?


Lexical Decision
----------------

In a lexical decision experiment, a string of characters is flashed at
the center of the screen and the participant has to decide if it is a actual
word or not, indicating his/her decision by pressing a left or right
button. Reaction time is measured from the word onset, providing an
estimate of the speed of word recognition.

Let us program such a task.


Step 1: stimuli listed in constants
+++++++++++++++++++++++++++++++++++

Modify the :download:`parity task script <../experiments/xpy_parity_decision/parity.py>` to display either a word or a pseudoword at each trial (in a random order).

For testing purposes, you can use the following variables::

words = ['bonjour', 'chien', 'président']
pseudos = ['lopadol', 'mirance', 'clapour' ]

A solution is proposed in :download:`lexdec_v1.py <../experiments/xpy_lexical_decision/lexdec_v1.py>`

Step 2: read stimuli from a csv file
++++++++++++++++++++++++++++++++++++

Then modify the lexical decision script to read the stimuli from a comma-separated text file (`stimuli.csv`) with two columns. Here is the content of ``stimuli.csv``::

item,category
bonjour,W
chien,W
président,W
lopadol,P
mirance,P
clapour,P

(hint: To read a csv file, one can use ``pandas.read_csv()``)

A solution is proposed in :download:`lexdec_v2.py <../experiments/xpy_lexical_decision/lexdec_v2.py>`

Note: You can use a file comparator, e.g. `meld <https://meldmerge.org/>`__, to compare the two versions::

meld lexdec_v1.py lexcdec_v2.py


Select words in a lexical dabatase
++++++++++++++++++++++++++++++++++

1. Go to http://www.lexique.org

Click on “Recherche en Ligne” and play with the interface:

- enter ``5...5`` in the ``nbletters`` field
- enter ``^b.t$`` in the field ``ortho`` field (see
http://www.lexique.org/?page_id=101 for more examples of pattern
that can be used)

2. how many words of grammatical category (``cgram``) ‘NOM’, and of
length 5 (``nblettres``), of lexical frequency (``freqfilms2``)
comprised between 10 and 100 per millions are there in this database?
(answer=367). Save these words (i.e. the content of the field
``Words``) into a ``words.csv`` file (you may have to clean manually,
ie. remove unwanted columns, using Excel or Libroffice Calc).



Automatising database searches with R and Python
++++++++++++++++++++++++++++++++++++++++++++++++

To select words, rather than using the interface at
http://www.lexique.org, one can write scripts in R or Python. This
promotes reproducible science.

1. Open
https://github.com/chrplr/openlexicon/tree/master/documents/Interroger-Lexique-avec-R
and follow the instructions in the document
``interroger-lexique-avec-R.pdf``

2. Read
https://github.com/chrplr/openlexicon/tree/master/scripts#selecting-lexical-items-with-python

To select 100 five letters long nouns for our lexical decision, execute::

import pandas
lex = pandas.read_csv("http://www.lexique.org/databases/Lexique382/Lexique382.tsv", sep='\t')
subset = lex.loc[(lex.nblettres == 5) & (lex.cgram == "NOM") & (lex.freqfilms2 > 10) & (lex.nombre == 's')]
samp = subset.sample(100)
samp2 = samp.rename(columns = {'ortho':'item'})
samp2.item.to_csv('words.csv', index=False)

This creates ``words.csv``.


Generate nonwords
+++++++++++++++++

1. Write a function that returns a nonword (a string containing random
characters)

::

def pseudo(length):
""" returns a nonword of length `length` """

Solution at :download:`create_nonwords.py <../experiments/xpy_lexical_decision/create_nonwords.py>`


2. Use this function to create a list of 100 nonwords and save it in a
file ``"pseudowords.csv"`` (one pseudoword per line) (see
https://www.pythontutorial.net/python-basics/python-write-text-file/)



Create a stimuli file
+++++++++++++++++++++

Merge ``words.csv`` and ``pseudowords.csv`` into a single
``stimuli2.csv`` file::

import pandas
w = pandas.read_csv('words.csv')
w['category'] = 'W'
p = pandas.read_csv('pseudowords.csv')
p['category'] = 'P'
allstims = pandas.concat([w, p])
allstims.to_csv('stimuli2.csv', index=False)


Use `sys.argv` to pass the name of the file containing the list of stimuli
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

Modify `lexdec_v2.py` to be able to pass the name of the stimuli file as an argument on the command line::

python lexdec_v3.py stimuli2.csv

(hint: use `sys.argv[]`; see https://www.geeksforgeeks.org/how-to-use-sys-argv-in-python/)

Solution at :download:`lexdec_v3.py <../experiments/xpy_lexical_decision/lexdec_v3.py>`



Improving the pseudowords
+++++++++++++++++++++++++

1. Check out the pseudoword generator
`UniPseudo <http://www.lexique.org/?page_id=582>`__

2. Generate a new list of pseudowords and add them to a new
``stimuli3.csv`` file


Data analysis
+++++++++++++

After running::

python lexdec_v3.py stimuli2.csv

the subject's responses are stored in the subfolder ``data/`` contains a file ``lexdec...xpd``

You can download this :download:`xpd file <../experiments/xpy_lexical_decision/data/lexdec_v3_02_202112131227.xpd>` as an example.

1. Use ``pandas.read_csv(..., comment='#')`` to read the responses into a pandas dataframe.

2. Compute the average reaction times for words and for pseudo-words.

3. Plot the distribution of reactions times using ``seaborn.boxplot()``

4. Use ``scipy.stats.ttest_ind()`` to perform a Student t-test compairn gthe RTs of Words and Non-Words.

Check a solution :download:`analyze_RT.py <../experiments/xpy_lexical_decision/analyze_RT.py>`

Auditory Lexical Decision
+++++++++++++++++++++++++

Transform ``lexdec_v1.py`` into an auditory lexical decision script using the sound files
from the `lexical decision folder <../experiments/xpy_lexical_decision/>`::

bonjour.wav
chien.wav
président.wav
clapour.wav
lopadol.wav
mirance.wav


Solution at :download:`lexdec_audio.py <../experiments/xpy_lexical_decision/lexdec_audio.py>`


Finally
+++++++

Check out an example of a “real” lexical decision experiment at
https://chrplr.github.io/PCBS-LexicalDecision


Posner’s attentional cueing task
--------------------------------

Exercise (\*\*): Read about `Posner’s attentional cueing task <https://en.wikipedia.org/wiki/Posner_cueing_task>`__ and program the experiment.

See a solution in :download:`Posner-attention/posner_task.py <../experiments/xpy_Posner_attention_networks_task/posner_task.py>`
See a solution in :download:`Posner-attention/posner_task.py <../experiments/xpy_Posner_attention_networks_task/posner_task.py>` (you will need :download:`Posner-attention/right-arrow.png <../experiments/xpy_Posner_attention_networks_task/right-arrow.png>`, :download:`Posner-attention/star.png <../experiments/xpy_Posner_attention_networks_task/star.png>` and :download:`Posner-attention/left-arrow.png <../experiments/xpy_Posner_attention_networks_task/left-arrow.png>`

Stroop Effect
-------------
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