In a lexical decision task, a stimulus is presented at each trial and the subject must decide if it is a word or not (and indicate his response by pressing one of two keys). Let’s program such a task.
First of all, modify the parity task script 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' ]
Solution at lexdec_v1.py
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)
Solution at lexdec_v2.py
Note: You can use a file comparator, e.g. meld, to compare the two versions:
meld lexdec_v1.py lexcdec_v2.py
Go to http://www.lexique.org
Click on “Recherche en Ligne” and play with the interface:
5...5 in the nbletters field^b.t$ in the field ortho field (see http://www.lexique.org/?page_id=101 for more examples of pattern that can be used)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).
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.
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
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.
Write a function that returns a nonword (a string containing random characters)
def pseudo(length):
""" returns a nonword of length `length` """
Solution at create_nonwords.py
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/)
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)
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 lexdec_v3.py
Check out the pseudoword generator UniPseudo
Generate a new list of pseudowords and add them to a new stimuli3.csv file
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import scipy
results = pd.read_csv('data/lexdec_v3_02_202112131227.xpd', comment='#')
results.head()
sns.boxplot(x="cat", y="RT", hue="respkey", data=results)
plt.show()
results.groupby(results.cat).describe()
scipy.stats.ttest_ind(results.RT.loc[results.cat=='W'],
results.RT.loc[results.cat=='P'],
equal_var=False)
Check out the example of a Lexical decision experiment at https://chrplr.github.io/PCBS-LexicalDecision/)