Infant-mother interaction

This tutorial is based on an example extracted from the data analyzed in {cite:p}DobaEtAl22 article see [DoPN22]_.

Behavioral interaction between mother and her infant are video recorded while playing. Behaviors are encoded according to several categories. They are recorded during a first session before the mother leave temporally the room and after the mother comes back. The software used to encode the videos is called codix see [PDPN24]_

Read data

import scyseq as sq
from scyseq import io
from scyseq import viz as V

data_S1 = io.read_codix('data/209_S1')
data_S2 = io.read_codix('data/209_S2')

The read_codix function returns a dictionary organized as data[person][code] which value is a symbolic Sequence coded according to a specific Alphabet:

for person in data_S1.keys():
    print(person+': ')
    for code in data_S1[person].keys():
        print("\t"+code+': \t', data_S1[person][code].alphabet)

Bebe:
        Mouvement:       ((0 | Non), (1 | Oui))
        Expession_faciale:       ((0 | neutre), (1 | sourit), (2 | negatif))
        Regard:          ((0 | ailleurs), (1 | vers_la_mere))
        Sons:    ((0 | Silence), (1 | vocalisation), (2 | negatif))
Mere:
        Expression_faciale:      ((0 | Neutre), (1 | Expressif))
        Sti_motrices:    ((0 | Absence), (1 | Avec_contact), (2 | Sans_contact))
        Regard:          ((0 | Ailleurs), (1 | vers_bebe))
        Sti_verbale:     ((0 | Silence), (1 | Inference), (2 | Sons))
        Jeu:     ((0 | Absence), (1 | Avec_objet), (2 | Sans_objet))

In the study we discarded the facial expression code since the face of the mother and the infant could not be seen all the time.

bmv1 = data_S1['Bebe']['Mouvement']
bcv1 = data_S1['Bebe']['Sons']
bre1 = data_S1['Bebe']['Regard']
bmv2 = data_S2['Bebe']['Mouvement']
bcv2 = data_S2['Bebe']['Sons']
bre2 = data_S2['Bebe']['Regard']

mmv1 = data_S1['Mere']['Sti_motrices']
mcv1 = data_S1['Mere']['Sti_verbale']
mre1 = data_S1['Mere']['Regard']
mmv2 = data_S2['Mere']['Sti_motrices']
mcv2 = data_S2['Mere']['Sti_verbale']
mre2 = data_S2['Mere']['Regard']

# rename motor behavior in English :-)
eng_mvt = {0:'NoMvt', 1:'Touch', 2:'NoTouch'}
mmv1.alphabet.rename(eng_mvt)
mmv2.alphabet.rename(eng_mvt)

Recode and transform sequences

The infant’s behavior was also recoded according to a general level of activity.

First, the three sequences (motor, verbal and gaze) are recoded according to the cartesian product of the alphabets:

bbstate1 = sq.recode([bmv1, bcv1, bre1], new_alphabet=True, names=['Mvt','Verb','Gaz'])
bbstate2 = sq.recode([bmv2, bcv2, bre2], new_alphabet=True, names=['Mvt','Verb','Gaz'])
print(bbstate1.alphabet) # some pretty print would be better...

((0 | Mvt_Non+Verb_Silence+Gaz_ailleurs), (1 | Mvt_Non+Verb_Silence+Gaz_vers_la_mere), (2 | Mvt_Non+Verb_vocalisation+Gaz_ailleurs), (3 | Mvt_Non+Verb_vocalisation+Gaz_vers_la_mere), (4 | Mvt_Non+Verb_negatif+Gaz_ailleurs), (5 | Mvt_Non+Verb_negatif+Gaz_vers_la_mere), (6 | Mvt_Oui+Verb_Silence+Gaz_ailleurs), (7 | Mvt_Oui+Verb_Silence+Gaz_vers_la_mere), (8 | Mvt_Oui+Verb_vocalisation+Gaz_ailleurs), (9 | Mvt_Oui+Verb_vocalisation+Gaz_vers_la_mere), (10 | Mvt_Oui+Verb_negatif+Gaz_ailleurs), (11 | Mvt_Oui+Verb_negatif+Gaz_vers_la_mere))

Then, states are transformed according to a correspondance table:

naint = sq.Alphabet(['Low','Moderate', 'High'])
bbglo1 = sq.transform(bbstate1, [0,0,0,1,0,1,0,1,1,2,1,2], new_alphabet=naint)
bbglo2 = sq.transform(bbstate2, [0,0,0,1,0,1,0,1,1,2,1,2], new_alphabet=naint)
bbglo1

Sequence: [0 0 1 1 0 1 0 2 1 0 0 0 1 1 0 1 1 1 1 0 2 1 1 1 1 1 1 0 1 1 0 0 0 0 0 1 1
 1 1 1 2 1 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 2 1 1 1 1 1 1 2 1
 1 1 1 1 0 0 1 1 1 1 2 1 2 1 1 1 0 0 1 1 2 0 0 0 2 1 2 2 2 2 2 2 2 0 0 1 0
 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 1 2 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1
 2 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 1 2 2 1 2
 2 1 1 1 0 0 1 1 2 2 2 1 1 1 2 2 1 0 0 0 0 2 2 2 1 1 0 0 0 0 0 1 1 2 0 0 0
 0 0 0 0 0 0 0 0 0 0 0]
Alphabet(Symbol(0 | Low), Symbol(1 | Moderate), Symbol(2 | High))
N = 233 ; k = 3

Visualise sequences

One sequence

V.plot(bbglo1)

V.plot_bar(bbglo1)

(<Figure size 1200x400 with 1 Axes>,
 <Axes: title={'center': 'Bar plot'}, xlabel='Time', ylabel='States'>)

V.plot_color(bbglo1)

(<Figure size 1000x240 with 1 Axes>,
 <Axes: title={'center': 'Sequence'}, xlabel='Time'>)

Two sequences

V.plot_grid(mmv1, bbglo1, xlabel='Mother', ylabel='Infant', title='Initial', titlesize=20)

(<Figure size 640x480 with 1 Axes>,
 <Axes: title={'center': 'Initial'}, xlabel='Mother', ylabel='Infant'>)

V.plot_grid(mmv2, bbglo2, xlabel='Mother', ylabel='Infant', title='Reunion', titlesize=20)

(<Figure size 640x480 with 1 Axes>,
 <Axes: title={'center': 'Reunion'}, xlabel='Mother', ylabel='Infant'>)

V.plot_independence(bcv1,mcv1, scale=2500)