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)
11
Yulök Revista de Innovación Académica, ISSN 2215-5147, Vol. 5, N.º 2
Junio- diciembre 2021, pp. 10-19
Sánchez, J. La interpretación neuro-semiótica en Mafalda; un análisis exploratorio.
Introducción
El trabajo desarrollado aborda al personaje de Mafalda,
la obra maestra del caricaturista argentino Joaquín Salva-
dor Lavado, conocido como Quino, así como a varios de
sus personajes secundarios desde un enfoque de análisis
neuro-conductual, es decir, considerando las diversas re-
giones y activaciones cerebrales que parecen, y de forma
inductiva, precisarse en las piezas gráficas revisadas, para
estos efectos se detalla un análisis a la luz de la interpreta-
ción neural semiótica de tres tiras cómicas seleccionadas,
de forma que logren evidenciar los elementos neuro-con-
ductuales presentes en los personajes. Se recurre al uso
del método inductivo e interpretativo, así como a la her-
menéutica investigativa, permitiendo realizar un análisis
cruzado entre las tiras cómicas y la teoría referente a las
Neurociencias del comportamiento humano.
Es de interés señalar que el personaje de Mafalda, es crea-
do por Quino y publicado por primera vez el 29 de sep-
tiembre de 1964, esto en el Semanario Primera Plana de
Buenos Aires Argentina, continuando con su publicación
hasta 1973 (en.quino.com.ar, 2021). La tira cómica está
basada en una típica niña argentina de clase media, donde
se presentan las situaciones vivenciales que experimenta
tanto con sus padres como con sus amigos, las que deno-
tan un fuerte contenido social así como su correspondien-
te crítica.
Sin duda que al leer al personaje de Mafalda, puede
encontrarse que su contenido de fondo, su mensaje so-
cio-económico, e incluso su manejo neuro-conductual es
mucho más profundo de lo que de forma evidente puede
detallarse. La profundidad de sus mensajes y los ejem-
plos que reflejan la neuro-conducta económica, social y
personal, son desarrollados de forma diligente, tema que
es precisado en el estudio presentado en este trabajo, en
el cual se abordan las diferentes regiones cerebrales que
parecen tener un ligamen específico a la conducta de los
personajes aquí analizados.
Cabe señalar que la neuro-conducta humana hace men-
ción a las activaciones cerebrales de diferentes regiones
que permiten entender, y hasta cierto punto, justificar las
acciones humanas (Pérez-Llantada, 2005), destaca aquí
la existencia de diversas teorías ligadas a las Neurocien-
cias, las cuales permiten una mejor comprensión de las
decisiones humanas. Ahora bien, aunque en el trabajo
desarrollado se analizan personajes de tiras cómicas, el
abordaje dado demuestra un enfoque profundo en cuan-
to al manejo de las emociones neurales, así como de los
procesos cerebrales que parecen ser activados en estos in-
dividuos, lo que permite definir diferentes patrones con-
ductuales de interés en ellos.
Se recurren a algunas teóricas básicas de las Neurocien-
cias del comportamiento humano, para el análisis de las
tiras cómicas, tales como el cerebro Tri-uno, que plantea
la división en tres sistemas (instintivo, límbico y córtex),
así como el detalla del circuito motivacional neural, que
señala la activación de diferentes neurotransmisores pro-
pios de esta conducta, además de señalar otros conceptos
tales como la activación de lóbulos cerebrales por estí-
mulos particulares, las sinapsis neural, asociada al pen-
samiento complejo, entre otros. Su detalle explicativo es
planteado en la discusión de resultados, con lo cual, se
logra realizar el cruce entre la interpretación conceptual y
su aplicación a las tiras cómicas analizadas.
El análisis realizado en cuestión recurre a una revisión
hermenéutica y a una exégesis puntual de las diferentes
teorías de las Neurociencias aplicadas a la conducta hu-
mana, permite de esta forma, realizar un ligamen cientí-
co entre las piezas grácas analizadas y el enfoque cien-
tíco de la Neurociencia y la neuro-conducta aplicada,
en este caso, a las acciones y decisiones humanas, pero
reejadas en las caricaturas del personaje de Mafalda, así
como de los otros individuos presentes en esta historieta
cómica, que conlleva a su vez una gran crítica social.
Elemento innovador
El presente trabajo precisa la innovación dada en térmi-
nos sociales, pero relacionada especícamente al estudio
de la conducta humana, se realiza un ligamen, primera-
mente, entre el concepto de las Neurociencias, denidas
como: “(…) el estudio de todos los aspectos del sistema
nervios: su anatomía, su química, siología, desarrollo
y funcionamiento. La investigación en Neurociencia es
muy amplia y comprende estudios y campos tan distintos
como la genética molecular o la conducta social” (Soria-
no, Guillazo, Redolar, Torras y Martínez, 2007, p.15).
Y posteriormente la conducta humana, pero observable
en este caso, por medio de la aplicación inductiva, inter-
pretativa y hermenéutica del reejo del comportamiento
neural de los personajes presentados en la tira cómica del
personaje de Mafalda.
Metodología
La metodología utilizada para el presente trabajo es dada
por la técnica de la revisión documental, basada en la in-