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CONCEPT LEARNING, VERBAL-NONVERBAL
TAXONOMY: A STUDY FROM ENGRAM THEORY
AND CONCEPTUAL ATOMISM
APRENDIZAJE DE CONCEPTOS, TAXONOMÍA
VERBAL-NO VERBAL: UN ESTUDIO DESDE LA TEORÍA
DE ENGRAMAS Y EL ATOMISMO CONCEPTUAL
Miguel Ángel Muñoz López
Universidad Pedagógica de Durango, México
María del Rocío Hernández Pozo
Universidad Nacional Autónoma de México, México
Omar David Almaráz Rodríguez
Universidad Pedagógica de Durango, México
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DOI: https://doi.org/10.37811/cl_rcm.v8i2.10702
Concept learning, Verbal-Nonverbal Taxonomy: A Study from Engram
Theory and Conceptual Atomism
Miguel Ángel Muñoz López1
miguelarcangel@comunidad.unam.mx
https://orcid.org/0000-0002-8056-0061
Universidad Pedagógica de Durango
Durango – México
María del Rocío Hernández Pozo
rochpoz@co-educa.org
https://orcid.org/0000-0001-5781-2825
Universidad Nacional Autónoma de México
Cuernavaca – México
Omar David Almaráz Rodríguez
almarax@hotmail.com
https://orcid.org/0000-0002-4740-1923
Universidad Pedagógica de Durango
Durango – México
ABSTRACT
This article focuses on concept learning, exploring the variability of this process in relation to verbal
and nonverbal taxonomy. The theories of concept and memory, particularly conceptual atomism and
engram theory, are examined, which provide a solid foundation for understanding learning processes
and concept formation. A novel definition of "dynamic concept" is elaborated based on these theories.
Considering this, an experiment was designed to measure conceptual learning, finding statistically
significant differences in learning between verbal and non-verbal concepts. Non-verbal concepts were
recalled more extensively than verbal ones, with a significant effect size, consistent with prior research.
Demographically, women and men showed similar learning patterns in verbal concepts, but men
exhibited highest number of correct answers during the test in non-verbal concepts.
Keywords: concept, memory, learning, engrams, conceptual, atomism
1
Autor principal
Correspondencia: miguelarcangel@comunidad.unam.mx
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Aprendizaje de Conceptos, Taxonomía Verbal-No Verbal: Un Estudio desde
la Teoría de Engramas y el Atomismo Conceptual
RESUMEN
Este artículo se enfoca en el aprendizaje conceptual, explorando la variabilidad de este proceso en
relación con la taxonomía verbal y no verbal. Se examinan las teorías del concepto y la memoria,
particularmente el atomismo conceptual y la teoría del engrama, que proporcionan una sólida base para
entender los procesos de aprendizaje y formación de conceptos. Se elabora una nueva definición de
"concepto dinámico" basada en estas teorías. Considerando esto, se diseñó un experimento para medir
el aprendizaje conceptual, encontrando diferencias estadísticamente significativas en el aprendizaje
entre conceptos verbales y no verbales. Los conceptos no verbales fueron recordados de manera más
extensa que los verbales, con un tamaño de efecto significativo, consistente con investigaciones previas.
Demográficamente, las mujeres y los hombres mostraron patrones de aprendizaje similares en
conceptos verbales, pero los hombres exhibieron el mayor número de respuestas correctas durante la
prueba en conceptos no verbales.
Palabras claves: concepto, memoria, aprendizaje, engramas, atomismo, conceptual
Artículo recibido 04 marzo 2024
Aceptado para publicación: 05 abril 2024
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INTRODUCTION
The Concept
Memory plays a crucial role in behavioral sciences and education. The study of memory has been of
great relevance, and various theories have been developed to explain its functioning, such as cognitive
schemas (Piaget & Warden, 1926) and their relationship with behavior (Beck, 1964, 1967). One notable
advancement in this field is the discovery of memory engrams, which explain how information is
processed and retrieved in the brain (Liu et al., 2015; Ramirez et al., 2013; Ryan et al., 2015).
However, in the study of engram theory, there is naturally a lack of research in the domain of semantic
and declarative memory, as the optogenetics technique used for studying it is not suitable for application
in humans. As a result, this aspect of memory has not been studied thus far, despite its importance in
our current culture (Eichenbaum, 2016).
The concept represents the building blocks of thoughts, crucial for psychological processes like memory
(Margolis, Eric & Laurence, Stephen, 2019). However, its nature has been the subject of intense debate
(Margolis & Laurence, 1999; Margolis & Laurence, 2015). To explain the concept, numerous theories
have been developed; with the most common one, that consider concepts as definitions that consist of
two components: their reference and their meaning (Fodor & Pylyshyn, 2015), or as a "mental
representation associated with a linguistic signifier" (Real Academia Española [RAE], 2022). This
semantic variable inherently excludes nonverbal concepts or blocks of information that do not involve
semantic or declarative information. This popular view serves as the basis for important situations such
as education, where traditionally, only what can be declared is assessed. According to Fodor, cognitive
science is mired in areas dependent on this topic (Fodor, as cited in Rodríguez, 2007).
Thus, Fodor proposes conceptual atomism, which suggests a concept without structure where most of
them are atomic and determined by nomologically supported informational relationships between the
individual and their context (Rodríguez, 2007). In conceptual atomism, the content of a concept depends
on its relation to the world, resulting in its psychological variability as a consequence of causal
relationships between the subject and the world.
A concept is recalled when the individual experiences an appropriate causal relationship with the
property of the world to which the concept refers (Margolis et al., 2019).
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Fodor's definition of the concept has an analogical resemblance to the dynamics of memory formation
(Figure 1) as discovered in neuroscience by Tonegawa in his reframe of the engram theory. This theory
was based on Semon's work (1923) and reformulated as follows: When a subject experiences an event,
selected stimuli from the experience activate sets of neurons that produce lasting physical and/or
chemical changes (engrams) in those cells and their connections, facilitating memory storage. Later,
when a part of the original stimulus returns, these cells connected by the created engram are reactivated
to evoke the recall of a memory (Liu et al., 2015).
Figure 1. Learning from engram theory and conceptual atomism
Based on this approach, within this research is proposed a definition of “concept” in accordance with the engram theory (Liu
et al., 2015) and conceptual atomism (Fodor, 1975).
The dynamic concept
Based on the analysis of terms used in the engram theory and the conceptual atomism theory, certain
terms were chosen to describe the phenomenon of concept formation and evocation (Table 1). The
reasons for their use are explained, along with efforts to avoid terms conflicting with the learning theory
or others.
Table 1. Main Terms
Term
Description
Property
Characteristics of objects and phenomena in the environment.
Stimulus
Effect of properties on the senses.
Pairing
A relationship established between the stimulus that causes a property of an object or
phenomenon with a part of the memory.
Activate
Lasting chemical or physical changes that occur in neural engrams and their connections for
the first time, in response to contact between the subject and their environment.
Reactivate
A situation in which a fraction or all of an original stimulus returns and then, reactivates
previously paired clusters of neurons or engrams.
Evoke
It causes the phenomenon of remembering.
Remembering or
Remembrance
An act of re-experiencing a specific memory.
Storage
Memory formation resulting from the activation of neuronal engrams.
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Next, the function of each term adopted for the definition of the dynamic concept proposed in this
research is broken down, marking it between < > for its distinction, while conceptual learning is
explained from the already established theoretical framework:
Conceptual learning is crucial for survival, commencing when an individual engages with their
environment, where objects and phenomena within it, possess <properties> that distinguish them. These
properties <stimulate> the subject's senses; for instance, a flower emits molecules that reach the
subject's nose, <stimulating> the sense of smell with the <property> of odor.
Once the sense is stimulated, its connection with the nervous system <activates> <engrams> or clusters
of neurons and their connections, associated with the stimulus nature and the subject's perception. It's
important to distinguish between <activation> and <reactivation>; activation occurs when something
new is learned, while reactivation happens when a previously perceived or learned stimulus is received.
In other words, an engram has already been <paired> with the object's property.
Note that the term <pairing> is used here instead of Fodor's (1982) "locking" in conceptual atomism to
explain the established connection between an object's property and a part of memory. In neuroscience,
this phenomenon is referred to as "association" and/or "consolidation." While "consolidate" might seem
preferable, it implies a permanent situation (RAE, 2014). According to Liu et al. (2014), "pairing" can
change in certain situations, such as shifts in emotional valence associated with a memory. Thus,
<pairing> is used as it aligns with engram theory and encompasses the phenomenon of "unpairing",
avoiding in this way permanence, and maintaining memory and concepts as dynamic entities.
From activation or reactivation arise two representative memory phenomena, activation involves the
first contact between the subject and an external object is <properties>, leading to lasting chemical or
physical changes in cellular engrams and their connections, contributing to <storage> of memory. The
term <storage> is widely used in reviewed studies of engram theory (Table 1).
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Figure 2. From the stimulus to memory.
Conversely, reactivation is used to describe the situation where a fraction or the entire original stimulus
returns and reactivates previously paired cell clusters or engrams, <evoking> the <recall> of a specific
<memory> (Figure 2).
In this sense, the term <evoke> is used to explain the triggering of the <remembering> phenomenon
that involves, the re-experiencing of a specific memory. It is crucial to note that remembering is
<evoked>, and memory is <recalled>.
This circumvents phrases like "bringing to consciousness" or "memory retrieval," which, although used
in neuroscience, may be confused with other theories like information theory.
The concept, as addressed in this research, lacks an internal structure (Fodor, 1982), as it is formed by
cellular <engrams> dynamically activated or reactivated based on the stimuli received by the subject.
Objects and phenomena in the environment have distinguishing properties, analogically learned by the
individual in his neuronal engrams, by being activated or reactivated according to the presence of some
specific property.
It should be noted that cellular-level storage occurs with the activation of specific neurons, a stimulus
activates a cluster of neurons, not just one cell. Thus, although a single property, such as a color, is
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perceived, it does not imply the activation of a single neuron but rather multiple neurons in a complex
manner; reactivation of a cell engram evokes a property.
Based on this analysis on conceptual learning from the conceptual atomism and engram theory, the
following definition of a (dynamic) concept is proposed:
The concept is a basic element of thought, crucial to intellectual processes; it lacks structure, becoming
dynamic and relative on the experience of each subject. It consists of the evocation of recollections from
memory, because of the reactivation of neural engrams and in response to stimulation elicited by
properties of previously paired objects or external or internal phenomena by the quantitative or
qualitative exposure between them and the subject. This activation caused lasting physical and/or
chemical changes between specific neurons, forming engrams.
Table 2. Regularities between theories for the conformation of the concept
Conceptual atomism
Hebb Theory
Natural properties
Artificial properties
Evocation
Mental state
Locking
Plasticity by exposure and/or
quality
Exposition
Analyzing both theories (Table 2) the design of conceptual learning experiment was developed, making
possible to analyze learning from this perspective, its relation to verbal and nonverbal taxonomy and
the effects of anxiety on the conceptual evocation.
METHOD
The subjects were students who can follow instructions. Individuals with psychological disorders,
blindness or visual impairments, and those unable to use the experimental materials were excluded as
criteria.
The participants were from different places in Mexico. The sample consisted of 268 individuals with
an average age of 17.33 (SD = 1.059), 158 (59%) women, and 109 (40.7%) males. Participants were
asked for their informed consent, which involved clarifying the privacy of their data, stating their
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participation was voluntary, and explaining the purpose of the research, the consent is conducted in
electronic format due to the online nature of the experiment.
Based on the primary objective of the research, a hypothesis was formulated for resolution, which
justifies the calculation of the sample size (Quispe, 2020):
H0 = There are no statistically significant differences between the mean of accuracy in verbal concept
learning and the mean of accuracy in nonverbal concept learning in the experiment.
The sampling was probabilistic, to calculate the sample size, RStudio software and the 'pwr' package
titled "Basic Functions for Power Analysis" (Champely et al., 2017) were used. This package contains
functions for Cohen's statistical power analysis (1988).
The hypothesis involves two means from different populations with two tails, i.e., H0: µ2 = µ1 vs HA:
µ2 ≠ µ1. To calculate the sample size, the effect size "d" or design effect needs to be obtained using the
formula: (µ1 - µ2) / standard deviation. With µ1: 56, µ2: 53.43, and standard deviation: 7.024, the result
was d = 0.36588838268792710706150341685649. The command for calculating the sample size for
this type of hypothesis in RStudio is as follows:
Sample = pwr.t.test(d = 0.36588838268792710706150341685649, sig.level = 0.05, power = 0.50, type
= 'two.sample', alternative = 'two.sided')
This resulted in a requirement of 58 subjects per group. The subjects were matched based on their
baseline anxiety, which represents the possibility of a person reacting with anxiety to life situations,
since baseline anxiety is a variable that can affect learning (Blumer & Benson, 1975). To reduce
confounding factors, the decision was made to match subjects based on this criterion. As a result of the
matching process, three groups were obtained: high anxiety, medium anxiety, and low anxiety.,Each
group was randomly divided by the software into an experimental group and a control group (Figure
3), where the independent variable is verbal or nonverbal information. The evaluation is performed
automatically by the software at the moment that the subject interacts with it.
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Figure 3. Research design
Data collection was conducted by the software application used in the experiment. For the assessment
of subjects regarding the anxiety variable, the ASI-3 questionnaire was used: New Scale for the
Assessment of Anxiety Sensitivity (Hernández-Pozo, Alvarado-Bravo, Espinosa-Luna, et al., 2022).
The questionnaire was validated in the Mexican population with a total scale reliability of Cronbach's
alpha equal to 0.919.
The data collected by the software were automatically sent to a spreadsheet on the network that captured
data from all applications. This approach eliminated errors caused by the researcher's expectations
during the experiment evaluation or personal observation.
Experiment
The experiments follow a process of discrimination and generalization. Although different concepts
were learned, they share a common design. Initially, a situation is created wherein the subject is intended
to learn a concept without the use of text. This is achieved through the continuous exposure of the
subject to an object or phenomenon, as posited by Hebb's theory, Conceptual Atomism and Engram’s
theory.
The experiment consisted of two stages. In the first stage, the subjects were repeatedly exposed to a set
of related stimuli to create a concept, theoretically pairing the object properties with the subject neuronal
engrams. The nature of the stimulus presented changed according to a verbal and nonverbal taxonomy,
which defined the control and experimental groups.
Diagnosis
ASI-3
Anxiety (Ax.)
Mating
Group with High Ax.
Group with Medium Ax
Group with Low Ax
verbal –non verbal
experiment
verbal group
non verbal group
verbal group
non verbal group
verbal group
non verbal group
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After the conceptual creation process, the learning of the concept was verified. This involved the
retrieval of information from memory as a second step in the experiment. Without the use of text and
considering the engram theory, the subject is presented with only one feature of the entire object or
phenomenon. The aim is to determine if, based on that single feature, the subject can discriminate
between the features that constitute the concept and those that do not, while also generalizing the
characteristics of an object as a consequence.
Figure 4. Comparison of experiments between control and experimental groups
The experiment with the control group aims to create concepts using semantic information. In the
second stage, the goal is to retrieve the concepts using words. The process was virtually the same as in
the experimental group, with the only difference being the use of semantic information in the control
group (Figure 4).
The experiments were double balanced in terms of learning and probing. In the training or learning
phase, the training was balanced based on concepts. For each concept, seven exposures were conducted,
resulting in a total of four concepts.
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Table 3. Distribution example
Trial
Discriminative Stimuli
Concept 1
Concept 2
1
2
1
2
0
4
3
1
2
4
4
0
5
3
3
There were 5 probing trials per concept. Each trial consisted of a sample stimulus, six comparison
stimuli, and discriminative stimuli presented with a balanced distribution of correct responses ranging
from 0 to 4 for each concept (Table 3).
RESULTS
The main hypothesis of the research was tested using the data obtained from the experiment, a modified
Welch's t-test was performed (Table 4), which yielded a p-value indicating statistically significant
differences between the groups with verbal and nonverbal concept learning, with a higher number of
nonverbal correct responses.
Table 4. Test for verbal and nonverbal groups
Group
N
Mean
SD
P
Non verbal
121
104.4298
14.49645
.003
Verbal
154
98.9870
15.72101
The evaluation of the hypothesis was initially conducted solely based on the accuracy achieved in the
experiment. However, data on errors made by the subjects during the experiment, were also collected.
This is relevant because one of the characteristics of a concept is the ability to discriminate, which is
reflected in the results through the quantity of "selection errors."
These errors occur when properties of unrelated objects are selected. Additionally, there is another type
of error known as "generalization error," which occurs when the subject fails to select a quality of the
base object, indicating a failure to generalize the concept.
To assess conceptual learning according to these considerations, errors were subtracted from the correct
responses to re-evaluate the previously stated hypotheses. The same sample size (n) and statistical tests
mentioned in Table 5 were used for this analysis.
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Table 5. Overall test
Group
N
Mean
SD
Two-tailed p
Non verbal
121
88.8595
28.992
.003
Verbal
154
78.0455
31.317
The results once again showed a significant difference where nonverbal learning accuracy was higher,
rejecting the null hypothesis. Finally, to determine the effect size of the difference between the two
groups, Cohen's d-test was used, yielding a result of 1.1.
This value suggests a large effect, as it exceeds 0.8. To analyze the variables' behavior in more detail, a
structural equation model (SEM) was conducted, considering sex, nonverbal taxonomy, baseline
anxiety (Axb), and nonverbal concept learning represented by the number of correct responses. The
bootstrap process with 1000 samples (nboot) and a significance level of 0.05 (Alpha) was employed for
this analysis.
Figure 5. SEM Axb, Gender, nonverbal/verbal taxonomy, and nonverbal concept learning
Figure 5 shows that, the variable woman seems to have a negative effect on concept learning, both
verbal and nonverbal. However, this effect did not turn out to be statistically significant. On the other
hand, a significant relationship was found between the variable female and baseline anxiety, indicating
that women was more frequently associated with higher levels of baseline anxiety. Regarding the
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variable "non verbal taxonomy" a significant weight was found on the dependent variable of concept
learning. This was evidenced by a T statistic of 2.8, surpassing the minimum threshold of 1.6, and the
confidence interval (CI) values between the 2.5% CI and 97.5% CI not including zero.
In general, regarding verbal and nonverbal concept learning, women learned similarly in response to
these variables. However, men had nonverbal and verbal concept learning scores of 107.2 and 98.8,
respectively, with a higher number of correct responses for nonverbal concepts. After analyzing the
means using the modified Welch's T-test, a p-value of 0.002 was obtained. When comparing the results
between men and women in non-verbal learning, disparities were observed, as men achieved an average
of 107.2 correct responses, while women had an average of 102.7 correct responses. However, when
performing the statistical analysis using Welch's T-test, a p-value of 0.07 was obtained, indicating that
there is no statistically significant difference between the sexes.
DISCUSSION
Addressing the research hypothesis, statistically significant differences were found between the mean
scores in the learning of verbal and non-verbal concepts. It was observed that non-verbal concepts were
retrieved to a greater extent than verbal concepts, with a large effect size. This finding is consistent with
previous research reported in recent years (Margolis & Laurence, 2015).
The higher retrieval of non-verbal concepts compared to verbal concepts could be explained by the wide
diversity of object-related properties that can be associated with a single word. This phenomenon is
context-based and requires a greater amount of mental processing compared to a visual stimulus, which
directly relates to a set of properties without the need for prior processing (Malt et al., 1999), while a
concept without the use of verbs would theoretically require fewer mental operations.
Regarding demographics, women learned verbal and non-verbal concepts in the same way, while men
showed greater learning in non-verbal concepts.
In terms of non-verbal learning, which encompasses storage and reactivation/recall, the first experiment
found that subjects were able to construct concepts by combining properties without the need for verbs.
They were also able to solve problems that required knowledge of these non-verbal concepts. In other
words, the subjects recalled non-verbal information to solve each trial. From this, we can deduce that
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human beings are capable of acquiring knowledge and acting accordingly without the need to verbalize
it.
Another consideration, as stated by Margolis and Laurence (2015), is that learning is not necessarily
tied to words. However, a problem arises when trying to express what has been learned in a non-verbal
manner. This occurs in various environments, such as the academic setting, where students who do not
speak the native language declare their main problem as not being able to find or remember the
appropriate vocabulary to communicate, even though they know the answer (Sifrar, 2006).
The experiment conducted in this research was based on the conceptual atomism proposed by Fodor
(1998), as well as Tonegawa's engram theory (Tonegawa, 2015) for the creation and retrieval of
concepts. Additionally, Hebb's theory (Hebb, 1932) was used to explain offline association or locking
between properties. Taking this set of theories as a starting point, a specific definition of dynamic
concepts was elaborated to be applied in this research.
CONCLUSION
The main objective was to contribute to the debate on the nature of concepts, drawing upon Fodor's
ideas on concepts and considering the scientific advancements made at MIT with engram theory. All of
this was conducted from the perspective of cognitive neuroscience, which is dedicated to the study of
how the brain facilitates cognitive functions and how the functions of the physical brain can generate
seemingly intangible thoughts, ideas, and beliefs (Gazzaniga, Ivry, & Mangun, 2019).
This description of the concept allows us to approach the study of cognitive learning to the real
characteristics of the development of human knowledge, taking into account its individuality. This has
been considered from various perspectives, starting with Ausubel (2002) and his concept of meaningful
learning, which emphasizes the importance of each student's prior knowledge for the achievement of
new cognitive structures. It also extends to neuroscientific studies indicating that individuals'
experiences over the years lead to completely individual brain anatomy (Valizadeh, Liem, Mérillat,
Hänggi, & Jäcke, 2018).
This implies that each concept is unique in each person and develops based on their experiences when
interacting with the environment. In this new description, the concept is recognized as structureless and
dynamic, which is closer to real learning, where knowledge constantly changes through feedback. The
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semantic aspect becomes just one property among others, and for social agreements in communication,
knowledge about an object is conveyed through common criteria assigned to a symbol (Ausubel, 1983).
Acknowledgments
No funding was received for this research.
Ethics
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by
the Institutional Review Board (or Ethics Committee) from Universidad Pedagógica de Durango (Acta
No.001/23).
REFERENCES
Ausubel, D., Novak., J, & Hanesian, H. (1983). Psicología educativa: un punto de vista cognoscitivo.
México: Trillas.
Ausubel. D. (2002). Adquisición y retención del conocimiento. Una perspectiva cognitiva. 2ª edición,
Barcelona: Paidós Ibérica.
Beck, A. T. (1964). Thinking and depression: II. Theory and therapy. Archives of General Psychiatry,
10(6), 561-571.
Blumer, D. & Benson, D.F. (1975). Personality changes with frontal and temporal lobe lesions. En D.F.
Benson y D. Blumer (Eds.), Psychiatric apsects of neurologcal disease. Nueva York: Grune and
Stratton
Champely, S., Ekstrom, C., Dalgaard, P., Gill, J., Weibelzahl, S., Anandkumar, A., Ford, C., Volcic, R.
& De Rosario, H. (2017). pwr: Basic functions for power analysis. Software https://cran.r-
project.org/web/packages/pwr/
Cohen, J. (1988) Statistical power analysis for the behavioral sciences. New York: Routkedge.
https://doi.org/10.4324/9780203771587
Cadenas Bogantes, D., & Castro Miranda, J. C. (2021). Analysis Of the Effectiveness of The Action
Oriented Approach in The New English Program Proposed by The Ministry of Public Education
in The Year 2018. Sapiencia Revista Científica Y Académica , 1(1), 45-60. Recuperado a partir
de https://revistasapiencia.org/index.php/Sapiencia/article/view/13
pág. 2683
Eichenbaum, H. (2016). Still searching for the engram. Learn Behav 44, 209–222 (2016). doi:
doi.org/10.3758/s13420-016-0218-1
Fodor, J. (1975). The Language of thought, Cambridge, MA: Harvard University Press.
Fodor, J. (1998). Concepts: where cognitive science went wrong. Oxford: Clarendon.
Fodor, J. & Pylyshyn, Z. (2015). Minds without meanings. Cambridge: The MIT press. Pp. 21-40
Gazzaniga, M., Ivry, R. & Mangun, G. (2019). Cognitive Neuroscience [Quinta edición]. Canadá: W.W.
Norton & company
Hebb, D. (1949). The organization of behavior. New York: John Wiley & Sons
Hernández-Pozo, M. R., Alvarado-Bravo, B. G., Espinosa-Luna, C., Barahona-Torres, I., Arenas-
Cortes, M., Soriano-Lucio, E., Hernández Pérez, J. F., Govea-Martínez, L., González-García,
C. D., Robles Beltrán M. A., López-Morales, U., Salazar-Serna, K. & Bobadilla-Odriozola, J.
J. (2021). Sensibilidad a la ansiedad en Mexicanos durante la pandemia Covid19: Evaluación
mediante el cuestionario ASI3. Escrito sometido a dictamen para su publicación.
Liu, X., Ramirez, S., Redondo, R., & Tonegawa, S. (2015). Identification and Manipulation of Memory
Engram Cell. Cold Spring Harb Symp Quant Biol 79. 59-65 DOI: 10.1101/sqb.2014.79.024901
Malt, B., Sloman, S., Gennari, S., Shi, M. & Wang, Y. (1999). Knowing versus naming: Similarity and
the linguistic categorization of artifacts. Journal of Memory and Language 40. 230 – 262.
Margolis, E. & Laurence, S. (1999). Concepts: Core Readings, Cambridge, MA: MIT Press.
Margolis, E. & Laurence, S. (2015). The conceptual mind: new directions in the study of concepts,
Cambridge, MA: MIT Press.
Margolis, E. & Laurence, S. (2019). Concepts. the Stanford encyclopedia of philosophy (Summer 2019
Edition). Edward N. Zalta (ed.). Recuperado el 9 de marzo de 2020 de:
https://plato.stanford.edu/archives/sum2019/entries/concepts/.
Nallapati, R., Zhou, B., dos Santos, C. N., Gulcehre, C., Xiang, B. (2016). Abstractive text
summarization using sequence-to-sequence RNNs and beyond.
López, M. (2023). El Manejo de las Transacciones Online y la Protección al Consumidor. Emergentes
- Revista Científica, 3(1), 77-96. https://doi.org/10.60112/erc.v3i1.22
pág. 2684
Piaget, J. & Gabain, M. (1926). The language and thought of the child. Londres: K. Paul, Trench,
Trubner & Co., Ltd
Quispe, A., Pinto, D., Huamán, M., Bueno G. & Valle-Campos, A. (2020). Metodologías cuantitativas:
Cálculo del tamaño de muestra con STATA y R. Revista del cuerpo médico. 13(1). 78-83. DOI:
https://doi.org/10.35434/rcmhnaaa.2020.131.627
Ramirez, S., Liu, X., Lin, P., Suh, J., Pignatelli, M., Redondo, R., Ryan, T. & Tonegawa, S. (2013).
Creating a False Memory in the Hippocampus. Science. 341 387-391
Real Academia Española. (2019). concepto. Recuperado el 17 de noviembre de 2020 de:
https://dle.rae.es/concepto?m=form (RAE,2019)
Rodríguez, M. (2007). Sobre conceptos primitivos - atomismo conceptual. SUMMA Psicológica UST.
4 (1), 31-45
Ryan, T., Roy, D., Pignatelli, M., Arons, A. & Tonegawa, S. (2015). Engram cells retain memory under
retrograde amnesia. Science. 28. 1007-1013
Rodríguez Gómez, J. C. (2023). La importancia de la diversidad y la inclusión en el ámbito educativo.
Estudios Y Perspectivas Revista Científica Y Académica , 3(2), 16-47.
https://doi.org/10.61384/r.c.a.v3i2.30
Ríos Castro , N. (2022). La Evaluación y el Manejo del Dolor en Pacientes con Enfermedad Terminal.
Revista Científica De Salud Y Desarrollo Humano, 3(2), 80-95.
https://doi.org/10.61368/r.s.d.h.v3i2.37
Semon, R. (1923). Mnemic philosophy. Australia: Allen & Unwin.
Sifrar, M. (2006). Las dificultades lingüísticas y afectivas de la expresión oral en clase y en la vida real.
XVII Congreso Internacional de la Asociación del Español como lengua extranjera (ASELE).
Logroño, 27-30 de septiembre de 2006
Tonegawa, S., Pignatelli, M., Roy, D. & Ryan, T. (2015). Memory engram storage and retrieval.
Sciencedirect.35.101-109
Valizadeh, S., Liem, F., Mérillat, S., Hänggi, J. & Jäncke, L. (2018). Identification of individual subjects
on the basis of their brain anatomical features. Scientific Reports, April 4, 2018.
DOI:10.1038/s41598-018-23696-6