Content uploaded by Sebastián Javier Lipina
Author content
All content in this area was uploaded by Sebastián Javier Lipina on Aug 10, 2020
Content may be subject to copyright.
24
Fracchia, Carolina S.
a
*, Segretin, María Soledada, Hermida, María Juliab, Prats, Lucíaa, y Lipina,
Sebastián J.a
Artículo Original
Abstract
Resumen
Tabla de
Contenido
The association between environmental factors and
cognitive performance during childhood could be mediated
by poverty (i.e., households with Unsatisfied or Satisfied
Basic Needs). This study explored such mediating roles in
preschoolers from different socioeconomic backgrounds.
Tasks to assess executive attention, working memory,
inhibitory control, planning, and fluid reasoning were
administered to 250 children aged 4 and 5 years. The
results suggested that poverty mediated the effects of
family composition, child health, health risk factors, children
and adults at home, maternal age, and literacy activities on
the performance of executive attention, fluid reasoning, and
inhibitory control. These results contribute to our
understanding of the relationship between environmental
factors and cognitive development through the identification
of the mediating role of poverty.
El rol mediador de la pobreza en la asociación
entre factores ambientales y el desempeño
cognitivo de preescolares.
La asociación entre los factores ambientales y el
desempeño cognitivo durante la infancia podría
estar mediada por la pertenencia a hogares
pobres (i.e., hogares con necesidades básicas
insatisfechas o satisfechas). Este estudio exploró
tal mediación en preescolares de diferentes
contextos socioeconómicos. Para tal fin, se
administraron tareas que demandaron atención
ejecutiva, memoria de trabajo, control inhibitorio,
planificación y razonamiento fluido a 250 niños/as
de 4 y 5 años. Los resultados sugirieron que la
pobreza medió los efectos de la composición
familiar, la salud infantil, los factores de riesgo
para la salud, cantidad de niños/as y adultos en el
hogar, la edad materna y las actividades de
alfabetización sobre la atención ejecutiva, el
razonamiento fluido y el control inhibitorio. Estos
resultados contribuyen a la comprensión de la
relación entre los factores ambientales y el
desarrollo cognitivo a través de la identificación de
la pobreza como variable mediadora.
Introduction
Methods
Participants
Study desing
and
procedures
Cognitive
measures
Environmental
factors
Data analysis
Results
Discussión
References
24
26
26
26
26
27
27
28
32
33
Keywords: poverty, environmental factors, mediation,
cognitive development, preschoolers.
Palabras clave: pobreza, factores ambientales,
mediación, desempeño cognitivo, preescolares.
Recibido el 20 de noviembre de 2019. Aceptado el 26 de febrero de 2020
Editaron este artículo: Silvana Montes, Paula Abate, Verónica Ramírez y Sofía Sambre.
a Unidad de Neurobiología Aplicada (UNA, CEMIC-CONICET), Buenos Aires, Argentina.
b Universidad Nacional de Hurlingham, Instituto de Educación, Villa Tesei, Buenos Aires, Argentina.
*Enviar correspondencia a: Fracchia, C. S. E-mail: carolinafracchia@gmail.com
Citar este artículo como: Fracchia, C. S., Segretin, M. S. Hermida, M. J., Prats, L., y Lipina, S. J. (2020) Mediating role of poverty in the
association between environmental factors and cognitive performance in preschoolers. Revista Argentina de Ciencias del
Comportamiento, 12(2), 24-38
Introduction
Cognitive development and poverty during
childhood are complex phenomena that involve
biological and psychosocial components (Bradley
& Corwyn, 2002; Hackman, Farah, & Meany,
2010; Segretin et al., 2016). Although several
environmental factors (e.g., maternal age, literacy
activities) could influence basic cognitive functions
(Sameroff, 1998; Zauche, Thul, Mahoney, &
Stapel-Wax, 2016), the effects of some of them
could vary according to whether the person lives in
a poor home or not (Bradley & Corwyn, 2002;
Sarsour et al., 2011). The literature has explored
Revista Argentina de Ciencias del Comportamiento
ISSN 1852-4206
Agosto 2020, Vol. 12,
N°2, 24-38
revistas.unc.edu.ar/inde
x.php/racc
Mediating role of poverty in the association between
environmental factors and cognitive performance in
preschoolers
Fracchia, C. S. et al. / RACC, 2020, Vol. 12, N°2, 24-38
25
two main proposals: (a) one that analyzes how
poverty impacts child cognitive development
(Johnson, Riis, & Noble, 2016; Kishiyama, Boyce,
Jimenez, Perry, & Knight, 2009; Segretin et al.,
2016; Stevens, Lauinger, & Neville, 2009;
Yoshikawa, Aber, & Beardslee, 2012); and (b)
another that shows how environmental variables
(e.g., health variables) affect cognition (Hackman
et al., 2010; Rao et al., 2010; Ursache & Noble,
2016).
In general, these studies are based on
associations between two variables. For example,
a vast amount of literature indicates that growing
in a poor home can modulate children’s academic
outcomes and the emergence and development of
different aspects of cognition and emotional
behavior (Blair & Raver, 2016; Brooks-Gunn &
Duncan, 1997; Dickerson & Popli, 2016; Luby et
al., 2013). In addition, other studies documented
the association between environmental factors
(e.g., maternal stress, literacy activities) and
cognition. Most of them have shown only direct
associations between those variables (Finegood,
Raver, DeJoseph, & Blair, 2017; Rhoades,
Greenberg, Lanza, & Blair, 2011; Sharkins, Leger,
& Ernest, 2016). However, these studies contrast
with reality, where these relationships (poverty,
environment, and cognition) are the result of the
interaction of a large number of variables (Bradley
& Corwyn, 2002; Bronfenbrenner, 1992; Lipina &
Colombo, 2009).
On the other hand, vast literature about
mediation analysis attempts to explain in a more
comprehensive way the complex interactions
among poverty, environmental factors (other than
poverty factors), and cognitive development. In
general, these studies are focused on how poverty
affects cognition and analyzes how this effect is
mediated by other factors (Lipina et al., 2013;
Rubio-Codina, Attanasio, & Grantham-McGregor,
2016). The most frequently analyzed mediating
mechanisms are (a) physical health and nutrition
of children, (b) type and quality of interactions
between parents and children, (c) parental mental
health, (d) possibilities/opportunities for affective
and cognitive stimulation at home, and (e)
material, health, educational, and institutional
resources of the neighborhoods (Guo & Mullan
Harris, 2000; Hackman et al., 2010; Sarsour et al.,
2011; Sulik et al., 2015). In short, although various
studies have introduced environmental factors as
mediators of poverty effects on cognition (Blair et
al., 2011; Noble, McCandliss, & Farah, 2007), less
is known about the opposite relationship: how
poverty mediates the effects of environmental
factors on cognition (Ronfani et al., 2015).
In such a context of analysis, we focused on
self-regulation processes. Self-regulation is a
multidimensional and complex construct that
involves a set of cognitive and emotional
processes occurring at different levels of
organization implicated in the regulation of
thoughts, emotions, and actions, and aimed at
adaptation to several circumstances in everyday
life (Bell & Deater-Deckard, 2007; Hofmann,
Schmeichel, & Baddeley, 2012; McClelland,
Ponitz, Messersmith, & Tominey, 2010; Montroy,
Bowles, Skibbe, McClelland, & Morrison, 2016;
Nigg, 2017).
Specifically, we analyzed executive attention,
inhibitory control, working memory, and planning
processes, which are fundamental to cognitive
activity and social behavior throughout life (Moffitt
et al., 2011; Posner, Rothbart, & Tang, 2013).
Particularly, executive attention is strongly
activated in situations that entail attentional
control, such as when there is conflict between
responses suggested by stimulus dimensions
(Posner & Raichle, 1998; Rueda, Rothbart,
McCandliss, Saccomanno, & Posner, 2005).
Inhibitory control involves the ability to control
attention, behavior, thoughts, emotions, and/or
external stimuli to suppress strong predispositions
to act and allow more appropriate responses
(Diamond, 2013). Working memory is the ability to
maintain and manipulate online relevant
information to perform a task (Diamond, 2013;
Schelble, Therriault, & Miller, 2012; Sdoia, Di
Nocera, & Ferlazzo, 2019). It makes it possible to
retain a limited amount of information to generate
possible solutions, while it is no longer
perceptually present (Baddeley & Hitch, 1994;
Bergman Nutley et al., 2011; D'Esposito & Postle,
2015; Smith & Jonides, 1999). Finally, planning
can be defined as the ability to solve a problem by
creating a strategy and an action plan that consist
in executing and evaluating different steps
(Debelak, Egle, Köstering, & Kaller, 2016; Shallice,
1982). Particularly, the importance of such
competencies is that they are part of everyday
behavior, and they are essential in the regulation
of complex behaviors and the acquisition of early
school learning (Bull & Lee, 2014; Diamond, 2013;
Garon, Bryson, & Smith, 2008; Rothbart, Sheese,
Fracchia, C. S. et al. / RACC, 2020, Vol. 12, N°2, 24-38
26
& Posner, 2008). We focused on preschool
children because the early development of these
cognitive processes could be susceptible to
environmental influences, such as home and
school experiences (Lipina et al., 2013; Rao et al.,
2010; Ursache, Blair, & Raver, 2012; Vernon-
Feagans, Willoughby, & Garrett-Peters, 2016).
Fluid reasoning is a complex human ability
related to solving new problems independently of
the knowledge previously acquired (Jaeggi,
Buschkuehl, Jonides, & Perrig, 2008). It is critical
for solving different cognitive tasks and for
adapting thinking to new situations. In addition,
this skill is also involved in daily activities during
child development and, specifically, in educational
success (Green, Bunge, Chiongbian, Barrow, &
Ferrer, 2017).
In this context, the research questions that
guided this study were (1) does poverty mediate
the association among environmental factors and
the performance of cognitive processes? and, (2)
does this mediation vary with each process?
It is important to highlight that one way to
characterize poverty is the Unsatisfied/Satisfied
Basic Needs (UBN/SBN) approach introduced in
the 1980s by Economic Commission for Latin
America and the Caribbean (CEPAL). It allows the
identification of the structural causes of poverty
(Minujin, 1992). Although this method determines
whether a list of basic needs for a dignified life are
satisfied in the households, it is not clear how this
factor is related to other environmental variables
(Martínez & Nicolini, 2017). Therefore, to answer
these questions, the present study proposed to
analyze poor homes (in terms of UBN or SBN) as
a mediator in the associations between
environmental factors and cognitive performance
in a sample of preschoolers in the city of Buenos
Aires.
Out hypotheses were as follows: (1) poverty
will mediate the associations between attention,
inhibitory control, working memory, planning, and
fluid reasoning and specific environmental factors
(i.e., family composition, reception of social
benefits, child health, health risk factors, children
and adults at home, maternal age, years of
preschool attendance, literacy activities, and
access to computer resources) (e.g., Ronfani et
al., 2015); (2) different patterns of mediation will be
identified based on cognitive processes and
environmental factors (Hackman, Gallop, Evans, &
Farah, 2015; Lawson et al., 2014; Lipina et al.,
2013); and (3) cognitive differences will be based
on socioeconomic disparities (Fracchia et al.,
2016; Segretin et al., 2014, 2016).
Methods
Participants
Two-hundred and fifty healthy Argentinean
children (134 girls; 116 boys) aged 4-5 years (M =
4.87, SD = 0.59) were recruited from three schools
in the City of Buenos Aires in 2009. Informed
consent was obtained from parents/caregivers,
and ethical approval was obtained from the
CEMIC ethical review committee (Protocol N°
320). The study was conducted in accordance with
APA’s ethical standards and international and
national children’s rights laws.
Study design and procedures
A cross-sectional study was implemented to
evaluate the associations among poverty,
environmental factors, and cognitive performance.
No atypical cases were identified, and therefore
the entire sample was considered. In addition,
missing cases were charged when they were less
than 20% in each task.
Cognitive measures
Children were assessed with a set of tasks
administered by examiners (psychologists, or
psychology or psychopedagogy students), in two
sessions of about 40 min each, in a quiet school
room conditioned for this purpose. The order of the
sessions was the same for all participating
children. Examiners were blind to the objectives of
the study and the composition of the groups. We
had no psychometric information about the tasks
used to assess the children’s cognitive
performance. These tasks were as follows:
Attention Network Test (ANT). The
computerized version for children was used to
assess different aspects of attention processing
(Rueda et al., 2004). In each trial, children pressed
a right or left button depending on the direction an
animal was facing on the computer screen. Total
efficiency (i.e., the proportion of correct responses
to the total administered) was the dependent
variable of interest.
Stroop-like Heart-Flower. This computerized
task was designed to evaluate inhibitory control
and cognitive flexibility processes (Davidson,
Amso, Cruess Anderson, & Diamond, 2006). It
consisted in presenting three contingencies of
stimuli: (a) congruent: children were asked to
Fracchia, C. S. et al. / RACC, 2020, Vol. 12, N°2, 24-38
27
press the button on the same side in which a heart
appeared; (B) incongruent: children were asked to
press the button on the opposite side of a flower;
(C) mixed: congruent and incongruent stimuli were
combined randomly. The efficiency of the mixed
condition (i.e., the proportion of correct responses
to the mixed condition administered) was the
dependent variable of interest.
Self-ordered searching. This is a
computerized test used to evaluate the spatial
working memory of objects (Luciana & Nelson,
2002). The purpose was to select all the pictures
of objects, one at a time; each time an object was
selected, the others disappeared from the screen
and reappeared, but in a different order. Four
blocks were administered, two of six and two of
eight items. The dependent variable considered
was a composite variable generated from the sum
of scores that corresponded to blocks 1 and 4.
Corsi Block task. This was used to assess
visuo-spatial working memory (Berch, Krikorian, &
Huha, 1998; Huang, Klein, & Leung, 2016). During
administration, the child was asked to reproduce a
sequence of lights (from one to eight, lighting time
1000 ms), which were turned on inside a series of
boxes arranged randomly in the device. Difficulty
levels increased with the number of lights. The
dependent variable of interest was the total score,
which was computed as the sum of correct
responses multiplied by the level of difficulty.
Tower of London (TOL). This was used to
assess planning (Berg & Byrd, 2002; Shallice,
1982). In each trial, the children were required to
reach a goal configuration of three colored balls
from an initial configuration, following a set of
rules, and they were asked to generate the
appropriate action sequence to reach the
configuration model. Difficulty levels included
exercises with 1 to 9 movements. The dependent
variable was the total score, computed as the sum
of correct responses multiplied by the level of
difficulty.
Kaufman Brief Intelligence Test (K-BITM).
The matrices subscale was administered to obtain
an overall measure of fluid reasoning performance
(Kaufman & Kaufman, 1990). The dependent
variable analyzed was the total score, computed
as the sum of correct answers.
Environmental factors
Individual interviews were conducted during
the school year in a private room with parents or
legal caregivers to obtain information from the
home environments. In this context, we
administered a socioeconomic background scale
(NES) (Lipina, Martelli, Vuelta, & Colombo, 2005;
Segretin et al., 2014) to identify indicators of UBN
(Boltvinik, 1995) and other individual and
environmental factors associated with children's
daily life experiences. In addition, all the
information was validated with the school records
about the family´s environmental characteristics,
which were available in the kindergartens.
Based on the literature in this area (Bradley &
Corwyn, 2002; Hackman et al., 2010; Lipina et al.,
2013), we selected a set of variables from the
scale to evaluate each household: family
composition (in relation with the presence of both
parents, single parent or other caregivers at
home), reception of social benefits (number of
benefits), child health (number of child health
records, including low weight at birth, preterm
birth, neurological disorders, perinatal disorders),
health risk factors (number of peri-, pre-, and
postnatal risk factors for child heath), children at
home (number of children under 14 years of age
living at home), adults at home (number of adults
living at home), maternal age, years of preschool
attendance (number of years that the child was
previously enrolled at school or in a childcare
institution), literacy activities (a composite variable
was created based on the number of books
available at home and the frequency of book
reading to the children), and computer resources
(a composite variable was created based on
whether a computer and internet connection were
available in the household). UBN criteria are
based on the identification of at least one of the
following conditions: (a) inappropriate dwelling
conditions (precarious houses that were not
intended for housing purposes), (b) absence of
waste discharge systems in the household, or (c)
overcrowding conditions (three or more people
sleeping in one bedroom). Based on this
information, two groups of children were
generated: UBN homes and SBN homes.
Data analysis
Standard descriptive analysis and
correlation analysis for each independent variable
were performed to identify associations, from the
set of 10 environmental variables. Before running
mediation analysis, two composites were
generated based on a previous approach: (1)
Fracchia, C. S. et al. / RACC, 2020, Vol. 12, N°2, 24-38
28
literacy activities, generated by averaging the z
scores of the variables amount of books at home,
and frequency of book reading to children; and (2)
computer resources, generated by averaging the z
scores of the variables computer use and internet
use. To compare differences between
socioeconomic groups in the independent
variables, univariate analysis and Mann-Whitney U
test (if appropriate) were used.
Univariate analysis of variance was
implemented to compare performance among
children from UBN and SBN homes. The fulfillment
of assumptions of normality, homoscedasticity,
and independence were previously verified. In
cases where non-compliance with one or more of
these assumptions was detected, quadratic or
trigonometric transformations were applied, as
appropriate. In the univariate variance models,
poverty (UBN/SBN) was included as an
independent variable, performance in cognitive
tasks and environmental factors were dependent
variables, and age was a covariable.
First, a correlation analysis was
implemented to identify associations between
dependent variables. Then, each dependent
variable was analyzed separately to identify
significant mediators. Before the inclusion of each
dependent variable in the mediation analysis, their
scores were transformed into z-scores, to obtain a
common metric for comparisons across tasks. For
each task, only one dependent variable was
included in the analyses (see Cognitive
measures).
Finally, Sobel-Goodman mediation tests
were implemented, which included poverty as a
mediator variable, each environmental factor as an
independent variable, and cognitive performance
as the dependent variable (Figure 1). In this paper,
we considered a full mediation when there was an
indirect effect, but no direct effect. When there
were both indirect and direct effects, we
considered it a partial mediation (Baron & Kenny,
1986; Zhao, Lynch, & Chen, 2010).
All analyses were adjusted for age. For the
number of comparisons (n = 10), the Bonferroni
correction was used for a significance level of .05
(the final value of p was .005).
COGNITIVE
PERFORMANCE
(VD)
ENVIRONMENTAL
FACTOR
(VI)
BASIC
NEEDS
(M)
POVERTY
Figure 1. Diagram of mediation analysis model that
tests the mediating effect of poverty on the relationship
between environmental factors and cognitive
processes.
Results
Independent variables
The correlation analysis between the
independent variables and poverty resulted in low
and non-significant associations between them,
except for the association between literacy
activities and poverty, where the association was
moderate (Table 1).
Dependent variables
Results from the correlation analysis between
the dependent variables showed non-significant
associations, except for the relation among
executive attention and inhibitory control, where
the association was moderate (Table 2).
Socioeconomic condition
The results of the univariate analysis
regarding the environmental conditions indicated
some significant differences between children from
UBN and SBN homes. In particular, families from
UBN homes had more adults at home (z = -
2.25; p = .025). Children from SBN conditions
were more likely to be in the care of a single
person (z = -2.17; p = .030). Children from UBN
homes had more child health (z = -4.85; p = .000)
and health risk factors (z = -5.95; p = .000). In
addition, in comparison to children from the SBN
group, the children from the UBN group yielded
the following findings: (a) almost one more year of
preschool attendance (z = -1.91; p = .056); (b)
fewer books at home and lower frequency of book
reading to children (z = -7.87; p = .000); (d) lower
frequency of computer and internet use (z = -7.13;
p = .000); and (e) younger mothers (f = 5.28; p =
.023). There were no significant differences in the
other variables analyzed (Table 3).
As expected, comparisons between BN
groups showed that the UBN group obtained
significantly lower efficacy levels and scores in
Fracchia, C. S. et al. / RACC, 2020, Vol. 12, N°2, 24-38
29
most variables analyzed: executive attention,
visuo-spatial working memory, inhibitory control,
planning, and fluid reasoning. The SBN group
obtained significantly lower efficacy in object
spatial working memory (Table 4).
Table 1.
Spearman correlation analysis between the independent variables (environmental factors) and the mediator
(poverty) of children coming from different socioeconomic contexts in Buenos Aires, Argentina.
Poverty
FC
NB
CH
HRF
NC
NA
MA
YPA
LA
Family
composition
(FC)
.15*
Number of
benefits (NB)
.09
-.08
Child health
(CH)
.34***
-.06
.48***
Health risks
factors (HRF)
.41***
.02
.52***
.59***
Number of
children
under 14
(NC)
.12
-.20**
.61***
.48***
.49***
Number of
adults (NA)
.16*
-.21**
.40***
.41***
.46***
.59***
Maternal age
(MA)
-.15*
.23***
-.27***
-.27***
-.30***
-.35***
-.37***
Years of
preschool
attendance
(YPA)
-.14
-.03
.34***
.23***
.19**
.31***
.24***
-.03
Literacy
activities (LA)
-.56***
-.02
-.33***
-.47***
-.44***
-.32***
-.27***
.32***
-.07
Computer
resources
(CR)
-.51***
.07
-.16*
-.33***
-.30***
-.20**
-.26***
.22***
.15*
.51***
Note. *p < .05; **p < .01; ***p < .001
Table 2.
Pearson correlation analysis between the dependent variables (cognitive performance) of children coming from
different socioeconomic contexts in Buenos Aires, Argentina.
Inhibitory control
Planning
Working
memory
Fluid reasoning
Planning
.38***
Working memory
.33***
.13*
Fluid reasoning
.39***
.29***
.23***
Executive
attention
.56***
.44***
.33***
.38***
Note. *p < .05; ***p < .001
Fracchia, C. S. et al. / RACC, 2020, Vol. 12, N°2, 24-38
30
Table 3.
Differences between socioeconomic groups (SBN/UBN) in the independent variables (environmental factors) of
preschoolers from Buenos Aires, Argentina.
Variables
n
SBN
UBN
Z
Sig
Mdn
Mdn
Number of adults at home
200
5
6
-2.25
.025
Family composition
206
1
1
-2.17
.030
Child health records
201
3
3
-4.85
.000
Health risk factors
207
2
3
-5.95
.000
Years of preschool attendance
198
3
3
-1.91
.056
Literacy activities
199
2
1
-7.87
.000
Computer resources
197
3
1
-7.13
.000
Number of children under 14 at home
200
5
5
-1.69
.091
Number of public benefits
205
1
1
-1.33
.181
M (SD)
M (SD)
F
Sig
Maternal age
204
35.15 (6.44)
33.16 (5.83)
5.284
.023
Note. The significant scores are highlighted in bold.
Table 4.
Comparison of dependent variables (cognitive performance) in preschoolers from two different socioeconomic
groups in Buenos Aires, Argentina.
Task
Dependent Variable
SBN
UBN
df
F
Sig
n
M(SD)
n
M(SD)
ANT
Total efficiency
147
0.22 (0.88)
98
-0.33
(1.07)
1.245
23.80
.000
Stroop
Efficiency mixed
condition
144
0.17 (0.99)
98
-0.26
(0.96)
1.242
13.31
.000
Self-ordered
Proportion of corrects
answers
147
-0.14
(0.81)
98
0.20 (0.82)
1.245
10.01
.002
Corsi
Total score
147
0.21 (1.08)
98
-0.31
(0.76)
1.245
18.58
.000
TOL
Total score
147
0.14 (0.99)
98
-0.20
(0.98)
1.245
8.38
.004
K-BITM
Total score
147
0.24 (0.96)
98
-0.37
(0.95)
1.245
26.32
.000
Note. SBN: Satisfied Basic Needs; UBN: Unsatisfied Basic Needs. All analyses were adjusted for age. The
significant scores are highlighted in bold.
Mediation analysis
According to the criteria to determine a total
or partial mediation, results from the Sobel-
Goodman test showed the following results.
Total mediation.
(a) The effects of maternal age on executive
attention and fluid reasoning were totally mediated
by poverty; (b) the effects of children at home and
adults at home on executive attention were largely
mediated by poverty (Table 5).
Partial mediation.
(a) The effects of family composition on
executive attention, fluid reasoning, and inhibitory
control were partially mediated by poverty; (b) the
effects of health risk factors on executive attention
and fluid reasoning were partially mediated by
poverty; (c) the effects of literacy activities on
executive attention and fluid reasoning were
partially mediated by poverty; (d) the effects of
child health, children at home, and adults at home
Fracchia, C. S. et al. / RACC, 2020, Vol. 12, N°2, 24-38
31
on fluid reasoning were partially mediated by
poverty (Table 6).
Table 5.
Mediation model with dependent variables (cognitive performances) regressed on mediator (poverty) and independent
variables (environmental factors) for total mediation for preschoolers from two different socioeconomic groups in
Argentina.
Environmental
variable (IV)
Cognitive
domain
(DV)
Path a
Path b
Path c
Effect
%M
Coef.
SE
Sig.
Coef.
SE
Sig.
Coef.
SE
Sig.
Indirect
Direct
Maternal age
Executive
attention
-.013
.006
.019
-.443
.122
.000
.021
.010
.037
.047
.124
0.280
Maternal age
Fluid
intelligence
-.013
.006
.019
-.525
.133
.000
.024
.011
.027
.043
.102
0.284
Number of
children under
14
Executive
attention
.055
.018
.003
-.454
.130
.001
-.062
.034
.068
.021
.270
0.402
Number of
adults
Executive
attention
.054
.020
.008
-.452
.130
.001
-.073
.038
.051
.033
.185
0.331
Note. DV: Dependent variable; IV: Independent variable; MV: Mediator variable; %M: Proportion of total effect that is
mediated. Analysis was adjusted for age and gender.
Table 6.
Mediation model with dependent variables (cognitive performances) regressed on mediator (poverty) and independent
variables (environmental factors) for partial mediation for preschoolers from two different socioeconomic groups in
Argentina.
Environmental
variable (IV)
Cognitive
domain
(DV)
Path a
Path b
Path c
Effect
%M
Coef.
SE
Sig.
Coef.
SE
Sig.
Coef.
SE
Sig.
Indirect
Direct
Family
composition
Executive
attention
-.150
.037
.000
-.429
.128
.001
.237
.070
.001
.010
.014
0.272
Family
composition
Fluid
reasoning
-.150
.037
.000
-.455
.134
.001
.267
.073
.000
.009
.007
0.256
Family
composition
Inhibitory
control
-.149
.038
.000
-.266
.128
.039
.213
.070
.002
.066
.014
0.185
Health risk
factors
Executive
attention
.194
.028
.000
-.361
.136
.009
-.204
.056
.000
.013
.027
0.343
Health risk
factors
Fluid
reasoning
.194
.028
.000
-.375
.144
.010
-.259
.059
.000
.014
.003
0.281
Literacy
activities
Executive
attention
-.314
.033
.000
-.310
.154
.045
.271
.0272
.000
.048
.043
0.360
Literacy
activities
Fluid
reasoning
-.314
.033
.000
-.295
.159
.064
.362
.073
.000
.067
.002
0.256
Child health
Fluid
reasoning
.163
.030
.000
-.330
.136
.016
-.299
.059
.000
.027
.000
0.180
Number of
children under
14
Fluid
reasoning
.055
.178
.002
-.483
.133
.000
-.134
.035
.000
.019
.001
0.193
Number of
adults
Fluid
reasoning
.054
.020
.008
-.479
.131
.000
-.172
.038
.000
.030
.000
0.150
Note. DV: Dependent variable; IV: Independent variable; %M: Proportion of total effect that is mediated. Analysis was
adjusted for age and gender.
Fracchia, C. S. et al. / RACC, 2020, Vol. 12, N°2, 24-38
32
Discusión
The literature has traditionally analyzed the
relation between poverty, environmental factors,
and cognitive development, and the studies tend
to focus on the direct associations between them
(Blair, Ursache, Greenberg, Vernon-Feagans, &
The Family Life Project Investigators, 2015;
Raghubar, Barnes, & Hecht, 2010; Ursache,
Noble, & Blair, 2015; Weiland & Yoshikawa, 2013).
More recently, other studies have explored how
these associations are mediated by environmental
factors (Hackman et al., 2015; Liberzon et al.,
2015; Luby et al., 2013). Using such an approach,
we analyzed the contribution of poverty to the
association between specific environmental factors
and cognitive skills. We identified the specific
mediating role of poor and non-poor homes in the
association between environmental factors –
family composition, maternal age, health risk
factors, child health, literacy activities, children and
adults at home – and executive attention, inhibitory
control, and fluid reasoning.
First, the results of this study show that
children from poor homes had lower performance
in tasks that demanded the identification of stimuli
from the environment, flexibility to look for different
sources of information to solve tasks where
contingencies changed, interference control, and
generation of sequences of actions to solve the
tasks. These results add evidence to the literature
on childhood poverty and cognition studies about
the differences in the performance of children from
different socioeconomic backgrounds (Bradley &
Corwyn, 2002; Farah et al., 2006, 2008; Hackman
& Farah, 2009; Lipina & Colombo, 2009;
Yoshikawa et al., 2012).
Second, in agreement with previous results
(Fracchia et al., 2016; Lipina et al., 2005, 2013;
Lipina & Colombo, 2009; Segretin et al., 2014,
2016), we identified significant differences in
several environmental factors between
socioeconomic groups. Specifically, families from
the poverty group were exposed to more adults at
home, younger mothers, a higher number of child
health and health risk factors, a tendency to have
more than one caregiver, almost one more year of
preschool attendance, fewer books at home, lower
frequency of book reading to children, and lower
frequency of computer and internet use.
The results of our mediation analysis
suggested that depending on the environmental
factor analyzed, the proportion of poverty
mediation varied from .15 to .40. For total
mediation, the relation of the maternal age variable
on the executive attention and fluid reasoning
competencies was mediated largely by poverty.
Several studies have indicated the association
between maternal age and childhood cognitive
and behavioral outcomes (Fall et al., 2015;
Fergusson & Lynskey, 1993). However, our results
suggested that whether a child lived in a poor
home or not determined the correlation of this
environmental factor on the child’s performance.
Also, poverty mediated the effects of children
and adults at home on executive attention
processes. This means that these relationships
were fully explained by poverty or non-poverty
backgrounds. Some evidence suggests that the
number of people at home (whether children or
adults) resulted in a lack of personal space or
privacy and enforced intimate proximity to
household members with communicable diseases
and that the potentially excessive social or
external demands could have harmful effects on
cognition (Goux & Maurin, 2005; Leventhal &
Newman, 2010).
The results of partial mediation analyses
showed that the associations between family
composition and performance in executive
attention, fluid reasoning, and inhibitory control
varied according to the socioeconomic
backgrounds. Previous studies have indicated that
children who lived with both parents had higher
cognitive performance (e.g., Sarsour et al., 2011).
However, the fact that this relationship varied
according to poverty implied that beyond the direct
effect of having one or both parents at home on
executive attention, fluid reasoning, and inhibitory
control, a large proportion of the association of this
environmental factor depended on the
socioeconomic conditions of the households.
Hence, the effect of such a factor in the case of
children living in poverty was different from those
who lived in non-poor homes.
Likewise, the variable health risk factors
affected children’s performance in executive
attention and fluid reasoning tasks, and this
relation was mediated by poverty. In accordance
with our results, the literature showed that the
presence of health risk factors in childhood was
associated with impacts on cognitive development
(Lengua et al., 2015; Weitzman, 2007).
Nevertheless, the fact that poverty was a mediator
Fracchia, C. S. et al. / RACC, 2020, Vol. 12, N°2, 24-38
33
implies that beyond the direct effect that health risk
factors have on cognitive competences, their
presence or absence influences children from poor
homes and children from non-poor homes in
different ways.
Literacy activities were associated with
executive attention and fluid reasoning, and this
association was mediated by poverty. Kegel and
Bus (2014) suggested that children who had more
literacy stimuli in their homes had higher cognitive
performance. However, our results suggested that
literacy activities do not have an identical effect on
poor and non-poor contexts, beyond the direct
relation that literacy activities have on executive
attention and fluid reasoning.
Finally, the variables child health and children
and adults at home were associated with fluid
reasoning, and this relationship varied according
to poverty.
These results showed that the environmental
factors that we analyzed had different types of
relationships when they were present in both UBN
and SBN contexts. Because the frequency of
single-parent households and children and adults
at home were higher in poverty contexts, and the
frequency of literacy activities was lower in those
children, it is important to consider these variables
as potential targets for future interventions aimed
at optimizing cognitive processes skills in
preschoolers from those contexts. Therefore,
poverty did not mediate the relationship between
environmental factors and cognitive performance
in a uniform way, but its influence differed
depending on the type of environmental factor.
Additionally, results of the mediating effects of
poverty were verified for three of the five cognitive
processes analyzed. Thus, results indicated a
differential sensitivity of each process to different
environmental factors and the mediating role of
poverty. This variation is consistent with other
studies that indicated that not all aspects of the
socioeconomic backgrounds affected the
associations between environmental factors and
cognitive development (Duncan & Magnuson,
2012; Duncan, Magnuson, & Votruba-Drzal, 2017;
Lipina, 2016). In addition, this variation suggested
different patterns of cognitive integration through
development (Garon et al., 2008). These findings
should not be generalized since this study has
certain limitations that should be covered in future
studies with different cognitive tasks for the same
processes, a wider age range, different
environmental factors, and different levels of
organization (e.g., molecular, neural, and
behavioral). Another limitation of the present work
was the lack of psychometric information about the
cognitive tasks, an issue that should be solved in
future studies. Therefore, this generates the need
to continue exploring (a) the application of this
model of analysis with a more diverse set of self-
regulatory tasks (e.g., flexibility); (b) more diverse
samples in terms of individual and environmental
factors; and (c) the influences of interventions, to
better understand the development and integration
of different cognitive processes during learning
processes. Understanding these cognitive
processes is necessary not only for improving
cognitive performance but also for improving the
general well-being of these populations (Campbell
et al., 2002; Evans, 2016; Hoelscher, Moag-
Stahlberg, Ellis, Vandewater, & Malkani, 2016).
Specifically, social policy aimed at promoting
human development in general, and child
development in particular, should be designed
together with scientific policies that provide
information on what issues should be investigated
based on the needs of each society. Although the
information in this work must be taken cautiously
due to the limitations mentioned above, it is useful
since it contributes to optimizing the design of
interventions aimed at fostering child cognitive
development in populations exposed to poverty.
References
Baddeley, A. D., & Hitch, G. J. (1994). Developments in
the concept of working memory. Neuropsychology,
8(4), 485-493. doi: 10.1037/0894-4105.8.4.485
Baron, R. M., & Kenny, D. A. (1986). The moderator–
mediator variable distinction in social psychological
research: Conceptual, strategic, and statistical
considerations. Journal of Personality and Social
Psychology, 51(6), 1173-1182.
Bell, M. A., & Deater-Deckard, K. (2007). Biological
systems and the development of self-regulation:
Integrating behavior, genetics, and
psychophysiology. Journal of Developmental &
Behavioral Pediatrics, 28(5), 409-420. doi:
10.1097/DBP.0b013e3181131fc7
Berch, D. B., Krikorian, R., & Huha, E. M. (1998). The
corsi block-tapping task: Methodological and
theoretical considerations. Brain and Cognition,
38(3), 317-338. doi: 10.1006/brcg.1998.1039
Berg, W. K., & Byrd, D. L. (2002). The Tower of London
spatial problem-solving task: Enhancing clinical
and research implementation. Journal of Clinical
and Experimental Neuropsychology, 24(5), 586-
Fracchia, C. S. et al. / RACC, 2020, Vol. 12, N°2, 24-38
34
604. doi: 10.1076/jcen.24.5.586.1006
Bergman Nutley, S., Söderqvist, S., Bryde, S., Thorell,
L. B., Humphreys, K., & Klingberg, T. (2011). Gains
in fluid intelligence after training non‐verbal
reasoning in 4‐year‐old children: A controlled,
randomized study. Developmental Science, 14(3),
591-601. doi: 10.1111/j.1467-7687.2010.01022.x
Blair, C., Granger, D. A., Willoughby, M., Mills‐Koonce,
R., Cox, M., Greenberg, M. T., … Fortunato, C. K.
(2011). Salivary cortisol mediates effects of poverty
and parenting on executive functions in early
childhood. Child Development, 82(6), 1970-1984.
doi: 10.1111/j.1467-8624.2011.01643.x
Blair, C., & Raver, C. C. (2016). Poverty, stress, and
brain development: New directions for prevention
and intervention. Academic Pediatrics, 16(3), S30-
S36. doi: 10.1016/j.acap.2016.01.010
Blair, C., Ursache, A., Greenberg, M., Vernon-Feagans,
L., & The Family Life Project Investigators. (2015).
Multiple Aspects of Self-Regulation Uniquely
Predict Mathematics but Not Letter–Word
Knowledge in the Early Elementary Grades.
Developmental Psychology, 51(4), 459-472. doi:
10.1037/a0038813
Boltvinik, J. (1995). Poverty Measurement Methods: An
overview. Washington, DC.: UNDP, Social
Development & Poverty Elimination Division.
Bradley, R. H., & Corwyn, R. F. (2002). Socioeconomic
status and child development. Annual Review of
Psychology, 53(1), 371-399. doi:
10.1146/annurev.psych.53.100901.135233
Bronfenbrenner, U. (1992). Ecological systems theory.
En R. Vasta (Ed.), Six theories of child
development: Revised formulations and current
issues (pp. 187-249). London, England: Jessica
Kingsley Publishers.
Brooks-Gunn, J., & Duncan, G. J. (1997). The effects of
poverty on children. The Future of Children, 7(2),
55-71. doi: 10.2307/1602387
Bull, R., & Lee, K. L. (2014). Executive functioning and
mathematics achievement. Child Development
Perspectives, 8(1), 36-41. doi: 10.1111/cdep.12059
Campbell, F. A., Ramey, C. T., Pungello, E., Sparling,
J., Miller-Johnson, S., & Early Childhood
Education. (2002). Young adult outcomes from the
Abecedarian Project. Applied Developmental
Science, 6(1), 42-57. doi:
10.1207/S1532480XADS0601_05
Davidson, M. C., Amso, D., Cruess Anderson, L., &
Diamond, A. (2006). Development of cognitive
control and executive functions from 4-13 years:
Evidence from manipulations of memory, inhibition,
and task switching. Neuropsychologia, 44(11),
2037-2078. doi:
10.1016/j.neuropsychologia.2006.02.006
Debelak, R., Egle, J., Köstering, L., & Kaller, C. P.
(2016). Assessment of planning ability:
Psychometric analyses on the unidimensionality
and construct validity of the Tower of London Task
(TOL-F). Neuropsychology, 30(3), 346-360. doi:
10.1037/neu0000238
D'Esposito, M., & Postle, B. R. (2015). The cognitive
neuroscience of working memory. Annual Review
of Psychology, 66(1), 115-142. doi:
10.1146/annurev-psych-010814-015031
Diamond, A. (2013). Executive functions. Annual
Review of Psychology, 64(1), 135-168. doi:
10.1146/annurev-psych-113011-143750
Dickerson, A., & Popli, G. K. (2016). Persistent poverty
and children's cognitive development: evidence
from the UK Millennium Cohort Study. Journal of
the Royal Statistical Society: Series A (Statistics in
Society), 179(2), 535-558. doi: 10.1111/rssa.12128
Duncan, G. J., & Magnuson, K. (2012). Socioeconomic
status and cognitive functioning: Moving from
correlation to causation. Wiley Interdisciplinary
Reviews: Cognitive Science, 3(3), 377-386. doi:
10.1002/wcs.1176
Duncan, G. J., Magnuson, K., & Votruba-Drzal, E.
(2017). Moving beyond correlations in assessing
the consequences of poverty. Annual Review of
Psychology, 68(1), 413-434. doi: 10.1146/annurev-
psych-010416-044224
Evans, G. W. (2016). Childhood poverty and adult
psychological well-being. Proceedings of the
National Academy of Sciences, 113(52), 14949-
14952. doi: 10.1073/pnas.1604756114
Fall, C. H., Sachdev, H. S., Osmond, C., Restrepo-
Mendez, M. C., Victora, C., Martorell, R., …
Richter, L. M. (2015). Association between
maternal age at childbirth and child and adult
outcomes in the offspring: a prospective study in
five low-income and middle-income countries
(COHORTS collaboration). The Lancet Global
Health, 3(7), e366-e377. doi: 10.1016/S2214-
109X(15)00038-8
Farah, M. J., Betancourt, L., Shera, D. M., Savage, J.
H., Giannetta, J. M., Brodsky, N. L., … Hurt, H.
(2008). Environmental stimulation, parental
nurturance and cognitive development in humans.
Developmental Science, 11(5), 793-801. doi:
10.1111/j.1467-7687.2008.00688.x
Farah, M. J., Shera, D. M., Savage, J. H., Betancourt,
L., Giannetta, J. M., Brodsky, N. L., … Hurt, H.
(2006). Childhood poverty: specific associations
with neurocognitive development. Brain Research,
1110(1), 166-174. doi:
10.1016/j.brainres.2006.06.072
Fergusson, D. M., & Lynskey, M. T. (1993). Maternal
Fracchia, C. S. et al. / RACC, 2020, Vol. 12, N°2, 24-38
35
age and cognitive and behavioral outcomes in middle
childhood. Paediatric and Perinatal Epidemiology,
7(1), 77-91. doi: 10.1111/j.1365-
3016.1993.tb00604.x
Finegood, E. D., Raver, C. C., DeJoseph, M. L., & Blair,
C. (2017). Parenting in poverty: Attention bias and
anxiety interact to predict parents’ perceptions of
daily parenting hassles. Journal of Family
Psychology, 31(1), 51-60. doi:
10.1037/fam0000291
Fracchia, C. S., Giovannetti, F., Gili, J., Lopez-
Rosenfeld, M., Hermida, M. J., Prats, L. M., …
Lipina, S. J. (2016). Individuality and Self-
regulation in Preschoolers. Diskurs Kindheits-und
Jugendforschung, 11(4), 457-471. doi:
10.3224/diskurs.v11i4.25604
Garon, N., Bryson, S. E., & Smith, I. M. (2008).
Executive function in preschoolers: A review using
an integrative framework. Psychological Bulletin,
134(1), 31-60. doi: 10.1037/0033-2909.134.1.31
Goux, D., & Maurin, E. (2005). The effect of
overcrowded housing on children's performance at
school. Journal of Public Economics, 89(5), 797-
819. doi: 10.1016/j.jpubeco.2004.06.005
Green, C. T., Bunge, S. A., Chiongbian, V. B., Barrow,
M., & Ferrer, E. (2017). Fluid reasoning predicts
future mathematical performance among children
and adolescents. Journal of Experimental Child
Psychology, 157, 125-143. doi:
10.1016/j.jecp.2016.12.005
Guo, G., & Mullan Harris, K. (2000). The mechanisms
mediating the effects of poverty on children’s
intellectual development. Demography, 37(4), 431-
447. doi: 10.1353/dem.2000.0005
Hackman, D. A., & Farah, M. J. (2009). Socioeconomic
status and the developing brain. Trends in
Cognitive Sciences, 13(2), 65-73. doi:
10.1016/j.tics.2008.11.003
Hackman, D. A., Farah, M. J., & Meany, M. J. (2010).
Socioeconomic status and the brain: Mechanistic
insights from human and animal research. Nature
Reviews Neuroscience, 11(9), 651-659. doi:
10.1038/nrn2897
Hackman, D. A., Gallop, R., Evans, G. W., & Farah, M.
J. (2015). Socioeconomic status and executive
function: developmental trajectories and mediation.
Developmental Science, 18(5), 686-702. doi:
10.1111/desc.12246
Hoelscher, D. M., Moag-Stahlberg, A., Ellis, K.,
Vandewater, E. A., & Malkani, R. (2016).
Evaluation of a student participatory, low-intensity
program to improve school wellness environment
and students’ eating and activity behaviors. The
International Journal of Behavioral Nutrition and
Physical Activity, 13(1), 59. doi: 10.1186/s12966-
016-0379-5
Hofmann, W., Schmeichel, B. J., & Baddeley, A. D.
(2012). Executive functions and self-regulation.
Trends in Cognitive Sciences, 16(3), 174-180. doi:
10.1016/j.tics.2012.01.006
Huang, A. S., Klein, D. N., & Leung, H. C. (2016). Load-
related brain activation predicts spatial working
memory performance in youth aged 9-12 and is
associated with executive function at earlier ages.
Developmental Cognitive Neuroscience, 17, 1-9.
doi: 10.1016/j.dcn.2015.10.007
Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W.
J. (2008). Improving fluid intelligence with training
on working memory. Proceedings of the National
Academy of Sciences, 105(19), 6829-6833. doi:
10.1073/pnas.0801268105
Johnson, S. B., Riis, J. L., & Noble, K. G. (2016). State
of the art review: Poverty and the developing brain.
Pediatrics, 137(4), e20153075. doi:
10.1542/peds.2015-3075
Kaufman, A. S., & Kaufman, N. D. (1990). Kaufman
brief intelligence test: KBIT. Circle Pines, MN:
AGS, American Guidance Service.
Kegel, C. A., & Bus, A. G. (2014). Evidence for causal
relations between executive functions and
alphabetic skills based on longitudinal data. Infant
and Child Development, 23(1), 22-35. doi:
10.1002/icd.1827
Kishiyama, M. M., Boyce, W. T., Jimenez, A. M., Perry,
L. M., & Knight, R. T. (2009). Socioeconomic
disparities affect prefrontal function in children.
Journal of Cognitive Neuroscience, 21(6), 1106-
1115. doi: 10.1162/jocn.2009.21101
Lawson, G. M., Hook, C. J., Hackman, D. A., Farah, M.
J., Griffin, J. A., Freund, L. S., & McCardle, P.
(2014). Socioeconomic status and neurocognitive
development: Executive function. En J. A. Griffin, L.
S. Freund, & P. McCardle (Eds.), Executive
Function in Preschool Children: Integrating
Measurement, Neurodevelopment, and
Translational Research (pp. 1-28). Washington,
DC: American Psychological Association Press.
Lengua, L. J., Moran, L., Zalewski, M., Ruberry, E., Kiff,
C., & Thompson, S. (2015). Relations of growth in
effortful control to family income, cumulative risk,
and adjustment in preschool-age children. Journal
of Abnormal Child Psychology, 43(4), 705-720. doi:
10.1007/s10802-014-9941-2
Leventhal, T., & Newman, S. (2010). Housing and child
development. Children and Youth Services Review,
32(9), 1165-1174. doi:
10.1016/j.childyouth.2010.03.008
Liberzon, I., Ma, S. T., Okada, G., Ho, S. S., Swain, J.
E., & Evans, G. W. (2015). Childhood poverty and
recruitment of adult emotion regulatory
neurocircuitry. Social Cognitive and Affective
Neuroscience, 10(11), 1596-1606. doi:
10.1093/scan/nsv045
Lipina, S. J. (2016). Critical considerations about the
Fracchia, C. S. et al. / RACC, 2020, Vol. 12, N°2, 24-38
36
use of poverty measures in the study of cognitive
development. International Journal of Psychology,
52(3), 241-250. doi: 10.1002/ijop.12282
Lipina, S. J., & Colombo, J. A. (2009). Poverty and
brain development during childhood: An approach
from Cognitive Psychology and Neuroscience.
Washington, DC: American Psychological
Association. doi: 10.1037/11879-000
Lipina, S. J., Martelli, M. I., Vuelta, B. L., & Colombo, J.
A. (2005). Performance on the AnoB task of
Argentinean infants from Unsatisfied Basic Needs
Homes. Interamerican Journal of Psychology,
39(1), 49-60.
Lipina, S. J., Segretin, M. S., Hermida, M. J., Prats, L.,
Fracchia, C., López-Camelo, J., & Colombo, J. A.
(2013). Linking childhood poverty and cognition:
Individual and environmental predictors of non-
verbal executive control in an Argentine sample.
Developmental Science, 16(5), 697-707. doi:
10.1111/desc.12080
Luby, J., Belden, A., Botteron, K., Marrus, N., Harms,
M. P., Babb, C., … Barch, D. (2013). The effects of
poverty on childhood brain development: the
mediating effect of caregiving and stress1ful life
events. JAMA Pediatrics, 167(12), 1135-1142. doi:
10.1001/jamapediatrics.2013.3139
Luciana, M., & Nelson, C. A. (2002). Assessment of
neuropsychological function through use of the
Cambridge Neuropsychological Testing Automated
Battery: performance in 4- to 12-year-old children.
Developmental Neuropsychology, 22(3), 595-624.
doi: 10.1207/S15326942DN2203_3
Martínez, A. A., & Nicolini, E. A. (2017). The long
memory of poverty: The Historical Unsatisfied
Basic Needs and the geographic patterns of
standards of living in Argentina (and Spain) in the
last 100 years. Trabajo presentado en Iberometric
VIII: Eight Iberian Cliometrics Workshop, Institute
of Advanced Research in Business and Economics
(INARBE), Universidad Pública de Navarra,
Pamplona.
McClelland, M. M., Ponitz, C. C., Messersmith, E. E., &
Tominey, S. (2010). Self‐regulation: The integration
of cognition and emotion. En Lerner, R. M. (Ed.),
The Handbook of Life
‐
Span Development (pp. 510-
553). doi: 10.1002/9780470880166.hlsd001015
Minujin, A. (1992). Cuesta abajo. Los nuevos pobres:
efectos de la crisis en la sociedad argentina.
Buenos Aires, Argentina: Editorial Losada.
Moffitt, T. E., Arseneault, L., Belsky, D., Dickson, N.,
Hancox, R. J., Harrington, H., & Sears, M. R.
(2011). A gradient of childhood self-control predicts
health, wealth, and public safety. Proceedings of
the National Academy of Sciences, 108(7), 2693-
2698. doi: 10.1073/pnas.1010076108
Montroy, J. J., Bowles, R. P., Skibbe, L. E., McClelland,
M. M., & Morrison, F. J. (2016). The development
of self-regulation across early childhood.
Developmental Psychology, 52(11), 1744-1762.
doi: 10.1037/dev0000159
Nigg, J. T. (2017). Annual Research Review: On the
relations among self‐regulation, self‐control,
executive functioning, effortful control, cognitive
control, impulsivity, risk‐taking, and inhibition for
developmental psychopathology. Journal of Child
Psychology and Psychiatry, 58(4), 361-383. doi:
10.1111/jcpp.12675
Noble, K. G., McCandliss, B. D., & Farah, M. J. (2007).
Socioeconomic gradients predict individual
differences in neurocognitive abilities.
Developmental Science, 10(4), 464-480. doi:
10.1111/j.1467-7687.2007.00600.x
Posner, M. I., & Raichle, M. E. (1998). The
neuroimaging of human brain function.
Proceedings of the National Academy of Sciences,
95(3), 763-764. doi: 10.1073/pnas.95.3.763
Posner, M. I., Rothbart, M. K., & Tang, Y. (2013).
Developing self-regulation in early childhood.
Trends in Neuroscience and Education, 2(3), 107-
110. doi: 10.1016/j.tine.2013.09.001
Raghubar, K. P., Barnes, M. A., & Hecht, S. A. (2010).
Working memory and mathematics: A review of
developmental, individual difference, and cognitive
approaches. Learning and Individual Differences,
20(2), 110-122. doi: 10.1016/j.lindif.2009.10.005
Rao, H., Betancourt, L., Giannetta, J. M., Brodsky, N.
L., Korczykowski, M., Avants, B. B., … Detre, J. A.
(2010). Early parental care is important for
hippocampal maturation: evidence from brain
morphology in humans. NeuroImage, 49(1), 1144-
1150. doi: 10.1016/j.neuroimage.2009.07.003
Rhoades, B. L., Greenberg, M. T., Lanza, S. T., & Blair,
C. (2011). Demographic and Familial Predictors of
Early Executive Function Development:
Contribution of a person-centered perspective.
Journal of Experimental Child Psychology, 108(3),
638-662. doi: 10.1016/j.jecp.2010.08.004
Ronfani, L., Vecchi Brumatti, L., Mariuz, M., Tognin, V.,
Bin, M., Ferluga, V., … & Barbone, F. (2015). The
Complex Interaction between Home Environment,
Socioeconomic Status, Maternal IQ and Early Child
Neurocognitive Development: A Multivariate
Analysis of Data Collected in a Newborn Cohort
Study. PLoS ONE, 10(5), e0127052. doi:
10.1371/journal.pone.0127052
Rothbart, M. K., Sheese, B. E., & Posner, M. I. (2008).
Executive attention and effortful control: Linking
temperament, brain networks, and genes. Child
Development Perspectives, 1(1), 2-7. doi:
10.1111/j.1750-8606.2007.00002.x
Rubio-Codina, M., Attanasio, O., & Grantham-
McGregor, S. (2016). Mediating pathways in the
socio-economic gradient of child development:
Evidence from children 6–42 months in Bogota.
Fracchia, C. S. et al. / RACC, 2020, Vol. 12, N°2, 24-38
37
International Journal of Behavioral Development, 40(6),
483-491. doi: 10.1177/0165025415626515
Rueda, M. R., Fan, J., McCandliss, B. D., Halparin, J.
D., Gruber, D. B., Pappert Lercari, L., & Posner, M.
I. (2004). Development of attentional networks in
childhood. Neuropsychologia, 42(8), 1029-1040.
doi: 10.1016/j.neuropsychologia.2003.12.012
Rueda, M. R., Rothbart, M. K., McCandliss, B. D.,
Saccomanno, L., & Posner, M. I. (2005). Training,
maturation, and genetic influences on the
development of executive attention. Proceedings of
the National Academy of Sciences, 102(41),
14931-14936. doi: 10.1073/pnas.0506897102
Sameroff, A. J. (1998). Environmental risk factors in
infancy. Pediatrics, 102, 1287-1292.
Sarsour, K., Sheridan, M., Jutte, D., Nuru-Jeter, A.,
Hinshaw, S., & Boyce, W. T. (2011). Family
socioeconomic status and child executive
functions: the roles of language, home
environment, and single parenthood. Journal of
International Neuropsychology Society, 17(1), 120-
132. doi: 10.1017/S1355617710001335
Schelble, J. L., Therriault, D. J., & Miller, M. D. (2012).
Classifying retrieval strategies as a function of
working memory. Memory & Cognition, 40(2), 218-
230. doi: 10.3758/s13421-011-0149-1
Sdoia, S., Di Nocera, F., & Ferlazzo, F. (2019). Memory
for positional movements as a component of the
visuospatial working memory. Cognitive
Processing, 20(3), 363-369. doi: 10.1007/s10339-
019-00917-y
Segretin, M. S., Hermida, M. J., Prats, L. M., Fracchia,
C. S., Ruetti, E., & Lipina, S. J. (2016). Childhood
Poverty and Cognitive Development in Latin
America in the 21st Century. New Directions for
Child and Adolescent Development, 152, 9-29. doi:
10.1002/cad.20162
Segretin, M. S., Lipina, S. J., Hermida, M. J., Sheffield,
T., Nelson, J. M., Espy, K. A., & Colombo, J. A.
(2014). Predictors of cognitive enhancement after
training in a sample of Argentinean preschoolers
from diverse socioeconomic backgrounds.
Frontiers in Developmental Psychology, 13, A205.
doi: 10.3389/fpsyg.2014.00205
Shallice, T. (1982). Specific impairments of planning.
Philosophical Transcripts of the Royal Society of
London, 298(1089), 199-209. doi:
10.1098/rstb.1982.0082
Sharkins, K. A., Leger, S. E., & Ernest, J. M. (2016).
Examining Effects of Poverty, Maternal
Depression, and Children’s Self-Regulation
Abilities on the Development of Language and
Cognition in Early Childhood: An Early Head Start
Perspective. Early Childhood Education Journal,
45(4), 493-498. doi: 10.1007/s10643-016-0787-9
Smith, E. E., & Jonides, J. (1999). Storage and
executive processes in the frontal lobes. Science,
283(5408), 1657-1661. doi:
10.1126/science.283.5408.1657
Stevens, C., Lauinger, B., & Neville, H. (2009).
Differences in the neural mechanisms of selective
attention in children from different socioeconomic
backgrounds: an event-related brain potential
study. Developmental Science, 12(4), 634-646. doi:
10.1111/j.1467-7687.2009.00807.x
Sulik, M. J., Blair, C., Mills-Koonce, R., Berry, D.,
Greenberg, M., & The Family Life Project
Investigators. (2015). Early Parenting and the
Development of Externalizing Behavior Problems:
Longitudinal Mediation through Children’s
Executive Function. Child Development, 86(5),
1588-1603. doi: 10.1111/cdev.12386
Ursache, A., Blair, C., & Raver, C. C. (2012). The
promotion of self-regulation as a means of
enhancing school readiness and early achievement
in children at risk for school failure. Child
Development Perspectives, 6(2), 122-128. doi:
10.1111/j.1750-8606.2011.00209.x
Ursache, A., & Noble, K. G. (2016). Neurocognitive
development in socioeconomic context: Multiple
mechanisms and implications for measuring
socioeconomic status. Psychophysiology, 53(1),
71-82. doi: 10.1111/psyp.12547
Ursache, A., Noble, K. G., & Blair, C. (2015).
Socioeconomic status, subjective social status, and
perceived stress: Associations with stress
physiology and executive functioning. Behavioral
Medicine, 41(3), 145-154. doi:
10.1080/08964289.2015.1024604
Vernon-Feagans, L., Willoughby, M., & Garrett-Peters,
P. (2016). Predictors of Behavioral Regulation in
Kindergarten: Household Chaos, Parenting, and
Early Executive Functions. Developmental
Psychology, 52(3), 430-441. doi:
10.1037/dev0000087
Weiland, C., & Yoshikawa, H. (2013). Impacts of a
prekindergarten program on children´s
mathematics, language, literacy, executive
functions, and emotional skills. Child Development,
84(6), 2112-2130. doi: 10.1111/cdev.12099
Weitzman, M. (2007). Low income and its impact on
psychosocial child development. En R. E.
Tremblay, M. Boivin, & R. De V. Peters (Eds.),
Encyclopedia on Early Childhood Development
(pp. 1-8). Montreal, Quebec: Centre of Excellence
for Early Childhood Development and Strategic
Knowledge Cluster on Early Child Development.
Yoshikawa, H., Aber, J. L., & Beardslee, W. R. (2012),
The effects of poverty on the mental, emotional,
and behavioral health of children and youth.
American Psychologist, 67(4), 272-284. doi:
10.1037/a0028015
Zauche, L. H., Thul, T. A., Mahoney, A. E. D., & Stapel-
Wax, J. L. (2016). Influence of language nutrition
Fracchia, C. S. et al. / RACC, 2020, Vol. 12, N°2, 24-38
38
on children’s language and cognitive development: An
integrated review. Early Childhood Research
Quarterly, 36(3), 318-333. doi:
10.1016/j.ecresq.2016.01.015
Zhao, X., Lynch, J. G., & Chen, Q. (2010).
Reconsidering Baron and Kenny: Myths and truths
about mediation analysis. Journal of Consumer
Research, 37(2), 197-206. doi: 10.1086/651257
