The neurological development of the child with the educational enrichment in mind

Abstract

Early life events can exert a powerful influence on both the pattern of brain architecture and behavioral development. The paper examines the nature of nervous system plasticity, the nature of functional connectivities in the nervous system, and the application of connectography to better understand the concept of a functional neurology that can shed light on approaches to instruction in preschool and primary education. The paper also examines the genetic underpinnings of brain development such as synaptogenesis, plasticity, and critical periods as they relate to numerosity, language and perceptual development. Discussed is how the child's environment in school and home interact with and modify the structures and functions of the developing brain. The role of experience for the child is to both maintain and expand the child's early wiring diagram necessary for effective cognitive as well as neurological development beyond early childhood.

Full text

Please
cite
this
article
in
press
as:
Leisman,
G.,
et
al.
The
neurological
development
of
the
child
with
the
educational
enrichment
in
mind.
Psicología
Educativa
(2015),
http://dx.doi.org/10.1016/j.pse.2015.08.006
ARTICLE IN PRESS
G Model
PSE-29;
No.
of
Pages
18
Psicología
Educativa
xxx
(2015)
xxx–xxx
www.elsevier.es/psed
Psicología
Educativa
Original
The
neurological
development
of
the
child
with
the
educational
enrichment
in
mind
Gerry
Leismana,b,c,∗,
Raed
Mualema,d,
Safa
Khayat
Mughrabia
aThe
National
Institute
for
Brain
and
Rehabilitation
Sciences,
Nazareth,
Israel
bBiomechanics
Laboratory,
O.R.T.
–
Braude
College
of
Engineering,
Karmiel,
Israel
cFacultad
Manuel
Fajardo,
Universidad
de
Ciencias
Médicas
de
la
Habana,
Cuba
dOranim
Academic
College,
Qiriat,
Tivon,
Israel
a
r
t
i
c
l
e
i
n
f
o
Article
history:
Received
19
June
2015
Accepted
31
August
2015
Available
online
xxx
Keywords:
Enrichment
Synaptogenesis
Neural
architecture
Hemispheric
asymmetry
Numerosity
Genetic
Environment
Language
development
Visual
processing
Education
a
b
s
t
r
a
c
t
Early
life
events
can
exert
a
powerful
influence
on
both
the
pattern
of
brain
architecture
and
behavioral
development.
The
paper
examines
the
nature
of
nervous
system
plasticity,
the
nature
of
functional
con-
nectivities
in
the
nervous
system,
and
the
application
of
connectography
to
better
understand
the
concept
of
a
functional
neurology
that
can
shed
light
on
approaches
to
instruction
in
preschool
and
primary
edu-
cation.
The
paper
also
examines
the
genetic
underpinnings
of
brain
development
such
as
synaptogenesis,
plasticity,
and
critical
periods
as
they
relate
to
numerosity,
language
and
perceptual
development.
Dis-
cussed
is
how
the
child’s
environment
in
school
and
home
interact
with
and
modify
the
structures
and
functions
of
the
developing
brain.
The
role
of
experience
for
the
child
is
to
both
maintain
and
expand
the
child’s
early
wiring
diagram
necessary
for
effective
cognitive
as
well
as
neurological
development
beyond
early
childhood.
©
2015
Colegio
Oficial
de
Psicólogos
de
Madrid.
Published
by
Elsevier
España,
S.L.U.
This
is
an
open
access
article
under
the
CC
BY-NC-ND
license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
El
desarrollo
neurológico
del
ni˜
no
con
el
enriquecimiento
educativo
en
mente
Palabras
clave:
Enriquecimiento
Sinaptogénesis
Arquitectura
neural
Asimetría
hemisférica
Numerosidad
Genética
Ambiente
Desarrollo
del
lenguaje
Procesamiento
visual
Educación
r
e
s
u
m
e
n
Los
primeros
acontecimientos
vitales
pueden
ejercer
una
enorme
influencia
tanto
en
el
patrón
de
arqui-
tectura
cerebral
como
en
el
desarrollo
del
comportamiento.
En
este
trabajo
exploraremos
la
naturaleza
de
la
plasticidad
del
sistema
nervioso,
la
naturaleza
de
sus
conexiones
funcionales
y
la
aplicación
de
la
tractografía,
para
lograr
una
mejor
explicación
del
concepto
de
neurología
funcional
que
pueda
arrojar
luz
sobre
las
teorías
de
la
instrucción
en
la
ense˜
nanza
preescolar
y
primaria.
El
trabajo
analiza
también
los
fundamentos
genéticos
del
desarrollo
del
cerebro
tales
como
la
sinaptogénesis,
la
plasticidad
y
los
periodos
críticos
en
lo
que
respecta
a
su
relación
con
el
desarrollo
numérico,
lingüístico
y
perceptivo.
Se
aborda
cómo
interactúa
el
entorno
del
ni˜
no
en
la
escuela
y
en
casa
con
las
estructuras
y
funciones
del
cerebro
en
desarrollo
y
las
modifica.
El
papel
de
la
experiencia
temprana
será
tanto
mantener
como
expandir
los
circuitos
neurales
necesarios
para
un
desarrollo
efectivo
(tanto
cognitivo
como
neurológico)
más
allá
de
la
temprana
infancia.
©
2015
Colegio
Oficial
de
Psicólogos
de
Madrid.
Publicado
por
Elsevier
España,
S.L.U.
Este
es
un
artículo
Open
Access
bajo
la
licencia
CC
BY-NC-ND
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
∗Corresponding
author.
The
National
Institute
for
Brain
&
Rehabilitation
Sciences.
ORT
Braude
College
of
Engineering,
51
Snunit,
POB
78.
Karmiel,
Israel
21982.
E-mail
address:
gerry.leisman@staff.nazareth.ac.il
(G.
Leisman).
http://dx.doi.org/10.1016/j.pse.2015.08.006
1135-755X/©
2015
Colegio
Oficial
de
Psicólogos
de
Madrid.
Published
by
Elsevier
España,
S.L.U.
This
is
an
open
access
article
under
the
CC
BY-NC-ND
license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Please
cite
this
article
in
press
as:
Leisman,
G.,
et
al.
The
neurological
development
of
the
child
with
the
educational
enrichment
in
mind.
Psicología
Educativa
(2015),
http://dx.doi.org/10.1016/j.pse.2015.08.006
ARTICLE IN PRESS
G Model
PSE-29;
No.
of
Pages
18
2
G.
Leisman
et
al.
/
Psicología
Educativa
xxx
(2015)
xxx–xxx
From
Camillo
Golgi
and
Santiago
Ramón
y
Cajal
in
the
late
1890s,
with
their
extensive
observations,
descriptions,
and
categoriza-
tions
of
neurons
throughout
the
brain
and
the
formation
of
the
neuron
doctrine
and
the
start
of
modern
Neuroscience,
we
have
come
a
long
way
in
understanding
the
nature
of
the
nervous
system
in
the
control
of
human
behavior.
Little
of
that
work
has
actually
wound
its
way
into
the
classroom
and
even
less
into
public
policy
in
education.
Basic
principles
have
emerged
that
allow
application
to
educa-
tional
practice,
especially
in
the
early
years
from
birth
to
five
years
that
place
great
responsibility
for
brain
development
in
the
hands
of
parents
and
early
childhood
teachers.
These
principles
include
the
following:
1.
The
human
brain
develops
from
conception
to
the
early
twenties
from
the
bottom
up
with
vital
and
autonomic
functions
and
con-
trol
coming
first
and
cognitive-motor
sensory
and
perceptual
processes
later
and
integration
and
decision
making
last
(Melillo
&
Leisman,
2009).
2.
The
child’s
brain
is
influenced
by
the
combined
roles
of
genetics
and
experience
(Leisman,
Machado,
Melillo,
&
Mualem,
2012;
Leisman
&
Melillo,
2012;
Melillo
&
Leisman,
2009).
3.
The
brain’s
capacity
for
change
decreases
with
age
(Leisman,
2011).
4.
Cognitive,
emotional,
and
social
capacities
are
inextricably
inter-
twined
throughout
the
life
course
(Leisman,
Braun-Benjamin,
&
Melillo,
2014).
5.
Motor
and
cognitive
functions
interact
with
our
brains,
being
the
direct
result
of
bipedalism
(Melillo
&
Leisman,
2009).
6.
Toxic
stress
damages
developing
brain
architecture,
which
can
lead
to
life-long
problems
in
learning,
behavior,
and
physical
and
mental
health.
7.
The
child’s
environment
directly
affects
synaptogenesis
and
allows
for
neurological
optimization
(Gilchreist
2011;
Leisman,
Rodriguez-Rojas
et
al.,
2014).
The
Effect
of
Environmental
Enrichment
on
the
Child’s
Brain:
Playing
with
the
Genetics
Early
life
events
can
exert
a
powerful
influence
on
both
the
pat-
tern
of
brain
architecture
and
behavioral
development.
Both
early
as
well
as
later
experiences
contribute
to
the
wiring
diagram
of
the
child’s
brain,
but
experiences
during
critical
periods
establish
the
basis
for
development
beyond
the
early
years.
The
role
of
the
kindergarten
and
nursery
teachers
becomes
critical
in
establish-
ing
the
solid
functional
footing
of
the
developing
child
and
the
neurological
adult.
The
foundations
of
brain
architecture
are
established
early
in
life
through
a
continuous
series
of
dynamic
interactions
between
genetic
influences,
environmental
conditions,
and
experiences
(Friederici,
2006;
Majdan
&
Shatz,
2006).
We
have
come
to
learn
that
the
child’s
environment
significantly
impacts
the
timing
and
nature
of
gene
expression
directly
affecting
the
child’s
brain
archi-
tecture.
Because
specific
experiences
potentiate
or
inhibit
neural
con-
nectivity
at
key
developmental
stages,
these
time
points
are
referred
to
as
critical
periods
(Knudsen,
2004).
Brain,
cognitive,
sen-
sory,
and
perceptual
development
does
not
occur
simultaneously
but
rather
at
different
developmental
stages
as
represented
below
in
Fig.
1.
Each
one
of
our
perceptual,
cognitive,
and
emotional
capabilities
is
built
upon
the
scaffolding
provided
by
early
life
expe-
riences.
Examples
can
be
found
in
both
the
visual
and
auditory
systems,
where
the
foundation
for
later
cognitive
architecture
is
laid
down
during
sensitive
periods
for
basic
neural
circuitry.
The
capacity
to
perceive
stereoscopic
depth
requires
early
expe-
rience
with
binocular
vision,
(Crawford,
Pesch,
&
von
Noorden,
1996),
which
at
a
later
point
in
development
may
have
impli-
cations
for
perceptual
and
cognitive
development.
Likewise,
the
capacity
to
perceive
a
range
of
tones
requires
variation
in
the
tonal
environment,
and
exposure
to
such
variation
later
leads
to
lan-
guage
processing
and
proficiency
(Kuhl,
2004;
Newport,
Bavelier
&
Neville,
2001;
Weber-Fox
&
Neville,
2001).
The
absence
of
tones
associated
with
a
given
language
will
eradicate
the
discrimination
of
those
developmentally
unheard
tones
by
the
time
the
infant
is
one-year-old
(Werker
&
Tees,
1983).
Second
language
acquisition
obtained
early
enough
will
have
the
same
brain
representation
as
the
first
language
throughout
the
lifespan,
but
that
second
lan-
guage,
learned
later
in
development,
even
when
spoken
at
native
level,
will
be
represented
differently
in
the
brain
relative
to
the
first
language
(cf.
Leisman,
2012;
Leisman
&
Melillo,
2015).
Although
early
experiences
are
reflected
in
behavior,
behavioral
measures
tend
to
underestimate
(in
part
because
of
a
lack
of
sensi-
tivity
and
specificity)
the
magnitude
and
persistence
of
the
effects
of
early
neuronal
development
(Knudsen,
2004).
In
order
to
explore
the
role
of
timing
and
quality
of
early
experiences
on
later
cogni-
tive
function,
we
must
therefore
have
a
genetic
framework
of
the
developing
brain.
We
see
no
fundamental
difference
between
the
task
of
the
educational
system,
rehabilitation
after
neurological
insult
or
developmental
disabilities,
the
task
of
parenting,
the
effects
of
social
interaction,
the
effects
on
the
nervous
system
of
sport,
or
even
the
ability
to
intervene
in
the
natural
consequences
of
cogni-
tive
aging.
The
term
education
can
be
used
interchangeably
with
rehabilitation
as
all
directly
relate
to
measurable
dynamic
plastic
changes
in
neural
connectivities.
Education
has
been
grabbing
at
straws
for
a
long
time.
Often
when
a
preliminary
finding
is
reported
in
the
neuroscience
liter-
ature
or
presented
at
a
conference,
it
is
grabbed
and
expounded
upon
with
little
consideration
of
the
fundamental
nature
of
biolog-
ical
processes
that
underlie
those
changes.
For
better
or
worse,
over
the
last
10
years,
education
has
been
actively
and
aggressively
look-
ing
to
the
biological
sciences
in
order
to
inform
education
policy
and
practice.
A
good
example
is
that
of
the
1998
decision
in
Georgia
to
fund
an
expensive
program,
to
provide
CDs
of
Mozart’s
music
to
all
new
mothers.
In
establishing
this
policy,
the
governor
of
Geor-
gia
drew
heavily
on
work
in
cognitive
neuroscience
conducted
at
the
University
of
California,
Irvine.
The
actions
were
taken
in
the
hope
of
“harnessing
the
‘Mozart
effect’
for
Georgia’s
new-
borns
–
that
is,
playing
classical
music
to
spur
brain
development.”
Despite
what
the
program
implied,
Mozart
effect
research,
upon
close
examination,
had
little
to
offer
education.
One
study,
reported
in
Nature
(Rauscher,
Shaw,
&
Ky,
1993),
found
that
listening
to
Mozart
raised
the
IQs
of
college
students
for
a
brief
period
of
time.
Another
study
found
that
keyboard
music
lessons
boosted
the
spa-
tial
skills
of
three-year-olds
(Schlaug,
Norton,
Overy,
&
Winner,
2005).
Cognitive
neuroscientists
responsible
for
this
work,
were
baffled
by
Georgia’s
program
and
actions
based
on
their
work.
Since
this
debacle,
major
figures
in
the
sciences
have
published
articles
emphasizing
caution
and
care
as
scientists,
educators,
and
practitioners
proceed
down
this
exciting,
but
pitfall-laden
road.
These
cautionary
articles
have
laid
the
groundwork
for
relation-
ships
between
neuroscience
and
education.
However,
there
is
a
paucity
of
publications
that
systematically
examine
an
area
of
research
where
conservative
but
confident
claims
can
be
made
of
the
benefits
of
interdisciplinarity.
Most
currently
prevailing
patterns
of
education
are
heavily
biased
towards
left
cerebral
functioning
and
are
antithetical
to
right
cerebral
functioning.
Reading,
writing,
and
arithmetic
are
all
logical
linear
processes,
and
for
most
of
us
are
fed
into
the
brain
through

Please
cite
this
article
in
press
as:
Leisman,
G.,
et
al.
The
neurological
development
of
the
child
with
the
educational
enrichment
in
mind.
Psicología
Educativa
(2015),
http://dx.doi.org/10.1016/j.pse.2015.08.006
ARTICLE IN PRESS
G Model
PSE-29;
No.
of
Pages
18
G.
Leisman
et
al.
/
Psicología
Educativa
xxx
(2015)
xxx–xxx
3
–9
Conception Neurulation
Birth
Death
Months
(18-24 prenatal days)
Months
Myelination
(–2 months to 5-10 years)
Synaptogenesis
(–3 months to 15-18 years?)
(Prefrontal cortex)
Age
Years Decades
–8 –7 –6 –5
Cell migration
(6-24 prenatal
weeks) Adult levels of synapses
Neurogenesis in the hippocampus
Experience-dependent synapse formation
–4 –3 –2 –1 0
1
2
3
4
5
6
7
8
910 11 12
1
2
3
4
5
6
7
8
910 11 1213 14 15 16 17 18 19 20 30 40 50 60 70
H
i
g
h
e
r
c
o
g
n
i
t
i
v
e
f
u
n
c
t
i
o
n
s
(
p
r
e
f
r
o
n
t
a
l
c
o
r
t
e
x
)
R
e
c
e
p
t
i
v
e
l
a
n
g
u
a
g
e
/
s
p
e
e
c
h
p
r
o
d
u
c
t
i
o
n
(
A
n
g
u
l
a
r
g
y
r
u
s
/
B
r
o
c
a
’
s
a
r
e
a
)
S
e
e
i
n
g
/
h
e
a
r
i
n
g
(
v
i
s
u
a
l
c
o
r
t
e
x
/
a
u
d
i
t
o
r
y
c
o
r
t
e
x
)
Figure
1.
Human
Brain
Development:
Neurogenesis
in
the
Hippocampus
through
Experience-dependent
Synapse
Formation.
our
right
hand.
Most
educational
policies
have
tended
to
aggravate
and
prolong
this
one-sidedness.
There
is
a
kind
of
damping
down
of
fantasy,
imagination,
clever
guessing,
and
visualization
in
the
interests
of
rote
learning,
reading,
writing,
and
arithmetic.
Great
emphasis
is
placed
upon
being
able
to
say
what
one
has
on
one’s
mind
clearly
and
precisely
the
first
time.
The
atmosphere
empha-
sizes
intra-verbal
skills,
“Using
words
to
talk
about
words
that
refer
to
still
other
words”
(Bruner,
1971).
If
there
is
any
truth
in
the
assertion
that
our
culture
stresses
left
hemisphere
skills
and
discriminates
against
the
right
hemisphere,
this
is
especially
true
of
school
systems.
Our
society’s
overemphasis
on
“propositionality”
at
the
cost
of
“appositionality”
does
not
only
result
in
adjustment
difficulties
but
also
in
a
lopsided
education
for
the
entire
student
body.
Our
students
are
not
being
offered
the
edu-
cation
they
require
to
understand
the
complex
nature
of
the
world
and
themselves,
an
education
for
the
whole
brain.
Sperry
wrote:
“Our
education
system
and
modern
society
generally
(with
its
very
heavy
emphasis
on
communication
and
on
early
training
in
the
three
R’s)
discriminates
against
one
whole
half
of
the
brain.
I
refer,
of
course,
to
the
nonverbal,
non-mathematical,
minor
hemisphere,
which
we
find
has
its
own
perceptual,
mechanical,
and
spatial
mode
of
apprehension
and
reasoning.
In
our
present
school
system,
the
attention
given
to
the
minor
hemisphere
of
the
brain
is
minimal
compared
with
training
lavished
on
the
left,
or
major
hemisphere.”
(Sperry,
1975)
Educational
institutions
have
placed
a
great
premium
on
the
verbal/numerical
categories
and
have
systematically
eliminated
those
experiences
that
would
assist
young
children’s
development
of
visualization,
imagination
and/or
sensory/perceptual
abilities.
The
over-analytic
models
so
often
presented
to
children
in
their
textbooks
emphasize
linear
thought
processes
and
discourage
intu-
itivity,
analogical,
and
metaphorical
thinking.
These
factors
of
neural
functioning
among
children
have
been
left
to
modification
by
random
environmental,
rather
than
systematic,
institutional
means.
Education,
which
is
predominantly
abstract,
verbal
and
bookish,
does
not
have
room
for
raw,
concrete,
esthetic
experience,
especially
of
the
subjective
happenings
inside
oneself.
Education
imposes
a
structure
of
didactic
instruction,
right-
wrong
criteria,
and
dominance
of
the
logical-objective
over
the
intuitive-subjective
on
the
learning
child
so
early
in
the
course
of
emergent
awareness
of
his
world
and
of
himself
that,
except
in
rare
cases,
creative
potential
is
inhibited,
or
at
least
diminished
(cf.
Melillo
&
Leisman,
2009).
This
leads
us
to
affirm
that
our
sys-
tem
of
education
is
one,
which
leads
to
the
underdevelopment
of
the
right
hemisphere.
As
a
result
of
excessive
emphasis
on
intel-
lectualizing,
verbalizing,
analyzing,
and
conceptualizing
processes,
‘curriculum’
has
become
equated
with
mere
‘understanding’.
This
imposes
a
‘neurotogenic
limitation’
and
binds
mental
processes
so
tightly
that
they
impede
the
perception
of
new
data.
In
the
words
of
Gazzaniga
(1975)
a
long
time
ago,
curriculum
is
“inordi-
nately
skewed
to
reward
only
one
part
of
the
human
brain
leaving
half
an
individual’s
potential
unschooled.”
The
traditional
preoc-
cupation
with
formal
intellectual
education
effectively
blocks
the
possibility
for
the
students
to
recognize
and
cultivate
creativity
and
transcendence.
It
has
been
the
adaptation
by
educators
of
appli-
cations
of
brain
sciences
into
the
classroom
and
the
culture
of
dichotomies
of
the
Behavioral
Sciences
over
the
past
150
years
that
have
placed
undo
reliance
by
our
educational
systems
on
functional
brain
models
that
may
be
irrelevant
at
best
and
damaging
at
worst
to
children’s
classroom
performance
and
its
evaluation.
What
emerges
as
the
central
proposition
of
this
paper
is
that
(A)
the
examination
and
study
of
regional
cerebral
differences
in
brain
function
as
a
way
of
explaining
and
evaluating
the
learn-
ing
process
within
the
educational
system
is
irrelevant
(cf.
Figs.
6A
&
B);
(B)
the
evaluation
of
students
by
standardized
aptitude
and
achievement
tests
is
not
sufficient
although
probably
still
neces-
sary;
and
(C)
the
educational
systems
had
better
examine
student
performance
and
teach
towards
“cognitive
efficiency”
rather
than
simply
mastery
vs.
non-mastery
with
methods
that
employ
both
psychophysics
that
examine
person-environment
interaction
and
mathematical
means
of
examining
optimization
and
the
strategy
used
to
get
there
as
well
as
how
far
or
close
a
student
is
functioning
from
a
mathematically
derived
optimization
regression
line
or,
in
fact,
how
quickly
the
learner
is
progressing
in
that
direction.
Educators,
although
perhaps
not
palatable
to
conceive
of
early
childhood
education
as
such,
are
producing
a
product
and
produc-
tion
management
techniques
that
should
be
useful
for
evaluating
not
just
the
product
but
the
process
or
“manufacture”
of
that
prod-
uct
as
well.
Genetic
and
Environmental
Interplay
in
the
Developing
Brain
The
uniquely
large
number
of
cells
and
their
potential
for
asso-
ciation
as
well
as
asymmetry
is
directly
the
result
of
bipedalism
along
with
genetic
mutations
(cf.
Melillo
&
Leisman,
2009).
Once
the
large
cell
assemblies
were
established
and
pressures
for
bi-
symmetry
were
released,
humans
then
could
develop
asymmetric
functions
in
their
brains
that
were
not
directly
tied
to
motor
or
autonomic
control.
Hemispheric
specialization
then
could
develop
different
control
centers
consistent
with
the
previous
function
of
that
hemisphere,
creating
most
of
the
unique
human
charac-
teristics.
The
other
demand
that
bipedalism
would
place
on
the
brain
would
be
the
need
to
be
more
precise
and
complex
in
the

Please
cite
this
article
in
press
as:
Leisman,
G.,
et
al.
The
neurological
development
of
the
child
with
the
educational
enrichment
in
mind.
Psicología
Educativa
(2015),
http://dx.doi.org/10.1016/j.pse.2015.08.006
ARTICLE IN PRESS
G Model
PSE-29;
No.
of
Pages
18
4
G.
Leisman
et
al.
/
Psicología
Educativa
xxx
(2015)
xxx–xxx
synchronization
of
muscles
to
be
able
to
walk,
run,
and
jump.
This
increased
synchronization
would
require
greater
frequency
of
oscillation
of
control
centers
within
the
inferior
olive
and
cerebel-
lum
and
their
feedback
to
the
intralaminar
nucleus
of
the
thalamus
and
its
reciprocal
thalamo-cortical
projections.
This
increase
in
oscillation
into
the
40-Hertz
range
is
thought
to
be
required
to
achieve
binding
within
various
cortical
sites
into
one
continuous
conscious
percept
of
the
world.
This
appears
to
be
the
foundation
of
human
consciousness,
which
is
thought
to
be
unique
in
humans
and
due
to
unique
connectivities
in
the
human
brain.
Therefore,
a
proposal
of
an
increase
in
neuroblast
proliferation
in
the
human
brain
is
consistent
with
the
concept
of
neoteny
in
the
human
evolution.
This
concept
states
that
certain
characters
are
delayed
in
their
development
with
respect
to
others
(pedeo-
genesis)
(cf.
Fig.
1)
(Bjorklund,
1997).
This
resulted
in
changes
in
adult
morphology
during
evolution.
This
is
thought
to
be
the
process
in
the
human
skull
in
which
infantile
dimensions
are
com-
parable
to
other
primates.
This
first
factor
explains
the
increase
in
cell
size
that
concurs
with
minimal
genetic
change.
However,
the
maintenance
of
these
cells
would
not
continue
without
the
appro-
priate
activity,
presynaptically
and
postsynaptically.
In
essence,
they
require
a
power
source
as
well
as
they
would,
in
turn,
require
connections
to
expanded
areas
sub-cortically.
Bipedalism
would
provide
both
by
increasing
exponentially
the
amount
of
temporal
and
spatial
summation
within
sensory
motor
networks,
especially
cerebellum,
thalamus,
and
cortex.
This
would
require
expanded
areas
of
cerebellum
and
thalamus
that
would
evolve
in
parallel
with
the
expanded
areas
of
cortex
and
could
provide
a
site
for
connection
to
these
increased
numbers
of
neurons.
This
would
take
place
because
although
the
genetic
change
would
increase
cell
number,
it
would
do
so
with
a
non-directional
force,
which
would
not
specify
any
specific
shape.
Posterior
epigenetic
reor-
ganization
(synaptic
stabilization)
would
determine
the
shape
and
configuration
of
the
networks
within
the
brain
itself.
There-
fore,
genetic
factors
would
produce
the
density
of
cells
required
but
environmental
factors
would
trim
and
shape
it
in
a
specific
fashion.
Therefore,
it
can
be
speculated
that
there
are
no
genes
spec-
ifying
particular
types
of
neuronal
networks
involved
in
higher
cognitive
function.
The
human
brain
is
about
four
times
larger
than
those
of
primates,
because
the
brain
cells
or
neurons
are
spread
about
(Nieuwenhuys,
1999).
The
thicker
cortices
of
the
large
mam-
mals
and
humans
as
well
seem
to
be
primarily
a
function
of
larger
nerve
cell
bodies,
more
extensive
dendritic
and
axonal
systems,
and
more
numerous
glial
cells.
Although
neurons
do
not
repro-
duce
after
birth,
glial
cells
can.
They
reproduce
based
on
increased
metabolic
demand
of
the
neurons
or
increased
stimulation.
This
increase
in
growth
of
glial
cells
allows
the
neurons
to
make
more
connections,
which
increases
the
ability
and
speed
of
the
cell
to
transmit
signal.
The
increase
in
size
and
strength
of
connections
allows
both
to
happen
more
efficiently.
The
growth
in
size
and
com-
plexity
of
the
human
brain
comes
from
the
number
of
supporting
cells
which
in
essence
feeds
the
neurons
with
more
fuel
and
encour-
ages
the
growth
of
new
connections.
It
is
not
the
increased
number
of
neurons,
but
the
increase
in
connections
between
the
cells
and
the
increase
in
separating
and
supporting
cells
that
accounts
for
the
large
growth
of
the
human
brain.
This
is
the
very
definition
of
“plasticity.”
Plasticity
is
the
ability
of
the
brain
to
grow
and
whether
it
is
growing
on
a
short-term
basis
or
on
a
long-term
basis
in
the
case
of
evolution,
the
facts
of
plasticity
are
consistent.
This
can
only
mean
that
there
was
some
increase
in
the
frequency,
duration,
and
intensity
of
stimulation
of
the
human
brain
over
time
for
it
to
have
evolved
as
uniquely
as
it
has.
There
are
two
things
that
make
humans
unique
among
other
organisms:
1)
we
have
a
larger
cortex
and,
2)
we
stand
upright
(bipedal).
Refinements
in
the
neural
circuits
that
mediate
sensory,
emo-
tional,
and
social
behaviors
are
driven
by
experience
(Feldman
&
Knudsen,
1998;
Leisman
et
al.,
2012).
Specifically,
postnatal
expe-
riences
drive
a
protracted
process
of
maturation
at
the
structural
and
functional
level,
but
the
very
ability
of
such
developmental
processes
to
occur
successfully
is
dependent
in
large
part
on
the
prenatal
establishment
of
the
fundamental
brain
architecture
that
provides
the
basis
for
receiving,
interpreting,
and
acting
on
infor-
mation
from
the
world
around
us
(Hammock,
2006).
While
the
term
“blueprint”
has
been
utilized
in
the
past
to
describe
a
fixed
set
of
genes
with
inflexible
interactions,
the
term
is
used
here
as
an
analogy
to
a
rough
draft,
or
design
–
the
framework
from
which
a
more
defined
structure
will
evolve,
alternatively,
an
operating
system
in
which
programs
have
yet
to
be
laid
down.
The
emergence
of
the
architecture
in
all
vertebrate
species
begins
early;
in
humans,
this
occurs
within
the
first
two
months
post-
fertilization
(Levitt,
2003).
The
cerebral
cortex
has
garnered
substantial
attention
in
defin-
ing
key
developmental
features
across
species.
This
is
due
in
part
to
the
technical
advantages
of
studying
a
well-organized,
layered
structure,
and
the
functional
relevance
of
linking
typical
and
atyp-
ical
maturation
of
complex
behaviors
and
neurodevelopment.
The
neocortex
in
all
mammalian
species
is
comprised
of
six
layers
of
neurons,
the
architecture,
connectivity
and
chemistry
of
which
are
distinct
depending
upon
their
location.
The
neo-
cortex
is
organized
to
receive
information
from
the
organism’s
surrounding
environment,
typically
through
connections
with
the
thalamus.
It
does
so
by
integrating
information
within
and
across
architecturally
distinct
functional
domains,
and
then
relays
this
information
to
other
brain
centers
that
generate
an
appropriate
functional
response.
There
are
two
major
organizing
principles
of
the
neocortex
influenced
by
gradients
of
gene
networks
that
have
developed
evo-
lutionarily.
First,
the
precursors
of
different
functional
areas
emerge
during
roughly
the
first
and
second
trimester
of
pregnancy
in
the
human
(cf.
Leary,
Chou,
&
Sahara,
2007).
Regional
specification
is
not
absolute,
but
involves
networks
controlling
the
expression
of
axon
guidance
molecules
that
control
the
initial
input
and
output
wiring
plan.
Expansion
of
the
size
of
the
neocortex
during
evolu-
tion
(e.g.,
1000-fold
between
mouse
and
human)
occurs
mostly
in
this
period
(Rakic,
2005).
The
‘inside-out’
pattern
of
neuron
production
and
migration
provides
the
basis
for
building
cell
connectivities
forming
func-
tional
areas,
with
small
variations
in
the
ratio
of
excitatory
to
inhibitory
neurons
in
different
regions.
In
fact,
this
organization
provides
a
framework
for
later-developing
refinement
of
circuits
influenced
extensively
by
patterns
of
physiological
activity
through
experience
and
training.
Experiments
in
genetically
manipulated
mice
demonstrate
that
altering
the
expression
of
just
one
genetic
transcription
factor,
cortical
regions
can
be
changed
(Cholfin
&
Rubenstein,
2007).
For
example,
the
genetic
factor
emx2
controls
the
expression
of
the
Fgf8
factor
near
the
anterior
end
of
the
cerebrum.
Fgf8
alone
is
suf-
ficient
to
specify
the
cortical
regions
that
will
eventually
receive
connections
that
are
typical
of
frontal
and
somatosensory
cortices
(Fukuchi-Shimogori
&
Grove,
2003).
This
type
of
early
genetic
re-
specification
is
functionally
relevant.
For
example,
the
Fgf17
is
responsible
for
initial
patterning
of
different
frontal
cortex
areas
(Cholfin
&
Rubenstein,
2007).
It
is
not
our
function
here
to
pursue
this
notion
in
detail
other
than
to
indicate
that
the
early
specification
and
re-specification
of
the
neocortex
by
genetic
factors
is
powerful
because
additional
axon
guidance
molecules
serve
as
important
chemical
cues
for
get-
ting
axons
to
grow
into
their
correct
target
region
prior
to
beginning
the
extended
process
of
synapse
formation
(cf.
Alcamo
et
al.,
2008).
Gene
regulatory
networks
also
can
influence
the
initial
size
of

Please
cite
this
article
in
press
as:
Leisman,
G.,
et
al.
The
neurological
development
of
the
child
with
the
educational
enrichment
in
mind.
Psicología
Educativa
(2015),
http://dx.doi.org/10.1016/j.pse.2015.08.006
ARTICLE IN PRESS
G Model
PSE-29;
No.
of
Pages
18
G.
Leisman
et
al.
/
Psicología
Educativa
xxx
(2015)
xxx–xxx
5
cortical
areas
by
modulating
the
number
of
neurons
produced.
The
long-distance
circuit
projections
that
help
to
define
functional
cor-
tical
areas,
and
even
functional
differences
in
superficial
and
deep
projecting
neurons,
are
altered
when
the
disruption
of
early
gene
networks
modifies
guidance
cues
so
that
atypical
connections
are
made.
Though
we
tend
to
think
that
genetic
mechanisms
are
immutable,
it
is
important
to
stress
that
expression
of
early
gene
networks
can
be
perturbed
not
only
by
catastrophic
genetic
mutations
that
disrupt
important
regulatory
genes,
but
also
by
pre-
natal
environmental
influences,
such
as
drugs,
alcohol,
toxins,
and
inflammatory
responses.
These
may
have
less
profound
impacts
on
brain
patterning,
but
nonetheless
can
result
in
long-term
dis-
ruption
of
cellular
differentiation
and
behavioral
development
(Stanwood
&
Levitt,
2008).
In
all
mammalian
species,
this
early
period
of
specified
pattern-
ing
to
generate
a
unique
architecture
is
followed
by
an
extended
period
of
synapse
formation,
adjustment,
and
pruning
that
typi-
cally
extends
from
the
last
quarter
of
gestation
through
puberty
(Bourgeois,
Goldman-Rakic,
&
Rakic,
1999).
Experience-based
Adjustments
to
Neural
Architecture
in
Early
Childhood
Although
genetics
provides
an
important
foundation
for
early
development,
it
is
only
a
framework
upon
which
the
early
child-
hood
environment
can
influence
future
structure
and
function.
This
can
best
be
illustrated
through
studies
of
the
sensory
systems,
which
demonstrate
the
crucial
role
of
environment
in
the
early
development
and
maintenance
of
the
nervous
system
(Leisman,
2011).
Such
work
also
demonstrates
the
need
for
patterned
phys-
iologic
activity
during
development,
as
well
as
refinement
and
maintenance
of
detailed
sensory
maps.
Synaptic
reorganization
takes
place
most
predominantly
dur-
ing
childhood
and
adolescence
(Blakemore,
2012).
During
these
periods
the
brain
becomes
sensitive
to
change
which
allows
it
to
develop
in
unique
ways
dependent
upon
the
individual
age,
gen-
der,
and
environment
along
with
many
other
variables
(Andersen,
2003).
The
concept
of
“self-organization”
indicates
that
the
brain
actually
organizes
itself
based
on
the
individual’s
experiences.
Envi-
ronment
stimulation
and
training
can
affect
how
the
brain
develops
and
at
what
pace
(Andersen,
2003;
Leisman,
2011).
The
environ-
ment
can
include
factors
like
location
and
surroundings,
home,
parenting,
and
of
course
the
classroom,
as
well
as
circumstances
in
each
of
those
environments
(Blakemore,
2012;
Tau
&
Peterson,
2010).
Environment
can
also
be
identified
as
a
child’s
emotions
or
responses
to
certain
stimuli,
in
this
case,
the
concept
of
self-
organization
which
postulates
that
the
brain
organizes
itself
based
on
each
child’s
unique
experiences.
The
fact
that
humans
have
a
greater
capacity
than
rats
or
even
chimps
for
self-organizing,
plastic,
or
flexible
behavior
provides
no
implication
that
we
are
either
all
stereotyped
or
flexible
in
our
behavior
and
brain
organization.
Stereotypy
creates
for
efficien-
cies
but
plasticity
or
flexibility
allow
for
adaptation
due
to
the
exigencies
of
one’s
environment.
We,
given
the
notions
of
stability
and
flexibility
(Leisman,
1980),
have
a
basis
for
rehabilitation
and
effective
adaptive
function.
The
concept
of
the
interplay
between
stability
and
flexibility
and
its
implications
for
the
education
of
the
normally
developing
child’s
brain
needs
to
be
viewed
as
a
relativis-
tic
notion,
viewed
against
the
features
of
the
organism
that
are
not
plastic.
In
order
to
identify
flexibility
or
plasticity,
one
must
be
able
to
identify
the
invariant
and
constant.
The
identification
of
plastic-
ity
requires
us
to
be
able
to
know
the
constraints
of
the
system.
The
fact,
however,
that
we
are
more
plastic
than
other
organisms
is
expressed
even
in
our
adult
lives
as
organisms.
This
suggests
that
our
capacity
for
systematic
change
and
the
fact
that
we
retain
flex-
ibility
across
our
later
developmental
periods
allows
application
of
rehabilitation
thinking
and
the
measurement
of
optimization
throughout
the
life
span.
Hebb
had
postulated
in
1949
that
when
one
cell
excites
another
repeatedly,
a
change
takes
place
in
one
or
both
cells
such
that
one
cell
becomes
more
efficient
at
firing
the
other
(Hebb,
1949).
It
is
this
view
that
is
not
only
limited
to
a
particular
cell
and
its
arborized
neuronal
connections
but
to
definable
anatomical
regions.
It
is
this
notion
that
forms
the
basis
of
our
concept
of
plasticity.
Hebb
was
the
first
to
propose
the
‘enriched
environment’
as
an
experimental
concept.
He
reported
anecdotally
that
laboratory
rats
that
nurtured
at
home
as
pets
were
behaviorally
different
than
their
littermates
kept
at
the
laboratory.
Hebb
was
not
the
only
one
who
conceptualized
the
effects
of
enriched
nurturance
hav-
ing
an
effect
on
nervous
system
structure
and
function.
Hubel
and
Wiesel
examined
the
effects
of
selective
visual
deprivation
dur-
ing
development
on
the
anatomy
and
physiology
of
the
visual
cortex
(Hubel
&
Wiesel,
1970;
Wiesel
&
Hubel,
1965)
and
Rosen-
zweig
and
colleagues
(Rosenzweig,
1966;
Rosenzweig
&
Bennett,
1996;
Rosenzweig
et
al.,
1978)
introduced
enriched
environments
as
a
testable
scientific
concept
by
measuring
the
effects
of
envi-
ronment
on
‘total
brain
weight,’
‘total
DNA
or
RNA
content,’
or
‘total
brain
protein’.
Numerous
researchers
have
demonstrated
a
significant
linkage
between
enrichment
and
neurological
plasticity
that
have
included
biochemical
changes,
gliogenesis,
neurogen-
esis,
dendritic
arborization,
and
improved
learning
and
memory
(Greenough,
West,
&
DeVoogd,
1978;
Kempermann,
Kuhn,
&
Gage,
1997).
An
example
is
provided
below
in
Figure
2.
In
an
experimental
setting,
an
enriched
environment
is
‘enriched’
in
relation
to
standard
laboratory
housing
conditions
in
that
experimental
animals
in
larger
cages
than
their
non-enriched
peers
have
greater
opportunity
at
social
interaction
with
nesting
material,
toys
and
food
locations
frequently
changed.
The
enriched
animals
were
also
given
opportunities
for
voluntary
activity
on
treadmills.
These
experiences
have
allowed
researchers
to
for-
mulate
a
definition
of
enrichment
as
“a
combination
of
complex
inanimate
and
social
stimulation”
(Rosenzweig
et
al.,
1978).
In
the
landmark
studies
of
vision
by
Wiesel
and
Hubel
(1965),
it
was
demonstrated
that
kittens
reared
with
normal
visual
experi-
ence
resulted
in
each
eye
having
sole
access
to
alternating
columns
of
neurons
in
layer
IV
of
the
striate
cortex.
At
birth,
however,
both
Figure
2.
Dendritic
morphology
of
pyramidal
neurons
in
layer
III
of
the
somatosen-
sory
cortex
in
rat
housed
in
(left)
standard
and
(right)
enriched
environments.
Bar
=
25
␮m.
The
enrichment
significantly
increases
dendritic
branching
as
well
as
the
number
of
dendritic
spines
(cf.
Johansson
&
Belichenko,
2001).

Please
cite
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article
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G.,
et
al.
The
neurological
development
of
the
child
with
the
educational
enrichment
in
mind.
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Educativa
(2015),
http://dx.doi.org/10.1016/j.pse.2015.08.006
ARTICLE IN PRESS
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xxx–xxx
eyes
synapsed
on
all
neurons
in
layer
IV.
In
order
to
assure
that
a
neuron
is
stimulated
by
experience
coming
from
only
one
eye,
a
competitive
process
occurs
in
which
activation
and
neighboring
inhibition
result
in
an
alternating
pattern
of
connectivity
between
columns
of
neurons
in
layer
IV
and
each
eye
(Wiesel
&
Hubel,
1965).
When
kittens
were
reared
with
one
eye
closed
for
a
period
of
time
after
birth,
the
occluded
eye
became
essentially
functionally
blind.
This
blindness
is
due
to
the
elimination
of
connections
of
the
closed
eye
to
layer
IV
and
the
lack
of
exposure
to
patterned
activity.
If
occlusion
extends
beyond
a
certain
time
period,
the
typ-
ical
pattern
of
ocular
representation
cannot
be
recovered
despite
the
restoration
of
visual
input
to
both
eyes
(Wiesel
&
Hubel,
1965).
It
has
been
hypothesized
that
the
initial
ingrowth
of
axons
from
the
thalamus
to
ocular
dominance
columns
in
visual
cortex
is
gov-
erned
by
molecular
cues
(Crair,
Horton,
Antonini,
&
Stryker,
2001;
Crowley
&
Katz,
2000).
It
has
recently
been
shown,
for
example,
that
the
decreased
visual
acuity
seen
in
the
adult
rat
suffering
from
chronic
monocular
deprivation
is
reversed
if
the
adult
rat
is
treated
with
dark
exposure
prior
to
removal
of
the
occlusion
(He,
Ray,
Dennis,
&
Quinlan,
2007).
The
increased
plasticity
induced
by
the
dark
environment
may
be
due
to
a
lack
of
input
to
visual
cor-
tex
through
the
functioning
eye,
and
therefore
a
reduction
in
the
strength
of
previously
established
connections.
A
similar
restoration
of
visual
acuity
can
also
be
induced
with
chronic
administration
of
fluoxetine
(Maya
Vetencourt
et
al.,
2008).
Such
dramatic
changes
in
sensory
system
connectivity
sug-
gest
that
activity-dependent
potentiation
of
these
initial
axons
is
required
to
maintain
connections
among
cortical
regions.
In
the
case
of
primary
visual
cortex,
local
circuit
neurons
have
been
implicated
in
activity-dependent
plasticity
through
GABAergic
inhibition
over
a
wide
range
of
neighboring
axonal
paths
(Fagiolini
et
al.,
2004;
Hensch
&
Stryker,
2004).
An
altered
pattern
of
activity
through
one
circuit
can
thus
radically
change
neighboring
cir-
cuits
through
an
increase
or
decrease
in
inhibition
of
mediating
cells.
The
early
development
of
visual
pathways
may
be
likened
to
the
laying
of
a
foundation
and
scaffolding
for
a
building.
If
the
scaf-
folding
pattern
is
changed,
the
building
may
not
be
constructed
in
its
original
form,
though
a
functional
alternative
may
be
reached.
Thus,
irreversible
changes
at
the
synaptic
level
do
not
necessarily
translate
into
irreversible
changes
in
a
complex
behavior
(Feldman
&
Knudsen,
1998).
For
example,
we
now
understand
that
the
sen-
sitive
period
for
visual
representation
reflects,
predominantly,
the
critical
period
for
thalamic
input
to
layer
IV
(Pascual-Leone,
Amedi,
Fregni,
&
Merabet,
2005),
but
that
plasticity
of
other
sensory
sys-
tems
may
allow
a
blind
person
to
demonstrate
normal
–
and
possibly
enhanced
–
spatial
awareness
(Amedi
et
al.,
2007).
Plas-
ticity
in
higher
regions
involved
in
spatial
awareness
feeds
back
upon
lower
pathways,
thus
compensating
for
an
abnormal
visual
representation.
Advanced
perceptual
processes
are
also
dependent
upon
the
normal
development
of
basic
visual
systems.
For
example,
early
visual
deprivation
due
to
congenital
cataracts
can
lead
to
subtle
but
persistent
deficits
in
face
processing,
even
when
the
cataracts
are
removed
in
the
first
months
of
life
(LeGrand,
Mondloch,
Maurer,
&
Brent,
2001).
Similarly,
experience
with
specific
faces,
such
as
same
vs.
different
species,
powerfully
shapes
subsequent
face
specializa-
tion.
For
example,
monkeys
deprived
of
viewing
faces
since
birth
are
capable
of
discriminating
both
monkey
and
human
faces
follow-
ing
the
selective
restoration
of
faces
in
the
visual
environment,
but
what
kind
of
faces
determines
whether
that
same
monkey
will
be
able
to
subsequently
discriminate
human
or
monkey
faces
–
thus,
monkeys
selectively
exposed
to
human
faces
can
only
discriminate
human
faces
not
monkey
faces,
and
monkeys
selectively
exposed
to
monkey
faces
can
only
discriminate
monkey
faces,
not
human
faces
(Sugita,
2008).
Critical
and
Sensitive
Periods
in
the
Early
Development
of
Higher
Cognitive
Processes
Critical
periods
are
important
stages
in
the
lifespan
of
the
child
as
he
or
she
acquires
a
particular
developmental
skill
that
is
indis-
pensable
and
which
can
influence
later
development.
If
the
child
does
not
receive
appropriate
stimulation
during
a
given
critical
period
to
learn
a
given
skill
or
trait,
it
may
be
difficult,
ultimately
less
successful,
or
even
impossible,
to
develop
some
functions
later
in
life.
This
is
fundamentally
different
from
the
sensitive
period,
which
is
a
more
extended
period
of
time
during
development
when
the
child
or
adolescent
is
more
receptive
to
specific
types
of
environmental
stimuli,
usually
because
the
nervous
system
devel-
opment
is
especially
sensitive
to
certain
sensory
stimuli
at
that
given
time.
For
example,
the
critical
period
for
the
development
of
a
human
child’s
binocular
vision
is
thought
to
be
between
three
and
eight
months,
with
sensitivity
to
damage
extending
up
to
at
least
three
years
of
age.
Further
critical
periods
have
been
identified
for
the
development
of
hearing
and
the
vestibular
system
(Melillo
&
Leisman,
2009;
Robson,
2002).
Confirming
the
existence
of
a
crit-
ical
period
for
a
particular
ability
requires
evidence
that
there
is
a
point
after
which
the
associated
behavior
is
no
longer
correlated
with
age
and
ability
stays
at
the
same
level.
Sensitive
periods
of
the
child’s
cognitive
development
associated
with
the
development
of
his
or
her
nervous
system
is
represented
in
Figs.
3
below.
Here
we
can
see
the
learning
sensitivity
for
numerous
cognitive
as
well
as
social
skills.
Hubel
and
Wiesel’s
experiments
involving
visual
deprivation
brought
about
the
concept
of
“sensitive”
and
“critical”
periods
in
early
cognitive
development.
“Sensitive”
periods
are
defined
as
a
time
in
development
during
which
the
brain
is
particularly
respon-
sive
to
experiences
in
the
form
of
patterns
of
activity
(Daw,
1997).
Further,
this
time
point
may
be
termed
a
“critical”
period
if
the
presence
or
absence
of
an
experience
results
in
irreversible
change
(Newport
et
al.,
2001;
Trachtenberg
&
Stryker,
2001).
Those
factors
that
allow
a
circuit
underlying
cognition
to
be
plastic
–
or
render
it
unchangeable
–
are
not
yet
well
understood.
In
the
area
of
speech
and
language,
the
“maturational
hypothesis,”
predicts
that
native
language
proficiency
cannot
be
obtained
when
learning
begins
after
puberty
(Werker
&
Tees,
2005).
Studies
supporting
this
theory
have
correlated
the
degree
of
accent
in
a
second
language
to
age
at
the
time
of
acquisition
of
that
language
(Birdsong
&
Molis,
2001).
Adults
exposed
to
a
second
language
in
early
childhood
were
found
to
have
native-like
accents
and
pattern
of
tone
(Gordon,
2000;
Stein
et
al.,
2006).
Other
researchers
have
also
found
a
neg-
ative
correlation
between
age
at
acquisition
and
grammaticality
judgments
(Komarova
&
Nowak,
2001).
However,
as
seen
in
Figs.
4
below,
brain
areas
representing
early
bilingual
language
acquisition
overlap
as
compared
to
late
bilingual
language
acquisition.
Several
investigators
have
used
the
theory
of
neural
networks,
originally
developed
for
vision
research,
to
model
the
activity
of
individual
neurons
and/or
groups
of
neurons
in
the
brain
during
learning
(Morton
&
Munakata,
2005).
These
neural
network
models
are
particularly
useful
for
comparing
the
experience-independent
and
experience-based
accounts
of
sensitive
periods,
because
the
network
can
be
kept
constant
with
regard
to
features
affected
by
maturation,
motivation,
and
amount
of
exposure.
Returning
to
the
work
of
Hubel
and
Wiesel,
it
is
important
to
note
that
the
loss
of
binocular
function
in
the
kitten
did
not
arise
simply
because
of
the
absence
of
input
to
the
occluded
eye.
Occluding
both
eyes
during
the
same
time
period
of
development
was
proven
not
to
result
in
loss
of
binocular
vision
(Cynader
&
Mitchell,
1980).
It
is
nec-
essary
for
one
eye
to
have
access
to
layer
IV
of
the
visual
cortex
while
the
other
eye
is
denied
access,
allowing
exclusive
connec-
tivity
of
the
unoccluded
eye
to
striate
cortex.
The
irreversible
loss
of
binocular
vision
during
development
must
therefore
be
due
to

Please
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Leisman,
G.,
et
al.
The
neurological
development
of
the
child
with
the
educational
enrichment
in
mind.
Psicología
Educativa
(2015),
http://dx.doi.org/10.1016/j.pse.2015.08.006
ARTICLE IN PRESS
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/
Psicología
Educativa
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(2015)
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7
High Pre-school years
A
B
School years
Numbers
Peer social skills
Symbol
Habitual ways of responding
Emotional control
Vision
Hearing
Low 0
1
2
3
Years
4
5
6
7
Sensitivity
–0.6
Z-scores
–1.2
0
0.6
1.2
20’s
30’s
40’s
Performance is
preserved over
age for world
knowledge
Performance declines with
increasing age of speed of
processing, working memory,
and long-term memory
50’s
Age (years)
Speed of processing
Working memory
Long-term memory
World knowledge
60’s
70’s
Digit symbol
Letter comparison
Letter rotation
Benton
Shipley vocabulary
Antonym vocabulary
Synonym vocabulary
Rey
Cued recall
Free recall
Line span
Computation span
Reading span
Pattern comparison
80’s
Figure
3.
Sensitive
periods
during
early
brain
development
for
(A)
Sensation,
emotional
control,
numerosity
and
symbolic
representation
and
(B)
Speed
of
processing,
working
memory,
long-term
memory
and
vocabulary.
a
combination
of
environmental
experience
and
cortical
learning
processes
(Knudsen,
2004).
The
fact
that
the
existence
of
a
sensitive
period
can
depend
upon
occurrence
of
a
particular
environment
suggests
that
in
early
development,
portions
of
networks
become
perceptually
biased,
making
future
modifications
more
difficult.
For
example,
in
the
lit-
erature
on
both
speech
and
face
perception,
the
perceptual
window
through
which
faces
and
speech
is
initially
processed
is
broadly
tuned,
then
narrows
with
experience.
For
example,
Pascalis
et
al.
(2005)
demonstrated
that
six-
and
nine-month-old
infants
and
adults
can
readily
discriminate
two
human
faces,
but
only
6-
month-old
infants
can
discriminate
two
monkey
faces.
Similarly,
six
month
olds
given
three
months
of
experience
viewing
mon-
key
faces
can
readily
discriminate
monkey
faces
at
nine
months
of
age,
whereas
nine-month-old
infants
not
afforded
such
experience
cannot
(Pascalis
et
al.,
2005).
As
a
rule,
circuits
that
process
lower
level
information
mature
earlier
than
those
that
process
higher
level
information
(Scherf,
Behrmann,
Humphreys,
&
Luna,
2007).
For
example,
in
the
neu-
ral
hierarchy
that
analyzes
visual
information,
low-level
circuits
that
analyze
the
color,
shape,
or
motion
of
stimuli
are
fully
mature
long
before
the
high-level
circuits
that
analyze
or
identify
biolog-
ically
important
stimuli,
such
as
faces,
food,
or
frequently
used
objects
(Knudsen,
2004;
Scherf
et
al.,
2007).
The
process
by
which
initial
learning
leads
to
a
constraint
on
later
learning
is
termed
entrenchment,
and
is
equally
apparent
in
the
development
of
speech
(Munakata
&
Pfaffly,
2004;
Seidenberg
&
Zevin,
2006).
Sev-
eral
studies
have
shown,
for
example,
that
adults
are
often
better
at
discriminating
non-native
phonetic
contrasts
when
they
differ
substantially
from
phonemes
of
their
native
language
(Kuhl,
2004).
Adults
are
poorer
at
discriminating
when
the
phonetic
contrasts
are
similar
to
phonetic
contrasts
of
their
native
language.
This
is
akin
to
the
nature
of
the
developing
auditory
system,
which
is
more
capable
of
discriminating
tones
outside
of
the
tonal
environment
of
hearing.
At
both
the
level
of
tone,
and
of
speech
phonetic
discrim-
ination,
there
is
evidence
for
a
fixed
bias
of
the
neural
network.
As
discussed
in
the
case
of
visual
networks,
however,
neurons
may
be
constantly
modifying
connectivity,
allowing
learning
from
new
environments
to
compete
against
already
existing
tendencies.
The
role
of
environment
and
inputs
to
the
brain
may
therefore
be
seen
as
critical
in
the
bias
of
network
formation
during
early
life.
Altered
patterns
of
enhancement
and
inactivity
are
thought
to
be
the
basis
for
neural
plasticity
and
have
been
suggested
in
humans
by
studies
of
tactile
and
auditory
perception
in
the
blind,
where
such
systems
may
even
activate
“visual”
cortex
(Merabet,
Rizzo,
Amedi,
Somers,
&
Pascual-Leone,
2005).
It
is
likely
that
changes
in
experience
have
a
greater
impact
on
an
untrained
‘young’
network
as
compared
to
the
same
experience
on
an
‘older’
trained
network.
This
biasing
feature
is
suggested
by
studies
on
aphasia
that
show
that
words
learned
earlier
in
life
are
more
resistant
to
loss,
and
are
more
easily
accessed
in
naming
tasks
as
compared
to
words
learned
later
(Greenough,
Black,
&
Wallace,
1987).
It
has
been
suggested
that
learning
through
experience
leads
to
the
capacity
to
understand
specific
environments
and
the
responses
needed
for
these
environments
(Anisman,
Zaharia,
Meaney,
&
Merali,
1998).
Similarly,
changes
in
the
environment
–
particularly
when
they
are
dramatic
and
pervasive
–
may
have
the
power
to
alter
neural
connectivity
and
cognitive
processing
between
systems.
Examples
can
be
found
in
studies
of
sensory
deprivation,
such
as
blindfolding,
as
well
as
sensory
enhancement
Native 1 (Turkish)
0
0
R
A
B
R
Native 2 (English)
Common
Centre-of-mass
Native (English)
Second (French)
Centre-of-mass
Figure
4.
(A)
and
(B)
represent
the
effect
of
brain
on
early
as
opposed
to
late
exposure
to
a
second
language.
The
figures
clearly
indicate
the
nature
of
the
optimization
and
efficiency
of
brain
function
connections
when
notions
that
related
to
early
training
and
critical
periods
are
applied.

Please
cite
this
article
in
press
as:
Leisman,
G.,
et
al.
The
neurological
development
of
the
child
with
the
educational
enrichment
in
mind.
Psicología
Educativa
(2015),
http://dx.doi.org/10.1016/j.pse.2015.08.006
ARTICLE IN PRESS
G Model
PSE-29;
No.
of
Pages
18
8
G.
Leisman
et
al.
/
Psicología
Educativa
xxx
(2015)
xxx–xxx
through
technology.
In
studies
of
deaf
children
receiving
cochlear
implants,
it
is
clear
that
language
learning
improves
with
earlier
correction
(Tomblin,
Barker,
Spencer,
Zhang,
&
Gantz,
2005).
It
remains
to
be
determined,
however,
whether
this
effect
upon
learn-
ing
is
due
to
actual
changes
in
cognitive
capacity
or
changes
in
the
learning
environment
brought
about
by
the
ability
to
interact
with
others
through
spoken
language.
Global
Development:
Higher-level
Functions
Build
on
Lower-level
Functions
The
nature
of
the
child’s
experiences,
particularly
during
a
time-
limited
period
in
early
development,
can
profoundly
affect
the
mental
framework
we
use
to
understand
the
world
around
us.
Sen-
sitive
periods
in
child
development
are
of
interest
because
they
represent
a
timeframe
in
which
our
capabilities
can
be
modified
and
perhaps
enhanced.
The
quality
of
experiences
during
such
periods
–
be
they
adverse
or
enhancing
–
is
also
of
importance
in
understanding
why
it
may
be
difficult
to
restore
normal
func-
tion
once
development
has
been
altered.
While
explanatory
models
for
the
timing
of
early
experiences
have
generally
been
based
at
the
genetic
or
neural
circuit
level,
our
direct
observations
of
the
effects
of
early
environments
are
often
made
at
the
behavioral
level.
Through
the
study
of
sensitive
periods,
we
are
better
able
to
understand
the
impact
that
early
experience
may
have
upon
devel-
opment.
To
cite
but
one
example,
it
has
recently
been
demonstrated
that
otherwise-typically
developing
young
children
institutional-
ized
at
birth
have
IQs
in
the
low
70s.
However,
placing
such
children
in
high
quality
foster
care
before
the
age
of
two
years
leads
to
a
dramatic
increase
in
IQ
(Nelson
et
al.,
2007).
A
similar
trend
also
occurs
for
language
(Windsor,
Glaze,
&
Koga,
2007)
and
the
development
of
the
EEG
(Marshall,
Reeb,
Fox,
Nelson,
&
Zeanah,
2008),
although
in
the
case
of
the
former,
the
sensitive
period
occurs
around
16-18
months.
It
is
important
to
note
recent
work
suggesting
that
the
brain
retains
the
capacity
to
adapt
and
change
throughout
the
lifespan
(Keuroghlian
&
Knudsen,
2007).
However,
the
foundation
of
brain
architecture
must
lie
in
the
early
developmental
years,
and
that
the
influence
of
childhood
environment
is
much
more
salient
in
such
basic
cognitive
processes
as
sensory
perception
(Amedi
et
al.,
2007;
Knudsen,
2004;
Pascual-Leone
et
al.,
2005).
Each
sensory
and
cog-
nitive
system
reaches
a
unique
sensitive
period
(Daw,
1997),
and
thus
identical
environmental
conditions
will
result
in
very
different
cognitive
and
emotional
experiences
for
a
child,
depending
upon
his
or
her
age
(Amedi
et
al.,
2007;
Trachtenberg
&
Stryker,
2001;
Tritsch,
Yi,
Gale,
Glowatski,
&
Bergles,
2007).
Behavioral
analysis
can
demonstrate
the
value
of
early
experi-
ences
in
the
development
of
the
brain.
It
must
be
remembered,
however,
that
information
is
processed
in
a
series
of
networks,
each
reflecting
the
effects
of
environment
at
varying
time
points.
Higher
level
processing
may
mask
modifications
in
lower
levels
networks
(Daw,
1997;
Feldman
&
Knudsen,
1998;
Trachtenberg
&
Stryker,
2001).
Thus,
behavioral
outcomes
may
be
shaped
by
later
experience,
even
though
circuits
at
the
lowest
levels
in
a
pathway
remain
irreversibly
altered.
In
addition,
studies
of
the
plasticity
of
sensory
processing
reveal
that
similar
information
can
be
derived
from
alternative
pathways
(Akins,
2006;
Pascual-Leone
et
al.,
2005;
Melchner,
Pallas,
&
Sur,
2000).
For
example,
when
using
sound
devices
to
assess
space,
blind
individuals
have
been
shown
to
activate
lateral
occipital
cortex
in
the
same
manner
as
sighted
individuals
do
through
vision
(Amedi
et
al.,
2007).
It
has
been
suggested
that
loss
of
sensory
input
–
such
as
occurs
in
late
blindness
–
may
in
fact
lead
to
the
unmasking
and
strengthening
of
alternative
pathways
stemming
from
multisen-
sory
integration
regions
of
the
brain
(Pascual-Leone
et
al.,
2005).
These
pathways
may
not
only
substitute
for
the
original
sensory
inputs,
but
may
enhance
previously
existing
capabilities.
This
form
of
sensory
enhancement
can
often
be
seen
in
the
highly
tuned
audi-
tory
and
tactile
perception
of
blind.
High-level
neural
circuits
that
carry
out
sophisticated
mental
functions
depend
on
the
quality
of
the
information
that
is
provided
to
them
by
lower
level
cir-
cuits.
Low-level
circuits
whose
architecture
was
shaped
by
healthy
experiences
early
in
life
provide
high-level
circuits
with
precise,
high-quality
information.
High-quality
information,
combined
with
sophisticated
expe-
rience
later
in
life,
allows
the
architecture
of
circuits
involved
in
higher
functions
to
take
full
advantage
of
their
genetic
potential.
Thus,
early
learning
lays
the
foundation
for
later
learning
and
is
essential
(though
not
sufficient)
for
the
development
of
optimized
brain
architecture.
Stated
simply,
rich
early
experience
must
be
followed
by
rich
and
more
sophisticated
experience
later
in
life,
when
high-level
circuits
are
maturing,
in
order
for
full
potential
to
be
achieved
(DeBello
&
Knudsen,
2004;
Karmarkar
&
Dan,
2006;
Sabatini
et
al.,
2007).
Elevated
cerebral
glucose
metabolism
can
be
observed
during
ages
3-10
yrs.,
which,
corresponds
to
an
era
of
exuberant
connec-
tivity
that
is
needed
for
energy
needs
of
neuronal
processes.
In
childhood
it
is
measurably
greater
by
a
factor
of
2
compared
to
adults.
PET
scans
show
the
relative
glucose
metabolic
rate.
We
see
the
complexity
of
dendritic
structures
of
cortical
neurons
consis-
tent
with
the
expansion
of
synaptic
connectivities
and
increases
in
capillary
density
in
the
frontal
cortex.
During
early
childhood
cross-
modal
plasticity
is
more
evident
(Bavelier
&
Neville,
2002)
with,
as
seen
in
Figure
4,
exuberant
connectivities
between
auditory
and
visual
areas
that
will
gradually
decrease
in
most
children
between
6
and
36
months
of
age
(Neville
&
Bavelier,
2002).
PET
and
fMRI
studies
have
shown
that
elderly
people
are
more
less
“optimized,”
activating
greater
regions
of
the
brain
than
younger
individuals
for
a
variety
of
motor
tasks
including
simple
one.
Accuracy
is
not
affected,
but
the
results
of
greater
areas
of
brain
involved
in
motor
tasks
among
the
elderly
is
highly
asso-
ciated
with
increases
in
reaction
time,
with
greater
surface
area
activation,
and
with
the
recruitment
of
additional
cortical
and
sub-
cortical
regions
as
compared
to
that
found
in
younger
individuals
(Ward
&
Frackowiak,
2003).
Mind,
Brain,
and
Education
Numerosity
Knowing
what
we
do
about
the
neuroscience
of
plasticity
and
development
under
normal
and
enriched
environments,
we
can
understand
that
much
of
the
knowledge
base
is
predicated
on
a
fair
amount
of
research
in
lower
organisms.
This
is
not
to
say
that
there
is
no
validity
in
the
ability
to
extrapolate
to
normal
child
devel-
opment.
We
know
that
brain
networks,
not
necessarily
structure,
support
cognitive
function
in
the
examples
provided
in
the
pre-
vious
section.
Classroom-based
educational
practice
is
supported
by
the
knowledge
base
of
Cognitive
Psychology
and
it
has
been
applied
in
the
classroom
to
the
analysis
of
reading
by
studying
the
component
skills
of
word
recognition,
grammar
and
syntax
text
analysis,
and
metacognition.
Additionally,
the
encoding
of
visual
and
auditory
information
from
the
printed
words
has
been
exten-
sively
examined
as
well
as
lexical
access,
which
can
determine
if
the
visual
representation
matches
a
word
in
the
reader’s
language.
The
tools
of
Cognitive
Psychology
have
allowed
the
educator
to
understand
better
the
component
processes,
skills,
and
knowledge
structures
underlying
reading,
mathematics,
writing,
and
science
(Bruer,
1993;
Leisman,
Machado,
&
Mualem,
2013;
Skemp,
1987).
The
result
of
Cognitive
Psychology’s
presence
in
the
classroom
has

Please
cite
this
article
in
press
as:
Leisman,
G.,
et
al.
The
neurological
development
of
the
child
with
the
educational
enrichment
in
mind.
Psicología
Educativa
(2015),
http://dx.doi.org/10.1016/j.pse.2015.08.006
ARTICLE IN PRESS
G Model
PSE-29;
No.
of
Pages
18
G.
Leisman
et
al.
/
Psicología
Educativa
xxx
(2015)
xxx–xxx
9
Figure
5.
Neuronal
Modeling
for
Numerosity
(Dehaene
&
Changeux,
1993).
directly
led
to
numerous
instructional
tools
and
technologies
and
this
is
not
the
forum
in
which
to
enumerate
those
advances
(Carver
&
Klahr,
2013).
The
missing
piece
is
in
the
application
of
the
Cognitive
Neu-
rosciences
and
engineering
methods
and
methodology
in
the
classroom.
While
applications
in
brain
imaging
have
revealed
much
about
the
nature
of
thinking,
problem
solving,
reading,
sensory
and
perceptual
processes
and
understanding,
because
of
the
lack
of
temporal
resolution
in
these
technologies,
it
is
difficult
to
apply
the
findings
in
practical
ways
in
classroom
performance
and
in
its
evaluation.
Stanislas
Dehaene
and
Jean-Pierre
Chageux
(Dehaene
&
Changeux,
1993)
developed
a
neuronal
network
model
of
num-
ber
processing,
which
made
the
prediction
that
the
parietal
cortex
contain
“numerosity
detectors”
(cf.
Fig.
5)
These
detectors
are
neu-
rons
tuned
to
a
specific
number,
and
thus
firing
preferentially
for
instance
to
sets
of
3
objects.
While
it
is
very
easy
to
fall
into
the
trap
of
phrenology,
with
specific
brain
sites
controlling
specific
function,
Dehaene
and
colleagues
have
actually
strongly
argued
for
network
approaches
to
understand
brain
and
cognition.
And
it
is
those
networks
that
need
development
inside
the
framework
of
formal
education.
An
example
of
that
type
of
network
may
be
seen
in
Figure
6.
Dehaene
argues
that
multiple
brain
areas
contribute
to
the
cerebral
processing
of
numbers;
the
inferior
parietal
quantity
rep-
resentation
is
only
one
node
in
a
distributed
circuit.
The
triple-code
model
of
number
processing
(Dehaene
&
Cohen,
1995)
makes
explicit
hypotheses
about
where
these
areas
lie,
what
they
encode,
and
how
their
activity
is
coordinated
in
different
tasks
(Fig.
6).
Functionally,
the
model
rests
on
three
fundamental
hypotheses.
First,
numerical
information
can
be
manipulated
mentally
in
three
formats:
an
analogical
representation
of
quantities,
in
which
num-
bers
are
represented
as
distributions
of
activation
on
the
mental
number
line;
a
verbal
format,
in
which
numbers
are
represented
as
strings
of
words
(e.g.,
thirty-seven);
and
a
visual
Arabic
repre-
sentation,
in
which
numbers
are
represented
as
a
string
of
digits
(e.g.
37).
Second,
transcoding
procedures
enable
information
to
be
translated
directly
from
one
code
to
the
other.
Third,
each
calcu-
lation
procedure
rests
on
a
fixed
set
of
input
and
output
codes.
For
instance,
the
operation
of
number
comparison
takes
as
input
numbers
coded
as
quantities
on
the
number
line.
Likewise,
the
model
postulates
that
multiplication
tables
are
memorized
as
ver-
bal
associations
between
numbers
represented
as
string
of
words,
and
that
multi-digit
operations
are
performed
mentally
using
the
visual
Arabic
code.
Dehaene
(Dehaene,
1996)
designed
an
experiment
to
test
a
serial
model
of
numerical
comparison.
In
his
experiment,
right-
handed
college
students
had
to
decide
if
a
number
flashed
on
a
computer
screen
was
larger
or
smaller
than
five,
then
press
a
key
to
indicate
their
response.
Dehaene
manipulated
three
inde-
pendent
factors,
where
each
factor
was
assumed
to
influence
processing
within
only
one
of
the
model’s
stages.
For
the
stimulus
Left hemisphere Right hemisphere
Parietal lobe
Frontal
lobe
Frontal
lobe
Temporal lobe Temporal lobe
Occipital lobe
Parietal lobe
Magnitude
representation
Magnitude
representation
Comparison
Verbal
system
Visual
number
form
Visual
number
form
Arithmetic
facts
Comparsion
Figure
6.
Schematic
Functional
and
Anatomical
Architecture
of
the
Triple-code
Model
(Dehaene
&
Cohen,
1995).
The
localization
of
the
main
areas
thought
to
be
involved
in
the
three
numerical
codes
is
depicted
on
a
lateral
view
of
the
left
and
right
hemispheres.
The
arrows
indicate
a
functional
transmission
of
information
across
numerical
codes
and
are
not
meant
as
a
realistic
depiction
of
existing
neural
fiber
pathways,
whose
organization
is
not
fully
understood
in
humans.

Please
cite
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article
in
press
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Leisman,
G.,
et
al.
The
neurological
development
of
the
child
with
the
educational
enrichment
in
mind.
Psicología
Educativa
(2015),
http://dx.doi.org/10.1016/j.pse.2015.08.006
ARTICLE IN PRESS
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PSE-29;
No.
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Pages
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10
G.
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Psicología
Educativa
xxx
(2015)
xxx–xxx
identification
stage,
he
contrasted
subjects’
performance
when
given
Arabic
(1,
4,
6,
9)
versus
verbal
notation
(one,
four,
six,
nine).
For
the
magnitude
comparison
stage,
he
compared
subjects’
per-
formance
on
close
(4,
6
and
four,
six)
versus
far
(1,
9
and
one,
nine)
comparisons
to
the
standard
5.
His
reason
for
choosing
this
factor
is
the
well-established
distance
effect
(Moyer
&
Landauer,
1967).
The
distance
effects
showed
that
it
takes
subjects
longer,
and
they
make
more
errors,
when
asked
to
compare
numbers
that
are
close
in
numerical
value
than
when
asked
to
compare
numbers
that
are
farther
apart
in
numerical
value.
In
Dehaene’s
experiment,
half
the
comparisons
were
close
comparisons
and
half
were
far
comparisons,
a
factor
that
should
affect
only
the
magnitude
com-
parison
stage.
Finally,
on
half
the
trials,
subjects
had
to
respond
“larger”
using
their
right
hand
and
“smaller”
using
their
left
hand,
and
on
half
the
trials,
“larger”
with
their
left
and
“smaller”
with
their
right.
This
factor
should
influence
reaction
times
only
for
the
motor
preparation
and
execution
stage.
When
Dehaene
analyzed
subjects’
reaction
times
on
the
numer-
ical
comparison
task,
he
found
that
the
overall
median
(correct)
reaction
time
was
around
400
milliseconds.
Subjects
needed
less
than
one
half
second
to
decide
if
a
number
was
greater
or
less
than
5.
Furthermore,
he
found
that
each
of
the
three
factors
had
an
independent
influence
on
reaction
time.
Reactions
to
Arabic
stimuli
were
38
milliseconds
faster
than
those
for
verbal
notation,
far
com-
parisons
were
18
milliseconds
faster
than
close
comparisons,
and
right-hand
responses
were
10
milliseconds
faster
than
left-hand
responses.
Finally,
the
three
factors
had
an
additive
effect
on
sub-
jects’
total
reaction
times,
just
as
one
would
expect
if
subjects
were
using
the
serial-processing
model.
Dehaene’s
experiment,
however,
went
beyond
the
typical
cog-
nitive
experiment
that
would
have
stopped
with
the
analysis
of
reaction
times.
Dehaene
also
recorded
event-related
potentials
(ERPs),
while
his
subjects
performed
the
number
comparison
task.
His
ERP
system
measured
electrical
currents
emerging
from
the
scalp
at
64
sites,
currents
that
presumably
were
generated
by
the
electrical
activity
of
large
numbers
of
nearby
neurons.
ERPs
have
relatively
poor
spatial
resolution,
but
relatively
precise
temporal
resolution.
Significant
changes
in
the
electrical
activity
recorded
at
each
of
the
64
sites
as
subjects
compared
numbers
might
give
general
indications
about
where
the
neural
structures
were
in
the
brain
that
implemented
the
three
processing
stages.
The
ERPs’
more
precise
temporal
resolution
might
indicate
the
time
course
of
the
three
processing
stages.
Together,
the
spatial
and
temporal
data
would
allow
Dehaene
to
trace,
at
least
approximately,
the
neural
circuitry
that
is
active
in
numerical
comparison.
A
cognitive
model
together
with
brain
recording
techniques
created
the
possibility
of
mapping
sequences
of
elementary
cognitive
operations
onto
their
underlying
neural
structures
and
circuits.
This
first
significant
ERP
effect
Dehaene
observed
occurred
100
milliseconds
after
the
subjects
saw
either
the
Arabic
or
ver-
bal
stimulus.
This
change
in
brain
activity
was
not
influenced
by
any
of
the
experimental
factors.
It
appeared
to
occur
in
the
right
posterior
portion
of
the
brain.
Based
on
this
and
other
imaging
and
recording
experiments,
early
activation
in
that
part
of
the
brain
is
most
likely
the
result
of
the
brain’s
initial,
nonspecific
processing
of
visual
stimuli.
At
approximately
146
milliseconds
after
stimulus
presentation,
Dehaene
observed
a
notation
effect.
When
subjects
processed
number
words,
they
showed
a
signifi-
cant
negative
electrical
wave
on
the
electrodes
that
recorded
from
the
left
posterior
occipital-temporal
brain
areas.
In
contrast,
when
subjects
processed
Arabic
numerals,
they
showed
a
similar
neg-
ative
electrical
wave
on
electrodes
recording
from
both
the
left
and
right
posterior
occipital-temporal
areas.
This
suggested
that
number
words
are
processed
primarily
on
the
left
side
of
the
brain,
but
that
Arabic
numerals
are
processed
on
both
the
left
and
right
sides.
To
look
for
a
distance
effect
and
the
timing
and
localization
of
the
magnitude
comparison
stage,
Dehaene
compared
the
ERPs
for
digits
close
to
5
(4,
four
and
6,
six)
with
the
ERPs
for
digits
far
from
5
(1,
one
and
9,
nine).
This
comparison
revealed
a
parieto-occipto-
temporal
activation
in
the
right
hemisphere
that
was
associated
with
the
distance
effect.
This
effect
was
greatest
approximately
210
milliseconds
before
the
subjects
gave
their
responses.
What
is
significant
here,
according
to
Dehaene,
is
that
the
timing
and
distribution
of
the
electrical
currents
was
similar
for
both
Ara-
bic
digits
and
verbal
numerals.
This
supports
the
claim,
Dehaene
argues,
that
there
is
a
common,
abstract,
notation
independent
magnitude
representation
in
the
brain
that
we
use
for
numeri-
cal
comparison.
To
make
a
numerical
comparison,
we
apparently
translate
both
number
words
and
Arabic
digits
into
this
abstract
magnitude
representation.
Finally,
Dehaene
found
a
response-side
effect
that
occurred
approximately
332
milliseconds
after
the
stimulus
or
equivalently
140
milliseconds
before
the
key
press.
This
appeared
as
a
substan-
tial
negative
wave
over
motor
areas
in
the
brain.
The
motor
area
in
the
left
hemisphere
controls
movement
of
the
right
side
of
the
body,
and
the
motor
area
in
the
right
hemisphere
controls
move-
ment
of
the
left
side
of
the
body.
Thus,
as
expected,
this
negative
wave
appeared
over
the
left
hemisphere
for
right-hand
responses
and
over
the
right
hemisphere
for
left-hand
responses.
Dehaene’s
experiment
exemplifies
how
cognitive
neuroscien-
tists
use
cognitive
theories
and
models
in
brain
imaging
and
recording
experiments.
Well-designed,
interpretable
imaging
and
recording
studies
demand
analyses
of
cognitive
tasks,
construction
of
cognitive
models,
and
use
of
behavioral
data,
like
reaction
times,
to
validate
the
models.
Experiments
like
these
suggest
how
neural
structures
implement
cognitive
functions,
tell
us
new
things
about
brain
organization,
and
suggest
new
hypotheses
for
further
experi-
ments.
Dehaene’s
experiment
traces
the
approximate
circuitry
the
brain
uses
to
identify,
compare,
and
respond
to
number
stimuli.
The
experiment
reveals
several
new
things
about
brain
organi-
zation
that
suggest
hypotheses
for
further
experiments.
First,
the
experiment
points
to
a
bilateral
neural
system
for
identifying
Arabic
digits.
This
is
something
that
one
could
not
discover
by
analyzing
behavioral
data
from
normal
subjects.
Nor
is
it
a
finding
found
as
a
result
of
the
neuropsychological
study
of
patients
with
brain
lesions
and
injuries
that
could
reliably
and
unambiguously
be
supported.
In
fact,
Dehaene
suggests,
the
existence
of
such
a
bilateral
system
could
explain
some
of
the
puzzling
features
about
the
patterns
of
lost
versus
retained
number
skills
neuropsychologists
see
in
these
patients.
Second,
this
experiment
suggests
there
is
a
brain
area
in
the
right
hemisphere
that
is
used
in
numerical
comparison.
This
area
might
be
the
site
of
an
abstract
representation
of
numerical
mag-
nitude,
a
representation
that
is
independent
of
our
verbal
number
names
and
written
number
symbols.
This,
too,
runs
counter
to
com-
mon
neuropsychological
wisdom.
Neuropsychologists
commonly
hold
that
the
left
parieto-occipito-temporal
junction,
not
the
right,
is
the
critical
site
for
number
processing
because
damage
to
this
area
in
the
left
hemisphere
causes
acalculia.
Dehaene’s
finding
of
right
hemisphere
involvement
during
the
comparison
phase
suggests
that
neuropsychologists
should
look
more
carefully
than
they
might
have
in
the
past
at
numerical
reasoning
impairments
among
patients
who
have
suffered
damage
to
the
right
poste-
rior
brain
areas.
They
might
find,
for
example,
patients
who
are
able
to
read
Arabic
numerals
and
perform
rote
arithmetic
calcu-
lations,
but
who
are
unable
to
understand
numerical
quantities,
make
numerical
comparisons,
or
understand
approximate
numer-
ical
relations.
Dehaene’s
work
is
just
one
example
of
how
cognitive
neuroscience
is
advancing
our
understanding
of
how
brain
struc-
tures
might
support
cognitive
function.
Cognitive
neuroscientists
at
numerous
institutions
are
starting
to
trace
the
neural
circuitry
for
other
cognitive
constructs
and
culturally
transmitted
skills.

Please
cite
this
article
in
press
as:
Leisman,
G.,
et
al.
The
neurological
development
of
the
child
with
the
educational
enrichment
in
mind.
Psicología
Educativa
(2015),
http://dx.doi.org/10.1016/j.pse.2015.08.006
ARTICLE IN PRESS
G Model
PSE-29;
No.
of
Pages
18
G.
Leisman
et
al.
/
Psicología
Educativa
xxx
(2015)
xxx–xxx
11
Several
studies
suggest
that
automatic
and
controlled
processing
rely
on
distinct
brain
circuits
(Raichle
et
al.,
1994).
Other
studies
show
how
attention
can
reorder
the
sequence
in
which
component
cognitive
skills
are
executed
in
a
task:
the
areas
of
brain
activation
remain
the
same,
but
the
sequence
in
which
the
areas
become
active
changes
(Posner
&
Raichle,
1994).
We
are
beginning
to
understand
the
different
brain
systems
that
underlie
language
processing
and
their
developmental
time
course
(Neville
&
Bavelier,
2002).
Using
our
rather
detailed
cognitive
models
of
reading
–
particularly
word
recognition
–,
PET,
fMRI,
and
ERP
stud-
ies
allow
us
to
trace
the
neural
circuitry
for
early
reading
skills
and
to
document
the
developmental
course
of
this
circuitry
in
children
between
the
ages
of
5
to
12
years
(Posner,
Abdullaev,
McCandliss,
&
Sereno,
1999).
However,
in
most
cases,
we
are
still
far
from
under-
standing
how
these
results
might
contribute
to
advances
in
the
clinic,
let
alone
in
the
classroom.
It
is
not
yet
clear
how
we
move
from
results
like
these
across
the
bridge
to
educational
research
and
practice.
The
example
does,
however,
make
two
things
clear.
First,
there
is
no
way
that
we
could
possibly
understand
how
the
brain
processes
numbers
by
looking
at
children’s
classroom
or
every-
day
use
of
numbers
or
by
looking
at
math
curricula.
Second,
there
is
no
way
we
could
possibly
design
a
math
curriculum
based
on
Dehaene’s
results.
It
is
the
cognitive
research
that
creates
both
of
those
possibilities.
When
we
do
begin
to
understand
how
to
apply
cognitive
neuroscience
in
instructional
contexts,
it
is
likely
that
it
will
first
be
of
most
help
in
addressing
the
educational
needs
of
special
populations.
Cognitive
psychology
allows
us
to
understand
how
learning
and
instruction
support
the
acquisition
of
culturally
transmitted
skills
like
numeracy
and
literacy.
Cognitive
Psychology
in
combination
with
brain
imaging
and
electrophysiological
recording
technologies
also
allows
us
to
see
how
learning
and
instruction
alter
brain
circuitry.
It
opens
the
pos-
sibility
of
being
able
to
see
and
to
compare
these
learning-related
changes
in
normal-versus-special
learning
populations.
Such
com-
parative
studies
might
yield
insights
into
specific
learning
problems
and,
more
importantly,
into
alternative,
compensatory
strategies,
representations,
and
neural
circuits
that
children
who
learn
with
greater
difficulty
than
others
in
the
traditional
learning
settings.
might
exploit.
These
insights
could
in
turn
help
us
develop
better
instructional
interventions
to
address
specific
learning
problems.
Language
Skills
Sensory
information
undergoes
extensive
organization
into
associative
networks
necessary
for
incorporation
into
texture
of
cognition.
The
normal
operation
of
such
a
system
allows
for
the
integration
of
motor
and
cognitive
function
of
the
kind
that
one
sees
in
reading
and
language.
Damage
to
or
dysfunction
in
this
system
of
the
kind
often
found
in
post-stroke
individuals
can
be
exemplified
in
disconnection
syndromes
such
as
alexia
without
agraphia
and
a
color-naming
deficit
with
no
other
form
of
anomia
evidenced
(Leisman,
1976;
Leisman,
2011;
Leisman,
Braun-Benjamin
et
al.,
2014).
This
process
of
integration
occurs
along
a
synaptic
hier-
archy,
which
includes
the
primary
sensory,
up-
and
downstream
unimodal,
hetero-modal,
paralimbic
and
limbic
zones
of
the
cere-
bral
cortex.
Connections
from
one
zone
to
another
are
reciprocal
and
allow
higher
synaptic
levels
to
exert
a
feedback
(top-down)
influence
upon
earlier
levels
of
processing.
Each
cortical
area
pro-
vides
a
nexus
for
the
convergence
of
afferents
and
divergence
of
efferents.
The
resultant
synaptic
organization
allows
each
sensory
event
to
initiate
multiple
cognitive
and
behavioral
outcomes.
Upstream
sectors
of
unimodal
association
areas
encode
basic
features
of
sensation
such
as
color,
motion,
form,
and
pitch.
More
complex
contents
of
sensory
experience
such
as
objects,
faces,
word-forms,
spatial
locations,
and
sound
sequences
become
encoded
within
downstream
sectors
of
unimodal
areas
by
groups
of
coarsely
tuned
neurons.
Hetero-modal,
paralimbic
and
limbic
cortices,
collectively
known
as
trans-modal
areas,
occupy
the
high-
est
synaptic
levels
of
sensory-fugal
processing.
The
unique
role
of
these
areas
is
to
bind
multiple
unimodal
and
other
trans-modal
areas
into
distributed
but
integrated
multimodal
representations.
Trans-modal
areas
in
the
mid-temporal
cortex,
Wernicke’s
area,
the
hippocampal-entorhinal
complex
and
the
posterior
parietal
cortex
provide
critical
gateways
for
transforming
perception
into
recogni-
tion,
word-forms
into
meaning,
scenes
and
events
into
experiences,
and
spatial
locations
into
targets
for
exploration.
All
cognitive
pro-
cesses
arise
from
analogous
associative
transformations
of
similar
sets
of
sensory
inputs.
The
differences
in
the
resultant
cognitive
operation
are
determined
by
the
anatomical
and
physiological
properties
of
the
trans-modal
node
that
acts
as
the
critical
gateway
for
the
dominant
transformation.
Interconnected
sets
of
trans-
modal
nodes
provide
anatomical
and
computational
epicenters
for
large-scale
neurocognitive
networks.
In
keeping
with
the
principles
of
selectively
distributed
processing,
each
epicenter
of
a
large-scale
network
displays
a
relative
specialization
for
a
specific
behavioral
component
of
its
principal
neuropsychological
domain.
The
human
brain
contains
at
least
five
anatomically
distinct
networks.
The
network
for
spa-
tial
awareness
is
based
on
trans-modal
epicenters
in
the
posterior
parietal
cortex
and
the
frontal
eye
fields;
the
language
network
on
epicenters
in
Wernicke’s
and
Broca’s
areas;
the
explicit
mem-
ory/emotion
network
on
epicenters
in
the
hippocampal-entorhinal
complex
and
the
amygdala;
the
face-object
recognition
network
on
epicenters
in
the
mid-temporal
and
temporopolar
cortices;
and
the
working
memory-executive
function
network
on
epicenters
in
the
lateral
prefrontal
cortex
and
perhaps
the
posterior
pari-
etal
cortex.
Individual
sensory
modalities
give
rise
to
streams
of
processing
directed
to
trans-modal
nodes
belonging
to
each
of
these
networks.
The
fidelity
of
sensory
channels
is
actively
pro-
tected
through
approximately
four
synaptic
levels
of
sensory-fugal
processing.
The
modality-specific
cortices
at
these
four
synaptic
levels
encode
the
most
veridical
representations
of
experience.
Attentional,
motivational,
and
emotional
modulations,
including
those
related
to
working
memory,
novelty-seeking,
and
mental
imagery,
become
increasingly
more
pronounced
within
down-
stream
components
of
unimodal
areas,
where
they
help
to
create
a
highly
edited
subjective
version
of
the
world.
The
synaptic
architecture
of
large-scale
networks
and
the
man-
ifestations
of
working
memory,
novelty-seeking
behaviors,
and
mental
imagery
collectively
help
to
loosen
the
rigid
stimulus-
response
bonds
that
dominate
the
behavior
of
lower
animal
species.
This
phylogenetic
trend
has
helped
to
shape
the
unique
prop-
erties
of
human
consciousness
and
to
induce
the
emergence
of
second
order
(symbolic)
representations
related
to
language.
Through
the
advent
of
language
and
the
resultant
ability
to
com-
municate
abstract
concepts,
the
critical
pacemaker
for
human
cognitive
development
has
shifted
from
the
extremely
slow
pro-
cess
of
structural
brain
evolution
to
the
much
more
rapid
one
of
distributed
computations
where
each
individual
intelligence
can
become
incorporated
into
an
interactive
lattice
that
promotes
the
trans-generational
transfer
and
accumulation
of
knowledge.
The
transfer
of
knowledge
from
the
environment
and
the
devel-
opment
of
skills
to
interact
with
that
environment
is
a
direct
consequence
of
the
ability
to
organize
physical
and
measurable
associational
networks.
Examples
of
such
networks
for
language
are
represented
in
Figs.
7
(A
and
B)
and
in
turn
represent
language
embodiment
in
the
networks
rather
than
language
ascribed
to
one
particular
brain
region.
Figs.
8
represent
the
power
of
Connectog-
raphy
in
measuring
the
efficiencies
of
practical
learning
based
on
graph
theory
and
Connectography.
What
we
can
learn
from
the
characterization,
organization,
and
development
of
large-scale
brain
networks
in
children
using

Please
cite
this
article
in
press
as:
Leisman,
G.,
et
al.
The
neurological
development
of
the
child
with
the
educational
enrichment
in
mind.
Psicología
Educativa
(2015),
http://dx.doi.org/10.1016/j.pse.2015.08.006
ARTICLE IN PRESS
G Model
PSE-29;
No.
of
Pages
18
12
G.
Leisman
et
al.
/
Psicología
Educativa
xxx
(2015)
xxx–xxx
a
b
a
c
b
Via higher-order frontal networks
A
B
Dorsal stream
Input from
other sensory
modalities
Leg-relate words Arm-related words Face-related words
WB
WPB
BPT BPO
PFC
Leg
Arm
Face PMC
M1
A1
Ventral stream
Articulatory network
pIFG, PM, anterior insula
(left dominant)
Phonological network
Mid-post STS
(bilateral)
Conceptual network
Widely distributed
Sensorimotor interface
Parietal-temporal Spt
(left dominant)
Combinatorial network
aMTG, aITS
(left dominant?)
Lexical interface
pMTG, pITS
(weak left-hemisphere bias)
Spectrotemporal analysis
Dorsal STG
(bilateral)
Figure
7.
(A)
Multiple
stream
models
of
receptive
language
functions
organized
into
multiple
self-organizing
simultaneously
active
networks.
(B)
Grounded
meaning
indicates
that
the
meaning
of
words
and
sentences
are
“embodied.”.
graph-theoretical
metrics
(Leisman,
Rodriguez-Rojas
et
al.,
2014)
is
that
functional
brain
networks
in
children
and
young-adults
show
small-world
properties.
In
mathematics,
physics,
and
sociology,
a
small-world
network
is
a
type
of
mathematical
graph
in
which
most
nodes
are
not
neighbors
of
one
another,
but
most
nodes
can
be
reached
from
every
other
node
by
a
small
number
of
steps.
Specif-
ically,
a
small-world
network
is
defined
as
a
network
where
the
typical
distance
L
between
two
randomly
chosen
nodes
(the
num-
ber
of
steps
required)
grows
proportionally
to
the
logarithm
of
the
number
of
nodes
N
in
the
network
(Watts
&
Strogatz,
1998).
Functional
connectivity
networks
of
brain
from
EEG
(Leisman,
2011)
as
well
as
MEG
(Stam,
2004)
have
also
been
shown
to
possess
small-world
architecture.
Large-scale
brain
networks
in
7-9-y-old
children
show
similar
small-world,
functional
organi-
zation.
Functional
brain
networks
in
children
show
lower
levels
of
hierarchical
organization
compared
to
young-adults.
Children
and
young-adults
possess
different
interregional
connectivity
pat-
terns,
stronger
subcortical-cortical
connectivities
in
young
adults
and
weaker
cortico-cortical
connectivities
in
children.
Large-scale
brain
connectivity
involves
functional
segregation
and
integra-
tion,
stronger
short-range
connections
in
children,
and
stronger
long-range
connections
in
young-adults.
In
taking
this
concept
further,
we
note
that
represented
in
Fig.
8(A)
and
is
a
represen-
tation
of
functional
connectivity
along
the
posterior-anterior
and
ventral-dorsal
axes
showing
elevated
subcortical
connectivity
and
decreased
paralimbic
connectivity
in
children,
compared
to
young-
adults.
This
clearly
demonstrates
that
the
wiring
and
connectivities
of
young
children
is
significantly
different
that
that
of
teenagers.
The
change
in
organization
of
these
connectivities
directly
speaks
to
the
issue
of
optimization
of
pathways
and
is
a
direct
consequence
of
training
and
therefore
of
education.
In
attempting
to
apply
graph
theory
to
an
understanding
of
language
acquisition,
Fig.
8(B)
shows
the
responses
of
both
typically
developing
(TD)
and
of
at-risk,
late-talkers
(LT).
There
exists
a
significant
and
apparent
visual
difference
in
the
networks
with
the
TD’s
network
showing
higher
clustering
coefficient
and
higher
median
in-degree,
but
lower
geodesic
distance
than
the
LT’s
connectivity
networks
of
brain
from
EEG
(Leisman,
2011)
as
well
and
MEG
(Stam,
2004)
have
also
been
shown
to
possess
small-world
architecture.
Large-scale
brain
networks
in
7-9-y-
old
children
show
similar
small-world,
functional
organization.
Functional
brain
networks
in
children
show
lower
levels
of
0.75 Functional connectivity greater in children
A
B
**
**
**
0.65
0.55
Correlation
0.5
0.45
0.4
0.35
Subcortical - primary
sensory
Subcortical -
association
Subcortical -
paralimbic
0.6
0.7
Functional connectivity greater in young-adults
Network graphs of individual children:
Typical talker (60 words) and late talker (61 words)
Typical talker
Moon
Moon
Snow
Comb
Drink
Bee
Button
Hair
Sweater
Bubble
Soap
Lamp
Potty
Potty
Scissor
Cake Backyard
Tummy
Short
Block
Pizza
Aunt
Church
Snow
Pillow Crib
Pool
Boot
TV
Lion
Fish
Duck
Penguin Squirrel
Sister
Nose
Bug
Toe
Block
Puppy
Bus
Trash
Trash
Balloon
Star
Star
Airplane
Airplane
Late talker
Child 1 (TD)
60 words
(40%, 17mo)
Clustering coefficient 0.641
0.392
0.485
2049
13.95
2863
9
1526
25
Geodesic dist.(mean)
In-degree (median)
Child 2 (LT)
61 words
(10%, 24mo)
Random
acquisition
network
**
**
**
0.55
Children
Young-adults
Correlation
0.5
0.45
0.4
0.35
Paralimbic -
limbic
Paralimbic -
association
Association -
limbic
0.6
Figure
8.
(A)
Characterization,
Organization
&
Development
of
Large-Scale
Brain
Networks
in
Children
Using
Graph-Theoretical
Metrics.
(B)
The
graph
on
the
left
is
a
typically
developing
(TD)
child
(17
mo,
40%)
and
the
graph
on
the
right
is
of
an
at-risk,
late-talker
(LT)
(24
mo.,
10%).
The
network
of
the
TD
child
includes
the
60
words
in
the
child’s
productive
vocabulary
and
the
network
of
the
at-risk
LT
child
includes
the
61
words
in
the
child’s
productive
vocabulary.
The
apparent
visual
differences
in
the
networks
are
supported
by
the
differences
in
the
corresponding
table,
with
the
typical
talker’s
network
showing
higher
clustering
coefficient
and
higher
median
in-degree,
but
lower
geodesic
distance,
than
the
LT.
These
differences
are
consistent
at
both
the
individual
and
population
level
(cf.
Leisman,
2013).

Please
cite
this
article
in
press
as:
Leisman,
G.,
et
al.
The
neurological
development
of
the
child
with
the
educational
enrichment
in
mind.
Psicología
Educativa
(2015),
http://dx.doi.org/10.1016/j.pse.2015.08.006
ARTICLE IN PRESS
G Model
PSE-29;
No.
of
Pages
18
G.
Leisman
et
al.
/
Psicología
Educativa
xxx
(2015)
xxx–xxx
13
hierarchical
organization
compared
to
young-adults.
Children
and
young-adults
possess
different
interregional
connectivity
pat-
terns,
stronger
subcortical-cortical
connectivities
in
young
adults
and
weaker
cortico-cortical
connectivities
in
children.
Large-scale
brain
connectivity
involves
functional
segregation
and
integration,
stronger
short-range
connections
in
children,
and
stronger
long-
range
connections
in
young-adults.
In
taking
this
concept
further,
we
note
that
what
is
shown
in
Figs.
8(A)
and
(B)
is
a
represen-
tation
of
functional
connectivity
along
the
posterior-anterior
and
ventral-dorsal
axes
showing
elevated
subcortical
connectivity
and
decreased
paralimbic
connectivity
in
children,
compared
to
young-
adults.
This
clearly
demonstrates
that
the
wiring
and
connectivities
of
young
children
are
significantly
different
from
those
of
teenagers
and
beyond
and
the
change
in
organization
of
these
connectivities
directly
speaks
to
the
issue
of
optimization
of
pathways
and
is
a
direct
consequence
of
training
and
therefore
of
education.
Sensory
Perceptual
Skills
Visual
processing
Vision
is
thought
to
be
like
all
other
high
level
abilities
and
therefore
does
not
involve
a
single
process.
The
function
of
early
childhood
play
in
general,
and
formal
education
in
particular,
is
to
integrate
the
various
subsystems
which
compute
information
about
the
spatial
properties
of
objects,
movement,
shape,
color,
etc.
One
type
of
visual
processing
is
accomplished
by
the
so-called
ventral
system
because
computations
take
place
in
the
more
ven-
tral
occipito-temporal
and
inferior-temporal
cortices.
This
system
has
also
been
characterized
as
the
“what
system”
(Ungerleider
&
Mishkin,
1982)
as
opposed
to
the
“where
system”
functioning
in
the
parietal
lobe
which
focuses
on
object
recognition.
The
two
hemi-
spheres
are
known
to
act
differently
in
the
way
they
encode
shapes
(Leisman,
1976;
Melillo
&
Leisman,
2009).
It
has
been
argued
that
many
different
functions
of
vision
could
be
achieved
effectively
if
the
system
could
encode
infor-
mation
at
multiple
levels
of
scale
(Marr,
1982).
In
other
words,
one
way
to
distinguish
whether
you
were
seeing
an
edge
or
just
a
change
in
texture
is
to
determine
whether
changes
in
intensity
are
present
at
multiple
scales.
For
instance,
if
they
are
noticeable
with
only
high
resolution,
they
are
most
likely
texture
variations;
if
they
are
present
at
multiple
levels,
they
are
probably
edges
(DeValois
&
DeValois,
1988).
The
evidence
from
research
at
this
time
suggests
that
the
two
hemispheres
focus
on
different
types
of
features
of
visual
input
when
forming
object
representations.
The
left
hemisphere
is
thought
to
focus
on
smaller
parts,
higher
spatial
frequencies,
or
details.
The
right
hemisphere
is
thought
to
focus
on
the
global
form,
lower
spatial
frequencies
or
course
patterns.
To
explain
this
asymmetry
there
are
two
theories
that
have
been
proposed
(Brown
&
Kosslyn,
1995).
One,
a
structural
theory,
proposes
that
one
or
more
processing
subsystems
have
become
specialized
in
the
hemispheres.
The
allocation
theory
states
that
the
hemispheres
tend
to
employ
different
strategies
that
often
pro-
duce
these
results
but
there
are
not
specific
structural
differences
between
hemispheres.
Visual
processing
can
be
divided
into
three
phases
of
low,
inter-
mediate,
and
high
levels
(Marr,
1982)
and
the
hemispheres
can
differ
in
their
allocation
of
resources
at
any
of
these
levels.
While,
as
in
most
lateralized
functions,
each
type
of
processing
is
found
in
both
hemispheres,
to
different
degrees,
the
hemispheres
differ
in
the
relative
efficiency
of
the
individual
subsystems
for
a
particu-
lar
type
of
processing
(structural
theory)
or
in
their
predominance
for
using
certain
strategies
(allocation
theory)
(Brown
&
Kosslyn,
1995).
The
neurodevelopmental
skills
represented
in
pre-primary
and
primary
education
should
be
consistent
with
the
normal
develop-
ment
of
visual
processing.
At
the
lowest
level,
subsystems
organize
the
input
so
that
distinct
figures
are
separated
from
the
ground.
This
processing
takes
place
in
a
structure
known
as
the
visual
buffer
(Kosslyn,
1987).
Computations
in
the
visual
buffer
specify
edges,
regions
of
common
color,
and
texture,
and
other
character-
istics
that
distinguish
one
object
from
its
background.
It
is
thought
that
not
all
the
information
in
the
visual
buffer
can
be
consid-
ered
in
detail,
therefore,
some
information
is
chosen
for
additional
processing.
This
has
been
referred
to
as
an
attention
window
that
can
be
focused
to
a
specific
size,
shape,
and
location
to
select
a
specific
area
of
the
visual
buffer
for
more
processing
(Treisman
&
Gelade,
1980).
According
to
structural
theories,
the
right
hemisphere
may
more
effectively
detect
large
variations
in
light
intensity
over
space.
The
left
hemisphere
more
efficiently
detects
small
variations
in
light
intensity
over
space.
This
would
suggest
that
the
hemispheres
dif-
fer
in
their
sensitivity
to
different
spatial
frequencies
(Sergent
&
Hellige,
1986).
At
the
intermediate
level
of
visual
processing,
stimuli
are
orga-
nized
into
perceptual
groups
useful
for
later
object
recognition.
In
this
model,
the
contents
of
the
attention
window
are
sent
to
a
preprocessing
subsystem
in
the
occipito-temporal
regions.
Fea-
tures
such
as
texture
gradients
and
color
are
found
at
this
level
(Kosslyn,
Flynn,
Amsterdam,
&
Wang,
1990).
According
to
the
struc-
tural
theories,
the
subsystem
is
focused
to
detect
different
kinds
of
information
in
the
two
hemispheres.
For
instance,
a
child
may
wish
to
look
at
an
overall
pattern
like
a
face,
whereas
in
other
instances
the
child
may
want
to
look
at
one
of
its
components
such
as
an
eye.
The
two
functions
are
not
compatible
with
each
other.
The
global
function
needs
to
incorporate
into
a
whole
the
same
things
that
need
to
be
separated
out
by
the
local
process.
Therefore,
it
may
be
more
efficient
to
have
separate
process
operate
in
parallel
at
the
two
levels.
These
concerns
should
be
addressed
in
the
approach
that
nursery,
kindergarten,
and
primary
teachers
take
in
working
with
perceptual
problem
solving
and
with
the
organization
of
visually
based
classroom
materials.
High-level
vision
is
concerned
with
matching
input
to
represen-
tations
in
stored
memory.
It
is
thought
that
an
object
is
reorganized
when
a
match
is
made.
In
this
same
model,
the
output
from
the
pre-
processing
subsystem
serves
as
the
input
to
the
pattern
activation
subsystem
found
in
the
inferior
temporal
lobes.
It
is
here
that
the
perceptual
input
is
compared
to
the
stored
visual
information,
and
recognition
is
achieved
if
a
match
is
made.
If
the
input
does
not
match
a
previously
stored
representation
well
enough,
then
the
new
pattern
is
stored.
It
is
thought
that
size
per
se
is
not
likely
to
be
represented
at
the
level
of
object
recognition.
It
is
thought
that
neurons
in
the
inferior
temporal
lobe
that
are
sensitive
to
high
level
visual
properties
are
insensitive
to
changes
in
visual
angle
(Leisman,
1976;
Melillo
&
Leisman,
2009;
Plaut
&
Farah,
1990).
The
hemispheres
may
function
differently
in
their
ability
for
encoding
parts
in
the
whole
or
at
different
levels
of
hierarchy
in
a
structural
description.
A
structural
description
specifies
how
components
are
organized
to
compose
a
whole.
The
shape
of
a
person
is
one
exam-
ple
given
to
illustrate
how
parts
are
organized
to
compose
a
whole.
In
this
example,
a
person
is
represented
as
a
tree
diagram,
with
the
body
at
the
top,
head,
trunk,
arms,
and
legs
as
branches;
upper
arms,
forearm,
and
hand
as
branches
from
the
arm
(Marr,
1982).
It
is
possible
according
to
one
theory
that
one
hemisphere
could
store
the
(larger)
wholes
and
the
other
could
store
the
(smaller)
parts.
Another
theory
suggests
that
it
is
not
size
that
is
different
but
that
the
hemispheres
store
by
preferred
level
of
hierarchy.
The
left
hemisphere
may
compute
input
farther
down
in
a
structural
hier-
archy
whereas
the
right
hemisphere
may
compute
parts
or
wholes
on
higher
or
lower
levels
in
hierarchy.
Several
experiments
have
been
used
to
verify
the
functional
differences
between
the
two
hemispheres.
These
are
useful
to
review
because
they
emphasize
the
functional
differences
in
a
practical
way.
In
addition,
the
same

Please
cite
this
article
in
press
as:
Leisman,
G.,
et
al.
The
neurological
development
of
the
child
with
the
educational
enrichment
in
mind.
Psicología
Educativa
(2015),
http://dx.doi.org/10.1016/j.pse.2015.08.006
ARTICLE IN PRESS
G Model
PSE-29;
No.
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Pages
18
14
G.
Leisman
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Psicología
Educativa
xxx
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xxx–xxx
techniques
that
are
used
to
identify
functions
can
be
used
to
diag-
nose
dysfunction,
or
if
one
hemisphere
is
decreased
in
activation
as
compared
to
the
other.
Additionally,
if
we
know
what
each
hemi-
sphere
responds
to
we
can
later
use
this
information
to
concentrate
rehabilitation
on
the
performance
of
one
hemisphere.
One
of
the
most
common
experiments
(Heinke
&
Humphreys,
2003)
involves
use
of
letter
stimuli
that
are
most
commonly
used
in
global
precedence
studies;
the
features
of
the
global-level
object
(e.g.,
two
vertical
lines
and
one
horizontal
line
forming
an
H)
are
determined
by
the
positioning
of
the
local
elements.
Therefore,
there
is
a
confounding
between
size
and
level
of
hierarchy;
the
larger
letter
is
made
up
of
smaller
letters.
In
this
case,
the
term
hier-
archy
refers
to
objects
that
are
made
up
only
of
their
constituent
parts.
For
example,
a
dog’s
body
is
hierarchically
structured
because
it
is
made
out
of
head,
trunk,
legs,
and
so
on;
if
these
parts
are
removed,
nothing
remains.
In
contrast,
patterns
on
a
shirt
are
not
hierarchically
related
to
the
shirt,
if
one
removes
them,
the
shirt
would
remain
intact.
In
other
experiments
(Paquet
&
Merikle,
1988),
investigators
removed
these
confounding
four
letter
stimuli.
In
addition
to
letter
stimuli,
picture
stimuli
could
be
employed
that
are
not
hierar-
chically
arranged
removing
the
possibility
of
processes
that
are
specialized
for
reading.
In
most
real
world
objects,
global
features
provide
general
information
about
object
identity,
whereas
local
feature
can
be
used
to
identify
specific
information.
In
one
experi-
ment
(Martin,
1979),
stimuli
were
presented
consisting
of
pictures
of
garments
with
smaller
pictures
on
them,
the
larger
pictures
were
not
composed
of
the
smaller
ones,
and
therefore
there
was
not
a
hierarchic
relation
between
the
two.
The
smaller
pictures
were
also
garments,
providing
the
same
types
of
objects
at
the
local
and
global
level
without
a
hierarchic
arrangement.
In
one
divided
visual
feed
study
of
global
precedence
(Martin,
1979)
it
was
found
that
the
global
(larger)
was
processed
faster
than
the
local
(smaller)
level
in
both
hemispheres
when
the
global
and
local
level
letters
were
different.
However,
the
global
level
was
processed
faster
follow-
ing
right
hemisphere
presentation
than
following
left
hemisphere
presentation
when
subjects
attended
to
the
global
level.
In
addi-
tion,
there
was
a
greater
interference
from
the
global
level
letter
when
stimuli
were
presented
originally
to
the
right
hemisphere
than
when
the
subjects
selectively
attended
to
the
local
level.
In
this
case,
the
subjects
evaluated
the
stimuli
more
quickly
when
they
were
presented
initially
to
the
left
hemisphere
than
to
the
right
hemisphere.
To
test
whether
local-global
hemisphere
specialization
is
dif-
ferent
for
hierarchic
letter
or
non-hierarchic
pictures
(Lamb,
Robertson,
&
Knight,
1990;
Martin,
1979),
which
would
suggest
high-level
visual
processing,
subjects
were
shown
two
types
of
stimuli:
letters
composed
of
smaller
letters
and
articles
of
cloth-
ing
with
patterns
of
smaller
articles
of
clothing
printed
on
them.
Results
showed
that
for
pictures,
subjects
did
detect
targets
at
the
local
level
faster
when
stimuli
were
shown
initially
to
the
left
hemi-
sphere
than
the
right
hemisphere;
however,
they
evaluated
targets
at
global
level
equally
well
with
both
hemispheres.
This
confirms
a
local
precedence
and
a
trend
toward
the
expected
pattern
of
hemi-
spheric
specialization.
In
comparison,
although
a
global
precedence
was
found
for
the
letter
stimuli,
the
effect
was
the
same
when
stimuli
were
presented
initially
to
the
right
hemisphere
or
to
the
left.
For
letters,
subjects
responded
faster
and
made
fewer
errors
when
the
targets
were
at
the
global
level
than
when
they
were
at
the
local
level.
In
an
attempt
to
explain
the
attention
allocation
hypothesis,
Kosslyn,
Chabris,
Marsolek,
&
Koenig
(1992)
propose
a
specific
mechanism
where
they
suggest
that
the
right
hemisphere
prefer-
entially
monitors
outputs
from
neurons
that
have
relatively
large
receptor
fields.
Also
the
hemispheres
may
differ
in
their
ability
to
monitor
the
outputs
from
different
size
receptive
fields
even
if
the
same
outputs
are
available
in
both
hemispheres
(Kosslyn,
Chabris,
Marsolek,
&
Koenig,
1992).
Kosslyn
et
al.
(1992)
suggest
that
the
bias
to
encode
outputs
from
neurons
with
different
size
receptive
fields
allows
the
ventral
(object)
and
dorsal
(spatial)
systems
to
be
coordinated.
It
is
thought
that
an
individual
during
movement
not
only
needs
to
know
the
precise
metric
distances
of
objects
(dorsal)
but
also
the
specific
shapes
of
objects
(ventral).
The
two
types
of
processing
need
to
be
linked
and
it
is
thought
that
this
is
best
achieved
if
they
are
both
in
the
same
hemisphere
–
the
right
(Kosslyn
et
al.,
1992;
Marsolek,
Kosslyn,
&
Squire,
1992).
In
addition,
when
an
individual
attempts
to
identify
objects,
they
may
need
to
ignore
variations
in
shape
among
specific
examples
and
may
need
only
to
know
the
type
of
spatial
relations
among
parts,
not
the
specific
positions
of
parts
of
a
given
object.
For
example,
to
reorganize
the
shape
of
a
dog,
one
would
ignore
the
type
of
dog
and
the
exact
position
of
limbs.
The
left
hemisphere
is
thought
to
have
a
special
role
both
in
generalizing
over
shapes
(Marsolek
et
al.,
1992)
and
in
categorizing
spatial
relations
(Kosslyn
et
al.,
1992).
There-
fore,
differences
in
receptive
field
size
may
act
to
coordinate
the
encoding
of
shapes
and
spatial
relations,
and
could
therefore
cause
the
right
hemisphere
to
specialize
in
computing
metric
spatial
rela-
tions
and
specific
shapes
and
the
left
hemisphere
to
specialize
in
computing
categorical
spatial
relations
and
categories
of
shapes.
So
far
we
have
suggested
that
hemispheres
function
differently
in
their
effectiveness
of
focusing
attention
at
scales
of
different
size,
and
that
the
underlying
mechanism
involves
sampling
out-
puts
from
neurons
with
different
size
receptive
fields.
This
is
similar
to
a
spatial-frequency
hypothesis.
In
fact,
the
receptive
field
and
spatial-frequency
theories
predict
similar
results.
The
two
concepts
are
closely
related.
The
smaller
its
recep-
tive
field,
the
higher
the
spatial
frequency
a
cell
will
respond
to;
on
the
other
hand,
the
larger
the
receptive
field,
the
lower
the
spatial
frequency.
In
this
case
it
is
thought
therefore
that
a
large
receptive
field,
or
a
lower
spatial
frequency,
is
more
of
a
right
hemi-
sphere
function,
whereas
small
receptive
fields
and
higher
spatial
frequency
is
more
of
a
left
hemisphere
function
with
the
effects
modulated
by
attentional
variables.
Normal
processing
of
global
aspects
is
thought
to
depend
on
the
posterior
superior
temporal
lobe
of
the
right
hemisphere.
Normal
processing
of
local
elements
depends
on
the
posterior
superior
temporal
lobe
of
the
left
hemi-
sphere
(Lamb
et
al.,
1990).
Another
important
quality
of
visual
information
processing
is
the
ability
to
localize
a
visual
image
in
space.
The
observer
is
required
to
detect
whether
one
object
touches
another
object,
this
would
be
an
on
and
off
quality.
Another
requirement
is
the
dis-
cernment
of
near
or
far
and
above
or
below.
There
are
hemispheric
performance
differences
for
all
of
these
tasks.
Studies
have
shown
that
there
is
a
left
hemisphere
advantage
for
the
on-off
tasks
and
a
right
hemisphere
advantage
for
distance
judgment
tasks
(Kosslyn,
Sokolov,
&
Chen,
1989).
Hellige
and
Michimata
(1989)
tested
indi-
viduals
by
having
observers
indicate
whether
a
dot
was
within
2
cm
of
the
line
(a
coordinate
or
distance
task
called
near-far
task).
For
the
above-below
task,
there
is
a
left
hemisphere
advantage
whereas
the
right
hemisphere
shows
an
advantage
for
the
near-far
task.
Researchers
have
examined
how
these
different
asymmetries
arise
during
the
course
of
ontogenetic
development.
Compared
to
adults,
the
visual
sensory
system
of
newborns
is
especially
limited
in
its
transformation
of
information
carried
by
high
spatial
frequen-
cies
(DeSchonen
&
Mathivet,
1989).
It
has
been
suggested
that
the
development
of
various
brain
areas
is
more
advanced
in
the
right
hemisphere
than
in
the
left
hemisphere
at
the
time
of
birth
and
possibly
for
a
short
time
after.
Hellige
(1993)
postulates
a
certain
critical
period
for
incoming
visual
input
modification,
which
occurs
earlier
for
the
right
hemisphere
than
for
the
left
hemisphere.
Once
modified
by
highly
degraded
visual
input,
the
right
hemisphere
is
not
only
predisposed
to
become
dominant
for
processing
low

Please
cite
this
article
in
press
as:
Leisman,
G.,
et
al.
The
neurological
development
of
the
child
with
the
educational
enrichment
in
mind.
Psicología
Educativa
(2015),
http://dx.doi.org/10.1016/j.pse.2015.08.006
ARTICLE IN PRESS
G Model
PSE-29;
No.
of
Pages
18
G.
Leisman
et
al.
/
Psicología
Educativa
xxx
(2015)
xxx–xxx
15
spatial
frequencies
but
is
also
less
able
than
the
left
to
take
full
advantage
of
higher
frequencies
when
they
finally
do
appear.
If
this
notion
is
accurate,
then
it
would
also
follow
that
the
result-
ing
hemispheric
differences
in
visual
processing
would
influence
asymmetry
for
any
task
that
depends
on
the
relevant
aspects
of
visual
information
whether
the
activity
requires
stimulus
identifi-
cation
or
stimulus
location.
Enrichment
Should
be
Synonym
for
Early
Childhood
Education
Although
studies
of
the
adverse
effects
of
deprivation
on
brain
development
are
powerful
and
compelling,
they
tell
us
little
about
the
benefits
of
enrichment.
Much
of
what
we
know
about
the
impact
of
early
experience
on
brain
architecture
comes
from
animal
or
human
studies
of
deprivation.
As
we
work
to
clarify
further
the
patterns
of
genetic
expression
required
for
normal
neural
structure,
we
have
also
recognized
that
an
optimal
level
of
environmental
input
or
“expectable”
environment
must
exist
in
parallel.
Increas-
ing
evidence
suggests
that
this
“expectable
environment”
of
early
development
requires
not
only
the
variation
in
light
necessary
for
vision,
or
the
tones
heard
in
a
spoken
language,
but
also
the
emo-
tional
support
and
familiarity
of
a
caregiver
(Nelson
et
al.,
2007).
The
well-documented
negative
impacts
of
deprivation
on
brain
circuitry
do
not
mean
that
excessive
enrichment
produces
mea-
surable
enhancements
in
brain
function.
A
small
number
of
case
reports
exist
in
which
neglected
children
with
very
little
lan-
guage
experience
in
early
childhood
were
given
enriched
language
exposure
in
a
protective
environment
(Curtiss,
1977;
Itard,
1932).
Longitudinal
follow-up
studies
of
these
children
demonstrated
that,
after
several
years
of
language
exposure,
they
were
unable
to
achieve
adult-level
native
language
abilities.
Most
recently,
early
intervention
to
correct
a
deeply
impov-
erished
early
environment
has
been
shown
to
greatly
improve
cognitive,
linguistic,
and
emotional
capabilities
in
humans
(Nelson
et
al.,
2007;
Windsor
et
al.,
2007).
Activity-dependent
mechanisms
of
network
formation
may
be
responsible
for
such
changes
when
children
were
placed
into
a
stimulating
environment
for
learning
and
exploration.
With
continued
research
into
the
modification
of
sensitive
periods,
as
well
as
the
factors
influencing
cortical
plasticity
throughout
life,
we
may
remain
optimistic
about
the
possibility
of
recovery
from
early
deprivation.
This
in
turn
may
provide
hope
for
children
who
lack
the
biological
framework,
or
necessary
environment
required
for
optimal
neural
and
cognitive
growth.
The
possibility
of
cognitive
and
neural
rehabilitation
leads
to
theories
of
enrichment
beyond
the
norm
to
a
level
of
enhanced
development.
Educational
and
environmental
enrichment
of
preschool
children
from
impoverished
economic
settings
has
been
shown
to
improve
cognitive
measures
through
early
adulthood
(Campbell,
Pungello,
Miller-Johnson,
Burchinal,
&
Ramey,
2001).
Cognitive
capabilities,
however,
may
follow
a
pattern
similar
to
the
growth
curve
of
the
human
body;
that
is,
while
it
is
possible
to
enhance
the
environment
of
a
child
to
assume
the
pattern
of
the
normal
curve,
it
is
not
possible
to
exceed
the
predicted
trajectory
to
a
significant
extent
without
causing
some
potential
harm.
This
is
suggested
by
large
studies
of
children
from
varying
socioeco-
nomic
status,
which
demonstrated
an
improvement
in
cognitive
performance
only
in
those
born
to
a
low
socioeconomic
class,
with
no
significant
difference
between
those
of
middle-class
and
high-
income
families
(Jeffaris,
Power,
&
Hertzman,
2002).
If
the
possibility
for
enhancement
exists,
it
is
perhaps
related
to
forms
of
enrichment
that
lie
outside
access
to
unlimited
resources
–
i.e.,
beyond
the
expectable
environment.
Creativity,
for
exam-
ple,
is
a
key
component
to
enhanced
cognitive
functioning,
yet
we
have
not
been
able
to
define
the
neural
processes
or
envi-
ronmental
attributes
that
can
enrich
this
aspect
of
cognition,
nor
are
there
sure-fire
ways
of
boosting
creativity
among
the
popula-
tion
at
large.
Similarly,
exposure
to
art
or
music
or
great
literature
or
horseback
riding
may
not
confer
any
evolutionary
advantage
(i.e.,
reproductive
success),
yet
these
activities
may
confer
some
advantage
among
certain
strata
of
society.
Thus,
perhaps
it
would
be
useful
to
draw
a
distinction
between
enrichment
as
applied
to
those
experiencing
downward
deviations
from
the
expectable
environment
(such
as
those
reared
in
situations
of
neglect
or
depri-
vation)
and
enhanced
enrichment
applied
to
those
reared
in
typical
(expectable)
environments.
Enrichment
may
lead
to
a
restoration
of
typical
development
whereas
enhanced
enrichment
may
lead
to
exceeding
the
typical
environment.
Of
course,
a
challenge
here
lies
in
accounting
for
individual
differences,
as
some
individuals
have
greater
potential
to
benefit
from
art
or
music
lessons
than
others.
Individual
heterogeneity
may
be
under
control
of
gene
x
gene,
gene
x
environment,
and
environment
x
environment
factors.
For
example,
animal
studies
show
that
epigenetic
mechanisms
–
whereby
environmental
factors
and
experiences
early
in
life
can
permanently
alter
the
genome
of
an
individual
through
chemical
modification
–
will
impact
long-term
cognitive
and
social-
emotional
functioning
(Szyf,
McGowan,
&
Meany,
2008).
The
field
awaits
translation
of
this
type
of
mechanism
into
human
experi-
ences.
Finally,
how
might
the
field
of
developmental
psychology
benefit
from
advances
being
made
in
developmental
neuroscience?
First,
given
that
our
genome
contains
many
fewer
genes
than
we
surmised
even
a
decade
ago
(approximately
20,000),
and
given
advances
being
made
in
the
field
of
epigenetics,
renewed
attention
should
be
paid
to
the
origins
and
elaboration
of
complex
human
behaviors.
Second,
those
working
in
the
field
of
intervention
should
take
stock
in
what
is
now
known
about
neural
plasticity;
for
exam-
ple,
it
is
quite
possible
that
we
could
witness
a
revolution
in
new
treatment
approaches
based
on
what
we
know
about
the
mal-
leability
of
the
human
brain.
Finally,
for
the
millions
of
children
around
the
world
who
begin
their
lives
in
adverse
circumstances,
we
should
be
mindful
of
what
is
known
about
sensitive
periods,
and
act
with
alacrity
to
improve
the
lives
of
these
children
before
neural
circuits
become
well-established
and
thus,
difficult
to
mod-
ify.
To
borrow
an
analogy
from
economics,
by
investing
early
and
well
in
our
children’s
development
we
increase
the
rate
of
return
later
in
life,
and
in
so
doing
improve
not
only
the
lives
of
individuals
but
of
societies
as
well.
Resumen
ampliado
Desde
los
tiempos
de
Camillo
Golgi
y
Santiago
Ramón
y
Cajal,
en
la
década
de
los
90
del
siglo
XIX,
hasta
la
moderna
neurocien-
cia,
se
han
producido
grandes
avances
en
nuestro
conocimiento
del
cerebro.
Sin
embargo,
muy
poco
de
ese
conocimiento
se
ha
trasladado
al
aula,
y
aún
menos
a
las
políticas
educativas.
Fruto
de
ese
extenso
recorrido
científico,
han
surgido
una
serie
de
prin-
cipios
básicos
de
funcionamiento
y
desarrollo
del
sistema
nervioso
que
son
del
mayor
interés
para
la
educación.
Entre
estos
principios
cabe
destacar
los
siguientes:
1.
El
cerebro
humano
se
desarrolla
desde
la
concepción
hasta
el
comienzo
de
la
segunda
década
de
vida,
empezando
por
los
procesos
más
básicos.
Es
decir,
se
comienza
por
las
funciones
y
controles
vitales
y
autónomos,
siguen
los
procesos
cognitivo-
motores
sensoriales
y
perceptivos
y
culmina
con
los
procesos
de
integración
y
toma
de
decisiones.
2.
El
cerebro
del
ni˜
no
recibe
la
influencia
combinada
de
la
genética
y
la
experiencia.
3.
La
capacidad
del
cerebro
para
modificarse
decrece
con
la
edad.

Please
cite
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article
in
press
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Leisman,
G.,
et
al.
The
neurological
development
of
the
child
with
the
educational
enrichment
in
mind.
Psicología
Educativa
(2015),
http://dx.doi.org/10.1016/j.pse.2015.08.006
ARTICLE IN PRESS
G Model
PSE-29;
No.
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Pages
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16
G.
Leisman
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/
Psicología
Educativa
xxx
(2015)
xxx–xxx
4.
Las
capacidades
cognitivas,
emocionales
y
sociales
están
inex-
orablemente
unidas
a
lo
largo
de
toda
la
vida.
5.
Las
funciones
cognitivas
y
motoras
interactúan
en
nuestro
cere-
bro
como
consecuencia
directa
de
nuestra
postura
bípeda.
6.
La
presencia
de
tóxicos
da˜
na
la
arquitectura
cerebral,
lo
que
puede
conducir
a
problemas
para
el
aprendizaje,
la
conducta
y
la
salud
mental
y
física
de
por
vida.
7.
El
entorno
del
ni˜
no
afecta
directamente
a
la
sinaptogénesis
y
permite
la
optimización
neurológica.
Una
de
las
primeras
consecuencias
de
estos
principios
es
que
el
papel
de
las
escuelas
infantiles
y
de
los
profesores
en
esta
etapa
de
la
vida
resultará
crítico
para
el
establecimiento
de
unos
sólidos
cimientos
funcionales
que
serán
necesarios
para
el
desarrollo
ulte-
rior
del
ni˜
no
y
para
la
neurobiología
del
adulto.
Se
ha
constatado
que
el
ambiente
del
ni˜
no
tiene
un
impacto
directo
en
los
tiempos
y
la
naturaleza
de
la
expresión
génica
que
afecta
directamente
a
la
arquitectura
cerebral
del
ni˜
no.
El
desarrollo
cerebral,
cognitivo,
sensorial
y
perceptivo
no
ocurre
simultáneamente
sino
más
bien
en
diferentes
etapas
de
desarrollo,
como
se
puede
apreciar
en
la
figura
1.
Lo
importante
a
destacar
aquí
es
que
cada
una
de
las
capacidades
perceptivas,
cognitivas
y
emocionales
se
fundamenta
en
el
andamiaje
proporcionado
por
las
primeras
etapas
de
la
vida.
Todo
esto
ocurre
porque
aunque
existan
instrucciones
genéticas
para
formar
numerosas
neuronas
y
sus
conexiones,
esto
ocurre
de
manera
no
direccional,
es
decir,
sin
especificación
de
su
forma
concreta.
Es
más
bien
la
reorganización
epigenética
posterior
(la
estabilización
sináptica,
consecuencia
del
uso
y
la
experiencia)
la
que
determina
la
forma
y
configuración
de
los
circuitos
neurales
del
cerebro.
En
este
sentido,
la
neurociencia
ha
demostrado
la
estrecha
relación
que
existe
entre
el
grado
de
enriquecimiento
ambiental
y
procesos
neurobiológicos
tan
impor-
tantes
como
la
bioquímica
cerebral,
la
gliogénesis,
la
neurogénesis
o
la
arborización
dendrítica,
entre
otros.
De
ahí
la
crucial
importancia
de
la
experiencia
en
las
primeras
etapas
de
la
vida.
Se
sabe
además
que
los
precursores
de
lo
que
posteriormente
serán
las
distintas
áreas
funcionales
del
cerebro
emergen
aproximadamente
durante
los
dos
primeros
trimestres
de
la
gestación
en
el
humano.
Esta
organización
es
la
base
sobre
la
que
se
fundamenta
el
detallado
desarrollo
posterior
de
los
cir-
cuitos
neurales.
Por
lo
tanto,
estas
etapas
del
desarrollo
serán
también
fundamentales
y
cualquier
influencia
sobre
las
mismas
(p.
ej.,
exposición
a
tóxicos)
puede
tener
consecuencias
de
largo
alcance.
Es
cierto
que
nuestro
cerebro
es
más
plástico
que
el
de
muchos
otros
organismos,
manteniendo
esta
propiedad
incluso
en
el
adulto.
Sin
embargo,
el
fundamento
de
la
arquitectura
cerebral
del
adulto
se
establece
en
edades
tempranas
y
se
sabe
que
la
influencia
del
ambiente
durante
la
infancia
es
tremendamente
significativa
para
los
mismos
procesos
sensoriales
de
la
percepción.
Y
es
sobre
estos
procesos
sobre
los
que
se
fundamenta
el
funcionamiento
de
los
procesos
cognitivos
superiores
alcanzados
en
etapas
posteriores
y
utilizados
en
la
adultez.
Cada
sistema
sensorial
y
cognitivo
tiene
su
propio
período
sensible
y
los
que
se
alcanzan
más
tarde
tienen
su
fundamento
en
los
alcanzados
previamente.
De
ahí
que
unas
mis-
mas
condiciones
ambientales
puedan
tener
efectos
muy
distintos
dependiendo
de
la
edad
del
ni˜
no
de
la
que
hablemos.
Efectivamente,
los
circuitos
neurales
de
alto
nivel,
que
llevan
a
cabo
operaciones
mentales
sofisticadas,
dependen
para
su
correcto
funcionamiento
de
la
calidad
de
la
información
que
les
propor-
cionan
los
sistemas
de
bajo
nivel.
Los
sistemas
de
bajo
nivel
cuya
arquitectura
haya
sido
moldeada
por
experiencias
saludables
en
las
primeras
etapas
de
la
vida
proporcionarán
a
los
circuitos
de
alto
nivel
información
precisa
y
de
alto
nivel.
Ésta,
combinada
con
una
experiencia
rica
y
sofisticada
en
etapas
posteriores
de
la
vida,
permitirá
que
la
arquitectura
de
los
circuitos
implicados
en
funciones
cognitivas
superiores
alcance
todo
su
potencial
genético.
Por
lo
tanto,
el
aprendizaje
temprano
es
el
fundamento
del
apren-
dizaje
posterior
y
es
esencial
(aunque
no
suficiente)
para
un
desarrollo
óptimo
de
la
arquitectura
cerebral.
Dicho
de
otra
forma,
una
experiencia
temprana
enriquecida
debe
de
ir
seguida
de
más
experiencia
enriquecida
y
sofisticada,
especialmente
cuando
los
circuitos
de
orden
superior
están
madurando.
La
neurociencia
ha
constatado,
en
este
sentido,
que
el
metabolismo
cerebral
de
la
glu-
cosa
entre
los
3
y
los
10
a˜
nos
de
edad
–período
que
se
corresponde
con
una
etapa
de
conectividad
neural
exuberante–
es
de
en
torno
al
doble
del
de
un
adulto.
El
enriquecimiento
ambiental
debería
ser
por
tanto
sinónimo
de
educación
infantil.
Para
los
millones
de
ni˜
nos
de
todo
el
mundo
que
comienzan
la
vida
en
circunstancias
adversas,
deberíamos
tener
presente
lo
que
se
sabe
acerca
de
los
períodos
sensibles
del
desar-
rollo
y
actuar
con
diligencia
para
mejorar
su
vidas
antes
de
que
los
circuitos
neurales
se
establezcan
y
afiancen
y,
por
tanto,
sean
más
difíciles
de
modificar.
Empleando
una
metáfora
en
términos
económicos,
podríamos
decir
que
al
invertir
bien
y
pronto
en
el
desarrollo
de
los
ni˜
nos
incrementaremos
la
cantidad
de
retorno
en
etapas
posteriores
de
la
vida.
De
este
modo,
no
sólo
mejoraríamos
la
vida
de
las
personas
sino
también
la
de
toda
la
sociedad.
Conflict
of
Interest
The
authors
of
this
article
declare
no
conflict
of
interest.
Financial
Support
This
work
was
supported
in
part
by
the
government
of
Israel
through
the
Kamea
Dor-Bet
program
and
by
the
Children’s
Autism
Help
Project-USA.
References
Akins,
M.
R.,
&
Biederer,
T.
(2006).
Cell-cell
interactions
in
synaptogenesis.
Current
Opinion
in
Neurobiology,
16,
83–89.
Alcamo,
E.
A.,
Chiriella,
L.,
Dautzenberg,
M.,
Dobreva,
G.,
Farinas,
I.,
&
Grosschedl,
R.
(2008).
Satb2
regulates
callosal
projection
neuron
identity
in
the
developing
cerebral
cortex.
Neuron,
57,
364–377.
Amedi,
A.,
Stern,
W.
M.,
Camprodon,
J.
A.,
Bermpohl,
F.,
Merabet,
L.,
&
Rotman,
S.
(2007).
Shape
conveyed
by
visual-to-auditory
sensory
substitution
activates
the
lateral
occipital
complex.
Nature
Neuroscience,
10,
687–689.
Andersen,
S.
L.
(2003).
Trajectories
of
brain
development:
point
of
vulnerabil-
ity
or
window
of
opportunity?
Neuroscience
and
Biobehavioral
Reviews,
27,
3–18.
Anisman,
H.,
Zaharia,
M.
D.,
Meaney,
M.
J.,
&
Merali,
Z.
(1998).
Do
early-life
events
permanently
alter
behavioral
and
hormonal
responses
to
stressors?
Interna-
tional
Journal
of
Developmental
Neuroscience,
16,
149–164.
Bavelier,
D.,
&
Neville,
H.
J.
(2002).
Cross-modal
plasticity:
where
and
how?
Nature
Reviews
Neurosciences,
3,
443–452.
Birdsong,
D.,
&
Molis,
M.
(2001).
On
the
Evidence
for
Maturational
Constraints
in
Second-Language
Acquisition.
Journal
of
Memory
and
Language,
44,
235–249.
Bjorklund,
D.
F.
(1997).
The
role
of
immaturity
in
human
development.
Psychological
Bulletin,
122,
153–169.
Blakemore,
S.
J.
(2012).
Imaging
brain
development:
the
adolescent
brain.
Neuroim-
age,
61,
397–406.
Bourgeois,
J.-P.,
Goldman-Rakic,
P.
S.,
&
Rakic,
P.
(1999).
Formation,
elimination,
and
stabilization
of
synapses
in
the
primate
cerebral
cortex.
In
M.
S.
Gazzaniga
(Ed.),
The
New
Cognitive
Neurosciences
(pp.
45–53).
Cambridge,
MA:
MIT
Press.
Brown,
H.
D.,
&
Kosslyn,
S.
M.
(1995).
Hemispheric
differences
in
visual
object
processing:
Structural
versus
allocation
theories.
In
R.
J.
Davidson,
&
K.
Hugdahl
(Eds.),
Brain
asymmetry
(pp.
77–97).
Cambridge,
MA:
MIT
Press.
Bruer,
J.
T.
(1993).
Schools
for
though:
The
science
of
learning
in
the
classroom.
Cambridge,
MA:
MIT
Press.
Bruner,
J.
(1971).
The
Relevance
of
Education.
London:
Allen
and
Unwin.
Campbell,
F.
A.,
Pungello,
E.
P.,
Miller-Johnson,
S.,
Burchinal,
M.,
&
Ramey,
C.
T.
(2001).
The
development
of
cognitive
and
academic
abilities:
Growth
curves
from
and
early
childhood
educational
experiment.
Developmental
Psychology,
37,
231–242.
Carver,
S.
M.,
&
Klahr,
D.
(Eds.).
(2013).
Cognition
and
instruction:
25
years
of
progress..
Mahwah,
NJ:
Lawrence
Erlbaum
Associates.
Cholfin,
J.
A.,
&
Rubenstein,
J.
L.
R.
(2007).
Patterning
of
frontal
cortex
subdivisions
by
fgf17.
Proceedings
of
the
National
Academy
of
Sciences,
104,
7652–7657.

Please
cite
this
article
in
press
as:
Leisman,
G.,
et
al.
The
neurological
development
of
the
child
with
the
educational
enrichment
in
mind.
Psicología
Educativa
(2015),
http://dx.doi.org/10.1016/j.pse.2015.08.006
ARTICLE IN PRESS
G Model
PSE-29;
No.
of
Pages
18
G.
Leisman
et
al.
/
Psicología
Educativa
xxx
(2015)
xxx–xxx
17
Crair,
M.
C.,
Horton,
J.
C.,
Antonini,
A.,
&
Stryker,
M.
P.
(2001).
Emergence
of
ocular
dominance
columns
in
cat
visual
cortex
by
2
weeks
of
age.
Journal
of
Comparative
Neurology,
430,
235–249.
Crawford,
M.
L.,
Pesch,
T.
W.,
&
von
Noorden,
G.
K.
(1996).
Excitatory
binocular
neurons
are
lost
following
prismatic
binocular
dissociation
in
infant
monkeys.
Behavioural
Brain
Research,
79,
227–232.
Crowley,
J.
C.,
&
Katz,
L.
C.
(2000).
Early
development
of
ocular
dominance
columns.
Science,
290,
1321–1324.
Curtiss,
S.
G.
(1977).
A
psycholinguistic
study
of
a
modern-day
“wild
child”.
New
York:
Academic
Press.
Cynader,
M.,
&
Mitchell,
D.
E.
(1980).
Prolonged
sensitivity
to
monocular
deprivation
in
dark-reared
cats.
Journal
of
Neurophysiology,
43,
1026–1040.
Daw,
N.
W.
(1997).
Critical
periods
and
strabismus:
what
questions
remain?
Optom-
etry
and
Vision
Science,
74,
690–694.
DeBello,
W.
M.,
&
Knudsen,
E.
I.
(2004).
Multiple
sites
of
adaptive
plasticity
in
the
owl’s
auditory
localization
pathway.
The
Journal
of
Neuroscience,
24,
6853–6861.
Dehaene,
S.
(1996).
The
organization
of
brain
activations
in
number
comparison:
Event-related
potentials
and
the
additive-factors
methods.
Journal
of
Cognitive
Neuroscience,
8,
47–68.
Dehaene,
S.,
&
Changeux,
J.-P.
(1993).
Development
of
elementary
numerical
abili-
ties:
A
neuronal
model.
Journal
Cognitive
Neuroscience,
5,
390–407.
Dehaene,
S.,
&
Cohen,
L.
(1995).
Towards
an
anatomical
and
functional
model
of
number
processing.
Mathematical
Cognition,
1,
83–120.
deSchonen,
S.
M.,
&
Mathivet,
E.
(1989).
First
come
first
served:
A
scenario
about
the
development
of
hemispheric
specialization
in
face
recognition
during
infancy.
European
Bulletin
of
Cognitive
Psychology,
9,
3–44.
DeValois,
R.
L.,
&
DeValois,
K.
K.
(1988).
Spatial
vision.
New
York,
NY:
Oxford
Univer-
sity
Press.
Fagiolini,
M.,
Fritschy,
J.
M.,
Low,
M.,
Mohler,
H.,
Rudolph,
U.,
&
Hensch,
T.
K.
(2004).
Specific
GABA
circuits
for
visual
cortical
plasticity.
Science,
303,
1681–1683.
Feldman,
D.
E.,
&
Knudsen,
E.
I.
(1998).
Experience-dependent
plasticity
and
the
maturation
of
glutamatergic
synapses.
Neuron,
20,
1067–1071.
Friederici,
A.
D.
(2006).
The
neural
basis
of
language
development
and
its
impair-
ment.
Neuron,
52,
941–952.
Fukuchi-Shimgori,
T.,
&
Grove,
E.
A.
(2003).
Emx2
patterns
the
neocortex
by
regu-
lating
FGF
positional
signaling.
Nature
Neuroscience,
6,
825–831.
Gazzaniga,
M.
S.
(1975).
Editorial:
Review
of
the
split
brain.
Journal
of
Neurology,
209,
75–79.
Gilchriest,
J.
(2011).
A
method
for
quantifying
visual
search
scanpath
efficiency.
Functional
Neurology,
Rehabilitation,
and
Ergonomics,
1,
181–196.
Gordon,
N.
(2000).
The
acquisition
of
a
second
language.
European
Journal
of
Pediatric
Neurology,
4,
3–7.
Greenough,
W.
T.,
Black,
J.
E.,
&
Wallace,
C.
S.
(1987).
Experience
and
brain
develop-
ment.
Child
Development,
58,
539–559.
Greenough,
W.
T.,
West,
R.
W.,
&
DeVoogd,
T.
J.
(1978).
Postsynaptic
plate
perfora-
tions:
changes
with
age
and
experience
in
the
rat.
Science,
202,
1096–1098.
Hammock,
E.
A.
D.,
&
Levitt,
P.
(2006).
The
discipline
of
neurobehavioral
devel-
opment:
the
emerging
interface
that
builds
processes
and
skills.
Human
Development,
49,
294–309.
He,
H.
Y.,
Ray,
B.,
Dennis,
K.,
&
Quinlan,
E.
M.
(2007).
Experience-dependent
recov-
ery
of
vision
following
chronic
deprivation
amblyopia.
Nature
Neuroscience,
10,
1134–1136.
Hebb,
D.
O.
(1949).
The
organization
of
behavior.
New
York:
Wiley.
Heinke,
D.,
&
Humphreys,
G.
W.
(2003).
Attention,
spatial
representation,
and
visual
neglect:
simulation
emergent
attention
and
spatial
memory
in
the
selective
attention
for
identification
model
(SAIM).
Psychological
Review,
110,
29–87.
Hellige,
J.
B.
(1993).
Hemispheric
asymmetry:
What’s
right
and
what’s
left.
Cambridge,
MA:
Harvard
University
Press.
Hellige,
J.
B.,
&
Michimata,
C.
(1989).
Categorization
versus
distance:
Hemi-
spheric
differences
for
processing
spatial
information.
Memory
and
Cognition,
17,
770–776.
Hensch,
T.
K.,
&
Stryker,
M.
P.
(2004).
Columnar
architecture
sculpted
by
GABA
circuits
in
developing
cat
visual
cortex.
Science,
303,
1678–1681.
Hubel,
D.
N.,
&
Wiesel,
T.
N.
(1970).
The
period
of
susceptibility
to
the
physiological
effects
of
unilateral
eye
closure
in
kittens.
Journal
of
Physiology,
206,
419–436.
Itard,
J.
M.
G.
(1932).
The
wild
boy
of
Aveyron.
(Translation
by
Humphrey
G.
and
Humphrey
M.
translators.
New
York:
Century,
1967.
Original
work
published
1801).
Jeffaris,
H.,
Power,
C.,
&
Hertzman,
C.
(2002).
Birth
weight,
childhood
socioeconomic
environment
and
cognitive
development
in
the
1958
British
birth
cohort
study.
British
Medical
Journal.,
325,
305–311.
Johansson,
B.
B.,
&
Belichenko,
P.
V.
(2001).
Environmental
influence
on
neuronal
and
dendritic
spine
plasticity
after
permanent
focal
brain
ischemia.
In
N.
G.
Bazan,
U.
Ito,
V.
L.
Marcheselli,
T.
Kuroiwa,
&
I.
Klatzo
(Eds.),
Maturation
phenomenon
in
cerebral
ischemia
IV
(pp.
23–77).
Berlin:
Springer.
Karmarkar,
U.
R.,
&
Dan,
Y.
(2006).
Experience-dependent
plasticity
in
adult
visual
cortex.
Neuron,
52,
577–585.
Kempermann,
G.,
Kuhn,
H.
G.,
&
Gage,
F.
H.
(1997).
More
hippocampal
neurons
in
adult
mice
living
in
an
enriched
environment.
Nature,
386,
493–495.
Keuroghlian,
A.
S.,
&
Knudsen,
E.
I.
(2007).
Adaptive
auditory
plasticity
in
developing
and
adult
animals.
Progress
in
Neurobiology,
82,
109–121.
Knudsen,
E.
I.
(2004).
Sensitive
periods
in
the
development
of
the
brain
and
behavior.
Journal
of
Cognitive
Neuroscience,
16,
1412–1425.
Komarova,
N.
L.,
&
Nowak,
M.
A.
(2001).
Natural
selection
of
the
critical
period
for
language
acquisition.
Proceedings
of
the
Royal
Society
of
London.
Series
B,
Biological
Sciences,
268,
1189–1196.
Kosslyn,
S.
M.
(1987).
Seeing
and
imaging
in
the
cerebral
hemispheres:
A
computa-
tional
approach.
Psychological
Review,
94,
148–175.
Kosslyn,
S.
M.,
Chabris,
C.,
Marsolek,
C.
J.,
&
Koenig,
O.
(1992).
Categorical
versus
coordinate
spatial
representations:
Computational
analyses
and
computer
sim-
ulations.
Journal
of
Experimental
Psychology:
Human
Perception
and
Performance,
18,
562–577.
Kosslyn,
S.
M.,
Flynn,
R.
A.,
Amsterdam,
J.
B.,
&
Wang,
G.
(1990).
Components
of
high-level
vision:
A
cognitive
neuroscience
analysis
and
accounts
of
neurological
syndromes.
Cognition,
34,
203–277.
Kosslyn,
S.
M.,
Sokolov,
M.
A.,
&
Chen,
J.
C.
(1989).
The
lateralization
of
BRIAN:
A
com-
putational
theory
and
model
of
visual
hemispheric
specialization.
In
D.
Klahr,
&
K.
Kotovsky
(Eds.),
Complex
Information
Processing
Comes
of
Age.
Hillsdale,
NJ:
Lawrence
Erlbaum
Associates.
Kuhl,
P.
K.
(2004).
Early
language
acquisition:
cracking
the
speech
code.
Nature
Reviews
Neuroscience,
5,
831–843.
Lamb,
M.
R.,
Robertson,
L.
C.,
&
Knight,
R.
T.
(1990).
Component
mechanisms
underly-
ing
the
processing
of
hierarchically
organized
patterns:
inferences
from
patients
with
unilateral
cortical
lesions.
Journal
of
Experimental
Psychology:
Learning,
Memory,
and
Cognition,
16,
471–483.
Le
Grand,
R.,
Mondloch,
C.
J.,
Maurer,
D.,
&
Brent,
H.
P.
(2001).
Neuroperception:
Early
visual
experience
and
face
processing.
Nature,
410,
890.
Leisman,
G.
(1976).
The
neurophysiology
of
visual
processing:
Implications
for
learn-
ing
disability.
In
G.
Leisman
(Ed.),
Basic
Visual
Processes
and
Learning
Disability
(pp.
124–187).
Springfield,
Il:
Charles
C.
Thomas.
Leisman,
G.
(1980).
Stability
and
flexibility
in
natural
systems.
International
Journal
of
Neuroscience,
11,
153–155.
Leisman,
G.
(2011).
Brain
networks,
plasticity,
and
functional
connectivities
inform
current
directions
in
functional
neurology
and
rehabilitation.
Functional
Neurol-
ogy,
Rehabilitation,
and
Ergonomics,
1,
315–356.
Leisman,
G.
(2012).
Children’s
language
production:
How
cognitive
neuroscience
&
industrial
engineering
can
inform
public
education
policy
and
practice.
Forum
on
Public
Policy:
A
Journal
of
the
Oxford
Roundtable,
1,
1–14.
Leisman,
G.,
Braun-Benjamin,
O.,
&
Melillo,
R.
(2014).
Cognitive-motor
interactions
of
the
basal
ganglia
in
development.
Frontiers
in
Systems
Neuroscience,
8(16).
http://dx.doi.org/10.3389/fnsys.2014.00016
Leisman,
G.,
Machado,
C.,
Melillo,
R.,
&
Mualem,
R.
(2012).
Intentionality
and
“free-will”
from
a
neurodevelopmental
perspective.
Frontiers
of
Integrative
Neu-
roscience,
6(36).
http://dx.doi.org/10.3389/fnint.2012.00036
Leisman,
G.,
Machado,
C.,
&
Mualem,
R.
(2013).
The
merging
the
neurosciences
principles
with
educational
practice
in
the
treatment
of
ADHD:
Function
spe-
cific
treatment
for
rehabilitation.
Frontiers
of
Public
Health:
Frontiers
of
Child
Health
and
Human
Development,
1(22).
http://dx.doi.org/10.3389/fpubh.2013.
00022
Leisman,
G.,
&
Melillo,
R.
(2012).
The
Development
of
the
Frontal
Lobes
in
Infancy
and
Childhood:
Asymmetry
and
the
Nature
of
Temperament
and
Adjustment.
In
A.
E.
Cavanna
(Ed.),
Frontal
Lobe:
Anatomy,
Functions
and
Injuries.
Hauppauge,
NY:
Nova
Scientific
Publishers.
Leisman,
G.,
&
Melillo,
R.
(2015).
The
plasticity
of
brain
networks
as
a
basis
for
a
science
of
nervous
system
rehabilitation.
International
Journal
of
Neurorehabili-
tation,
2(155).
http://dx.doi.org/10.4172/2376-0281.1000155
Leisman,
G.,
Rodriguez-Rojas,
R.,
Batista,
K.,
Carballo,
M.,
Morales,
J.
M.,
Iturria,
Y.,
&
Machado,
C.
(2014).
Measurement
of
axonal
fiber
connectivity
in
conscious-
ness
evaluation.
In
Proceedings
of
the
2014
IEEE
28th
convention
of
electrical
and
electronics
engineers
in
Israel.
Minneapolis,
MN:
IEEE.
http://dx.doi.org/
10.13140/2.1.4845.7289
Levitt,
P.
(2003).
Structural
and
functional
maturation
of
the
developing
primate
brain.
Journal
of
Pediatrics,
143,
S35–S45.
Majdan,
M.,
&
Shatz,
C.
J.
(2006).
Effects
of
visual
experience
on
activity-dependent
gene
regulation
in
cortex.
Nature
Neuroscience,
9,
650–659.
Marr,
D.
(1982).
Vision.
New
York,
NY:
W.
H.
Freeman
&
Co.
Marshall,
P.
J.,
Reeb,
B.
C.,
Fox,
N.
A.,
Nelson,
C.
A.,
3rd,
&
Zeanah,
C.
H.
(2008).
Effects
of
early
intervention
on
EEG
power
and
coherence
in
previously
institutionalized
children
in
Romania.
Developmental
Psychopathology,
20,
861–880.
Marsolek,
C.
J.,
Kosslyn,
S.
M.,
&
Squire,
L.
R.
(1992).
Form-specific
visualpriming
in
the
right
cerebral
hemisphere.
Journal
of
Experimental
Psycholology:
Learning
Memory
and
Cognition,
18,
492–508.
Martin,
M.
(1979).
Hemispheric
specialization
for
local
and
global
processing.
Neu-
ropsychologia,
17,
33–40.
Maya
Vetencourt,
J.
J.,
Sale,
A.,
Viegi,
A.,
Baroncelli,
L.,
De
Pasquale,
R.,
&
O’Leary,
O.
F.
(2008).
The
antidepressant
fluoxetine
restores
plasticity
in
the
adult
visual
cortex.
Science,
320,
385–388.
Melillo,
R.,
&
Leisman,
G.
(2009).
Neurobiological
Disorders
of
Childhood:
An
Evolu-
tionary
Approach.
New
York:
Springer.
Merabet,
L.
B.,
Rizzo,
J.
F.,
Amedi,
A.,
Somers,
D.
C.,
&
Pascual-Leone,
A.
(2005).
What
blindness
can
tell
us
about
seeing
again:
merging
neuroplasticity
and
neuro-
prostheses.
Nature
Reviews
Neuroscience,
6,
71–77.
Morton,
J.
B.,
&
Munakata,
Y.
(2005).
What’s
the
difference?
Contrasting
modular
and
neural
network
approaches
to
understanding
developmental
variability.
Journal
of
Development
and
Behavioral
Pediatrics,
26,
128–139.
Moyer,
R.
S.,
&
Landauer,
T.
K.
(1967).
Time
required
for
judgments
of
numerical
inequality.
Nature,
215,
1519–1520.
Munakata,
Y.,
&
Pfaffly,
J.
(2004).
Hebbian
Learning
and
development.
Developmental
Science,
7,
141–148.
Nelson,
C.
A.,
Zeanah,
C.
H.,
Fox,
N.
A.,
Marshall,
P.
J.,
Smyke,
A.,
&
Guthrie,
D.
(2007).
Cognitive
Recovery
in
socially
deprived
young
children:
The
Bucharest
Early
Intervention
Project.
Science,
318,
1937–1940.

Please
cite
this
article
in
press
as:
Leisman,
G.,
et
al.
The
neurological
development
of
the
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with
the
educational
enrichment
in
mind.
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Educativa
(2015),
http://dx.doi.org/10.1016/j.pse.2015.08.006
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PSE-29;
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Pages
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Leisman
et
al.
/
Psicología
Educativa
xxx
(2015)
xxx–xxx
Neville,
H.,
&
Bavelier,
D.
(2002).
Human
brain
plasticity:
evidence
from
sensory
deprivation
and
altered
language
experience.
Progress
in
Brain
Research,
138,
177–188.
Newport,
E.
L.,
Bavelier,
D.,
&
Neville,
H.
J.
(2001).
Critical
thinking
about
critical
periods:
Perspectives
on
a
critical
period
for
language
acquisition.
In
E.
Doupoux
(Ed.),
Language,
Brain
and
Cognitive
Development:
Essays
in
Honor
of
Jacques
Mehler
(pp.
481–502).
Cambridge,
MA:
MIT
Press.
Nieuwenhuys,
R.
(1999).
The
morphological
pattern
of
the
vertebrate
brain.
Euro-
pean
Journal
of
Morphology,
37,
81–84.
O’Leary,
D.
D.
M.,
Chou,
S.
J.,
&
Sahara,
S.
(2007).
Area
patterning
of
the
mammalian
cortex.
Neuron,
56,
252–269.
Paquet,
L.,
&
Merikle,
P.
M.
(1988).
Global
precedence
in
attended
and
nonattended
objects.
Journal
of
Experimental
Psychology:
Human
Perception
and
Performance,
14,
89–100.
Pascalis,
O.,
Scott,
L.
S.,
Kelly,
D.
J.,
Dufour,
R.
W.,
Shannon,
R.
W.,
&
Nicholson,
E.
(2005).
Plasticity
of
face
processing
in
infancy.
Proceedings
of
the
National
Academy
of
Sciences,
102,
5297–5300.
Pascual-Leone,
A.,
Amedi,
A.,
Fregni,
F.,
&
Merabet,
L.
B.
(2005).
The
plastic
human
brain
cortex.
Annual
Review
of
Neuroscience,
28,
377–401.
Plaut,
D.
C.,
&
Farah,
M.
J.
(1990).
Visual
object
representation:
Interpreting
neu-
rophysiological
data
within
a
computational
framework.
Journal
of
Cognitive
Neuroscience,
2,
320–343.
Posner,
M.
I.,
Abdullaev,
Y.
G.,
McCandliss,
B.
D.,
&
Sereno,
S.
C.
(1999).
Neuroanatomy,
circuitry
and
plasticity
of
word
reading.
Neuroreport.,
10,
R12–R23.
Posner,
M.
I.,
&
Raichle,
M.
E.
(1994).
Images
of
Mind.
New
York,
NY:
Scientific
Amer-
ican
Library.
Raichle,
M.
E.,
Fiez,
J.
A.,
Videen,
T.
O.,
MacLeod,
A.
M.,
Pardo,
J.
V.,
Fox,
P.
T.,
&
Petersen,
S.
E.
(1994).
Practice-related
changes
in
human
brain
functional
anatomy
during
nonmotor
learning.
Cerebral
Cortex,
4,
8–26.
Rakic,
P.
(2005).
Less
is
more:
progenitor
cell
death
and
cortical
size.
Nature
Neuro-
science,
8,
981–982.
Rauscher,
F.
H.,
Shaw,
G.
L.,
&
Ky,
K.
N.
(1993).
Music
and
spatial
task
performance.
Nature,
365,
611.
Robson,
A.
L.
(2002).
Critical/sensitive
periods.
In
N.
J.
Salkind
(Ed.),
Child
Develop-
ment.
(pp.
101–103).
New
York:
Macmillan.
Rosenzweig,
M.
R.
(1966).
Environmental
complexity,
cerebral
change,
and
behavior.
American
Psychologist,
21,
321–332.
Rosenzweig,
M.
R.,
&
Bennett,
E.
L.
(1996).
Psychobiology
of
plasticity:
effects
of
training
and
experience
on
brain
and
behavior.
Behavioral
Brain
Research,
78,
57–65.
Rosenzweig,
M.
R.,
Bennett,
E.
L.,
Hebert,
M.,
&
Morimoto,
H.
(1978).
Social
grouping
cannot
account
for
cerebral
effects
of
enriched
environments.
Brain
Research,
153,
563–576.
Sabatini,
M.
J.,
Ebert,
P.,
Lewis,
D.
A.,
Levitt,
P.,
Cameron,
J.
L.,
&
Mirnics,
K.
(2007).
Amygdala
gene
expression
correlates
of
social
behavior
in
monkeys
experienc-
ing
maternal
separation.
Journal
of
Neuroscience,
27,
3295–3304.
Scherf,
K.
S.,
Behrmann,
M.,
Humphreys,
K.,
&
Luna,
B.
(2007).
Visual
category-
selectivity
for
faces,
places
and
objects
emerges
along
different
developmental
trajectories.
Developmental
Science,
10,
F15–F30.
Schlaug,
G.,
Norton,
A.,
Overy,
K.,
&
Winner,
E.
(2005).
Effects
of
music
training
on
the
child’s
brain
and
cognitive
development.
Annals
of
the
New
York
Academy
of
Science,
1060,
219–230.
Seidenberg,
M.
S.,
&
Zevin,
J.
D.
(2006).
Connectionist
models
in
developmental
cog-
nitive
neuroscience:
Critical
periods
and
the
paradox
of
success.
In
Y.
Munakata,
&
M.
Johnson
(Eds.),
Processes
of
Change
in
Brain
and
Cognitive
Development.
Attention
and
Performance
XXI..
Oxford,
UK:
Oxford
University
Press.
Sergent,
J.,
&
Hellige,
J.
B.
(1986).
Role
of
input
factors
in
visual-field
asymmetries.
Brain
and
Cognition,
5,
174–199.
Skemp,
R.
R.
(1987).
Mathematics
in
the
Primary
School.
Hillsdale,
NJ:
Lawrence
Erl-
baum
Associates.
Sperry,
R.
W.
(1975).
In
search
of
the
psyche.
In
F.
G.
Worden,
J.
P.
Swazey,
&
G.
Adelman
(Eds.),
The
neurosciences:
Paths
of
discovery.
Cambridge:
MIT
Press.
Stam,
C.
J.
(2004).
Functional
connectivity
patterns
of
human
magnetoencephalo-
graphic
recordings:
a
‘small-world’
network?
Neuroscience
Letters,
355,
25–28.
Stanwood,
G.
D.,
&
Levitt,
P.
(2008).
The
effects
of
monoamines
on
the
developing
nervous
system.
In
C.
A.
Nelson,
&
M.
Luciana
(Eds.),
Handbook
of
Developmental
Cognitive
Neuroscience
(2)
(pp.
83–94).
Cambridge,
MA:
MIT
Press.
Stein,
M.,
Dierks,
T.,
Brandeis,
D.,
Wirth,
M.,
Srtik,
W.,
&
Koenig,
T.
(2006).
Plasticity
in
the
adult
language
system:
a
longitudinal
electrophysiological
study
on
second
language
learning.
Neuroimage,
33,
774–783.
Sugita,
Y.
(2008).
Face
perception
in
monkeys
reared
with
no
exposure
to
faces.
Proceedings
of
the
National
Academy
of
Sciences,
105,
394–398.
Szyf,
M.,
McGowan,
P.,
&
Meany,
M.
J.
(2008).
The
social
environment
and
the
epigenome.
Environmental
and
Molecular
Mutagenesis,
49,
46–60.
Tau,
G.
Z.,
&
Peterson,
B.
S.
(2010).
Normal
Development
of
Brain
Circuits.
Neuropsy-
chopharmacology,
35,
147–168.
Tomblin,
J.
B.,
Barker,
B.
A.,
Spencer,
L.,
Zhang,
X.,
&
Gantz,
B.
J.
(2005).
The
effect
of
age
at
cochlear
implant
initial
stimulation
on
expressive
language
growth
in
infants
and
toddlers.
Journal
of
Speech,
Language
and
Hearing
Research,
48,
853–867.
Trachtenberg,
J.
T.,
&
Stryker,
M.
P.
(2001).
Rapid
anatomical
plasticity
of
hori-
zontal
connections
in
the
developing
visual
cortex.
Journal
of
Neuroscience,
15,
3476–3482.
Treisman,
A.
M.,
&
Gelade,
G.
(1980).
A
feature
integration
theory
of
attention.
Cog-
nitive
Psychology,
12,
97–136.
Tritsch,
N.
X.,
Yi,
E.,
Gale,
J.
E.,
Glowatski,
E.,
&
Bergles,
D.
E.
(2007).
The
origin
of
spontaneous
activity
in
the
developing
auditory
system.
Nature,
450,
50–55.
Ungerleider,
L.
G.,
&
Mishkin,
M.
(1982).
Two
Cortical
Visual
Systems.
Cambridge,
Massachusetts:
MIT
Press.
von
Melchner,
L.,
Pallas,
S.
L.,
&
Sur,
M.
(2000).
Visual
behaviour
mediated
by
retinal
projections
directed
to
the
auditory
pathway.
Nature,
404,
871–876.
Ward,
N.
S.,
&
Frackowiak,
R.
S.
(2003).
Age-related
changes
in
the
neural
correlates
of
motor
performance.
Brain,
126,
873–888.
Watts,
D.
J.,
&
Strogatz,
S.
H.
(1998).
Collective
dynamics
of
‘small-world’
networks.
Nature,
393,
440–442.
Weber-Fox,
C.,
&
Neville,
H.
J.
(2001).
Sensitive
periods
differentiate
processing
of
open-
and
closed-class
words:
an
ERP
study
of
bilinguals.
Journal
of
Speech,
Language,
and
Hearing
Research,
44,
1338–1353.
Werker,
J.
F.,
&
Tees,
R.
C.
(1983).
Developmental
changes
across
childhood
in
the
perception
of
non-native
speech
sounds.
Canadian
Journal
of
Psychology,
37,
278–286.
Werker,
J.
F.,
&
Tees,
R.
C.
(2005).
Speech
perception
as
a
window
for
understand-
ing
plasticity
and
commitment
in
language
systems
of
the
brain.
Developmental
Psychobiology,
46,
233–251.
Wiesel,
T.
N.,
&
Hubel,
D.
N.
(1965).
Extent
of
recovery
from
the
effects
of
visual
deprivation
in
kittens.
Journal
of
Neurophysiology,
28,
1060–1072.
Windsor,
J.,
Glaze,
L.
E.,
&
Koga,
S.
F.
(2007).
Language
acquisition
with
limited
input:
Romanian
institution
and
foster
care.
Journal
of
Speech,
Language,
and
Hearing
Research,
50,
1365–1381.