Social re-orientation and brain development: An expanded and updated view

Abstract

Social development has been the focus of a great deal of neuroscience based research over the past decade. In this review, we focus on providing a framework for understanding how changes in facets of social development may correspond with changes in brain function. We argue that (1) distinct phases of social behavior emerge based on whether the organizing social force is the mother, peer play, peer integration, or romantic intimacy; (2) each phase is marked by a high degree of affect-driven motivation that elicits a distinct response in subcortical structures; (3) activity generated by these structures interacts with circuits in prefrontal cortex that guide executive functions, and occipital and temporal lobe circuits, which generate specific sensory and perceptual social representations. We propose that the direction, magnitude and duration of interaction among these affective, executive, and perceptual systems may relate to distinct sensitive periods across development that contribute to establishing long-term patterns of brain function and behavior.

Full text

Please
cite
this
article
in
press
as:
Nelson,
E.E.,
et
al.,
Social
re-orientation
and
brain
development:
An
expanded
and
updated
view.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.008
ARTICLE IN PRESS
G Model
DCN-336;
No.
of
Pages
10
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
Contents
lists
available
at
ScienceDirect
Developmental
Cognitive
Neuroscience
j
o
ur
nal
ho
me
pa
ge:
http://www.elsevier.com/locate/dcn
Review
Social
re-orientation
and
brain
development:
An
expanded
and
updated
view
Eric
E.
Nelsona,∗,
Johanna
M.
Jarchoa,
Amanda
E.
Guyerb
aSection
on
Development
and
Affective
Neuroscience,
National
Institute
of
Mental
Health,
Bethesda,
MD
20892,
United
States
bDepartment
of
Human
Ecology,
Center
for
Mind
and
Brain,
University
of
California-Davis,
Davis,
CA
95618,
United
States
a
r
t
i
c
l
e
i
n
f
o
Article
history:
Received
24
October
2014
Received
in
revised
form
12
June
2015
Accepted
19
December
2015
Available
online
xxx
Keywords:
Motivation
Sensitive
periods
Learning
Attention
a
b
s
t
r
a
c
t
Social
development
has
been
the
focus
of
a
great
deal
of
neuroscience
based
research
over
the
past
decade.
In
this
review,
we
focus
on
providing
a
framework
for
understanding
how
changes
in
facets
of
social
development
may
correspond
with
changes
in
brain
function.
We
argue
that
(1)
distinct
phases
of
social
behavior
emerge
based
on
whether
the
organizing
social
force
is
the
mother,
peer
play,
peer
integration,
or
romantic
intimacy;
(2)
each
phase
is
marked
by
a
high
degree
of
affect-driven
motivation
that
elicits
a
distinct
response
in
subcortical
structures;
(3)
activity
generated
by
these
structures
interacts
with
circuits
in
prefrontal
cortex
that
guide
executive
functions,
and
occipital
and
temporal
lobe
circuits,
which
generate
specific
sensory
and
perceptual
social
representations.
We
propose
that
the
direction,
magnitude
and
duration
of
interaction
among
these
affective,
executive,
and
perceptual
systems
may
relate
to
distinct
sensitive
periods
across
development
that
contribute
to
establishing
long-term
patterns
of
brain
function
and
behavior.
Published
by
Elsevier
Ltd.
This
is
an
open
access
article
under
the
CC
BY-NC-ND
license
(http://
creativecommons.org/licenses/by-nc-nd/4.0/).
Contents
1.
Introduction
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
00
2.
Beyond
adolescence:
other
periods
of
social
re-orientation
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
00
3.
Sensitive
periods
and
the
role
of
the
environment
in
canalizing
maturation
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
00
4.
Developmental
changes
in
perceptual
regions
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
00
5.
Developmental
changes
in
affective/motivational
regions
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
00
6.
Developmental
changes
in
executive
systems
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
00
7.
Summary
and
conclusions
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
00
Acknowledgements:
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
00
References
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
00
1.
Introduction
Humans
evolved
in
an
environment
where
integration
with
the
social
world
was
critical
for
survival.
Because
many
factors
moder-
ate
social
dynamics
(e.g.,
dominance
relations,
alliance
formation,
cooperation,
deception
(de
Wall,
1996)),
the
need
to
maintain
social
cohesion
under
such
complex
conditions
necessitated
the
dedica-
tion
of
substantial
neuronal
resources
to
processing
social
signals
in
the
environment
(Pinker,
2002;
Shultz
and
Dunbar,
2007;
Dunbar,
∗Corresponding
author
at:
National
Institute
of
Mental
Health,
Building
15K,
9000
Rockville
Pike,
Bethesda,
MD
20892,
United
States.
E-mail
address:
nelson@mail.nih.gov
(E.E.
Nelson).
2012).
Social
cognition,
like
many
complex
cognitive
processes,
is
not
fully
functional
at
birth,
but
rather
matures
slowly
across
devel-
opment.
One
remarkable
feature
of
human
social
behavior,
and
one
that
we
believe
is
a
key
aspect
of
normative
maturation,
is
the
dramatic
change
in
social
focus
across
development.
Social
behavior
is
the
culmination
of
input
from
many
neural
networks
that
mediate
different
aspects
of
responding
to
various
classes
of
social
stimuli
or
contexts
(Kennedy
and
Adolphs,
2012).
For
example,
distinct
brain
networks
have
been
identified
for
social
processes
such
as
motor
mimicry
(Gallese
et
al.,
2004),
joint
atten-
tion
(Happe
and
Frith,
2014),
mentalizing
(Saxe
and
Baron-Cohen,
2006),
empathy
(Singer
and
Lamm,
2009),
fairness
(Guroglu
et
al.,
2011),
social
bonding
(Insel,
2010),
and
even
deception
(Yang
et
al.,
2014).
Thus,
the
neural
mechanisms
underlying
social
behavior
do
http://dx.doi.org/10.1016/j.dcn.2015.12.008
1878-9293/Published
by
Elsevier
Ltd.
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:
Nelson,
E.E.,
et
al.,
Social
re-orientation
and
brain
development:
An
expanded
and
updated
view.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.008
ARTICLE IN PRESS
G Model
DCN-336;
No.
of
Pages
10
2
E.E.
Nelson
et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
not
reflect
a
single
“social
brain”,
but
rather
distinct
neural
circuits
that
are
implicated
in
fundamentally
different
and
dissociable
func-
tional
brain
processes
that
evolve
and
adapt
to
the
social
demands
of
a
given
environment
and
a
specific
phase
of
development.
In
a
previous
review
on
adolescent
neurodevelopment,
we
argued
that
the
brain
processes
governing
social
behavior
could
be
parsed
into
three
broad
functional
clusters
or
nodes:
the
perceptual
node;
the
affective
node;
and
the
cognitive-regulatory
node
(Nelson
et
al.,
2005).
This
provided
a
framework
for
mapping
changes
in
social
behavior
during
adolescence
onto
maturational
changes
that
take
place
in
the
brain.
The
present
paper
has
two
primary
goals.
The
first
is
to
expand
the
scope
of
this
social
re-orientation
perspec-
tive
beyond
adolescence
to
encompass
a
number
of
other
inflection
points
in
social
development.
The
second
is
to
update
the
empirical
evidence
described
in
the
original
review
and
highlight
important
gaps
that
need
to
be
addressed
in
future
work.
2.
Beyond
adolescence:
other
periods
of
social
re-orientation
We
believe
at
least
five
distinct
social
phases
occur
in
devel-
opment,
each
of
which
can
be
largely
defined
by
the
social
target
and
type
of
social
behavior
expressed.
During
infancy,
sociality
pri-
marily
consists
of
engagement
with
the
mother/caregiver.
In
the
juvenile
phase
between
weaning
and
puberty,
the
mother–infant
dyad
is
gradually
replaced
with
peer-focused
play
behavior,
while
maintaining
the
mother/caregiver
as
a
base.
In
the
adolescent
phase
between
puberty
and
full
maturity,
social
behavior
transitions
to
full
integration
with
larger
groups
of
peers.
This
transitions
into
the
reproductive/intimacy
phase,
which
is
accompanied
by
social
bond-
ing
and
reproductive
behavior.
Finally,
in
the
mature
adult
phase,
social
behavior
is
characterized
by
interactions
within
a
relatively
stable
multigenerational
group,
with
the
expression
of
intimate
relations,
and
directed
care
of
offspring.
Although
this
develop-
mental
pattern
is
not
universal,
it
is
widely
expressed
among
most
primates
and
in
many
socially
living
mammalian
species
(Hinde
and
Spencer-Booth,
1967;
Biben,
1983;
Panksepp
et
al.,
1984;
Pusey
and
Packer,
1987;
Spinka
et
al.,
2001;
Ekernas
and
Cords,
2007;
Konner,
2010;
Zhang
et
al.,
2012).
We
believe
the
conservation
of
this
pattern
of
social
phases
across
development
likely
indicates
that
the
brain
plays
an
important
role
in
shaping
maximally
adap-
tive
social
behavior.
An
important
feature
of
social
behavior
in
each
of
these
phases
is
that
within
phase
social
engagement
is
highly
motivated.
Individuals
will
energetically
seek
out
the
developmentally
appro-
priate
form
of
social
behavior
and
they
will
experience
distress
when
this
target
is
impeded.
Highly
motivated
social
engagement
is
directed
at
caregivers
in
infancy,
at
playmates
during
the
juvenile
phase,
at
integration
with
peers
during
adolescence,
at
potential
mates
during
early
adulthood
in
the
social
intimacy
phase,
and
at
offspring
and
stable
group
members
in
the
mature
adult
phase
(Hennessy
et
al.,
1995;
Rubin
et
al.,
2010;
Trezza
et
al.,
2011;
Abrams
et
al.,
2013;
Hostinar
et
al.,
2015).
This
motivation
facilitates
attention
to
and
behavioral
engagement
with
specific
social
contexts
at
developmentally
appropriate
times.
There
is
even
some
indication
that,
like
other
motivated
behaviors,
phase
specific
social
behaviors
might
be
under
homeostatic
control
with
built-in
patterns
of
compensatory
drives
and
satiation.
For
instance,
periods
of
maternal
separation
interspersed
with
brief,
rather
than
extended
maternal
contact,
elicits
heightened
distress
in
both
human
and
rodent
infants
(Shair
et
al.,
2015).
One
explanation
for
this
effect
is
that
insufficient
time
with
the
mother
leaves
a
hunger
like
state
in
the
offspring
which
is
blunted
if
the
contact
is
of
sufficient
duration
to
satiate
a
social
need.
Likewise,
compensatory
increases
in
play
behavior
have
been
observed
in
juvenile
rats
following
periods
of
social
separation,
and
the
longer
the
separation
the
more
play
behavior
is
expressed.
This
suggests
that
rats
are
motivated
to
obtain
a
certain
amount
of
play
in
this
specific
developmental
phase
(Panksepp
et
al.,
1984).
The
role
of
motivation
in
guiding
phase-specific
social
behav-
ior
is
also
suggested
by
several
studies
indicating
that
when
new
targets
of
social
behavior
emerge,
the
motivational
response
to
pre-
vious
social
categories
diminish.
For
instance,
the
duration
and
intensity
of
distress
during
maternal
separation
declines
across
infancy
for
a
number
of
species
at
the
same
time
that
play
behavior
increases
(Hinde
and
Spencer-Booth,
1967;
Barr,
1990;
Brunelli
and
Hofer,
1996;
Zhang
et
al.,
2012).
Likewise,
a
recent
study
in
humans
demonstrated
that
maternal
presence
can
blunt
the
cortisol
stress
response
in
young
pre-adolescent
children,
but
has
no
effect
on
adolescents,
who
have
shifted
their
motivated
behavior
toward
peer
integration
(Hostinar
et
al.,
2015).
Finally,
peer
group
directed
behavior
becomes
less
important
for
adolescents
and
young
adults
after
romantic
engagements
have
become
established
(Collins
et
al.,
2009).
Taken
together,
these
data
provide
evidence
that
suggests
the
focus
of
social
goal
undergoes
systematic
shifts
across
develop-
ment,
and
is
accompanied
by
changes
in
motivation
to
obtain
and
maintain
specific
types
of
social
experience.
Another
feature
of
social
behavior
within
each
developmental
phase
is
that
the
behaviors
themselves
are
malleable
and
become
adapted
to
the
local
social
environment.
This
is
perhaps
most
clearly
demonstrated
in
the
emergence
of
face
processing
biases.
A
classic
example
of
this
phenomenon
was
demonstrated
in
cross-
species
comparisons
of
face
recognition
capabilities
during
the
first
year
of
life.
While
motivation
to
attend
to
face-like
stimuli
is
present
at
birth,
experiences
during
the
early
infancy
phase
sculpt
subse-
quent
face-processing
capabilities.
For
example,
at
six
months
of
age,
humans
and
monkeys
demonstrate
equivalent
capacity
to
rec-
ognize
differences
between
individuals
of
both
species.
However,
over
the
course
of
the
following
six
months,
recognition
capabilities
for
individuals
of
one’s
own
species
increases
while
similar
capa-
bilities
for
other
species
diminishes
(Pascalis
et
al.,
2002).
Hence,
a
same-species
bias
emerges
in
perceptual
processing
as
a
conse-
quence
of
social
experience.
Similar
tuning
of
social
perception
has
also
been
characterized
for
specific
facial
features
associated
with
race,
gender,
and
age
(Scherf
and
Scott,
2012).
Each
processing
bias
appears
to
be
sculpted
by
specific
contextual
experiences
during
unique
developmental
windows.
A
similar
perceptual-tuning
process
may
also
occur
in
the
auditory
domain.
Despite
being
pre-linguistic,
infants
are
able
to
respond
selectively
to
emotional
content
in
voices
from
an
early
age
(Grossmann
et
al.,
2010),
and
recognize
identity
based
on
vocal
characteristics.
However,
these
biases
are
only
observed
when
the
speaker
uses
the
infant’s
native
language
(Johnson
et
al.,
2011).
This
suggests
that,
like
experience-tuned
biases
in
face
processing,
maturation-related
biases
in
voice
processing
are
molded
by
the
contexts
a
child
experiences
(Perrachione
et
al.,
2011).
Another
example
of
this
context-matching
aspect
of
social
behaviors
in
development
can
be
found
in
juvenile
play.
While
anthropological
studies
have
revealed
that
both
physical
and
imag-
inary
play
in
virtually
all
human
cultures
peaks
during
middle
childhood
(Konner,
2010),
the
form
that
the
play
takes
can
vary
markedly
across
cultures.
In
some
cultures,
play
involves
ritualis-
tic
dance,
in
others
it
has
more
elements
of
hunting
and
chasing,
and
in
still
others
it
involves
pretending
to
take
on
different
adult
roles
(Whiting
and
Edwards,
1992;
Rogoff,
2003;
Konner,
2010).
Similarly,
during
adolescence,
adaptation
to
the
peer
group
often
involves
adoption
of
specific
cultural
norms
expressed
by
spe-
cific
local
groups
into
which
they
are
attempting
to
integrate
(e.g.,
“goths”
vs
“jocks”)
(O’Brien
and
Bierman,
1988).
These
findings
suggest
that
while
there
is
a
developmentally
specific
aspect
in
the
timing
of
learned
social
behaviors,
the
specific
form
of
that

Please
cite
this
article
in
press
as:
Nelson,
E.E.,
et
al.,
Social
re-orientation
and
brain
development:
An
expanded
and
updated
view.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.008
ARTICLE IN PRESS
G Model
DCN-336;
No.
of
Pages
10
E.E.
Nelson
et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
3
expression
is
strongly
molded
by
the
particulars
of
environmental
conditions.
Although
specific
motivated
behaviors
and
adaptative
responses
are
unique
to
each
social
phase,
it
is
clear
that
experiences
in
each
phase
are
interdependent
insofar
as
their
effects
cascade
into
successive
periods.
The
experiences
that
occur
in
each
phase
may
impact
the
expression
of
social
behaviors
and
motivations
in
subsequent
phases.
For
instance,
experiences
with
a
caretaker
in
infancy
can
moderate
social
behavior
with
peers
in
adolescence,
and
parenting
style
with
offspring
in
adulthood
(Fleming
et
al.,
1999;
Kumsta
et
al.,
2012;
Olsavsky
et
al.,
2013).
Studies
on
play
behavior
have
indicated
that
play
experience
in
the
juvenile
period
may
promote
flexibility
and
enhanced
cortical
function
in
adult
social
contexts
(Fagen,
1981;
Himmler
et
al.,
2014)
Additionally,
competence
with
peers
in
adolescence
is
predictive
of
romantic
competence
in
adulthood
(Roisman
et
al.,
2004).
Thus,
while
each
new
social
phase
in
development
generates
a
shift
in
motivational
direction,
behavioral
re-organization,
and
adaptation
to
localized
experiences,
these
experiences
can
have
both
short
and
long-term
consequences
on
subsequent
behavior
which
is
likely
mediated
by
sustained
changes
to
underlying
neural
circuits.
3.
Sensitive
periods
and
the
role
of
the
environment
in
canalizing
maturation
Conceptualizing
maturation
in
terms
of
distinct
phases,
each
with
specific
socially
motivated
goals,
is
not
a
novel
concept.
In
fact
this
has
been
the
foundation
for
various
theories
of
human
development
for
generations
(Havighurst,
1972;
Erikson,
1993;
Masten
et
al.,
1995;
Roisman
et
al.,
2004).
Stage
based
approaches
also
appear
in
“dynamic
systems
approaches”
to
maturation,
in
which
development
is
construed
in
terms
of
system-wide
inte-
grated
adaptation
to
emerging
and
shifting
environmental
contexts
or
niches
(Gubernick
and
Alberts,
1984;
Thelen
and
Smith,
1994;
Barr
et
al.,
2009;
Johnson,
2010;
Scherf
and
Scott,
2012).
Such
approaches
tend
to
run
counter
to
reductionism
of
neuroscience,
where
mechanisms
are
often
stripped
of
contextual
function.
More
recently,
however,
integrative
models
that
incorporate
dynamic
change
across
maturation
have
begun
to
emerge
in
developmental
cognitive
neuroscience
as
well
(Crone
and
Dahl,
2012;
Scherf
and
Scott,
2012;
Scherf
et
al.,
2013;
Nelson
et
al.,
2014).
One
concept
in
developmental
neuroscience
that
resonates
with
stage
approaches
common
in
developmental
psychology
is
that
of
sensitive
or
critical
maturational
periods.
In
this
approach,
brain
and
behavioral
organization
are
considered
particularly
amenable
to
specific
types
of
information
during
specific
developmental
windows
(Nelson
and
Panksepp,
1998;
Bell
et
al.,
2010;
Meaney,
2010;
Nelson
and
Guyer,
2011;
Crone
and
Dahl,
2012;
Takesian
and
Hensch,
2013;
Nelson
et
al.,
2014).
Sensitive
period
models
of
brain
development
generally
regard
neuronal
circuit
maturation
as
a
confluence
of
endogenous
maturation
and
experiential
sculpting.
When
neural
systems
begin
to
mature,
they
are
weakly
respon-
sive
to
a
wide
scope
of
stimuli,
and
generate
diffuse
patterns
of
activation
within
and
between
other
circuits.
However,
this
pat-
tern
changes
as
experience
with
categorical
exemplars
accumulate.
Brain
responses
become
stronger,
more
efficient,
and
automatic
as
the
boundaries
of
relevant
stimuli
becomes
narrower,
while
responses
to
non-experienced
stimuli
are
dampened
(Greenough
et
al.,
1987;
Knudsen,
2004;
Stiles,
2008;
Leppanen
and
Nelson,
2009;
Werker
and
Hensch,
2015).
Interestingly,
while
the
timing
of
the
opening
and
closing
of
sensitive
windows
has
generally
been
considered
an
internally
mediated
phenomenon
with
pre-determined
timing
parameters,
under
some
conditions,
environmental
experiences
can
also
affect
the
timing
and
pace
of
the
period
of
heightened
sensitivity.
For
example,
in
animal
models,
complete
light
restriction
prolongs
the
closing
of
the
window
for
organization
of
the
visual
cortex
(Johnson,
2005),
while
restricted
physical
exposure
to
conspecifics
can
delay
the
closing
of
sexual
imprinting
(Bischof
et
al.,
2002;
Bischof,
2007)
and
delay
the
narrowing
of
face
perception
capabilities
(Sugita,
2008).
In
humans,
several
factors,
including
body
weight
and
life
stress,
influence
the
timing
of
puberty
onset
(Ellis
et
al.,
2011;
Lee
and
Styne,
2013).
Moreover,
recent
findings
suggest
that
the
timing
of
neural
circuit
organization
during
human
development
may
also
be
susceptible
to
extreme
differences
in
environmental
conditions,
whereby
brain
maturation
may
be
accelerated
in
order
to
facilitate
social
goals
in
the
face
of
adversity
(Gee
et
al.,
2013).
Although
the
extent
to
which
environmental
influences
affect
timing
parameters
of
brain
development
has
not
been
extensively
investigated,
par-
ticularly
in
humans,
the
existing
data
are
suggestive
of
this
being
an
important
factor
to
consider
in
future
studies.
Advances
in
research
on
molecular
contributions
to
develop-
mental
plasticity
may
inform
our
understanding
of
factors
that
contribute
to
the
onset
and
offset
of
sensitive
periods
during
both
early
life
and
later
phases,
as
cortical
organization
continues
to
occur
(Blakemore,
2014).
For
instance,
recent
studies
indicate
that
changes
in
the
plasticity
of
local
circuits
are
dependent
on
the
maturation
of
local
inhibitory
connections,
which
may
reg-
ulate
sensitive
periods
at
a
molecular
level
by
shifting
the
local
excitatory–inhibitory
balance
within
local
circuits
(Takesian
and
Hensch,
2013;
Werker
and
Hensch,
2015).
Isolating
regionally
spe-
cific
markers
for
heightened
plasticity
across
sensitive
periods
in
development
may
help
us
understand
how
neural
organiza-
tional
sculpting
influences
specific
social
functions
during
different
phases
of
development.
In
addition
it
may
be
possible
to
re-open
a
closed
critical
period
with
targeted
pharmacological
approaches
(Gervain
et
al.,
2013).
The
difference
between
these
mechanistic
approaches
to
developmental
periods
and
more
traditional
holistic
stage-based
approaches
in
developmental
psychology
may
be
sim-
ply
a
matter
of
semantics
or
scale.
An
important
point
of
emphasis
in
both
approaches,
however,
is
that
experiences
are
not
uniformly
effective
in
inducing
change
across
maturation.
Rather
the
degree
to
which
different
experiences
can
affect
developmental
trajectory
depends
to
a
large
extent
on
when
in
the
trajectory
the
experiences
occur.
4.
Developmental
changes
in
perceptual
regions
One
approach
to
better
understanding
how
social
behavior
emerges
across
development
is
to
characterize
the
neural
cir-
cuitry
that
mediate
cognitions,
emotions,
and
behavior
as
they
mature.
Although
some
of
the
basic
neural
systems
engaged
by
social
processing
are
functional
in
infancy,
others
come
online
only
later
in
development.
Developmental
changes
in
the
neurobiologi-
cal
substrates
engaged
by
social
stimuli
have
been
the
focus
of
many
neuroscience
studies
in
recent
years.
We
will
provide
an
overview
of
this
work
and
suggest
some
generalizations
that
can
be
made
from
this
literature
to
date.
As
described
above,
one
of
the
three
key
features
of
social
cogni-
tion
is
perceptual.
The
ability
to
categorize,
recognize,
and
appraise
a
social
stimulus
is
critical
for
generating
social
behavior.
Within
the
perceptual
stream,
developmental
studies
have
generally
found
support
for
several
patterns.
First,
social
stimuli
are
highly
salient
for
infants
and
a
remarkable
ability
to
perceptually
process
social
stimuli
is
evident
from
early
in
life.
Second,
there
is
good
evidence
for
experience
expectant
tuning
of
brain
responses
to
some
social
stimuli
which
also
occurs
early
in
life.
Third,
in
spite
of
the
fact
that
social
perception
capabilities
are
grossly
competent
in
infancy,
a
protracted
period
of
refinement
in
these
capabilities
lasts
through
at
least
late
adolescence
and
likely
into
middle
adulthood.

Please
cite
this
article
in
press
as:
Nelson,
E.E.,
et
al.,
Social
re-orientation
and
brain
development:
An
expanded
and
updated
view.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.008
ARTICLE IN PRESS
G Model
DCN-336;
No.
of
Pages
10
4
E.E.
Nelson
et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
Face
processing,
or
the
ability
to
extract
information
such
as
emotional
expression,
group
membership,
and
identity
from
a
face,
is
a
prototypical
perceptual
function.
Soon
after
birth,
infants
demonstrate
attentional
orienting
toward
faces,
facial
features
like
eyes,
and
displays
of
biologically
based
movement
(Simion
et
al.,
2008;
Leppanen
and
Nelson,
2009;
McKone
et
al.,
2012;
Senju
et
al.,
2013;
Bidet-Ildei
et
al.,
2014).
Directing
attention
to
the
eyes
may
be
a
congenital
feature
as
even
infants
of
blind
parents,
for
whom
eyes
contain
no
inherent
social
information,
tend
to
focus
on
the
eyes
(Senju
et
al.,
2013).
Infants
also
orient
toward
voices
–
especially
of
their
caretakers
(DeCasper
and
Fifer,
1980;
Grossmann
et
al.,
2010),
and
are
able
to
discern
some
emotional
content
contained
in
voices.
In
recent
years,
a
growing
body
of
research
on
face
processing
in
both
humans
and
nonhuman
primates
has
demonstrated
that
per-
ceptual
processing
occurs
in
several
distinct
areas
of
occipital
and
temporal
lobes.
This
includes
the
occipital
face
area
(OFA),
fusiform
face
area
(FFA),
and
several
other
regions
across
the
superior
tem-
poral
sulcus
(STS)
and
inferior
temporal
lobe
(IT)
(Freiwald
and
Tsao,
2010;
Goesaert
and
Op
de
Beeck,
2013;
Morin
et
al.,
2014;
Pitcher
et
al.,
2014;
Hung
et
al.,
2015;
Weiner
and
Grill-Spector,
2015).
In
general,
neural
processing
of
social
percepts
advance
along
the
temporal
lobe
in
a
step-like
fashion,
with
each
step
adding
an
increasing
degree
of
integration
of
the
various
salient
aspects
of
the
stimulus
such
as
identity,
gaze
direction,
and
emotional
expression
(Morin
et
al.,
2014).
Increased
integration
is
the
result
of
brain
regions
directly
related
to
face
processing,
but
also
other
regions
that
are
engaged
by
affective
and
goal-related
processes,
such
as
amygdala
and
prefrontal
cortex
(Fusar-Poli
et
al.,
2009;
Cohen
Kadosh
et
al.,
2010;
Freiwald
and
Tsao,
2010;
Hadj-Bouziane
et
al.,
2012;
Morin
et
al.,
2014).
As
the
neural
percept
advances
along
the
rostral
axis
of
the
temporal
lobe,
integration
results
in
a
neural
signal
that
is
increasingly
invariant,
combinatorial,
and
holistically
representative
of
the
stimulus.
Similar
regions
in
the
temporal
lobe
have
also
been
impli-
cated
in
the
perceptual
processing
of
other
key
features
of
social
stimuli
including
specific
body
parts,
speech,
and
biological
motion
(Schwarzlose
et
al.,
2005;
Galvan
et
al.,
2007;
Peelen
et
al.,
2009;
Belin
and
Grosbras,
2010;
Grossmann
et
al.,
2010;
Perrachione
et
al.,
2011)
Although
much
less
work
has
been
devoted
to
the
neural
systems
engaged
during
processing
of
social
stimuli
from
these
domains,
similar
stepwise
hierarchical
processes
may
also
occur
with
these
functions
as
well.
However,
faces
are
particu-
larly
powerful
for
human
social
interaction.
Thus,
it
is
worth
noting
that
differences
have
been
found
in
the
developmental
trajectory
between
face
and
body
processing
across
maturation
(Peelen
et
al.,
2009).
While
many
studies
have
demonstrated
that
gross
levels
of
com-
petence
in
social
perception
are
evident
very
early
in
life,
other
findings
have
shown
that
maturational
changes
continue
for
a
sub-
stantial
period
of
time.
Indeed,
in
a
study
that
included
60,000
participants
from
a
wide
age
range,
Germine
et
al.
demonstrated
that
facial
recognition
capabilities
continue
to
improve
through
age
30
(Germine
et
al.,
2011).
Other
capabilities
such
as
the
abil-
ity
to
use
configural
relations
among
facial
features
and
to
flexibly
attend
to
different
aspects
of
the
face
stimulus
also
continue
to
improve
through
the
adolescent
period
(Mondloch
et
al.,
2006;
Cohen
Kadosh
et
al.,
2013a).
Structural
imaging
studies
have
indicated
that
developmental
modifications
continue
to
occur
in
occipital
and
temporal
lobes
including
FFA,
STS,
and
TPJ
through
at
least
the
late
adolescent
period
(Blakemore
and
Mills,
2014).
Indeed,
the
temporal
lobe
is
one
of
the
latest
to
reach
maturity
(Gogtay
et
al.,
2004).
These
structural
changes
generally
reflect
relatively
large
scale
increases
in
brain
volume
and
gray
matter
intensity
during
the
first
several
years
of
life,
followed
by
a
more
gradual
reduction
in
gray
matter,
and
a
subsequent
increase
in
white
matter
in
late
juvenile
and
early
adolescent
phases
(Mills
et
al.,
2014).
Developmental
differences
in
behavioral
sensitivity
and
functional
activity
within
social
percep-
tion
regions
likewise
suggest
a
developmental
profile
of
large
scale
changes
during
early
life
followed
by
more
subtle
refinements
that
continue
into
adulthood.
Neuroimaging
studies
have
shown
that
regions
such
as
the
FFA,
STS
and
TPJ
show
selectivity
for
social
stimuli
by
early
childhood
if
not
sooner.
However,
changes
in
the
patterns
of
activation,
and
in
the
networks
associated
with
activation
of
these
perceptual
regions,
continue
to
occur
through
at
least
late
adolescence
(Carter
and
Pelphrey,
2008;
Guroglu
et
al.,
2011;
Johnson,
2011;
Pfeifer
and
Blakemore,
2012;
Pfeifer
and
Peake,
2012;
Cohen
Kadosh
et
al.,
2013b;
Blakemore
and
Mills,
2014).
For
example,
face
sensitive
regions
have
been
shown
to
increase
in
both
spatial
extent
and
response
magnitude
through
adolescence
(Aylward
et
al.,
2005;
Golarai
et
al.,
2007;
Scherf
et
al.,
2007).
These
changes
have
been
attributed
to
both
age
and
pubertal
status
(Moore
et
al.,
2012).
As
the
face
response
network
extends
to
other
regions
of
the
brain,
it
also
becomes
more
flexibly
engaged
during
different
goal
states.
For
example,
task
demands
elicit
developmental
differences
in
brain
function
during
face
processing
through
early
adulthood
(Cohen
Kadosh
et
al.,
2010,
2013c).
Whether
a
face
is
being
scanned
for
purposes
of
identity,
emotional
content,
or
feature
detection
influences
the
extent
to
which
perceptual
regions
co-activated
with
other
networks
(Cohen
Kadosh
et
al.,
2010;
Johnson,
2011;
Cohen
Kadosh
et
al.,
2013a).
During
social
perception
tasks,
increased
functional
connectivity
has
been
observed
with
non-perceptual
regions
with
increased
age
(van
den
Bos
et
al.,
2011;
Pfeifer
and
Blakemore,
2012;
Cohen
Kadosh
et
al.,
2013b).
However,
this
may
depend
in
part
on
demands
of
the
task
and
strategy
adopted
by
the
subjects
(Cohen
Kadosh
et
al.,
2010;
Moor
et
al.,
2012;
Cohen
Kadosh
et
al.,
2013c).
Scherf
and
Scott
have
suggested
that
develop-
mental
differences
in
engagement
patterns
may
reflect
differences
in
social
demand.
For
instance,
the
most
important
social
signals
during
the
juvenile
phase
may
relate
to
valence,
but
during
the
peer
integration
phase
of
adolescence
social
features
that
tap
social
cog-
nition
processes
like
dominance
and
trustworthiness
may
become
more
important
(Scherf
and
Scott,
2012).
In
summary,
the
regions
involved
in
social
perceptual
processing
such
as
the
FFA,
STS
and
TPJ
undergo
developmental
changes
that
reflect
an
innate
sensitivity
to
inherently
salient
social
stimuli.
During
infancy
and
early
childhood
these
regions
are
fine-
tuned
to
respond
to
specific
features
in
the
social
world.
Then,
over
the
course
of
a
decades
long
protracted
developmental
process,
functional
responses
in
these
brain
regions
become
increasingly
networked
and
integrated
with
other
maturing
neuronal
compo-
nents,
culminating
in
a
highly
refined,
adaptive,
and
flexible
system.
5.
Developmental
changes
in
affective/motivational
regions
In
human
neuroimaging
studies,
the
most
prominent
structures
implicated
in
social
affect
and
motivation
are
the
amygdala
and
striatum,
ventral
prefrontal
cortex,
and
anterior
insula.
The
animal
literature
has
identified
a
much
more
extensive
network
of
regions
involved
in
the
expression
of
emotional
or
motivational
behav-
ior
within
social
environments
including
the
hypothalamus,
bed
nucleus
of
the
stria
terminalis,
and
brain
stem
regions
(Panksepp,
1998).
This
discrepancy
is
most
likely
due
to
limitations
of
neu-
roimaging
techniques,
rather
than
a
fundamental
difference
in
brain
structures
mediating
social
emotions
between
animals
and
humans.
It
is
clear
that
the
amygdala
and
striatum,
in
particular,
interface
with
both
perceptual
regions
in
temporal
lobe
(Hadj-
Bouziane
et
al.,
2008;
Cassia
et
al.,
2009;
Hadj-Bouziane
et
al.,
2012;
Miyahara
et
al.,
2013;
Haeger
et
al.,
2015)
and
executive
systems
in

Please
cite
this
article
in
press
as:
Nelson,
E.E.,
et
al.,
Social
re-orientation
and
brain
development:
An
expanded
and
updated
view.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.008
ARTICLE IN PRESS
G Model
DCN-336;
No.
of
Pages
10
E.E.
Nelson
et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
5
prefrontal
cortex
(Guyer
et
al.,
2008a;
Crone
and
Dahl,
2012;
Guyer
et
al.,
2012;
Jarcho
et
al.,
2015a;
Smith
et
al.,
2015)
to
guide
social
behavior
under
varying
conditions.
In
contrast
to
the
developmen-
tal
changes
in
the
perceptual
regions,
which
can
be
characterized
as
early
large
scale
organization
followed
by
protracted
refinement,
the
most
consistently
reported
developmental
profile
of
both
the
amygdala
and
striatum
is
an
inverted
U-shaped
pattern
in
which
responsiveness
to
social
stimuli
increases
from
the
late
juvenile
phase
into
adolescence,
and
then
diminishes
again
from
adoles-
cence
into
adulthood
(Guyer
et
al.,
2008b;
Hare
et
al.,
2008;
Galvan,
2010;
Somerville
and
Casey,
2010;
Chein
et
al.,
2011;
Spear,
2011;
Scherf
et
al.,
2013;
Somerville
et
al.,
2013).
However,
this
pat-
tern
is
not
always
observed
(Spear,
2011;
Crone
and
Dahl,
2012;
Pfeifer
and
Allen,
2012).
For
example,
a
linear
decrease
has
been
reported
in
amygdala
response
to
unfamiliar
adult
faces
between
early
childhood
and
mid
adolescence
(Tottenham
et
al.,
2012),
and
several
studies
have
found
relative
increases
between
adolescents
and
adults
in
activity
of
both
striatum
and
amygdala
in
response
to
various
social
stimuli
(Ernst
et
al.,
2006;
Carter
and
Pelphrey,
2008;
Guyer
et
al.,
2009;
Bjork
et
al.,
2010;
Galvan,
2010;
Gunther
Moor
et
al.,
2010;
Scherf
et
al.,
2013;
Casey
et
al.,
2014).
Indeed
a
key
feature
of
our
revised
framework
is
that,
in
regards
to
social
processes,
the
activity
of
both
amygdala
and
striatum
vary
as
a
function
of
stimuli,
context,
and
task
demands.
Activity
from
the
subcortical,
affective,
and
motivation-related
regions
generate
intrinsic
salience
signals,
which
highlight
distinct
experiences
or
stimuli
that
are
developmentally
relevant
in
the
social
environment
(Leppanen
and
Nelson,
2009;
Scherf
et
al.,
2013;
Nelson
et
al.,
2014).
Similar
models
have
been
suggested
by
Scherf
in
relation
to
devel-
opmental
changes
in
face
processing
(Scherf
and
Scott,
2012;
Scherf
et
al.,
2013).
Salience
signals
from
subcortical
limbic
structures
may
guide
approach
and
engagement
or
withdrawal
and
avoidance
behaviors,
or
may
simply
direct
cognitive
systems
such
as
attention
and
memory
toward
developmentally
appropriate
social
cues
with-
out
inducing
affective
change
(Leppanen
and
Nelson,
2009).
These
salience
signals
also
promote
networking
between
perceptual
sys-
tems
and
other
brain
regions
related
to
motivation
and
behavioral
execution.
We
suggest
the
primary
function
of
affective
engage-
ment
in
development
is
to
influence
neural
circuit
formation
during
sensitive
organizational
periods
(Cohen
Kadosh
and
Johnson,
2007;
Cohen
Kadosh
et
al.,
2013a,
2014;
Scherf
et
al.,
2013;
Jarcho
et
al.,
2014).
From
a
more
general
perspective,
rodent
studies
have
found
a
developmental
trend
in
both
brain
activation
and
behavioral
responses
that
are
elicited
from
widespread
and
nonspecific
activa-
tion
to
more
refined
and
selective
responses
(Wiedenmayer,
2009;
Simon
and
Moghaddam,
2015).
This
change
is
probably
a
reflection
of
both
maturation
and
experience
(Wiedenmayer,
2009).
In
the
context
of
the
five
social
phases,
this
pattern
should
be
reflected
in
amplified
(and
more
extensive)
signals
to
the
mother
in
infancy,
to
peer
play
in
juvenile
development,
to
peer
accep-
tance
and
integration
in
adolescence,
and
to
romantic
or
close
intimacy
signals
in
late
adolescence.
Some
support
has
been
found
for
these
predictions
in
animal
studies,
although
this
hypothesis
has
not
been
extensively
tested
in
neuroimaging
paradigms.
For
example,
differential
patterns
of
amygdala
activity
were
evident
when
monkeys
were
separated
from
their
mother
at
1
week
vs
one
month
of
postnatal
life
(Sabatini
et
al.,
2007).
This
differen-
tial
amygdala
response
correlated
with
a
distinct
social
behavioral
profile
later
in
development
(Sabatini
et
al.,
2007),
suggesting
age
specific
tuning
of
behavioral
profiles
mediated
by
specific
amyg-
dala
response
patterns.
Another
example
of
this
phenomenon
can
be
found
in
the
rodent
literature.
In
rats,
social
play
is
a
highly
moti-
vated
experience
that
engages
many
regions
of
the
brain
including
the
striatum,
amygdala,
and
thalamus
(Trezza
et
al.,
2010;
Siviy
and
Panksepp,
2011).
The
experience
of
social
play
during
the
juvenile
phase
appears
to
affect
the
maturation
of
the
medial
prefrontal
cortex,
and
the
flexible
engagement
of
this
region
in
social
encoun-
ters
later
in
development
(Bell
et
al.,
2010;
Himmler
et
al.,
2013,
2014).
Different
types
of
play
experience
at
distinct
developmental
periods
may
therefore
differentially
shape
both
brain
and
behavior
in
adulthood.
While
the
proposed
role
of
subcortical
signals
in
sculpting
corti-
cal
patterns
in
development
is
admittedly
speculative,
there
is
some
support
for
a
similar
process
in
the
animal
learning
literature.
In
a
motivated
learning
context,
responses
in
ventral
striatum,
amyg-
dala,
and
hippocampus
have
been
shown
to
subside
as
habitual
responses
and
memory
consolidation
emerge.
Control
of
behav-
ior
and
orchestration
of
distributed
memory
networks
is
thought
to
shift
from
ventral
to
dorsal
striatum
or
from
hippocampus
to
dorsal
prefrontal
cortex
for
example
(Vanderschuren
et
al.,
2005;
Euston
et
al.,
2012;
Clark
et
al.,
2013).
If
subcortical
emotion
related
structures
serve
as
a
signal
to
guide
formation
of
interconnected
circuits,
one
might
expect
to
see
not
only
differences
in
sensitiv-
ity,
but
also
patterns
of
structural
and
functional
connectivity
with
other
regions.
While
the
systematic
investigation
of
developmen-
tal
changes
in
functional
connectivity
has
only
just
begun,
several
findings
provide
support
for
the
presence
of
age-related
changes
in
connectivity.
Age-related
increase
in
PFC
coupling
occurs
for
both
the
amygdala
and
ventral
striatum,
in
both
a
threat
assessment
and
a
social
evaluation
context
during
adolescence
(Guyer
et
al.,
2008a;
Pfeifer
et
al.,
2011;
van
den
Bos
et
al.,
2012;
Somerville
et
al.,
2013;
Spielberg
et
al.,
2014a,
2015).
This
suggests
an
impor-
tant
anatomical
mechanism
for
integrating
salient
signals
across
development.
While
there
are
a
number
of
reports
of
enhanced
coupling
between
subcortical
and
cortical
structures
in
adolescence,
there
have
also
been
some
reports
of
a
decrease
in
functional
cou-
pling
between
amygdala
and
orbitofrontal
activity
in
adolescence
(Spielberg
et
al.,
2014b).
Our
framework
suggests
that
stimulus
or
task
demands
are
another
critical
feature
impacting
differences
in
connectivity
observed
across
maturation.
Tasks
that
are
devel-
opmentally
relevant
(e.g.,
play
for
juveniles,
peer
acceptance
for
adolescents)
would
likely
result
in
transient
increases
in
coupling,
while
those
that
are
not
developmentally
relevant
may
result
in
transient
decreases;
both
would
impact
the
way
in
which
environ-
mental
stimuli
are
processed
across
development.
One
important
task
for
future
developmental
studies
is
to
iden-
tify
the
mechanisms
that
mediate
developmental
shifts
in
affective
responding.
Some
of
changes
in
affect/motivation-related
brain
responses
appear
to
be
linked
to
physiological
events
(e.g.,
the
effect
of
sex
steroids
on
brain
function)
(Forbes
and
Dahl,
2010;
Op
de
Macks
et
al.,
2011;
Crone
and
Dahl,
2012;
Scherf
et
al.,
2013;
Spielberg
et
al.,
2014a,
2015),
whereas
others
may
relate
to
differ-
ent
salient
developmental
events
like
weaning
or
transition
into
novel
social
contexts
(Sullivan
and
Holman,
2010).
An
interesting
corollary
to
the
effects
of
sex
steroids
on
limbic
system
responses
in
puberty
is
in
rat
pups,
maternal
presence,
and
suckling
in
par-
ticular,
has
an
inhibitory
effect
on
cortisol
secretions
and
the
HPA
axis.
As
the
relationship
with
the
mother
changes,
infant
rats
move
out
of
the
stress
hypo-responsive
period,
begin
secreting
cortisol
in
response
to
stressors
and
gain
the
capability
of
forming
aversive
associations
with
odors
(Sullivan
and
Holman,
2010).
This
relation-
ship
is
thought
to
be
mediated
by
the
effects
of
cortisol
on
amygdala
function
(Sullivan
and
Holman,
2010).
In
an
influential
model,
which
differs
to
some
extent
in
the
details
of
our
own
model,
but
results
in
similar
proposed
modi-
fications
of
developmental
learning,
Crone
and
Dahl
have
argued
that
in
adolescents,
pubertal
steroids
temporarily
disrupt
some
of
the
top-down
inhibitory
controls
on
subcortical
activity,
which
results
in
both
greater
emotionality
and
reduced
automaticity
in
task
completion.
This,
in
turn,
may
promote
more
flexible
and
adaptive
problem
solving
in
adolescence
(Crone
and
Dahl,
2012;

Please
cite
this
article
in
press
as:
Nelson,
E.E.,
et
al.,
Social
re-orientation
and
brain
development:
An
expanded
and
updated
view.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.008
ARTICLE IN PRESS
G Model
DCN-336;
No.
of
Pages
10
6
E.E.
Nelson
et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
Spielberg
et
al.,
2014b),
and
facilitate
individually
tailored
long
term
behavioral
solutions.
Regardless
of
the
specific
model,
there
is
a
growing
consensus
that
changes
in
subcortical
responding
to
social
stimuli
take
place
across
development,
and
these
changes
impact
and
guide
adaptive
circuit
formation
in
social
development
in
important
ways.
Several
recent
studies
have
also
found
evidence
for
“linger-
ing”
effects
of
differential
experiences
in
previous
developmental
periods
on
concurrent
responses
to
social
stimuli.
Positive
emo-
tional
parenting
in
childhood
has
been
associated
with
attenuated
growth
of
amygdala
and
accelerated
cortical
thinning
in
ventral
prefrontal
regions
in
adolescents
(Whittle
et
al.,
2014).
Functional
studies
have
found
that
negative
parenting
blunts
responses
to
peer
acceptance
in
several
regions
including
amygdala,
striatum,
and
anterior
insula,
whereas
constructive
authoritative
parenting
is
associated
with
a
blunted
caudate
response
to
peer
rejection
(Tan
et
al.,
2014;
Guyer
et
al.,
2015).
Finally,
Gee
et
al.
have
shown
that
rearing
under
conditions
of
extreme
deprivation
can
fundamentally
alter
the
relationship
between
the
amygdala
and
the
prefrontal
cor-
tex
much
later
in
development
(Gee
et
al.,
2013).
Taken
together,
while
interactions
between
emotional
systems
and
both
executive
and
perceptual
systems
play
an
active
role
in
guiding
develop-
ment
within
each
maturational
phase
(Field
et
al.,
2009),
the
responsivity
of
these
regions
themselves
is
sensitive
to
social
experiences
that
occurred
in
previous
phases
of
development
as
well.
6.
Developmental
changes
in
executive
systems
Executive
functions
(EFs),
such
as
voluntary
attention
control,
intentional
response
selection,
or
contextual
framing
of
stimuli,
serve
to
guide
behavior,
attention,
and
memory
toward
or
away
from
predetermined
goals.
EFs
interface
with
perceptual
systems
to
enhance
or
blunt
sensory
processing
of
stimuli
(Shi
et
al.,
2014).
EFs
can
also
augment
or
diminish
emotional
experience
and
associ-
ated
subcortical
signals
(Ochsner
et
al.,
2004;
Giuliani
et
al.,
2008)
Conversely,
emotional
activity
can
diminish
efficacy
of
executive
functions
when
they
are
irrelevant
to
task
demands,
or
enhance
functioning
when
they
are
congruent
with
them
(Blair
et
al.,
2007;
Robinson
et
al.,
2013).
EFs
have
a
protracted
developmental
tra-
jectory.
For
example,
improvements
in
working
memory,
selective
attention,
response
inhibition,
and
flexible
engagement
of
behav-
ior
occur
from
birth
through
the
mid
to
late
adolescent
years
(Luna
et
al.,
2004;
Crone,
2009;
Luna,
2009;
Diamond,
2013).
This
improvement
is
particularly
marked
in
the
social
domain
(Germine
et
al.,
2011;
Gur
et
al.,
2012).
A
similar
protracted
and
largely
linear
developmental
pattern
has
been
observed
specifically
in
the
ability
to
use
EF
to
modulate
emotional
responses
(McRae
et
al.,
2012).
EFs
are
instantiated
in
dorsal,
medial,
and
ventrolateral
regions
of
prefrontal
cortex
(Miller
and
Cohen,
2001).
However,
the
ventral
region
of
PFC,
which
includes
the
orbitofrontal
cortex,
plays
more
of
an
intermediary
role
between
“hot”
functions,
like
subjective
valuation,
and
“cold”
executive
functions
like
response
selection
(Rudebeck
et
al.,
2013).
Similarly
the
anterior
cingulate
cortex
(ACC),
while
not
traditionally
viewed
as
a
region
directly
involved
in
cold
executive
functions,
plays
an
important
role
in
cognitive
control,
particularly
in
the
context
of
affective
experiences,
learn-
ing,
and
error
monitoring
(Totah
et
al.,
2009;
Medalla
and
Barbas,
2010;
Shackman
et
al.,
2011;
Tamnes
et
al.,
2013).
A
number
of
studies
have
indicated
that
the
ACC
and
vPFC
play
an
important
role
in
various
aspects
of
social
interaction
(Somerville
et
al.,
2006;
Guyer
et
al.,
2008b;
Masten
et
al.,
2009,
2011;
Somerville
et
al.,
2013;
Blakemore,
2014;
Guyer
et
al.,
2015;
Jarcho
et
al.,
2015a,b).
These
regions
might
play
a
particularly
important
role
in
integrat-
ing
social
behavior
with
more
traditional
(cold)
EFs.
Both
structural
and
functional
maturational
changes
in
the
PFC
occur
into
late
adolescence
in
humans
(Gogtay
et
al.,
2004;
Crone,
2009;
Luna
et
al.,
2010;
Mills
et
al.,
2014).
From
a
structural
stand-
point,
the
PFC
matures
in
a
manner
similar
to
the
social
perception
regions
in
the
occipital
and
temporal
lobe.
In
early
life
there
is
an
increase
in
volume
and
gray
matter,
followed
by
a
more
pro-
tracted
reduction
in
gray
matter,
and
a
linear
increase
in
white
matter.
Functionally,
however,
there
may
be
important
differences
in
development
between
perceptual
and
executive
mechanisms.
As
indicated
above,
while
several
“sensitive
periods”
have
been
identified
for
social
perceptual
processes,
like
face
processing
and
language
acquisition,
no
such
windows
have
been
identified
for
EFs
in
the
social
domain
(but
see
Nelson
et
al.
(2007)
for
a
possible
exception).
Rather
than
develop
in
a
pattern
of
rapid
orienting
and
pro-
tracted
narrowing
and
refining,
EFs
tend
to
develop
in
a
manner
suggesting
slow
linear
improvement
of
function
as
the
brain
matures
with
age
(Gur
et
al.,
2012;
Tamnes
et
al.,
2013).
This
rel-
atively
slow
improvement
in
EFs
may
help
explain
the
protracted
and
gradual
ability
to
match
social
behavior
to
specific
goals
and
task
demands
(Cohen
Kadosh
et
al.,
2013b).
It
may
also
reflect
the
gradual
transition
of
motivated
behavior
across
different
phases
of
development
into
individualized
patterned
circuits
(Euston
et
al.,
2012).
Several
models
contrast
the
slow
linear
maturation
of
brain
regions
implicated
in
EF
with
the
inverted-U
shaped
maturational
pattern
of
brain
regions
implicated
in
affective
processing
in
gen-
eral
(Ernst
et
al.,
2006;
Somerville
et
al.,
2010),
and
affective
processing
in
the
social
domain
in
particular
(Steinberg,
2008;
Smith
et
al.,
2013).
These
models
have
generally
highlighted
the
transition
from
adolescence
to
adulthood
as
a
risky
period
when
behavioral
responses
are
strongly
impacted
by
affective
responses
since
EF
systems
reach
full
functional
maturation
in
adulthood.
While
there
is
much
evidence
for
a
protracted
functional
mat-
uration
of
EFs
and
a
mismatch
in
maturation
of
brain
regions
supporting
affective
and
cognitive
control
processes,
a
strict
lin-
ear
transition
from
hot
affective
to
cold
executive
control
of
social
behavior
seems
unlikely
in
the
face
of
changing
affective
response
profiles
highlighted
above.
The
fact
that
EFs
interact
with
affec-
tive
response
systems
rather
than
simply
controlling
them,
and
the
complex
maturational
profile
of
social
affect
across
development,
we
suggest
a
more
nuanced
developmental
profile
of
EFs
in
the
con-
text
of
social
behavior.
While
“cold”
EFs
are
likely
to
guide
social
behavior
to
a
greater
extent
across
development,
factors
such
as
the
specific
emotional
context
(playmates
vs
peers
integration
vs
romantic
partner),
and
whether
EFs
are
congruent
or
incongruent
with
this
context,
are
likely
to
be
important
factors
as
well.
Finally,
within
the
context
of
social
development,
one
of
the
more
consistent
findings
is
a
decrease
in
medial
and
dorsal
PFC
activity
during
the
transition
from
adolescence
to
adulthood.
These
differences
have
been
observed
in
response
to
tasks
that
involve
face
processing,
mentalizing,
accessing
social
knowledge
about
self
and
others,
and
socially
interactive
decision
making
(Blakemore,
2008;
Burnett
et
al.,
2011;
van
den
Bos
et
al.,
2011;
Blakemore,
2012;
Pfeifer
and
Blakemore,
2012).
There
is
also
some
evidence
for
an
increase
in
mPFC
and
dPFC
activation
between
childhood
and
adolescence,
which
precedes
this
decline
(Pfeifer
et
al.,
2011).
Thus,
under
many
conditions,
the
PFC
and
subcortical
structures
may
have
a
similar
inverted
U-shaped
developmental
profile.
How-
ever,
the
developmental
changes
across
these
two
domains
are
not
a
unitary
process.
In
fact,
discontinuity
or
segregation
between
prefrontal
and
subcortical
activity
may
underlie
some
aspects
of
aberrant
adolescent
behavior
(Ernst
et
al.,
2006).
As
with
both
the
perceptual
and
affective
regions
discussed
above,
the
specific
dynamics
of
EF
development,
as
well
as
the
interaction
between
PFC,
affective,
and
perceptual
regions,
are
highly
dependent
upon
stimulus,
context,
and
task
demands
(Pfeifer
and
Allen,
2012).

Please
cite
this
article
in
press
as:
Nelson,
E.E.,
et
al.,
Social
re-orientation
and
brain
development:
An
expanded
and
updated
view.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.008
ARTICLE IN PRESS
G Model
DCN-336;
No.
of
Pages
10
E.E.
Nelson
et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
7
Given
the
dynamic
nature
of
these
systems,
one
should
be
careful
not
to
overgeneralize
or
oversimplify
this
process.
From
a
functional
development
perspective,
the
decline
in
PFC
activity
during
the
transition
from
adolescence
to
adulthood
tends
to
coincide
with
a
relative
increase
in
functional
activity
in
posterior
regions
of
the
brain,
such
as
TPJ
and
pSTS
(Pfeifer
and
Blakemore,
2012).
This
may
reflect
a
general
principle
of
rostral–caudal
mat-
uration,
or
the
emergence
of
more
automaticity
in
the
execution
of
socially
oriented
behaviors
like
mentalizing
or
face
processing
functions.
Both
enhanced
skill
proficiency
and
experience
with
diverse
functional
networks
may
result
in
less
need
for
effortful
EF
engagement
across
development
(Johnson,
2001;
Pfeifer
and
Blakemore,
2012).
Learning
and
the
establishment
of
habitual
pat-
terns
of
social
behavior
are
likely
to
be
an
important
feature
of
development
(Jarcho
et
al.,
2015b),
and
this
transition
may
be
a
reflection
of
this
process.
However,
this
may
also
result
in
less
flexibility
and
compromised
ability
to
adapt
to
changing
social
con-
ditions
(Crone
and
Dahl,
2012).
7.
Summary
and
conclusions
Although
social
behavior
is
embedded
in
many
diverse
con-
texts,
there
are
some
generalizations
about
development
that
can
be
made.
First,
although
social
engagement
is
highly
motivated
across
the
lifespan,
the
object
of
social
motivation
and
the
struc-
ture
of
social
behavior
change
markedly
across
development.
In
the
early
years,
the
primary
focus
of
social
motivation
is
contact
with
the
caretaker.
Social
behaviors
then
transition
to
peer
play
in
the
early
juvenile
period,
to
peer
acceptance
and
integration
in
early
adolescence,
and
to
romantic
intimacy
in
later
adolescence.
From
the
neural
standpoint,
most
social
behavior
involves
activa-
tion
of
complex
networks.
We
have
construed
these
networks
as
belonging
to
three
functional
areas
–
those
involved
in
perceptual
and
sensory
processing
of
the
stimulus;
those
involved
in
coding
the
value
or
affective
significance
of
the
stimulus
to
the
individual;
and
those
involved
in
executive
functions
related
to
the
effortful
manipulation
of
the
stimulus
and
integration
with
other
cognitive
and
behavioral
functions.
In
the
framework
outlined
in
the
present
paper,
we
have
argued
that
changes
occur
in
all
three
of
these
regions
throughout
develop-
ment
as
nested
within
age-dependent
social
demands.
Perceptual
systems
are
inherently
responsive
to
social
stimuli,
and
postna-
tal
brain
development
proceeds
in
two
steps.
Rapid,
large
scale
tuning
occurs
in
early
postnatal
life
upon
exposure
to
species
spe-
cific
exemplars;
more
refinements
and
integration
then
continues
through
early
adulthood
(Leppanen
and
Nelson,
2009).
In
contrast,
maturation
of
brain
activation
within
the
affective
domain
cor-
responds
with
shifting
patterns
of
social
salience
and
behavioral
engagement.
Thus,
subcortical
structures
may
be
most
sensitive
to
cues
related
to
the
mother
in
infancy
and
toddlerhood,
to
peer
play
in
the
juvenile
period
to
peer
acceptance
in
early
adolescence
and
to
intimacy
in
late
adolescence,
and
ultimately
to
care
of
offspring
in
adulthood.
These
“bottom
up”
signals
may
engage
both
EFs
in
prefrontal
cortex
and
sensory/perceptual
systems
in
the
occipital
and
temporal
lobes
(though
connectivity
is
likely
bidirectional).
What
factors
contribute
to
the
transfer
of
salience
attribution
asso-
ciated
with
the
phase
shifts
are
largely
unknown,
although
steroids
and
learning
may
contribute.
Characterizing
the
factors
that
con-
tribute
to
phase
shifts
is
an
important
task
for
future
developmental
studies.
Finally,
the
executive
systems,
which
display
a
particularly
protracted
pattern
of
maturation,
exhibit
a
decrease
in
functional
activity
during
social
tasks,
particularly
across
the
transition
from
adolescence
to
adulthood.
This
decrease
may
correspond
with
the
emergence
of
habitual
and/or
flexible
patterns
of
social
behav-
ior
or
less
effortful
activation
of
social/perceptual
systems
for
the
completion
of
social
tasks.
Thus,
these
three
functional
processes
act
in
concert
to
tune
different
social
behaviors
at
different
points
in
development
contingent
on
the
demands
of
that
phase.
The
sensitive
period
model
of
social
development
may
have
important
implications
for
understanding
and
treating
psy-
chopathology.
Most
chronic
mood
and
anxiety
disorders
first
appear
during
juvenile
or
adolescent
development,
and
often
have
a
strong
social
component
(Pine
et
al.,
1998;
Kessler
et
al.,
2005;
Paus
et
al.,
2008).
Although
the
sensitive
period
framework
emphasizes
the
importance
of
affective
experiences
in
shaping
brain
networks,
this
shaping
can
be
either
adaptive
or
maladaptive
for
adult
func-
tioning.
Considerations
of
how
affective
reactivity
changes
across
development
and
is
integrated
with
changes
in
executive
and
per-
ceptual
systems
across
development
are
likely
to
have
important
treatment
implications
(Monk,
2008).
Thus
the
phase
of
social
development
and
the
timing
of
social
experiences
will
likely
play
an
important
role
in
the
efficacy
of
different
types
of
intervention.
Acknowledgements:
The
authors
wish
to
thank
Daniel
Pine
for
his
thoughtful
com-
ments
on
an
earlier
version
of
this
manuscript.
This
research
was
supported
by
the
Intramural
Research
Program
of
NIMH
(EEN
&
JMJ),
NIH
grants
R01-MH098370,
R01-
MH093605
(AEG),
and
a
William
T.
Grant
Scholars
Award
(AEG).
References
Abrams,
D.A.,
Lynch,
C.J.,
Cheng,
K.M.,
Phillips,
J.,
Supekar,
K.,
Ryali,
S.,
Uddin,
L.Q.,
Menon,
V.,
2013.
Underconnectivity
between
voice-selective
cortex
and
reward
circuitry
in
children
with
autism.
Proc.
Natl.
Acad.
Sci.
U.
S.
A.
110
(29),
12060–12065.
Aylward,
E.H.,
Park,
J.E.,
Field,
K.M.,
Parsons,
A.C.,
Richards,
T.L.,
Cramer,
S.C.,
Meltzoff,
A.N.,
2005.
Brain
activation
during
face
perception:
evidence
of
a
developmental
change.
J.
Cogn.
Neurosci.
17
(2),
308–319.
Barr,
G.A.,
Moriceau,
S.,
Shionoya,
K.,
Muzny,
K.,
Gao,
P.,
Wang,
S.,
Sullivan,
R.M.,
2009.
Transitions
in
infant
learning
are
modulated
by
dopamine
in
the
amygdala.
Nat.
Neurosci.
12
(11),
1367–1369.
Barr,
R.G.,
1990.
The
normal
crying
curve:
what
do
we
really
know?
Dev.
Med.
Child
Neurol.
32
(4),
356–362.
Belin,
P.,
Grosbras,
M.H.,
2010.
Before
speech:
cerebral
voice
processing
in
infants.
Neuron
65
(6),
733–735.
Bell,
H.C.,
Pellis,
S.M.,
Kolb,
B.,
2010.
Juvenile
peer
play
experience
and
the
development
of
the
orbitofrontal
and
medial
prefrontal
cortices.
Behav.
Brain
Res.
207
(1),
7–13.
Biben,
M.,
1983.
Comparative
ontogeny
of
social
behaviour
in
three
South
American
canids,
the
maned
wolf,
crab-eating
fox
and
bush
dog:
implications
for
sociality.
Anim.
Behav.
31,
814–826.
Bidet-Ildei,
C.,
Kitromilides,
E.,
Orliaguet,
J.P.,
Pavlova,
M.,
Gentaz,
E.,
2014.
Preference
for
point-light
human
biological
motion
in
newborns:
contribution
of
translational
displacement.
Dev.
Psychol.
50
(1),
113–120.
Bischof,
H.J.,
2007.
Behavioral
and
neuronal
aspects
of
developmental
sensitive
periods.
NeuroReport
18
(5),
461–465.
Bischof,
H.J.,
Geissler,
E.,
Rollenhagen,
A.,
2002.
Limitations
of
the
sensitive
period
for
sexual
imprinting:
neuroanatomical
and
behavioral
experiments
in
the
zebra
finch
(Taeniopygia
guttata).
Behav.
Brain
Res.
133
(2),
317–322.
Bjork,
J.M.,
Smith,
A.R.,
Chen,
G.,
Hommer,
D.W.,
2010.
Adolescents,
adults
and
rewards:
comparing
motivational
neurocircuitry
recruitment
using
fMRI.
PLoS
ONE
5
(7),
e11440.
Blair,
K.S.,
Smith,
B.W.,
Mitchell,
D.G.,
Morton,
J.,
Vythilingam,
M.,
Pessoa,
L.,
Fridberg,
D.,
Zametkin,
A.,
Sturman,
D.,
Nelson,
E.E.,
Drevets,
W.C.,
Pine,
D.S.,
Martin,
A.,
Blair,
R.J.,
2007.
Modulation
of
emotion
by
cognition
and
cognition
by
emotion.
NeuroImage
35
(1),
430–440.
Blakemore,
S.J.,
2008.
The
social
brain
in
adolescence.
Nat.
Rev.
Neurosci.
9
(4),
267–277.
Blakemore,
S.J.,
2012.
Development
of
the
social
brain
in
adolescence.
J.
R.
Soc.
Med.
105
(3),
111–116.
Blakemore,
S.J.,
2014.
Brain
development
in
adolescence.
J.
Neurol.
Neurosurg.
Psychiatry
85
(8),
e3.
Blakemore,
S.J.,
Mills,
K.L.,
2014.
Is
adolescence
a
sensitive
period
for
sociocultural
processing?
Annu.
Rev.
Psychol.
65,
187–207.
Brunelli,
S.A.,
Hofer,
M.A.,
1996.
Development
of
ultrasonic
vocalization
responses
in
genetically
heterogeneous
National
Institute
of
Health
(N:NIH)
rats:
II.
Associations
among
variables
and
behaviors.
Dev.
Psychobiol.
29
(6),
517–528.
Burnett,
S.,
Sebastian,
C.,
Cohen
Kadosh,
K.,
Blakemore,
S.J.,
2011.
The
social
brain
in
adolescence:
evidence
from
functional
magnetic
resonance
imaging
and
behavioural
studies.
Neurosci.
Biobehav.
Rev.
35
(8),
1654–1664.

Please
cite
this
article
in
press
as:
Nelson,
E.E.,
et
al.,
Social
re-orientation
and
brain
development:
An
expanded
and
updated
view.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.008
ARTICLE IN PRESS
G Model
DCN-336;
No.
of
Pages
10
8
E.E.
Nelson
et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
Carter,
E.J.,
Pelphrey,
K.A.,
2008.
Friend
or
foe?
Brain
systems
involved
in
the
perception
of
dynamic
signals
of
menacing
and
friendly
social
approaches.
Soc.
Neurosci.
3
(2),
151–163.
Casey,
B.J.,
Oliveri,
M.E.,
Insel,
T.,
2014.
A
neurodevelopmental
perspective
on
the
research
domain
criteria
(RDoC)
framework.
Biol.
Psychiatry
76
(5),
350–353.
Cassia,
V.M.,
Kuefner,
D.,
Picozzi,
M.,
Vescovo,
E.,
2009.
Early
experience
predicts
later
plasticity
for
face
processing:
evidence
for
the
reactivation
of
dormant
effects.
Psychol.
Sci.
20
(7),
853–859.
Chein,
J.,
Albert,
D.,
O’Brien,
L.,
Uckert,
K.,
Steinberg,
L.,
2011.
Peers
increase
adolescent
risk
taking
by
enhancing
activity
in
the
brain’s
reward
circuitry.
Dev.
Sci.
14
(2),
F1–F10.
Clark,
J.J.,
Collins,
A.L.,
Sanford,
C.A.,
Phillips,
P.E.,
2013.
Dopamine
encoding
of
Pavlovian
incentive
stimuli
diminishes
with
extended
training.
J.
Neurosci.
33
(8),
3526–3532.
Cohen
Kadosh,
K.,
Heathcote,
L.C.,
Lau,
J.Y.,
2014.
Age-related
changes
in
attentional
control
across
adolescence:
how
does
this
impact
emotion
regulation
capacities?
Front.
Psychol.
5,
111.
Cohen
Kadosh,
K.,
Henson,
R.N.,
Cohen
Kadosh,
R.,
Johnson,
M.H.,
Dick,
F.,
2010.
Task-dependent
activation
of
face-sensitive
cortex:
an
fMRI
adaptation
study.
J.
Cogn.
Neurosci.
22
(5),
903–917.
Cohen
Kadosh,
K.,
Johnson,
M.H.,
2007.
Developing
a
cortex
specialized
for
face
perception.
Trends
Cogn.
Sci.
11
(9),
367–369.
Cohen
Kadosh,
K.,
Johnson,
M.H.,
Dick,
F.,
Cohen
Kadosh,
R.,
Blakemore,
S.J.,
2013a.
Effects
of
age,
task
performance,
and
structural
brain
development
on
face
processing.
Cereb.
Cortex
23
(7),
1630–1642.
Cohen
Kadosh,
K.,
Johnson,
M.H.,
Henson,
R.N.,
Dick,
F.,
Blakemore,
S.J.,
2013b.
Differential
face-network
adaptation
in
children,
adolescents
and
adults.
NeuroImage
69,
11–20.
Cohen
Kadosh,
K.,
Linden,
D.E.,
Lau,
J.Y.,
2013c.
Plasticity
during
childhood
and
adolescence:
innovative
approaches
to
investigating
neurocognitive
development.
Dev.
Sci.
16
(4),
574–583.
Collins,
W.A.,
Welsh,
D.P.,
Furman,
W.,
2009.
Adolescent
romantic
relationships.
Annu.
Rev.
Psychol.
60,
631–652.
Crone,
E.A.,
2009.
Executive
functions
in
adolescence:
inferences
from
brain
and
behavior.
Dev.
Sci.
12
(6),
825–830.
Crone,
E.A.,
Dahl,
R.E.,
2012.
Understanding
adolescence
as
a
period
of
social-affective
engagement
and
goal
flexibility.
Nat.
Rev.
Neurosci.
13
(9),
636–650.
de
Wall,
F.,
1996.
Good
Natured.
The
Origins
of
Right
and
Wrong
in
Humans
and
Other
Animals.
Harvard
University
Press,
Cambridge,
MA.
DeCasper,
A.J.,
Fifer,
W.P.,
1980.
Of
human
bonding:
newborns
prefer
their
mothers’
voices.
Science
208
(4448),
1174–1176.
Diamond,
A.,
2013.
Executive
functions.
Annu.
Rev.
Psychol.
64,
135–168.
Dunbar,
R.I.,
2012.
The
social
brain
meets
neuroimaging.
Trends
Cogn.
Sci.
16
(2),
101–102.
Ekernas,
L.S.,
Cords,
M.,
2007.
Social
and
environmental
factors
influencing
natal
dispersal
in
blue
monkeys
Cercopithecus
mitis
stuhlmanni.
Anim.
Behav.
73,
1009–1020.
Ellis,
B.J.,
Shirtcliff,
E.A.,
Boyce,
W.T.,
Deardorff,
J.,
Essex,
M.J.,
2011.
Quality
of
early
family
relationships
and
the
timing
and
tempo
of
puberty:
effects
depend
on
biological
sensitivity
to
context.
Dev.
Psychopathol.
23
(1),
85–99.
Erikson,
E.H.,
1993.
Childhood
and
Society,
Reissue
edition.
WW
Norton
&
Company,
New
York,
NY.
Ernst,
M.,
Pine,
D.S.,
Hardin,
M.,
2006.
Triadic
model
of
the
neurobiology
of
motivated
behavior
in
adolescence.
Psychol.
Med.
36
(3),
299–312.
Euston,
D.R.,
Gruber,
A.J.,
McNaughton,
B.L.,
2012.
The
role
of
medial
prefrontal
cortex
in
memory
and
decision
making.
Neuron
76
(6),
1057–1070.
Fagen,
R.M.,
1981.
Animal
Play.
Oxford
University
Press,
Oxford,
UK.
Field,
T.,
Diego,
M.,
Hernandez-Reif,
M.,
2009.
Depressed
mothers’
infants
are
less
responsive
to
faces
and
voices.
Infant
Behav.
Dev.
32
(3),
239–244.
Fleming,
A.S.,
O’Day,
D.H.,
Kraemer,
G.W.,
1999.
Neurobiology
of
mother–infant
interactions:
experience
and
central
nervous
system
plasticity
across
development
and
generations.
Neurosci.
Biobehav.
Rev.
23
(5),
673–685.
Forbes,
E.E.,
Dahl,
R.E.,
2010.
Pubertal
development
and
behavior:
hormonal
activation
of
social
and
motivational
tendencies.
Brain
Cogn.
72
(1),
66–72.
Freiwald,
W.A.,
Tsao,
D.Y.,
2010.
Functional
compartmentalization
and
viewpoint
generalization
within
the
macaque
face-processing
system.
Science
330
(6005),
845–851.
Fusar-Poli,
P.,
Placentino,
A.,
Carletti,
F.,
Landi,
P.,
Allen,
P.,
Surguladze,
S.,
Benedetti,
F.,
Abbamonte,
M.,
Gasparotti,
R.,
Barale,
F.,
Perez,
J.,
McGuire,
P.,
Politi,
P.,
2009.
Functional
atlas
of
emotional
faces
processing:
a
voxel-based
meta-analysis
of
105
functional
magnetic
resonance
imaging
studies.
J.
Psychiatry
Neurosci.
34
(6),
418–432.
Gallese,
V.,
Keysers,
C.,
Rizzolatti,
G.,
2004.
A
unifying
view
of
the
basis
of
social
cognition.
Trends
Cogn.
Sci.
8
(9),
396–403.
Galvan,
A.,
2010.
Adolescent
development
of
the
reward
system.
Front.
Hum.
Neurosci.
4,
6.
Galvan,
A.,
Hare,
T.,
Voss,
H.,
Glover,
G.,
Casey,
B.J.,
2007.
Risk-taking
and
the
adolescent
brain:
who
is
at
risk?
Dev.
Sci.
10
(2),
F8–F14.
Gee,
D.G.,
Gabard-Durnam,
L.J.,
Flannery,
J.,
Goff,
B.,
Humphreys,
K.L.,
Telzer,
E.H.,
Hare,
T.A.,
Bookheimer,
S.Y.,
Tottenham,
N.,
2013.
Early
developmental
emergence
of
human
amygdala-prefrontal
connectivity
after
maternal
deprivation.
Proc.
Natl.
Acad.
Sci.
U.
S.
A.
110
(39),
15638–15643.
Germine,
L.T.,
Duchaine,
B.,
Nakayama,
K.,
2011.
Where
cognitive
development
and
aging
meet:
face
learning
ability
peaks
after
age
30.
Cognition
118
(2),
201–210.
Gervain,
J.,
Vines,
B.W.,
Chen,
L.M.,
Seo,
R.J.,
Hensch,
T.K.,
Werker,
J.F.,
Young,
A.H.,
2013.
Valproate
reopens
critical-period
learning
of
absolute
pitch.
Front.
Syst.
Neurosci.
7,
102.
Giuliani,
N.R.,
McRae,
K.,
Gross,
J.J.,
2008.
The
up-
and
down-regulation
of
amusement:
experiential,
behavioral,
and
autonomic
consequences.
Emotion
8
(5),
714–719.
Goesaert,
E.,
Op
de
Beeck,
H.P.,
2013.
Representations
of
facial
identity
information
in
the
ventral
visual
stream
investigated
with
multivoxel
pattern
analyses.
J.
Neurosci.
33
(19),
8549–8558.
Gogtay,
N.,
Giedd,
J.N.,
Lusk,
L.,
Hayashi,
K.M.,
Greenstein,
D.,
Vaituzis,
A.C.,
Nugent
3rd,
T.F.,
Herman,
D.H.,
Clasen,
L.S.,
Toga,
A.W.,
Rapoport,
J.L.,
Thompson,
P.M.,
2004.
Dynamic
mapping
of
human
cortical
development
during
childhood
through
early
adulthood.
Proc.
Natl.
Acad.
Sci.
U.
S.
A.
101
(21),
8174–8179.
Golarai,
G.,
Ghahremani,
D.G.,
Whitfield-Gabrieli,
S.,
Reiss,
A.,
Eberhardt,
J.L.,
Gabrieli,
J.D.,
Grill-Spector,
K.,
2007.
Differential
development
of
high-level
visual
cortex
correlates
with
category-specific
recognition
memory.
Nat.
Neurosci.
10
(4),
512–522.
Greenough,
W.T.,
Black,
J.E.,
Wallace,
C.S.,
1987.
Experience
and
brain
development.
Child
Dev.
58
(3),
539–559.
Grossmann,
T.,
Oberecker,
R.,
Koch,
S.P.,
Friederici,
A.D.,
2010.
The
developmental
origins
of
voice
processing
in
the
human
brain.
Neuron
65
(6),
852–858.
Gubernick,
D.J.,
Alberts,
J.R.,
1984.
A
specialization
of
taste
aversion
learning
during
suckling
and
its
weaning-associated
transformation.
Dev.
Psychobiol.
17
(6),
613–628.
Gunther
Moor,
B.,
van
Leijenhorst,
L.,
Rombouts,
S.A.,
Crone,
E.A.,
Van
der
Molen,
M.W.,
2010.
Do
you
like
me?
Neural
correlates
of
social
evaluation
and
developmental
trajectories.
Soc.
Neurosci.
5
(5–6),
461–482.
Gur,
R.C.,
Richard,
J.,
Calkins,
M.E.,
Chiavacci,
R.,
Hansen,
J.A.,
Bilker,
W.B.,
Loughead,
J.,
Connolly,
J.J.,
Qiu,
H.,
Mentch,
F.D.,
Abou-Sleiman,
P.M.,
Hakonarson,
H.,
Gur,
R.E.,
2012.
Age
group
and
sex
differences
in
performance
on
a
computerized
neurocognitive
battery
in
children
age
8–21.
Neuropsychology
26
(2),
251–265.
Guroglu,
B.,
van
den
Bos,
W.,
van
Dijk,
E.,
Rombouts,
S.A.,
Crone,
E.A.,
2011.
Dissociable
brain
networks
involved
in
development
of
fairness
considerations:
understanding
intentionality
behind
unfairness.
NeuroImage
57
(2),
634–641.
Guyer,
A.E.,
Choate,
V.R.,
Pine,
D.S.,
Nelson,
E.E.,
2012.
Neural
circuitry
underlying
affective
response
to
peer
feedback
in
adolescence.
Soc.
Cogn.
Affect
Neurosci.
7
(1),
81–92.
Guyer,
A.E.,
Jarcho,
J.M.,
Perez-Edgar,
K.,
Degnan,
K.A.,
Pine,
D.S.,
Fox,
N.A.,
Nelson,
E.E.,
2015.
Temperament
and
parenting
styles
in
early
childhood
differentially
influence
neural
response
to
peer
evaluation
in
adolescence.
J.
Abnorm.
Child
Psychol.
Guyer,
A.E.,
Lau,
J.Y.,
McClure-Tone,
E.B.,
Parrish,
J.,
Shiffrin,
N.D.,
Reynolds,
R.C.,
Chen,
G.,
Blair,
R.J.,
Leibenluft,
E.,
Fox,
N.A.,
Ernst,
M.,
Pine,
D.S.,
Nelson,
E.E.,
2008a.
Amygdala
and
ventrolateral
prefrontal
cortex
function
during
anticipated
peer
evaluation
in
pediatric
social
anxiety.
Arch.
Gen.
Psychiatry
65
(11),
1303–1312.
Guyer,
A.E.,
McClure-Tone,
E.B.,
Shiffrin,
N.D.,
Pine,
D.S.,
Nelson,
E.E.,
2009.
Probing
the
neural
correlates
of
anticipated
peer
evaluation
in
adolescence.
Child
Dev.
80
(4),
1000–1015.
Guyer,
A.E.,
Monk,
C.S.,
McClure-Tone,
E.B.,
Nelson,
E.E.,
Roberson-Nay,
R.,
Adler,
A.D.,
Fromm,
S.J.,
Leibenluft,
E.,
Pine,
D.S.,
Ernst,
M.,
2008b.
A
developmental
examination
of
amygdala
response
to
facial
expressions.
J.
Cogn.
Neurosci.
20
(9),
1565–1582.
Hadj-Bouziane,
F.,
Bell,
A.H.,
Knusten,
T.A.,
Ungerleider,
L.G.,
Tootell,
R.B.,
2008.
Perception
of
emotional
expressions
is
independent
of
face
selectivity
in
monkey
inferior
temporal
cortex.
Proc.
Natl.
Acad.
Sci.
U.
S.
A.
105
(14),
5591–5596.
Hadj-Bouziane,
F.,
Liu,
N.,
Bell,
A.H.,
Gothard,
K.M.,
Luh,
W.M.,
Tootell,
R.B.,
Murray,
E.A.,
Ungerleider,
L.G.,
2012.
Amygdala
lesions
disrupt
modulation
of
functional
MRI
activity
evoked
by
facial
expression
in
the
monkey
inferior
temporal
cortex.
Proc.
Natl.
Acad.
Sci.
U.
S.
A.
109
(52),
E3640–E3648.
Haeger,
A.,
Lee,
H.,
Fell,
J.,
Axmacher,
N.,
2015.
Selective
processing
of
buildings
and
faces
during
working
memory:
the
role
of
the
ventral
striatum.
Eur.
J.
Neurosci.
41
(4),
505–513.
Happe,
F.,
Frith,
U.,
2014.
Annual
research
review:
towards
a
developmental
neuroscience
of
atypical
social
cognition.
J.
Child
Psychol.
Psychiatry
55
(6),
553–557.
Hare,
T.A.,
Tottenham,
N.,
Galvan,
A.,
Voss,
H.U.,
Glover,
G.H.,
Casey,
B.J.,
2008.
Biological
substrates
of
emotional
reactivity
and
regulation
in
adolescence
during
an
emotional
go-nogo
task.
Biol.
Psychiatry
63
(10),
927–934.
Havighurst,
R.J.,
1972.
Developmental
Tasks
and
Education.
McKay,
New
York,
NY.
Hennessy,
M.B.,
Nigh,
C.K.,
Sims,
M.L.,
Long,
S.J.,
1995.
Plasma
cortisol
and
vocalization
responses
of
postweaning
age
guinea
pigs
to
maternal
and
sibling
separation:
evidence
for
filial
attachment
after
weaning.
Dev.
Psychobiol.
28
(2),
103–115.
Himmler,
B.T.,
Bell,
H.C.,
Horwood,
L.,
Harker,
A.,
Kolb,
B.,
Pellis,
S.M.,
2014.
The
role
of
the
medial
prefrontal
cortex
in
regulating
interanimal
coordination
of
movements.
Behav.
Neurosci.
128
(5),
603–613.
Himmler,
B.T.,
Pellis,
S.M.,
Kolb,
B.,
2013.
Juvenile
play
experience
primes
neurons
in
the
medial
prefrontal
cortex
to
be
more
responsive
to
later
experiences.
Neurosci.
Lett.
556,
42–45.
Hinde,
R.A.,
Spencer-Booth,
Y.,
1967.
The
behaviour
of
socially
living
rhesus
monkeys
in
their
first
two
and
a
half
years.
Anim.
Behav.
15
(1),
169–196.

Please
cite
this
article
in
press
as:
Nelson,
E.E.,
et
al.,
Social
re-orientation
and
brain
development:
An
expanded
and
updated
view.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.008
ARTICLE IN PRESS
G Model
DCN-336;
No.
of
Pages
10
E.E.
Nelson
et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
9
Hostinar,
C.E.,
Johnson,
A.E.,
Gunnar,
M.R.,
2015.
Parent
support
is
less
effective
in
buffering
cortisol
stress
reactivity
for
adolescents
compared
to
children.
Dev.
Sci.
18
(2),
281–297.
Hung,
C.C.,
Yen,
C.C.,
Ciuchta,
J.L.,
Papoti,
D.,
Bock,
N.A.,
Leopold,
D.A.,
Silva,
A.C.,
2015.
Functional
mapping
of
face-selective
regions
in
the
extrastriate
visual
cortex
of
the
marmoset.
J.
Neurosci.
35
(3),
1160–1172.
Insel,
T.R.,
2010.
The
challenge
of
translation
in
social
neuroscience:
a
review
of
oxytocin,
vasopressin,
and
affiliative
behavior.
Neuron
65
(6),
768–779.
Jarcho,
J.M.,
Romer,
A.L.,
Shechner,
T.,
Galvan,
A.,
Guyer,
A.E.,
Leibenluft,
E.,
Pine,
D.S.,
Nelson,
E.E.,
2015a.
Forgetting
the
best
when
predicting
the
worst:
Preliminary
observations
on
neural
circuit
function
in
adolescent
social
anxiety.
Dev.
Cogn.
Neurosci.
13,
21–31.
Jarcho,
J.M.,
Tanofsky-Kraff,
M.,
Nelson,
E.E.,
Engel,
S.G.,
Vannucci,
A.,
Field,
S.E.,
Romer,
A.L.,
Hannallah,
L.,
Brady,
S.M.,
Demidowich,
A.P.,
Shomaker,
L.B.,
Courville,
A.B.,
Pine,
D.S.,
Yanovski,
J.A.,
2015b.
Neural
activation
during
anticipated
peer
evaluation
and
laboratory
meal
intake
in
overweight
girls
with
and
without
loss
of
control
eating.
NeuroImage
108,
343–353.
Johnson,
E.K.,
Westrek,
E.,
Nazzi,
T.,
Cutler,
A.,
2011.
Infant
ability
to
tell
voices
apart
rests
on
language
experience.
Dev.
Sci.
14
(5),
1002–1011.
Johnson,
M.H.,
2001.
Functional
brain
development
in
humans.
Nat.
Rev.
Neurosci.
2
(7),
475–483.
Johnson,
M.H.,
2005.
Sensitive
periods
in
functional
brain
development:
problems
and
prospects.
Dev.
Psychobiol.
46
(3),
287–292.
Johnson,
M.H.,
2011.
Interactive
specialization:
a
domain-general
framework
for
human
functional
brain
development?
Dev.
Cogn.
Neurosci.
1
(1),
7–21.
Johnson,
T.D.,
2010.
Developmental
systems
theory.
In:
Blumberg,
M.S.,
Freeman,
J.H.,
Robinson,
S.R.
(Eds.),
Oxford
Handbook
of
Developmental
Behavioral
Neuroscience.
Oxford
University
Press,
New
York,
NY,
pp.
12–29.
Kennedy,
D.P.,
Adolphs,
R.,
2012.
The
social
brain
in
psychiatric
and
neurological
disorders.
Trends
Cogn.
Sci.
16
(11),
559–572.
Kessler,
R.C.,
Chiu,
W.T.,
Demler,
O.,
Merikangas,
K.R.,
Walters,
E.E.,
2005.
Prevalence,
severity,
and
comorbidity
of
12-month
DSM-IV
disorders
in
the
National
Comorbidity
Survey
Replication.
Arch.
Gen.
Psychiatry
62
(6),
617–627.
Knudsen,
E.I.,
2004.
Sensitive
periods
in
the
development
of
the
brain
and
behavior.
J.
Cogn.
Neurosci.
16
(8),
1412–1425.
Konner,
M.,
2010.
The
Evolution
of
Childhood:
Relationships,
Emotion
Mind.
Belknap
Press,
Cambridge,
MA.
Kumsta,
R.,
Sonuga-Barke,
E.,
Rutter,
M.,
2012.
Adolescent
callous-unemotional
traits
and
conduct
disorder
in
adoptees
exposed
to
severe
early
deprivation.
Br.
J.
Psychiatry
200
(3),
197–201.
Lee,
Y.,
Styne,
D.,
2013.
Influences
on
the
onset
and
tempo
of
puberty
in
human
beings
and
implications
for
adolescent
psychological
development.
Horm.
Behav.
64
(2),
250–261.
Leppanen,
J.M.,
Nelson,
C.A.,
2009.
Tuning
the
developing
brain
to
social
signals
of
emotions.
Nat.
Rev.
Neurosci.
10
(1),
37–47.
Luna,
B.,
2009.
Developmental
changes
in
cognitive
control
through
adolescence.
Adv.
Child
Dev.
Behav.
37,
233–278.
Luna,
B.,
Garver,
K.E.,
Urban,
T.A.,
Lazar,
N.A.,
Sweeney,
J.A.,
2004.
Maturation
of
cognitive
processes
from
late
childhood
to
adulthood.
Child
Dev.
75
(5),
1357–1372.
Luna,
B.,
Padmanabhan,
A.,
O’Hearn,
K.,
2010.
What
has
fMRI
told
us
about
the
development
of
cognitive
control
through
adolescence?
Brain
Cogn.
72
(1),
101–113.
Masten,
A.S.,
Coatsworth,
J.D.,
Neemann,
J.,
Gest,
S.D.,
Tellegen,
A.,
Garmezy,
N.,
1995.
The
structure
and
coherence
of
competence
from
childhood
through
adolescence.
Child
Dev.
66
(6),
1635–1659.
Masten,
C.L.,
Eisenberger,
N.I.,
Borofsky,
L.A.,
McNealy,
K.,
Pfeifer,
J.H.,
Dapretto,
M.,
2011.
Subgenual
anterior
cingulate
responses
to
peer
rejection:
a
marker
of
adolescents’
risk
for
depression.
Dev.
Psychopathol.
23
(1),
283–292.
Masten,
C.L.,
Eisenberger,
N.I.,
Borofsky,
L.A.,
Pfeifer,
J.H.,
McNealy,
K.,
Mazziotta,
J.C.,
Dapretto,
M.,
2009.
Neural
correlates
of
social
exclusion
during
adolescence:
understanding
the
distress
of
peer
rejection.
Soc.
Cogn.
Affect
Neurosci.
4
(2),
143–157.
McKone,
E.,
Crookes,
K.,
Jeffery,
L.,
Dilks,
D.D.,
2012.
A
critical
review
of
the
development
of
face
recognition:
experience
is
less
important
than
previously
believed.
Cogn.
Neuropsychol.
29
(1–2),
174–212.
McRae,
K.,
Gross,
J.J.,
Weber,
J.,
Robertson,
E.R.,
Sokol-Hessner,
P.,
Ray,
R.D.,
Gabrieli,
J.D.,
Ochsner,
K.N.,
2012.
The
development
of
emotion
regulation:
an
fMRI
study
of
cognitive
reappraisal
in
children,
adolescents
and
young
adults.
Soc.
Cogn.
Affect
Neurosci.
7
(1),
11–22.
Meaney,
M.J.,
2010.
Epigenetics
and
the
biological
definition
of
gene
×
environment
interactions.
Child
Dev.
81
(1),
41–79.
Medalla,
M.,
Barbas,
H.,
2010.
Anterior
cingulate
synapses
in
prefrontal
areas
10
and
46
suggest
differential
influence
in
cognitive
control.
J.
Neurosci.
30
(48),
16068–16081.
Miller,
E.K.,
Cohen,
J.D.,
2001.
An
integrative
theory
of
prefrontal
cortex
function.
Annu.
Rev.
Neurosci.
24,
167–202.
Mills,
K.L.,
Lalonde,
F.,
Clasen,
L.S.,
Giedd,
J.N.,
Blakemore,
S.J.,
2014.
Developmental
changes
in
the
structure
of
the
social
brain
in
late
childhood
and
adolescence.
Soc.
Cogn.
Affect
Neurosci.
9
(1),
123–131.
Miyahara,
M.,
Harada,
T.,
Ruffman,
T.,
Sadato,
N.,
Iidaka,
T.,
2013.
Functional
connectivity
between
amygdala
and
facial
regions
involved
in
recognition
of
facial
threat.
Soc.
Cogn.
Affect
Neurosci.
8
(2),
181–189.
Mondloch,
C.J.,
Maurer,
D.,
Ahola,
S.,
2006.
Becoming
a
face
expert.
Psychol.
Sci.
17
(11),
930–934.
Monk,
C.S.,
2008.
The
development
of
emotion-related
neural
circuitry
in
health
and
psychopathology.
Dev.
Psychopathol.
20
(4),
1231–1250.
Moor,
B.G.,
Macks,
Z.A.,
Guroglu,
B.,
Rombouts,
S.A.,
Molen,
M.W.,
Crone,
E.A.,
2012.
Neurodevelopmental
changes
of
reading
the
mind
in
the
eyes.
Soc.
Cogn.
Affect
Neurosci.
7
(1),
44–52.
Moore
3rd,
W.E.,
Pfeifer,
J.H.,
Masten,
C.L.,
Mazziotta,
J.C.,
Iacoboni,
M.,
Dapretto,
M.,
2012.
Facing
puberty:
associations
between
pubertal
development
and
neural
responses
to
affective
facial
displays.
Soc.
Cogn.
Affect
Neurosci.
7
(1),
35–43.
Morin,
E.L.,
Hadj-Bouziane,
F.,
Stokes,
M.,
Ungerleider,
L.G.,
Bell,
A.H.,
2014.
Hierarchical
encoding
of
social
cues
in
primate
inferior
temporal
cortex.
Cereb.
Cortex.
Nelson
3rd,
C.A.,
Zeanah,
C.H.,
Fox,
N.A.,
Marshall,
P.J.,
Smyke,
A.T.,
Guthrie,
D.,
2007.
Cognitive
recovery
in
socially
deprived
young
children:
the
Bucharest
Early
Intervention
Project.
Science
318
(5858),
1937–1940.
Nelson,
E.E.,
Guyer,
A.E.,
2011.
The
development
of
the
ventral
prefrontal
cortex
and
social
flexibility.
Dev.
Cogn.
Neurosci.
1
(3),
233–245.
Nelson,
E.E.,
Lau,
J.Y.,
Jarcho,
J.M.,
2014.
Growing
pains
and
pleasures:
how
emotional
learning
guides
development.
Trends
Cogn.
Sci.
18
(2),
99–108.
Nelson,
E.E.,
Leibenluft,
E.,
McClure,
E.B.,
Pine,
D.S.,
2005.
The
social
re-orientation
of
adolescence:
a
neuroscience
perspective
on
the
process
and
its
relation
to
psychopathology.
Psychol.
Med.
35
(2),
163–174.
Nelson,
E.E.,
Panksepp,
J.,
1998.
Brain
substrates
of
infant–mother
attachment:
contributions
of
opioids,
oxytocin,
and
norepinephrine.
Neurosci.
Biobehav.
Rev.
22
(3),
437–452.
O’Brien,
S.F.,
Bierman,
K.L.,
1988.
Conceptions
and
perceived
influence
of
peer
groups:
interviews
with
preadolescents
and
adolescents.
Child
Dev.
59
(5),
1360–1365.
Ochsner,
K.N.,
Ray,
R.D.,
Cooper,
J.C.,
Robertson,
E.R.,
Chopra,
S.,
Gabrieli,
J.D.,
Gross,
J.J.,
2004.
For
better
or
for
worse:
neural
systems
supporting
the
cognitive
down-
and
up-regulation
of
negative
emotion.
NeuroImage
23
(2),
483–499.
Olsavsky,
A.K.,
Telzer,
E.H.,
Shapiro,
M.,
Humphreys,
K.L.,
Flannery,
J.,
Goff,
B.,
Tottenham,
N.,
2013.
Indiscriminate
amygdala
response
to
mothers
and
strangers
after
early
maternal
deprivation.
Biol.
Psychiatry
74
(11),
853–860.
Op
de
Macks,
Z.A.,
Gunther
Moor,
B.,
Overgaauw,
S.,
Guroglu,
B.,
Dahl,
R.E.,
Crone,
E.A.,
2011.
Testosterone
levels
correspond
with
increased
ventral
striatum
activation
in
response
to
monetary
rewards
in
adolescents.
Dev.
Cogn.
Neurosci.
1
(4),
506–516.
Panksepp,
J.,
1998.
Affective
Neuroscience.
Oxford
University
Press,
New
York.
Panksepp,
J.,
Siviy,
S.,
Normansell,
L.,
1984.
The
psychobiology
of
play:
theoretical
and
methodological
perspectives.
Neurosci.
Biobehav.
Rev.
8
(4),
465–492.
Pascalis,
O.,
de
Haan,
M.,
Nelson,
C.A.,
2002.
Is
face
processing
species-specific
during
the
first
year
of
life?
Science
296
(5571),
1321–1323.
Paus,
T.,
Keshavan,
M.,
Giedd,
J.N.,
2008.
Why
do
many
psychiatric
disorders
emerge
during
adolescence?
Nat.
Rev.
Neurosci.
9
(12),
947–957.
Peelen,
M.V.,
Glaser,
B.,
Vuilleumier,
P.,
Eliez,
S.,
2009.
Differential
development
of
selectivity
for
faces
and
bodies
in
the
fusiform
gyrus.
Dev.
Sci.
12
(6),
F16–F25.
Perrachione,
T.K.,
Del
Tufo,
S.N.,
Gabrieli,
J.D.,
2011.
Human
voice
recognition
depends
on
language
ability.
Science
333
(6042),
595.
Pfeifer,
J.H.,
Allen,
N.B.,
2012.
Arrested
development?
Reconsidering
dual-systems
models
of
brain
function
in
adolescence
and
disorders.
Trends
Cogn.
Sci.
16
(6),
322–329.
Pfeifer,
J.H.,
Blakemore,
S.J.,
2012.
Adolescent
social
cognitive
and
affective
neuroscience:
past,
present,
and
future.
Soc.
Cogn.
Affect
Neurosci.
7
(1),
1–10.
Pfeifer,
J.H.,
Masten,
C.L.,
Moore
3rd,
W.E.,
Oswald,
T.M.,
Mazziotta,
J.C.,
Iacoboni,
M.,
Dapretto,
M.,
2011.
Entering
adolescence:
resistance
to
peer
influence,
risky
behavior,
and
neural
changes
in
emotion
reactivity.
Neuron
69
(5),
1029–1036.
Pfeifer,
J.H.,
Peake,
S.J.,
2012.
Self-development:
integrating
cognitive,
socioemotional,
and
neuroimaging
perspectives.
Dev.
Cogn.
Neurosci.
2
(1),
55–69.
Pine,
D.S.,
Cohen,
P.,
Gurley,
D.,
Brook,
J.,
Ma,
Y.,
1998.
The
risk
for
early-adulthood
anxiety
and
depressive
disorders
in
adolescents
with
anxiety
and
depressive
disorders.
Arch.
Gen.
Psychiatry
55
(1),
56–64.
Pinker,
S.,
2002.
The
Blank
Slate.
Penguin
Books,
New
York.
Pitcher,
D.,
Duchaine,
B.,
Walsh,
V.,
2014.
Combined
TMS
and
FMRI
reveal
dissociable
cortical
pathways
for
dynamic
and
static
face
perception.
Curr.
Biol.
24
(17),
2066–2070.
Pusey,
A.E.,
Packer,
C.,
1987.
In:
Smuts,
B.B.,
Cheney,
D.L.,
Seyfarth,
R.M.,
Wrangham,
R.W.,
Struhsaker,
T.T.
(Eds.),
Dispersal
and
Philopatry.
Primate
Societies.
University
of
Chicago
Press,
Chicago,
IL,
pp.
25–266.
Robinson,
O.J.,
Vytal,
K.,
Cornwell,
B.R.,
Grillon,
C.,
2013.
The
impact
of
anxiety
upon
cognition:
perspectives
from
human
threat
of
shock
studies.
Front.
Hum.
Neurosci.
7,
203.
Rogoff,
B.,
2003.
The
Cultural
Nature
of
Human
Development.
Oxford
University
Press,
New
York,
NY.
Roisman,
G.I.,
Masten,
A.S.,
Coatsworth,
J.D.,
Tellegen,
A.,
2004.
Salient
and
emerging
developmental
tasks
in
the
transition
to
adulthood.
Child
Dev.
75
(1),
123–133.
Rubin,
K.H.,
Root,
A.K.,
Bowker,
J.,
2010.
Parents,
peers,
and
social
withdrawal
in
childhood:
a
relationship
perspective.
New
Dir.
Child
Adolesc.
Dev.
2010
(127),
79–94.
Rudebeck,
P.H.,
Saunders,
R.C.,
Prescott,
A.T.,
Chau,
L.S.,
Murray,
E.A.,
2013.
Prefrontal
mechanisms
of
behavioral
flexibility,
emotion
regulation
and
value
updating.
Nat.
Neurosci.
16
(8),
1140–1145.
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
experiencing
maternal
separation.
J.
Neurosci.
27
(12),
3295–3304.

Please
cite
this
article
in
press
as:
Nelson,
E.E.,
et
al.,
Social
re-orientation
and
brain
development:
An
expanded
and
updated
view.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.008
ARTICLE IN PRESS
G Model
DCN-336;
No.
of
Pages
10
10
E.E.
Nelson
et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
Saxe,
R.,
Baron-Cohen,
S.,
2006.
The
neuroscience
of
theory
of
mind.
Soc.
Neurosci.
1
(3–4),
i–ix.
Scherf,
K.S.,
Behrmann,
M.,
Humphreys,
K.,
Luna,
B.,
2007.
Visual
category-selectivity
for
faces,
places
and
objects
emerges
along
different
developmental
trajectories.
Dev.
Sci.
10
(4),
F15–F30.
Scherf,
K.S.,
Scott,
L.S.,
2012.
Connecting
developmental
trajectories:
biases
in
face
processing
from
infancy
to
adulthood.
Dev.
Psychobiol.
54
(6),
643–663.
Scherf,
K.S.,
Smyth,
J.M.,
Delgado,
M.R.,
2013.
The
amygdala:
an
agent
of
change
in
adolescent
neural
networks.
Horm.
Behav.
64
(2),
298–313.
Schwarzlose,
R.F.,
Baker,
C.I.,
Kanwisher,
N.,
2005.
Separate
face
and
body
selectivity
on
the
fusiform
gyrus.
J.
Neurosci.
25
(47),
11055–11059.
Senju,
A.,
Tucker,
L.,
Pasco,
G.,
Hudry,
K.,
Elsabbagh,
M.,
Charman,
T.,
Johnson,
M.H.,
2013.
The
importance
of
the
eyes:
communication
skills
in
infants
of
blind
parents.
Proc.
Biol.
Sci.
280
(1760),
20130436.
Shackman,
A.J.,
Salomons,
T.V.,
Slagter,
H.A.,
Fox,
A.S.,
Winter,
J.J.,
Davidson,
R.J.,
2011.
The
integration
of
negative
affect,
pain
and
cognitive
control
in
the
cingulate
cortex.
Nat.
Rev.
Neurosci.
12
(3),
154–167.
Shair,
H.N.,
Rupert,
D.D.,
Rosko,
L.M.,
Hofer,
M.A.,
Myers,
M.M.,
Welch,
M.G.,
2015.
Effects
of
maternal
deprivation
and
the
duration
of
reunion
time
on
rat
pup
ultrasonic
vocalization
responses
to
isolation:
possible
implications
for
human
infant
studies.
Dev.
Psychobiol.
57
(1),
63–72.
Shi,
Y.,
Meindl,
T.,
Szameitat,
A.J.,
Muller,
H.J.,
Schubert,
T.,
2014.
Task
preparation
and
neural
activation
in
stimulus-specific
brain
regions:
an
fMRI
study
with
the
cued
task-switching
paradigm.
Brain
Cogn.
87,
39–51.
Shultz,
S.,
Dunbar,
R.I.,
2007.
The
evolution
of
the
social
brain:
anthropoid
primates
contrast
with
other
vertebrates.
Proc.
Biol.
Sci.
274
(1624),
2429–2436.
Simion,
F.,
Regolin,
L.,
Bulf,
H.,
2008.
A
predisposition
for
biological
motion
in
the
newborn
baby.
Proc.
Natl.
Acad.
Sci.
U.
S.
A.
105
(2),
809–813.
Simon,
N.W.,
Moghaddam,
B.,
2015.
Neural
processing
of
reward
in
adolescent
rodents.
Dev.
Cogn.
Neurosci.
11,
145–154.
Singer,
T.,
Lamm,
C.,
2009.
The
social
neuroscience
of
empathy.
Ann.
N.
Y.
Acad.
Sci.
1156,
81–96.
Siviy,
S.M.,
Panksepp,
J.,
2011.
In
search
of
the
neurobiological
substrates
for
social
playfulness
in
mammalian
brains.
Neurosci.
Biobehav.
Rev.
35
(9),
1821–1830.
Smith,
A.R.,
Chein,
J.,
Steinberg,
L.,
2013.
Impact
of
socio-emotional
context,
brain
development,
and
pubertal
maturation
on
adolescent
risk-taking.
Horm.
Behav.
64
(2),
323–332.
Smith,
A.R.,
Steinberg,
L.,
Strang,
N.,
Chein,
J.,
2015.
Age
differences
in
the
impact
of
peers
on
adolescents’
and
adults’
neural
response
to
reward.
Dev.
Cogn.
Neurosci.
11,
75–82.
Somerville,
L.H.,
Casey,
B.J.,
2010.
Developmental
neurobiology
of
cognitive
control
and
motivational
systems.
Curr.
Opin.
Neurobiol.
20
(2),
236–241.
Somerville,
L.H.,
Heatherton,
T.F.,
Kelley,
W.M.,
2006.
Anterior
cingulate
cortex
responds
differentially
to
expectancy
violation
and
social
rejection.
Nat.
Neurosci.
9
(8),
1007–1008.
Somerville,
L.H.,
Jones,
R.M.,
Casey,
B.J.,
2010.
A
time
of
change:
behavioral
and
neural
correlates
of
adolescent
sensitivity
to
appetitive
and
aversive
environmental
cues.
Brain
Cogn.
72
(1),
124–133.
Somerville,
L.H.,
Jones,
R.M.,
Ruberry,
E.J.,
Dyke,
J.P.,
Glover,
G.,
Casey,
B.J.,
2013.
The
medial
prefrontal
cortex
and
the
emergence
of
self-conscious
emotion
in
adolescence.
Psychol.
Sci.
24
(8),
1554–1562.
Spear,
L.P.,
2011.
Rewards,
aversions
and
affect
in
adolescence:
emerging
convergences
across
laboratory
animal
and
human
data.
Dev.
Cogn.
Neurosci.
1
(4),
392–400.
Spielberg,
J.M.,
Forbes,
E.E.,
Ladouceur,
C.D.,
Worthman,
C.M.,
Olino,
T.M.,
Ryan,
N.D.,
Dahl,
R.E.,
2014a.
Pubertal
testosterone
influences
threat-related
amygdala-orbitofrontal
cortex
coupling.
Soc.
Cogn.
Affect
Neurosci.
Spielberg,
J.M.,
Olino,
T.M.,
Forbes,
E.E.,
Dahl,
R.E.,
2014b.
Exciting
fear
in
adolescence:
does
pubertal
development
alter
threat
processing?
Dev.
Cogn.
Neurosci.
8,
86–95.
Spielberg,
J.M.,
Jarcho,
J.M.,
Dahl,
R.E.,
Pine,
D.S.,
Ernst,
M.,
Nelson,
E.E.,
2015.
Anticipation
of
peer
evaluation
in
anxious
adolescents:
divergence
in
neural
activation
and
maturation.
Soc.
Cogn.
Affect.
Neurosci.
10
(8),
1084–1091.
Spinka,
M.,
Newberry,
R.C.,
Bekoff,
M.,
2001.
Mammalian
play:
training
for
the
unexpected.
Q.
Rev.
Biol.
76
(2),
141–168.
Steinberg,
L.,
2008.
A
social
neuroscience
perspective
on
adolescent
risk-taking.
Dev.
Rev.
28
(1),
78–106.
Stiles,
J.,
2008.
The
Fundamentals
of
Brain
Development.
Harvard
University
Press,
Cambridge,
MA.
Sugita,
Y.,
2008.
Face
perception
in
monkeys
reared
with
no
exposure
to
faces.
Proc.
Natl.
Acad.
Sci.
U.
S.
A.
105
(1),
394–398.
Sullivan,
R.M.,
Holman,
P.J.,
2010.
Transitions
in
sensitive
period
attachment
learning
in
infancy:
the
role
of
corticosterone.
Neurosci.
Biobehav.
Rev.
34
(6),
835–844.
Takesian,
A.E.,
Hensch,
T.K.,
2013.
Balancing
plasticity/stability
across
brain
development.
Prog.
Brain
Res.
207,
3–34.
Tamnes,
C.K.,
Walhovd,
K.B.,
Torstveit,
M.,
Sells,
V.T.,
Fjell,
A.M.,
2013.
Performance
monitoring
in
children
and
adolescents:
a
review
of
developmental
changes
in
the
error-related
negativity
and
brain
maturation.
Dev.
Cogn.
Neurosci.
6,
1–13.
Tan,
P.Z.,
Lee,
K.H.,
Dahl,
R.E.,
Nelson,
E.E.,
Stroud,
L.J.,
Siegle,
G.J.,
Morgan,
J.K.,
Silk,
J.S.,
2014.
Associations
between
maternal
negative
affect
and
adolescent’s
neural
response
to
peer
evaluation.
Dev.
Cogn.
Neurosci.
8,
28–39.
Thelen,
E.,
Smith,
L.B.,
1994.
A
Dynamic
Systems
Approach
to
the
Development
of
Cognition
and
Action.
MIT
Press,
Cambridge,
MA.
Totah,
N.K.,
Kim,
Y.B.,
Homayoun,
H.,
Moghaddam,
B.,
2009.
Anterior
cingulate
neurons
represent
errors
and
preparatory
attention
within
the
same
behavioral
sequence.
J.
Neurosci.
29
(20),
6418–6426.
Tottenham,
N.,
Shapiro,
M.,
Telzer,
E.H.,
Humphreys,
K.L.,
2012.
Amygdala
response
to
mother.
Dev.
Sci.
15
(3),
307–319.
Trezza,
V.,
Baarendse,
P.J.,
Vanderschuren,
L.J.,
2010.
The
pleasures
of
play:
pharmacological
insights
into
social
reward
mechanisms.
Trends
Pharmacol.
Sci.
31
(10),
463–469.
Trezza,
V.,
Campolongo,
P.,
Vanderschuren,
L.J.,
2011.
Evaluating
the
rewarding
nature
of
social
interactions
in
laboratory
animals.
Dev.
Cogn.
Neurosci.
1
(4),
444–458.
van
den
Bos,
W.,
Cohen,
M.X.,
Kahnt,
T.,
Crone,
E.A.,
2012.
Striatum-medial
prefrontal
cortex
connectivity
predicts
developmental
changes
in
reinforcement
learning.
Cereb.
Cortex
22
(6),
1247–1255.
van
den
Bos,
W.,
van
Dijk,
E.,
Westenberg,
M.,
Rombouts,
S.A.,
Crone,
E.A.,
2011.
Changing
brains,
changing
perspectives:
the
neurocognitive
development
of
reciprocity.
Psychol.
Sci.
22
(1),
60–70.
Vanderschuren,
L.J.,
Di
Ciano,
P.,
Everitt,
B.J.,
2005.
Involvement
of
the
dorsal
striatum
in
cue-controlled
cocaine
seeking.
J.
Neurosci.
25
(38),
8665–8670.
Weiner,
K.S.,
Grill-Spector,
K.,
2015.
The
evolution
of
face
processing
networks.
Trends
Cogn.
Sci.
19
(5),
240–241.
Werker,
J.F.,
Hensch,
T.K.,
2015.
Critical
periods
in
speech
perception:
new
directions.
Annu.
Rev.
Psychol.
66,
173–196.
Whiting,
B.B.,
Edwards,
C.P.,
1992.
Children
of
Different
Worlds:
The
Formation
of
Social
Behavior.
Harvard
University
Press,
Cambridge,
MA.
Whittle,
S.,
Simmons,
J.G.,
Dennison,
M.,
Vijayakumar,
N.,
Schwartz,
O.,
Yap,
M.B.,
Sheeber,
L.,
Allen,
N.B.,
2014.
Positive
parenting
predicts
the
development
of
adolescent
brain
structure:
a
longitudinal
study.
Dev.
Cogn.
Neurosci.
8,
7–17.
Wiedenmayer,
C.P.,
2009.
Plasticity
of
defensive
behavior
and
fear
in
early
development.
Neurosci.
Biobehav.
Rev.
33
(3),
432–441.
Yang,
Z.,
Huang,
Z.,
Gonzalez-Castillo,
J.,
Dai,
R.,
Northoff,
G.,
Bandettini,
P.,
2014.
Using
fMRI
to
decode
true
thoughts
independent
of
intention
to
conceal.
NeuroImage
99,
80–92.
Zhang,
B.,
Suarez-Jimenez,
B.,
Hathaway,
A.,
Waters,
C.,
Vaughan,
K.,
Noble,
P.L.,
Fox,
N.A.,
Suomi,
S.J.,
Pine,
D.S.,
Nelson,
E.E.,
2012.
Developmental
changes
of
rhesus
monkeys
in
response
to
separation
from
the
mother.
Dev.
Psychobiol.
54
(8),
798–807.