The Dual Systems Model: Review, Reappraisal, and Reaffirmation

Abstract

According to the dual systems perspective, risk taking peaks during adolescence because activation of an early-maturing socioemotional-incentive processing system amplifies adolescents’ affinity for exciting, pleasurable, and novel activities at a time when a still immature cognitive control system is not yet strong enough to consistently restrain potentially hazardous impulses. We review evidence from both the psychological and neuroimaging literatures that has emerged since 2008, when this perspective was originally articulated. Although there are occasional exceptions to the general trends, studies show that, as predicted, psychological and neural manifestations of reward sensitivity increase between childhood and adolescence, peak sometime during the late teen years, and decline thereafter, whereas psychological and neural reflections of better cognitive control increase gradually and linearly throughout adolescence and into the early 20s. While some forms of real-world risky behavior peak at a later age than predicted, this likely reflects differential opportunities for risk-taking in late adolescence and young adulthood, rather than neurobiological differences that make this age group more reckless. Although it is admittedly an oversimplification, as a heuristic device, the dual systems model provides a far more accurate account of adolescent risk taking than prior models that have attributed adolescent recklessness to cognitive deficiencies.

Full text

Please
cite
this
article
in
press
as:
Shulman,
E.P.,
et
al.,
The
dual
systems
model:
Review,
reappraisal,
and
reaffirmation.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.010
ARTICLE IN PRESS
G Model
DCN-338;
No.
of
Pages
15
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
The
dual
systems
model:
Review,
reappraisal,
and
reaffirmation
Elizabeth
P.
Shulmana,∗,1,
Ashley
R.
Smithb,1,
Karol
Silvab,
Grace
Icenogleb,
Natasha
Duellb,
Jason
Cheinb,
Laurence
Steinbergb,c
aBrock
University,
Psychology
Department,
1812
Sir
Isaac
Brock
Way,
St.
Catharines,
ON
L2S
3A1,
Canada
bTemple
University,
Department
of
Psychology,
1701
N.
13th
Street,
Philadelphia,
PA
19122,
USA
cKing
Abdulaziz
University,
Abdullah
Sulayman,
Jeddah
22254,
Saudi
Arabia
a
r
t
i
c
l
e
i
n
f
o
Article
history:
Received
22
January
2015
Received
in
revised
form
17
July
2015
Accepted
19
December
2015
Available
online
xxx
Keywords:
Adolescents
Risk
taking
Dual
systems
Sensation-seeking
Reward
sensitivity
Cognitive
control
a
b
s
t
r
a
c
t
According
to
the
dual
systems
perspective,
risk
taking
peaks
during
adolescence
because
activation
of
an
early-maturing
socioemotional-incentive
processing
system
amplifies
adolescents’
affinity
for
exciting,
pleasurable,
and
novel
activities
at
a
time
when
a
still
immature
cognitive
control
system
is
not
yet
strong
enough
to
consistently
restrain
potentially
hazardous
impulses.
We
review
evidence
from
both
the
psychological
and
neuroimaging
literatures
that
has
emerged
since
2008,
when
this
perspective
was
originally
articulated.
Although
there
are
occasional
exceptions
to
the
general
trends,
studies
show
that,
as
predicted,
psychological
and
neural
manifestations
of
reward
sensitivity
increase
between
childhood
and
adolescence,
peak
sometime
during
the
late
teen
years,
and
decline
thereafter,
whereas
psychological
and
neural
reflections
of
better
cognitive
control
increase
gradually
and
linearly
throughout
adolescence
and
into
the
early
20s.
While
some
forms
of
real-world
risky
behavior
peak
at
a
later
age
than
predicted,
this
likely
reflects
differential
opportunities
for
risk-taking
in
late
adolescence
and
young
adulthood,
rather
than
neurobiological
differences
that
make
this
age
group
more
reckless.
Although
it
is
admittedly
an
oversimplification,
as
a
heuristic
device,
the
dual
systems
model
provides
a
far
more
accurate
account
of
adolescent
risk
taking
than
prior
models
that
have
attributed
adolescent
recklessness
to
cognitive
deficiencies.
©
2016
The
Authors.
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.
The
emergence
of
dual
systems
models
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The
current
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3.
Are
adolescents
particularly
prone
to
risk
taking?
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3.1.
Risk
taking
in
the
laboratory
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4.
The
development
of
sensation
seeking
and
reward
sensitivity
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4.1.
Sensation
seeking
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4.2.
Behavioral
manifestations
of
reward
sensitivity
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4.3.
Neuroimaging
of
reward
sensitivity
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5.
The
development
of
self-regulation
and
cognitive
control
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Self-reported
impulsivity
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5.2.
Behavioral
measures
of
self-regulation
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5.3.
Neuroimaging
of
cognitive
control.
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6.
Is
risk
taking
during
adolescence
related
to
heightened
reward
sensitivity
and
immature
cognitive
control?
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Unresolved
questions
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future
directions
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8.
Concluding
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References
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∗Corresponding
author.
E-mail
addresses:
eshulman@brocku.ca
(E.P.
Shulman),
tuc69946@temple.edu
(A.R.
Smith),
karol.silva@temple.edu
(K.
Silva),
grace.icenogle@temple.edu
(G.
Icenogle),
ntduell@temple.edu
(N.
Duell),
jchein@temple.edu
(J.
Chein),
lds@temple.edu
(L.
Steinberg).
1These
authors
made
equal
contributions
to
the
article
and
should
be
considered
co-first
authors.
http://dx.doi.org/10.1016/j.dcn.2015.12.010
1878-9293/©
2016
The
Authors.
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:
Shulman,
E.P.,
et
al.,
The
dual
systems
model:
Review,
reappraisal,
and
reaffirmation.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.010
ARTICLE IN PRESS
G Model
DCN-338;
No.
of
Pages
15
2
E.P.
Shulman
et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
Social
scientists
and
casual
observers
of
human
development
have
long
noted
that
the
transitional
period
between
childhood
and
adulthood
is
a
time
of
heightened
risk-taking.
Indeed,
despite
the
relative
absence
of
illness
and
disease
during
this
period,
rates
of
morbidity
and
mortality
increase
substantially
in
adoles-
cence,
largely
due
to
risk
taking.
The
question
of
why
adolescents
seem
predisposed
toward
recklessness
is
age-old;
however,
work
in
the
field
of
developmental
psychology,
and
more
recently,
developmental
neuroscience,
has
provided
new
insights
into
the
phenomenon.
For
many
years
psychologists
had
attempted
to
explain
ado-
lescent
recklessness
as
a
consequence
of
cognitive
deficiencies
in
young
people’s
thinking,
including
irrationality,
poor
information
processing,
and
ignorance
about
risk.
As
we
have
noted
in
previ-
ous
publications
(e.g.,
Steinberg,
2008),
these
accounts
have
been
largely
undermined
by
available
evidence.
Generally
speaking,
by
age
15
or
so,
adolescents
perform
as
well
as
adults
on
tasks
measur-
ing
logical
reasoning,
information
processing,
and
risk
perception.
1.
The
emergence
of
dual
systems
models
About
a
decade
ago,
the
budding
field
of
developmental
cog-
nitive
neuroscience
began
to
provide
insight
into
how
patterns
of
brain
development
might
explain
aspects
of
adolescent
decision-
making
(see,
e.g.
Dahl,
2004).
In
2008,
our
lab
at
Temple
University
(Steinberg,
2008;
Steinberg
et
al.,
2008)
and
Casey’s
lab
at
Cor-
nell
(Casey
et
al.,
2008)
simultaneously
proposed
similar
variations
of
a
“dual
systems”
account
of
adolescent
decision-making.
This
perspective
attributes
adolescents’
vulnerability
to
risky,
often
reckless,
behavior
in
part
to
the
divergent
developmental
courses
of
two
brain
systems:
one
(localized
in
the
striatum,
as
well
as
the
medial
and
orbital
prefrontal
cortices)
that
increases
motivation
to
pursue
rewards
and
one
(encompassing
the
lateral
prefrontal,
lateral
parietal,
and
anterior
cingulate
cortices)
that
restrains
imprudent
impulses
(see
e.g.,
Casey
et
al.,
2008;
Duckworth
and
Steinberg,
2015;
Evans
and
Stanovich,
2013;
Luna
and
Wright,
2016;
Metcalfe
and
Mischel,
1999;
Steinberg,
2008).
Specifically,
it
proposes
that
risk-taking
behaviors
peak
during
adolescence
because
activation
of
an
early-maturing
incentive-processing
sys-
tem
(the
“socioemotional
system”)
amplifies
adolescents’
affinity
for
exciting,
novel,
and
risky
activities,
while
a
countervailing,
but
slower
to
mature,
“cognitive
control”
system
is
not
yet
far
enough
along
in
its
development
to
consistently
restrain
potentially
haz-
ardous
impulses.
Several
variations
on
this
dual
systems
model
have
been
pro-
posed.
The
version
that
guides
our
work
(Steinberg,
2008)
is
very
similar
to
that
proposed
by
Casey
et
al.
(2008).
Both
conceive
of
a
slowly
developing
cognitive
control
system,
which
contin-
ues
to
mature
through
late
adolescence.
However,
whereas
we
propose
that
the
socioemotional
system
follows
an
inverted-U
shaped
developmental
course,
such
that
responsiveness
to
reward
increases
in
early
adolescence
and
declines
in
early
adulthood,
Casey
et
al.
have
portrayed
the
socioemotional
system
as
increas-
ing
in
arousability
until
mid-adolescence,
at
which
point
it
reaches
a
plateau,
remaining
at
this
level
into
adulthood.
Furthermore,
our
version
of
the
dual
systems
model
posits
that
the
decline
in
socioe-
motional
arousability
occurs
independently
of
the
development
of
the
control
system,
whereas
Casey
et
al.’s
model
proposes
that
the
strengthening
of
the
cognitive
control
system
causes
the
socioe-
motional
system
to
become
less
arousable.
More
recently,
Luna
and
Wright
(2016)
have
proposed
another
variation
on
the
dual
systems
model
(the
“driven
dual
systems”
model),
which,
like
our
model,
hypothesizes
an
inverted-U
shaped
trajectory
of
socioemo-
tional
arousability,
but,
unlike
our
model,
hypothesizes
a
trajectory
of
cognitive
control
that
plateaus
in
mid-adolescence
rather
than
continuing
to
increase
into
the
20s,
as
suggested
by
us
and
by
Casey
et
al.
In
a
similar
vein,
Luciana
and
Collins
(2012)
endorse
a
model
that
emphasizes
the
role
of
a
hyperactive
socioemotional
system
(“subcortical
limbic-striatal
systems”
in
their
terminology)
under-
mining
the
regulatory
ability
of
the
cognitive
control
system
(the
“prefrontal
executive
system”)
resulting
in
greater
risk-taking
dur-
ing
adolescence.
Like
Luna
and
Wright,
Luciana
and
Collins
argue
that
the
development
of
cognitive
control
is
complete
by
mid-
adolescence,
as
evidenced
by
adolescents’
adult-like
performance
on
non-affective
measures
of
cognitive
capacity.
Fig.
1
illustrates
the
similarities
and
differences
between
these
versions
of
the
dual
systems
model.
Another
perspective,
Ernst’s
(2014)
triadic
model,
expands
on
the
dual
systems
concept
by
hypothesizing
that
a
third
brain
system—one
responsible
for
emotional
intensity
and
avoidance,
anchored
in
the
amygdala—is
also
important
for
understanding
the
developmental
differences
in
“motivated
behavior.”
With
respect
to
the
type
of
reward-seeking
risky
behavior
that
the
dual
systems
models
seek
to
explain,
Ernst
(2014)
speculates
that
this
emo-
tion/avoidance
system
may
serve
to
boost
impulsive
decisions
in
adolescence
by
amplifying
the
perceived
cost
of
delay.
She
also
proposes
that
this
system
may
become
hypoactive—dampening
avoidance
impulses—in
the
face
of
a
potential
reward
that
acti-
vates
the
socioemotional
system.
While
this
model
is
intuitively
appealing,
there
is
not
much
evidence
to
date
indicating
that
the
emotion/avoidance
system
and
its
developmental
trajectory
help
to
explain
heightened
levels
of
risk
taking
in
adolescence.
Also,
the
role
of
the
amygdala
in
decision-making
is
not
yet
clear
(see
e.g.,
Somerville
et
al.,
2014).
Therefore,
our
review
does
not
address
this
third
hypothesized
system.
2.
The
current
article
In
this
article,
we
review
evidence
from
both
the
behavioral
and
neuroimaging
literatures
that
has
emerged
since
the
dual
systems
model
was
originally
articulated
in
2008.
In
particular,
we
consider
the
degree
to
which
extant
research
findings
support,
extend,
mod-
ify,
and
challenge
the
theory.
We
focus
our
discussion
on
three
main
propositions
of
the
model:
(1)
that
reward
sensitivity
peaks
in
ado-
lescence;
(2)
that
cognitive
control
increases
linearly
during
this
period;
and
(3)
that
heightened
risk-taking
during
adolescence
is
the
product
of
heightened
reward-seeking
and
relatively
weaker
cognitive
control.
We
begin
by
addressing
a
recent
criticism
of
the
basic
premise
that
middle
adolescence
is
an
especially
intensified
period
of
risky
behavior.
We
then
examine
evidence
regarding
the
trajectory
of
sensation
seeking
across
development,
the
reward
processing
circuitry
that
might
underlie
developmental
changes
in
sensation-
seeking
behavior,
and
the
extent
to
which
heightened
sensation
seeking
and
reward
sensitivity
are
related
to
pubertal
develop-
ment.
Next,
we
survey
evidence
on
the
developmental
trajectory
of
the
ability
to
control
impulsive
behavior
through
self-regulatory
processes,
and
on
the
maturation
of
the
brain’s
cognitive
control
network,
which
is
proposed
to
undergird
this
ability.
Finally,
we
consider
evidence
concerning
the
interaction
of
the
two
proposed
systems
during
risky
decision
making,
identify
several
unresolved
issues,
and
offer
some
recommendations
for
how
they
might
be
addressed
in
future
research.
In
examining
how
recent
evidence
informs
the
dual
systems
model,
we
are
cognizant
of
critiques
of
this
viewpoint,
including
contentions
that
the
model
inadequately
accounts
for
studies
that
do
not
find
adolescents
to
be
particularly
sensitive
to
reward
(Pfeifer
and
Allen,
2012;
but
see
Strang
et
al.,
2013
for
a
response
to
this
critique),
that
cognitive
control
does
not
unequivocally
improve
during
adolescence
(Crone
and
Dahl,
2012),
and
that

Please
cite
this
article
in
press
as:
Shulman,
E.P.,
et
al.,
The
dual
systems
model:
Review,
reappraisal,
and
reaffirmation.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.010
ARTICLE IN PRESS
G Model
DCN-338;
No.
of
Pages
15
E.P.
Shulman
et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
3
Dual Systems ModelA.
(Steinberg, 2008)
Strength
Age
Strength
Age
Strength
Age
socioemoonal system cognive control system
Maturaonal Imbalance ModelB.
(Casey et al., 2008)
Driven Dual Systems ModelC.
(Luna & Wright, 2015)
Fig.
1.
Alternative
theoretical
models
of
the
development
of
the
socioemotional
(reward
processing)
and
cognitive
control
systems
from
about
age
10
to
age
25.
adolescence
may
not
actually
be
a
peak
period
of
vulnerability
to
risk-taking
(e.g.,
Defoe
et
al.,
2014;
Willoughby
et
al.,
2013;
but
see
Ernst,
2014
for
a
response
to
Willoughby
et
al.).
We
briefly
address
these
critiques
here.
We
do
not
disagree
with
a
fourth
critique
of
the
dual
sys-
tems
model—that
it
is
insufficiently
nuanced
(Pfeifer
and
Allen,
2012)—because
this
is
almost
certainly
correct.
However,
we
believe
that
even
an
admittedly
simplified
model
can
serve
as
a
useful
heuristic
and,
more
important,
can
help
to
motivate
research
needed
to
flesh
out
the
details
of
an
initially
simplistic
account
(for
a
full
discussion
see
Strang
et
al.,
2013).
Moreover,
given
the
influ-
ence
this
perspective
continues
to
have
on
legal
policy
and
practice,
public
health,
and
popular
discourse
about
adolescence
(Steinberg,
2014),
it
is
important
to
ask
whether
this
simplified
account
is
helpful
or
misguided.
It
may
be
useful
at
this
juncture
to
clarify
our
terminology.
To
begin,
the
term
“adolescence”
warrants
discussion.
Largely
as
a
matter
of
convenience,
scholars
generally
agree
that
adolescence
begins
when
pubertal
development
becomes
evident,
around
age
10
(somewhat
later
among
males).
The
end
of
adolescence—the
attainment
of
adult
status—is
not
easily
pegged
to
any
single
biolog-
ical
or
social
event,
however.
In
research,
adulthood
is
often
defined
as
beginning
at
either
age
18
or
21,
the
two
ages
most
often
tied
to
legal
majority
in
the
developed
world.
However,
given
that
18-
to
21-year-olds
in
industrialized
societies
are
rarely
regarded
out-
side
the
legal
system
as
fully
mature
adults,
and
typically
have
not
attained
many
of
the
traditional
markers
of
adult
status
(e.g.,
finan-
cial
independence,
completion
of
formal
education,
stable
romantic
relationships,
full-time
employment,
parenthood),
we
prefer
to
refer
to
this
age
range
as
“late
adolescence.”
For
purposes
of
this
paper,
our
focus
is
mainly
on
the
second
decade
of
life,
from
about
ages
10
to
21,
which
we
subdivide
into
early
adolescence
(10–13),
middle
adolescence
(14–17),
and
late
adolescence
(18–21).
Another
source
of
confusion
in
discussions
of
the
dual
systems
perspective
concerns
levels
of
analysis,
since
the
perspective
refers
to
overt
behaviors
(such
as
risk
taking),
the
psychological
states
hypothesized
to
motivate
them
(such
as
sensation
seeking),
and
the
neural
processes
believed
to
undergird
these
states
(such
as
reward
sensitivity).
In
an
earlier
paper
(Smith
et
al.,
2013),
we
sug-
gested
that
“reward
sensitivity”
and
“cognitive
control”
be
used
to
refer
to
the
neurobiological
constructs
that
are
measured
in
stud-
ies
of
brain
structure
or
function
(see
Fig.
2).
These
neurobiological
phenomena
have
psychological
manifestations
(in
our
terminol-
ogy,
“sensation
seeking”
and
“self-regulation”)
that
are
measured
by
assessing
psychological
states
or
traits
through
the
subjective
reports
of
individuals
or
their
evaluators.
For
heuristic
purposes,
we
use
“sensation
seeking”
as
an
over-
arching
label
for
a
number
of
interrelated
constructs
that
refer
to
the
inclination
to
pursue
“varied,
novel,
complex,
and
intense
sensations
and
experiences
and
the
willingness
to
take
physical,
social,
legal,
and
financial
risks
for
the
sake
of
such
experi-
ences”
(Zuckerman,
1994,
p.
26).
Recruitment
of
brain
regions
and
systems
implicated
in
reward-processing
(e.g.,
ventral
striatum,
orbitofrontal
cortex)
has
been
linked
to
measures
of
sensation
seek-
ing
in
humans
and
other
animals
(Abler
et
al.,
2006;
Leyton
et
al.,
2002;
Lind
et
al.,
2005).
In
a
similar
vein,
we
use
the
label
“self-
regulation”
to
refer
to
a
group
of
interrelated
but
distinguishable
constructs
that
refer
to
the
capacity
to
deliberately
modulate
one’s
thoughts,
feelings,
or
actions
in
the
pursuit
of
planned
goals;
among
these
constructs
are
impulse
control,
response
inhibition,
emotion
regulation,
and
attentional
control.
Aspects
of
self-regulation
have
been
linked
to
the
functioning
of
brain
regions
and
systems
that
subserve
cognitive
control
(e.g.,
lateral
prefrontal,
lateral
parietal,
and
anterior
cingulate
cortices)
(Luna
et
al.,
2010;
Mennigen
et
al.,
2014).
Variations
in
sensation
seeking
and
self-regulation,
in
turn,
are
associated
with
variations
in
behaviors,
including
risk
taking,
which
can
be
measured
through
objective
reports
or
observations.
In
our
model,
risk
taking
is
a
subset
of
many
aspects
of
decision
making
that
share
some,
but
not
all,
characteristics
in
common.
Further-
more,
as
Fig.
2
indicates,
all
decision
making
takes
place
within
a
broader
context
that
encourages
and
enables
some
acts
but
discour-
ages
and
prohibits
others.
As
we
discuss,
the
fact
that
adolescents’
risk
taking
is
influenced
by
the
broader
context
in
which
it
occurs
makes
it
difficult
to
move
seamlessly
between
laboratory
studies
and
the
real
world.
3.
Are
adolescents
particularly
prone
to
risk
taking?
Allusions
to
adolescence
as
a
time
of
rash
behavior
and
poor
decision
making
predate
the
articulation
of
the
dual
systems
model
by
centuries.
And
yet,
empirical
evidence
of
a
mid-adolescent
peak
in
risk
taking
(at
least
in
humans)
is
not
unequivocal.
As
pointed
out
in
a
recent
review
of
epidemiological
data,
the
peak
age
for
risk
taking
varies
across
different
behaviors,
and
very
often
NEUROBIOLOGICAL
PSYCHOLOGICAL
Reward
Sensivity
Sensaon
Seeking Self-Regulaon
Cognive
Control
BEHAVIORAL
Decision Making
Context
Risk Taking
Fig.
2.
Constructs
implicated
in
the
dual
systems
model
of
adolescent
risk-taking
arranged
by
level
of
analysis.

Please
cite
this
article
in
press
as:
Shulman,
E.P.,
et
al.,
The
dual
systems
model:
Review,
reappraisal,
and
reaffirmation.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.010
ARTICLE IN PRESS
G Model
DCN-338;
No.
of
Pages
15
4
E.P.
Shulman
et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
it
is
late
adolescents,
not
middle
adolescents,
who
exhibit
the
highest
levels
of
recklessness
(see
Willoughby
et
al.,
2013).
For
example,
one
of
the
most
dangerous
forms
of
substance
use—binge
drinking—is
most
common
during
the
early
20s
(Chassin
et
al.,
2002;
Willoughby
et
al.,
2013).
Although
some
argue
that
these
data
pose
a
problem
for
the
dual
systems
model,
we
disagree.
The
model
does
not
posit
that
middle
adolescents
necessarily
demonstrate
the
highest
levels
of
all
forms
of
risk
taking
in
the
real
world.
Rather,
it
asserts
that
risk-taking
propensity
is
highest
in
mid-adolescence,
but
that
the
expression
of
this
propensity
is
expected
to
vary
depending
on
the
context
(as
noted
in
Fig.
2).
Our
position
is
that
late
adolescents
are
less
biologi-
cally
predisposed
to
risk
taking
than
middle
adolescents
(consistent
with
the
dual
systems
model),
but
that
they
exhibit
higher
levels
of
many
forms
of
real-world
risk-taking
due
to
greater
opportunity.
Compared
to
younger
individuals,
people
in
their
early
20s
typi-
cally
experience
less
supervision
from
adults,
have
more
financial
resources,
and
are
afforded
greater
legal
access
to
many
forms
of
risk
taking
(e.g.,
driving,
alcohol,
and
gambling).
Thus,
we
contend
that
maturational
factors
predispose
middle
adolescents
to
greater
risk
taking,
but
that
social
and
legal
factors
constrain
their
oppor-
tunities
to
realize
this
predisposition.
Simply
put,
it
is
far
easier
for
the
average
21-year-old
to
take
risks
with
alcohol,
cars,
and
gam-
bling
than
it
is
for
the
average
15-year-old.
If
15-year-olds
were
permitted
to
drive,
purchase
alcohol,
and
enter
casinos
legally,
our
prediction
is
that
they
would
likely
crash,
binge
drink,
and
gamble
more
than
people
in
their
early
20s.
3.1.
Risk
taking
in
the
laboratory
In
an
effort
to
investigate
age
differences
in
risk-taking
propen-
sity,
unconfounded
by
age
differences
in
opportunity,
researchers
have
tested
adolescent
and
adult
participants
using
artificial
tasks—typically
gambling
games
and
driving
simulations—that
give
them
the
option
to
take
risks
in
the
safety
of
a
laboratory
set-
ting.
While
such
tasks
are
often
lacking
in
ecological
validity,
they
do
have
the
advantage
of
controlling
for
contextual
differences
between
adolescents
and
other
age
groups,
as
well
as
for
age
dif-
ferences
in
behavior
preferences.
These
studies
yield
inconsistent
results,
with
some
finding
greater
risk
taking
in
adolescence
than
in
adulthood
(e.g.,
Burnett
et
al.,
2010;
Mitchell
et
al.,
2008;
Van
Leijenhorst
et
al.,
2008,
2010a),
others
finding
no
age
effects
(e.g.,
Bjork
et
al.,
2007;
Eshel
et
al.,
2007;
de
Water
et
al.,
2014),
and
still
others
finding
that
adolescents
engage
in
less
risk
taking
com-
pared
to
children
(Paulsen
et
al.,
2011).
These
inconsistent
findings
suggest
that
if
there
is
an
increased
risk
taking
propensity
in
ado-
lescence
it
may
only
manifest
under
certain
conditions
(see
Defoe
et
al.,
2014
for
a
recent
meta-analysis).
Recently,
researchers
have
used
laboratory
tasks
and
manip-
ulations
that
better
approximate
certain
aspects
of
real-life
risky
decision-making.
These
studies
have
helped
to
delineate
the
con-
ditions
under
which
adolescents
may
be
more
predisposed
than
other
age
groups
to
take
risks.
For
example,
noting
that
during
most
real-world
risk
taking
the
actual
chances
of
a
positive
or
negative
outcome
are
unknown,
researchers
recently
tested
whether
age
differences
in
risk
taking
depend
on
whether
the
probabilities
of
a
successful
outcome
are
known
or
unknown
(Tymula
et
al.,
2012,
2013).
Tymula
and
colleagues
(2012)
had
adolescents
and
adults
complete
a
risk-taking
task
with
two
different
conditions:
a
“known
risk”
condition
and
an
“ambiguous
risk”
condition.
In
the
“known
risk”
condition,
participants
chose
between
a
sure
bet
(100%
chance
of
receiving
$5)
and
a
“risky”
bet
with
known
reward
probabili-
ties
(e.g.,
a
50%
chance
of
winning
$50,
versus
$0
if
they
lost).
In
the
“ambiguous
risk”
condition,
participants
again
chose
between
a
sure
and
risky
option,
but
this
time
the
likelihood
of
winning
or
losing
on
the
risky
option
was
unknown.
Compared
to
adults,
adolescents
made
fewer
risky
decisions
when
the
probabilities
of
loss
were
known
(i.e.,
adolescents
were
less
risk
tolerant).
However,
when
the
probabilities
were
unknown,
adolescents
made
signif-
icantly
more
risky
decisions
than
adults.
Thus,
under
conditions
that
are
more
representative
of
real-life
risk-taking
(where
risk
probabilities
are
typically
unknown),
adolescents
evince
a
greater
risk-taking
propensity
than
adults.
Another
way
in
which
real-life
risk
taking
differs
from
risk
tak-
ing
in
the
laboratory
is
with
respect
to
emotional
arousal.
Contexts
in
which
risk
taking
occurs
outside
the
lab
are
often
thrilling
or
frightening;
in
the
lab,
both
the
nature
of
the
risk
taking
(the
stakes
and
considerations
involved)
and
the
surrounding
environment
are
typically
less
exciting.
Scholars
have
argued
that
differences
in
arousal
give
rise
to
fundamentally
different
ways
of
processing
information
(e.g.,
Luna
and
Wright,
2016;
Metcalfe
and
Mischel,
1999).
The
dual
systems
model
holds
that,
to
the
extent
that
decision-making
occurs
under
conditions
that
arouse
the
socioe-
motional
system
(e.g.,
conditions
that
are
relatively
more
thrilling),
differences
between
adolescent
and
adult
decision-making
and,
hence,
risk
taking
will
be
more
pronounced.
This
pattern
was
observed
in
one
study
that
experimentally
manipulated
the
degree
to
which
a
card
game
risk-taking
task
was
affectively
arousing
(Figner
et
al.,
2009).
Consistent
with
the
dual
systems
account,
ado-
lescents
evinced
greater
risk
taking
and
poorer
use
of
risk-relevant
information
than
adults,
but
only
in
the
more
arousing
version
of
the
task.
A
third
difference
between
most
laboratory
risk-taking
tasks
and
real-life
risk
taking
is
that,
in
the
laboratory,
adolescents
are
asked
to
make
decisions
when
they
are
alone,
whereas
the
major-
ity
of
risky
behaviors
during
adolescence
occur
in
groups
(Albert
et
al.,
2013).
To
mimic
this
context
in
the
lab,
researchers
have
employed
experimental
manipulations
in
which
adolescents
com-
plete
risk-taking
tasks
in
the
presence
of
peers
(real
or
illusory).
Some
studies
have
asked
participants
to
bring
same-aged
peers
to
the
lab
(Chein
et
al.,
2011;
Gardner
and
Steinberg,
2005;
Kretsch
and
Harden,
2014),
while
others
have
deceived
participants
into
believ-
ing
that
they
are
being
observed
remotely
by
a
peer
(Smith
et
al.,
2014a).
Not
only
does
the
“presence”
of
peers
increase
the
ecolog-
ical
validity
of
the
risk-taking
task
(because
adolescent
risk
taking
often
occurs
in
groups),
but
it
also
appears
to
elevate
emotional
arousal,
which
further
increases
the
comparability
to
real-world
risk-taking
contexts.
Studies
that
have
manipulated
the
social
context
have
found
that
adolescents
are
more
induced
by
peer
presence
to
take
risks
than
are
adults
(Chein
et
al.,
2011;
Gardner
and
Steinberg,
2005;
Smith
et
al.,
2014a).
These
findings,
which
are
largely
consis-
tent
with
other
studies
of
peer
effects
on
adolescent
driving
[e.g.,
Segalowitz
et
al.,
2012;
see
Lambert
et
al.
(2014)
for
a
review],
sug-
gest
that
adolescents
are
particularly
vulnerable
to
the
effects
of
peer
presence
on
risk-taking
behaviors.
Moreover,
neuroimaging
data
suggest
that
the
effect
of
peer
presence
on
risk
taking
is
due
to
increased
affective
arousal,
as
evidenced
by
greater
activation
of
brain
regions
within
the
socioemotional
system
(Chein
et
al.,
2011).
A
recent
extension
of
this
line
of
work
in
our
lab
using
a
rodent
model
found
that
adolescent
mice,
but
not
adult
mice,
con-
sume
more
alcohol
in
the
presence
of
same-aged
conspecifics
than
when
alone
(Logue
et
al.,
2014).
Overall,
then,
there
is
evidence
for
increased
risk
taking
in
adolescence
compared
to
adulthood,
though
developmental
dif-
ferences
may
only
be
evident
under
certain
conditions,
such
as
emotional
arousal,
ambiguous
risk,
and
the
presence
of
others.
The
tendency
for
adolescents
to
engage
in
more
risky
behaviors
in
highly-arousing
contexts
together
with
increased
engagement
of
their
socioemotional
system
during
peer
observation
point
to
the
importance
of
reward
processing
in
decision
making
during
this
period
of
life,
a
topic
to
which
we
now
turn.

Please
cite
this
article
in
press
as:
Shulman,
E.P.,
et
al.,
The
dual
systems
model:
Review,
reappraisal,
and
reaffirmation.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.010
ARTICLE IN PRESS
G Model
DCN-338;
No.
of
Pages
15
E.P.
Shulman
et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
5
4.
The
development
of
sensation
seeking
and
reward
sensitivity
Increased
adolescent
risk
taking
in
contexts
that
are
emotion-
ally
arousing
is
consistent
with
one
of
the
central
tenets
of
the
dual
systems
model—that
activation
and
reactivity
of
the
socioe-
motional
system
reaches
its
peak
during
mid-
to
late
adolescence.
A
growing
literature
interrogates
this
aspect
of
the
model
by
exam-
ining
the
psychological
and
neurological
evidence
for
heightened
responsiveness
of
the
socioemotional
system
during
adolescence,
including
in
situations
that
do
not
involve
risky
decision-making.
This
is
important
because
the
dual
systems
model
proposes
that
the
socioemotional
system
is
more
responsive
generally
in
adolescence
than
at
other
ages,
not
only
in
the
context
of
risk
taking.
Moreover,
the
model
hypothesizes
that
the
developmental
course
of
the
socioemotional
system
is,
unlike
the
development
of
the
cognitive
control
system,
closely
tied
to
pubertal
develop-
ment
(for
review
see
Smith
et
al.,
2013).
Around
age
12
(for
boys)
or
11
(for
girls),
pubertal
hormones
inundate
the
brain,
trigger-
ing
a
series
of
changes
in
neural
structure
and
function
(Euling
et
al.,
2008;
Schulz
et
al.,
2009),
especially
in
dopamine-rich
lim-
bic
regions
associated
with
reward
processing
(Blakemore
et
al.,
2010;
Sinclair
et
al.,
2014).
It
is
thought
that
these
hormone-related
changes
sensitize
the
adolescent
brain
to
reward
(Forbes
and
Dahl,
2010;
Peper
and
Dahl,
2013),
as
appears
to
be
the
case
in
ani-
mal
studies
(Alexander
et
al.,
1994;
Clark
et
al.,
1996;
Miele
et
al.,
1988).
More
specifically,
the
reward
system
is
particularly
sensitive
to
the
sudden
surge
of
hormones
at
the
start
of
puberty,
height-
ening
sensitivity
to
affective
stimuli.
Although
pubertal
hormones
do
not
decline
into
adulthood,
we
posit
that
a
decrease
in
reward
sensitivity
ensues
during
later
adolescence
and
into
young
adult-
hood
as
the
reward
system
becomes
desensitized
to
the
effects
of
these
hormones
(Smith
et
al.,
2013).
While
admittedly
limited,
recent
evidence
integrating
measures
of
puberty
into
psycholog-
ical,
behavioral,
and
neuroscience
studies
supports
this
claim
as
well.
4.1.
Sensation
seeking
One
psychological
manifestation
of
socioemotional
reactivity
is
sensation
seeking.
As
anticipated
by
the
dual
systems
model,
measures
of
sensation
seeking
are
often
found
to
be
predictive
of
self-reported
risk
taking
(e.g.,
Kong
et
al.,
2013;
MacPherson
et
al.,
2010).
True
sensation-seeking
behavior
is
difficult
to
elicit
in
laboratory
environments
(at
least,
among
human
subjects);
conse-
quently,
the
vast
majority
of
studies
examining
age-related
changes
in
sensation
seeking
rely
on
self-report.
As
would
be
expected
within
the
dual
systems
account,
longitudinal
and
cross-sectional
studies
generally
find
evidence
of
a
peak
in
self-reported
sensation
seeking
around
mid-adolescence
and
a
decrease
into
adulthood
(Harden
and
Tucker-Drob,
2011;
Peach
and
Gaultney,
2013;
Quinn
and
Harden,
2013;
Romer
and
Hennessy,
2007;
Shulman
et
al.,
2014a,b;
Steinberg
and
Chein,
2015;
Steinberg
et
al.,
2008).
This
overall
pattern
is
further
corroborated
by
a
number
of
longitudi-
nal
studies
following
individuals
from
childhood
into
adolescence,
which
find
that
sensation
seeking
increases
across
this
time
period
(Collado
et
al.,
2014;
Lynne-Landsman
et
al.,
2011;
MacPherson
et
al.,
2010).
For
example,
using
the
Brief
Sensation-seeking
Scale
(Hoyle
et
al.,
2002),
Collado
and
colleagues
(2014)
found
a
linear
increase
in
sensation
seeking
in
individuals
aged
9–13.
Fewer
longi-
tudinal
studies
of
sensation
seeking
have
followed
individuals
from
adolescence
into
adulthood.
However,
two
recent
studies
using
a
large,
longitudinal
data
set
(the
National
Longitudinal
Study
of
Youth
1979
Child
and
Young
Adult
Survey)
have
helped
to
address
this
gap
and
clarify
the
developmental
pattern
of
sensation
seek-
ing
across
adolescence.
Harden
and
Tucker-Drob
(2011)
found
that
self-reported
sensation
seeking
increased
from
age
10
to
mid-
adolescence,
and
then
decreased
thereafter
into
early
adulthood.
Analyzing
the
same
data
set,
Shulman
and
colleagues
(2014a,b)
found
that
females
demonstrated
an
earlier
peak
in
sensation
seek-
ing
(age
16–17)
than
males
(age
18–19),
and
a
steeper
decline
thereafter.
Overall,
these
studies
suggest
that,
as
the
dual
systems
model
would
predict,
sensation
seeking
follows
an
inverted-U
pat-
tern
over
time,
consistent
with
the
proposed
pattern
of
change
in
the
socioemotional
system.
The
hypothesis
that
pubertal
development
drives
developmen-
tal
change
in
the
socioemotional
system
in
adolescence
is
derived
in
part
from
older
studies
linking
higher
levels
of
sensation
seek-
ing
to
more
advanced
pubertal
status
(Martin
et
al.,
2002;
Resnick
et
al.,
1993).
Newer
studies
have
replicated
this
result
(Castellanos-
Ryan
et
al.,
2013;
Gunn
and
Smith,
2010;
Quevedo
et
al.,
2009;
Uroˇ
sevi´
c
et
al.,
2014)
and
have
found
evidence
that
the
correla-
tion
between
self-reported
pubertal
development
and
sensation
seeking
may
be
stronger
for
boys
than
for
girls
(Castellanos-Ryan
et
al.,
2013;
Steinberg
et
al.,
2008).
Also,
as
would
be
expected
based
on
the
link
between
puberty
and
sensation
seeking,
recent
studies
have
found
that
more
advanced
pubertal
status
in
adolescents
is
associated
with
greater
involvement
in
behaviors
that
are
closely
related
to
sensation
seeking,
such
as
substance
use
(Castellanos-
Ryan
et
al.,
2013;
de
Water
et
al.,
2013;
Gunn
and
Smith,
2010),
law-breaking
(Collado
et
al.,
2014;
Kretschmer
et
al.,
2014),
and
risk
taking
in
laboratory
contexts
(Collado
et
al.,
2014;
Kretsch
and
Harden,
2014;
Steinberg
et
al.,
2008;
but
see
van
Duijvenvoorde
et
al.,
2014
who
did
not
find
a
correlation
between
pubertal
status
and
performance
on
a
gambling
task).
4.2.
Behavioral
manifestations
of
reward
sensitivity
Compared
to
self-report
studies
of
sensation
seeking,
there
are
markedly
fewer
behavioral
studies
examining
the
development
of
reward
sensitivity,
and
these
have
heterogeneous
methodologies
and
findings,
which
makes
it
difficult
to
draw
firm
conclusions
about
age
differences.
One
large-scale
study
utilized
the
Iowa
Gambling
Task
(IGT;
Cauffman
et
al.,
2010)
to
explore
age-related
changes
in
reward
sensitivity.
In
the
standard
version
of
the
IGT,
participants
are
presented
with
four
decks
of
cards,
two
that
will
win
them
money
over
repeated
play
(advantageous
decks)
and
two
that
will
lose
them
money
over
repeated
play
(disadvantageous
decks);
participants
are
permitted
to
choose
freely
from
the
four
decks
(e.g.,
Smith
et
al.,
2011b).
However,
Cauffman
et
al.
(2010)
modified
the
task
such
that
the
computer
pseudorandomly
selected
a
deck
on
each
trial
and
the
participant
was
asked
to
decide
whether
to
play
or
pass.
This
modification
allowed
the
researchers
to
disen-
tangle
affinity
for
the
advantageous
decks—a
measure
of
reward
sensitivity—from
avoidance
of
disadvantageous
decks.
The
results
indicated
that
mid-adolescents
aged
14–17
and
older
adolescents
aged
18–21
learned
to
play
from
advantageous
decks
faster
than
either
younger
adolescents
(ages
10–13)
or
adults
(ages
22–25),
a
finding
that
was
recently
replicated
in
an
international
sample
of
more
than
5000
individuals
(Steinberg
and
Chein,
2015).
This
outcome
suggests
that
ages
14–21
are
a
period
of
heightened
sen-
sitivity
to
reward.
Using
the
same
data
set,
Steinberg
(2010)
also
found
that
self-reported
sensation
seeking,
but
not
impulsivity,
was
associated
with
overall
rate
of
plays
from
rewarding
decks
at
the
end
of
the
task.
Another
way
researchers
have
examined
developmental
differ-
ences
in
reward
sensitivity
is
by
substituting
neutral
stimuli
(e.g.,
letters)
with
rewarding
ones
(e.g.,
happy
faces)
in
traditional
behav-
ioral
tasks
(e.g.,
measures
of
impulse
control),
and
then
observing
the
extent
to
which
the
presence
of
rewarding
stimuli
impacts
performance.
Two
such
studies
have
examined
age
differences
(comparing
children,
adolescents,
and
adults)
in
performance
on

Please
cite
this
article
in
press
as:
Shulman,
E.P.,
et
al.,
The
dual
systems
model:
Review,
reappraisal,
and
reaffirmation.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.010
ARTICLE IN PRESS
G Model
DCN-338;
No.
of
Pages
15
6
E.P.
Shulman
et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
an
“Emotional
Go/No-Go”
task.
In
all
Go/No-Go
tasks,
participants
are
presented
with
a
rapid
sequence
of
target
and
non-target
stim-
uli,
both
of
which
are
typically
emotionally
neutral.
Participants
are
instructed
to
press
a
button
when
a
target
stimulus
is
presented
(a
“go”
trial)
and
to
withhold
the
button
press
(do
nothing)
when
a
non-target
stimulus
is
presented
(a
“no-go”
trial).
Non-target
events
occur
relatively
infrequently,
making
it
challenging
for
par-
ticipants
to
restrain
the
impulse
to
press
the
button
on
no-go
trials.
[As
with
most
measures
of
self-regulation,
performance
improves
linearly
with
age
on
traditional
Go/No-Go
tasks
(see
Casey
et
al.,
2002
for
a
review).]
In
the
Emotional
Go/No-Go
task
employed
by
Somerville
et
al.
(2011),
the
stimuli
were
photographs
of
either
happy
or
calm
faces.
They
found
that
for
adolescents
(ages
13–17)
more
than
for
chil-
dren
(ages
6–12)
or
adults
(ages
18–29),
withholding
a
button
press
was
more
difficult
when
the
no-go
stimulus
was
a
happy
face—a
rewarding
stimulus—than
when
it
was
a
calm
face.
In
fact,
only
adolescents
showed
impaired
impulse
control
in
the
happy,
rela-
tive
to
the
calm,
no-go
condition.
The
researchers
proposed
that
adolescents’
greater
emotional
response
to
the
(rewarding)
happy
face
made
it
harder
for
them
to
restrain
the
impulse
to
“approach”
it
(i.e.,
press
the
“go”
button).
If
so,
these
results
support
the
proposi-
tion
that
adolescents
are
particularly
sensitive
to
reward.
However,
another
study
(Tottenham
et
al.,
2011)
using
a
similar,
but
not
identical,
Emotional
Go/No-Go
task
did
not
find
this
pattern
(i.e.,
they
found
no
emotion
by
condition
by
age
group
interaction
for
erroneous
button
presses).
Though
there
were
methodological
dif-
ferences
between
these
two
studies,
the
failure
to
find
the
effect
in
one
of
the
two
underscores
the
need
for
further
research
in
this
vein.
It
also
highlights
the
benefits
of
being
able
to
incorporate
neuroimaging
methods.
Engagement
of
the
socioemotional
system
may
not
always
be
robust
enough
(especially
in
laboratory
settings)
to
consistently
bias
behavior.
Neuroimaging
enables
researchers
to
detect
age
differences
in
the
engagement
of
this
system,
even
absent
behavioral
consequences.
4.3.
Neuroimaging
of
reward
sensitivity
In
recent
years,
many
neuroimaging
studies
have
asked
whether
adolescents
are
particularly
sensitive
to
reward.
Beyond
iden-
tifying
differences
between
adolescents
and
other
age
groups,
these
studies
help
address
questions
about
the
neural
mechanisms
underlying
adolescents’
heightened
reward-seeking.
For
example,
among
those
who
agree
that
adolescents
are
more
inclined
than
adults
to
seek
out
rewards,
there
has
been
disagreement
over
whether
this
results
from
the
fact
that
rewards
are
experienced
as
exceedingly
pleasurable
during
adolescence
(and
are
therefore
more
enticing)
or
because
they
are
experienced
as
less
so
(and
are
therefore
less
satisfying).
Indeed,
one
early
notion,
now
largely
dis-
credited,
posited
that
adolescents
suffer
from
a
“reward
deficiency
syndrome”
which
impels
them
to
seek
out
exciting
experiences
because
mundane
ones
are
not
sufficiently
rewarding,
much
like
addicts
who
seek
out
drugs
because
quotidian
experiences
no
longer
excite
them
(for
a
discussion,
see
Spear,
2002).
To
date,
most
of
the
developmental
neuroscience
literature
has
focused
on
developmental
differences
in
the
striatum,
and
more
specifically
in
the
ventral
portion
of
the
striatum,
which
is
con-
sidered
one
of
the
main
regions
involved
in
the
calculation
of
reward
(Knutson
et
al.,
2001;
Luciana
and
Collins,
2012).
In
our
dual
systems
account,
increases
in
risk
taking
and
other
reward-
seeking
behaviors
are
thought
to
be
a
consequence
of
increased
engagement
of
the
striatum
during
decision-making,
thus
biasing
adolescents
toward
more
rewarding
choices.
Heightened
sensitiv-
ity
to
rewarding
outcomes
of
prior
decisions
may
contribute
to
adolescent
risk-taking
as
well.
There
is
evidence,
for
example,
that
the
volume
of
the
nucleus
accumbens
(part
of
the
ventral
striatum
and
presumed
to
be
the
central
structure
in
the
reward
system)
increases
during
the
first
part
of
adolescence
and
then
shrinks
thereafter
(Luciana
and
Collins,
2012).
As
discussed
by
Steinberg
(2008),
the
neuroscience
literature
includes
both
studies
that
support
and
challenge
the
dual
systems
account
of
heightened
striatal
engagement
during
adolescence
(e.g.,
Bjork
et
al.,
2004;
Galvan
et
al.,
2006).
Since
that
2008
publi-
cation,
several
reviews
have
discussed
methodological
differences
across
these
studies
that
may
have
contributed
to
the
inconsistent
findings
(see
Galvan,
2010;
Richards
et
al.,
2013).
Indeed,
the
devel-
opmental
neuroimaging
literature
on
reward
processing
has
grown
substantially
over
the
last
several
years,
and
we
believe
there
are
patterns
to
be
noted,
and
conclusions
to
be
drawn,
that
help
explain
what
appear
to
be
contradictory
findings.
In
its
current
state,
the
literature
provides
considerable
evi-
dence
that
when
developmental
differences
in
striatal
activation
are
present
during
reward
processing
(both
during
the
anticipa-
tion
and
the
receipt
of
reward)
adolescents
engage
the
striatum
to
a
greater
extent
than
both
children
and
adults
(Barkley-Levenson
and
Galvan,
2014;
Christakou
et
al.,
2011;
Galvan
and
McGlennen,
2013;
Galvan
et
al.,
2006;
Geier
et
al.,
2010;
Hoogendam
et
al.,
2013;
Jarcho
et
al.,
2012;
Padmanabhan
et
al.,
2011;
Silverman
et
al.,
2015;
Van
Leijenhorst
et
al.,
2010b).
For
example,
a
recent
longitudinal
study
found
that,
across
mid-adolescence
(roughly
ages
15–18),
ventral
striatal
activation
in
response
to
“risk
taking”
on
the
bal-
loon
analogue
task
(which
also
reflects
reward-seeking)
declines
intra-individually
over
time,
and
that
striatal
activation
during
the
task
is
correlated
with
self-reported
risk
taking
outside
the
lab-
oratory
(Qu
et
al.,
2015).
However,
a
handful
of
studies
find
the
opposite
pattern—dampened
striatal
response
during
adolescence
relative
to
adulthood
(Bjork
et
al.,
2004,
2010;
Hoogendam
et
al.,
2013;
Lamm
et
al.,
2014)—and
others
fail
to
demonstrate
any
age
differences
(Krain
et
al.,
2006;
Teslovich
et
al.,
2014;
Van
Leijenhorst
et
al.,
2006).
In
trying
to
explain
this
inconsistency,
it
is
important
to
note
that
disparate
findings
emerge
only
for
contrasts
that
focus
on
the
anticipation
of
a
reward.
Studies
focusing
on
striatal
engagement
during
the
receipt
of
a
reward
consistently
find
that
adolescents
engage
the
striatum
to
a
greater
extent
than
adults
(Galvan
and
McGlennen,
2013;
Hoogendam
et
al.,
2013;
Van
Leijenhorst
et
al.,
2010b),
suggesting
that
adolescents
are—as
the
dual
systems
model
claims—more
sensitive
to
rewarding
outcomes.
The
fact
that
striatal
engagement
is
relatively
higher
among
ado-
lescents
than
among
children
or
adults
during
receipt
of
rewards
but
not
necessarily
during
reward
anticipation
potentially
chal-
lenges
our
conception
of
adolescent
risk
taking
as
being
driven
by
the
prospect
of
a
reward.
However,
nuances
in
task
design,
modeling
of
the
anticipatory
event
in
imaging
analyses,
and
the
relationship
between
striatal
engagement
and
behavioral
reward
sensitivity
may
account
for
these
seemingly
inconsistent
results,
for
several
reasons.
First,
the
time
points
at
which
events
are
mod-
eled,
and
the
specific
trial
periods
that
are
included
within
the
model,
can
dramatically
affect
the
observed
neural
response
(e.g.,
Geier
et
al.,
2010).
One
factor
that
seems
to
differentiate
studies
that
do
and
don’t
report
increased
adolescent
engagement
of
the
striatum
during
reward
anticipation
is
the
degree
to
which
antic-
ipatory
cues
reliably
predict
the
delivery
of
the
reward.
Studies
using
a
task
design
in
which
the
reward
cue
signals
not
only
the
opportunity
for
reward,
but
also
an
increased
likelihood
of
earning
that
reward,
tend
to
find
increased
adolescent
activity
in
the
stria-
tum
during
anticipation
(e.g.,
Barkley-Levenson
and
Galvan,
2014;
Van
Leijenhorst
et
al.,
2010b).
Meanwhile,
studies
using
tasks
for
which
the
anticipatory
cue
signals
the
possibility
to
earn
a
reward,
but
is
equivocal
with
respect
to
the
likelihood
of
succeeding
in
obtaining
the
reward
(as
in
typical
implementations
of
the
Mon-
etary
Incentive
Delay
task),
do
not
yield
a
consistent
pattern
of

Please
cite
this
article
in
press
as:
Shulman,
E.P.,
et
al.,
The
dual
systems
model:
Review,
reappraisal,
and
reaffirmation.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.010
ARTICLE IN PRESS
G Model
DCN-338;
No.
of
Pages
15
E.P.
Shulman
et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
7
developmental
differences
(e.g.,
Bjork
et
al.,
2007;
Teslovich
et
al.,
2014).
Second,
developmental
findings
regarding
striatal
outputs
dur-
ing
reward
anticipation
are
more
consistent
in
studies
where
there
is
also
concomitant
behavioral
evidence
that
the
adolescents
are
relatively
more
sensitive
to
the
rewards
being
presented
(e.g.,
faster
reaction
times
on
rewarded
trials,
more
reward-related
errors,
etc.).
While
most
reward
processing
tasks
used
in
neuroimaging
stud-
ies
do
not
include
a
behavioral
measure
or
control
for
behavioral
differences
in
reward
sensitivity
across
development,
the
handful
of
studies
that
do
(Barkley-Levenson
and
Galvan,
2014;
Christakou
et
al.,
2011;
Geier
et
al.,
2010;
Padmanabhan
et
al.,
2011;
Somerville
et
al.,
2011)
all
report
both
greater
recruitment
of
the
striatum
dur-
ing
anticipation
of
reward
and
higher
reward
sensitivity
among
adolescents
compared
to
adults,
reflected
in
the
behavioral
out-
comes.
Unfortunately,
the
majority
of
reward
tasks
used
in
the
developmental
literature
lack
a
useful
behavioral
index
of
reward
sensitivity—an
issue
that
may
also
account
for
variability
in
striatal
findings
across
development.
Our
lab
recently
explored
how
socioemotional
arousal
influ-
ences
adolescents’
neural
responses
to
reward
by
testing
whether
the
presence
of
peers
increased
striatal
activation
during
a
reward-
processing
task
in
which
no
risk
was
involved
(Smith
et
al.,
2015).
In
this
study,
we
examined
the
effects
of
peer
observation
on
ado-
lescents’
and
adults’
neural
response
to
reward
using
a
modified
version
of
the
High/Low
Card
Guessing
Task
(Delgado
et
al.,
2003;
May
et
al.,
2004).
During
the
receipt
of
reward,
adolescents
who
completed
the
task
in
the
presence
of
their
peers
recruited
the
stria-
tum
to
a
greater
degree
than
when
they
completed
the
task
alone.
Furthermore,
only
when
peers
were
present
did
adolescents
evince
greater
striatal
activation
than
adults.
These
findings
provide
cor-
roborating
evidence
that,
during
adolescence,
social
context
is
an
important
modulator
of
reward
processing,
even
when
this
pro-
cessing
is
uncoupled
from
risk
taking.
Consistent
with
this
claim,
we
have
shown
that,
in
the
presence
of
peers,
adolescents
evince
a
stronger
preference
for
immediate
(as
opposed
to
delayed)
rewards
on
a
Delay
Discounting
task
that
does
not
involve
risk
taking
(O’Brien
et
al.,
2011;
Weigard
et
al.,
2014).
Recent
neuroimaging
studies
also
support
the
idea
that,
in
addi-
tion
to
having
profound
effects
on
brain
structure
[a
topic
not
covered
in
the
present
article;
see
Blakemore
et
al.
(2010)
and
Smith
et
al.
(2013)
for
reviews],
pubertal
development
plays
a
role
in
developmental
change
in
the
sensitivity
of
the
striatum
to
reward
(Braams
et
al.,
2015;
Forbes
et
al.,
2010;
Op
de
Macks
et
al.,
2011).
For
example,
a
landmark,
longitudinal
neuroimaging
study
of
chil-
dren,
adolescents
and
young
adults
(N
=
299,
ages
8–27)
found,
as
previous
studies
have,
that
activation
of
the
nucleus
accumbens
in
response
to
monetary
reward
(relative
to
loss)
was
higher
in
mid-adolescence
than
at
other
ages
(Braams
et
al.,
2015).
Moreover,
activation
of
this
region
was
related
both
to
greater
self-reported
pubertal
stage
and
higher
levels
of
salivary
testosterone
(Braams
et
al.,
2015).
This
finding
provides
strong
support
for
the
claim
that
the
heightened
responsiveness
of
the
socioemotional
system
dur-
ing
adolescence
is,
at
least
in
part,
a
result
of
pubertal
development.
Thus
far,
we
have
discussed
reward
sensitivity
specifically
with
respect
to
striatal
activation.
However,
there
also
have
been
advances
in
how
we
understand
developmental
changes
in
the
functioning
of
other
regions
hypothesized
to
participate
in
reward
processing,
including
the
dorsal
portion
of
the
stria-
tum
(Benningfield
et
al.,
2014;
Hoogendam
et
al.,
2013;
Lamm
et
al.,
2014),
mPFC
(Christakou
et
al.,
2011),
OFC
(Galvan
et
al.,
2006;
Galvan
and
McGlennen,
2013;
Hoogendam
et
al.,
2013;
Van
Leijenhorst
et
al.,
2010b),
and
the
anterior
insular
cortex
(AIC)
(Galvan
and
McGlennen,
2013;
Van
Leijenhorst
et
al.,
2010b).
In
a
recent
paper,
we
posited
that
continuing
maturation
of
con-
nectivity
between
the
striatum
and
the
AIC,
which
appears
to
act
as
connective
hub
that
influences
the
engagement
of
both
the
con-
trol
and
reward
processing
networks,
may
account
for
inconsistent
recruitment
of
the
striatum
in
adolescent
reward
processing
(Smith
et
al.,
2014b).
Because
reward
processing
entails
the
coordinated
action
of
a
network
of
regions,
developmental
studies
examining
the
reward
system
as
a
whole,
rather
than
focusing
on
activa-
tion
of
specific
regions
considered
in
isolation,
will
likely
yield
greater
insight
into
changes
in
reward
processing
during
adoles-
cence,
including
the
reasons
for
the
inconsistent
recruitment
of
the
striatum
in
adolescent
reward
processing
(Smith
et
al.,
2014b).
One
study
has
already
demonstrated
the
potential
value
of
such
a
network-based
approach.
Using
resting
state
data,
Cho
and
colleagues
(2012)
examined
functional
connectivity
between
the
striatum,
thalamus,
and
AIC
as
adolescents
and
adults
completed
a
reward
processing
task.
They
found
that
during
anticipation
of
reward
(i.e.,
during
cue
presentation)
adolescents
and
adults
did
not
differ
in
the
functional
connectivity
between
these
regions.
Fur-
ther,
they
observed
that
activity
in
the
AIC
and
thalamus
preceded
VS
activation
in
both
adolescents
and
adults.
These
results
sug-
gest
that
the
bottom-up
processing
of
rewards
(as
demonstrated
by
communication
between
these
three
regions)
is
adequately
devel-
oped
by
adolescence.
Therefore,
it
may
be
that
developmental
differences
between
adolescents
and
adults
in
reward
sensitivity
are
not
due
to
immature
connectivity,
but
rather
to
differences
in
top-down
influences
on
the
subjective
valuation
of
reward.
More
studies
considering
the
socioemotional
system
as
a
coordi-
nated
network
are
needed
to
inform
our
understanding
of
how
the
development
of
this
system
relates
to
age-differences
in
reward
processing.
In
summary,
despite
occasional
inconsistencies
in
the
literature,
self-reported
sensation
seeking,
behavioral
measures
of
reward
sensitivity,
and
neuroimaging
studies
of
reward
processing
sup-
port
the
contention
that
reward
sensitivity
reaches
its
apex
during
adolescence
(e.g.,
Barkley-Levenson
and
Galvan,
2014;
Christakou
et
al.,
2011;
Collado
et
al.,
2014;
Galvan
and
McGlennen,
2013;
MacPherson
et
al.,
2010;
Shulman
et
al.,
2014a,b;
Somerville
et
al.,
2011;
Van
Leijenhorst
et
al.,
2010b).
The
bulk
of
developmen-
tal
research
on
this
topic
provides
evidence
for
a
mid-adolescent
peak
in
reward
sensitivity,
and
although
the
neuroimaging
litera-
ture
does
not
allow
for
a
precise
estimation
of
age
of
peak
striatal
response,
the
weight
of
the
evidence
indicates
that
adolescents
engage
the
striatum
(and
other
components
of
the
reward
net-
work)
to
a
greater
extent
than
adults,
particularly
during
receipt
of
reward
and
when
differences
in
reward
sensitivity
are
reflected
in
decision-making
behavior.
Also
consistent
with
the
dual
systems
account,
studies
that
have
incorporated
measures
of
puberty
typi-
cally
find
that
sensation
seeking
and
reward
sensitivity
are
higher
among
those
(particularly
boys)
who
are
more
pubertally
advanced.
5.
The
development
of
self-regulation
and
cognitive
control
5.1.
Self-reported
impulsivity
A
second
major
claim
of
the
dual
systems
model
is
that
cognitive
control
increases
linearly
across
adolescence
and
does
not
reach
full
maturity
until
several
years
after
the
peak
period
of
reward
sensitivity.
In
the
developmental
literature,
impulse
control
(or
its
inverse,
impulsivity)
is
the
psychological
variable
most
often
used
to
assess
self-regulation
(or
its
absence).
Impulsiveness—acting
in
an
unplanned
and
reactive,
or
less
thought
out,
fashion—is
often
considered
a
quintessential
adolescent
characteristic
that
predisposes
adolescents
to
engage
in
reckless
behaviors
(Romer,
2010).
To
date,
studies
examining
age
differences
in
self-reported
impulsivity—both
cross-sectional
(Leshem
and
Glicksohn,
2007;
Steinberg
et
al.,
2008)
and
longitudinal
(Harden
and
Tucker-Drob,

Please
cite
this
article
in
press
as:
Shulman,
E.P.,
et
al.,
The
dual
systems
model:
Review,
reappraisal,
and
reaffirmation.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.010
ARTICLE IN PRESS
G Model
DCN-338;
No.
of
Pages
15
8
E.P.
Shulman
et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
2011)—find
that
impulsivity
decreases
with
age
across
the
second
decade
of
life.
Importantly,
the
protracted
maturation
of
impulse
control
is
believed
to
continue
into
young
adulthood,
where
even
18–19
year
olds
report
higher
impulsivity
(i.e.,
less
impulse
control)
than
indi-
viduals
in
their
early
twenties
(Vaidya
et
al.,
2010).
Although
adults
sometimes
engage
in
impulsive
acts,
by
the
early-to-mid
20s
the
frequency
of
impulsive
behavior
appears
to
stabilize
at
levels
much
lower
than
those
exhibited
by
adolescents
(Steinberg
et
al.,
2008;
Quinn
and
Harden,
2013).
For
example,
using
a
three-item
impul-
sivity
scale,
Quinn
and
Harden
(2013)
found
a
linear
decrease
in
self-reported
impulsivity
between
the
ages
of
15
and
21,
but
no
further
age
differences
among
individuals
between
21
and
25.
5.2.
Behavioral
measures
of
self-regulation
Self-regulation
is
commonly
assessed
in
behavioral
tasks
that
require
response
inhibition,
a
form
of
cognitive
control
that
involves
overcoming
automatic
or
inappropriate
responses
in
favor
of
goal-relevant
information
processing
and
actions
(Casey
et
al.,
2002).
The
most
widely
used
measures
of
response
inhibition
(e.g.,
Go/NoGo,
antisaccade,
and
Stroop
paradigms)
are
typically
configured
to
assess
“reactive
inhibition,”
which
refers
to
the
out-
right
restraint
of
motor
and
perceptual
impulses
in
response
to
an
external
stimulus
(e.g.,
canceling
a
prepotent
response
upon
seeing
a
signal,
or
maintaining
attention
in
the
presence
of
dis-
tractions).
A
wealth
of
behavioral
evidence
on
reactive
inhibitory
control
demonstrates
that
self-regulation
improves
from
childhood
to
adulthood
(Bezdjian
et
al.,
2014;
Bunge
et
al.,
2002;
Casey
et
al.,
1997,
2002;
Durston
et
al.,
2002;
Marsh
et
al.,
2006;
Paulsen
et
al.,
2015;
Rubia
et
al.,
2006,
2013;
Smith
et
al.,
2011a;
Tamm
et
al.,
2002;
Velanova
et
al.,
2009;
Veroude
et
al.,
2013).
Within
this
literature,
adolescents
and
adults
consistently
demonstrate
better
inhibitory
control
compared
to
children;
however,
differences
between
adolescents
and
adults
are
not
con-
sistently
found
unless
the
behavioral
paradigm
is
particularly
challenging.
For
example,
studies
that
use
the
traditional
Stroop
color-word
task
find
no
differences
in
cognitive
control
between
adolescents
and
adults
(e.g.,
Andrews-Hanna
et
al.,
2011),
whereas
studies
that
use
an
emotional
version
of
the
Stroop
to
assess
the
effect
of
emotional
interference
in
cognitive
control
report
improvements
in
self-regulation
from
adolescence
to
adulthood
(e.g.,
Veroude
et
al.,
2013).
Thus,
while
adolescents’
ability
to
inhibit
impulses
appears
to
be
comparable
to
adults’
in
relatively
simple
tasks,
the
sort
of
self-regulatory
skills
necessary
to
appropriately
respond
to
more
cognitively
demanding
situations
continue
to
improve
from
adolescence
to
adulthood.
This
developmental
pat-
tern
is
also
observed
in
measures
of
proactive
(as
opposed
to
reactive)
inhibitory
control,
which
involves
advance
planning
and
monitoring
in
anticipation
of
the
need
to
stop
a
response
or
to
not
engage
in
a
future
action
(e.g.,
slowing
down
responses
to
go-stimuli
in
anticipation
of
a
no-go
signal
approaching,
therefore
allowing
more
time
to
appropriately
cancel
a
response
when
the
no-go
signal
appears;
Vink
et
al.,
2014).
These
findings
suggest
that
while
basic
response
inhibition
mechanisms
may
be
mature
by
adolescence,
self-regulatory
mechanisms
underlying
challeng-
ing
reactive
response
inhibition
tasks
and
proactive
response
inhibition
(e.g.,
planning)
may
still
be
developing
into
the
early
20s.
The
proposition
that
the
prolonged
development
of
self-
regulation
is
more
evident
under
challenging
conditions
has
been
demonstrated
using
the
Tower
of
London
task.
In
this
task,
which
requires
strategic
planning,
participants
must
rearrange
objects
on
pegs
(either
real
or
depicted
on
a
computer
monitor)
to
produce
a
specific
pattern
in
the
fewest
possible
moves
(De
Luca
et
al.,
2003;
Steinberg
et
al.,
2008).
Researchers
manipulate
the
difficulty
of
trials
by
increasing
the
number
of
moves
required
to
complete
the
rearrangement
successfully.
The
amount
of
time
a
participant
spends
deliberating
before
making
his
or
her
first
move
(latency
to
first
move)
is
used
as
a
measure
of
impulse
control
(because
making
an
initial
move
too
rashly
can
extend
the
number
of
moves
needed
to
solve
the
problem).
A
study
from
our
lab
found
no
dif-
ferences
between
children,
adolescents,
and
adults
in
latency
to
first
move
or
in
the
number
of
moves
taken
to
complete
the
trial
on
easy
trials
(i.e.,
those
that
can
be
solved
in
3
moves)
(Albert
and
Steinberg,
2011).
However,
on
difficult
trials
(i.e.,
those
that
required
5
or
more
moves
to
be
solved),
performance
improved
with
age
from
childhood
to
adulthood,
and
this
trend
coincided
with
greater
deliberation
time
prior
to
the
initial
move.
These
find-
ings
suggest
that
when
difficult
tasks
are
used,
such
as
those
that
require
strategic
planning,
improvement
in
self-regulation
contin-
ues
throughout
adolescence
and
into
the
early
20s,
consistent
with
the
dual
systems
model.
Importantly,
the
ongoing
development
of
self-regulation
into
early
adulthood
is
also
in
line
with
the
idea
that
the
develop-
ment
of
self-regulation
is
independent
of
pubertal
development
(Smith
et
al.,
2013).
In
the
most
comprehensive
test
of
the
rela-
tionship
between
self-regulation,
and
pubertal
status
Steinberg
and
colleagues
(2008)
found
that
self-reported
and
behavioral
self-
regulation
was
correlated
with
age
but
not
pubertal
status.
Instead,
pubertal
status
was
more
closely
tied
to
sensation
seeking.
While
this
is
the
only
study
we
know
of
that
simultaneously
examines
the
relationship
between
age,
pubertal
status,
self-regulation,
and
sensation
seeking,
thus
far
the
findings
support
the
notion
that
the
development
of
the
socioemotional
system
is
dependent
on
puber-
tal
status
while
self-regulation
seems
to
develop
independently.
Other
findings
suggest
that
adolescents’
ability
to
exert
adult-
like
self-control
also
may
vary
depending
on
whether
rewards
are
offered
for
better
performance
(Luna
et
al.,
2001).
In
several
stud-
ies
that
have
rewarded
participants
for
better
performance
on
an
antisaccade
task,
researchers
have
found
that
incentives
boost
ado-
lescents’
performance
to
adult
levels
(Geier
et
al.,
2010;
Jazbec
et
al.,
2006;
Padmanabhan
et
al.,
2011).
For
example,
using
an
antisac-
cade
task
where
some
trials
were
rewarded
and
some
were
not,
Geier
and
colleagues
(2010)
found
that
adolescents
performed
bet-
ter
on
rewarded
trials,
compared
to
non-rewarded
trials,
though
their
overall
task
performance
did
not
differ
from
that
of
adults.
At
first
blush,
it
may
appear
that
these
results
are
incompatible
with
the
dual
systems
model,
since
its
basic
claim
is
that
height-
ened
awareness
of
the
availability
of
rewards
should
undermine
cognitive
control
in
adolescents,
not
strengthen
it.
It
is
important
to
note,
though,
that
this
proposition
of
the
dual
systems
model
is
posited
specifically
with
respect
to
situations
in
which
reward-
seeking
impulses
conflict
with
self-regulatory
efforts,
as
do
most
instances
of
risk
taking.
In
contexts
where
increased
sensitivity
to
the
opportunity
for
reward
serves
to
motivate
faster
and
more
attentive
responding,
without
disturbing
relevant
cognitive
pro-
cesses,
adolescents’
relatively
heightened
sensitivity
to
reward
may
be
helpful
rather
than
harmful.
For
that
matter,
even
in
certain
risk-
taking
scenarios—in
particular,
those
in
which
increased
risk
taking
results
in
more
optimal
performance,
such
as
in
certain
gambling
tasks
for
which
risky
choices
have
a
higher
expected
value—greater
reward
sensitivity
can
offer
an
advantage.
Overall,
the
self-report
and
behavioral
literatures
on
self-
regulation
suggest
that
this
capacity
improves
with
age
across
childhood,
adolescence,
and
into
adulthood.
Furthermore,
it
may
be
that
adolescents’
ability
to
self-regulate
is
more
dependent
than
adults’
on
contextual
factors,
such
as
task
difficulty,
the
prospect
of
a
reward
for
better
self-control,
and
the
manner
in
which
rewards
are
presented.
Although
adolescents
may
exhibit
adult-
like
self-regulation
under
ideal
circumstances
by
around
age
15,
this
capacity
is
still
tenuous,
and
maturation
of
self-regulation
may

Please
cite
this
article
in
press
as:
Shulman,
E.P.,
et
al.,
The
dual
systems
model:
Review,
reappraisal,
and
reaffirmation.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.010
ARTICLE IN PRESS
G Model
DCN-338;
No.
of
Pages
15
E.P.
Shulman
et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
9
be
best
indexed
by
the
consistency
with
which
individuals
demon-
strate
self-control
across
different
contextual
circumstances.
As
we
have
noted
previously
(Strang
et
al.,
2013),
it
is
imprudent
to
conclude
that
heightened
reward
sensitivity
is
inherently
disad-
vantageous,
or
that
impulsivity
is
always
problematic.
In
situations
in
which
greater
attentiveness
to
reward
or
more
impetuous
behav-
ior
is
desirable,
adolescents
may
enjoy
a
distinct
advantage
over
adults.
Indeed,
one
of
the
tenets
of
the
dual
systems
model
is
that
adolescence
evolved
as
a
period
during
which
individuals
are
more
likely
to
engage
in
sensation
seeking
and
less
likely
to
restrain
urges
to
pursue
immediate
rewards
because
this
combination
may
con-
fer
a
reproductive
advantage
during
a
period
of
heightened
fertility
(Steinberg,
2014).
5.3.
Neuroimaging
of
cognitive
control
In
recent
years,
developmental
neuroimaging
has
helped
eluci-
date
the
neural
mechanisms
underlying
age-related
improvements
in
cognitive
control.
Continuing
maturation
of
response
inhibition
is
often
examined
in
terms
of
development
of
the
prefrontal
cor-
tex,
and
particularly
the
lateral
prefrontal
cortex
(lPFC).
In
line
with
the
dual
systems
framework,
we
postulate
that
develop-
mental
improvements
in
cognitive
control
are
supported
by
the
concurrent
maturation
of
these
underlying
neural
regions
and
by
enhancements
in
top-down
connectivity
between
frontal
cognitive
control
regions
and
other
cortical
and
subcortical
areas
associated
with
motor
processing,
affective
processing,
and
the
execution
of
selected
actions.
Irrespective
of
age,
individuals
who
perform
better
on
response
inhibition
tasks
(i.e.,
Go/No-Go,
Flanker,
Stroop,
Stop
Signal,
anti-
saccade)
exhibit
greater
activation
of
the
lPFC
compared
to
those
who
perform
poorly
(Durston
et
al.,
2006;
Rubia
et
al.,
2006,
2013;
Velanova
et
al.,
2009).
Across
adolescence,
performance
on
response
inhibition
tasks
improves
with
age—a
pattern
that
appears
to
be
explained
by
continuing
maturation
of
the
lPFC,
with
most
studies
finding
either
a
linear
increase
in
lPFC
recruitment
with
age
(Adleman
et
al.,
2002;
Bunge
et
al.,
2002;
Durston
et
al.,
2006;
Marsh
et
al.,
2006;
Paulsen
et
al.,
2015;
Spielberg
et
al.,
2015;
Tamm
et
al.,
2002;
Velanova
et
al.,
2009;
Vink
et
al.,
2014)
or
sig-
nificantly
increased
engagement
of
the
lPFC
from
adolescence
to
adulthood
(Rubia
et
al.,
2000,
2006,
2013;
Veroude
et
al.,
2013).
Fur-
thermore,
several
studies
have
demonstrated
a
direct
relationship
between
age-related
increases
in
lPFC
engagement
and
success-
ful
cognitive
control
(Adleman
et
al.,
2002;
Andrews-Hanna
et
al.,
2011;
Bunge
et
al.,
2002;
Casey
et
al.,
1997;
Durston
et
al.,
2006;
Rubia
et
al.,
2006,
2007,
2013;
Velanova
et
al.,
2009).
Whereas
the
behavioral
and
neuroimaging
literatures
gener-
ally
indicate
a
relationship
between
increases
in
cognitive
control
and
engagement
of
the
lPFC
from
adolescence
into
adulthood,
the
relationship
between
age,
behavior,
and
neural
engagement
from
childhood
to
adolescence
is
not
as
consistent
(Alahyane
et
al.,
2014;
Booth
et
al.,
2003;
Braet
et
al.,
2009;
Casey
et
al.,
1997;
Durston
et
al.,
2002).
In
fact,
some
studies
find
that
children
utilize
more
frontal
regions
than
adults—in
terms
of
overall
volume
and/or
mag-
nitude
of
activity—in
order
to
successfully
withhold
a
prepotent
action.
These
findings
have
led
researchers
to
posit
that
increases
in
self-regulation
from
childhood
to
adolescence
and
into
adult-
hood
may
be
due
to
a
developmental
progression
from
diffuse
to
focal
activation
(Durston
et
al.,
2002).
In
this
account,
during
child-
hood
and
early
adolescence,
the
brain
is
inefficient
and
needs
to
“work
harder,”
recruiting
neurons
across
a
larger
frontal
area
in
order
to
successfully
inhibit
a
response
(though
see
Poldrack,
2014
for
a
critique
of
the
explanatory
value
of
the
term
“efficiency”
in
this
context).
As
the
brain
undergoes
continued
reorganization
across
adolescence,
necessary
neural
connections
are
strengthened
and
unnecessary
ones
are
pruned,
creating
a
more
efficient
brain
and
leading
to
more
focal
recruitment
of
regions
within
the
lPFC
during
successful
inhibition.
Cognitive
control
encompasses
the
integration
of
several
(often
simultaneous)
processes
that
support
planning
behavior
in
accord
with
one’s
intentions
(Miller,
2000).
The
effective
integration
of
these
processes
relies
not
only
on
the
functional
recruitment
of
implicated
brain
regions,
but
also
on
the
strength
of
connectiv-
ity
among
them
(Hwang
et
al.,
2010;
van
Belle
et
al.,
2014).
For
example,
a
study
by
Hwang
and
colleagues
(2010)
examined
devel-
opmental
changes
in
connectivity
underlying
inhibitory
control
and
found
that
connectivity
between
the
prefrontal
cortex
and
other
cortical
areas
increased
from
childhood
into
adolescence,
with
some
connections
continuing
to
strengthen
from
adoles-
cence
to
adulthood.
The
increases
they
observed
in
the
number
and
strength
of
frontal
connections
to
both
cortical
and
subcorti-
cal
regions
during
the
transition
from
adolescence
into
adulthood
suggest
that
developmental
improvements
in
cognitive
control
may
be
supported
by
age
related
enhancements
in
the
top-down
regulation
of
task-engaged
regions.
Results
such
as
these
under-
score
the
potential
benefit
to
the
field
of
fMRI
studies
moving
beyond
simplistic
models
of
regional
activation
toward
more
elab-
orate
models
that
consider
connectivity
among
regions
throughout
development,
as
well
as
the
strength
and
efficiency
of
those
connections,
which
likely
support
age-related
increases
in
the
acquisition
and
execution
of
complex
cognitive
control
skills
(see,
e.g.,
Satterthwaite
et
al.,
2013).
This
is
particularly
true
because,
as
noted
earlier,
there
is
reason
to
believe
that
continuing
changes
in
connectivity
account
for
the
observation
that
some
aspects
of
cog-
nitive
control
continue
to
strengthen
into
early
adulthood,
instead
of
plateauing
in
adolescence.
6.
Is
risk
taking
during
adolescence
related
to
heightened
reward
sensitivity
and
immature
cognitive
control?
As
reviewed
above,
research
largely
supports
the
dual
systems
model’s
characterization
of
adolescence
as
a
time
of
heightened
socioemotional
reactivity
(relative
to
earlier
and
later
periods)
and
still
maturing
cognitive
control.
Moreover,
there
is
considerable
evidence
consistent
with
the
proposition
that
the
developmen-
tal
trajectories
of
reward
sensitivity
and
cognitive
control
(and,
by
extension,
sensation
seeking
and
self-regulation)
differ,
with
the
former
following
an
inverted
U-shaped
pattern
and
the
latter
evincing
protracted,
linear
improvement
that
extends
into
the
third
decade
of
life.
How
well
does
the
literature
support
the
claim
that
devel-
opmental
change
in
these
two
systems
explains
heightened
risk
taking
during
adolescence?
The
model
posits
that
it
is
the
con-
fluence
of
the
developmental
patterns
of
the
socioemotional
and
cognitive
control
systems—relatively
high
responsiveness
to
reward
combined
with
relatively
weak
self-regulation—that
ren-
ders
adolescents
particularly
vulnerable
to
risk
taking.
If
the
two
systems
contribute
to
risk
taking
in
an
additive
manner,
we
should
find
independent
correlations
between
the
functional
state
of
each
system
and
risk-taking
propensity.
Indeed,
there
is
evidence
for
this
pattern
in
the
literature.
In
order
to
serve
as
a
test
of
the
dual
systems
model
in
predict-
ing
risk
taking,
a
behavioral
study
must
include
measures
of
both
socioemotional
reactivity
and
cognitive
control.
Unfortunately,
constructs
reflecting
the
functional
status
of
the
socioemotional
and
cognitive
control
systems,
like
sensation
seeking
and
impulsiv-
ity,
tend
to
be
highly
correlated
(e.g.,
Shulman
and
Cauffman,
2014;
Steinberg
et
al.,
2008),
despite
being
theoretically
and
empirically
separable
(Duckworth
and
Kern,
2011;
Duckworth
and
Steinberg,
2015).
Thus,
for
studies
that
examine
the
relationship
between
only
one
of
these
constructs
and
risk
taking,
the
correlation
will

Please
cite
this
article
in
press
as:
Shulman,
E.P.,
et
al.,
The
dual
systems
model:
Review,
reappraisal,
and
reaffirmation.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.010
ARTICLE IN PRESS
G Model
DCN-338;
No.
of
Pages
15
10
E.P.
Shulman
et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
be
contaminated
by
the
contribution
of
the
unmeasured
construct.
Some
of
this
overlap
between
sensation
seeking
and
self-regulation
may
be
artifactual—a
result
of
the
difficulty
of
developing
mea-
sures
that
cleanly
assess
one
construct
and
not
the
other.
(For
example,
items
like
“I
often
get
myself
into
trouble”
could
reflect
either
sensation
seeking
or
impulsivity.)
But
some
of
the
observed
association
between
these
constructs
may
be
attributable
to
an
ongoing
dynamic
interplay
between
the
socioemotional
and
cogni-
tive
control
systems;
for
example,
when
the
socioemotional
system
generates
an
impulse
to
pursue
an
intrinsically
rewarding
expe-
rience
and
the
cognitive
control
system
counters
with
a
signal
meant
to
restrain
the
impulse.
Although
a
large
number
of
stud-
ies
have
examined
risky
behavior
in
relation
to
measures
of
either
sensation
seeking
or
impulse
control,
very
few
have
examined
the
concurrent
contributions
to
risk
taking
of
psychological
manifesta-
tions
of
socioemotional
activation
and
cognitive
control.
Even
fewer
have
examined
this
question
in
a
sample
spanning
childhood,
ado-
lescence,
and
adulthood,
which
would
be
necessary
to
fully
test
whether
variation
in
the
functional
status
of
these
two
systems
explains
age-related
patterns
in
risk
taking.
In
the
few
studies
that
have
simultaneously
assessed
constructs
reflective
of
the
socioemotional
and
cognitive
control
systems
(e.g.,
sensation
seeking
and
impulse
control)
along
with
measures
of
risk
taking,
the
anticipated
correlations
are
found.
Both
higher
levels
of
sensation
seeking
and
lower
levels
of
impulse
control
explain
variation
in
risk
taking,
over
and
above
the
effects
of
one
another
(Cyders
et
al.,
2009;
Donohew
et
al.,
2000;
Quinn
and
Harden,
2013).
For
example,
one
study
of
college
students
found
that
sensation
seeking
uniquely
predicted
increases
in
the
frequency
of
alcohol
use,
over
and
above
several
measures
of
impulsivity
(Cyders
et
al.,
2009).
Another
found
that
both
sensation
seeking
and
“impulsive
decision
making”
were
independently
associated
with
greater
odds
of
ninth-graders
engaging
in
sex,
non-coital
sexual
behavior,
alco-
hol
use,
and
marijuana
use
(Donohew
et
al.,
2000).
Moreover,
these
associations
were
comparable
in
magnitude,
except
that
impulsive
decision-making
was
more
strongly
associated
with
having
sex
and
sensation
seeking
was
more
strongly
associated
with
marijuana
use.
Similarly,
unpublished
data
from
our
lab—based
on
a
sample
of
283
10–30-year
olds
and
using
a
scale
that
surveyed
involvement
in
a
wide
range
of
risk-taking
behaviors
(see
Shulman
and
Cauffman,
2014)—suggests
that
impulse
control
and
sensation
seeking
con-
tribute
equally
(betas
=
−.21
and
.21,
respectively)
to
self-reported
engagement
in
risky
behaviors
(controlling
for
age,
sex,
and
each
other).
An
obvious
shortcoming
of
these
studies
is
that
they
rely
exclusively
on
self-report.
However,
if
common
method
variance
alone
were
driving
the
findings,
one
would
not
expect
to
see
inde-
pendent
relations
between
risk
taking
and
either
sensation
seeking
or
impulsivity
once
the
other
predictor
was
controlled.
Another
limitation
of
these
studies
is
that
it
is
not
yet
clear
how
well
self-
report
measures
reflect
the
functional
status
of
the
socioemotional
and
cognitive
control
systems.
Neuroimaging
studies
have
the
advantage
over
behavioral
studies
of
being
able
to
measure
activation
within
distinguishable
regions
thought
to
correspond
to
the
socioemotional
and
cognitive
control
systems
(although
heightened
activity
in
these
regions
during
a
laboratory
task
does
not
constitute
a
direct
measure
of
the
functional
status
of
these
systems).
A
few
studies
have
examined
engagement
of
regions
associated
with
the
socioemotional
and/or
cognitive
control
systems
during
adolescent
decision
making
(e.g.,
Cascio
et
al.,
2015;
Kahn
et
al.,
2015;
Paulsen
et
al.,
2011;
van
Duijvenvoorde
et
al.,
2015b).
However,
only
Chein
et
al.
(2011)
found
increased
engagement
of
structures
within
the
socioemo-
tional
system
and
decreased
activation
of
structures
within
the
cognitive
control
system
simultaneously
within
a
risk-taking
task
(a
driving
simulation).
One
additional
study
(Paulsen
et
al.,
2011)
found
age
effects
in
both
the
PFC
and
striatum
during
risk
taking.
During
risky
(i.e.,
varying
expected
values)
compared
to
sure
(i.e.,
guaranteed
reward)
decisions,
several
PFC
regions
showed
increased
activation
with
age,
consistent
with
Chein
et
al.
(2011).
On
the
other
hand,
striatal
activation
was
inconsistent,
making
these
results
difficult
to
interpret.
Another
recent
study
examined
the
extent
to
which
age
differ-
ences
in
impatience
during
a
temporal
discounting
task—in
which
participants
choose
between
a
smaller
immediate
reward
and
a
larger
delayed
reward—are
explained
by
variations
in
self-reported
“present
hedonism”
(i.e.,
reward
sensitivity)
and
“future
orien-
tation”
(i.e.,
impulse
control)
and
engagement
of
neural
regions
and
networks
during
decision
making
(van
den
Bos
et
al.,
2015).
Though
the
decision
making
task
did
not
involve
risk,
the
study
is
nonetheless
relevant
to
the
dual
systems
model
because
it
was
designed
to
probe
the
degree
to
which
adolescents’
tendency
to
discount
the
future
is
due
to
greater
reward
sensitivity
or
weaker
self
control.
The
results
indicated
that
choices
to
delay
gratifica-
tion
in
the
decision
task
were
associated
with
greater
self-reported
future
orientation
and
increased
engagement
of
frontoparietal
con-
trol
circuitry,
but
not
with
variation
in
self
reported
hedonism.
Also,
improvements
in
frontostriatal
connectivity
mediated
the
link
between
age
and
willingness
to
wait
for
a
larger
reward.
The
authors
interpreted
these
results
as
showing
that
weak
cognitive
control,
rather
than
heightened
reward
sensitivity,
explains
ado-
lescents’
tendency
to
discount
the
future.
However,
limitations
of
the
methodology
(e.g.,
limited
range
on
the
present
hedonism
scale,
lumping
immediate
rewards
together
with
rewards
to
be
received
in
two
weeks
for
analysis
of
the
discounting
data,
and
the
unemo-
tional
context
of
the
laboratory)
may
have
biased
the
study
against
finding
linkages
between
reward
sensitivity
and
impatience
(see
Steinberg
and
Chein,
2015).
Whereas
other
studies
have
not
demonstrated
simultaneously
heightened
socioemotional
activation
and
dampened
cognitive
control
within
the
same
task,
a
few
recent
ones
have
observed
heightened
striatal
activation
when
adolescents
receive
a
reward
following
a
decision
(Braams
et
al.,
2014,
2015).
In
one
further
relevant
study
(Cascio
et
al.,
2015),
researchers
recruited
recently
licensed
drivers
(∼age
16)
to
complete
a
response
inhibition
task
and,
one
week
later,
a
driving
simulation
in
the
presence
of
a
peer
confederate.
The
peer
either
encouraged
risky
driving
or
safe
driv-
ing.
In
the
latter
condition
(encouragement
of
safe
driving),
greater
engagement
of
cognitive
control
circuitry
(i.e.,
IFG
and
BG)
during
the
response
inhibition
task
(indicative
of
better
cognitive
control)
predicted
safer
driving
behavior
in
the
simulated
driving
task.
Par-
ticipants
who
exhibited
higher
cognitive
control
also
showed
no
increase
in
risky
driving
in
the
condition
in
which
the
peer
encour-
aged
risk
taking,
suggesting
that
individuals
who
evince
greater
engagement
of
cognitive
control
circuitry
may
be
more
resistant
socioemotional
arousal.
These
findings
indicate
that
poor
cogni-
tive
control,
as
expected,
also
plays
a
role
in
risk-taking
behavior.
However,
because
the
study
did
not
compare
age
groups,
it
cannot
address
whether
maturation
of
cognitive
control
helps
to
account
for
developmental
patterns
in
risk
taking.
In
another
recent
study,
van
Duijvenvoorde
and
colleagues
(2015b)
had
children,
adolescents,
and
adults
complete
a
risk-
taking
task
(Columbia
Card
Task).
While
overall
risk-taking
tendency
did
not
differ
by
age,
adolescents
showed
greater
acti-
vation
of
control
circuitry
(including
the
dmPFC)
as
the
riskiness
of
the
decision
increased.
This
effect
was
not
seen
in
children
or
adults.
The
authors
suggest
that
heightened
recruitment
of
control
circuitry
was
necessary
due
to
the
heightened
emotional
response
to
risk
during
this
age.
Although
there
is
good
reason
to
believe
that
the
functional
status
of
both
the
socioemotional
and
cognitive
control
systems
during
adolescence
contribute
to
heightened
risk
taking
during
this
stage
of
development,
the
dual
systems
model
still
awaits
a
comprehensive
study
that
confirms
(or
disconfirms)

Please
cite
this
article
in
press
as:
Shulman,
E.P.,
et
al.,
The
dual
systems
model:
Review,
reappraisal,
and
reaffirmation.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.010
ARTICLE IN PRESS
G Model
DCN-338;
No.
of
Pages
15
E.P.
Shulman
et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
11
the
purported
joint
effects
of
the
developmental
trajectories
of
the
socioemotional
and
cognitive
control
systems
on
risk-taking
behavior.
7.
Unresolved
questions
and
future
directions
There
are
many
unresolved
issues
in
the
literature
that
await
further
research
attention.
Here
we
highlight
just
a
few
of
them.
First,
because
of
differences
in
opportunity
to
engage
in
risky
behavior
outside
the
laboratory
environment,
the
effects
of
matura-
tion
of
the
socioemotional
system
and
cognitive
control
system
on
real-world
risk
taking
are
likely
to
be
modest
and
difficult
to
detect.
Undoubtedly,
contextual
constraints
on
the
behavior
of
adolescents
relative
to
adults
overwhelm
any
putative
effect
on
actual
risk
tak-
ing.
To
take
an
obvious
example,
even
if
15-year-olds
are
higher
in
sensation
seeking
and
lower
in
self-regulation
than
people
in
their
20s,
these
differences
will
not
be
reflected
in
age
differences
in
reckless
driving
in
a
country
where
15-year-olds
are
not
per-
mitted
to
drive.
Thus,
tests
of
the
dual
systems
model
will
require
the
continued
development
of
laboratory
tasks
that
are
ecologically
valid
but
that
afford
individuals
of
different
ages
equal
opportu-
nity
to
take
risks.
Future
efforts
to
test
the
dual
systems
model’s
claims
also
would
benefit
from
collaboration
between
behavioral
researchers
and
neuroscientists
to
develop
measures
that
more
precisely
reflect
the
functioning
of
the
neural
systems
underlying
the
socioemotional
and
cognitive
control
systems.
Second,
still
unresolved
is
the
question
of
why
the
socioemo-
tional
system
declines
in
responsiveness
between
adolescence
and
adulthood.
Luciana
and
Collins
(2012)
have
speculated
that
experi-
ence
with
rewards
and
learning
lead
to
lower
background
levels
of
dopamine,
a
proposition
that
has
not
yet
been
tested.
Casey
et
al.’s
(2008)
model
implies
that
decreases
in
risk
taking
after
the
maturation
of
the
socioemotional
system,
which
in
their
view
is
complete
by
mid-adolescence,
are
ultimately
attributable
to
the
continued
strengthening
of
the
cognitive
control
system.
Given
the
evidence
of
reduced
reward
responsiveness
in
the
key
node
of
the
socioemotional
system
in
adulthood
(relative
to
adolescence),
it
would
seem
that
their
version
of
the
model
suggests
that
strength-
ening
of
the
cognitive
control
system
prospectively
dampens
the
reactivity
of
the
socioemotional
system.
One
study
from
our
lab
has
tested
this
hypothesis:
Shulman
et
al.
(2014b)
examined
the
effects
of
self-reported
impulse
control
(a
reflection
of
the
cog-
nitive
control
system)
and
sensation
seeking
(a
reflection
of
the
socioemotional
system)
on
one
another
over
time
in
a
large
sample
of
youth,
ages
10–25,
who
were
assessed
biennially
as
part
of
the
NLSY79
Children
and
Young
Adults
Study.
The
analysis
failed
to
find
evidence
that
increases
in
impulse
control
prospectively
predict
decreases
in
sensation
seeking;
in
general,
these
two
traits
devel-
oped
independently.
Recently,
a
neuroimaging
study
using
intrinsic
connectivity
found
that
increases
in
dlPFC-subcortical
(thalamus
and
striatum
when
not
controlling
for
age2)
connectivity
across
adolescence
were
associated
with
increases
in
cognitive
control
but
not
with
decreases
in
reward
sensitivity
(van
Duijvenvoorde
et
al.,
2015a).
Instead,
decreases
in
reward
sensitivity
were
related
to
age-
related
decreases
in
connectivity
within
the
socioemotional
system
(vmPFC,
OFC,
and
striatum).
Together
these
findings
lend
support
to
the
notion
that
these
traits
develop
independently.
However,
further
investigation
of
this
question
is
warranted;
in
particular,
longitudinal
studies
employing
more
closely
spaced
measures
and
more
sensitive
assessments
of
the
functional
states
of
the
socioe-
motional
and
cognitive
control
systems
are
needed.
2When
the
analysis
controls
for
age
this
relationship
is
not
significant.
As
the
authors
suggest
this
relationship
may
be
more
sensitive
to
age
than
their
measure
of
cognitive
control.
A
third
issue
concerns
the
operationalization
of
mature
cog-
nitive
control.
It
is
clear
that
in
some
respects,
key
nodes
of
the
cognitive
control
system
have
reached
adult
levels
of
structural
and
functional
maturation
by
mid-adolescence,
a
point
made
recently
by
Luna
et
al.
(2014).
Yet
there
are
other
signs
that
aspects
of
cog-
nitive
control
are
immature,
unreliable,
or
easily
disrupted
during
mid-adolescence
relative
to
adulthood,
which
challenges
Luna
and
Wright’s
(2016)
notion
that
cognitive
control
is
mature
by
mid-
adolescence.
Part
of
this
inconsistency
stems,
we
believe,
from
heterogeneous
operationalizations
of
“mature”
cognitive
control.
It
is
now
eminently
clear
that
whether
activation
of
this
system
is
weaker
or
stronger
is
not
a
useful
way
of
conceptualizing
the
maturity
of
the
cognitive
control
system,
because
on
some
tasks
adolescents
show
greater
or
wider
activation
than
adults,
whereas
on
others
the
reverse
is
true.
As
we
have
argued
(Strang
et
al.,
2013),
a
more
sensible
index
of
maturation
of
cognitive
control
would
focus
on
structural
and
functional
connectivity
within
the
cognitive
control
network
and
between
this
system
and
other
brain
regions
(DeWitt
et
al.,
2014;
Jacobus
et
al.,
2013).
In
order
to
pursue
this
idea
further,
research
is
needed
that
correlates
psychological
and
behav-
ioral
measures
of
self-regulation
with
indices
of
structural
and
functional
connectivity
that
involve
the
cognitive
control
system.
Recent
connectivity
analyses
have
demonstrated
that
increases
in
control
behaviors
across
adolescence
are
associated
with
an
increase
in
connectivity
between
striatal
and
prefrontal
regions
(van
den
Bos
et
al.,
2015;
van
Duijvenvoorde
et
al.,
2015a;
Vink
et
al.,
2014).
However,
some
studies
have
also
found
that
same-
aged
individuals
with
more
well-developed
connections
between
cortical
and
subcortical
areas
engage
in
relatively
more
risk
taking
(Berns
et
al.,
2009;
DeWitt
et
al.,
2014).
This
apparent
inconsis-
tency
between
studies
of
development
and
studies
of
individual
differences
among
same-aged
adolescents
warrants
further
study.
Finally,
it
will
almost
certainly
be
necessary
to
consider
the
development
of,
and
interactions
among,
brain
systems
that
are
not
included
in
the
dual
systems
model
in
order
to
account
for
the
full
array
of
evidence
on
the
development
of
risky
behavior
across
the
period
from
preadolescence
into
adulthood.
Such
expansion
of
the
model
is
consistent
with
the
triadic
model
(Ernst,
2014)
and
with
our
recent
efforts
to
consider
how
development
in
other
pathways,
including
those
linking
the
AIC
to
the
cognitive
control
and
reward
systems
(Smith
et
al.,
2014b),
may
impact
risk-taking
behavior.
According
to
our
recent
work,
the
transition
between
adolescence
and
adulthood
may
involve
a
shift
in
the
ways
in
which
the
VS,
PFC,
and
AIC
are
functionally
connected,
with
relatively
the
stronger
connections
between
the
insula
and
striatum
characteristic
of
ado-
lescence
giving
way
to
stronger
connections
between
the
PFC
and
insula
in
adulthood.
8.
Concluding
comment
The
dual
systems
model
attributes
elevated
levels
of
risk
tak-
ing
in
adolescence
to
the
heightened
arousal
of
the
socioemotional
system
before
the
cognitive
control
system
fully
attains
functional
maturity.
Moreover,
the
decrease
in
risky
behavior
between
adoles-
cence
and
adulthood
is
attributed
to
the
continued
strengthening
of
the
cognitive
control
system
and
the
attenuation
of
arousal
within
the
socioemotional
system.
Whether
and
in
what
respects
the
contributions
of
these
changes
in
the
cognitive
control
and
socioe-
motional
systems
are
independent,
interactive,
or
reciprocal—or
a
combination
of
all
three—are
important
questions
for
future
research.
It
is
important
to
note,
however,
that
the
ways
in
which
these
systems
work
together
in
motivating
increases
in
risky
behav-
ior
between
childhood
and
adolescence
are
not
necessarily
the
same
as
the
ways
in
which
they
combine
to
create
a
decline
in

Please
cite
this
article
in
press
as:
Shulman,
E.P.,
et
al.,
The
dual
systems
model:
Review,
reappraisal,
and
reaffirmation.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.010
ARTICLE IN PRESS
G Model
DCN-338;
No.
of
Pages
15
12
E.P.
Shulman
et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
risky
behavior
between
adolescence
and
adulthood.
It
is
entirely
possible,
for
example,
that
the
increase
in
recklessness
seen
in
early
adolescence
is
due
mainly
to
increases
in
reward
sensitivity
whereas
the
decrease
in
recklessness
seen
in
young
adulthood
is
driven
mainly
by
improvements
in
cognitive
control.
It
is
also
possible
(albeit
unlikely)
that
the
initial
increase
and
later
decline
in
risk
taking
seen
during
the
transition
between
childhood
and
adulthood
is
entirely
explained
by
the
rise
and
fall
in
socioemo-
tional
reactivity
and
not
related
to
changes
in
cognitive
control.
While
advances
in
neuroscience
have
permitted
researchers
to
distinguish
between
these
systems
in
studies
of
brain
structure
and
function,
these
systems
likely
engage
in
ongoing
interactions
with
one
another,
and
it
is
therefore
unwise
to
think
about
them
as
if
they
are
independent
entities.
As
we
have
asserted
throughout
this
review,
the
weight
of
the
evidence
amassed
to
date
is
consistent
with
the
dual
sys-
tems
perspective.
Although
there
are
occasional
exceptions
to
the
general
trends,
self-report,
behavioral,
and
neuroimaging
stud-
ies
generally
support
the
model,
finding
that
psychological
and
neural
manifestations
of
reward
sensitivity
increase
between
child-
hood
and
adolescence,
peak
sometime
during
the
late
teen
years,
and
decline
thereafter,
whereas
psychological
and
neural
reflec-
tions
of
better
cognitive
control
increase
gradually
and
linearly
throughout
adolescence
and
into
the
early
20s,
and
that
the
combination
of
amplified
reward
sensitivity
and
still-developing
cognitive
control
makes
middle
and
late
adolescence
a
time
of
heightened
predisposition
to
risky
and
reckless
behavior.
Whether
this
inclination
translates
into
real-world
risk-taking,
however,
is
contingent
on
the
context
in
which
adolescent
development
occurs.
In
our
view,
the
published
research
that
has
appeared
since
the
introduction
of
this
viewpoint
has
strengthened,
rather
than
called
into
question,
the
model’s
utility.
Of
course,
there
have
been
studies
yielding
results
inconsistent
with
one
or
more
aspects
of
the
dual
systems
model.
This
is
to
be
expected
given
the
large
number
of
relevant
studies
and
wide
variety
of
methodologies
employed.
Importantly,
studies
that
have
failed
to
support
the
dual
systems
model
have
not
provided
consistent
evidence
for
an
alternative
developmental
model.
They
do,
however,
serve
as
a
reminder
that
there
may
be
conditions
under
which
the
general
finding
of
heightened
reward
sensitivity
in
adolescence
or
age-
related
increases
in
cognitive
control
may
not
apply.
This
highlights
the
fact
that,
as
we
have
pointed
out,
the
dual
systems
perspective
is
at
times
overly
simplistic.
As
a
heuristic
device,
however,
the
model
provides
a
far
better
account
of
adolescent
risk
taking
than
prior
models
that
have
attributed
this
period
of
transient
reckless-
ness
to
adolescents’
cognitive
deficiencies.
It
also
continues
to
be
generative,
and
has
informed
ongoing
research
in
multiple
fields,
research
that
will
almost
certainly
support
continued
refinement
of
the
model
(refinements
already
partially
reflected
in
the
mul-
tiple
variations
of
the
perspective
advanced
by
different
research
groups).
Importantly,
the
dual
systems
model
does
not
suggest
that
ado-
lescents
are
universally
risky
or
incompetent
decision
makers.
On
the
contrary,
the
model
recognizes
that
basic
reasoning
capacity
is
almost
fully
mature
by
mid-adolescence.
Indeed,
under
conditions
that
minimize
arousal
of
the
socioemotional
system
and
allow
for
deliberative,
calculated
decision
making,
adolescents
tend
to
make
decisions
and
judgments
that
are
quite
similar
to
those
of
adults
(e.g.,
Chein
et
al.,
2011;
Figner
et
al.,
2009;
Van
Leijenhorst
et
al.,
2008).
Instead,
what
the
dual
systems
model
suggests
is
that
when
decision
making
occurs
under
conditions
that
excite,
or
activate,
the
socioemotional
system
(e.g.,
when
decisions
are
made
in
the
pres-
ence
of
friends,
under
emotionally
arousing
circumstances,
or
when
there
is
a
potential
to
obtain
an
immediate
reward)
adolescents
are
more
prone
than
other
age
groups
to
pursue
exciting,
novel,
and
risky
courses
of
action.
Far
from
being
a
biologically
determinis-
tic
model,
the
dual
systems
perspective
explicitly
emphasizes
the
context
in
which
decision
making
takes
place.
References
Abler,
B.,
Walter,
H.,
Erk,
S.,
Kammerer,
H.,
Spitzer,
M.,
2006.
Prediction
error
as
a
linear
function
of
reward
probability
is
coded
in
human
nucleus
accumbens.
Neuroimage
31
(2),
790–795.
Adleman,
N.E.,
Menon,
V.,
Blasey,
C.M.,
White,
C.D.,
Warsofsky,
I.S.,
Glover,
G.H.,
Reiss,
A.L.,
2002.
A
developmental
fMRI
study
of
the
Stroop
color-word
task.
Neuroimage
16
(1),
61–75.
Alahyane,
N.,
Brien,
D.C.,
Coe,
B.C.,
Stroman,
P.W.,
Munoz,
D.P.,
2014.
Developmental
improvements
in
voluntary
control
of
behavior:
effect
of
preparation
in
the
fronto-parietal
network.
Neuroimage
(Epub
ahead
of
print).
Albert,
D.,
Chein,
J.,
Steinberg,
L.,
2013.
Peer
influences
on
adolescent
decision-making.
Curr.
Dir.
Psychol.
Sci.
22,
80–86.
Albert,
D.,
Steinberg,
L.,
2011.
Age
differences
in
strategic
planning
as
indexed
by
the
Tower
of
London.
Child
Dev.
82,
1501–1517.
Alexander,
G.M.,
Packard,
M.G.,
Hines,
M.,
1994.
Testosterone
has
rewarding
affective
properties
in
male
rats:
implications
for
the
biological
basis
of
sexual
motivation.
Behav.
Neurosci.
108
(2),
424–428.
Andrews-Hanna,
J.R.,
Mackiewicz
Seghete,
K.L.,
Claus,
E.D.,
Burgess,
G.C.,
Ruzic,
L.,
Banich,
M.T.,
2011.
Cognitive
control
in
adolescence:
neural
underpinnings
and
relation
to
self-report
behaviors.
PLoS
ONE
6
(6),
e21598.
Barkley-Levenson,
E.,
Galvan,
A.,
2014.
Neural
representation
of
expected
value
in
the
adolescent
brain.
Proc.
Natl.
Acad.
Sci.
U.
S.
A.
111
(4),
1646–1651.
Benningfield,
M.M.,
Blackford,
J.U.,
Ellsworth,
M.E.,
Samanez-Larkin,
G.R.,
Martin,
P.R.,
Cowan,
R.L.,
Zald,
D.H.,
2014.
Caudate
responses
to
reward
anticipation
associated
with
delay
discounting
behavior
in
healthy
youth.
Accid.
Anal.
Prev.,
43–52.
Berns,
G.S.,
Moore,
S.,
Capra,
C.M.,
2009.
Adolescent
engagement
in
dangerous
behaviors
is
associated
with
increased
white
matter
maturity
of
frontal
cortex.
PLoS
ONE
4,
e6773.
Bezdjian,
S.,
Tuvblad,
C.,
Wang,
P.,
Raine,
A.,
Baker,
L.A.,
2014.
Motor
impulsivity
during
childhood
and
adolescence:
a
longitudinal
biometric
analysis
of
the
go/no-go
task
in
9-
to
18-year-old
twins.
Dev.
Psychol.
50
(11),
2549.
Bjork,
J.M.,
Knuston,
B.,
Fong,
G.W.,
Caggiano,
D.M.,
Bennett,
S.M.,
Hommer,
D.W.,
2004.
Incentive-elicited
brain
activation
in
adolescents:
similarities
and
differences
from
young
adults.
J.
Neurosci.
24
(8),
1793–1802.
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.
Bjork,
J.M.,
Smith,
A.R.,
Danube,
C.L.,
Hommer,
D.W.,
2007.
Developmental
differences
in
posterior
mesofrontal
cortex
recruitment
by
risky
rewards.
J.
Neurosci.
27
(18),
4839–4849.
Blakemore,
S.-J.,
Burnett,
S.,
Dahl,
R.E.,
2010.
The
role
of
puberty
in
the
developing
adolescent
brain.
Hum.
Brain
Mapp.
31,
926–933.
Booth,
J.R.,
Burman,
D.D.,
Meyer,
J.R.,
Lei,
Z.,
Trommer,
B.L.,
Davenport,
N.D.,
Li,
W.,
Parrish,
T.B.,
Gitelman,
D.R.,
Mesulam,
M.M.,
2003.
Neural
development
of
selective
attention
and
response
inhibition.
Neuroimage
20,
737–751.
Braams,
B.R.,
Peters,
S.,
Peper,
J.S.,
Güro˘
glu,
B.,
Crone,
E.A.,
2014.
Gambling
for
self,
friends,
and
antagonists:
differential
contributions
of
affective
and
social
brain
regions
on
adolescent
reward
processing.
Neuroimage
100,
281–289.
Braams,
B.R.,
van
Duijenvoorde,
A.C.K.,
Peper,
J.S.,
Crone,
E.A.,
2015.
Longitudinal
changes
in
adolescent
risk-taking:
a
comprehensive
study
of
neural
responses
to
rewards,
pubertal
development,
and
risk-taking
behavior.
J.
Neurosci.
25
(18),
7226–7238.
Braet,
W.,
Johnson,
K.A.,
Tobin,
C.T.,
Acheson,
R.,
Bellgrove,
M.A.,
Robertson,
I.H.,
Garavan,
H.,
2009.
Functional
developmental
changes
underlying
response
inhibition
and
error-detection
processes.
Neuropsychologia
47
(14),
3143–3151.
Bunge,
S.,
Dudukovic,
N.M.,
Thomason,
M.E.,
Vaidya,
C.J.,
Gabrieli,
J.D.,
2002.
Immature
frontal
lobe
contributions
to
cognitive
control
in
children:
evidence
from
fMRI.
Neuron
33,
301–311.
Burnett,
S.,
Bault,
N.,
Coricelli,
G.,
Blakemore,
S.-J.,
2010.
Adolescents’
heightened
risk-seeking
in
a
probabilistic
gambling
task.
Cogn.
Dev.
25
(2),
183–196.
Cascio,
C.N.,
Carp,
J.,
O’Donnell,
M.B.,
Tinney,
F.J.,
Bingham,
C.R.,
Shope,
J.T.,
Ouimet,
M.C.,
Pradhan,
A.K.,
Simons-Morton,
B.G.,
Falk,
E.B.,
2015.
Buffering
social
influence:
neural
correlates
of
response
inhibition
predict
driving
safety
in
the
presence
of
a
peer.
J.
Cogn.
Neurosci.
27
(1),
83–95.
Casey,
B.J.,
Getz,
S.,
Galvan,
A.,
2008.
The
adolescent
brain.
Dev.
Rev.
28
(1),
62–77.
Casey,
B.J.,
Tottenham,
N.,
Fossella,
J.,
2002.
Clinical,
imaging,
lesion,
and
genetic
approaches
toward
a
model
of
cognitive
control.
Dev.
Psychobiol.
40
(3),
237–254.
Casey,
B.J.,
Trainor,
R.J.,
Orendi,
J.L.,
Schubert,
A.B.,
Nystrom,
L.E.,
Giedd,
J.N.,
Castellanos,
F.X.,
Haxby,
J.V.,
Noll,
D.C.,
Cohen,
J.D.,
Forman,
S.D.,
Dahl,
R.E.,
Rapoport,
J.L.,
1997.
A
developmental
functional
MRI
study
of
prefrontal
activation
during
performance
of
a
Go-No-Go
task.
J.
Cogn.
Neurosci.
9,
835–847.
Castellanos-Ryan,
N.,
Parent,
S.,
Vitaro,
F.,
Tremblay,
R.E.,
Séguin,
J.R.,
2013.
Pubertal
development,
personality,
and
substance
use:
a
10-year
longitudinal
study
from
childhood
to
adolescence.
J.
Abnorm.
Psychol.
122
(3),
782–796.

Please
cite
this
article
in
press
as:
Shulman,
E.P.,
et
al.,
The
dual
systems
model:
Review,
reappraisal,
and
reaffirmation.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.010
ARTICLE IN PRESS
G Model
DCN-338;
No.
of
Pages
15
E.P.
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et
al.
/
Developmental
Cognitive
Neuroscience
xxx
(2015)
xxx–xxx
13
Cauffman,
E.,
Shulman,
E.P.,
Steinberg,
L.,
Claus,
E.,
Banich,
M.T.,
Graham,
S.,
Woolard,
J.,
2010.
Age
differences
in
affective
decision
making
as
indexed
by
performance
on
the
Iowa
Gambling
Task.
Dev.
Psychol.
46,
193–207.
Chassin,
L.,
Pitts,
S.C.,
Prost,
J.,
2002.
Binge
drinking
trajectories
from
adolescence
to
emerging
adulthood
in
a
high-risk
sample:
predictors
and
substance
abuse
outcomes.
J.
Consult.
Clin.
Psychol.
70
(1),
67.
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.
Cho,
Y.T.,
Fromm,
S.,
Guyer,
A.E.,
Detloff,
A.,
Pine,
D.S.,
Fudge,
J.L.,
Ernst,
M.,
2012.
Nucleus
accumbens,
thalamus
and
insula
connectivity
during
incentive
anticipation
in
typical
adults
and
adolescents.
Neuroimage
66
(C),
508–521.
Christakou,
A.,
Brammer,
M.,
Rubia,
K.,
2011.
Maturation
of
limbic
corticostriatal
activation
and
connectivity
associated
with
developmental
changes
in
temporal
discounting.
Neuroimage
54
(2),
1344–1354.
Clark,
A.S.,
Lindenfield,
R.C.,
Gibbons,
C.H.,
1996.
Anabolic–androgenic
steroids
and
brain
reward.
Pharmacol.
Biochem.
Behav.
53
(3),
741–745.
Collado,
A.,
Felton,
J.W.,
MacPherson,
L.,
Lejuez,
C.W.,
2014.
Longitudinal
trajectories
of
sensation
seeking,
risk
taking
propensity,
and
impulsivity
across
early
to
middle
adolescence.
Addict.
Behav.
39
(11),
1580–1588.
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.
Cyders,
M.A.,
Flory,
K.,
Rainer,
S.,
Smith,
G.T.,
2009.
The
role
of
personality
dispositions
to
risky
behavior
in
predicting
first-year
college
drinking.
Addiction
104
(2),
193–202.
Dahl,
R.E.,
2004.
Adolescent
brain
development:
a
period
of
vulnerabilities
and
opportunities.
Keynote
address.
Ann.
N.
Y.
Acad.
Sci.
1021,
1–22.
Defoe,
I.N.,
Dubas,
J.S.,
Figner,
B.,
van
Aken,
M.A.,
2014.
A
meta-analysis
on
age
differences
in
risky
decision
making:
adolescents
versus
children
and
adults.
Psychol.
Bull.
141,
48–84.
De
Luca,
C.R.,
Wood,
S.J.,
Anderson,
V.,
Buchanan,
J.A.,
Proffitt,
T.M.,
Mahony,
K.,
Pantelis,
C.,
2003.
Normative
data
from
the
CANTAB.
I:
Development
of
executive
function
over
the
lifespan.
J.
Clin.
Exp.
Neuropsychol.
25
(2),
242–254.
de
Water,
E.,
Braams,
B.R.,
Crone,
E.A.,
Peper,
J.S.,
2013.
Pubertal
maturation
and
sex
steroids
are
related
to
alcohol
use
in
adolescents.
Horm.
Behav.
63
(2),
392–397.
de
Water,
E.,
Cillessen,
A.H.N.,
Scheres,
A.,
2014.
Distinct
age-related
differences
in
temporal
discounting
and
risk
taking
in
adolescents
and
young
adults.
Child
Dev.
85
(5),
1881–1897.
Delgado,
M.R.,
Locke,
H.M.,
Stenger,
V.A.,
Fiez,
J.A.,
2003.
Dorsal
striatum
responses
to
reward
and
punishment:
effects
of
valence
and
magnitude
manipulations.
Cogn.
Affect.
Behav.
Neurosci.
3
(1),
27–38.
DeWitt,
S.J.,
Aslan,
S.,
Filbey,
F.M.,
2014.
Adolescent
risk-taking
and
resting
state
functional
connectivity.
Psychiatry
Res.:
Neuroimaging
222
(3),
157–164.
Donohew,
L.,
Zimmerman,
R.,
Cupp,
P.S.,
Novak,
S.,
Colon,
S.,
Abell,
R.,
2000.
Sensation
seeking,
impulsive
decision-making,
and
risky
sex:
implications
for
risk-taking
and
design
of
interventions.
Personal.
Individ.
Differ.
28
(6),
1079–1091.
Duckworth,
A.L.,
Kern,
M.L.,
2011.
A
meta-analysis
of
the
convergent
validity
of
self-control
measures.
J.
Res.
Personal.
45
(3),
259–268.
Duckworth,
A.,
Steinberg,
L.,
2015.
Unpacking
self-control.
Child
Dev.
Perspect.
9,
32–37.
Durston,
S.,
Davidson,
M.C.,
Tottenham,
N.,
Galvan,
A.,
Spicer,
J.,
Fossella,
J.A.,
Casey,
B.,
2006.
A
shift
from
diffuse
to
focal
cortical
activity
with
development.
Dev.
Sci.
9
(1),
1–20.
Durston,
S.,
Thomas,
K.M.,
Yang,
Y.,
Ulug,
A.Z.,
Zimmerman,
R.D.,
Casey,
B.J.,
2002.
A
neural
basis
for
the
development
of
inhibitory
control.
Dev.
Sci.
5
(4),
F9–F16.
Ernst,
M.,
2014.
The
triadic
model
perspective
for
the
study
of
adolescent
motivated
behavior.
Brain
Cogn.
89,
104–111.
Eshel,
N.,
Nelson,
E.E.,
Blair,
R.J.,
Pine,
D.S.,
Ernst,
M.,
2007.
Neural
substrates
of
choice
selection
in
adults
and
adolescents:
development
of
the
ventrolateral
prefrontal
and
anterior
cingulate
cortices.
Neuropsychologia
45
(6),
1270–1279.
Euling,
S.Y.,
Herman-Giddens,
M.E.,
Lee,
P.A.,
Selevan,
S.G.,
Juul,
A.,
Sørensen,
T.I.A.,
Dunkel,
L.,
Himes,
J.H.,
Teilmann,
G.,
Swan,
S.H.,
2008.
Examination
of
US
puberty-timing
data
from
1940
to
1994
for
secular
trends:
panel
findings.
Pediatrics
121
(Suppl.),
S172–S191.
Evans,
J.S.B.T.,
Stanovich,
K.E.,
2013.
Dual-process
theories
of
higher
cognition:
advancing
the
debate.
Perspect.
Psychol.
Sci.
8
(3),
223–241.
Figner,
B.,
Mackinlay,
R.J.,
Wilkening,
F.,
Weber,
E.U.,
2009.
Affective
and
deliberative
processes
in
risky
choice:
age
differences
in
risk
taking
in
the
Columbia
Card
Task.
J.
Exp.
Psychol.:
Learn.
Mem.
Cogn.
35
(3),
709–730.
Forbes,
E.E.,
Dahl,
R.E.,
2010.
Pubertal
development
and
behavior:
hormonal
activation
of
social
and
motivational
tendencies.
Brain
Cogn.
72
(1),
66–72.
Forbes,
E.E.,
Ryan,
N.D.,
Phillips,
M.L.,
Manuck,
S.B.,
Worthman,
C.M.,
Moyles,
D.L.,
Tarr,
J.A.,
Sciarrillo,
S.R.,
Dahl,
R.E.,
2010.
Healthy
adolescents’
neural
response
to
reward:
associations
with
puberty,
positive
affect,
and
depressive
symptoms.
J.
Am.
Acad.
Child
Adolesc.
Psychiatry
49
(2),
162–172.
Galvan,
A.,
2010.
Adolescent
development
of
the
reward
system.
Front.
Hum.
Neurosci.
4,
1–9.
Galvan,
A.,
McGlennen,
K.M.,
2013.
Enhanced
striatal
sensitivity
to
aversive
reinforcement
in
adolescents
versus
adults.
J.
Cogn.
Neurosci.
25
(2),
284–296.
Galvan,
A.,
Hare,
T.A.,
Parra,
C.E.,
Penn,
J.,
Voss,
H.,
Glover,
G.,
Casey,
B.J.,
2006.
Earlier
development
of
the
accumbens
relative
to
orbitofrontal
cortex
might
underlie
risk-taking
behavior
in
adolescents.
J.
Neurosci.
26
(25),
6885–6892.
Gardner,
M.,
Steinberg,
L.,
2005.
Peer
influence
on
risk
taking,
risk
preference,
and
risky
decision
making
in
adolescence
and
adulthood:
an
experimental
study.
Dev.
Psychol.
41
(4),
625–635.
Geier,
C.F.,
Terwilliger,
R.,
Teslovich,
T.,
Velanova,
K.,
Luna,
B.,
2010.
Immaturities
in
reward
processing
and
its
influence
on
inhibitory
control
in
adolescence.
Cereb.
Cortex
20,
1613–1629.
Gunn,
R.L.,
Smith,
G.T.,
2010.
Risk
factors
for
elementary
school
drinking:
pubertal
status,
personality,
and
alcohol
expectancies
concurrently
predict
fifth
grade
alcohol
consumption.
Psychol.
Addict.
Behav.
24
(4),
617–627.
Harden,
K.P.,
Tucker-Drob,
E.M.,
2011.
Individual
differences
in
the
development
of
sensation
seeking
and
impulsivity
during
adolescence:
further
evidence
for
a
dual
systems
model.
Dev.
Psychol.
47
(3),
739–746.
Hoogendam,
J.M.,
Kahn,
R.S.,
Hillegers,
M.H.J.,
van
Buuren,
M.,
Vink,
M.,
2013.
Different
developmental
trajectories
for
anticipation
and
receipt
of
reward
during
adolescence.
Dev.
Cogn.
Neurosci.
6C,
113–124.
Hoyle,
R.H.,
Stephenson,
M.T.,
Palmgreen,
P.,
Lorch,
E.P.,
Donohew,
R.L.,
2002.
Reliability
and
validity
of
a
brief
measure
of
sensation
seeking.
Personal.
Individ.
Differ.
32
(3),
401–414.
Hwang,
K.,
Velanova,
K.,
Luna,
B.,
2010.
Strengthening
of
top-down
frontal
cognitive
control
networks
underlying
the
development
of
inhibitory
control:
a
functional
magnetic
resonance
imaging
effective
connectivity
study.
J.
Neurosci.
30
(46),
15535–15545.
Jacobus,
J.,
Thayer,
R.E.,
Trim,
R.S.,
Bava,
S.,
Frank,
L.R.,
Tapert,
S.F.,
2013.
White
matter
integrity,
substance
use,
and
risk
taking
in
adolescence.
Psychol.
Addict.
Behav.
27
(2),
431.
Jarcho,
J.M.,
Benson,
B.E.,
Plate,
R.C.,
Guyer,
A.E.,
Detloff,
A.M.,
Pine,
D.S.,
Leibenluft,
E.,
Ernst,
M.,
2012.
Developmental
effects
of
decision-making
on
sensitivity
to
reward:
an
fMRI
study.
Dev.
Cogn.
Neurosci.
2
(4),
437–447.
Jazbec,
S.,
Hardin,
M.G.,
Schroth,
E.,
McClure,
E.,
Pine,
D.S.,
Ernst,
M.,
2006.
Age-related
influence
of
contingencies
on
a
saccade
task.
Exp.
Brain
Res.
174
(4),
754–762.
Kahn,
L.E.,
Peake,
S.J.,
Dishion,
T.J.,
Stormshak,
E.A.,
Pfeifer,
J.H.,
2015.
Learning
to
play
it
safe
(or
not):
stable
and
evolving
neural
responses
during
adolescent
risky
decision-making.
J.
Cogn.
Neurosci.
27
(1),
13–25.
Knutson,
B.,
Fong,
G.W.,
Adams,
C.M.,
Varner,
J.L.,
Hommer,
D.,
2001.
Dissociation
of
reward
anticipation
and
outcome
with
event-related
fMRI.
Neuroreport
12
(17),
3683–3687.
Kong,
G.,
Smith,
A.E.,
McMahon,
T.J.,
Cavallo,
D.A.,
Schepis,
T.S.,
Desai,
R.A.,
Potenza,
M.N.,
Krishnan-Sarin,
S.,
2013.
Pubertal
status,
sensation-seeking,
impulsivity,
and
substance
use
in
high
school-aged
boys
and
girls.
J.
Addict.
Med.
7
(2),
116–121.
Krain,
A.L.,
Hefton,
S.,
Pine,
D.S.,
Ernst,
M.,
Castellanos,
X.F.,
Klein,
R.G.,
Milham,
M.P.,
2006.
An
fMRI
examination
of
developmental
differences
in
the
neural
correlates
of
uncertainty
and
decision-making.
J.
Child
Psychol.
Psychiatry
47
(10),
1023–1030.
Kretsch,
N.,
Harden,
K.P.,
2014.
Pubertal
development
and
peer
influence
on
risky
decision
making.
J.
Early
Adolesc.
34
(3),
339–359.
Kretschmer,
T.,
Oliver,
B.R.,
Maughan,
B.,
2014.
Pubertal
development,
spare
time
activities,
and
adolescent
delinquency:
testing
the
contextual
amplification
hypothesis.
J.
Youth
Adolesc.
43,
1346–1360.
Lambert,
A.E.,
Simons-Morton,
B.G.,
Cain,
S.A.,
Weisz,
S.,
Cox,
D.J.,
2014.
Considerations
of
a
dual-systems
model
of
cognitive
development
and
risky
driving.
J.
Res.
Adolesc.
24
(3),
541–550.
Lamm,
C.,
Benson,
B.E.,
Guyer,
A.E.,
Perez-Edgar,
K.,
Fox,
N.A.,
Pine,
D.S.,
Ernst,
M.,
2014.
Longitudinal
study
of
striatal
activation
to
reward
and
loss
anticipation
from
mid-adolescence
into
late
adolescence/early
adulthood.
Brain
Cogn.
89,
51–60.
Leshem,
R.,
Glicksohn,
J.,
2007.
The
construct
of
impulsivity
revisited.
Personal.
Individ.
Differ.
43
(4),
681–691.
Leyton,
M.,
Boileau,
I.,
Benkelfat,
C.,
Diksic,
M.,
Baker,
G.,
Dagher,
A.,
2002.
Amphetamine-induced
increases
in
extracellular
dopamine,
drug
wanting,
and
novelty
seeking:
a
PET/[11C]
raclopride
study
in
healthy
men.
Neuropsychopharmacology
27
(6),
1027–1035.
Lind,
N.M.,
Gjedde,
A.,
Moustgaard,
A.,
Olsen,
A.K.,
Jensen,
S.B.,
Jakobsen,
S.,
Cumming,
P.,
2005.
Behavioral
response
to
novelty
correlates
with
dopamine
receptor
availability
in
striatum
of
Göttingen
minipigs.
Behav.
Brain
Res.
164
(2),
172–177.
Logue,
S.,
Chein,
J.,
Gould,
T.,
Holliday,
E.,
Steinberg,
L.,
2014.
Adolescent
mice,
unlike
adults,
consume
more
alcohol
in
the
presence
of
peers
than
alone.
Dev.
Sci.
17,
79–85.
Luciana,
M.,
Collins,
P.F.,
2012.
Incentive
motivation,
cognitive
control,
and
the
adolescent
bra:
is
it
time
for
a
paradigm
shift?
Child
Dev.
Perspect.
6
(4),
392–399.
Luna,
B.,
Padmanabhan,
A.,
Geier,
C.,
2014.
The
adolescent
sensation
seeking
period:
development
of
reward
processing
and
its
effects
on
cognitive
control.
In:
Reyna,
V.F.,
Zayas,
V.
(Eds.),
The
Neuroscience
of
Risky
Decision
Making.
American
Psychological
Association,
Washington,
DC,
pp.
93–121.
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.
Luna,
B.,
Thulborn,
K.R.,
Munoz,
D.P.,
Merriam,
E.P.,
Garver,
K.E.,
Minshew,
N.J.,
Keshavan,
M.S.,
Genovese,
C.R.,
Eddy,
W.F.,
Sweeney,
J.A.,
2001.
Maturation
of

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E.P.,
et
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The
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Review,
reappraisal,
and
reaffirmation.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.010
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widely
distributed
brain
function
subserves
cognitive
development.
Neuroimage
13
(5),
786–793.
Luna,
B.,
Wright,
C.,
2016.
Adolescent
brain
development:
implications
to
the
juvenile
criminal
justice
system.
In:
Heilbrun,
K.,
DeMatteo,
D.,
Goldstein,
N.E.S.
(Eds.),
APA
Handbooks
in
Psychology:
APA
Handbook
of
Psychology
and
Juvenile
Justice.
American
Psychological
Association,
Washington,
DC
(in
press).
Lynne-Landsman,
S.D.,
Graber,
J.A.,
Nichols,
T.R.,
Botvin,
G.J.,
2011.
Is
sensation
seeking
a
stable
trait
or
does
it
change
over
time?
J.
Youth
Adolesc.
40
(1),
48–58.
MacPherson,
L.,
Magidson,
J.F.,
Reynolds,
E.K.,
Kahler,
C.W.,
Lejuez,
C.W.,
2010.
Changes
in
sensation
seeking
and
risk-taking
propensity
predict
increases
in
alcohol
use
among
early
adolescents.
Alcohol.
Clin.
Exp.
Res.
34
(8),
1400–1408.
Marsh,
R.,
Zhu,
H.,
Schultz,
R.,
Quackenbush,
G.,
Royal,
J.,
Skudlarski,
P.,
Peterson,
B.,
2006.
A
developmental
fMRI
study
of
self-regulatory
control.
Hum.
Brain
Mapp.
27
(11),
848–863.
Martin,
C.A.,
Kelly,
T.H.,
Rayens,
M.K.,
Brogli,
B.R.,
Brenzel,
A.,
Smith,
W.J.,
Omar,
H.A.,
2002.
Sensation
seeking,
puberty,
and
nicotine,
alcohol,
and
marijuana
use
in
adolescence.
J.
Am.
Acad.
Child
Adolesc.
Psychiatry
41
(12),
1495–1502.
May,
J.C.,
Delgado,
M.R.,
Dahl,
R.E.,
Stenger,
V.A.,
Ryan,
N.D.,
Fiez,
J.A.,
Carter,
C.S.,
2004.
Event-related
functional
magnetic
resonance
imaging
of
reward-related
brain
circuitry
in
children
and
adolescents.
Biol.
Psychiatry
55
(4),
359–366.
Mennigen,
E.,
Rodehacke,
S.,
Muller,
K.U.,
Ripke,
S.,
Goschke,
T.,
Smolka,
M.N.,
2014.
Exploring
adolescent
cognitive
control
in
a
combined
interference
switching
task.
Neuropsychologia
61,
175–189.
Metcalfe,
J.,
Mischel,
W.,
1999.
A
hot/cool-system
analysis
of
delay
of
gratification:
dynamics
of
willpower.
Psychol.
Rev.
106,
3–19.
Miele,
J.,
Rosellini,
R.A.,
Svare,
B.,
1988.
Estradiol
benzoate
can
function
as
an
unconditioned
stimulus
in
a
conditioned
taste
aversion
paradigm.
Horm.
Behav.
22,
116–130.
Miller,
E.K.,
2000.
The
prefontral
cortex
and
cognitive
control.
Nat.
Rev.
Neurosci.
1,
59–65.
Mitchell,
S.H.,
Schoel,
C.,
Stevens,
A.A.,
2008.
Mechanisms
underlying
heightened
risk
taking
in
adolescents
as
compared
with
adults.
Psychon.
Bull.
Rev.
15
(2),
272–277.
O’Brien,
L.,
Albert,
D.,
Chein,
J.,
Steinberg,
L.,
2011.
Adolescents
prefer
more
immediate
rewards
when
in
the
presence
of
their
peers.
J.
Res.
Adolesc.
21
(4),
747–753.
Op
de
Macks,
Z.A.,
Moor,
B.G.,
Overgaauw,
S.,
Güro˘
glu,
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.
Padmanabhan,
A.,
Geier,
C.F.,
Ordaz,
S.J.,
Teslovich,
T.,
Luna,
B.,
2011.
Developmental
changes
in
brain
function
underlying
the
influence
of
reward
processing
on
inhibitory
control.
Dev.
Cogn.
Neurosci.
1,
517–529.
Paulsen,
D.J.,
Carter,
R.M.,
Platt,
M.L.,
Huettel,
S.A.,
Brannon,
E.M.,
2011.
Neurocognitive
development
of
risk
aversion
from
early
childhood
to
adulthood.
Front.
Hum.
Neurosci.
5,
1–17.
Paulsen,
D.J.,
Hallquist,
M.N.,
Geier,
C.F.,
Luna,
B.,
2015.
Effects
of
incentives,
age,
and
behavior
on
brain
activation
during
inhibitory
control:
a
longitudinal
fMRI
study.
Dev.
Cogn.
Neurosci.
11,
105–115.
Peach,
H.D.,
Gaultney,
J.F.,
2013.
Sleep,
impulse
control,
and
sensation-seeking
predict
delinquent
behavior
in
adolescents,
emerging
adults,
and
adults.
J.
Adolesc.
Health
53
(2),
293–299.
Peper,
J.S.,
Dahl,
R.E.,
2013.
The
teenage
brain:
surging
hormones
–
brain–behavior
interactions
during
puberty.
Curr.
Dir.
Psychol.
Sci.
22
(2),
134–139.
Pfeifer,
J.H.,
Allen,
N.B.,
2012.
Arrested
development?
Reconsidering
dual-systems
models
of
brain
function
in
adolescence
and
disorders.
Trends
Cogn.
Sci.
16,
322–329.
Poldrack,
R.A.,
2014.
Is
“efficiency”
a
useful
concept
in
cognitive
neuroscience?
Dev.
Cogn.
Neurosci.
(Epub
ahead
of
print).
Qu,
Y.,
Galvan,
A.,
Fuligni,
A.J.,
Lieberman,
M.D.,
Telzer,
E.H.,
2015.
Longitudinal
changes
in
prefrontal
cortex
activation
underlie
declines
in
adolescent
risk
taking.
J.
Neurosci.
35,
11308–11314.
Quevedo,
K.M.,
Benning,
S.D.,
Gunnar,
M.R.,
Dahl,
R.E.,
2009.
The
onset
of
puberty:
effects
on
the
psychophysiology
of
defensive
and
appetitive
motivation.
Dev.
Psychopathol.
21
(1),
27–45.
Quinn,
P.D.,
Harden,
K.P.,
2013.
Differential
changes
in
impulsivity
and
sensation
seeking
and
the
escalation
of
substance
use
from
adolescence
to
early
adulthood.
Dev.
Psychopathol.
25
(1),
223–239.
Resnick,
S.M.,
Gottesman,
I.I.,
McGue,
M.,
1993.
Sensation
seeking
in
opposite-sex
twins:
an
effect
of
prenatal
hormones?
Behav.
Genet.
23
(4),
323–329.
Richards,
J.M.,
Plate,
R.C.,
Ernst,
M.,
2013.
A
systematic
review
of
fMRI
reward
paradigms
used
in
studies
of
adolescents
vs.
adults:
the
impact
of
task
design
and
implications
for
understanding
neurodevelopment.
Neurosci.
Biobehav.
Rev.
37
(5),
976–991.
Romer,
D.,
2010.
Adolescent
risk
taking,
impulsivity,
and
brain
development:
implications
for
prevention.
Dev.
Psychobiol.
52
(3),
263–276.
Romer,
D.,
Hennessy,
M.,
2007.
A
biosocial-affect
model
of
adolescent
sensation
seeking:
the
role
of
affect
evaluation
and
peer-group
influence
in
adolescent
drug
use.
Prev.
Sci.
8
(2),
89–101.
Rubia,
K.,
Lim,
L.,
Ecker,
C.,
Halari,
R.,
Giampietro,
V.,
Simmons,
A.,
Brammer,
M.,
Smith,
A.,
2013.
Effects
of
age
and
gender
on
neural
networks
of
motor
response
inhibition:
from
adolescence
to
mid-adulthood.
Neuroimage
83
(C),
690–703.
Rubia,
K.,
Overmeyer,
S.,
Brammer,
M.,
Williams,
S.C.R.,
Simmons,
A.,
Andrew,
C.M.,
Bullmore,
E.T.,
2000.
Functional
frontalisation
with
age:
mapping
neurodevelopmental
trajectories
with
fMRI.
Neurosci.
Biobehav.
Rev.
24,
13–19.
Rubia,
K.,
Smith,
A.B.,
Taylor,
E.,
Brammer,
M.,
2007.
Linear
age-correlated
functional
development
of
right
inferior
fronto-striato-cerebellar
networks
during
response
inhibition
and
anterior
cingulate
during
error-related
processes.
Hum.
Brain
Mapp.
28
(11),
1163–1177.
Rubia,
K.,
Smith,
A.B.,
Woolley,
J.,
Nosarti,
C.,
Heyman,
I.,
Taylor,
E.,
Brammer,
M.,
2006.
Progressive
increase
of
frontostriatal
brain
activation
from
childhood
to
adulthood
during
event-related
tasks
of
cognitive
control.
Hum.
Brain
Mapp.
27
(12),
973–993.
Satterthwaite,
T.D.,
Wolf,
D.H.,
Erus,
G.,
Ruparel,
K.,
Elliott,
M.A.,
Gennatas,
E.D.,
Hopson,
R.,
Jackson,
C.,
Prabhakaran,
K.,
Bilker,
W.B.,
Calkins,
M.E.,
Loughead,
J.,
Smith,
A.,
Roalf,
D.R.,
Hakonarson,
H.,
Verma,
R.,
Davatzikos,
C.,
Gur,
R.C.,
Gur,
R.E.,
2013.
Functional
maturation
of
the
executive
system
during
adolescence.
J.
Neurosci.
33
(41),
16249–16261.
Schulz,
K.M.,
Molenda-Figueira,
H.A.,
Sisk,
C.L.,
2009.
Back
to
the
future:
the
organization-activational
hypothesis
adapted
to
puberty
and
adolescence.
Horm.
Behav.
55
(5),
597–604.
Segalowitz,
S.J.,
Santesso,
D.L.,
Willoughby,
T.,
Reker,
D.L.,
Campbell,
K.,
Chalmers,
H.,
Rose-Krasnor,
L.,
2012.
Adolescent
peer
interaction
and
trait
surgency
weaken
medial
prefrontal
cortex
responses
to
failure.
Soc.
Cogn.
Affect.
Neurosci.
7
(1),
115–124.
Shulman,
E.P.,
Cauffman,
E.,
2014.
Deciding
in
the
dark:
age
differences
in
intuitive
risk
judgment.
Dev.
Psychol.
50
(1),
167.
Shulman,
E.P.,
Harden,
K.P.,
Chein,
J.M.,
Steinberg,
L.,
2014a.
Sex
differences
in
the
developmental
trajectories
of
impulse
control
and
sensation-seeking
from
early
adolescence
to
early
adulthood.
J.
Youth
Adolesc.
44,
1–17.
Shulman,
E.P.,
Harden,
K.P.,
Chein,
J.,
Steinberg,
L.,
2014b.
The
development
of
impulse
control
and
sensation-seeking
in
adolescence:
independent
or
interdependent
processes?
J.
Res.
Adolesc.,
http://dx.doi.org/10.1111/jora.
12181
(advance
online
publication).
Sinclair,
D.,
Purves-Tyson,
T.D.,
Allen,
K.M.,
Weickert,
C.S.,
2014.
Impacts
of
stress
and
sex
hormones
on
dopamine
neurotransmission
in
the
adolescent
brain.
Psychopharmacology
231,
1581–1599.
Silverman,
M.H.,
Jedd,
K.,
Luciana,
M.,
2015.
Neural
networks
involved
in
adolescent
reward
processing:
an
activation
likelihood
estimation
meta-analysis
of
functional
neuroimaging
studies.
Neuroimage
122,
427–439.
Smith,
A.B.,
Halari,
R.,
Giampetro,
V.,
Brammer,
M.,
Rubia,
K.,
2011a.
Developmental
effects
of
reward
on
sustained
attention
networks.
Neuroimage
56
(3),
1693–1704.
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,
323–332.
Smith,
A.R.,
Chein,
J.,
Steinberg,
L.,
2014a.
Peers
increase
adolescent
risk
taking
even
when
the
probabilities
of
negative
outcomes
are
known.
Dev.
Psychol.
50
(5),
1564–1568.
Smith,
A.R.,
Steinberg,
L.,
Chein,
J.,
2014b.
The
role
of
the
anterior
insular
cortex
in
adolescent
decision-making.
Dev.
Neurosci.
36,
196–209.
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.
Smith,
D.G.,
Xiao,
L.,
Bechara,
A.,
2011b.
Decision
making
in
children
and
adolescents:
impaired
Iowa
Gambling
Task
performance
in
early
adolescence.
Dev.
Psychol.
48
(4),
1180–1187.
Somerville,
L.H.,
Hare,
T.,
Casey,
B.J.,
2011.
Frontostriatal
maturation
predicts
cognitive
control
failure
to
appetitive
cues
in
adolescents.
J.
Cogn.
Neurosci.
23
(9),
2123–2134.
Somerville,
L.H.,
van
den
Bulk,
B.G.,
Skwara,
A.C.,
2014.
Response
to:
“The
triadic
model
perspective
for
the
study
of
adolescent
motivated
behavior”.
Brain
Cogn.
(89),
112–113.
Spielberg,
J.M.,
Galarce,
E.M.,
Ladouceur,
C.D.,
McMakin,
D.L.,
Olino,
T.M.,
Forbes,
E.E.,
Silk,
J.S.,
Ryan,
N.D.,
Dahl,
R.E.,
2015.
Adolescent
development
of
inhibition
as
a
function
of
SES
and
gender:
converging
evidence
from
behavior
and
fMRI.
Hum.
Brain
Mapp.
36
(8),
3194–3203.
Spear,
L.,
2002.
The
adolescent
brain
and
the
college
drinker:
biological
basis
of
propensity
to
use
and
misuse
alcohol.
J.
Stud.
Alcohol
Drugs
14,
71–81.
Steinberg,
L.,
2008.
A
social
neuroscience
perspective
on
adolescent
risk-taking.
Dev.
Rev.
28
(1),
78–106.
Steinberg,
L.,
2010.
A
dual
systems
model
of
adolescent
risk-taking.
Dev.
Psychobiol.
52,
216–224.
Steinberg,
L.,
2014.
Age
of
Opportunity:
Lessons
from
the
New
Science
of
Adolescence.
Houghton
Mifflin
Harcourt,
New
York.
Steinberg,
L.,
Albert,
D.,
Cauffman,
E.,
Banich,
M.,
Graham,
S.,
Woolard,
J.,
2008.
Age
differences
in
sensation
seeking
and
impulsivity
and
indexed
by
behavior
and
self-report:
evidence
for
a
dual
systems
model.
Dev.
Psychol.
44,
1764–1778.
Steinberg,
L.,
Chein,
J.M.,
2015.
Multiple
accounts
of
adolescent
impulsivity.
Proc.
Natl.
Acad.
Sci.
U.
S.
A.,
http://dx.doi.org/10.1073/pnas.1509732112
(advance
online
publication).
Strang,
N.M.,
Chein,
J.M.,
Steinberg,
L.,
2013.
The
value
of
the
dual
systems
model
of
adolescent
risk-taking.
Front.
Hum.
Neurosci.
7,
223.
Tamm,
L.,
Menon,
V.,
Reiss,
A.,
2002.
Maturation
of
brain
function
associated
with
response
inhibition.
J.
Am.
Acad.
Child
Adolesc.
Psychiatry
41
(10),
1231–1238.
Teslovich,
T.,
Mulder,
M.,
Franklin,
N.T.,
Ruberry,
E.J.,
Millner,
A.,
Somerville,
L.H.,
Simen,
P.,
Durston,
S.,
Casey,
B.J.,
2014.
Adolescents
let
sufficient
evidence

Please
cite
this
article
in
press
as:
Shulman,
E.P.,
et
al.,
The
dual
systems
model:
Review,
reappraisal,
and
reaffirmation.
Dev.
Cogn.
Neurosci.
(2015),
http://dx.doi.org/10.1016/j.dcn.2015.12.010
ARTICLE IN PRESS
G Model
DCN-338;
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Cognitive
Neuroscience
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(2015)
xxx–xxx
15
accumulate
before
making
a
decision
when
large
incentives
are
at
stake.
Dev.
Sci.
17,
59–70.
Tottenham,
N.,
Hare,
T.A.,
Casey,
B.J.,
2011.
Behavioral
assessment
of
emotion
discrimination,
emotion
regulation,
and
cognitive
control
in
childhood,
adolescence,
and
adulthood.
Front.
Psychol.
2,
2–39.
Tymula,
A.,
Belmaker,
L.A.R.,
Roy,
A.K.,
Ruderman,
L.,
Manson,
K.,
Glimcher,
P.W.,
Levy,
I.,
2012.
Adolescents’
risk-taking
behavior
is
driven
by
tolerance
to
ambiguity.
Proc.
Natl.
Acad.
Sci.
U.
S.
A.
109
(42),
17135–17140.
Tymula,
A.,
Belmaker,
L.A.R.,
Ruderman,
L.,
Glimcher,
P.W.,
Levy,
I.,
2013.
Like
cognitive
function,
decision
making
across
the
life
span
shows
profound
age-related
changes.
Proc.
Natl.
Acad.
Sci.
U.
S.
A.
110
(42),
17143–17148.
Uroˇ
sevi´
c,
S.,
Collins,
P.,
Muetzel,
R.,
Lim,
K.O.,
Luciana,
M.,
2014.
Pubertal
status
associations
with
reward
and
threat
sensitivities
and
subcortical
brain
volumes
during
adolescence.
Brain
Cogn.
89,
15–26.
Vaidya,
J.G.,
Latzman,
R.D.,
Markon,
K.E.,
Watson,
D.,
2010.
Age
differences
on
measures
of
disinhibition
during
young
adulthood.
Personal.
Individ.
Differ.
48
(7),
815–820.
van
Belle,
J.,
Vink,
M.,
Durston,
S.,
Zandbelt,
B.B.,
2014.
Common
and
unique
neural
networks
for
proactive
and
reactive
response
inhibition
revealed
by
independent
component
analysis
of
functional
MRI
data.
Neuroimage
103,
65–74.
van
den
Bos,
W.,
Rodriguez,
C.,
Schweitzer,
J.B.,
McClure,
S.M.,
2015.
Adolescent
impatience
decreases
with
increased
fronto-striatal
connectivity.
Proc.
Natl.
Acad.
Sci.
U.
S.
A.,
http://dx.doi.org/10.1073/pnas.1423095112
(advance
online
publication).
van
Duijvenvoorde,
A.C.K.,
Achterberg,
M.,
Braams,
B.R.,
Peters,
S.,
Crone,
E.A.,
2015a.
Testing
a
dual-systems
model
of
adolescent
brain
development
using
resting-state
connectivity
analyses.
Neuroimage
124
(A),
409–420.
van
Duijvenvoorde,
A.C.K.,
Huizenga,
H.M.,
Somerville,
L.H.,
Delgado,
M.R.,
Powers,
A.,
Weeda,
W.D.,
Casey,
B.J.,
Weber,
E.U.,
Figner,
B.,
2015b.
Neural
correlates
of
expected
risks
and
returns
in
risky
choice
across
development.
J.
Neurosci.
35
(4),
1549–1560.
van
Duijvenvoorde,
A.C.,
Op
de
Macks,
Z.A.,
Overgaauw,
S.,
Moor,
B.G.,
Dahl,
R.E.,
Crone,
E.A.,
2014.
A
cross-sectional
and
longitudinal
analysis
of
reward-related
brain
activation:
effects
of
age,
pubertal
stage,
and
reward
sensitivity.
Brain
Cogn.
89,
3–14.
Van
Leijenhorst,
L.,
Crone,
E.A.,
Bunge,
S.A.,
2006.
Neural
correlates
of
developmental
differences
in
risk
estimation
and
feedback
processing.
Neuropsychologia
44
(11),
2158–2170.
Van
Leijenhorst,
L.,
Moor,
B.G.,
Op
de
Macks,
Z.A.,
Rombouts,
S.A.R.B.,
Westenberg,
P.M.,
Crone,
E.A.,
2010a.
Adolescent
risky
decision-making:
neurocognitive
development
of
reward
and
control
regions.
Neuroimage
51,
345–355.
Van
Leijenhorst,
L.,
Westenberg,
P.M.,
Crone,
E.A.,
2008.
A
developmental
study
of
risky
decisions
on
the
cake
gambling
task:
age
and
gender
analyses
of
probability
estimation
and
reward
evaluation.
Dev.
Neuropsychol.
33
(2),
179–196.
Van
Leijenhorst,
L.,
Zanolie,
K.,
Van
Meel,
C.,
Westenberg,
P.M.,
Rombouts,
S.A.R.B.,
Crone,
E.A.,
2010b.
What
motivated
the
adolescent?
Brain
regions
mediating
reward
sensitivity
across
adolescence.
Cereb.
Cortex
20,
61–69.
Vink,
M.,
Zandbelt,
B.B.,
Gladwin,
T.,
Hillegers,
M.,
Hoogendam,
J.M.,
den
Wildenberg,
W.P.,
Du
Plessis,
S.,
Kahn,
R.S.,
2014.
Frontostriatal
activity
and
connectivity
increase
during
proactive
inhibition
across
adolescence
and
early
adulthood.
Hum.
Brain
Mapp.
35
(9),
4415–4427.
Velanova,
K.,
Wheeler,
M.E.,
Luna,
B.,
2009.
The
maturation
of
task
set-related
activation
supports
late
developmental
improvements
in
inhibitory
control.
J.
Neurosci.
29
(40),
12558–12567.
Veroude,
K.,
Jolles,
J.,
Croiset,
G.,
Krabbendam,
L.,
2013.
Changes
in
neural
mechanisms
of
cognitive
control
during
the
transition
from
late
adolescence
to
young
adulthood.
Dev.
Cogn.
Neurosci.
5,
63–70.
Weigard,
A.,
Chein,
J.,
Albert,
D.,
Smith,
A.,
Steinberg,
L.,
2014.
Effects
of
anonymous
peer
observation
on
adolescents’
preference
for
immediate
rewards.
Dev.
Sci.
17
(1),
71–78.
Willoughby,
T.,
Good,
M.,
Adachi,
P.J.C.,
Hamza,
C.,
Tavernier,
R.,
2013.
Examining
the
link
between
adolescent
brain
development
and
risk
taking
from
a
social-developmental
perspective.
Brain
Cogn.
83
(3),
315–323.
Zuckerman,
M.,
1994.
Behavioral
Expressions
and
Biosocial
Bases
of
Sensation
Seeking.
Cambridge
University
Press,
New
York,
NY.