Developmental changes in effects of risk and valence on adolescent decision-making

Abstract

Recent research on risky decision-making in adults has shown that both the risk in potential outcomes and their valence (i.e., whether those outcomes involve gains or losses) exert dissociable influences on decisions. We hypothesised that the influences of these two crucial decision variables (risk and valence) on decision-making would vary developmentally during adolescence. We adapted a risk-taking paradigm that provides precise metrics for the impacts of risk and valence. Decision-making in 11-16 year old female adolescents was influenced by both risk and valence. However, their influences assumed different developmental patterns: the impact of valence diminished with age, while there was no developmental change in the impact of risk. These different developmental patterns provide further evidence that risk and valence are fundamentally dissociable constructs and have different influences on decisions across adolescence.

Full text

Cognitive
Development
28 (2013) 290–
299
Contents
lists
available
at
SciVerse
ScienceDirect
Cognitive
Development
Developmental
changes
in
effects
of
risk
and
valence
on
adolescent
decision-making
夽
Laura
K.
Wolf
a,1
,
Nicholas
D.
Wright
b,∗,1
,
Emma
J.
Kilford
a
,
Raymond
J.
Dolan
b
,
Sarah-Jayne
Blakemore
a
a
UCL
Institute
of
Cognitive
Neuroscience,
17
Queen
Square,
London
WC1N
3AR,
UK
b
Wellcome
Trust
Centre
for
Neuroimaging,
UCL„
12
Queen
Square,
London
WC1N
3BG,
UK
a
r
t
i
c
l
e
i
n
f
o
Keywords:
Risk-taking
Loss
aversion
Valence
Decision-making
Adolescence
a
b
s
t
r
a
c
t
Recent
research
on
risky
decision-making
in
adults
has
shown
that
both
the
risk
in
potential
outcomes
and
their
valence
(i.e.,
whether
those
outcomes
involve
gains
or
losses)
exert
dissociable
influences
on
decisions.
We
hypothesised
that
the
influences
of
these
two
crucial
decision
variables
(risk
and
valence)
on
decision-making
would
vary
developmentally
during
adolescence.
We
adapted
a
risk-taking
paradigm
that
provides
precise
metrics
for
the
impacts
of
risk
and
valence.
Decision-making
in
11–16
year
old
female
ado-
lescents
was
influenced
by
both
risk
and
valence.
However,
their
influences
assumed
different
developmental
patterns:
the
impact
of
valence
diminished
with
age,
while
there
was
no
developmen-
tal
change
in
the
impact
of
risk.
These
different
developmental
patterns
provide
further
evidence
that
risk
and
valence
are
fun-
damentally
dissociable
constructs
and
have
different
influences
on
decisions
across
adolescence.
© 2013 The Authors. Published by Elsevier Inc. All rights reserved.
1.
Introduction
Value
based
decision-making
involves
an
agent
choosing
from
several
alternatives
based
on
the
subjective
values
of
available
options.
Two
powerful
influences
on
such
decisions
are
risk
in
potential
夽
This
is
an
open-access
article
distributed
under
the
terms
of
the
Creative
Commons
Attribution
License,
which
permits
unrestricted
use,
distribution
and
reproduction
in
any
medium,
provided
the
original
author
and
source
are
credited.
∗
Corresponding
author.
Tel.:
+44
20
7833
7472.
E-mail
addresses:
laura.wolf.10@ucl.ac.uk
(L.K.
Wolf),
n.wright@ucl.ac.uk,
NWright@ceip.org
(N.D.
Wright),
e.kilford.12@ucl.ac.uk
(E.J.
Kilford),
r.dolan@ucl.ac.uk
(R.J.
Dolan),
s.blakemore@ucl.ac.uk
(S.-J.
Blakemore).
1
Joint
first
authors.
0885-2014/$
–
see
front
matter ©
2013 The Authors. Published by Elsevier Inc. All rights reserved.
http://dx.doi.org/10.1016/j.cogdev.2013.04.001

L.K.
Wolf
et
al.
/
Cognitive
Development
28 (2013) 290–
299 291
outcomes
(Harrison
&
Rutstroem,
2008;
Kacelnik
&
Bateson,
1996)
and
valence,
meaning
whether
those
outcomes
involve
gains
or
losses
(Dayan
&
Seymour,
2008;
Kahneman
&
Tversky,
1979).
Risk
can
be
defined
as
a
state
in
which
the
decision-maker
lacks
precise
knowledge
about
which
outcome
will
follow
from
a
decision
–
there
is
uncertainty.
Individuals
may
be
risk-averse
(preferring
lower
risk
options
when
comparing
options
with
identical
expected
value
(EV)),
risk-neutral,
or
risk-seeking
(preferring
higher
to
lower
risk
options).
Valence
is
defined
as
whether
potential
outcomes
entail
punishment
(e.g.,
financial
losses
or
painful
electric
shocks)
or
rewards
(e.g.,
financial
gains
or
tasty
foods).
The
goal
of
the
present
study
was
to
investigate
the
development
of
responses
to
these
two
crucial
decision
variables,
risk
and
valence,
from
early-
to
mid-adolescence
(aged
11–16
years).
1.1.
The
effect
of
valence
on
decisions
in
adolescence
We
were
particularly
interested
in
the
development
of
the
impact
of
valence
on
decision-making,
prompted
in
part
by
a
recent
study
that
investigated
valence-dependent
reversal
learning
in
response
to
unexpected
reward
and
punishment
in
adolescence
(Van
der
Schaaf,
Warmerdam,
Crone,
&
Cools,
2011
).
Younger
adolescents
(age
10–11)
displayed
better
reversal
learning
scores
following
a
pun-
ishment
than
following
a
reward,
and
this
difference
in
performance
decreased
with
age
across
adolescence
(from
age
10
to
16).
We
investigated
whether
there
is
a
similar
development
in
the
effect
of
valence
on
risky
decision-making
in
early
to
mid-adolescence.
1.2.
Development
of
risk-taking
in
adolescence
Previous
studies
have
suggested
that
different
aspects
of
risky
decision-making
show
differ-
ent
developmental
patterns
(Blakemore
&
Robbins,
2012).
Developmental
trajectories
of
risk-taking
behaviour
differ
depending
on
whether
decisions
are
made
in
an
affective
or
“hot”
experimental
context
(e.g.,
when
emotions
are
involved
or
peers
are
present)
or
a
non-affective
or
“cold”
con-
text.
In
affective
contexts,
there
is
evidence
that
risk-taking
peaks
in
mid-adolescence.
A
peak
in
reward-sensitivity
in
mid-
to
late
adolescence
(14–21
years)
was
found
on
a
modified
version
of
the
Iowa
Gambling
Task
(IGT;
Cauffman
et
al.,
2010).
Participants
in
the
IGT
choose
among
four
packs
of
cards,
each
associated
with
different
profiles
of
monetary
gain
and
loss
(Bechara,
Damasio,
Damasio,
&
Anderson,
1994).
Two
packs
are
apparently
lucrative
but
eventually
result
in
significant
loss
(disad-
vantageous
packs).
The
other
two
packs
are
“steady
earners,”
with
small
wins
hardly
ever
penalised
by
even
smaller
losses
(advantageous
packs).
Adults
tend
to
sample
the
disadvantageous
packs
initially
but
then
settle
on
the
advantageous
options.
Cauffman
and
colleagues
(2010)
designed
a
modified
version
of
the
IGT
in
which
gambling
decisions
were
made
about
a
particular
deck
on
each
trial,
which
enabled
assessment
of
decision-making
in
response
to
gains
or
losses.
There
appeared
to
be
a
linear,
age-related
increase
in
the
tendency
to
avoid
the
disadvantageous
packs
over
the
course
of
the
task.
However,
compared
with
younger
adolescents
and
adults,
mid-
to
late
adolescents
learned
more
quickly
to
play
from
the
advantageous
packs,
suggesting
that
this
age
group
shows
a
heightened
sensitivity
to
approaching
rewards
(Cauffman
et
al.,
2010).
In
a
study
employing
a
gambling
task
designed
to
induce
relief
or
regret
(Burnett,
Bault,
Coricelli,
&
Blakemore,
2010),
a
quadratic
relationship
emerged
between
age
(9–35
years)
and
risk-taking,
which
peaked
in
mid-adolescence
(around
age
14).
In
a
further
study,
adolescents
(age
14–19)
and
adults
(age
20+)
played
a
card
game
in
which
cards
could
be
turned
over
as
long
as
gains
were
encountered,
but
as
soon
as
participants
received
a
loss
the
trial
terminated
(
Figner,
Mackinlay,
Wilkening,
&
Weber,
2009a).
Compared
with
adults,
adolescents
exhibited
sub-
optimal
decision-making,
failing
to
consider
value
and
probability
information
when
making
decisions
in
an
affective
but
not
a
non-affective
version
of
the
task.
In
a
follow-up
experiment,
10-year
olds
per-
formed
at
a
level
similar
to
adults,
suggesting
that
risk-taking
in
affective
contexts
peaks
in
adolescence
but
does
not
change
in
a
non-affective
context
(Figner,
Mackinlay,
Wilkening,
&
Weber,
2009b).
In
other
gambling
tasks
where
feedback
is
given
but
the
context
is
non-affective,
there
is
no
evidence
of
a
mid-adolescent
peak
in
risk-taking;
instead
these
tasks
show
a
gradual
decrease
in
risk-taking
or
no
developmental
change
(Paulsen,
Platt,
Huettel,
&
Brannon,
2011;
Rakow
&
Rahim,
2010;
Van
Leijenhorst
et
al.,
2010).
In
a
non-affective
task
in
which
participants
aged
8–18
chose

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Cognitive
Development
28 (2013) 290–
299
between
a
sure
outcome
and
a
gamble
option
(either
high-
or
low-risk),
risk-taking
decreased
across
adolescence.
Older
adolescents
chose
low-risk
gambles
more
frequently
than
high-risk
gambles,
and
this
difference
was
smaller
in
younger
participants
(Crone,
Bullens,
Van
Der
Plas,
Kijkuit,
&
Zelazo,
2008
).
These
studies
suggest
that
risk-taking
peaks
in
mid-adolescence
in
an
affective
context,
while
risk-taking
remains
stable
or
decreases
in
a
non-affective
context.
However,
it
must
be
noted
that
the
distinction
between
affective
versus
non-affective
contexts
is
not
always
straightforward,
which
could
lead
to
inconsistencies
in
the
literature,
especially
because
different
studies
employ
different
paradigms,
age
ranges,
sample
sizes,
and
measures
of
risk-taking.
While
the
affective
context
of
decision-making
tasks
has
been
modulated
in
previous
developmen-
tal
studies,
one
aspect
of
decision-making
that
has
not
yet
been
examined
is
the
differential
impact
of
valence
and
risk.
We
use
a
non-affective
task
to
isolate
the
effects
of
risk
and
valence
on
decisions
(and
developmental
change
in
those
effects),
without
studying
how
they
interact
with
emotion.
1.3.
Independent
effects
of
valence
and
risk
on
decision-making
in
adults
The
proposal
that
the
impacts
of
risk
and
valence
might
show
different
developmental
patterns
is
predicated
on
recent
research
suggesting
that
risk
and
valence
have
independent
effects
on
adult
decision-making
(Wright
et
al.,
2012).
The
prevailing
view
in
psychology
and
economics
has
been
that
risk
and
valence
are
related
in
a
specific
fashion,
with
risk-aversion
occurring
for
gains
and
risk-
seeking
for
losses,
given
medium
to
high
probabilities
for
both
gain
and
loss
outcomes
(Kahneman
&
Tversky,
1979).
An
alternative
hypothesis
is
that
valence
and
risk
exert
independent
influences
on
decisions
in
gambling
tasks
(Wright
et
al.,
2012).
This
alternative
hypothesis
was
motivated
by
evidence
that
multiple,
interacting
neural
valuation
systems
influence
decisions
(Dayan,
2008),
with
processing
of
risk
and
valence
by
distinct
neural
systems
being
consistent
with
independent,
rather
than
linked,
behavioural
effects.
Behavioural
and
neurobiological
evidence
for
a
dissociation
between
the
influences
of
risk
and
valence
on
decisions
has
been
derived
from
studies
that
employed
a
financial
gambling
task
that
separately
manipulated
risk
and
valence
(Wright
et
al.,
2012).
1.4.
The
present
study
We
adapted
the
financial
gambling
task
used
by
Wright
et
al.
(2012)
to
obtain
precise
metrics
for
developmental
changes
in
the
impacts
of
risk
and
valence
during
adolescence
(age
11–16).
We
first
investigated
whether
adolescent
decision-making
is
influenced
by
both
risk
and
valence
and
next
examined
whether
these
influences
are
independent
of
one
another.
We
then
asked
whether
the
influences
of
risk
and
valence
on
decision-making
show
different
developmental
patterns
during
adolescence.
Developmental
change
was
possible
in
the
impact
of
either
risk
or
valence,
or
of
both,
on
decisions
in
this
non-affective
risk-taking
task.
2.
Method
2.1.
Participants
Sixty-four
female
adolescents
(mean
age
13.9
years,
range
11–16
years)
took
part.
Data
from
three
participants
were
excluded
(two
were
unable
to
complete
the
task
and
one
confused
the
buttons).
All
were
recruited
from
the
same
academically
selective
secondary
school
in
North
London
and
were
well
matched
for
educational
background
and
socioeconomic
status.
Participants
were
individually
tested
in
a
quiet
classroom
at
their
school.
Verbal
IQ
was
assessed
with
the
BPVS
II
(Dunn,
Dunn,
Whetton,
&
Burley,
1997)
for
all
but
seven
participants
(whose
verbal
IQ
could
not
be
assessed
due
to
time
limitations
at
the
school).
Verbal
IQ
was
not
associated
with
age
(mean
=
114.28,
SD
=
13.38,
range
82–138;
ˇ
=
0.002,
r
2
<
0.001,
p
>
0.9)
and
covarying
verbal
IQ
did
not
affect
any
of
our
experimental
results.
Due
to
sex
differences
in
brain
maturation
(Giedd
et
al.,
1999),
we
tested
only
female
participants.

L.K.
Wolf
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Cognitive
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28 (2013) 290–
299 293
Fig.
1.
Experimental
design.
In
each
trial,
participants
were
instructed
to
choose
between
a
lottery
and
sure
option.
The
lottery
was
represented
by
a
pie
chart
with
three
segments
corresponding
to
the
three
possible
outcomes,
with
the
size
of
each
segment
corresponding
to
the
probability
of
that
outcome
occurring.
The
sure
option
was
indicated
on
the
upper
right
side
of
the
screen.
Half
the
trials
involved
winning
points
(“gain”
trials)
and
half
involved
losing
points
(“loss”
trials).
(a)
In
each
gain
trial,
participants
chose
either
to
accept
a
lottery
(three
varying
possible
outcomes,
all
≥0)
or
reject
it
in
favour
of
a
sure
gain
of
four
points.
(b)
In
each
loss
trial,
participants
chose
either
to
accept
a
lottery
(three
varying
possible
outcomes,
all
≤0)
or
reject
it
in
favour
of
a
sure
loss
of
four
points.
2.2.
Pretest
of
stimulus
understanding
We
first
employed
a
validity
check
to
ensure
that
participants
understood
basic
information
dis-
played
in
pie
chart
stimuli.
Participants
saw
eight
printed
pairs
of
pie
charts,
and
for
each
pair
were
told
they
should
try
to
win
as
many
points
as
possible
(in
the
gain
trials)
or
lose
as
few
points
as
possible
(in
the
loss
trials)
by
choosing
one
of
the
two
pie
charts.
We
tested
understanding
of
gains
and
losses
(four
pairs
of
pie
charts
contained
only
gains
and
four
only
losses),
magnitudes
(in
four
pairs,
magnitudes
differed
while
probabilities
were
identical
between
the
two
pie
charts),
and
probabilities
(magni-
tudes
were
identical
and
probabilities
differed).
Previous
work
has
shown
that
5-year
olds
understand
simple
probabilities
(Schlottmann,
2001),
and
8-year
olds
understand
how
risk
and
reward
outcome
contribute
to
a
gamble’s
EV
(Van
Leijenhorst,
Westenberg,
&
Crone,
2008).
As
expected,
all
participants
met
our
inclusion
criterion
of
over
75%
accuracy
(mean
correct
responses
=
96.7%,
SD
=
6.4%).
2.3.
The
gambling
task
Participants
completed
a
computer-based
financial
gambling
task
based
on
the
“accept/reject”
task
used
by
Wright
et
al.
(2012).
In
the
“accept/reject”
task
used
here
(Fig.
1),
participants
performed
112
trials
presented
in
random
order,
of
which
56
were
gain
trials
(all
possible
outcomes
≥0),
and
56
were

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Cognitive
Development
28 (2013) 290–
299
loss
trials
(all
outcomes
≤0).
In
each
trial,
participants
chose
to
accept
either
a
lottery
(with
three
possible
outcomes)
or
a
sure
outcome
(a
gain
of
four
points
in
gain
trials,
and
a
loss
of
four
in
loss
trials).
Each
trial
began
with
a
fixation
cross
presented
for
1–2
s
(mean
=
1.5
s),
followed
by
a
display
of
the
options
for
5
s.
Finally,
a
black
square
appeared
to
signal
that
participants
had
2
s
to
indicate
their
decision
by
pressing
a
button.
Participants
were
informed
that
not
responding
resulted
in
the
worst
possible
outcome,
corresponding
to
zero
points
in
the
gain
trials
and
loss
of
eight
points
in
the
loss
trials.
The
112
trials
were
split
into
two
blocks
of
56
trials,
with
each
block
lasting
approximately
8
min.
No
feedback
was
given.
We
manipulated
risk
by
using
a
set
of
56
lotteries
(three
possible
outcomes,
all
≥0),
in
which
we
parametrically
and
orthogonally
manipulated
degree
of
risk
(i.e.
variance;
eight
levels)
and
EV
(seven
levels).
In
adapting
the
paradigm
for
a
younger
age
group
it
was
not
possible
to
fully
control
for
skewness
(a
further
aspect
of
risk),
which
ranged
between
−40
and
40
in
the
current
sample.
Half
the
lotteries
had
an
EV
above
the
sure
amount,
and
half
were
below
it.
We
presented
each
lottery
in
this
set
once
to
produce
56
gain
trials.
To
manipulate
valence,
we
multiplied
all
outcome
amounts
by
−1
to
produce
56
loss
trials
(i.e.,
all
outcomes
≤0,
and
a
sure
option
of
−4).
This
created
a
set
of
gain
trials
and
a
set
of
matched
loss
trials.
Participants
were
told
to
treat
the
points
as
currency,
at
an
exchange
rate
of
one
point
equal
to
50
pence.
Participants
began
the
testing
session
with
an
endowment
of
eight
points.
After
the
experiment,
one
gain
trial
and
one
loss
trial
were
picked
at
random,
and
their
outcomes
were
added
to
this
endowment
to
determine
a
final
payment.
Participants
could
receive
0–16
points,
which
at
the
end
of
the
task
were
converted
into
GBP
(i.e.,
£0–8).
Participants
also
received
£1
for
participation.
2.4.
Stimulus
sets
We
generated
a
set
of
56
lotteries
by
orthogonally
manipulating
the
variance
(8
levels;
mean
=
7.5,
range
0.9–14.4)
and
EV
(7
levels;
mean
=
4.0;
range
=
2.2–5.8)
of
the
lottery.
We
created
this
stimulus
set
in
two
stages.
First,
we
generated
a
list
of
every
possible
trial
within
the
following
constraints:
each
lottery
had
three
outcomes
(three
pie
chart
segments);
outcomes
were
between
0
and
8
points;
the
smallest
allowable
probability
was
0.1
(to
avoid
probability
distortion);
and
the
smallest
allowable
probability
increment
was
0.05.
Next,
we
selected
the
56
trials
that
most
closely
matched
our
desired
eight
levels
of
variance
and
seven
levels
of
EV.
2.5.
Data
analysis
Expected
value
of
half
the
lotteries
was
above
the
sure
amount
and
half
below
(mean
EV
across
all
56
trials
was
equal
to
the
sure
option
in
both
gain
and
loss
trials).
Thus,
the
proportion
of
riskier
decisions
was
used
as
a
metric
of
participants’
risk
preference
(PropRisk:
risk-neutral
=
0.5;
risk-averse
<
0.5;
risk-seeking
>
0.5).
To
derive
an
individual
measure
for
the
effect
of
valence
on
decisions,
the
impact
of
valence
was
calculated
as
the
difference
in
proportion
of
riskier
decisions
between
gain
and
loss
trials
(ImpValence
=
PropRisk
gain
−
PropRisk
loss
).
Wright
et
al.
(2012)
used
the
same
metrics
for
risk
and
valence.
We
used
these
participant-derived
parameters
in
two
separate
analyses.
First,
we
assessed
whether
adolescent
decision-making
is
influenced
by
both
risk
and
valence.
To
examine
whether
risk
influ-
enced
decisions,
we
performed
a
one-sample
t-test
to
assess
whether
overall
PropRisk
(PropRiskall
i.e.,
collapsed
across
gains
and
losses)
was
significantly
different
from
0.5.
To
examine
whether
valence
influenced
decisions,
we
performed
a
one-sample
t-test
to
assess
whether
ImpValence
was
significantly
different
from
0.
Second,
we
used
regression
analyses
to
determine
whether
these
influences
on
decision-making
are
independent
and
whether
they
show
different
developmental
patterns
during
adolescence.
To
examine
whether
the
effect
of
age
on
valence
was
distinct
from
that
of
age
on
risk,
we
performed
a
forced
entry
multiple
regression
with
age
and
PropRisk
all
(proportion
of
riskier
decisions
collapsed
across
gain
and
loss
trials)
as
predictors
and
ImpValence
(the
difference
in
proportion
of
riskier
decisions
between
gain
and
loss
trials)
as
the
dependent
variable.

L.K.
Wolf
et
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/
Cognitive
Development
28 (2013) 290–
299 295
Fig.
2.
Risk
and
valence
both
influenced
decisions,
and
individuals’
preferences
for
both
were
not
associated
(a)
Individuals
were
significantly
risk-averse
overall
(PropRisk
all
<
0.5)
as
opposed
to
risk-neutral.
Valence
(ImpValence
=
PropRisk
gain
−
PropRisk
loss
)
also
significantly
influenced
decisions,
with
more
gambling
for
gains
than
losses.
(b)
Individuals’
preferences
related
to
risk
(PropRisk
all
)
and
valence
(ImpValence)
were
not
associated.
Error
bars
indicate
standard
error.
***p
<
0.001.
3.
Results
3.1.
Task
performance
Participants
performed
the
task
well,
with
non-response
rates
(total
2.1
±
2.8%,
gains
1.6
±
2.9%,
losses
2.6
±
3.2%)
similar
to
levels
previously
reported
in
adults
(Wright
et
al.,
2012),
and
not
associated
with
age
(ˇ
=
0.007,
r
2
<
0.001,
p
>
0.9).
3.2.
Risk
and
valence
influence
decisions
We
first
examined
whether
both
risk
and
valence
influence
decisions.
Half
the
lotteries
had
an
EV
above
the
sure
amount
and
half
below
(mean
EV
across
all
56
trials
was
equal
to
the
sure
option
with
both
gains
and
losses),
which
provided
a
simple
metric
of
risk
preference
for
each
participant
indexed
as
the
proportion
of
riskier
decisions
made
(PropRisk;
risk-neutral
=
0.5;
risk-
averse
<
0.5;
risk-seeking
>
0.5).
Individuals
were,
on
average,
significantly
averse
to
risk
(PropRisk
all
=
0.44
±
0.10)
as
opposed
to
risk-neutral,
t
(60)
=
−4.9,
p
<
0.001
(Fig.
2a).
We
also
extracted
a
simple
metric
for
the
impact
of
valence
for
each
participant
from
the
difference
in
riskier
decisions
between
gain
and
loss
trials
(ImpValence
=
PropRisk
gain
−
PropRisk
loss
).
Individuals
were
also
sensitive
to
valence
(ImpVa-
lence
=
0.12
±
0.13),
t
(60)
=
7.3,
p
<
0.001
(Fig.
2a).
Individuals
selected
the
riskier
option
more
frequently
in
gain
trials
than
in
loss
trials
(PropRisk
gain
=
0.50
±
0.12;
PropRisk
loss
=
0.38
±
0.12),
t
(60)
=
7.3,
p
<
0.001.
Participants
were
risk-neutral
with
gains,
t
(60)
=
−0.04,
p
>
0.9,
and
risk-averse
with
losses,
t
(60)
=
−8.0,
p
<
0.001.
In
sum,
both
risk
and
valence
influence
decisions,
as
has
previously
been
shown
with
adults
(Wright
et
al.,
2012).
We
next
asked
whether
risk
and
valence
had
independent
impacts
on
decision-making
using
regression
analysis.
Consistent
with
previous
adult
data
(Wright
et
al.,
2012),
the
impacts
of
risk
and
valence
on
individuals’
decisions
were
not
associated
with
each
other,
ˇ
=
−0.02,
r
2
<
0.001,
p
>
0.8
(Fig.
2b).
3.3.
Developmental
changes
in
the
influences
of
risk
and
valence
on
decisions
Finally,
we
asked
how
the
impacts
of
risk
and
valence
on
decisions
changed
with
age.
Our
data
revealed
that
the
influences
of
risk
and
valence
show
different
developmental
patterns
(Fig.
3).
The
influence
of
risk
on
decisions
did
not
change
with

296 L.K.
Wolf
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/
Cognitive
Development
28 (2013) 290–
299
Fig.
3.
The
impacts
of
risk
and
valence
have
different
developmental
patterns.
We
asked
how
the
impacts
of
risk
(PropRisk
all
)
and
valence
(ImpValence)
on
decisions
changed
with
age.
(a)
The
influence
of
risk
overall
(PropRisk
all
)
did
not
change
with
age.
(b)
The
impact
of
valence
(ImpValence)
decreased
significantly
with
age.
age,
ˇ
=
−0.04,
r
2
=
0.001,
p
>
0.7
(Fig.
3a).
In
contrast,
the
impact
of
valence
on
decision-making
decreased
with
age,
ˇ
=
−0.30,
r
2
=
0.09,
p
=
0.02
(Fig.
3b).
To
demonstrate
that
the
effect
of
age
on
valence
was
distinct
from
the
effect
of
age
on
risk,
we
performed
a
forced
entry
mul-
tiple
regression
with
age
and
PropRisk
all
as
predictors
and
ImpValence
as
the
dependent
variable.
Age,
as
a
single
independent
variable,
significantly
predicted
ImpValence,
ˇ
=
−0.30,
r
2
=
0.089,
p
=
0.02.
When
PropRisk
all
was
added
as
a
second
indepen-
dent
variable,
the
effect
of
age
on
ImpValence
remained
significant,
ˇ
=
−0.30,
p
=
0.02.
PropRisk
all
did
not
predict
ImpValence,
ˇ
=
−0.03,
r
2
=
0.001,
p
>
0.8.
The
change
in
the
impact
of
valence
during
adolescence
was
not
driven
by
a
change
in
responses
to
either
gains
or
losses
alone,
with
neither
PropRisk
gain
,
ˇ
=
−0.20,
r
2
=
0.04,
p
=
0.13,
nor
PropRisk
loss
,
ˇ
=
0.13,
r
2
=
0.02,
p
>
0.3,
significantly
predicted
by
age.
4.
Discussion
We
have
demonstrated
that
risk
and
valence
influence
decision-making
in
11–16-year-old
female
adolescents.
The
degrees
to
which
risk
and
valence
impact
individuals’
decisions
are
not
related,
con-
sistent
with
previous
data
for
adults
(Wright
et
al.,
2012).
Moreover,
the
influences
of
risk
and
valence
show
different
developmental
patterns
across
the
age
range
studied:
While
the
impact
of
risk
did
not
change
with
age,
the
degree
to
which
valence
influenced
decisions
diminished
with
age
(Fig.
3).
4.1.
Risk-taking
in
adolescence
is
stable
in
this
non-affective
task
Previous
work
has
suggested
that
the
development
of
risky
decision-making
in
affective
and
non-
affective
tasks
varies
during
adolescence.
Several
studies
have
reported
a
peak
in
risk-taking
in
mid-
adolescence
for
affective
tasks,
such
as
those
involving
emotions
(Burnett
et
al.,
2010;
Cauffman
et
al.,

L.K.
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Cognitive
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28 (2013) 290–
299 297
2010;
Figner
et
al.,
2009a;
Figner
et
al.,
2009b),
whereas
non-affective
tasks
demonstrate
either
no
change
or
a
decrease
in
risk-taking
with
age
(Crone
et
al.,
2008;
Figner
et
al.,
2009a;
Figner
et
al.,
2009b;
Paulsen
et
al.,
2011;
Rakow
and
Rahim,
2010).
In
the
present
study,
we
adapted
a
non-affective
task
used
by
Wright
et
al.
(2012)
to
dissociate
the
influences
of
risk
and
valence
on
decision-making
in
adults.
Adolescents
were
risk-averse
in
this
gambling
task,
meaning
that
they
made
fewer
riskier
than
safe
decisions.
We
also
demonstrated
distinct
developmental
patterns
in
the
impacts
of
risk
and
valence
during
early
to
mid-adolescence.
Developmental
stability
in
risk-taking
between
early
and
mid-adolescence
has
been
reported
in
previous
studies
examining
risky
decision-making
in
a
non-affective
task
(Figner
et
al.,
2009a).
In
contrast,
tasks
employing
an
affective
context
often
find
evidence
for
a
peak
in
risk-taking
around
mid-adolescence
(Burnett
et
al.,
2010;
Figner
et
al.,
2009a).
Our
results
support
those
by
Figner
et
al.,
suggesting
that,
in
the
absence
of
affective
task
components,
the
propensity
to
take
risks
does
not
change
during
adolescence.
4.2.
The
impact
of
valence
declines
across
early
to
mid-adolescence
Adolescent
decisions
were
influenced
by
valence,
such
that
fewer
riskier
decisions
were
made
for
losses
than
for
gains.
The
effect
of
valence
on
risky
decision-making
decreased
across
adolescence:
Compared
with
mid-adolescents,
younger
adolescents
were
more
biased
away
from
the
riskier
option
by
losses
relative
to
gains.
This
effect
was
not
driven
by
a
change
in
responses
to
gains
or
losses
alone;
thus,
the
decrease
in
the
effect
of
valence
could
be
explained
by
a
symmetrical
reduction
of
the
difference
between
risky
decisions
in
the
gain
and
loss
domains.
This
would
suggest
that
the
effect
does
not
derive
from
younger
adolescents
more
frequently
choosing
the
computationally
simpler
option
in
each
trial.
In
a
previous
study
requiring
adolescents
to
learn
to
select
advantageous
decks
of
cards
and
to
avoid
selecting
disadvantageous
decks,
there
was
an
age-related
increase
in
the
propen-
sity
to
avoid
the
disadvantageous
decks
over
the
course
of
the
experiment
(Cauffman
et
al.,
2010).
Older
participants
learned
more
quickly
to
avoid
playing
the
disadvantageous
decks;
this
was
inter-
preted
as
an
increase
in
loss-aversion
during
adolescence.
In
contrast,
the
reduction
in
the
impact
of
valence
during
adolescence
seen
here
is
consistent
with
a
recent
study
examining
changes
dur-
ing
adolescence
in
the
effect
of
valence
during
a
probabilistic
reversal
learning
decision
task
(Van
der
Schaaf
et
al.,
2011).
In
that
study
the
effect
of
valence
on
decisions
declined
with
increasing
age
(from
10
to
16
years),
such
that
younger
adolescents
displayed
greater
sensitivity
to
unexpected
financial
losses
compared
to
unexpected
gains
than
did
older
adolescents.
Thus,
the
developmental
change
in
the
effect
of
valence
seen
in
our
gambling
task
might
reflect
more
general
developmental
changes
in
the
influence
of
such
approach-avoidance
processes
on
decisions
(Van
der
Schaaf
et
al.,
2011
).
4.3.
Distinct
developmental
patterns
of
the
impacts
of
risk
and
valence
Our
finding
provides
a
new
source
of
evidence
in
a
debate
between
models
of
risky
economic
decision-making.
The
prevailing
view
in
psychology
and
economics
is
that
risk
and
valence
are
related
to
each
other
in
a
specific
fashion
(risk-aversion
for
gains
and
risk-seeking
for
losses
for
the
proba-
bilities
used
in
the
current
task),
and
that
these
preferences
arise
as
the
product
of
a
utility
function
concave
for
gains
and
convex
for
losses
(Kahneman
and
Tversky,
1979;
Tversky
and
Kahneman,
1992).
An
alternative
neurobiology-based
hypothesis
is
that
valence
and
risk
exert
independent
influences
on
economic
decisions
(Wright
et
al.,
2012).
This
hypothesis
is
supported
by
behavioural
evidence
for
a
dissociation
between
the
influences
of
risk
and
valence
on
adults’
decisions,
and
by
neural
evidence
for
dissociable
neural
substrates
related
to
the
manipulations
of
valence
and
risk
in
orbitofrontal
and
parietal
cortices,
respectively
(Wright
et
al.,
2012).
Our
finding
that
the
effects
of
risk
and
valence
show
different
developmental
patterns
during
adolescence
is
readily
accommodated
by
neurobio-
logical
models
where
multiple,
interacting
neural
decision-systems
contribute
to
decision-making
(
Dayan,
2008).
For
example,
decision-systems
themselves
(or
those
regulating
the
balance
between
them)
might
develop
differently
across
adolescence.
Further,
these
neurobiological
models
expand
upon
existing
risk-return
models
of
risky
decisions
(Bossaerts,
2010;
Markowitz,
1952),
which
do
not

298 L.K.
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28 (2013) 290–
299
include
effects
of
valence.
We
found
adolescent
participants
were
more
risk-averse
for
losses
than
gains,
which
parallels
the
findings
by
Wright
et
al.
(2012)
and
contrasts
with
other
studies,
which
often
report
that
participants
are
risk-seeking
in
the
loss
domain
and
risk-averse
in
the
gain
domain,
for
the
probabilities
used
in
this
study
(Kahneman
and
Tversky,
1979).
This
difference
could
be
due
to
the
format
of
the
tasks.
For
each
trial
in
the
present
study,
participants
considered
whether
to
accept
a
lottery
or
reject
it
in
favour
of
a
sure
option;
when
the
lottery
contains
losses
this
could
induce
participants
to
avoid
it.
When
instead
individuals
were
asked
to
evaluate
and
select
one
of
two
options,
they
could
not
express
avoidance
by
withdrawal
but
could
potentially
avoid
losses
by
selecting
the
riskier
option.
Such
a
variant
of
our
task
used
with
adults
(Wright
et
al.,
2012)
showed
pre-
cisely
this
reversal
in
the
direction
of
the
valence
effect
(i.e.,
more
gambling
for
gains
than
for
losses).
With
respect
to
Prospect
Theory
(Kahneman
and
Tversky,
1979),
while
our
data
do
not
support
the
‘reflection
effect’
(i.e.,
risk-seeking
with
losses
and
risk-aversion
with
gains),
an
approach-avoidance
account
is
consistent
with
‘loss
aversion’
where
losses
have
greater
weight
(‘loom
larger’)
than
gains.
4.4.
Limitations
and
implications
As
noted
earlier,
changes
to
the
task
format
can
change
the
direction
of
the
effect
of
valence.
Future
developmental
studies
could
examine
the
effects
of
valence
and
risk
on
decisions
in
other
paradigms
and
other
domains
of
risky
decision-making
–
for
example,
in
mixed
gain-loss
gambles.
This
study
used
simple
metrics
of
the
proportion
of
risky
decisions
made.
Future
work
could
also
dissociate
the
developmental
trajectories
of
responses
to
specific
components
of
risk,
such
as
variance
and
skewness,
which
have
both
been
shown
to
influence
adults’
decisions
(Symmonds
et
al.,
2011).
Despite
the
developmental
change
in
the
influence
of
valence
over
adolescence,
collapsing
across
age,
adolescents
show
similar
impacts
of
risk
and
valence
as
found
in
adults
(Wright
et
al.,
2012).
Therefore,
adolescents
and
adults
might
generally
use
similar
strategies
when
making
risky
decisions
in
a
non-affective
context.
One
explanation
for
the
gradual
decrease
in
the
influence
of
valence
during
adolescence
is
a
developmental
shift
in
cognitive
strategies
used
to
make
decisions
in
the
kind
of
task
employed
here.
The
previous
study
with
adults
employed
two
versions
of
the
task.
In
one,
for
each
trial
there
was
a
fixed
evaluation
period
before
the
decision
period
(as
in
the
present
study).
In
the
other,
individuals
were
free
to
respond
at
any
point
the
stimuli
were
displayed.
There
was
no
difference
in
the
effects
of
risk
or
valence
across
these
two
versions
of
the
task
for
adults
(Wright
et
al.,
2012),
but
we
have
not
examined
whether
this
is
true
for
adolescents.
We
intentionally
avoided
including
an
affective
component
in
our
task,
so
that
we
could
study
the
effects
of
risk
and
valence
on
decisions
without
the
influence
of
emotion.
Future
studies
could
examine
how
an
affective
context
(for
example,
the
presence
of
peers)
interacts
with
the
influence
of
risk
and
valence
on
decisions.
In
sum,
we
show
that
the
impacts
of
risk
and
valence
on
decision-making
in
a
non-affective
context
show
different
developmental
patterns.
While
the
influence
of
risk
does
not
change
with
age,
the
impact
of
valence
decreases
during
adolescence.
We
speculate
that
these
results
may
have
implications
for
public
health
policy.
Types
of
public
health
information
provided,
and
the
way
information
is
presented,
should
be
tailored
to
specific
age
groups
to
maximise
impact.
Parsing
the
influences
on
risky
decisions
and
understanding
how
they
develop
will
help
determine
which
to
stress
for
a
given
age
group.
Framing
something
as
a
loss,
for
example,
may
be
more
effective
with
younger
than
older
adolescents.
Acknowledgements
This
study
was
supported
by
the
Wellcome
Trust
and
the
Royal
Society.
LKW
was
supported
by
a
Wellcome
Trust
4-year
PhD
programme
in
neuroscience
at
UCL,
NDW
was
supported
by
a
Well-
come
Trust
Research
Training
Fellowship,
RJD
is
supported
by
a
Wellcome
Trust
Programme
Grant
078865/Z/05/Z,
and
SJB
is
supported
by
Royal
Society
University
Research
Fellowship.

L.K.
Wolf
et
al.
/
Cognitive
Development
28 (2013) 290–
299 299
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