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The ventral striatum displays hyper-responsiveness to reward in adolescents relative to other age groups, and animal research on the developmental trajectory of the dopaminergic system suggests that dopamine may underlie adolescent sensitivity to reward. However, practical limitations prevent the direct measurement of dopamine in healthy adolescents. Eye blink rate (EBR) shows promise as a proxy measure of striatal dopamine D2 receptor function. We investigated developmental differences in the relationship between EBR and reward-seeking behavior on a risky decision-making task. Increasing EBR was associated with greater reward maximization on the task for adolescent but not adult participants. Furthermore, adolescents demonstrated greater sensitivity to reward value than adults, as evinced by shifts in decision patterns based on increasing potential reward. These findings suggest that previously observed adolescent behavioral and neural hypersensitivity to reward may in fact be due to greater dopamine receptor activity, as represented by the relationship of blink rate and reward-seeking behavior. They also demonstrate the feasibility and utility of using EBR as a proxy for dopamine in healthy youth in whom direct measurements of dopamine are prohibitively invasive.
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SHORT REPORT
Eye blink rate predicts reward decisions in adolescents
Emily Barkley-Levenson
1
and Adriana Galv
an
2,3
1. Department of Psychology, Hofstra University, New York, USA
2. Department of Psychology, University of California Los Angeles, Los Angeles, USA
3. Brain Research Institute, University of California Los Angeles, Los Angeles, USA
Abstract
The ventral striatum displays hyper-responsiveness to reward in adolescents relative to other age groups, and animal research on
the developmental trajectory of the dopaminergic system suggests that dopamine may underlie adolescent sensitivity to reward.
However, practical limitations prevent the direct measurement of dopamine in healthy adolescents. Eye blink rate (EBR) shows
promise as a proxy measure of striatal dopamine D2 receptor function. We investigated developmental differences in the
relationship between EBR and reward-seeking behavior on a risky decision-making task. Increasing EBR was associated with
greater reward maximization on the task for adolescent but not adult participants. Furthermore, adolescents demonstrated
greater sensitivity to reward value than adults, as evinced by shifts in decision patterns based on increasing potential reward.
These findings suggest that previously observed adolescent behavioral and neural hypersensitivity to reward may in fact be due to
greater dopamine receptor activity, as represented by the relationship of blink rate and reward-seeking behavior. They also
demonstrate the feasibility and utility of using EBR as a proxy for dopamine in healthy youth in whom direct measurements of
dopamine are prohibitively invasive.
Research highlights
Study demonstrates the feasibility and utility of using
eye blink rate (EBR) as a proxy for dopamine in
healthy youth in whom direct measurements of
dopamine are prohibitively invasive.
EBR was associated with reward-seeking behavior in
adolescents but not adults
Adolescents show more value sensitivity than adults.
Introduction
Adolescents exhibit high sensitivity to rewards. Func-
tional magnetic resonance imaging (fMRI) has been used
to investigate the neural substrates of adolescent respon-
siveness to reward, and evidence generally demonstrates
that the ventral striatum (VS) is hyper-responsive to the
anticipation and experience of reward (May, Delgado,
Dahl, Stenger, Ryan et al., 2004; Ernst, Nelson, Jazbec,
McClure, Monk et al., 2005; Galv
an, Hare, Parra, Penn,
Voss et al., 2006; Cohen, Asarnow, Sabb, Bilder,
Bookheimer et al., 2010; Geier, Terwilliger, Teslovich,
Velanova & Luna, 2010; van Leijenhorst, Zanolie, Van
Meel, Westenberg, Rombouts et al., 2010) and to value
more generally (Barkley-Levenson & Galv
an, 2014) in
adolescents relative to other age groups, although such
findings are not universal (e.g. Bjork, Knutson, Fong,
Caggiano, Bennett et al., 2004; Bjork, Smith, Chen &
Hommer, 2010 and Bjork & Pardini, 2015, who suggest
that their divergent results may be due to task-specific
characteristics such as motor preparation and atten-
tional demands and individual differences among ado-
lescents on traits such as behavioral disinhibition). While
fMRI has been key in elucidating the importance of the
VS in adolescent reward responsiveness, complementary
techniques are required to explore the neurochemical
mechanisms underlying value processing in adolescents.
Of particular interest is striatal dopamine, which is
associated with the experience and expectation of reward
(e.g. Schultz, Apicella & Ljungberg, 1993; Schultz,
Dayan & Montague, 1997; Ikemoto & Panksepp, 1999).
Aspects of the dopamine system differ in adolescence
relative to childhood or adulthood. The number of D1
Address for correspondence: Adriana Galv
an, Department of Psychology, University of California Los Angeles, Los Angeles, CA 90095-1563, USA;
e-mail: agalvan@psych.ucla.edu
©2016 John Wiley & Sons Ltd
Developmental Science (2016), pp 1–11 DOI: 10.1111/desc.12412
and D2 dopamine receptors in the rat striatum peaks
during adolescence before undergoing pruning (Teicher,
Andersen & Hostetter, 1995), and adolescent rat
dopaminergic neurons release more dopamine in response
to environmental or pharmacological stimulation than
adult neurons do (Laviola, Pascucci & Pieretti, 2001;
Laviola, Macr
ı, Morley-Fletcher, and Adriani, 2003).
However, direct investigation of dopamine in the human
adolescent brain poses a methodological challenge. Inva-
sive electrode recordings of the human basal ganglia are
rare and are only conducted on patients with severe
neurological conditions such as ParkinsonsDiseaseor
obsessive-compulsive disorder (Engel, Moll, Fried & Oje-
mann, 2005; M
unte, Heldmann, Hinrichs, Marco-Pallares,
Kr
amer et al., 2007; M
unte, Heldmann, Hinrichs, Marco-
Pallares, Kr
amer et al., 2008). Positron emission tomogra-
phy (PET) allows for the measurement of components of
the dopamine system in healthy and clinical human
populations through the injection of radioactive ligands
(Volkow, Fowler, Gatley, Logan,Wang et al.,1996), but this
technique remains prohibitively invasive for use in non-
medical developmental research.
One technique that captures aspects of dopaminergic
functioning without the injection and radiation exposure
of PET is the measurement of eye blinks (Karson, 1983).
Spontaneous eye blink rate (EBR) in primates correlates
positively with D2-like receptor availability in the striatum
(Taylor, Elsworth, Lawrence, Sladek, Roth et al., 1999;
Groman, James, Seu, Tran, Clark et al., 2014) and
performance of a dopamine-driven learning task (Gro-
man et al., 2014). Human clinical research supports this,
demonstrating that EBR is suppressed in individuals with
Parkinsons disease (Karson, Lewitt, Calne & Wyatt,
1982) and elevated in unmedicated patients with
schizophrenia (Karson, 1983), disorders with known
dopaminergic dysfunction. Similarly, higher EBR corre-
lates with impaired motor response inhibition (Colzato,
van den Wildenberg, van Wouwe, Pannebakker & Hom-
mel, 2009), consistent with the role of dopamine in
impulsivity (Frank, Samanta, Moustafa & Sherman,
2007), and lower EBR correlates with greater learning
from negative outcomes, consistent with the role of the
dopamine D2 pathway in avoidance learning (Slagter,
Georgopoulou & Frank, 2015). The relationship between
dopamine and EBR appears to be striatum-specific: The
positive relationship between EBR and schizophrenia is
consistent with that disorders hyperactive mesolimbic
dopamine activity but not its hypoactive prefrontal
cortical dopamine activity (Brisch, Saniotis, Wolf, Bielau,
Bernstein et al., 2014), while in primates EBR has been
shown to correlate with dopamine levels specifically in the
rostral body of the ventromedial caudate, but not with
other nigrostriatal regions (Taylor et al., 1999). Given the
converging findings from primate (Groman et al., 2014)
and human (Slagter et al., 2015) research, EBR appears to
specifically reflect striatal dopamine D2 receptor avail-
ability or function, although this relationship has not yet
been directly observed in humans using PET. In addition,
clinical studies show that EBR increases when children
and adolescents are administered ziprasidone, an indirect
dopamine agonist (Sallee, Gilbert, Vinks, Miceli, Robarge
et al., 2003), suggesting that EBR is an effective proxy for
direct dopamine measurement in adolescents as well as in
adults.
The present study aims to leverage the well-established
finding from animals and adult humans that EBR is a
behavioral biomarker of striatal dopamine activity and
use it as a proxy for dopamine in the healthy adolescent
brain. Our goal is to investigate developmental differ-
ences in the relationship between dopamine and reward
sensitivity using a risky decision-making task and the
measurement of baseline EBR in adolescent and adult
participants. We focus on decision-making under risk for
three reasons. First, risky behavior is associated with
increased dopamine levels (Riba, Kr
amer, Heldmann,
Richter & M
unte, 2008; Zald, Cowan, Riccardi, Bald-
win, Ansari et al., 2008). Second, adolescents exhibit
high risky behavior in the real world. Third, risk-taking
tasks provide an easily understandable paradigm in
which to introduce the potential for losses as well as
gains, allowing us to explore the extent to which
adolescent sensitivity to reward is actually reflective of
overall sensitivity to value. The task employed here
allows for three different decision strategies: probability-
maximizing, gain-maximizing, and loss-minimizing. We
hypothesized that if dopamine is driving reward-seeking
behavior, participants with higher EBR would more
frequently select the gain-maximizing strategy. Further-
more, if adolescent neurobiological conditions produce
heightened dopamine-related reward sensitivity, we
would expect a stronger relationship between EBR and
gain-maximizing for adolescents versus adults.
Methods
Participants
Twenty-five middle and high school-aged adolescent
participants (age range 13,318,5, M=15.7, SD =1.6,
13 female) were recruited from the community. An
additional 26 adult participants (age range 18,1122,5,
M=20.4, SD =0.9, 16 female) were recruited from the
university undergraduate population. All participants
were fluent in English. Adult participants self-reported
that they had no history of psychiatric diagnoses and
©2016 John Wiley & Sons Ltd
2 Emily Barkley-Levenson and Adriana Galv
an
were not currently taking any psychoactive medications;
the parents/guardians of adolescent participants
reported the same information.
Materials
Baseline eye blink rate
Participants completed two 5-minute video recording
sessions for the purpose of measuring spontaneous eye
blink rate. Videos were collected using the Apple iSight
camera and PhotoBooth program. Participants were
seated approximately 25 inches from the camera during
recording. Participants were instructed to face a black
screen with a fixation cross and to remain awake while
behaving normally during the 5-minute period. Partici-
pants with corrected vision were allowed to wear either
glasses or contact lenses based on their preference, in the
interest of capturing naturalistic data.
Survey measures
Because sleepiness has been related to EBR (Barbato,
Ficca, Muscettola, Fichele, Beatrice et al., 2000) and
sleep-deprivation has previously been shown to affect
performance on the task used in this study (Venkatra-
man, Huettel, Chuah, Payne & Chee, 2011), we collected
self-reported sleepiness data from participants using the
Stanford Sleepiness Scale (SSS; Hoddes, Zarcone,
Smythe, Phillips & Dement, 1973), a measure of sleepi-
ness/alertness at the time of assessment.
Roulette Game
Participants completed a total of two runs of the
Roulette Game (RG; Figure 1), a novel version of a
task originally designed by Payne (2005) to assess
probability sensitivity in risky choice. In this task,
participants were presented with a series of wheel
gambles with a 1/3 probability of gaining money (ranging
from +$3.50 to +$8), a 1/3 probability of losing money
(ranging from $4 to $8.50) and a 1/3 probability of
receiving $0. A total of 400 trials were created and
divided among five runs of 80 trials each; the run
number and order were counterbalanced across partic-
ipants, and each participant completed two runs for a
total of 160 trials. After viewing the gamble for 1000 ms,
participants were presented with an amount of money
Figure 1 Example of a trial from the Roulette Game. Participants view a gamble for 1000 ms. Participants are then shown an
amount of money and are asked to select the space on the wheel to which they wish to add that amount. The decision phase is self-
paced. After making a selection, participants experience a 500 ms inter-trial interval before viewing the next gamble.
©2016 John Wiley & Sons Ltd
Eye blink rate in adolescents 3
(ranging from $1 to $2.50) and instructed to add that
amount of money to one of the three spaces on the
wheel, changing the value of that gamble. Thus, on
each trial the participant made a decision employing one
of three strategies. A gain-maximizing (GMax) decision
was one where the participant chose to add money to the
positive-value space on the wheel, increasing the
maximum possible amount they could win without
altering outcome probabilities. A probability-maximizing
(PMax) decision was one where the participant added
money to the reference ($0) space, increasing the
probability from 1/3 to 2/3 chance of winning some
amount of money without altering the range of values.
Finally, a loss-minimizing (LMin) decision was one
where the participant added money to the negative-value
space, reducing the value of the potential loss without
altering outcome probabilities. Because the probabilities
of each space are equal, the expected value of a given
value-added gamble is equal for each of the three
decision strategies. For example, the gamble [+$8, $0,
$6.50] shown in Figure 1 has an initial expected value
of $0.50. After participants add $1 to any of the three
spaces, the expected value is $0.83. Therefore, no one
strategy can be considered optimal because no one
strategy maximizes expected value, and different strate-
gies may be seen as reflecting different but equally valid
approaches to risk-taking. Across all trials, initial
expected values ranged from $1.67 to $1.33, and
value-added expected values ranged from $0.67 to
$2.17. Participants were informed that one trial (includ-
ing the money added by the participant to the chosen
space) would be selected at random at the end of the
study and its outcome would be resolved for real money,
with any gain or loss being added to or subtracted from
their $15 base pay for the session. This design incen-
tivized participants to respond based on their actual
preferences for every trial. In actuality, each trial was
resolved such that either the reference or gain amount
was selected at random, ensuring that all participants
received at least $15 for the testing session.
Procedure
All participants under the age of 18 completed informed
assent while their parents or guardians provided
informed consent; participants over the age of 18
provided informed consent. During testing, participants
completed baseline eye blink recording, surveys, and the
RG. Adolescent participants completed their testing in
two separate sessions (with one EBR recording and one
run of RG at each session) while adult participants
completed two EBR recordings and two runs of RG in a
single session. Adolescent EBR recordings were
separated by approximately one week and adult EBR
recordings were separated by approximately one hour.
No significant differences were observed within either the
adolescent sample or the adult sample when comparing
the two measurements of EBR, the number of GMax
trials selected in each run, the number of PMax trials
selected in each run, and the number of LMin trials
selected in each run (p>.05 for all paired t-tests).
Furthermore, correlations between each of these pairs
were highly significant (p<.01 and r>.5 for all
correlations), suggesting that the procedural difference
does not explain our findings.
Eye blink rate analysis
Three independent raters counted the total number of
blinks captured in each recording using a computerized
scoring program described by Groman et al. (2014).
Times during which participantseyes were not visible
were removed from the recorded total time, and eye blink
rate (EBR), measured as blinks per visible minute
(BPVM), was calculated for each recording. The intra-
class correlation coefficient between the three raters was
.988 (p<.001) for the first recording session and .986
(p<.001) for the second recording session; with satis-
factory inter-rater reliability, the ratersscores were
averaged for each recording.
Generalized linear mixed models
To assess the relationships among decision strategy
selection, EBR, sleepiness, and task gain and loss values,
we used generalized linear mixed models (Baayen,
Davidson & Bates, 2008; Bolker, Brooks, Clark, Geange,
Poulsen et al., 2009), which incorporate both random
effects (in this dataset, multiple trials and experimental
runs for each participant) and non-normally distributed
variables (in this dataset, binary yesor noresponses
for the selection of each decision strategy on each trial).
Models were fitted by maximum likelihood using a
Laplace approximation and all analyses were carried out
with the lme4package (Bates, Maechler, Bolker &
Walker, 2014) in R(R Core Team, 2015).
Results
EBR results
Spontaneous eye blink rates recorded during two 5-
minute baseline measurements demonstrated adequate
testretest reliability, r(49) =.87, p<.001, and were
averaged for subsequent analyses. EBR ranged from 3.05
©2016 John Wiley & Sons Ltd
4 Emily Barkley-Levenson and Adriana Galv
an
to 47.37 blinks per visible minute (BPM), with a mean of
20.04 BPM (SD =11.69). Blink rate did not differ
significantly between adolescent and adult participants,
although the data trend toward adults having a higher
average blink rate, t(49) =1.979, p=.053.
Roulette Game results
Mean reaction times (GMax M=3678 ms, SD =
2763 ms, PMax M=3589 ms, SD =2178 ms, LMin
M=3204 ms, SD =2160 ms) did not differ between any
of the decision strategies for the sample as a whole or for
adolescent and adult participants analyzed separately.
Adults responded significantly faster than adolescents
for all decision strategies (t(45) =3.571, p<.001 for
GMax, t(44) =3.569, p<.001 for PMax, t(47) =3.836,
p<.001 for LMin).
On average, adolescents employed the gain-maximiz-
ing (GMax) decision strategy on 22.9% of trials, the
probability-maximizing (PMax) decision strategy on
33.1% of trials, and the loss-minimizing (LMin) decision
strategy on 44.0% of trials. Adults employed the GMax
strategy on 24.9% of trials, the PMax strategy on 26.6%
of trials, and the LMin strategy on 48.5% of trials.
Adolescents and adults did not differ significantly in the
number of trials for which they employed the
GMax strategy (t(49) =.265, p=.792), the PMax
strategy (t(49) =.876, p=.385), or the LMin strategy
(t(49) =.551, p=.584) (Figure 2).
Paired-samples t-tests revealed that, overall,
participants employed the LMin strategy significantly
more than the GMax strategy (t(50) =3.314,
p=.002) and significantly more than the PMax strategy
(t(50) =2.401, p=.020). The frequencies of the GMax
and PMax strategies did not differ significantly from one
another, t(50) =.956, p=.344.
Effects of sleepiness
We investigated the effect of self-reported sleepiness on
decision strategy selection using a generalized linear
mixed model, with sleepiness as a fixed effect and
participant and run number as random effects The
results of this model are shown in Table 1. This model
demonstrated that sleepiness significantly increased the
selection of the GMax strategy and significantly
decreased the selection of the LMin strategy, consistent
with prior research (Venkatraman et al., 2011).
A separate generalized linear mixed model revealed no
significant effect of the interaction of sleepiness and age
group on any of the decision strategies (GMax B =
0.048, p=.954, PMax B =1.132, p=.085, LMin B
=0.128, p=.856), suggesting that this pattern does
not differ between adolescents and adults. We did not
observe a significant correlation between EBR and
reported sleepiness, r(74) =.128, p=.28.
EBR and decision strategy
To investigate the effects of EBR on decision strategy, we
conducted a generalized linear mixed model with blink
rate as a fixed effect and participant and run number as
random effects. The results of this model are shown in
Table 2. This model demonstrated that increased blink
rate was significantly associated with increased selection
of the GMax strategy and decreased selection of the
PMax strategy (Figure 3).
Effect of age on EBR and decision strategy
To explore whether the relationship between blink rate
and decision strategy selection differed as a function of
age, we conducted a generalized linear mixed model with
the interaction term of blink rate by age group
Figure 2 Percentage of trials employing each decision
strategy for adolescent and adult participants. **p>.01;
*p>.05. Error bars represent standard error of the mean.
Table 1 Effect of sleepiness on decision strategy selection
Decision strategy Estimate SE p-value
GMax 0.261 0.059 <0.001
PMax 0.032 0.053 0.553
LMin 0.182 0.052 <0.001
GMax =gain-maximizing; PMax =probability-maximizing;
LMin =loss-minimizing. Significant effects are in bold. Example of full
model in R: GMax ~sleepiness +(1 |participant) +(1 |run number),
family =binomial
©2016 John Wiley & Sons Ltd
Eye blink rate in adolescents 5
(adolescent or adult) as a fixed effect and participant and
run number as random effects (Table 3). This analysis
demonstrated a significant interaction of age group with
blink rate for the GMax strategy, with increasing blink
rate predicting increased gain-maximizing for adoles-
cents but not for adults; in adults, increasing blink rate
predicted decreased gain-maximizing (Figure 4A). Sim-
ilarly, increasing blink rate predicted decreased proba-
bility-maximizing for adolescents but not for adults
(Figure 4B). No significant interaction was observed for
the LMin strategy (Figure 4C). These observations were
confirmed by running the model of EBR on decision
strategy separately for the adolescent sample and the
adult sample (Table 2).
Effects of changing values on decision strategy
We explored the extent to which changes in the potential
gain and loss amounts presented in each gamble influ-
enced participantsselection of decision strategies, as
well as whether age group and EBR altered this
sensitivity to value. For this analysis, we conducted
generalized linear models predicting each of the three
decision strategies, with gain amount and loss amount
separately as fixed effects, for a total of six models
(Table 4). For each model we included the interaction
between age group and EBR, and included participant
and run number as random effects. These analyses
suggest that decision strategy selection is sensitive to
monetary value; for example, increasing gain amounts
are associated with reduced selection of the LMin
strategy and increased selection of PMax and GMax
strategies. Furthermore, age appears to affect value
sensitivity in some instances. Adolescents show more
value sensitivity than adults both in terms of the extent
to which increasing gain amounts reduce LMin strategy
Table 2 Effect of EBR on decision strategy selection, combined and by age group
All participants Adolescents only Adults only
Decision strategy Est. SE p Est. SE p Est. SE p
GMax 0.023 0.010 0.020 0.050 0.012 <0.001 0.040 0.019 0.033
PMax 0.024 0.011 0.023 0.055 0.013 <0.001 0.029 0.017 0.102
LMin 0.004 0.009 0.633 0.011 0.012 0.363 0.006 0.015 0.688
GMax =gain-maximizing; PMax =probability-maximizing; LMin =loss-minimizing. Significant effects are in bold. Example of full model in R:
GMax ~baselineEBR +(1 |participant) +(1 |run number), family =binomial
Figure 3 Effect of blink rate on selection of decision strategies.
Table 3 Interaction of EBR and age group on decision
strategy selection
Decision strategy Estimate SE p-value
GMax 0.093 0.022 <0.001
PMax 0.082 0.022 <0.001
LMin 0.017 0.018 0.355
GMax =gain-maximizing; PMax =probability-maximizing;
LMin =loss-minimizing. Significant effects are in bold. Example of full
model in R: GMax ~baselineEBR *age group +(1 |participant) +(1 |
run number), family =binomial
©2016 John Wiley & Sons Ltd
6 Emily Barkley-Levenson and Adriana Galv
an
selection (Figure 5A) and increase PMax strategy selec-
tion (Figure 5B).
Discussion
In this study, we observe a positive relationship between
EBR and reward-seeking behavior, consistent with the
role of striatal dopamine in reward-seeking and further
supporting the use of EBR as a dopamine proxy measure
in healthy youth. The positive relationship between EBR
and reward-seeking was driven by adolescents (and
indeed the effect appears to be reversed for adults). One
interpretation is that a stronger relationship between
dopamine and reward sensitivity exists in adolescents
than in adults, consistent with the adolescent ventral
striatums hypersensitivity to reward frequently observed
with neuroimaging. Furthermore, we demonstrate that
EBR can be measured inexpensively, reliably and non-
invasively in adolescents and adults.
Figure 4 The relationship between blink rate and GMax strategy selection (A) changes as a function of age group, as does the
relationship between blink rate and PMax strategy selection (B). The relationship between blink rate and LMin strategy selection (C)
does not change as a function of age group. Gray areas represent 95% confidence intervals.
Table 4 Effects of increasing gain amount, loss amount, and interactions with age group and EBR on decision strategy selection
GMax selection PMax selection LMin selection
Est. SE p Est. SE p Est. SE p
Gain Amount 0.231 0.074 0.002 0.278 0.067 <.001 0.510 0.065 <.001
Gain Amt *Age 0.033 0.047 0.471 0.118 0.043 0.006 0.179 0.039 <.001
Gain Amt *EBR 0.001 0.002 0.552 0.001 0.002 0.587 0.003 0.002 0.104
Loss Amount 0.160 0.076 0.035 0.109 0.071 0.123 0.175 0.070 0.012
Loss Amt *Age 0.249 0.049 <.001 0.161 0.047 <.001 0.191 0.043 <.001
Loss Amt *EBR 0.004 0.002 0.047 0.002 0.002 0.289 0.005 0.002 0.154
GMax =gain-maximizing; PMax =probability-maximizing; LMin =loss-minimizing. Significant effects are in bold. Example of full model in R:
GMax ~gain amount +gain amount *baselineEBR +gain amount *age_group +(1 |participant) +(1 |run number), family =binomial
Figure 5 Small increases in gain amount are more strongly
associated with decreased selection of the LMin strategy for
adolescents than for adults (A), and are more strongly
associated with increased selection of the PMax strategy for
adolescents than for adults (B). Gray areas represent 95%
confidence intervals.
©2016 John Wiley & Sons Ltd
Eye blink rate in adolescents 7
The trend in our data toward adults having higher
average EBR than adolescents, though initially appear-
ing to be at odds with adolescent dopaminergic hyper-
responsiveness, is in fact consistent with the observation
of lower basal levels of dopamine for periadolescent
versus adult rats in the synaptic cleft of the striatum
(Andersen & Gazzara, 1993). Despite lower basal
dopamine levels, adolescent rats actually release more
dopamine than adults when highly stimulated by envi-
ronmental or pharmacological challenges (Laviola et al.,
2003). Our findings are consistent with a potentially
similar dopaminergic profile in human development,
wherein adolescents have lower baseline dopamine mea-
surements but greater sensitivity to motivationally rele-
vant stimuli. Because our adult sample consisted of
young adults, many of whose neurochemical functioning
may still be similar to their adolescent counterparts,
investigating this effect with the inclusion of an older
adult sample would help understand the developmental
trajectory of human dopamine.
Our findings can be interpreted in the context of two
of the major developmental models of adolescent risky
decision making: the neurodevelopmental imbalance
model (e.g. Casey, Jones & Hare, 2008; Steinberg,
2007), which proposes that under certain conditions
adolescentshypersensitive motivational systems and
maturing cognitive control systems interact to produce
risk-taking behavior, and fuzzy-trace theory (Reyna,
2004; Reyna & Rivers, 2008), which proposes that
verbatim-based processing (reasoning based on costs,
benefits and probabilities) is more prevalent early in life
and that gist-based processing (rapid reasoning based on
heuristics formed through experience) increases from
childhood to adulthood (see Defoe, Dubas, Figner & van
Aken, 2015, for a meta-analysis incorporating both
models). Notably, both theories predict that adolescents
will be more sensitive than adults to changes in reward
magnitude, as we observe here. Under the neurodevel-
opmental imbalance model, this is due to heightened
value sensitivity in the adolescent ventral striatum (e.g.
Barkley-Levenson & Galv
an, 2014). Under fuzzy-trace
theory, adolescentsgreater reliance on verbatim-based
analysis leads to increased focus on reward magnitudes,
with heightened risk-taking in gain frames (Reyna,
Estrada, DeMarinis, Myers, Stanisz et al., 2011). The
relationship between EBR and increased reward-seeking
shown here in adolescents suggests the possibility of
dopamine playing a role in both of these theoretical
models. If striatal dopamine receptor availability con-
tributes to heightened reward sensitivity in the striatum,
we would expect to see EBR correlate with reward
sensitivity, as it does here. Similarly, dopamines role in
encoding value (e.g. Schultz, 2010) suggests that it may
provide cost and benefit signals necessary for verbatim-
based processing engaged more heavily in adolescence;
greater EBR in this model could reflect greater reliance
on verbatim-based relative to gist-based processing, and
would therefore correlate with increased focus on reward
magnitude (as seen in increased reward-seeking
responses on the RG).
Behaviorally, both adolescents and adults exhibited
loss aversion on the RG, demonstrating a preference for
avoiding losses over seeking gains or maximizing the
probability of winning. Although adolescents appear to
have a stronger relationship between dopamine and
reward-seeking, they did not actually differ from adult
participants in the proportion of trials in which they
chose the GMax strategy, nor did they differ on the
selection of the other strategies. A possible explanation is
that different underlying cognitive (and neurobiological)
processes lead participants to similar behavioral out-
comes. For example, in keeping with fuzzy-trace theory,
adolescents may rely more heavily on dopamine-driven
value signals to engage in deliberative (verbatim-based)
processing of risky decisions, leading to greater value
sensitivity (as discussed previously), while adults engage
in more rapid heuristic (gist-based) processing. Such a
distinction would be consistent with faster response
times for adults, which we observed on all trial types of
the RG.
Consistent with our previous work (Barkley-Levenson
& Galv
an, 2014), adolescents in this study exhibited
greater value sensitivity than adults. Adolescents were
more likely to increasingly select the probability-
maximizing decision and to reduce selection of the
loss-minimizing decision as the amount of the potential
gain increased. This decision suggests that relatively
small increases in monetary value held greater signifi-
cance for the adolescents versus adults.
One shortcoming of the decision-making task we
employed is the non-independence of the three decision
strategies (i.e. if selection of one strategy increases then
selection of the other two must necessarily decrease),
making the interpretation of correlations between EBR
and decision strategy selection more complicated than
for a binary choice task. However, the fact that only
GMax shows a positive relationship with EBR suggests
that this effect is genuine, rather than the less parsimo-
nious explanation that it is a byproduct of EBR
independently suppressing both PMax and LMin strate-
gies. Recent decision-making research has investigated
the process of trinary choice by applying a multi-
attribute drift diffusion model to behavioral and eye-
tracking data (Krajbich & Rangel, 2011). In future
studies a similar approach using a modified version of
the Roulette Game could more precisely characterize
©2016 John Wiley & Sons Ltd
8 Emily Barkley-Levenson and Adriana Galv
an
participant decision strategy selection on this trinary
choice task. Another shortcoming of the trinary choice
task, rather than the original design (a five-outcome
mixed gamble), is that we are unable to dissociate loss
aversion from risk aversion or to investigate framing
effects in risk attitudes that have been shown to differ
developmentally (Reyna & Ellis, 1994; Reyna et al.,
2011). This may explain why we did not observe in our
adult participants the bias towards the PMax strategy
reported elsewhere (Payne, 2005; Venkatraman, Payne,
Bettman, Luce & Huettel, 2009; Venkatraman et al.,
2011) and consistent with greater gist-based processing
as predicted by fuzzy-trace theory (Reyna & Farley,
2006). The binary and trinary versions of the task
address different questions but do not lend themselves to
direct comparison.
While the use of EBR to characterize dopamine
functioning in developmental research is promising, it
remains an indirect measure, with the limitations that
entails. Human research directly comparing EBR and
PET will be necessary to confirm the relationship
between blink rate and dopamine that has been
conclusively demonstrated in primates (Groman et al.,
2014). Slower event-related task designs (or blocked
designs) are required to observe phasic changes in EBR
in response to changes in stimulus value. Understand-
ing the extent to which environmental conditions affect
blink rate independently of dopamine (e.g. testing room
luminance, viewing screens versus physical stimuli) will
also be crucial in standardizing EBR collection proto-
cols to minimize noise. Furthermore, in the current
study we did not collect data on individual differences
in the sensitivity of participantseyes (e.g. seasonal
allergies, contact lenses, or chronically dry eyes). We
recommend that subsequent research measuring ado-
lescent and adult EBR should investigate whether any
such conditions systematically influence an individuals
blink rate. Nonetheless, EBR provides an opportunity
for researchers of vulnerable populations, those with
metal in the body (including braces, a common
limitation in human adolescent research) or other
contraindications for fMRI, or without access to PET
to readily explore the dopaminergic underpinnings of
behavior.
These findings suggest that previously observed
adolescent behavioral and neural hypersensitivity to
reward may in fact be due to heightened sensitivity to
dopamine, as represented by the relationship of blink
rate and reward-seeking behavior. This technique opens
the door for considering adolescent individual differ-
ences at the neurochemical as well as the behavioral
level when exploring responses to reward, value and
risk.
References
Andersen, S.L., & Gazzara, R.A. (1993). The ontogeny of
apomorphine-induced alterations of neostriatal dopamine
release: effects on spontaneous release. Journal of Neuro-
chemistry,61, 22472255.
Baayen, R.H., Davidson, D.J., & Bates, D.M. (2008). Mixed-
effects modeling with crossed random effects for subjects and
items. Journal of Memory and Language,3,1228.
Barbato, G., Ficca, G., Muscettola, G., Fichele, M., Beatrice,
M., et al. (2000). Diurnal variation in spontaneous eye-blink
rate. Psychiatry Research,93, 145151.
Barkley-Levenson, E., & Galv
an, A. (2014). Neural represen-
tation of expected value in the adolescent brain. Proceedings
of the National Academy of Sciences, USA,111, 16461651.
Bates, D., Maechler, M., Bolker, B., & Walker, S. (2014). lme4:
Linear mixed-effects models using Eigen and S4. R package
version 1.1-7, http://CRAN.R-project.org/package=lme4.
Bjork, J.M., Knutson, B., Fong, G.W., Caggiano, D.M.,
Bennett, S.M., et al. (2004). Incentive-elicited brain activa-
tion in adolescents: similarities and differences from young
adults. Journal of Neuroscience,24, 17931802.
Bjork, J.M., & Pardini, D.A. (2015). Who are those risk-taking
adolescents? Individual differences in developmental neu-
roimaging research. Developmental Cognitive Neuroscience,
11,5664.
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,
e11440.
Bolker, B.M., Brooks, M.E., Clark, C.J., Geange, S.W., Poulsen,
J.R., et al. (2009). Generalized linear mixed models: a
practical guide for ecology and evolution. Trends in Ecology
& Evolution,24, 127135.
Brisch, R., Saniotis, A., Wolf, R., Bielau, H., Bernstein, H.G.,
et al. (2014). The role of dopamine in schizophrenia from a
neurobiological and evolutionary perspective: old fashioned,
but still in vogue. Frontiers in Psychiatry,5, 47.
Casey, B.J., Jones, R.M., & Hare, T.A. (2008). The adolescent
brain. Annals of the New York Academy of Sciences,1124,
111126.
Cohen, J.R., Asarnow, R.F., Sabb, F.W., Bilder, R.M., Book-
heimer, S.Y., et al. (2010). A unique adolescent response to
reward prediction errors. Nature Neuroscience,13, 669671.
Colzato, L.S., van den Wildenberg, W.P., van Wouwe, N.C.,
Pannebakker, M.M., & Hommel, B. (2009). Dopamine and
inhibitory action control: evidence from spontaneous eye
blink rates. Experimental Brain Research,196, 467474.
Defoe, I.N., Dubas, J.S., Figner, B., & van Aken, M.A.G.
(2015). A meta-analysis on age differences in risky decision
making: adolescents versus children and adults. Psycholog-
ical Bulletin,141,4884.
Engel, A.K., Moll, C.K., Fried, I., & Ojemann, G.A. (2005).
Invasive recordings from the human brain: clinical insights
and beyond. Nature Reviews Neuroscience,6,3547.
Ernst, M., Nelson, E., Jazbec, S., McClure, E., Monk, C.S.,
et al. (2005). Amygdala and nucleus accumbens in responses
©2016 John Wiley & Sons Ltd
Eye blink rate in adolescents 9
to receipt and omission of gains in adults and adolescents.
NeuroImage,25, 12791291.
Frank, M.J., Samanta, J., Moustafa, A.A., & Sherman, S.J.
(2007). Hold your horses: impulsivity, deep brain stimulation,
and medication in parkinsonism. Science,318, 13091312.
Galv
an, A., Hare, T., Parra, C., Penn, J., Voss, H., et al. (2006).
Earlier development of the accumbens relative to orbito-
frontal cortex might underlie risk-taking behavior in adoles-
cents. Journal of Neuroscience,26, 68856892.
Geier, C.F., Terwilliger, R., Teslovich, T., Velanova, K., &
Luna, B. (2010). Immaturities in reward processing and its
influence on inhibitory control in adolescence. Cerebral
Cortex,20, 16131629.
Groman, S.M., James, A.S., Seu, E., Tran, S., Clark, T.A., et al.
(2014). In the blink of an eye: relating positive-feedback
sensitivity to striatal dopamine D2-like receptors through
blink rate. Journal of Neuroscience,34, 1444314454.
Hoddes, E., Zarcone, V., Smythe, H., Phillips, R., & Dement,
W.C. (1973). Quantification of sleepiness: a new approach.
Psychophysiology,10, 431436.
Ikemoto, S., & Panksepp, J. (1999). The role of nucleus
accumbens dopamine in motivated behavior: a unifying
interpretation with special reference to reward-seeking. Brain
Research Reviews,31,641.
Karson, C.N. (1983). Spontaneous eye-blink rates and
dopaminergic systems. Brain,106, 643653.
Karson, C.N., Lewitt, P.A., Calne, D.B., & Wyatt, R.J. (1982).
Blink rates in parkinsonism. Annals of Neurology,12, 580
583.
Krajbich, I., & Rangel, A. (2011). Multialternative drift-
diffusion model predicts the relationship between visual
fixations and choice in value-based decisions. Proceedings of
the National Academy of Sciences, USA,108, 1385213857.
Laviola, G., Macr
ı, S., Morley-Fletcher, S., & Adriani, W.
(2003). Risk-taking behavior in adolescent mice: psychobi-
ological determinants and early epigenetic influence. Neuro-
science and Biobehavioral Reviews,27,1931.
Laviola, G., Pascucci, T., & Pieretti, S. (2001). Striatal
dopamine sensitization to D-amphetamine in periadolescent
but not in adult rats. Pharmacology, Biochemistry, and
Behavior,68, 115124.
May, J.C., Delgado, M.R., Dahl, R.E., Stenger, V.A., Ryan,
N.D., et al. (2004). Event-related functional magnetic reso-
nance imaging of reward-related brain circuitry in children
and adolescents. Biological Psychiatry,55, 359366.
M
unte, T.F., Heldmann, M., Hinrichs, H., Marco-Pallares, J.,
Kr
amer, U.M., et al. (2007). Nucleus accumbens is involved
in human action monitoring: evidence from invasive electro-
physiological recordings. Frontiers in Human Neuroscience,1,
11.
M
unte, T.F., Heldmann, M., Hinrichs, H., Marco-Pallares, J.,
Kr
amer, U.M., et al. (2008). Contribution of subcortical
structures to cognition assessed with invasive electrophysiol-
ogy in humans. Frontiers in Neuroscience,2, 72.
Payne, J.W. (2005). It is whether you win or lose: the
importance of the overall probabilities of winning or losing
in risky choice. Journal of Risk and Uncertainty,30,519.
R Core Team (2015). R: A language and environment for
statistical computing. R Foundation for Statistical Comput-
ing, Vienna, Austria. URL http://www.R-project.org/.
Reyna, V.F. (2004). How people make decisions that involve
risk a dual-processes approach. Current Directions in Psy-
chological Science,13,6066.
Reyna, V.F., & Ellis, S.C. (1994). Fuzzy-trace theory and
framing effects in childrens risky decision making. Psycho-
logical Science,5, 275279.
Reyna, V.F., Estrada, S.M., DeMarinis, J.A., Myers, R.M.,
Stanisz, J.M., et al. (2011). Neurobiological and memory
models of risky decision making in adolescents versus young
adults. Journal of Experimental Psychology: Learning,
Memory and Cognition,37, 11251142.
Reyna, V.F., & Farley, F. (2006). Risk and rationality in
adolescent decision making: implications for theory, practice
and public policy. Psychological Science in the Public Interest,
7,144.
Reyna, V.F., & Rivers, S.E. (2008). Current theories of risk and
rational decision making. Developmental Review,28,111.
Riba, J., Kr
amer, U.M., Heldmann, M., Richter, S., & M
unte,
T.F. (2008). Dopamine agonist increases risk taking but
blunts reward-related brain activity. PLoS ONE,3, e2479.
Sallee, F.R., Gilbert, D.L., Vinks, .A.A, Miceli, J.J., & Robarge,
L. et al. (2003). Pharmacodynamics of ziprasidone in
children and adolescents: impact on dopamine transmission.
Journal of the American Academy of Child & Adolescent
Psychiatry,42, 902907.
Schultz, W. (2010). Dopamine signals for reward value and
risk: basic and recent data. Behavioral and Brain Functions,6,
24.
Schultz, W., Apicella, P., & Ljungberg, T. (1993). Responses of
monkey dopamine neurons to reward and conditioned
stimuli during successive steps of learning a delayed response
task. Journal of Neuroscience,13, 900913.
Schultz, W., Dayan, P., & Montague, P.R. (1997). A neural
substrate of prediction and reward. Science,275, 15931599.
Slagter, H.A., Georgopoulou, K., & Frank, M.J. (2015).
Spontaneous eye blink rate predicts learning from negative,
but not positive, outcomes. Neuropsychologia,71, 126132.
Steinberg, L. (2007). Risk-taking in adolescence: new perspec-
tives from brain and behavioral science. Current Directions in
Psychological Science,16,5559.
Taylor, J.R., Elsworth, J.D., Lawrence, M.S., Sladek, J.R., Roth,
R.H., et al. (1999). Spontaneous blink rates correlate with
dopamine levels in the caudate nucleus of MPTP-treated
monkeys. Experimental Neurology,158, 214220.
Teicher, M.H., Andersen, S.L., & Hostetter, J.C. (1995).
Evidence for dopamine receptor pruning between adoles-
cence and adulthood in striatum but not nucleus accumbens.
Developmental Brain Research,89, 167172.
Van Leijenhorst, L., Zanolie, K., Van Meel, C.S., Westenberg,
P.M., Rombouts, S.A.R.B., et al. (2010). What motivates the
adolescent? Brain regions mediating reward sensitivity across
adolescence. Cerebral Cortex,20,6169.
Venkatraman, V., Huettel, S.A., Chuah, L.Y., Payne, J.W., &
Chee, M.W. (2011). Sleep deprivation biases the neural
©2016 John Wiley & Sons Ltd
10 Emily Barkley-Levenson and Adriana Galv
an
mechanisms underlying economic preferences. Journal of
Neuroscience,31, 37123718.
Venkatraman, V., Payne, J.W., Bettman, J.R., Luce, M.F., &
Huettel, S.A. (2009). Separate neural mechanisms underlie
choices and strategic preferences in risky decision making.
Neuron,62, 593602.
Volkow, N.D., Fowler, J.S., Gatley, S.J., Logan, J., Wang, G.J.,
et al. (1996). PET evaluation of the dopamine system of the
human brain. Journal of Nuclear Medicine,37, 12421256.
Zald, D.H., Cowan, R.L., Riccardi, P., Baldwin, R.M., Ansari,
M.S., et al. (2008). Midbrain dopamine receptor availability
is inversely associated with novelty-seeking traits in humans.
Journal of Neuroscience,28, 1437214378.
Received: 21 July 2015
Accepted: 8 January 2016
©2016 John Wiley & Sons Ltd
Eye blink rate in adolescents 11
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Full-text available
Adolescents take more risks than adults in the real world, but laboratory experiments do not consistently demonstrate this pattern. In the current study, we examine the possibility that age differences in decision making vary as a function of the nature of the task (e.g., how information about risk is learned) and contextual features of choices (e.g., the relative favorability of choice outcomes), due to age differences in psychological constructs and physiological processes related to choice (e.g., weighting of rare probabilities, sensitivity to expected value, sampling, pupil dilation). Adolescents and adults made the same 24 choices between risky and safe options twice: once based on descriptions of each option, and once based on experience gained from sampling the options repeatedly. We systematically varied contextual features of options, facilitating a fine-grained analysis of age differences in response to these features. Eye-tracking and experience-sampling measures allowed tests of age differences in predecisional processes. Results in adolescent and adult participants were similar in several respects, including mean risk-taking rates and eye-gaze patterns. However, adolescents’ and adults’ choice behavior and process measures varied as a function of decision context in both description and experience, with age differences in probability weighting, expected-value sensitivity, experience sampling and pupil dilation patterns. Overall, results are consistent with the notion that adolescents are more prone than adults to take risks when faced with unlikely but costly negative outcomes, and broadly point to complex interactions between multiple psychological constructs that develop across adolescence.
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