Darren Schreiber, Greg Fonzo. 2012. Throwing a big party?

Darren Schreiber, Greg Fonzo. 2012. Throwing a big party?

Throwing a big party? Neurocorrelates of membership in the major political parties
Prepared for the Midwest Political Science Association
April 14, 2012
We compare brain activation patterns to identify correlates of membership in the two major
parties contrasted with non-partisans.
The few studies using functional brain imaging to investigate political phenomena have focused
upon members of the two main political parties in the United States. However a substantial
portion of the voters do not identify with either the Republicans or Democrats. In this paper, we
identify the neural differences that distinguish members of the two main parties, from nonpartisans. In a gambling task, members of the two main parties appear to have greater levels of
brain activity in Posterior Medial Cortices that have been previously identified as critical for
social cognition and sophisticated political cognition.
Darren Schreiber1*, Greg Fonzo2
1
Department of Political Science, University of California San Diego, La Jolla, CA 92103 USA
Joint Doctoral Program in Clinical Psychology, San Diego State University/University of
California-San Diego, San Diego, CA 92120, USA
2
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People evaluate risks and their willingness to take risks in different ways. This risk
orientation has been shown to play a role in how individuals react to political uncertainty and the
candidates and policies that they support. One way that humans evolved to mitigate risk is
through group affiliation, a modern manifestation of which is political parties. In this paper, we
compare brain activity in individuals that are affiliated with one of the major political parties (i.e.
registered Republicans or Democrats) with individuals not affiliated with a party while they are
doing a task that presents increasing levels of risk. We show differences in brain activity for
partisans and nonpartisans in a brain region that is part of the default mode network, a system
critical for social cognition, but also highly active when we are simply resting.
What it means to be a political animal
Aristotle (1996 [350 B.C.]) was right and we humans certainly are social and political
animals by nature. Many animals (Shettleworth 2009), plants (Braun-Blanquet, Fuller, and
Conard 1932), amoebas (Eichinger et al. 2005), bacteria (Overmann 2004), and even proteins
(Brodersen and Nissen 2005) exhibit various forms of broadly defined sociality. Ants, for
instance, can function in interconnected supercolonies that range across continents and have
billions of members (Sunamura et al. 2009). However, there is very little dynamism in their
social lives. Chemical signatures define coalition membership and this does not change. Thus,
navigating your social life as an ant requires very little brain activity.
Humans and a few other species are not merely social, but political. Dolphins have
multilevel alliances that are frequently changing. A trio might hunt together today and then go
their separate ways. And, these shifts can cause conflicts within the larger groupings (Connor
2007; Connor et al. 2010). Spotted hyenas also alter their social structures in a complex manner,
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forming and dissolving coalitions within larger associations (Holekamp, Sakai, and Lundrigan
2007). Pinyon jays track their complex social relationships to infer hierarchy even with regards
to strangers (Paz-Y-Miño C et al. 2004). For elephants, close cooperative relationships can last
over extremely long time periods and yet fluctuate with the fission and fusion dynamics of the
larger social groupings (Poole and Moss 2008).
The fact that all of these varied species have all evolved many common cognitive and
neural characteristics provides evidence of convergent evolution (Emery and Clayton 2004).
Bees, birds, and bats all have wings because light, flat structures that can be moved rapidly
enable them to generate sufficient lift to allow them to fly, not because they are closely related.
Likewise, animals that must maintain awareness of complex and dynamic alliance structures
have independently evolved larger brains than similarly sized species that have simpler social
lives (Dunbar 2009).
The complex cognition enabled by these large brains includes tool use and thinking about
mental states of others (Emery and Clayton 2004). Crows alter small sticks to scoop out insects
(Hunt 1996) and dolphins will use marine sponges to enable them to forage for fish on the
bottom of the bay (Krützen et al. 2005). Scrub jays hide their food from known enemies, but do
not fret if allies see where they have hidden a future snack (Emery and Clayton 2001). Elephants
can learn to anticipate the cooperation of other elephants (Plotnik et al. 2011). And, high status
chimpanzees will adjudicate disputes among other low status chimps (von Rohr et al. 2012).
However, not all members of such political species will equally partake in the life of their
polis. The rock hyrax, for instance, is the nearest relative of the elephant, but is about the size of
rabbit, with only very tiny tusks. It generally lives in complex social structures and its
vocalizations sing of its body weight, size, health, and social status (Koren and Geffen 2009).
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Some of male hyraxes live outside the community, forgoing the social interaction and paying a
cost in shorter life spans (Barocas et al. 2011). And, ill-mannered chimpanzees may be
ostracized from the community for their violations of social norms (Nishida et al. 1995) or when
they attempt an unwanted entry into a new community (Kerr and Levine 2008).
Causes and consequences of group affiliation
While the basic tendency to affiliate with groups appears extraordinarily strong in
humans (Tajfel 1970), like other political animals, we also vary in our tendency to affiliate.
Some of this appears to be biologically inherited (Weber, Johnson, and Arceneaux 2010), with
the additive genetic effect estimated between fifty and sixty percent. Partisan attachment in
particular has been shown to be heritable (Settle, Dawes, and Fowler 2009), and some genes
have already been identified as candidates. The A2 allele of the D2 dopamine receptor gene
appears to make one more likely to identify as affiliated with a party than the A1 allele (Dawes
and Fowler 2009). A less efficient version of the COMT gene is predicted to diminish partisan
identification compared with those whom have the more efficient allele, with extraversion
partially mediating the relationship (Dawes et al. 2010).
Group affiliation alters behaviors. Individuals are more likely to demonstrate consistent
biases in favor of ingroup members (Tajfel and Turner 1979). Priming a latent partisanship leads
people to become more consistent in their voting behavior and evaluations of party figures
(Gerber, Huber, and Washington 2010). But, it also appears that affiliating with others can alter
behavior more broadly.
Partisans are more likely than nonpartisans to engage in prosocial behaviors such as
contributing to the public good and punishing those who violate social norms. It appears likely
that it is membership in the partisan organization that drives the prosocial behaviors rather than
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vice versa. Formal evolutionary models suggest that “strong reciprocity depends on group
competition (and therefore partisanship) and not the other way around (Smirnov et al. 2010,
602).”
Another effect of affiliating with a political party appears to be a change in the way one
evaluates risk. Registered Republicans demonstrate elevated levels of activity in the right
amygdala when engaging in risky behaviors while Democrats activate portions of the left insula
(Schreiber et al. 2009). These differences are consistent with prior evidence of structural brain
differences between British conservatives and liberals (Kanai et al. 2011). However, the
differences in brain function are stronger than would be predicted by the models of heredity,
suggesting that party affiliation is altering the brain’s function and structure, rather than the
biological differences driving the affiliation with a particular party.
It is important to reiterate that there are many instances of behavior altering brain
function and structure, lest one get carried away with a simplistic belief in biological
determinism. Learning to juggle can alter both the grey (Draganski et al. 2004) and white matter
of the brain (Scholz et al. 2009). And, memorizing the knowledge required to navigate the
streets of London in a taxi can enlarge the hippocampus (Woollett and Maguire 2011).
In fact, the very demands that have driven the three million year cognitive arms race that
led to the human brain necessitate an extensive level of flexibility. Our political brain and the
complex and dynamic political environment it enables require that we are hardwired not to be
hardwired (Schreiber 2012 (in review)). Thus, affiliating with a political party can change not
only your outlook on politics, but also your worldview more generally.
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How we think about risks
Liberals and Democrats are more accepting of risk than conservatives and Republicans
(Kam and Simas 2010). Partisan differences in risk orientation appear consistent with evidence
of greater reactivity to threatening stimuli among social conservatives (Oxley et al. 2008) and the
brain differences described above. Toleration for risk has implications for a number of political
phenomena.
People tend to treat equal gains and losses unequally (Kahneman and Tversky 1979). We
are more averse to losses than motivated by similar gains. As a consequence the same scenario
can trigger different choices depending on how the payoffs are framed. While voters appear to
be less risk averse than previously believed (Berinsky and Lewis 2007), risk orientation can
change support for candidates (Tomz and Van Houweling 2009) and policy alternatives (Ehrlich
and Maestas 2010). Individuals who can tolerate risk are more likely to support the political
opposition and appear to have a tendency to punish weak economic performance (Morgenstern
and Zechmeister 2001).
Risk orientation changes how individuals approach decisions. Risk tolerant people tend
to make choices entirely on the basis of anticipated costs and benefits, whereas the risk averse
tend to give tremendous weight to the possibility of a worst case scenario (Nadeau, Martin, and
Blais 1999). The level of risk acceptance also changes the susceptibility of a person to framing
effects. People who can tolerate risk are less susceptible to changes in the way a risk is
presented (Kam and Simas 2010).
To explore the relationship between partisan affiliation and the way individuals processed
risky scenarios we analyzed data from an experiment in which individuals were asked to make a
series of gambles (see the Materials and Methods section below for a more detailed description
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of the experiment and data analysis.) In a scenario similar to doubling down at the blackjack
table, participants were given a choice to either take 20 cents or bet that it would double to 40
cents in the next turn or become a 40 cent loss. If they won the 40 cents, they could let it ride
and take the chance of winning 80 cents or losing 80 cents. The probabilities were selected such
that any pure strategy (e.g. always take the 20 cents) would be expected to yield the same payoff.
As they made these gambles, a functional magnetic resonance imaging (fMRI) device
generated a series of images of their brains. Comparing minute fluctuations in the magnetic
signal in particular areas enables inferences about changes in the level of cerebral blood flow.
Oxygen rich blood has a different magnetic signal from oxygen depleted blood and when a brain
region is hard at work on a cognitive task, additional oxygen rich blood flows into that area to
power its function. The fMRI images then reveal the changes in magnetic signals, leading to
inferences about changes in blood flow, which leads to inferences about neural activity, and thus
leads to inferences about changes in cognitive function.
After the experiment, we were able to match participants with publicly available voting
records. Thus, we could identify participants who were registered as Republicans or Democrats
(partisans) or who did not identify with any party (nonpartisans). In a series of statistical
analyses, we were able to discern differences in brain function during this risk-taking task.
Partisans have greater activity in the Posterior Medial Cortices than Nonpartisans
While a series of regions were tested initially due to prior studies implicating them in the
gambling task, none of these demonstrated significant differences between partisans and
nonpartisans. Subsequently, we conducted a broader analysis of the whole brain and discovered
a pattern of deactivation in the posterior medial cortices (PMC)(See Table 1). This region has
recently gained prominence for its role in the default mode network (DMN) and social cognition.
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Mask
ROI
WB
WB
WB
Hem
-L
R
L
Region
No significant differences
Posterior Cingulate/Precuneus
Posterior Cingulate/Precuneus
Cuneus/Precuneus
Vol.
(μl)
-1216
1152
1088
X
Y
Voxelwise Stats
Mean (sd)
Z
t
--5
19
-3
--55
-55
-78
-21
18
27
p
Mean
SD
Mean
SD
--2.47
-2.34
-2.40
-0.38
0.46
0.44
-0.021
0.029
0.024
-0.015
0.015
0.014
Cluster coordinates are for the cluster center of mass in Talairach space;
Hem.=hemisphere; L=left; R=right; sd=standard deviation; Vol.=volume; WB=whole-brain.
Table 1. Areas of Significant Activation Differences Between Partisans and Non-Partisans for
Risky vs. Safe Decisions
The blue regions in Figure 1 indicate areas where there were statistically significant
differences in brain activity for partisans and nonpartisans. These areas are known as the
posterior cingulate/precuneus and are components of the PMC. The differences in activity are
found in both the left and right hemispheres. Unfortunately, the bar graphs are a bit misleading.
This is a comparison of brain activity in the region when deciding to take a risk versus brain
activity when deciding not to take a risk. As illustrated in the graphs, the neural activity level
while choosing to take a risk is higher than when deciding to play it safe.
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Figure 1. Partisans and non-partisans differ in brain activity while performing a risk-taking task.
Partisans have higher activity in the bilateral posterior cingulate/precuneus, components of the
posterior medial cortices (PMC) a neural mechanism that has been previously identified as
critical for social cognition and sophisticated political cognition.
The same pattern can be observed in Figure 2 for the left cuneus/precuneus, another
component of the PMC. And, again we see higher brain activity for partisans compared with
nonpartisans. While the bar graphs accurately show differences in brain activity, the
complication is in considering an appropriate baseline.
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Figure 2. Partisans and non-partisans also differ in the level of brain activity present in the left
cuneus/precuneus, another part of the posterior medial cortices.
While studies using radioactive tracers in the blood stream (e.g. positron emission
tomography (PET) scanning) provide an absolute measure of cerebral blood flow, fMRI relies on
comparisons among conditions and has no meaningful absolute characteristic; the values are
arbitrary to the particular scan. In both instances (fMRI and PET), the estimate of changes in
neural activity is always made in terms of comparisons to a baseline. Typically, that baseline is
when the participant is merely resting in the scanner.
The Default Mode of the Brain
In 2001, Marcus Raichle and colleagues published a paper in which they had inverted the
typical experimental paradigm. They set the baseline as a variety of technical cognitive tasks
and observed which areas of the brain decreased in activity when the participant began to focus
on one of these technical cognitive tasks (e.g. conjugating verbs or imagining the rotation of a
shape.) They discovered a network of regions that became known as the default mode network
(DMN) since it was highly active when people were at rest (Raichle et al. 2001). This set off a
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firestorm of research as hundreds of neuroscientists sought to understand this default mode of the
brain its cognitive and physiological characteristics (Raichle and Snyder 2007).
When subjects were resting in the scanner, the DMN was extremely active, consuming a
disproportionate amount of the brain’s metabolic energy (Gusnard and Raichle 2001). It was a
bit odd that this activity was so important as to justify the consumption of so many evolutionarily
precious calories and yet would shut off whenever someone thought about any of a variety of
puzzles. What was this default mode of the brain?
It soon became clear that default mode of the brain was a mix of thoughts about self and
others. While each of the technical puzzles deactivated the DMN, a large variety of social
conditions caused the network to increase in activity above the resting baseline (Schreiber 2007).
People making personal moral judgments showed heightened activity in this network (Greene et
al. 2001). And, people who were knowledgeable about politics engaged it more when they
thought about political issues (Fowler and Schreiber 2008). In Figure 3, we can see the
increased activity in the medial prefrontal cortex (mPFC) and the posterior medial cortices
(PMC)(the convention in fMRI studies is to show warm colors like orange and red for activation
and cool colors like blue for deactivations.) Figure 4 illustrates a comparison of brain activity as
participants answered political questions with a resting baseline. The political club members
brains respond to the task as if it were a form of social cognition (e.g. playing on the playground
at recess), but for the political novices this is more akin to a technical task (i.e. being in the
classroom and taking a math test.)
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Figure 3. Two clusters of brain activity in the Default Mode Network show the contrast in brain
activity between people who are practiced at national politics and those who are novices.
Figure 4. Brain activity in two regions of the Default Mode Network show that political club
members activate the network above a resting baseline while political novices deactivate it.
In the present experiment, deciding how to gamble is more akin to the type of technical
tasks that typically deactivate regions in the DMN. The logic here is that metabolic energy is
scarce and the self-reflective social cognition taking place in the DMN is costly. Thus, when
facing a complex technical task, the DMN will deactivate to free up scarce energy for other
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mental processes. With practice the DMN does not need to deactivate as much to accomplish the
technical task (Mason et al. 2007).
While there is greater activity in the PMC for the partisans than the non-partisans, it is
more nuanced to note that the risky decisions deactivate the brain much less for the partisans
compared to the larger deactivations they have when making a safe decision. For the nonpartisans, there is a much smaller difference between the risky and the safe decisions, both
deactivate the brain to a similar degree.
Interpreting the differences in PMC activity
Two possible interpretations are that the partisans are more comfortable or practiced with
risk. As noted, a greater level of skill acquired with practice diminishes the computational
demands of a task. As we get better at conjugating verbs in a new language, regions such as
Broca’s area (known for its role in language) will diminish in their activity (Raichle 1994). The
risk-taking task described in this paper appears to typically deactivate the DMN as most
technical tasks do. Thus, practice with risk taking in general could explain the differences. This
would be a bit unusual, however, since decomposing the data shows that it is a smaller
deactivation for the biggest risks that appears to be driving the differences. It is not clear how
we would interpret practice related effects that would differentially change the brain’s processing
of big risks.
Another possible interpretation regards risk preference. Some individuals prefer risks
and uncertainty. As discussed above, this risk orientation varies with ideology. Perhaps it also
differs with partisanship. Exploring this possibility should be feasible with extant behavioral
data from the current experiment. And, it should be possible to confirm any results by
reanalyzing data from previously published experiments (Kam and Simas 2010).
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That the differences are found in the PMC is particularly intriguing. While those
practiced in national politics appear to differ in DMN activity from political novices during
political tasks, those differences do not carry over into non-political social tasks. In fact, the
functional connectivity of the network also appears to be generally consistent between the two
groups (Schreiber 2012 (in review)). Thus, the findings do not appear to be a direct consequence
of fundamental differences in DMN function as related to political practice.
Social group membership as insurance
Another possibility is that being a partisan alters one’s perception of risk. It appears that
affiliating with a party changes a variety of prosocial cognitive processes even beyond the
context of the party itself (Smirnov et al. 2010). Being a member of a social group has many
complications and many cognitive costs, but as seen with the example of the rock hyrax it also
pays off in terms of greater life span and health. Becoming more prosocial enables one to remain
in the group. Perhaps, a consequence is also the ability to tolerate a greater level of risk.
Group membership functions as a type of insurance. Because of the expectation of
reciprocity, individuals are able to take greater risks than they might otherwise take and have
stronger chance of having support if they fail than if they had been solo. If this is the case, we
might predict party members to be more risk tolerant than non-partisans. In fact, party members
might even prefer risk, knowing that they could obtain the potential higher payoffs and still be
protected against downside loss through their social connections.
In such a case, it might not be surprising to see brain activity consistent with a greater
tolerance of risk. Differences in the amygdala or ventromedial prefrontal cortex would be a
clearer sign of risk tolerance given these regions’ consistent activity for a variety of tasks with
uncertainty and risk. But the small deactivations for the partisans may indicate that the bigger
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risks are not as troubling or even that a choice to take the safe strategy is perceived as a loss of
the much larger potential gains that could be obtained from the bigger risks. Such an asymmetry
(and reversal of the predictions of prospect theory) would be rational if one was insured against
experiencing the larger losses by the prosocial provisions entailed through group membership.
The PMC, the self, and moral judgments
The function of the PMC in this task is curious. Antonio Damasio (2010) notes that over
evolutionary time the brain evolved hubs that integrated data from the various parts of the body.
The thalamus, for instance, sits atop the brainstem and integrates physical sensations from
throughout the body and then projects this information throughout the cerebral cortex. It also
receives connections from across the brain. Of these many hubs, the PMC appears to be the hub
of hubs. It integrates information from the other hubs and Damasio suggests that it might be the
place that the self comes to mind.
As such, while differences in the PMC may simply be the consequence of the demand for
cognitive effort, they may also indicate differences in the conscious processing of risk. The
PMC integrates information from across the brain and is active during the construction of
narratives (Awad et al. 2007). This self-awareness function may be altered for partisans as they
make risky decisions, especially since the DMN is so actively engaged in the extra social
cognition required by group membership.
The PMC also is involved in moral judgments. As mentioned above, when people make
moral judgments about those close to them the DMN increases in activity, signaling the
invocation of a rule based (deontological) moral judgment. In contrast, moral decisions about
those we are less connected to deactivate the DMN and are characterized by utilitarian judgments
(Greene 2007). Other work has shown that cognitive effort interferes with the capacity for
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utilitarian judgments (Greene et al. 2008). Thus, the level of activity in the PMC and the
cognitive effort we must expend affects the manner in which we weigh moral decisions.
As such, the variation in the function of the PMC might explain the differences in the
way that citizens evaluated a referendum on Quebec’s sovereignty. Individuals that were willing
to accept risk more readily tended to decide by comparing the costs and benefits (a utilitarian
judgment), but the risk averse tended to react to the worst possible outcome (which appears more
similar to a deontological judgment)(Nadeau, Martin, and Blais 1999). Similar findings were
described in the context of trade policy as well (Ehrlich and Maestas 2010).
Testing this would entail looking for systematic differences in the way that partisans and
nonpartisans evaluated policies and candidates and seeing if this lined up with differences in risk
tolerance. If so, it could be that the differences in the PMC are related to the differences in the
types of moral judgments that are being made. This would also have implications for how
policies ought to be framed if you were trying to persuade partisan or nonpartisan voters.
Nonpartisans’ brains were less responsive to variations in risk, so we might expect a more
consistent type of moral judgment.
Political scientists have become more conscious of the role of risk orientation in political
decision making over the past decade. While expected utility theory discounts the differences
between losses and gains, prospect theory emphasizes the asymmetry. However, prospect theory
does not sufficiently anticipate how differently individuals might make decisions if they are risk
accepting or risk averse. If these differences in risk orientation are connected to differences in
how the PMC processes risk then we could have a potential explanation for some of the
differences observed in partisans and nonpartisans.
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Further exploration from converging lines of evidence is what will be required to develop
a thorough understanding of how risk orientation and partisan affiliation alters the political
calculus. Field experiments provoking nonpartisans to affiliate with a party (Gerber, Huber, and
Washington 2010) can enable us to explore causality in realistic contexts. Survey experiments
can test the framing of political arguments and measure risk orientation (Kam and Simas 2010).
Behavioral economic games can test hypotheses about broad implications of party affiliation
(Smirnov et al. 2010). And, brain imaging studies can explore the neural mechanisms that
underpin the decisions.
We evolved as political animals. The reason we think and react as we do is a product of a
long evolutionary history that selected for greater levels of sociality in the midst of increased
social complexity. Understanding that our brains are built for politics changes our perceptions of
human nature and roots our theories of political behavior in the broader biological context.
Materials and Methods
Participants
Participant groups were composed of 82 partisan (60 Democrats and 22 Republicans) and
38 non-partisans who did not differ with regard to age (F(1,118)=2.075, p=.152; Partisan mean
age(SD)=23.82(8.73); Non-Partisan mean age(SD)=21.71(3.23)) or gender (Partisans: 47
females and 35 males; Non-Partisan: 16 females and 22 males; χ2=1.84, p=0.175.) Participants
gave informed written consent approved by the University of California, San Diego, Human
Research Protection Program.
The UCSD Institutional Review Board approved study procedures. All participants
provided written informed consent and were paid for their participation. One-hundred twenty
individuals were studied, including 60 Democrats, 22 Republicans, and 38 non-partisans. We
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acquired voter registration records from San Diego County in March 2008 that included party of
registration and electoral turnout history, and names, addresses, and phone numbers to ensure
exact matches to subjects who participated in the functional brain imaging study. Functional
imaging data was collected across 1.5T (n=15) and 3T (n=105) scanners. There was a difference
between Partisans and Non-Partisans on which scanner the data was acquired on (Fisher exact
probability test p = .035; Partisans: 14 on 1.5T, 68 on 3T; Non-Partisans: 1 on 1.5T, 37 on 3T).
Therefore, the scanner was entered as a covariate to control for any confounding effects.
Task
For the Risky-Gains task (Paulus et al. 2003), participants were presented with three
numbers in ascending order (20, 40, and 80). Each number was presented on the screen for one
second and if the participant pressed a button when the number was shown on the screen, he/she
received the number of points shown on the screen. The participants were informed that for both
40 and 80 points there was a chance that a 40 or 80 in red color might appear on the computer
screen which signaled that the participant lost 40 or 80 points, respectively. Thus, although the
participant may have gained more points per trial by waiting until a 40 or 80 appears on the
screen, there was also a risk of losing 40 or 80 points. The probabilities of presenting a negative
40 or 80 are such that a participant's final score would be identical were they to consistently
select 20, 40, or 80. Thus, there was no inherent advantage to select the risky response (40 or
80) over the safe response (20). Each trial lasted 3.5 s irrespective of the participant's choice and
the participant received rewarding feedback (stimulus on the screen and auditory sound)
immediately after selecting a response.
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Image acquisition
For 120 participants, during the task a BOLD-fMRI run was collected for each participant
using a Signa EXCITE (GE Healthcare, Milwaukee) 3.0T scanner (T2 * weighted echo planar
imaging, TR=2000ms, TE=32ms, FOV=250x250 mm3, 64×64 matrix, 30 2.6mm axial slices
with a 1.4mm gap, 290 scans). Functional MRI acquisitions were time-locked to the onset of
functional run. During the same experimental session, a high resolution T1-weighted image
(SPGR, TI=450ms, TR=8ms, TE=4ms, flip angle=12°, FOV=250x250, ~1 mm3 voxels) was
obtained for anatomical reference. For 15 participants, during the task a BOLD-fMRI run was
collected for each participant using a 1.5-T Siemens (Erlangen, Germany) scanner (T2*weighted echo planar imaging, TR=2,000 ms, TE=40 ms, 64×64 matrix, 20 4-mm axial slices,
256 repetitions). During the same experimental session, a T1-weighted image (MPRAGE,
TR=11.4 ms, TE=4.4 ms, flip angle=10°, FOV=256×256, 1 mm3 voxels) was obtained for
anatomical reference.
fMRI analysis pathway
The data were preprocessed and analyzed with the software AFNI (Cox 1996). The
echo-planar images were realigned to the temporal center of the longest stable head position and
time-corrected for slice acquisition order. To exclude the voxels showing an artifact related to
signal drop, a combined threshold/cluster-growing algorithm was applied to the mean of the
functional images to compute a region of interest brain mask. This screened out non-brain
voxels and voxels falling within the artifact region. A randomized, fast-event related design was
used with six resting trials interspersed between the 96 risky-gains trials. The preprocessed time
series data for each individual were analyzed using a multiple regression model where five
regressors of interest were constructed from the behavioral data obtained from each participant
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during the task. Specifically, response regressors were defined from the onset of the trial until the
individual selected an option and, for punished trials, until the appearance of negative 40 or 80.
These five regressors are referred to as (1) selecting 20 (safe response), (2) selecting 40 (risky
response), (3) selecting 80 (risky response), (4) punished with -40, and (5) punished with -80.
The subsequent time period, which included outcome and intertrial interval, as well as the null
trials, served as the baseline condition for this analysis. The regressors of interest were
convolved with a modified gamma variate function modeling a prototypical hemodynamic
response (Boynton et al. 1996) before inclusion in the regression model. In addition, three
regressors were used to account for residual motion (in the roll, pitch, and yaw direction).
Regressors for baseline and linear trends were used to eliminate slow signal drifts. The AFNI
program 3dDeconvolve was used to calculate the estimated voxel-wise response amplitude.
Finally, a participant-specific voxel-based linear contrast was used to identify brain activation
associated with selecting a winning risky response (40 or 80) vs. a safe response (20). A
Gaussian filter with FWHM of 4 mm was applied to the voxel-wise percent signal change data to
account for individual variations of the anatomical landmarks. Data of each participant were
normalized to Talairach coordinates.
Statistical analyses
Voxelwise “robust” multiple regression analyses implemented within the statistical
package R (www.r-project.org) were conducted on individual percent signal change statistics for
conditions of interest by modeling effects as a function of age, income, partisanship, and magnet
tesla. A-priori regions of interest (ROI) masks (defined by the Talairach Daemon atlas
(Lancaster et al. 2000)) in the bilateral amygdala, bilateral insula, and anterior cingulate/medial
prefrontal cortex (Brodmann Areas 24 and 32) as well as an exploratory whole-brain analysis
20
were used to examine between-group effects for the win risky versus safe decisions (contrasting
regressors 2 and 3 with regressor 1 in the list of regressors given above). On the basis of these
ROIs, a voxel-wise a-priori probability of 0.05 for each model factor, determined via MonteCarlo simulations, would result in a corrected cluster-wise a posteriori probability of 0.05 with a
minimum volume of 192 μl or three connected voxels (in the amygdala), 448 μl or 7 connected
voxels in the insula or anterior cingulate/medial prefrontal cortex, and 1024 μl or 16 connected
voxels for the whole-brain exploratory analysis. Using the thresholding and clustering
techniques described above, the corrected voxel-wise probabilities are as follows: amygdala
(p<0.0097), insular cortex (p<0.0018), anterior cingulate/medial PFC (p < 0.0017), and wholebrain (p < 0.00005). ROI masks were superimposed on each individual’s voxel-wise percent
signal change brain image. Only activations within the areas of interest, which also satisfied the
volume and voxel connection criteria, were extracted and used for further analysis. Significance
values reported in the cluster table were corrected for age, income, and magnet tesla. Behavioral
analyses were carried out with SPSS 12.0 (Chicago, Il).
21
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