1

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 non-partisans. 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 2 Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California-San Diego, San Diego, CA 92120, USA

2

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,

3

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-Mio 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 (Krtzen 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).

4

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

5

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 brains 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.

6

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

7

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.

8

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.

Voxelwise Stats Mean (sd) t p

Mask Hem Region Vol. (l) X Y Z

Mean SD Mean SD ROI -- No significant differences -- -- -- -- -- -- -- -- WB L Posterior Cingulate/Precuneus 1216 -5 -55 21 -2.47 0.38 0.021 0.015 WB R Posterior Cingulate/Precuneus 1152 19 -55 18 -2.34 0.46 0.029 0.015 WB L Cuneus/Precuneus 1088 -3 -78 27 -2.40 0.44 0.024 0.014

9

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.

10

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

11

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 brains 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.)

12

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

13

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 non-

partisans, 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

Brocas 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 brains 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).

14

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 ones 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

15

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

16

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 Quebecs 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.

17

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; 2=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

18

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.

19

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, 6464 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, 6464 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=256256, 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

20

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

21

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 Monte-

Carlo simulations, would result in a corrected cluster-wise a posteriori probability of 0.05 with a

minimum volume of 192 l or three connected voxels (in the amygdala), 448 l or 7 connected

voxels in the insula or anterior cingulate/medial prefrontal cortex, and 1024 l 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

22

Works Cited Aristotle. 1996 [350 B.C.]. Aristotle : the Politics and the Constitution of Athens. Edited by

Stephen Everson. Rev. student ed of Cambridge texts in the history of political thought. Cambridge ; New York: Cambridge University Press.

Awad, M, J. E Warren, S. K Scott, F. E Turkheimer, and R. J. S Wise. 2007. A Common System for the Comprehension and Production of Narrative Speech. Journal of Neuroscience 27 (43): 11455-11464.

Barocas, A, A Ilany, L Koren, M Kam, and E Geffen. 2011. Variance in Centrality within Rock Hyrax Social Networks Predicts Adult Longevity. PLoS ONE 6 (7): e22375.

Berinsky, Adam J, and Jeffrey B Lewis. 2007. An Estimate of Risk Aversion in the U.S. Electorate. Quarterly Journal of Political Science 2 (2): 139-154.

Boynton, G. M., S. A. Engel, G. H. Glover, and D. J. Heeger. 1996. Linear systems analysis of functional magnetic resonance imaging in human V1. J Neurosci 16 (13): 4207-21.

Braun-Blanquet, J., George D. Fuller, and Henry Shoemaker Conard. 1932. Plant sociology; the study of plant communities. 1st ed of McGraw-Hill publications in the agricultural and botanical sciences, E W Sinnott, consulting editor. New York, London,: McGraw-Hill book company, inc.

Brodersen, Ditlev E, and Poul Nissen. 2005. The social life of ribosomal proteins. FEBS J 272 (9): 2098-108.

Connor, R. C., J. J. Watson-Capps, W. B. Sherwin, and M. Krutzen. 2010. A new level of complexity in the male alliance networks of Indian Ocean bottlenose dolphins (Tursiops sp.). Biol Lett.

Connor, Richard C. 2007. Dolphin social intelligence: complex alliance relationships in bottlenose dolphins and a consideration of selective environments for extreme brain size evolution in mammals. Philos T Roy Soc B 362 (1480): 587-602.

Cox, R. W. 1996. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29 (3): 162-73.

Damasio, Antonio R. 2010. Self comes to mind : constructing the conscious brain. New York: Pantheon Books.

Dawes, C, and J Fowler. 2009. Partisanship, voting, and the dopamine D2 receptor gene. The Journal of Politics.

Dawes, Christopher T, James H Fowler, Sarah Sullivan, Fangwen Rao, Daniel T OConnor, Joel Gelernter, and Murray B Stein. 2010. COMT, Extraversion, and Partisan Attachment. Working Paper: 1-30.

Draganski, B., C. Gaser, V. Busch, G. Schuierer, U. Bogdahn, and A. May. 2004. Neuroplasticity: changes in grey matter induced by training. Nature 427 (6972): 311-2.

Dunbar, R. I. 2009. The social brain hypothesis and its implications for social evolution. Ann Hum Biol 36 (5): 562-72.

Ehrlich, S, and C Maestas. 2010. Risk Orientation, Risk Exposure, and Policy Opinions: The Case of Free Trade. Political Psychology 31 (5): 657-684.

Eichinger, L, J A Pachebat, G Gl|[Ouml]|Ckner, M -A Rajandream, R Sucgang, M Berriman, J Song, R Olsen, K Szafranski, Q Xu, B Tunggal, S Kummerfeld, M Madera, B A Konfortov, F Rivero, A T Bankier, R Lehmann, N Hamlin, R Davies, P Gaudet, P Fey, K Pilcher, G Chen, D Saunders, E Sodergren, P Davis, A Kerhornou, X Nie, N Hall, C Anjard, L Hemphill, N Bason, P Farbrother, B Desany, E Just, T Morio, R Rost, C

23

Churcher, J Cooper, S Haydock, N van Driessche, A Cronin, I Goodhead, D Muzny, T Mourier, A Pain, M Lu, D Harper, R Lindsay, H Hauser, K James, M Quiles, M Madan Babu, T Saito, C Buchrieser, A Wardroper, M Felder, M Thangavelu, D Johnson, A Knights, H Loulseged, K Mungall, K Oliver, C Price, M A Quail, H Urushihara, J Hernandez, E Rabbinowitsch, D Steffen, M Sanders, J Ma, Y Kohara, S Sharp, M Simmonds, S Spiegler, A Tivey, S Sugano, B White, D Walker, J Woodward, T Winckler, Y Tanaka, G Shaulsky, M Schleicher, G Weinstock, A Rosenthal, E C Cox, R L Chisholm, R Gibbs, W F Loomis, M Platzer, R R Kay, J Williams, P H Dear, A A Noegel, B Barrell, and A Kuspa. 2005. The genome of the social amoeba Dictyostelium discoideum. Nature 435 (7038): 43.

Emery, N J, and N S Clayton. 2001. Effects of experience and social context on prospective caching strategies by scrub jays. Nature 414 (6862): 443.

Emery, Nathan J, and Nicola S Clayton. 2004. The mentality of crows: convergent evolution of intelligence in corvids and apes. Science 306 (5703): 1903-7.

Fowler, J. H., and D. Schreiber. 2008. Biology, politics, and the emerging science of human nature. Science 322 (5903): 912-4.

Gerber, Alan S, Gregory A Huber, and Ebonya Washington. 2010. Party Affiliation, Partisanship, and Political Beliefs: A Field Experiment. American Political Science Review 104 (04): 720-744.

Greene, J. D., S. A. Morelli, K. Lowenberg, L. E. Nystrom, and J. D. Cohen. 2008. Cognitive load selectively interferes with utilitarian moral judgment. Cognition 107 (3): 1144-54.

Greene, J. D., R. B. Sommerville, L. E. Nystrom, J. M. Darley, and J. D. Cohen. 2001. An fMRI investigation of emotional engagement in moral judgment. Science 293 (5537): 2105-8.

Greene, Joshua D. 2007. Why are VMPFC patients more utilitarian? A dual-process theory of moral judgment explains. Trends Cogn Sci 11 (8): 322-3; author reply 323-4.

Gusnard, D. A., and M. E. Raichle. 2001. Searching for a baseline: functional imaging and the resting human brain. Nature Review Neuroscience 2 (10): 685-94.

Holekamp, Kay E, Sharleen T Sakai, and Barbara L Lundrigan. 2007. Social intelligence in the spotted hyena (Crocuta crocuta). Philos T Roy Soc B 362 (1480): 523-38.

Hunt, Gavin R. 1996. Manufacture and use of hook-tools by New Caledonian crows. 379 (6562): 249.

Kahneman, Daniel, and Amos Tversky. 1979. Prospect Theory: An Analysis of Decision under Risk. Econometrica 47 (2): 263-291.

Kam, C.D, and E.N Simas. 2010. Risk orientations and policy frames. The Journal of Politics 72 (02): 381-396.

Kanai, Ryota, Tom Feilden, Colin Firth, and Geraint Rees. 2011. Political Orientations Are Correlated with Brain Structure in Young Adults. Current biology : CB.

Kerr, N.L, and J.M Levine. 2008. The detection of social exclusion: Evolution and beyond. Group Dynamics: Theory, Research, and Practice 12 (1): 39.

Koren, Lee, and Eli Geffen. 2009. Complex call in male rock hyrax (Procavia capensis): a multi-information distributing channel. Behav Ecol Sociobiol 63 (4): 581-590.

Krtzen, Michael, Janet Mann, Michael R Heithaus, Richard C Connor, Lars Bejder, and William B Sherwin. 2005. Cultural transmission of tool use in bottlenose dolphins. Proceedings of the National Academy of Sciences of the United States of America 102 (25): 8939-43.

24

Lancaster, J. L., M. G. Woldorff, L. M. Parsons, M. Liotti, C. S. Freitas, L. Rainey, P. V. Kochunov, D. Nickerson, S. A. Mikiten, and P. T. Fox. 2000. Automated Talairach atlas labels for functional brain mapping. Hum Brain Mapp 10 (3): 120-31.

Mason, M. F., M. I. Norton, J. D. Van Horn, D. M. Wegner, S. T. Grafton, and C. N. Macrae. 2007. Wandering minds: the default network and stimulus-independent thought. Science 315 (5810): 393-5.

Morgenstern, S, and E Zechmeister. 2001. Better the devil you know than the saint you don't? Risk propensity and vote choice in Mexico. Journal of Politics 63 (1): 93-119.

Nadeau, R, P Martin, and A Blais. 1999. Attitude towards risk-taking and individual choice in the Quebec referendum on sovereignty. British Journal of Political Science 29 (3): 523-539.

Nishida, Toshisada, Kazuhiko Hosaka, Miho Nakamura, and Miya Hamai. 1995. A within-group gang attack on a young adult male chimpanzee: Ostracism of an ill-mannered member? Primates 36 (2): 207-211.

Overmann, J. 2004. Phototrophic consortia: a tight cooperation between non-related eubacteria. Symbiosis: 239-255.

Oxley, D. R., K. B. Smith, J. R. Alford, M. V. Hibbing, J. L. Miller, M. Scalora, P. K. Hatemi, and J. R. Hibbing. 2008. Political attitudes vary with physiological traits. Science 321 (5896): 1667-70.

Paulus, M. P., C. Rogalsky, A. Simmons, J. S. Feinstein, and M. B. Stein. 2003. Increased activation in the right insula during risk-taking decision making is related to harm avoidance and neuroticism. Neuroimage 19 (4): 1439-48.

Paz-Y-Mio C, Guillermo, Alan B Bond, Alan C Kamil, and Russell P Balda. 2004. Pinyon jays use transitive inference to predict social dominance. Nature 430 (7001): 778-81.

Plotnik, Joshua M, Richard Lair, Wirot Suphachoksahakun, and Frans B M de Waal. 2011. Elephants know when they need a helping trunk in a cooperative task. Proc Natl Acad Sci USA 108 (12): 5116-21.

Poole, J.H, and C.J Moss. 2008. "Elephant sociality and complexity: the scientific evidence." In Elephants and ethics: toward a morality of coexistence., eds. C. Wemmer and C. Christen. Baltmore: Johns Hopkins University Press. 6998.

Raichle, M. E., A. M. MacLeod, A. Z. Snyder, W. J. Powers, D. A. Gusnard, and G. L. Shulman. 2001. A default mode of brain function. Proceedings of the National Academy of Science USA 98 (2): 676-82.

Raichle, M. E., and A. Z. Snyder. 2007. A default mode of brain function: a brief history of an evolving idea. Neuroimage 37 (4): 1083-90; discussion 1097-9.

Raichle, Marcus E. 1994. Visualizing the mind. Scientific American 270 (4): 58-64. Scholz, J., M. C. Klein, T. E. Behrens, and H. Johansen-Berg. 2009. Training induces changes in

white-matter architecture. Nat Neurosci. Schreiber, Darren. 2007. "Political cognition as social cognition: Are we all political

sophisticates?" In The Affect Effect: Dynamics of Emotion in Political Thinking and Behavior, eds. Anne Crigler, Michael MacKuen, George E. Marcus and W. Russell Neuman. Chicago: University of Chicago. 48-70.

Schreiber, Darren. 2012 (in review). Your Brain is Built for Politics. Princeton. Schreiber, Darren, Alan N. Simmons, Christopher T. Dawes, Taru Flagan, James H. Fowler, and

Martin P. Paulus. 2009. "Red Brain, Blue Brain: Evaluative Processes Differ in

25

Democrats and Republicans." Presented at the American Political Science Association, Toronto, Canada.

Settle, JE, CT Dawes, and JH Fowler. 2009. The heritability of partisan attachment. Political Research Quarterly 62 (3): 601.

Shettleworth, Sara J. 2009. The evolution of comparative cognition: is the snark still a boojum? Behav Processes 80 (3): 210-7.

Smirnov, O, C Dawes, J Fowler, and T Johnson. 2010. The Behavioral Logic of Collective Action: Partisans Cooperate and Punish More Than Nonpartisan. Political Psychology 31 (4): 595-616.

Sunamura, E, X Espadaler, H Sakamoto, S Suzuki, M Terayama, and S Tatsuki. 2009. Intercontinental union of Argentine ants: behavioral relationships among introduced populations in Europe, North America, and Asia. Insect. Soc. 56 (2): 143-147.

Tajfel, Henri. 1970. Experiments in intergroup discrimination. Scientific American 23 (5): 96-102.

Tajfel, Henri, and John C. Turner. 1979. "An Integrative Theory of Intergroup Conflict." In The Social Psychology of Intergroup Relations, eds. W. G. Austin and S. Worchel. Monterey, California: Brooks-Cole.

Tomz, M, and R.P Van Houweling. 2009. The electoral implications of candidate ambiguity. American Political Science Review 103 (01): 83-98.

von Rohr, Claudia Rudolf, Sonja E Koski, Judith M Burkart, Clare Caws, Orlaith N Fraser, Angela Ziltener, and Carel P van Schaik. 2012. Impartial third-party interventions in captive chimpanzees: a reflection of community concern. PLoS ONE 7 (3): e32494.

Weber, Christopher, Martin Johnson, and Kevin Arceneaux. 2010. Genetic Influences on Group Politics. Working Paper: 1-40.

Woollett, Katherine, and Eleanor A Maguire. 2011. Acquiring "the Knowledge" of London's Layout Drives Structural Brain Changes. Current biology.