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Neural correlates of maintaining one’s political beliefs in the face of counterevidence

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People often discount evidence that contradicts their firmly held beliefs. However, little is known about the neural mechanisms that govern this behavior. We used neuroimaging to investigate the neural systems involved in maintaining belief in the face of counterevidence, presenting 40 liberals with arguments that contradicted their strongly held political and non-political views. Challenges to political beliefs produced increased activity in the default mode network—a set of interconnected structures associated with self-representation and disengagement from the external world. Trials with greater belief resistance showed increased response in the dorsomedial prefrontal cortex and decreased activity in the orbitofrontal cortex. We also found that participants who changed their minds more showed less BOLD signal in the insula and the amygdala when evaluating counterevidence. These results highlight the role of emotion in belief-change resistance and offer insight into the neural systems involved in belief maintenance, motivated reasoning, and related phenomena.
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Scientific RepoRts | 6:39589 | DOI: 10.1038/srep39589
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Neural correlates of maintaining
one’s political beliefs in the face of
counterevidence
Jonas T. Kaplan1, Sarah I. Gimbel1 & Sam Harris2
People often discount evidence that contradicts their rmly held beliefs. However, little is known about
the neural mechanisms that govern this behavior. We used neuroimaging to investigate the neural
systems involved in maintaining belief in the face of counterevidence, presenting 40 liberals with
arguments that contradicted their strongly held political and non-political views. Challenges to political
beliefs produced increased activity in the default mode network—a set of interconnected structures
associated with self-representation and disengagement from the external world. Trials with greater
belief resistance showed increased response in the dorsomedial prefrontal cortex and decreased activity
in the orbitofrontal cortex. We also found that participants who changed their minds more showed less
BOLD signal in the insula and the amygdala when evaluating counterevidence. These results highlight
the role of emotion in belief-change resistance and oer insight into the neural systems involved in
belief maintenance, motivated reasoning, and related phenomena.
Few things are as fundamental to human progress as our ability to arrive at a shared understanding of the world.
e advancement of science depends on this, as does the accumulation of cultural knowledge in general. Every
collaboration, whether in the solitude of a marriage or in a formal alliance between nations, requires that the
beliefs of those involved remain open to mutual inuence through conversation. Data on any topic—from climate
science to epidemiology—must rst be successfully communicated and believed before it can inform personal
behavior or public policy. Viewed in this light, the inability to change another persons mind through evidence
and argument, or to have ones own mind changed in turn, stands out as a problem of great societal importance.
Both human knowledge and human cooperation depend upon such feats of cognitive and emotional exibility.
It is well known that people oen resist changing their beliefs when directly challenged, especially when these
beliefs are central to their identity1–6. In some cases, exposure to counterevidence may even increase a persons
condence that his or her cherished beliefs are true7,8. Although neuroscientists have begun to study some of the
social aspects of persuasion9 and motivated reasoning10, little research is aimed directly at understanding the
neural systems involved in protecting our most strongly held beliefs against counterevidence.
One model of belief maintenance holds that when confronted with counterevidence, people experience neg-
ative emotions borne of conict between the perceived importance of their existing beliefs and the uncertainty
created by the new information11–14. In an eort to reduce these negative emotions, people may begin to think in
ways that minimize the impact of the challenging evidence: discounting its source, forming counterarguments,
socially validating their original attitude, or selectively avoiding the new information15. e degree to which such
rationalization occurs depends upon several factors, but the personal signicance of the challenged belief appears
to be crucial. Specically, beliefs that relate to ones social identity are likely to be more dicult to change16–19.
Based on this model, predictions can be made about the neural systems that govern resistance to belief change.
First, resistance to evidence may entail disengagement from external reality and increased inward focus. e
brains default mode network (DMN), including posterior and anterior midline structures and the lateral inferior
parietal lobes, appears to support these psychological processes20,21. Identity-related beliefs might also invoke
internal models of the self, a form of cognition that is associated with increased activity within the DMN22,23.
Second, if resistance to belief change is partly motivated by negative emotion, having ones beliefs contradicted
may produce activity in associated regions of the brain, such as the amygdala, the insular cortex, and other struc-
tures involved in emotion regulation.
1Brain and Creativity Institute and Department of Psychology, University of Southern California Los Angeles, CA,
90089, USA. 2Project Reason Los Angeles, CA, USA. Correspondence and requests for materials should be addressed
to J.T.K. (email: jtkaplan@usc.edu)
Received: 24 February 2016
Accepted: 25 November 2016
Published: 23 December 2016
OPEN
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In this study, we performed functional MRI to measure the brain activity of 40 individuals with strong politi-
cal views as they encountered arguments against their beliefs. All the subjects were self-identied as political lib-
erals of deep conviction. Inside the fMRI scanner, participants saw a series of statements they previously indicated
strongly believing, followed by several challenging counterarguments. Aer participants read all ve counterar-
guments, the original statement was shown again and they reported their post-challenge belief strength. e dif-
ference between pre-scan and post-challenge ratings was used as a measure of belief change. In order to compare
high belief persistence to low belief persistence, in one condition we challenged strongly held political beliefs, and
in another condition we challenged strongly-held non-political beliefs. While the non-political beliefs were just
as strongly held according to the participants who held them, we did not expect these beliefs to be defended with
the same vigor.
We predicted that the political condition would result in less belief change than the non-political condition,
and that resisting challenges to political beliefs would be associated with increased activity in brain systems
involved in contemplating identity and internally-focused cognition. Furthermore, we predicted that there would
be a relationship between activity in emotion-related brain structures and individual dierences in persuadability.
We also sought to identify brain activity that correlated with the strength with which specic beliefs were main-
tained in our sample.
Materials and Methods
Participants. Forty healthy participants with no history of psychological or neurological disorders were
recruited from the University of Southern California community and the surrounding Los Angeles Area (mean
age: 24.30 ± 0.92 years, range: 18–39 years, 20 male). All participants were right-handed according to their own
report. Subjects were paid $20 per hour for their participation and gave informed consent. All experimental pro-
tocols were approved by the Institutional Review Board of the University of Southern California and procedures
were carried out in accordance with the approved guidelines. All participants had spent the majority of their life
living in the United States and spoke uent English, identied themselves as politically liberal, and had strongly
held political and non-political beliefs. Specically, participants answered a screening questionnaire in which they
were asked about their political identication. On the question “Do you consider yourself a political person?”
answers ranged on a scale from 1 (not at all) to 5 (very much). Participants were only included if they answered at
least a 4 on this question. For the question “Which of the following describes your political self-identication?”
answers ranged from 1 (strongly liberal) to 7 (strongly conservative) and participants were only included if they
answered 1 or 2. Additionally, participants rated their agreement with several political and non-political state-
ments and were only included in the experiment if they strongly agreed with at least 8 political and 8 non-political
statements. Of 116 people who responded to our advertisements, 98 met the requirements for age, handedness,
and political orientation. From those 98 people, 40 subjects met the requirements for strongly agreeing to at least
8 statements in each category.
Stimuli. In this experiment, each participant read 8 political statements and 8 non-political statements with
which they had previously indicated strong agreement. Each statement was followed by 5 challenges. Each chal-
lenge was a sentence or two that provided a counter-argument or evidence against the original statement.
e 8 political statements for each participant were drawn from a pool of 9 political statements. ese state-
ments concerned policy issues on which we expected predictable, identity-consistent positions from our subjects,
such as “Abortion should be legal” and “Taxes on the wealthy should generally be increased”. e statements can
be found in full in TableS3. e 8 non-political statements were drawn from a pool of 14 non-political state-
ments. e pool of non-political statements was larger because while the inclusion criteria guaranteed the partic-
ipants would hold certain political beliefs, they did not guarantee belief in any specic non-political statement.
e non-political statements covered a wide range of topics including health (e.g. “Taking a daily multivitamin
improves ones health”), education (e.g. “A college education generally improves a persons economic prospects”),
and history (e.g. “omas Edison invented the light bulb”).
Each political and non-political statement was associated with 5 challenges. In order to be as compelling as
possible, the challenges oen contained exaggerations or distortions of the truth. For instance, one challenge to
the statement “e US should reduce its military budget” was “Russia has nearly twice as many active nuclear
weapons as the United States”. In truth, according to statistics published by the Federation of American Scientists:
Status of World Nuclear Forces (www.fas.org) in 2013, Russia has approximately 1,740 active nuclear warheads,
while the United States has approximately 2,150. Examples of the challenges are provided in TableS4.
The political and non-political statements did not differ in number of words (political: 11.22 ± 1.51,
non-political: 11.14 ± 1.33, p = 0.97), letters (political: 59.33 ± 7.71, non-political: 58.64 ± 6.04, p = 0.94), or
Flesch reading ease (political: 60.7 ± 17.89, non-political: 48.3 ± 28.64, p = 0.26)24. e political and non-political
challenges also did not dier in number of words (political: 20.44 ± 2.83, non-political: 18.92 ± 1.13, p = 0.15), let-
ters (political: 104.18 ± 15.50, non-political: 96.24 ± 6.08, p = 0.15), or Flesch reading ease (political: 53.9 ± 21.02,
non-political: 55.74 ± 19.11, p = 0.65).
Because we were interested in brain structures that are known to respond to social and mental stimuli, we used
a word counting method to count the frequency of social and cognitive words within the stimuli. is technique
is similar to linguistic inquiry word counting (LIWC25), but used the open-source soware tool TACIT26 version
1.0.0 in combination with the LIWC 2007 dictionary to count words in the social and cognitive process catego-
ries. We found that such words were infrequent in our stimuli, and similar across the two categories (social words
occurred with a frequency of 5.07% in the political challenges and 5.35% in the non-political challenges; cognitive
words occurred with a frequency of 10.14% in the political challenges and 11.9% in the non-political challenges).
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Experimental Procedure. In preparation for the study, participants lled out a survey of demographic
information, answered questions about their political and religious aliations, and indicated the degree to which
they agreed with political and non-political statements. Only statements for which participants chose 6 or 7
(where 1 was strongly disagree and 7 was strongly agree) were used during their scan. If a given subject strongly
believed more than 8 statements in a category, the statements were chosen for that subject as follows: rst, prefer-
ence was given to more strongly held beliefs (7 vs. 6). Second, all else being equal, preference was given for state-
ments that were not as commonly believed, in order to balance the frequency of statements in the experiment.
When participants arrived for their fMRI scan, they were given instructions and were given the opportunity
to ask questions of the experimenter. Aer the instructions, they performed a practice task, which consisted of a
shortened version of one trial of the experiment using the statement “Cats make better pets than dogs. followed
by three challenges to that statement. Following the practice task, participants underwent BOLD fMRI. For each
participant there were 4 belief-challenging scans (420 seconds each). During the belief-challenging scans, each
statement was presented for 10 seconds, followed by a variable delay of 4–6 seconds. Participants were instructed
to press a response button when they had read and understood the statement. Five challenges to the original state-
ment were then presented, each for 10 seconds. Again, participants pressed a response button when they had read
and understood the challenge. Aer all ve challenges had been presented, the original statement was presented
again and participants had 12 seconds to rate their strength of belief in the statement. e participant indicated
his or her response via a button press on an MRI-compatible button box held in the right hand. ey pressed
buttons to move a cursor le and right along a Likert scale to indicate the strength of their belief on a scale from
1 (strongly disbelieve) to 7 (strongly believe). e cursor started in the middle position of the scale. Two political
and two non-political statements were presented in each of the four fMRI scans. e order of these conditions was
randomized within each scan, and the statements within each condition were assigned random positions within
the experiment for each subject. e temporal structure of the trials and runs is depicted in Fig.S1.
Following the fMRI session, participants lled out a short questionnaire. ey were asked to rate how credible
they found the challenges in general, and how challenging they were to their beliefs. Participants did not make
separate ratings per item or per category, but rather answered these questions about their reaction to the stimulus
set in general. During the debrieng, subjects were given a packet of sourced information which detailed the
truth of each challenge they read inside the scanner and provided resources on where to nd further information.
MRI Scanning. Imaging was performed using a 3T Siemens MAGNETON Trio System with a 12-channel
matrix head coil at the Dana and David Dornsife Neuroscience Institute at the University of Southern
California. Functional images were acquired using a gradient-echo, echo-planar, T2*-weighted pulse sequence
(TR = 2000 msec, one shot per repetition, TE = 25 msec, ip angle = 90°, 64 × 64 matrix, phase encoding direc-
tion anterior to posterior, GRAPPA acceleration factor = 2, fat-sat fat suppression). Forty slices covering the entire
brain were acquired with an in-plane voxel resolution of 3.0 × 3.0 and a slice thickness of 3.4 mm with no gap.
Slices were acquired in interleaved ascending order, and 210 functional volumes were acquired in each run, not
including 3 volumes discarded by the scanner to account for T1 equilibrium eects. A gradient-echo eld map
was also acquired with the same slices and resolution as the functional images using a Siemens eld map sequence
(TR = 1000 ms, TE1 = 10 ms, TE2 = 12.45 ms, ip angle = 90°, 64 × 64 matrix).
A T1-weighted high-resolution (1 × 1 × 1 mm) image was acquired using a three-dimensional
magnetization-prepared rapid acquisition gradient (MPRAGE) sequence (TR = 2530 msec, TE = 3.13 msec, ip
angle = 10°, 256 × 256 matrix, phase encoding direction right to le, no fat suppression). Two hundred and eight
coronal slices covering the entire brain were acquired in interleaved order with a voxel resolution of 1 × 1 × 1 mm.
We also collected a T2-weighted anatomical scan (TR = 10,000 ms, TE = 88 ms, ip angle = 120°, 256 × 256
matrix) with 40 transverse slices with a voxel resolution of 0.82 × 0.82 × 3.5 mm that was reviewed by a radiolo-
gist to rule out incidental ndings.
fMRI Data Analysis. fMRI analysis was performed using FEAT version 6.00, FSL’s fMRI analysis tool
(FMRIB’s Soware Library http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/) and other FSL tools from FSL version 5.0.8. Data
were rst corrected for magnetic eld inhomogeneities using the eld maps acquired for each subject and FSLs
FUGUE utility for geometrically unwarping EPIs, unwarping in the anterior-posterior (y) direction with a 10%
signal loss threshold. Data were then preprocessed using standard steps in the following order: motion correction
using a rigid-body alignment to the middle volume of each run, slice-timing correction using Fourier-space
time-series phase-shiing, removal of skull using FSL’s BET brain extraction tool, 5 mm FWHM spatial smooth-
ing, and highpass temporal ltering using Gaussian-weighted least-squares straight line tting with a sigma of
60 s (corresponding to a period of 120 s). Finally, temporal autocorrelation was removed using FSL’s prewhitening
algorithm before statistical modeling27.
e skull was removed from the T1 images using the BET brain extraction tool with a fractional inten-
sity thresholding setting of 0.4, and specifying the voxel that represented the approximate center of the brain.
We then used FLIRT to register the functional images to the skull-stripped T1-weighted MP-RAGE using its
boundary-based registration (BBR) algorithm. Next, the MP-RAGE was registered to the standard MNI atlas with
a 12 degrees of freedom ane transformation, and then this transformation was rened using FNIRT nonlinear
registration with a warp resolution of 10 mm28.
Data were then analyzed within the General Linear Model using a multi-level mixed-eects design. Each
component of the task (statement, challenge, and rating) was modeled by convolving the task design with a
double-gamma hemodynamic response function with a phase of 0 s. e task periods were dened from stimulus
onset to stimulus oset. Political and non-political trials were modeled using separate regressors, yielding six
task regressors. e temporal derivative of each task regressor and six motion correction parameters were also
included in the design. At the individual subject level, statistical maps were generated for each functional scan.
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ese were then combined into individual participant-level maps in a xed eects analysis across each subject’s
four scans. Subject-level maps were then entered into a higher-level group analysis to examine group-level eects
using a “mixed eects” design with FLAME1.
To explore the relationship between the degree of belief change and brain activity in response to specic state-
ments, we also performed a whole brain item-wise analysis. In this analysis, we rst modeled each lower-level run
with a design that specied a single regressor for each trial’s statements and challenges. erefore, in this design,
there were 8 task regressors (4 statements and 4 challenge periods) in addition to the six motion parameters. Task
periods were modeled as in the previous analysis, using the time from stimulus onset to oset convolved with a
double-gamma hemodynamic response function with phase 0 s. We then computed brain-activity maps for each
specic stimulus item, combining across all subjects who read that stimulus using a second-level FLAME1 “mixed
eects” design to produce item-level activity maps. ese item-level activity maps were then tested for correla-
tion with the average belief change across items in a third-level FLAME1 design that included belief change as a
between-items covariate.
For all whole-brain analyses, statistical thresholding was performed using FSL’s cluster thresholding algorithm
to control for multiple comparisons. is algorithm uses Gaussian Random Field eory to estimate the proba-
bility of clusters of a given size taking into account the smoothness of the data. We used a Z threshold of 2.3, and
a cluster size probability threshold of p < 0.05.
In addition to whole brain analysis, we performed a region of interest (ROI) analysis focusing on a-priori ROIs
in the amygdala and insular cortex. We chose these two ROIs because of their well-known roles in emotion and
feeling. For this analyis, beta values from the GLM analysis were extracted for each subject, and averaged within
each ROI. e contrast used for this analysis combined activity from the period when participants were reading
all political and nonpolitical challenges to their beliefs. Because there was very little belief change for political
statements, we used belief change on non-political statements as our measure of individual variability. e beta
values and the average belief change scores were subjected to a Shapiro-Wilk test for normality. ese values were
then correlated with each participants average belief change score in a Pearsons correlation. e regions of inter-
est were dened as follows: For the amygdala, we used the Harvard-Oxford Atlas amygdala mask, thresholded at
25. For the insula, we used masks of the dorsal anterior, ventral anterior, and posterior insula dened by a study
that performed a cluster analysis of functional connectivity patterns29.
Followup Questionnaire. Following their participation in the fMRI portion of the study, participants were
sent an on-line questionnaire asking them to indicate how strongly they agreed with each statement they had
seen during their fMRI scan. e average time between a participant’s scan and completing the questionnaire was
48.36 ± 5.85 days.
Results
Behavioral results: Belief change. Aer reading the challenges to the statements, participants’ strength
of belief in the statement decreased more for non-political statements than for political statements (Fig.1A, polit-
ical: 0.31 ± 0.06, non-political: 1.28 ± 0.11, t(39) = 9.76, p < 0.001). Additionally, the degree of belief change for
political statements was correlated with the degree of belief change for non-political statements across subjects
(Fig.1B, r = 0.52, p = 0.001). In a follow-up questionnaire several weeks aer the scan, participants’ strength
of belief was still lower than their original strength of belief in the pre-test for both the political and non-polit-
ical statements (political: 0.20 ± 0.05, t(33) = 3.62, p = 0.001; non-political: 0.75 ± 0.1, t(33) = 7.82, p < 0.001).
ere was no dierence between belief strength in the follow-up compared to during the fMRI scan for political
statements (0.12 ± 0.06, t(33) = 1.83, p = 0.076), however there was a dierence between belief strength in the
follow-up compared to during the fMRI scan for non-political statements (0.51 ± 0.13, t(33) = 4.07, p < 0.001).
When separated by statement, average belief change across the 40 participants varied from 0.07 (abortion) to 0.32
(omas Edison). Generally, political statements showed the smallest degree of belief change.
Behavioral results: Response Times. On average, participants responded that they had read and
understood the statements faster for the political statements than for the non-political statements (political:
2.80 ± 0.12 seconds, non-political: 3.19 ± 0.14 seconds, t(39) = 4.21, p < 0.001). During the challenges, how-
ever, participants took longer to respond on the political challenges than the non-political challenges (political:
4.98 ± 0.20 seconds, non-political: 4.74 ± 0.18 seconds, t(39) = 4.02, p < 0.001). When participants were asked
to rate their belief in the statement following the challenges, their ratings for political statements were faster
than for non-political statements (political: 4.14 ± 0.18 seconds, non-political: 5.01 ± 0.22 seconds, t(39) = 5.50,
p < 0.001). Response time data are shown in Fig.1C.
Behavioral Results: Credibility and Challenging Ratings. Following the fMRI scan, participants rated
how credible they found the challenges in general, and how challenging they were to their beliefs. On average,
participants rated credibility of the challenges at 3.63 ± 0.21 and how challenging they were to their beliefs at
3.92 ± 0.20 on a scale from 1 to 7 where 1 was not credible/challenging at all and 7 was very credible/challenging.
e more credible participants found the challenges, the higher degree of belief change they showed during
the fMRI test both for the non-political statements (r = 0.55, p < 0.001) and for all statements (Fig.1D, r = 0.49,
p < 0.001). Additionally, those participants who found the challenges to be more challenging to their own beliefs
showed a greater degree of change in their beliefs overall (r = 0.321, p < 0.05).
Brain Imaging Results. Using the General Linear Model, we contrasted brain activity while participants
were considering the challenges compared to resting baseline. Reading and responding to the challenges resulted
in widespread activity throughout the brain, including medial and lateral occipital cortices, the inferior parietal
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lobule, the medial parietal cortex, large areas of the temporal lobes, the lateral frontal cortex in both hemispheres,
the medial frontal cortex, the striatum, and the cerebellum (Fig.S2).
e main contrast of interest compared brain activity during challenges in political versus non-political beliefs
(Fig.2, TableS1). Processing challenges to political beliefs was associated with relatively increased activity in
regions of the DMN, including the precuneus, the posterior cingulate cortex, the medial prefrontal cortex, the
inferior parietal lobe, and the anterior temporal lobe. e opposite contrast showed increased activity in the dor-
solateral prefrontal cortices and the orbitofrontal cortices bilaterally for non-political challenges compared with
political challenges.
To explore the relationship between the degree of belief change and brain activity, we next asked whether
item-level dierences in belief persistence were related to brain activity while reading challenges to those items
(Fig.3C depicts the degree of belief change across the dierent statements). Two brain regions showed activity
that signicantly correlated with belief persistence across items (Fig.3A,B, TableS2). Signal levels in the le OFC
correlated negatively with resistance to belief change, whereas a region in the le dorsomedial prefrontal cortex
(DMPFC) correlated positively. Figure3 shows how individual items corresponded to signal levels. We note that
the scatter plots in the gure do not represent an independent statistical analysis and are instead included as a
visualization of the pattern of activity in these regions.
In addition to item-wise correlations, we looked at dierences across individuals to examine how individual
variations in belief persistence related to brain activity while evaluating counterevidence using a region-of-interest
approach. Given that there was very little variability in subjects’ response to political challenges, we used belief
change from non-political challenges to characterize individual variability in belief-change resistance. In a region
of interest analysis, we examined signal in three subregions of the insular cortex and in the amygdala complex. A
Shapiro-Wilk test failed to reject the null hypothesis that the signal change data from the ROIs and the average
belief change data came from normal distributions. We found that individuals who showed greater resistance to
changing their beliefs showed greater activity in the dorsal anterior insular cortex (r = 0.35, p < 0.05) and in the
amygdala (r = 0.364, p < 0.05) when processing challenges to their beliefs (Fig.4). Signal in the posterior insula
(r = 0.293, p = 0.066) and ventral anterior insula (r = 0.255, p = 0.112) did not signicantly correlate with
belief change.
Discussion
As predicted, participants were especially resistant to arguments against their political beliefs. Post-challenge
belief strength was reduced for both political and non-political statements, indicating that the counterevi-
dence did, at least temporarily, aect reported belief strength. However, the change was signicantly greater for
non-political beliefs. Follow-up questionnaires completed weeks later showed that reduced belief strength per-
sisted for the non-political beliefs.
Figure 1. Behavioral results. (A) Belief strength before and aer counterevidence for political and non-
political trials. (B) Belief change on political trials correlated with belief change on non-political trials across
participants. (C) Response times for reading statements, reading challenges, and rating the strength of belief
at the end of the trial. (D) Increased belief change was associated with higher ratings of the credibility of the
challenging arguments.
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Defending ones beliefs against challenging evidence is a form of internally directed cognition, involving both
disconnection from the externally presented evidence and a search through memory for relevant counterargu-
ments15. Given the personal importance of political beliefs for the subjects enrolled in this study, we expected
our stimuli to evoke cognition related to social identity. Increased activation of the DMN during challenges to
political beliefs is, therefore, consistent with what is known about these structures20,22. In previous work, we have
found increased signal in the precuneus, the inferior parietal cortex, and the medial prefrontal cortex when par-
ticipants with strong beliefs about religion evaluated religious beliefs compared with nonreligious beliefs30. is is
consistent with the idea that the DMN is recruited when thinking about deeply held beliefs. A related fMRI study
recently found that many of the same regions correlated with certainty when people evaluated belief statements31.
Similarly, we have found this network to be preferentially activated when people read stories that appeal to values
that are perceived as strongly held and non-negotiable (i.e. “protected values”) compared to reading similar sto-
ries in which protected values are not perceived32. In that paper, we argue that the DMN is anatomically equipped
to function as a high-level coordinator across sensory, motor, and memory domains, giving it an important role in
the search and integration process that is required to create coherent meaning. Activation of the posterior medial
parietal cortex is also consistent with another study of motivated reasoning in politics10, which found increased
activity here when people processed threatening information about their candidate compared with exculpatory
information.
Several alternate explanations of this result appear to be ruled out by our data. One possibility is that DMN
activation here represents increased time “o-task” during the stimulation periods, since this network is known to
increase in activity during mind-wandering33. However, our response time data argue against this interpretation,
Figure 2. Brain activation during challenges to political vs. non-political beliefs. In red/yellow, brain regions
that showed increased signal while processing challenges to political beliefs (P > NP). In blue/green, brain
regions that showed increased signal during challenges to non-political beliefs (NP > P).
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as participants took longer to respond to political challenges compared with non-political challenges. Another
alternative is that stimuli in the political condition may have been more likely to describe social or mental
Figure 3. e relationship between belief change and brain activity. (A) Across all stimuli, BOLD signal in
orbitofrontal cortex during challenges correlated positively with amount of belief change. (B) Across all stimuli,
BOLD signal in dorsomedial prefrontal cortex correlated negatively with amount of belief change. Scatter plots
visualize the relationship found in the peak voxels in each region. (C) Stimulus items in order of average belief
change.
Figure 4. BOLD signal during challenges correlated with belief change across participants in (A) dorsal
anterior insular cortex and (B) amygdala, from region of interest analysis.
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phenomena. is explanation is not supported by our analysis of the stimuli that showed similar frequency of
social and cognitive words, with slightly higher frequency in the non-political stimuli.
e opposite contrast (non-political versus political) revealed increased signal in the orbitofrontal cortex
(OFC) and the dorsolateral prefrontal cortex (DLPFC). Interestingly, the OFC and the DLPFC appear critical
to various forms of cognitive exibility. Both regions, but particularly the OFC, are activated when previously
learned behavioral contingencies must be overridden in favor of new ones, as in the case of reversal learning34–36.
Relatedly, decreased activation in the DLPFC and the OFC is associated with cognitive inexibility, such as
that found in obsessive-compulsive disorder or addiction37–39. Our data suggest that the function of these brain
regions in adjusting learned associations may be important for the process of changing ones beliefs in response
to counterevidence.
In addition to showing greater activity for non-political trials, signal in the OFC showed an item-wise corre-
lation with belief change, again consistent with the OFC’s role in cognitive exibility. Another brain region, the
dorsomedial prefrontal cortex (DMPFC), showed the opposite relationship, correlating positively with resistance
to belief change. Increased activity in the DMPFC during challenges to more rmly held beliefs may relate to
the role of this region in regulating negative aect40. e DMPFC is one of the most frequently activated regions
during cognitive reappraisal41, an emotion regulation strategy in which a stimulus’s meaning is deliberately rein-
terpreted to reduce aect.
We found that individual dierences in resistance to belief change correlated with activity in the insular cor-
tex and in the amygdala. e insular cortex, which receives projections from interoceptive neural systems that
monitor the internal state of the body, is believed to be important for the generation of emotions and feelings42.
The anterior insula, in particular, is implicated in the process of integrating affective information into
decision-making43. In addition to reecting the strength of subjective feeling states in general44, the anterior
insula is activated by specic feelings that people are likely to encounter when their core beliefs are challenged,
including perceptions of threat45, uncertainty46, and anxiety47, and has been implicated in imaging studies of
politics during motivated reasoning10 and viewing faces of opposing political candidates48. e insula is also
closely connected anatomically and functionally to the amygdala49, whose role in responding to emotionally
salient stimuli is well established50. While recent data have shown that it signals the emotional value of a wide
variety of experiences, the amygdala is especially sensitive to fearful and threatening stimuli51. One interpretation
of these activations in the context of our study is that these structures are signaling threats to deeply held beliefs
in the same way they might signal threats to physical safety.
e amygdala also plays an important role in social judgments, particularly in assessing trustworthiness.
Patients with amygdala lesions show increased trust of strangers52, and functional imaging has revealed greater
activity in response to faces that are rated as untrustworthy53. Other studies have found the amygdala to be
directly involved in detecting deceit54. In the present study, the participants were presented with information and
engaged in evaluating its trustworthiness. Indeed, those who rated the counterevidence as less credible were less
likely to change their beliefs. Increased amygdala activity, then, may be associated with increased skepticism of
the material and could be an important neural signal of the persuasive potential of information. e relationship
between belief-change resistance and activity in the insular cortex and the amygdala supports the role of emotion
in this process and aligns with behavioral studies that have found correlations between negative aect and resist-
ance to changes in attitude11. Also, Howlett and Paulus recently found that insula activity while evaluating the
truth of propositions correlated with increased certainty in the truth or falsity of those propositions31.
We note that while activity in the amygdala and insula was correlated across individuals with belief persis-
tence, we did not nd these structures to be activated in general within the group while considering the chal-
lenges; nor did these structures appear in our direct comparison of political and non-political challenges. ere
are several possible explanations for this pattern of results. First, the eect may only show in those individuals
who resist belief change very strongly, and therefore does not appear in the group on average. Second, it appears
that greater belief change is associated with activity levels in the insula that are suppressed below resting baseline,
as opposed to activity levels that are enhanced above resting baseline in highly resistant individuals. Speculatively,
this could result from a successful suppression of threat-related insula activity in belief-changers.
We now turn to a discussion of the limitations of this study. First, belief change is intentionally confounded
with belief content: as predicted, participants were more likely to change their beliefs for non-political statements
than for political statements. us, brain regions that show correlations with belief change across statements may
be sensitive to qualities related to the political content of the beliefs or their challenges rather than reecting belief
change per se. Indeed, these regions appear in the direct contrast between processing political and non-political
challenges. However, our earlier neuroimaging studies of belief vs. disbelief suggest a content-independent role
for many of the involved structures30,55. Nevertheless, there are likely to be other notable dierences between
considering political and non-political beliefs. For example, given that our participants had strong political iden-
tication, they also may be more knowledgeable about the political issues referenced by our stimuli than about
the non-political issues. Relatedly, our participants are likely to have experience with challenges to their political
beliefs and may already have prepared counter-arguments against these challenges. Indeed, self-related activa-
tions in the DMN may be found as a result of the underlying role these structures play in memory retrieval, par-
ticularly in the posterior parietal nodes56,57.
Another potential dierence between political and non-political beliefs is that political beliefs tend to be more
prescriptive; that is, they imply a specic policy, while the non-political beliefs tend to be more straightforward
statements of fact (although some do imply a personal policy, e.g. “Taking a multivitamin improves ones health”).
As such, the non-political beliefs might be considered to be more amenable to empirical evaluation. Howlett and
Paulus recently made a related distinction between testable vs. non-testable beliefs, nding that the process of
evaluating the truth of these two dierent kinds of propositions engaged several dierent brain structures (as well
as some in common)31. However, in that study the posterior parietal cortex was more active for testable compared
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with non-testable beliefs, and so at least in that regard, a mapping between “political” and “non-testable” across
our two studies does not seem to hold. Instead, our “political” condition and their “testable” condition may elicit
greater depth of processing than their counterparts.
While we attempted to make the challenges to the political statements as strong as possible, we also cannot
rule out the possibility that the non-political challenges were somehow inherently more persuasive. Both catego-
ries involved exaggerations or distortions of the truth, but our political participants may have been more likely to
identify these distortions for the political issues, especially if they were more familiar with these issues. It is also
possible that people are generally more distrusting of political arguments if they have come to expect motivated
reasoning on the part of others in this domain. We did nd that participants who rated the challenges as more cred-
ible were more likely to change their minds, and it is well known that source credibility inuences persuasion58.
However, very few of our stimuli were attributed to specic sources. We view these subjective credibility ratings as
inseparable from how persuaded participants were by the arguments, and therefore did not intend to equate this
rating across the political and non-political categories.
Given that all of our participants were strong liberals, it is not clear how well these results would gener-
alize to conservatives, or to people with less polarized beliefs. Several studies have found structural or func-
tional dierences between the brains of conservatives and liberals59,60. One specically relevant dierence is the
nding of larger right amygdala volume in conservatives61. Relatedly, conservatism tends to be associated with
increased threat avoidance62. In our data, activity in the amygdala when beliefs were challenged was associated
with increased resistance to belief change. We note that while our participants expressed trait liberalism, in the
context of this experiment they were motivated to conserve their specic beliefs against a direct threat.
While extreme cognitive inexibility in the face of new information has the potential to be problematic, we
do not intend to suggest that the motivation to protect one’s beliefs is necessarily maladaptive. Indeed, there is
likely some benet to be gained from providing a degree of protection to useful beliefs, and changing one’s mental
models without sucient reason would cause problems of its own.
Our results show that when people are confronted with challenges to their deeply held beliefs, they pref-
erentially engage brain structures known to support stimulus-independent, internally directed cognition. Our
data also support the role of emotion in belief persistence. Individual dierences in persuasion were related to
dierences in activity within the insular cortex and the amygdala—structures crucial to emotion and feeling. e
brains systems for emotion, which are purposed toward maintaining homeostatic integrity of the organism63,
appear also to be engaged when protecting the aspects of our mental lives with which we strongly identify, includ-
ing our closely held beliefs.
References
1. Ahluwalia, . Examination of psychological processes underlying resistance to persuasion. J Consum es 27, 217–232, doi:
10.1086/314321 (2000).
2. Jacs, J. Z. & Devine, P. G. Attitude importance, forewarning of message content, and resistance to persuasion. Basic Appl Soc Psych
22, 19–29, doi: 10.1207/S15324834basp2201_3 (2000).
3. Pomerantz, E. M., Chaien, S. & Tordesillas, . S. Attitude strength and resistance processes. J Pers Soc Psychol 69, 408–419 (1995).
4. unda, Z. e case for motivated reasoning. Psychological bulletin 108, 480–498 (1990).
5. Lord, C. G., oss, L. & Lepper, M. . Biased Assimilation and Attitude Polarization - Eects of Prior eories on Subsequently
Considered Evidence. J Pers Soc Psychol 37, 2098–2109, doi: 10.1037/0022-3514.37.11.2098 (1979).
6. Munro, G. D. et al. Biased assimilation of sociopolitical arguments: Evaluating the 1996 US presidential debate. Basic Appl Soc Psych
24, 15–26, doi: 10.1207/153248302753439038 (2002).
7. Nyhan, B., eier, J., ichey, S. & Freed, G. L. Eective messages in vaccine promotion: a randomized trial. Pediatrics 133, e835–842,
doi: 10.1542/peds.2013-2365 (2014).
8. Nyhan, B. & eier, J. When Corrections Fail: e Persistence of Political Misperceptions. Polit Behav 32, 303–330, doi: 10.1007/
s11109-010-9112-2 (2010).
9. Fal, E. B. et al.e neural correlates of persuasion: a common networ across cultures and media. J Cogn Neurosci 22, 2447–2459,
doi: 10.1162/jocn.2009.21363 (2010).
10. Westen, D., Blagov, P. S., Harensi, ., ilts, C. & Hamann, S. Neural bases of motivated reasoning: an FMI study of emotional
constraints on partisan political judgment in the 2004 U.S. Presidential election. J Cogn Neurosci 18, 1947–1958, doi: 10.1162/
jocn.2006.18.11.1947 (2006).
11. Zuwerin, J. . & Devine, P. G. Attitude importance and resistance to persuasion: It’s not just the thought that counts. J Pers Soc
Psychol 70, 931–944, doi: 10.1037/0022-3514.70.5.931 (1996).
12. Harmon-Jones, E. In Emotions and beliefs (eds Frijda, N. H., Manstead, A. . S. & Bem, S.) 185–211 (Cambridge University Press,
2000).
13. Munro, G. D. e Scientic Impotence Excuse:Discounting Belief-reatening Scientic Abstracts. J Appl Soc Psychol 40, 579–600
(2010).
14. Burris, C. T., Harmon-Jones, E. & Tarpley, W. . “By faith alone”: eligious agitation and cognitive dissonance. Basic Appl Soc Psych
19, 17–31 (1997).
15. Jacs, J. Z. & Cameron, . A. Strategies for resisting persuasion. Basic Appl Soc Psych 25, 145–161, doi: 10.1207/S15324834basp2502_5
(2003).
16. Fleming, M. & Petty, . In Attitudes, behavior, and social context: e role of norms and group membership (eds Deborah, Terry &
Michael, Hogg) 171–199 (Lawrence Erlbaum Associates, 2000).
17. Cohen, G. L. Party over policy: e dominating impact of group inuence on political beliefs. J Pers Soc Psychol 85, 808–822, doi:
10.1037/0022-3514.85.5.808 (2003).
18. Conover, P. J. e ole of Social-Groups in Political ining. Brit J Polit Sci 18, 51–76 (1988).
19. Unsworth, . L. & Fielding, . S. It’s political: How the salience of one’s political identity changes climate change beliefs and policy
support. Global Environ Chang 27, 131–137, doi: 10.1016/j.gloenvcha.2014.05.002 (2014).
20. Bucner, . L., Andrews-Hanna, J. . & Schacter, D. L. e brain’s default networ: anatomy, function, and relevance to disease.
Annals of the New Yor Academy of Sciences 1124, 1–38, doi: 10.1196/annals.1440.011 (2008).
21. aichle, M. E. & Snyder, A. Z. A default mode of brain function: a brief history of an evolving idea. Neuroimage 37, 1083–1090,
discussion 1097–1089, doi: 10.1016/j.neuroimage.2007.02.041 (2007).
22. Whitfield-Gabrieli, S. et al. Associations and dissociations between default and self-reference networs in the human brain.
Neuroimage 55, 225–232, doi: 10.1016/j.neuroimage.2010.11.048 (2011).
www.nature.com/scientificreports/
10
Scientific RepoRts | 6:39589 | DOI: 10.1038/srep39589
23. Northo, G. et al. Self-referential processing in our brain–a meta-analysis of imaging studies on the self. Neuroimage 31, 440–457,
doi: 10.1016/j.neuroimage.2005.12.002 (2006).
24. Flesch, . A New eadability Yardstic. J Appl Psychol 32, 221–233, doi: 10.1037/h0057532 (1948).
25. Linguistic inquiry and word count: LIWC (Liwc.Net, Austin, TX, 2007).
26. Dehghani, M. et al. TACIT: An open-source text analysis, crawling, and interpretation tool. Social Science esearch Networ (2015).
27. Woolrich, M. W., ipley, B. D., Brady, M. & Smith, S. M. Temporal autocorrelation in univariate linear modeling of FMI data.
Neuroimage 14, 1370–1386, doi: 10.1006/nimg.2001.0931 (2001).
28. Andersson, J., Jeninson, M. & Smith, S. Non-linear registration, aa Spatial normalisation. FMIB technical report T07JA2 (2007).
29. Deen, B., Pitsel, N. B. & Pelphrey, . A. ree systems of insular functional connectivity identied with cluster analysis. Cereb
Cortex 21, 1498–1506, doi: 10.1093/cercor/bhq186 (2011).
30. Harris, S. et al. e Neural Correlates of eligious and Nonreligious Belief. Plos One 4, doi: 10.1371/Journal.Pone.0007272 (2009).
31. Howlett, J. . & Paulus, M. P. e neural basis of testable and non-testable beliefs. Plos One 10, e0124596, doi: 10.1371/journal.
pone.0124596 (2015).
32. aplan, J. T. et al. Processing Narratives Concerning Protected Values: A Cross-Cultural Investigation of Neural Correlates. Cereb
Cortex, doi: 10.1093/cercor/bhv325 (2016).
33. Smallwood, J. & Schooler, J. W. e science of mind wandering: empirically navigating the stream of consciousness. Annu ev
Psychol 66, 487–518, doi: 10.1146/annurev-psych-010814-015331 (2015).
34. Ghahremani, D. G., Monterosso, J., Jentsch, J. D., Bilder, . M. & Poldrac, . A. Neural components underlying behavioral
exibility in human reversal learning. Cereb Cortex 20, 1843–1852, doi: 10.1093/cercor/bhp247 (2010).
35. Schoenbaum, G., oesch, M. ., Stalnaer, T. A. & Taahashi, Y. . A new perspective on the role of the orbitofrontal cortex in
adaptive behaviour. Nat ev Neurosci 10, 885–892, doi: 10.1038/nrn2753 (2009).
36. Xue, G. et al. Common neural mechanisms underlying reversal learning by reward and punishment. Plos One 8, e82169, doi:
10.1371/journal.pone.0082169 (2013).
37. Gu, B. M. et al. Neural correlates of cognitive inexibility during tas-switching in obsessive-compulsive disorder. Brain 131,
155–164, doi: 10.1093/brain/awm277 (2008).
38. Verdejo-Garcia, A. et al. Neural substrates of cognitive exibility in cocaine and gambling addictions. Br J Psychiatry, doi: 10.1192/
bjp.bp.114.152223 (2015).
39. Chamberlain, S. . et al. Orbitofrontal dysfunction in patients with obsessive-compulsive disorder and their unaected relatives.
Science 321, 421–422, doi: 10.1126/science.1154433 (2008).
40. Silvers, J. A., Wager, T. D., Weber, J. & Ochsner, . N. e neural bases of uninstructed negative emotion modulation. Soc Cogn Aect
Neurosci 10, 10–18, doi: 10.1093/scan/nsu016 (2015).
41. Buhle, J. T. et al. Cognitive reappraisal of emotion: a meta-analysis of human neuroimaging studies. Cereb Cortex 24, 2981–2990,
doi: 10.1093/cercor/bht154 (2014).
42. Damasio, A. & Carvalho, G. B. e nature of feelings: evolutionary and neurobiological origins. Nat ev Neurosci 14, 143–152, doi:
10.1038/nrn3403 (2013).
43. Critchley, H. D. Neural mechanisms of autonomic, aective, and cognitive integration. J Comp Neurol 493, 154–166, doi: 10.1002/
cne.20749 (2005).
44. Craig, A. D. How do you feel–now? e anterior insula and human awareness. Nat ev Neurosci 10, 59–70, doi: 10.1038/nrn2555
(2009).
45. Wiech, . et al. Anterior insula integrates information about salience into perceptual decisions about pain. J Neurosci 30,
16324–16331, doi: 10.1523/JNEUOSCI.2087-10.2010 (2010).
46. Lamm, C. & Singer, T. e role of anterior insular cortex in social emotions. Brain Struct Funct 214, 579–591, doi: 10.1007/s00429-
010-0251-3 (2010).
47. Critchley, H. D., Wiens, S., otshtein, P., Ohman, A. & Dolan, . J. Neural systems supporting interoceptive awareness. Nat Neurosci
7, 189–195, doi: 10.1038/nn1176 (2004).
48. aplan, J. T., Freedman, J. & Iacoboni, M. Us versus them: Political attitudes and party aliation inuence neural response to faces
of presidential candidates. Neuropsychologia 45, 55–64, doi: 10.1016/j.neuropsychologia.2006.04.024 (2007).
49. Baur, V., Hanggi, J., Langer, N. & Jance, L. esting-state functional and structural connectivity within an insula-amygdala route
specically index state and trait anxiety. Biol Psychiatry 73, 85–92, doi: 10.1016/j.biopsych.2012.06.003 (2013).
50. Adolphs, ., Tranel, D., Damasio, H. & Damasio, A. Impaired recognition of emotion in facial expressions following bilateral
damage to the human amygdala. Nature 372, 669–672, doi: 10.1038/372669a0 (1994).
51. Adolphs, . Fear, faces, and the human amygdala. Curr Opin Neurobiol 18, 166–172, doi: 10.1016/j.conb.2008.06.006 (2008).
52. Adolphs, ., Tranel, D. & Damasio, A. . e human amygdala in social judgment. Nature 393, 470–474, doi: 10.1038/30982 (1998).
53. Engell, A. D., Haxby, J. V. & Todorov, A. Implicit trustworthiness decisions: automatic coding of face properties in the human
amygdala. J Cogn Neurosci 19, 1508–1519, doi: 10.1162/jocn.2007.19.9.1508 (2007).
54. Grezes, J., Berthoz, S. & Passingham, . E. Amygdala activation when one is the target of deceit: did he lie to you or to someone else?
Neuroimage 30, 601–608, doi: 10.1016/j.neuroimage.2005.09.038 (2006).
55. Harris, S., Sheth, S. A. & Cohen, M. S. Functional neuroimaging of belief, disbelief, and uncertainty. Ann Neurol 63, 141–147, doi:
10.1002/Ana.21301 (2008).
56. Araujo, H. F., aplan, J., Damasio, H. & Damasio, A. Involvement of cortical midline structures in the processing of autobiographical
information. PeerJ 2, e481, doi: 10.7717/peerj.481 (2014).
57. Sestieri, C., Corbetta, M., omani, G. L. & Shulman, G. L. Episodic memory retrieval, parietal cortex, and the default mode networ:
functional and topographic analyses. J Neurosci 31, 4407–4420, doi: 10.1523/JNEUOSCI.3335-10.2011 (2011).
58. Pornpitapan, C. e persuasiveness of source credibility: A critical review of ve decades’ evidence. J Appl Soc Psychol 34, 243–281,
doi: 10.1111/j.1559-1816.2004.tb02547.x (2004).
59. Amodio, D. M., Jost, J. T., Master, S. L. & Yee, C. M. Neurocognitive correlates of liberalism and conservatism. Nat Neurosci 10,
1246–1247, doi: 10.1038/nn1979 (2007).
60. Zamboni, G. et al. Individualism, conservatism, and radicalism as criteria for processing political beliefs: a parametric fMI study.
Soc Neurosci 4, 367–383, doi: 10.1080/17470910902860308 (2009).
61. anai, ., Feilden, T., Firth, C. & ees, G. Political orientations are correlated with brain structure in young adults. Curr Biol 21,
677–680, doi: 10.1016/j.cub.2011.03.017 (2011).
62. Jost, J. T. & Amodio, D. M. Political ideology as motivated social cognition: Behavioral and neuroscientic evidence. Motiv Emotion
36, 55–64, doi: 10.1007/s11031-011-9260-7 (2012).
63. Damasio, A. . Self comes to mind: constructing the conscious brain 1st edn (Pantheon Boos, 2010).
Acknowledgements
is work was supported by Project Reason and by the Brain and Creativity Institute at USC. Sam Harris, one
of the authors on this study, is also a cofounder and the CEO of Project Reason, a nonprot foundation devoted
to the spread of scientic thought and secular values in society. Project Reason has no nancial interest in the
outcome of this study.
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Scientific RepoRts | 6:39589 | DOI: 10.1038/srep39589
Author Contributions
J.K. and S.H. developed the concept for the study and designed the experimental procedures. J.K. and S.G.
collected and analyzed the data. All three authors participated in writing the manuscript. All authors approved
the nal version of the manuscript for submission and are responsible for its content.
Additional Information
Supplementary information accompanies this paper at http://www.nature.com/srep
Competing nancial interests: e authors declare no competing nancial interests.
How to cite this article: Kaplan, J. T. et al. Neural correlates of maintaining ones political beliefs in the face of
counterevidence. Sci. Rep. 6, 39589; doi: 10.1038/srep39589 (2016).
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and
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© e Author(s) 2016

Supplementary resource (1)

... Veći otpor uverenja dao je povišen odgovor u dorzomedijalnom PFCx i smanjenu aktivnost u OFCx. Ispitanici koji su promenili svoje mišljenje suočeni sa suprotstavljenim dokazima su imali manju aktivnost u insuli i amigdali tokom razmatranja dokaza (Kaplan et al., 2016). ...
... Na primer, veći otpor uverenja dao je povišen odgovor u dorzomedijalnom prefrontalnom korteksu i smanjenu aktivnost u orbitofrontalnom korteksu. Ispitanici koji su promenili svoje mišljenje suočeni sa suprotstavljenim dokazima su imali manju aktivnost u insuli i amigdali tokom razmatranja dokaza (Kaplan et al., 2016). ...
Thesis
Full-text available
Personality and environment interact to influence attitudes formation. Group processes, reasoning, traits, and brain activity have been used to explain political attitudes and behavior. Social phenomena have been associated with clinical indicators, albeit with inconclusive results. Psychobiological model of personality measures temperament dispositions in interaction with character, i.e., with mental health and success of social adaptation. They overcome a need for separate measurements of adaptive and maladaptive trait correlates. Such an efficient approach integrates traits measurement along the dimension of normalcy-pathology. We set out to investigate associations between the model and Right-Wing Authoritarianism, Conspiracy Mentality, and Manichean Worldview in a non-representative community sample. The model accounted for 24% of variance in RWA and negligible percentages in the rest. The strongest predictor was found to be Self-Transcendence, followed by Impulsiveness and Persistence, with Exploratory Excitability and Reward Dependence as negative predictors. There were no correlations between mental health and socio-political constructs. Implications are discussed.
... For example, top-down connectivity between the ventral ACC and AMG influences emotional regulation and reappraisal (Etkin et al., 2011). AMG circuitry is thought to respond to threats to both beliefs and physical safety (Kaplan et al., 2016). This implicates the AMG in ego resistance and defenses that may require resolution to produce ego dissolution experiences. ...
... The DMN also responds to nonphysical forms of threat. The DMN displayed activity increases in subjects whose political beliefs have been challenged (Kaplan et al., 2016). These findings suggest that DMN mentalizing functions predict the consequences of threats to beliefs like physical threats. ...
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