
==== Front
Soc Cogn Affect Neurosci
Soc Cogn Affect Neurosci
scan
Social Cognitive and Affective Neuroscience
1749-5016
1749-5024
Oxford University Press UK

39167464
10.1093/scan/nsae056
nsae056
Original Research & Neuroscience
AcademicSubjects/SCI01880
Tracking politically motivated reasoning in the brain: the role of mentalizing, value-encoding, and error detection networks
https://orcid.org/0000-0001-5212-1646
Lois Giannis Department of Psychology, School of Social Sciences, University of Crete, Rethymno 74100, Greece
Department of Microeconomics and Public Economics, School of Business and Economics, Maastricht University, Maastricht 6200, The Netherlands

Tsakas Elias Department of Microeconomics and Public Economics, School of Business and Economics, Maastricht University, Maastricht 6200, The Netherlands

Yuen Kenneth Neuroimaging Centre (NIC), Focus Program Translational Neuroscience (FTN), Johannes Gutenberg University Medical Center Mainz, Mainz 55131, Germany
Leibniz Institute for Resilience Research, Mainz 55122, Germany

Riedl Arno Department of Microeconomics and Public Economics, School of Business and Economics, Maastricht University, Maastricht 6200, The Netherlands

*Corresponding author. Department of Psychology, School of Social Sciences, University of Crete, Rethymno 74100, Greece. E-mail: i.lois@uoc.gr
‡ contributed equally to this work.

2024
21 8 2024
21 8 2024
19 1 nsae05611 4 2024
08 7 2024
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19 9 2024
© The Author(s) 2024. Published by Oxford University Press.
2024
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Abstract

Susceptibility to misinformation and belief polarization often reflects people’s tendency to incorporate information in a biased way. Despite the presence of competing theoretical models, the underlying neurocognitive mechanisms of motivated reasoning remain elusive as previous empirical work did not properly track the belief formation process. To address this problem, we employed a design that identifies motivated reasoning as directional deviations from a Bayesian benchmark of unbiased belief updating. We asked the members of a proimmigration or an anti-immigration group regarding the extent to which they endorse factual messages on foreign criminality, a polarizing political topic. Both groups exhibited a desirability bias by overendorsing attitude-consistent messages and underendorsing attitude-discrepant messages and an identity bias by overendorsing messages from in-group members and underendorsing messages from out-group members. In both groups, neural responses to the messages predicted subsequent expression of desirability and identity biases, suggesting a common neural basis of motivated reasoning across ideologically opposing groups. Specifically, brain regions implicated in encoding value, error detection, and mentalizing tracked the degree of desirability bias. Less extensive activation in the mentalizing network tracked the degree of identity bias. These findings illustrate the distinct neurocognitive architecture of desirability and identity biases and inform existing cognitive models of politically motivated reasoning.

motivated reasoning
belief updating
neural activity
mentalizing
valuation
Hellenic Foundation for Research and Innovation 10.13039/501100013209 7458 EU Horizon 2020 895685 Hellenic Foundation for Research and Innovation 10.13039/501100013209 7458 EU Horizon 2020 895685
==== Body
pmcIntroduction

Susceptibility to misinformation and belief polarization is often attributed to people’s motivation to protect their valuable identities and affirm their ideologies even at the defiance of truth (Kahan 2016). Although the neurocognitive processes that underlie politically motivated belief formation are still largely unknown, scholars have proposed competing cognitive models of this phenomenon (Hughes and Zaki 2015, Sharot and Garrett 2016, Van Bavel and Pereira 2018).

First, a value-encoding account (H1 in Fig. 1) posits that people derive utility from holding beliefs that align with their ideology or come from a valued source (Bromberg-Martin and Sharot 2020). Consistent with this account, brain areas that encode value and motivate behavior, such as the ventral striatum (VS) and ventromedial preforntal cortex (vmPFC), are recruited when people conform to social or in-group norms (Wu et al. 2016, Lin et al. 2018) or when they evaluate desirable and undesirable information (Bartra et al. 2013, Kuzmanovic and Rigoux 2017, Kuzmanovic et al. 2018).

Figure 1. Competing hypotheses based on the three proposed accounts of motivated belief formation. (H1a) Message value is encoded in vmPFC and VS and depends on its valence (Kuzmanovic and Rigoux 2017, Kuzmanovic et al. 2018, Van Bavel and Pereira 2018) or the identity of the source (Wu et al. 2016, Lin et al. 2018). Thus, activation of value-encoding brain regions predicts overendorsement of desirable and in-group messages, while deactivation of these regions predicts underendorsement of undesirable and out-group messages. (H1b) Activity in vmPFC and VS is associated with reward motivation and instrumental behavior (Haber and Knutson 2010, Kim 2013, Pool et al. 2022), which, in the present setting, can be expressed as ideology-affirming or identity-protective motives (Van Bavel and Pereira 2018). Thus, activation of these regions predicts not only overendorsement of desirable and in-group messages but also underendorsement of undesirable and out-group messages. (H2a) Asymmetric incorporation of good and bad news is driven by differential encoding of positive and negative prediction errors in the IFG and dmPFC (Sharot et al. 2011, Moutsiana et al. 2015, Sharot and Garrett 2016, Kappes et al. 2020). Thus, activation of these brain regions predicts overendorsement of desirable messages, while deactivation of these regions predicts underendorsement of undesirable messages. (H2b) Unfavorable information or deviations from in-group norms generate error signals in dmPFC, dACC, and AI, which lead to information discounting and social conformity (Izuma and Adolphs 2013, Nook and Zaki 2015, Lin et al. 2018). Thus, activation of prediction error detection brain regions predicts underendorsement of undesirable messages and out-group messages, while deactivation of these regions predicts overendorsement of desirable messages and in-group messages. (H3a,b) Activation of the mentalizing network is associated with updating (Kim et al. 2020; Park et al. 2021b) (H3a) or maintenance (Mende-Siedlecki 2018, Kim et al. 2020) (H3b) of strong and biased prior impressions about in-group and out-group members’ credibility and trustworthiness. Thus, deactivation (H3a) or activation (H3b) of core nodes of the mentalizing network (bilateral TPJ and anterior mPFC) predicts overendorsement of in-group messages and underendorsement of out-group messages.

Second, an error detection account (H2 in Fig. 1) postulates that the dorsomedial PFC (dmPFC), dorsal anterior cingulate cortex (dACC), anterior insula (AI), and inferior frontal gyrus (IFG) detect discrepancies between prior beliefs and new information by generating error signals that lead to behavioral adjustments (Garrison et al. 2013, Wu et al. 2016, De Martino et al. 2017). These error signals are associated with asymmetric belief updating in the face of desirable and undesirable information (Sharot et al. 2011, Sharot and Garrett 2016, Kappes et al. 2020) or with conformity to others’ or in-group’s opinions (Izuma and Adolphs 2013, Lin et al. 2018).

Third, a mentalizing account (H3 in Fig. 1) assumes that people form motivated beliefs in social settings by inferring the trustworthiness of the source of information through an effortful mentalizing process (Baek et al. 2020). Consistent with this account, the activity in core nodes of the mentalizing network such as the temporoparietal junction (TPJ) and anterior mPFC is associated with motivated updating or maintenance of strong prior impressions about others (Hughes et al. 2017, Kim et al. 2020, 2021, Park et al. 2021a).

Cognitive science approaches motivated reasoning phenomena as deviations from Bayesian rationality whereby belief updating depends on the strength of prior beliefs and the confidence in new information (Druckman and McGrath 2019, Tappin et al. 2020). Despite the prominence of the Bayesian approach, empirical work on the neural basis of politically motivated reasoning has failed to account for these factors (Westen et al. 2006, Kaplan et al. 2016, Haas et al. 2021, Moore et al. 2021).

Our study addresses this problem by setting a Bayesian benchmark for unbiased belief updating, thereby allowing the identification of motivated reasoning as directional deviations from this benchmark (Thaler 2024). Participants are asked to indicate how much they endorse factual messages on a polarizing topic that are either compatible or incompatible with participants’ attitude (Fig. 2a). The sources of these messages are individuals who belong to the same opinion-based group (i.e. in-group) or the opposing one (i.e. out-group). Although perceptions of source trustworthiness may differ for in-group and out-group sources, this is not the case in the present setting as participants are explicitly instructed that both sources are equally reliable. Moreover, in our setting, messages do not contain new information, and thus, Bayesian observers would not update their prior beliefs irrespective of message valence or message source identity. Therefore, our design addresses the recent critique of motivated reasoning paradigms by eliminating alternative explanations related to participants’ prior beliefs and perceptions of source trustworthiness (Tappin et al. 2020).

Figure 2. Trial structure, definition of biases, and behavioral results. (a) First, participants receive factual information about the percent of foreigners in a specific German city and report their median estimation of foreign criminality in this city. Then, a fixation cross (not shown) is presented for a jittered interstimulus interval (ISI), followed by the identity of the source and the message (i.e. presentation phase). After another jittered ISI, participants indicated the degree to which they endorse the message (i.e. message endorsement phase). Each trial ends with another fixation cross, which is presented for a jittered intertrial interval (ITI). The labels that designate group membership indicate attitudes toward migration policy. Proimmigration group members are labeled as “Welcome-Policy supporter”, while anti-immigration group members are labeled as “Strict-Policy supporter.” (b) Definition of biases as directional deviations of message endorsement from the Bayesian benchmark of 50%. Desirability (identity) biases are defined as overendorsement of desirable (in-group) and underendorsement of undesirable (out-group) messages. Behavioral results: (c) effects of message valence, source identity, and message congruence on average message endorsement (in %). Incongruent messages were characterized by a mismatch between message valence and source identity (e.g. undesirable messages from in-group sources or desirable messages from out-group sources). The horizontal line at 50% depicts the Bayesian benchmark. On average, desirable messages were endorsed more than undesirable messages (i.e. desirability bias) and in-group messages were endorsed more than out-group messages (i.e. identity bias). Undesirable messages and out-group messages were underendorsed compared with the Bayesian benchmark. (d) Effects of message valence, source identity, and message congruence on average response times (in seconds). Participants responded significantly faster to congruent than to incongruent messages. In both graphs (c and d), error bars represent 95% confidence intervals. * P < .05, ** P < .01, *** P < .001.

In this respect, directional deviations from the Bayesian benchmark would indicate that people engage in motivated reasoning and express two distinct biases (Fig. 2b): (i) a “desirability bias” whereby people distort their inference process in ways that confirm their desired beliefs (Kahan 2016, Flynn et al. 2017) and (ii) an “identity bias” whereby people incorporate information from in-group sources and resist influence from out-group sources (Abrams and Hogg 1990, Kahan 2016, Guilbeault et al. 2018, Kim et al. 2020). Our study design provides a clean setting to examine whether brain responses to the messages predict the subsequent expression of desirability and identity biases in polarized settings. Thus, our study aims to provide empirical support for the aforementioned models of motivated belief formation.

Materials and methods

Participants

We screened 628 native Germans using a two-step titration procedure (Fig. S1 and Supplementary Material). The screening procedure allowed us to recruit individuals who support a welcoming migration policy and believed that foreigners do not increase the crime rate in the country (i.e. proimmigration group) or individuals who support a strict migration policy and believed that foreigners increase the crime rate in the country (i.e. anti-immigration group). Our initial sample consisted mainly of students and highly educated individuals who are less likely to hold anti-immigrant attitudes. These characteristics of our initial sample may explain why recruiting participants from the anti-immigration group proved to be relatively difficult, which led to an unbalanced final sample of 30 participants in the proimmigration group and 18 participants in the anti-immigration group.

Participants were right handed, were free of current psychiatric or neurological conditions, did not currently take any neuropharmacological or psychopharmacological medication, and did not report any contraindication for participation in functional Magnetic Resonance Imaging (fMRI) experiments. Seven participants were excluded from the analysis due to excessive head movement (n = 5) or a high omission rate (n = 2), resulting in a final sample of 41 participants aged 18–49 years (M = 30.1, s.d. = 7.3, 16 women, 26 members of the proimmigration groups).

All participants gave written informed consent. The experiment lasted ∼90 min. All participants were debriefed at the end of the experiment. Importantly, participants received a bonus payment based on the accuracy of their estimations, on top of a fixed participation fee of €30 (total payment: M = €38.8, s.d. = €3.3). The study was approved by the local ethics committee (Medical Board of Rhineland-Palatinate, Mainz, Protocol number: 2022-16403) and was conducted in accordance with the Declaration of Helsinki.

Experimental design

Our design (Fig. 2a) was adapted by a recent study that identified politically motivated reasoning on factual topics (Thaler 2024). We asked participants to report their belief in messages about foreign criminality coming from in-group or out-group sources. The experiment consisted of 80 trials. In each trial, participants indicated their median estimation about the percent of crimes committed by foreigners in each of the 80 largest cities in Germany (see the Supplementary Material for detailed instructions). Subsequently, we presented messages from other participants who belonged either to the same opinion-based group (i.e. in-group source) or to the opposing opinion-based group (i.e. out-group source). The messages had the following form: “The foreign criminality is higher/lower than your estimation.” Members of the proimmigration or anti-immigration group should perceive these messages as desirable or undesirable. We first presented the identity of the source for 2 s, and then, the message appeared on the screen for 3 s. Following the message presentation phase, participants were asked to report how likely it is that the message is correct on a scale from 0 to 100.

We employed a within-subject design where the city of interest, identity of the source (i.e. member of the proimmigration or anti-immigration group), and valence of the message (i.e. foreign criminality is higher or lower than participants’ estimation) varied across trials. We truthfully informed participants in advance that half of the messages they received either from in-group or out-group sources were actually true. A median estimation indicates the percent of foreign criminality that participants think is equally likely to be above or below the actual foreign criminality. The message informs participants that their median estimation is higher or lower than the correct answer. For Bayesian observers, this type of message contains no new information, and thus, Bayesians should report the prior probability of a message being true (i.e. 50%) irrespective of the message valence or source identity. Consequently, we defined desirability bias as endorsing a desirable (undesirable) message more (less) than the 50% Bayesian benchmark (Fig. 2b). Similarly, we defined identity bias as endorsing an in-group (out-group) message more (less) than the Bayesian benchmark (Fig. 2b).

Each of the four possible message combinations (i.e. in-group-desirable, in-group-undesirable, out-group-desirable, and out-group-undesirable) was presented 20 times. The order of the trials and the matching of the 80 German cities with the source identity and message valence were randomized at the participant level. We incentivized participants to provide accurate median estimations and message endorsements using a binarized scoring rule (Hossain and Okui, n.d.). Specifically, participants were informed that they could earn a bonus of maximum €20 in addition to their participation fee. The size of the bonus was based either on the accuracy of their median estimation or on the deviation between message endorsement and message veracity in a randomly chosen trial (one of the two responses was randomly chosen to avoid risk hedging).

MRI data acquisition

Images were acquired on a Siemens 3T-Magnetom Tim Trio system (Siemens, Germany) running on software version Vb17, using a 32-channel head coil. Foam pads restricted head movement. Visual stimuli were presented on a screen at the head end of the scanner bore and projected to the subject’s visual field via a mirror that was fixed on the head coil. For blood oxygenation level–dependent (BOLD) fMRI, we used a multiband echo planar imaging (EPI) sequence (repetition time (TR) = 1000ms, echo time (TE) = 29ms, flip angle = 56°, field of view (FOV) = 210mm, voxel size = 2.5× 2.5× 2.5mm3, 60 slices, Multiband acceleration factor = 4, bandwidth = 2588Hz/px, no further GeneRalized Autocalibrating Partial Parallel Acquisition acceleration) from the Center for Magnetic Resonance Research, University of Minnesota, adopted from the Human Connectome Project (Feinberg & Setsompop, 2013). fMRI was complemented by a T1-MPRAGE sequence (TR = 1900ms, TE = 2.52ms, flip angle = 9°, FOV = 250mm, voxel size = 1×1×1 mm3) as well as a T2 sequence.

Behavioral data analysis

We fitted a 2 × 2 × 2 mixed analysis of variance model using message valence and source identity as within-subject predictors, participants’ group membership as the between-subject predictor, and the average message endorsement or average response times across the 80 trials as the outcome variable. Significant interaction effects were further explored by testing the effect of one variable separately in each level of the other. We also performed separate one-sample t-tests to compare the average endorsement of each message type with the 50% Bayesian benchmark.

fMRI data analysis

Imaging data were analyzed using SPM12 and Matlab 2017b (MathWorks). The first five EPI images of each run were discarded before preprocessing to ensure T1 saturation. The remaining images were realigned to the mean image and coregistered to the anatomical MPRAGE image. The anatomical images were segmented, and the estimated parameters from the segmentation were used to normalize functional images with the Montreal Neurological Institute (MNI) brain template. Normalized functional images were smoothed using a Gaussian filter with 6-mm Full Width at Half Maximum. Five participants with head movements exceeding translations of 3mm or rotations of 3° were discarded from the analysis. EPI images were temporally high-pass filtered with a cutoff of 128s.

A general linear model was then fitted to each participant’s BOLD signal. Using the canonical haemodynamic response function, we modeled individual BOLD signal changes induced by the median foreign criminality estimation phase (one regressor), the message presentation phase (two regressors), and the message endorsement phase (two regressors). The first regressor in the message presentation and message endorsement phase corresponds to the onset of trials in which participants engaged in motivated reasoning, and the second regressor corresponds to the parametric modulator (i.e. deviation from the Bayesian benchmark). Regressors were boxcar functions corresponding in length to the presentation of the message (5 s) or the response time in the median estimation phase and the message endorsement phase.

To identify brain responses that tracked the degree of desirability and identity bias, we estimated models that used directional deviations of participants’ message endorsement from the 50% benchmark as parametric modulators. For each bias, we ran two parametric modulation analyses to test our hypotheses (Fig. 1). In the first analysis, our aim was to test H1a and H2a-b by identifying brain regions whose activation predicts overendorsement of one message type (e.g. desirable) and their deactivation predicts underendorsement of the other message type (e.g. undesirable). In the second analysis, we aimed to identify brain regions whose activation (H1b and H3b) or deactivation (H3a) predicts overendorsement of desirable (or in-group) messages and underendorsement of undesirable (or out-group) messages. We also performed a parametric modulation analysis to explore whether brain responses to messages correlated with the absolute degree of message endorsement (irrespective of the Bayesian benchmark) or with any deviation from the Bayesian benchmark across all trials (i.e. irrespective of the presence or absence of motivated reasoning).

Four participants were excluded from the parametric analyses as they repeatedly reported the 50% benchmark, resulting in a sample of 37 participants (23 proimmigration group members) aged 18–49years (M=30.2, s.d. = 7.6, 15 females). Additional regressors of no interest included the six rigid body transformation parameters from spatial realignment. All first-level analyses were corrected for serial correlated errors by fitting a first-order autoregressive process to the error term.

At the group level, we performed random-effects one-sample t-tests on the single-subject beta images to identify brain regions that correlated positively or negatively with the degree of desirability bias or identity bias in both opinion-based groups. In the case of significant brain activation, we used the MarsBaR toolbox to extract percent of signal change from clusters of brain activity that significantly tracked motivated reasoning. Post hoc tests were performed on the extracted signal to further explore whether one of the two opinion-based groups drove the observed main effects. We also performed two-sample t-tests on the single-subject beta images to examine whether the two groups recruited different brain areas when engaging in motivated reasoning. However, the interpretation of the group comparisons is rather limited given the unequal and somewhat small sample sizes of each group (i.e. 26 proimmigration and 15 anti-immigration participants).

In addition to the parametric analysis, we performed a categorical analysis to examine whether source identity and message valence or the mismatch between source identity and message valence yield different brain activation during the presentation of the messages and the message endorsement phase. For these two phases, we created four regressors by crossing source identity with message valence (i.e. in-group-desirable, in-group-undesirable, out-group-desirable, and out-group-undesirable). At the group level, we performed one-sample t-tests to assess brain activation in the entire sample and two-sample t-tests to identify group differences in brain activation. We used the MarsBaR toolbox to extract percent of signal change from the significant clusters. Using the extracted signal, we explored whether a certain combination of message valence and source identity or a certain group (proimmigration or anti-immigration) drives the observed main effects.

Consistent with recent guidelines regarding fMRI statistical analysis (Eklund et al. 2016), statistical inference for both parametric modulation and categorical analyses was performed at a standard threshold of P < .05, and family-wise error was corrected at the cluster level across the whole brain, with an initial cluster forming a threshold of P < .001 uncorrected. Patterns of brain activation associated with motivated reasoning were submitted to Neurosynth (http://neurosynth.org) to strengthen reverse inference on the possible cognitive mechanisms involved (Supplementary Material).

Results

Message endorsement and response times

Most participants (37 out of 41) displayed a substantial within-subject trial-by-trial variation in message endorsement (s.d. = 19.14), which reflected a combination of desirability bias, identity bias, and no engagement in motivated reasoning (Supplementary Material and Table S1). Participants of both groups displayed a desirability bias as they endorsed desirable messages more than undesirable messages [F(1, 39) = 4.73, P = .036, ηp2 = 0.108] and an identity bias as they endorsed in-group messages more than out-group messages [F(1, 39) = 13.81, P < .001, ηp2 = 0.262] (Fig. 1c). We observed no significant main effect of group membership and no significant two-way or three-way interactions between source identity, message valence, and group membership.

Next, we compared the average endorsement of each message type with the 50% Bayesian benchmark. As shown in Fig. 2c, this analysis revealed that the average endorsement of out-group [t(40) = −2.16, P = .037, d = −0.337] and undesirable [t(40) = −2.02, P = .050, d = −0.315] messages was significantly lower than the 50% benchmark. Endorsement of desirable, in-group, congruent, and incongruent messages did not deviate significantly from the 50% benchmark.

The analysis of response times revealed no main effects of message valence, source identity, or group membership, but we observed a significant message valence by source identity interaction effect [F(1, 39) = 7.53, P = .009, η2 = 0.162]. Participants of both groups reacted significantly faster to congruent than to incongruent messages (Fig. 2d). Follow-up t-tests showed that this incongruence effect is predominantly driven by the fast responses to in-group-desirable messages (Supplementary Material). There were no differences in response times across trials characterized by desirability bias, identity bias, or no bias (Table S2).

Brain regions tracking the degree of desirability bias

After establishing the behavioral manifestation of motivated reasoning in the form of desirability and identity biases, we investigated whether BOLD responses during the message presentation phase predicted the expression of these two biases during the subsequent message endorsement phase. We first examined whether brain responses to the messages tracked the degree of desirability bias. Based on our hypotheses (Fig. 1), we ran two parametric modulation whole-brain analyses (see the Materials and methods section for model details). In the first analysis, our aim was to identify brain regions whose activation predicts overendorsement of one message type (e.g. desirable or undesirable) and their deactivation predicts underendorsement of the other message type (H1a and H2a–b). In the second analysis, we aimed to identify brain regions whose activation (H1b and H3b) or deactivation (H3a) predicts both overendorsement of desirable (or in-group) messages and underendorsement of undesirable (or out-group) messages.

Only the second analysis yielded significant brain activation in a host of brain regions implicated in value-encoding (H1b), error detection and adjustment (partly supporting H2a–b), and mentalizing (H3b). Specifically, during the processing of the message, increased activity in the bilateral VS, vmPFC, bilateral caudate, amPFC, dmPFC, dACC, left AI, right superior frontal gyrus, bilateral TPJ, and posterior cerebellum predicted the subsequent overendorsement of desirable messages and underendorsement of undesirable messages (Fig. 3a and Table 1). No brain area tracked deviations from the 50% benchmark in the opposite direction (H3a). Furthermore, no brain area tracked the reduction in desirability bias as there were no negative correlations between brain activity and desirability-consistent deviations from the 50% benchmark. Overall, this pattern suggests that the aforementioned brain activation selectively tracked directional deviations from the 50% benchmark that indicated desirability bias.

Figure 3. Brain regions tracking desirability bias. (a) Activity in dACC, dmPFC, amPFC, bilateral VS and caudate, left AI, bilateral TPJ, and bilateral posterior cerebellum in response to the messages tracked the degree of desirability bias. (b) The boxplots depict extracted percent of signal change tracking the degree of desirability bias separately for each group and each brain region. Activity in these brain regions tracked the degree of desirability bias in both groups. * P < .05.

Table 1. Brain regions whose activity positively predicted the degree of desirability and identity bias.

	MNI coordinates			
Brain region	x	y	z	Z-score	Cluster size (k)	
Brain regions tracking the desirability bias	
dACC	6	30	18	4.6	2865	
Dorsomedial PFC	−2	40	36	4.49		
Anterior medial PFC	8	62	18	4.34	-	
Ventromedial PFC	−10	54	−2	4.18	-	
Right middle frontal gyrus	24	42	36	4.36	157	
Left VS	−4	6	−6	4.29	841	
Left caudate	−16	−8	16	4.22	-	
Right VS	8	−2	−2	4.12	-	
Left posterior cerebellum (Crus II)	−22	−80	−36	4.25	1464	
Left posterior cerebellum (Crus I)	−44	−56	−38	4.11	-	
Right posterior cerebellum (Crus I and II)	32	−64	−38	4.09	-	
Right TPJ	44	−56	28	4.07	124	
Left AI	−32	18	14	4.06	284	
Right caudate	22	18	10	3.96	132	
Left TPJ	−48	−68	16	3.82	226	
Brain regions tracking the identity bias	
Left TPJ	−44	−66	14	4.14	197	
Right posterior cerebellum (Crus I and II)	32	−68	−20	4.04	305	
Left posterior cerebellum (Crus I)	−22	−84	−32	3.9	140	

A separate analysis for the proimmigration and anti-immigration groups showed that all aforementioned brain regions tracked the degree of desirability bias in both groups (Fig. 3b and Table S3). Moreover, a direct comparison of brain activation between the proimmigration and anti-immigration groups revealed no brain area that differentially tracked the degree of desirability bias across the two opinion-based groups.

Brain regions tracking the degree of identity bias

Similar analyses were performed to identify brain responses to political messages that selectively tracked the degree of identity bias during the subsequent message endorsement phase. Consistent with H3b, the parametric modulation analyses revealed that activation of a core node of the mentalizing network (i.e. left TPJ) and bilateral posterior cerebellum predicted the subsequent overendorsement of in-group messages and underendorsement of out-group messages (Fig. 4a and Table 1). No brain area tracked deviations from the 50% benchmark in the opposite direction (i.e. underendorsement of in-group messages and overendorsement of out-group messages), and no brain area tracked reductions in identity bias, measured as negative correlations between brain activity and the degree of identity bias.

Figure 4. Brain regions tracking identity bias. (a) Activity in left TPJ and bilateral posterior cerebellum in response to the messages tracked the degree of identity bias. (b) The boxplots depict the extracted percent of signal change tracking the degree of identity bias separately for each group and each brain region. Activity in these regions tracked the degree of identity bias in both groups, but this effect was more pronounced in the proimmigration group. * P < .05.

A separate analysis for the two opinion-based groups revealed that left TPJ and bilateral posterior cerebellum activation tracked the degree of identity bias mainly in the proimmigration group. However, the anti-immigration group displayed a nonsignificant trend in the same direction (Fig. 4b and Table S4). A direct comparison of brain activation between the proimmigration and anti-immigration groups revealed no brain area that differentially tracked the degree of identity bias across the two groups. Last, we found no brain activity that positively or negatively correlates with message endorsement across all trials (i.e. irrespective of the presence or absence of desirability and identity bias).

Brain responses to message valence, source identity, and their mismatch

We also investigated whether message valence, source identity, or their mismatch influence the pattern of neural responses irrespective of message endorsement. Whole-brain analysis revealed no differences in brain activation in response to message valence (desirable vs undesirable) or source identity (i.e. in-group vs out-group) during the presentation of messages and the message endorsement phase. However, compared with congruent messages, incongruent messages yielded significant neural activation in the left IFG, adjacent left AI, and left middle temporal gyrus (MTG) during the presentation of the messages (Fig. 5a and Table S5).

Figure 5. Brain responses to mismatch between message valence and source identity. (a) Left IFG and left MTG elicited stronger responses to incongruent (in-group-undesirable and out-group-desirable) compared with congruent (in-group-desirable and out-group-undesirable) messages. (b–e) The graphs depict the extracted percent of signal change for the incongruent vs congruent contrast separately for each group and each brain region. From left to right, each graph consists of raw data points, boxplots, and data distribution plots. Compared with other message types, out-group-desirable messages elicit the strongest responses in both brain regions (especially in left IFG) and across groups (more pronounced in the proimmigration group).

Follow-up analysis showed that the incongruence effect on both brain regions is significant only in the proimmigration group (Table S6). For the left IFG cluster, the incongruence effect is predominantly driven by out-group-desirable messages (Fig. 5b–e and Table S7). A direct comparison of the two groups revealed no significant differences between the two opinion-based groups in brain responses to source identity, message valence, or their mismatch.

Discussion

Our study examined the neurocognitive mechanisms of politically motivated reasoning by combining the fMRI technique with an experimental design that identifies desirability and identity biases as directional deviations from a Bayesian benchmark. Members of a proimmigration group and an anti-immigration group exhibited a desirability bias and an identity bias in message endorsement although they were informed that half of the messages were true and that these messages contained no new information. Brain responses to the messages predicted the engagement in motivated reasoning in the subsequent message endorsement phase. Specifically, brain regions implicated in encoding value, error detection and behavioral adjustment, and mentalizing tracked the degree of the desirability bias. Less extensive activation in the left TPJ and cerebellum tracked the degree of the identity bias.

The aforementioned pattern of brain activity selectively tracked the degree of desirability and identity biases, but not the absolute degree of message endorsement across all trials or bias-inconsistent deviations from the Bayesian benchmark, thus allowing us to rule out alternative interpretations that do not implicate motivated reasoning processes. Moreover, we showed that two ideologically opposing groups displayed similar behavioral and neural responses to message valence and source identity. This common pattern across ideologically opposing groups suggests that people with different attitudes employ the same basic cognitive tools to form opposing beliefs.

Independent of the presence of motivated reasoning, a direct comparison of desirable with undesirable messages or in-group with out-group messages did not yield significant brain activity, further supporting the interpretation that the aforementioned brain regions are selectively responsive to desirability and identity biases. However, incongruent, compared with congruent, messages elicited activity in brain areas implicated in error processing (left IFG and left AI) and semantic processing (left MTG) (Sharot et al. 2011, Turker et al. 2023). This result is consistent with evidence showing AI activation when participants perceive a mismatch between political candidates’ party affiliation and their stated policy position (Haas et al. 2017, 2021). Given the longer response times to incongruent messages, compared with congruent messages, this finding probably reflects the elaborate cognitive processing due to the conflict between ideology-affirming and identity-protective motives.

Consistent with the value-encoding account, the activation of vmPFC, bilateral VS, and bilateral caudate tracked the degree of the desirability bias. Previous work has implicated these brain regions in encoding the value of self-relevant and identity-relevant information (Sharot and Garrett 2016, Van Bavel and Pereira 2018). In the present setting, their recruitment may reflect the extent to which messages align with participants’ attitudes toward foreign criminality. This account (H1a) explains the overendorsement of desirable messages. However, it fails to explain why activation of these brain regions also predicted the rejection of undesirable messages. An alternative explanation (H1b) is that the VS and vmPFC are implicated in instrumental behavior and reward motivation (Haber and Knutson 2010, Kim 2013, Pool et al. 2022) and thus represent participants’ motivation to maintain their desired attitudes toward foreign criminality when processing political messages (Van Bavel and Pereira 2018). In this respect, the more these brain areas are active, the more motivated participants are to accept desirable messages and to reject undesirable messages.

Our findings are also consistent with the error detection account. Increased activity in the dACC, dmPFC, and left AI tracked the overendorsement of desirable messages. This finding is consistent with the hypothesis (H2a) that these brain regions encode discrepancies between prior beliefs and new information but are more responsive to positive than negative errors (Sharot et al. 2011, Sharot and Garrett 2016, Kappes et al. 2020). However, this account fails to explain why increased activity in these brain regions also tracked the underendorsement of undesirable messages. This finding is in line with the alternative hypothesis (H2b), which postulates that unfavorable information and norm violations generate error signals in these brain regions that lead to information discounting and social conformity, respectively (Berns et al. 2010, Izuma and Adolphs 2013, Nook and Zaki 2015, Lin et al. 2018).

To reconcile these competing hypotheses with our findings, we reformulated H2b to argue that these brain regions encode discrepancies between both desirable and undesirable information and one’s desired attitudes toward foreign criminality (Kahan 2016). That is, undesirable messages are perceived as threatening and are rejected, while desirable messages are overendorsed because they indicate that one’s prior belief is not sufficiently aligned with one’s attitudes. In line with this interpretation, a neuroimaging study has shown that both convergence to the preferences of the liked group and divergence from the preferences of the disliked group were mediated by dmPFC activity, which tracked the discrepancy between one’s prior preference and the socially desirable state (Izuma and Adolphs 2013).

Unlike desirability bias, the identity bias was not associated with the recruitment of brain areas implicated in encoding value (i.e. the VS and vmPFC) and error detection (i.e. the dmPFC, dACC, and AI). Instead, the left TPJ, a core node of the mentalizing network, tracked the degree of identity bias. This finding is consistent with the mentalizing account (H3b), whereby participants engage in mentalizing to infer the sources’ trustworthiness based on identity cues (Mende-Siedlecki 2018, Kim et al. 2020; Park et al. 2021b). It is of interest that the extensive activity within the mentalizing network (i.e. the bilateral TPJ and amPFC) also tracked the degree of desirability bias. One plausible explanation for the activation that tracked the identity bias is that left TPJ activity represents spontaneous judgments and heuristics about source’s trustworthiness based on superficial identity cues (Mende-Siedlecki et al. 2013, Schurz et al. 2014, Hyde et al. 2015, Kim et al. 2020, 2021). By contrast, for the desirability bias, the extensive activity in the mentalizing network may reflect the additional step of inferring source’s characteristics by the valence of their message rather than the identity of the source. In any case, the possible involvement of mentalizing processes in the expression of both biases highlights the importance of encouraging people to evaluate the source trustworthiness based on evidence rather than using partisanship or message valence as a heuristic (Van Bavel and Pereira 2018).

Our analyses also revealed that bilateral posterior cerebellum activation tracked the degree of both biases. Recent work has implicated this region in mentalizing (Van Overwalle et al. 2020, Beuriat et al. 2022) and in the detection of inconsistent trait-implying actions (Pu et al. 2022). This evidence reinforces the proposed link between mentalizing and motivated cognition and warrants further research on the potential role of the cerebellum in belief formation processes.

Taken together, the brain activation pattern tracking the desirability bias is consistent with the value-encoding, error detection, and mentalizing account. This multidimensional processing of message valence is not surprising in the present setting where message content is probably a stronger indicator of identity (i.e. opinion-based groups) than external identity cues (Bliuc et al. 2007). In this respect, the three proposed accounts of motivated reasoning are not mutually exclusive but may complement each other. For instance, discrepancies between political messages and attitudes (encoded in the dACC, dmPFC, and left AI) are probably linked with increased motivation to affirm one’s ideology (encoded in the VS and vmPFC). Moreover, the salience of ideology-affirming motives may influence the extent to which mentalizing about source’s trustworthiness will be based on superficial identity cues or on message content.

Our interpretations of the observed neural activity are based on the existing work on the neural basis of motivated reasoning and supported by the high correlations between the observed brain activation patterns and meta-analytic brain activation maps that are associated with reward processing, mentalizing, and error processing (see Neurosynth analysis in the Supplementary Material). Nevertheless, we note the potential risk of reverse inference. The brain regions reported earlier are implicated in multiple and diverse cognitive functions. For instance, the left TPJ is also associated with salience processing, attentional reorienting, and contextual updating. These cognitive processes may also play a role in motivated reasoning. However, the fact that an overlapping cluster of the left TPJ was active in both desirability bias and identity bias and the additional recruitment of other core nodes of the mentalizing network (at least for desirability bias) support our interpretation.

Future work can build on our findings by combining a motivated reasoning paradigm with localizer tasks that allow the identification of brain regions implicated in valuation, mentalizing, and error detection in the same participants. Moreover, given the correlational nature of our findings, an important next step in this line of research is the use of noninvasive brain stimulation techniques that can elucidate the causal role of targeted cortical regions in motivated reasoning.

As in most neuroimaging studies, the relatively small sample and the focus on a single issue (in our case, immigration) may limit the generalizability of our findings to different populations or sociopolitical contexts. Nonetheless, it is noteworthy that we replicated the behavioral effects of Thaler (2024) who used a similar experimental design but asked questions about various political topics, suggesting that people engage in motivated reasoning in different contexts not limited to the immigration issue. Moreover, the common behavioral and brain activation patterns across ideologically opposing groups add to the generalizability of our findings as they indicate that the relevant processes are not conditional on prior beliefs and attitudes.

Recent evidence suggests that people often engage in expressive responding and are willing to forgo payment in order to make political statements (Bursztyn et al. 2020). In our setting, the biased prior beliefs and the biased endorsement of the messages may reflect expressive responding rather than motivated reasoning (Schaffner and Luks 2018). However, expressive responding would not lead to asymmetric belief updating since participants have already expressed their bias in the prior belief. By contrast, motivated reasoning would also lead to asymmetric belief updating by selectively incorporating desirable messages into posterior beliefs. Our study did not measure belief updating. However, the study that used a similar design (Thaler 2024) showed that subjects are significantly more likely to update their beliefs in the direction of desirable rather than undesirable messages, and this asymmetric belief updating is entirely captured by the bias in message endorsement. We thus consider it unlikely that expressive responding is a main explanatory factor of the biased message endorsement.

The behavioral outcome of these neurocognitive processes is possibly contingent on contextual factors that influence which motives become salient, which discrepancies need to be corrected, and who is considered a trustworthy source of information. For instance, contextual factors such as moral and emotional language can activate partisan identities and undermine accuracy motives (Van Bavel and Pereira 2018). On the contrary, provision of accuracy prompts or inoculation by warning people about the potential presence of misinformation may blunt motivated reasoning and enhance discernment of true news from fake news (Lewandowsky and Van Der Linden 2021). According to this view, the neurocognitive processes that tracked motivated reasoning in the present setting may predict unbiased belief updating in a different setting.

In conclusion, the current work contributes to a burgeoning literature on the cognitive mechanisms of politically motivated reasoning and thus offers preliminary insights into the development of cognitive strategies that combat belief polarization phenomena and the spread of misinformation.

Supplementary Material

nsae056_Supp

Acknowledgements

We thank Katerina Petkanopoulou for her valuable comments and suggestions.

Supplementary data

Supplementary data is available at SCAN online.

Conflict of interest

None declared.

Funding

Financial support was provided by the EU Horizon 2020 Marie Curie Individual Fellowship (proposal number: 895685) to G.L. and Hellenic Foundation for Research and Innovation (H.F.R.I.) “3rd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers” (project number: 7458) to G.L. and A.R. received internal funds.

Data availability

The data underlying this article will be shared on reasonable request to the corresponding author.
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