Neural Basis of Cognitive Dissonance - Mathematical Models, Neural Activations, and Affective Responses

Cognitive Dissonance: Reexamining a Pivotal Theory in Psychology - Eddie Harmon-Jones 2019

Neural Basis of Cognitive Dissonance
Mathematical Models, Neural Activations, and Affective Responses

Keise Izuma and Kou Murayama

More than 6 decades after the original conceptualization of the theory (Festinger, 1957), research on cognitive dissonance has entered a new era; following an emergence of a new interdisciplinary field of social neuroscience in the 1990s, researchers have started investigating the neural basis underlying cognitive dissonance and subsequent attitude change using methods in cognitive neuroscience (Izuma, 2015). Compared with the long history of cognitive dissonance research (Harmon-Jones & Mills, 1999), the endeavor to explore its neural basis is still in its infancy. Nonetheless, social neuroscience studies from the past decade have provided initial findings on brain areas involved in cognitive dissonance and subsequent attitude change.

In this chapter, we first review such past social neuroscience studies that revealed several brain regions involved in cognitive dissonance. Second, we discuss possible neural mechanisms of cognitive dissonance (i.e., functional roles played in the brain regions) based on currently available evidence. Finally, we discuss challenges and difficulties in the field, which need to be carefully addressed in future research. In addition, we briefly discuss how neuroscience methods can not only reveal the neural bases of dissonance, but also has a great potential to advance our psychological understandings of cognitive dissonance in a way that would be not possible with traditional behavioral methods.


In 2008, Harmon-Jones and his colleagues applied electroencephalogram (EEG) for the first time in cognitive dissonance research (Harmon-Jones, Gerdjikov, & Harmon-Jones, 2008; Harmon-Jones, Harmon-Jones, Fearn, Sigelman, & Johnson, 2008). A year afterward, van Veen and his colleagues reported the first functional magnetic resonance imaging (fMRI) study that investigated the neural basis of cognitive dissonance (van Veen, Krug, Schooler, & Carter, 2009). Following these seminal papers, a number of social neuroscience studies have been published using both neuroimaging and brain-stimulation methods. These past studies converge to suggest that just like other complex social cognitive processes such as theory of mind and empathy, cognitive dissonance involves different interacting networks of neural structures (Izuma, 2015), which consists of the posterior medial frontal cortex (pMFC), anterior insula, and dorsolateral prefrontal cortex (DLPFC; see Figure 11.1). Although other brain regions have been also implicated, the involvement of these three brain regions seems to be the most consistently observed in past research.

FIGURE 11.1. Cognitive Dissonance Network


(a) Posterior medial frontal cortex (pMFC), pMFC consists of three sub-regions: (1) dorsal anterior cingulate cortex [dACC], (2) dorso-medial prefrontal cortex [dmPFC], and (3) supplementary motor area (SMA) and pre-supplementary motor area (pre-SMA); (b) insula, and (c) dorsolateral prefrontal cortex (DLPFC). From “Attitude Change and Cognitive Consistency,” by K. Izuma, in A. W. Toga (Ed.), Brain mapping: An encyclopedic reference (Vol. 3, p. 248). Oxford, England: Elsevier. Adapted with permission.

Posterior Medial Frontal Cortex

Among brain regions identified in past dissonance neuroimaging studies, the posterior medial frontal cortex (pMFC) is the most frequently reported (e.g., de Vries, Byrne, & Kehoe, 2015; Izuma et al., 2010; Kitayama, Chua, Tompson, & Han, 2013; van Veen et al., 2009). In 2009, van Veen and his colleagues used the classic “induced compliance” paradigm (Festinger & Carlsmith, 1959); participants were first asked to perform a long (45 minutes), boring task inside an fMRI scanner. Due to the big fMRI scanner noise and the limited freedom to move inside the scanner, performing such a long boring task in the scanner was an unpleasant experience for them. In the second fMRI task, they were asked to rate a number of statements regarding how they felt about the scanner and the task. Participants in the no-dissonance group were asked to respond to each statement as if they really enjoyed the task, and importantly, they were told that they would receive a monetary reward for pretending in this way. Thus, although they were asked to behave inconsistently with their attitudes, they presumably felt no cognitive dissonance because a monetary incentive was provided, which justifies the inconsistency between their attitude and behavior. On the other hand, participants in the dissonance group were also told to respond to each statement as if the task was enjoyable, but no external incentive for doing so was provided. Thus, they were asked to behave inconsistently with their attitude without sufficient justification, a situation known to induce strong cognitive dissonance. In addition to these critical statements recording their attitudes toward the task and scanner experience, participants in both groups were also asked to respond to neutral control statements honestly.

Van Veen et al. (2009) found that when participants in both groups were asked to rate the same statements about the scanner and task again, but this time honestly, participants in the dissonance group reported more positive attitudes compared with those in the no-dissonance group. This behavioral result indicates that when there was not sufficient justification to behave inconsistently with their attitudes, they changed their attitudes in order to make it consistent with their past behavior (i.e., dissonance reduction). Van Veen et al. further found that the pMFC was significantly activated when participants in the dissonance group rated critical statements compared to neutral statements, whereas there was no difference between the two conditions in the no-dissonance group (van Veen et al., 2009). More precisely, their pMFC activations lie in the dorsal anterior cingulate cortex (dACC) and pre-supplementary motor area (pre-SMA; see Figure 11.1a). Among other regions, the bilateral anterior insula also showed similar activation patterns (see below).

Subsequent studies also found activations in the pMFC using another classic dissonance paradigm called the “free-choice” paradigm (Brehm, 1956). Using this paradigm, Izuma and his colleagues (2010) asked participants to perform three different tasks inside an fMRI scanner—(a) first preference rating task, (b) choice task, and (c) second preference rating task. During the first preference rating task, participants rated how much they liked each food item presented on the screen. In the second choice task, pairs of food items were presented, and they were asked to select the one they preferred. Importantly, during the choice task, choice difficulty was systematically manipulated using each participant’s preference data obtained in the first rating task, so that two items in a pair were similarly liked by a participant in some trials (the difficult choice condition), while in other trials, one item was highly liked, but the other item was disliked (the easy choice condition).

Following the choice task, participants performed the second preference rating task. This task was the same as the first rating task except that below each food picture, the decision made by a participant during the choice task was presented (e.g., “You chose this item,” “You rejected this item”) to make inconsistency (or consistency) between their preferences and choices salient. For example, since participants had to pick one of two items during the choice task, they had to give up one of their favorite food items in the difficult choice trials. Thus, when they were presented with an image of a liked-but-rejected food item with “You rejected this item,” they perceived a big discrepancy between their behavior (e.g., rejected) and preference (the item was rated highly during the first rating task), a situation that induces high cognitive dissonance.

Their behavioral results showed that participants’ preferences for the liked-but-rejected items were significantly reduced. In other words, they justified the choices they made (I rejected it because I didn’t like it) (Izuma et al., 2010). When Izuma et al. (2010) analyzed the fMRI data obtained during the second preference rating task, they found that the pMFC (especially dACC and dmPFC; see Figure 11.1a) tracked the degree of discrepancy between their preferences and past choice behaviors on a trial-by-trial basis. Thus, when participants realized that their past behaviors contradicted with their preferences, the pMFC was strongly activated. Among other regions, dorsolateral prefrontal cortex (DLPFC) and posterior cingulate cortex (PCC) also tracked the degree of the discrepancy.

Kitayama and his colleagues (2013) also found the pMFC involvement in cognitive dissonance using the free-choice paradigm and found that the pMFC was activated when participants were making a difficult choice compared with an easy choice. Furthermore, although most past social neuroscience studies on dissonance used classic behavioral paradigms such as “induced compliance” and “free-choice” paradigm, de Vries and his colleagues (2015) took a different approach and also found the pMFC activation. In their fMRI study, participants were presented with an everyday situation that is likely to induce cognitive dissonance (e.g., “Have you ever broken a red light at a pedestrian crossing in front of small children?”). The study found that the pMFC (especially dACC) was significantly activated when participants reflected on their personal experience while reading these dissonance scenarios compared to control scenarios. Moreover, a few studies found that the pMFC activity was related to individual difference in attitude change following cognitive dissonance (Izuma et al., 2010; Qin et al., 2011; van Veen et al., 2009), although the direction of the relation is not consistent across the studies (see Izuma et al., 2010, for more discussion on this point).

Importantly, from these fMRI studies, it still remains unknown whether the pMFC activity simply reflects an epiphenomenon of cognitive dissonance (e.g., emotional reactions to perceived discrepancy between one’s attitude and behavior) or it plays an active role in inducing preference change to reduce inconsistency. To tease apart these two possibilities, Izuma and his colleagues (2015) conducted a study using a brain-stimulation method called transcranial magnetic stimulation (TMS) and directly manipulated the activity in the pMFC region. The study used a modified free-choice paradigm, and all participants performed the same three tasks (first preference rating task, choice task, and second preference rating task) as their previous fMRI study (Izuma et al., 2010), and Izuma et al. (2015) down-regulated the activity of the dmPFC using TMS before participants performed the second preference rating task (thus, participants performed the second rating task under the influence of TMS).

If the pMFC plays a role in representing cognitive dissonance and inducing preference change, down-regulating this region would eliminate preference change following difficult choices (participants no longer justify their past choices). Supporting this idea, their results showed that when their pMFC was stimulated by TMS, participants’ preference ratings were not influenced by the choices they made, while those who received sham TMS to the pMFC or real TMS to a control region (posterior parietal cortex) still showed typical choice-induced preference change (Izuma et al., 2015). Thus, extending the previous fMRI studies (Izuma et al., 2010; van Veen et al., 2009), this TMS study showed that the relation between the pMFC activity and preference change is not just correlational, but it is the pMFC that causes preference change following the perception of discrepancy between attitude and behavior.

Anterior Insula

The anterior insula is another region frequently reported in neuroimaging studies on cognitive dissonance. The four above-mentioned fMRI studies (de Vries et al., 2015; Izuma et al., 2010; Kitayama et al., 2013; van Veen et al., 2009) found that the anterior insula showed similar activation patterns as the pMFC. Van Veen et al. (2009) found bilateral anterior insula activations when participants were asked to behave inconsistently with their attitudes without sufficient justification. Izuma et al. (2010) also found that left anterior insula activation tracked the degree of discrepancy between behavior and attitude just like the pMFC, although only when the statistical threshold was slightly lowered (Izuma et al., 2010). Kitayama et al. (2013) found significant left anterior insula activation when participants made a difficult choice compared to an easy choice. Finally, de Vries et al. (2015) found left insula activations when participants were reading dissonance scenarios.

Furthermore, another fMRI study (Jarcho, Berkman, & Lieberman, 2011) used the free-choice paradigm and scanned participants’ brains only during the choice task (two preference rating tasks were performed outside of the scanner) and found that bilateral anterior insula activations were negatively correlated with preference change on a trial-by-trial bases. Thus, the anterior insula showed reduced activations when confronted with a choice pair for which participants would eventually show greater attitude change (choice justification). Similarly, using the free-choice paradigm, Qin et al. (2011) found that the activity of right anterior insula tracked preference change on an item-by-item basis during the second preference rating task.

Dorsolateral Prefrontal Cortex

A series of EEG studies conducted by Harmon-Jones and his colleagues consistently demonstrated that the DLPFC, especially in the left hemisphere, plays an important role in attitude change following cognitive dissonance (Harmon-Jones, Gerdjikov, et al., 2008; Harmon-Jones, Harmon-Jones, et al., 2008; Harmon-Jones, Harmon-Jones, Serra, & Gable, 2011). The initial study (Harmon-Jones, Gerdjikov, et al., 2008) used the induced-compliance paradigm and asked students (participants) to write a counterattitudinal essay supporting a tuition increase, and their EEG responses were measured while writing the essay. While one group of participants were provided with justification to write a counterattitudinal essay (no-dissonance group), the other group was not provided with such justification (dissonance group). Their behavioral results demonstrated the effect of cognitive dissonance on attitude change; those in the dissonance group showed significantly more positive attitudes toward tuition increase compared to those in the no-dissonance control group. The study further found that left DLPFC activity was higher for the dissonance (no justification) group compared to the no-dissonance group, while there was no difference in other brain regions. A similar finding was also obtained in their follow-up study (Harmon-Jones et al., 2011). The DLPFC activation has been also reported in some of the above-mentioned fMRI studies (e.g., Izuma et al., 2010; Kitayama et al., 2013).

The subsequent EEG study (Harmon-Jones, Harmon-Jones, et al., 2008) extended their initial EEG findings using a neurofeedback technique, which allows one to directly manipulate (not just measure) the activity in the left DLPFC. The study employed the free-choice paradigm, and after participants made a difficult choice between two items that were equally preferred, they were induced to increase or decrease their left DLPFC activity (all participants had undergone neurofeedback training sessions before the main task). The results demonstrated that while those who were induced to increase the left DLPFC activity showed typical patterns of attitude change, those who were induced to decrease the left DLPFC activity didn’t show attitude change, indicating that the left DLPFC plays a causal role in choice justification (Harmon-Jones, Harmon-Jones, et al., 2008).

Another piece of evidence supporting the causal role of the left DLPFC in cognitive dissonance comes from a study that used a brain stimulation method called transcranial direct current stimulation (tDCS; Mengarelli, Spoglianti, Avenanti, & di Pellegrino, 2015). Just like TMS, tDCS allows us to temporarily decrease cortical excitability of a target region (note that tDCS can also increase cortical excitability by placing an anodal, as opposed to cathodal, electrode on a target area). Mengarelli and her colleagues (2015) employed the free-choice paradigm and applied cathodal tDCS (which down-regulates the activity of a targeted area) to the left or the right DLPFC for 15 min after participants performed the choice task. Thus, just like the above-mentioned TMS study (Izuma et al., 2015), participants performed the second rating task under the influence of tDCS. The study found that cathodal tDCS over the left DLPFC reduced typical attitude change following difficult choices, while those who received cathodal tDCS to the right DLPFC (and also those who received sham stimulation) showed significant attitude changes (Mengarelli et al., 2015). Taken together with the EEG neurofeedback study (Harmon-Jones, Harmon-Jones, et al., 2008), the two studies consistently demonstrated that when the left DLPFC activity is weakened (through neurofeedback or tDCS), individuals no longer justify their choices, supporting the causal role played by the left DLPFC in attitude change following dissonance.

Other Brain Regions

Posterior Cingulate Cortex

Activation in a posterior part of the cingulate cortex has been reported in some past dissonance neuroimaging studies. For example, Izuma et al. (2010) found that the PCC activity also tracked the degree of discrepancy between attitude and behavior on a trial-by-trial basis just like the pMFC. Kitayama et al. (2013) further reported that PCC activity is correlated with preference change of chosen items during the choice task. A similar finding was also reported in Jarcho et al. (2011), although the activated region is slightly more posterior to PCC (i.e., precuneus). In contrast, although Qin et al. (2011) found that PCC is related to participants’ preferences for items, its activity was not related to preference change following choices. Furthermore, Tompson and his colleagues (2016) re-analyzed the fMRI data reported in Kitayama et al. (2013) and found that the connectivity between the medial prefrontal cortex (mPFC) and PCC predicted preference change following choices. Thus, although PCC activation has been reported in some previous neuroimaging studies, the PCC involvement reported in each study is not necessarily consistent with each other, and whether or not the PCC plays a key role (if so, what role) in cognitive dissonance remains unclear.

Ventral Striatum

A few studies reported the involvement of reward-related brain regions, especially ventral striatum (nucleus accumbens), which tracked change in individual’s preferences. In a typical free-choice paradigm, individuals increase their preference for a selected item and decrease their preference for a rejected item after making a difficult choice (known as “spreading of alternatives”). Two studies demonstrated that the activity in the ventral striatum showed the same patterns (increased activation for selected items and decreased activation for rejected items; Izuma et al., 2010; Sharot, De Martino, & Dolan, 2009), indicating that dissonance induced preference change can be seen not only at the self-report level, but also at the neural level. Similarly, two different studies showed that the ventral striatum activity during the choice task is related to subsequent changes in preferences (Jarcho et al., 2011; Kitayama et al., 2013).


Furthermore, although its involvement was not reported in previous studies, it is conceivable that brain regions related to memory, especially hippocampus, also play an important role in cognitive dissonance. One behavioral study (Salti, El Karoui, Maillet, & Naccache, 2014) demonstrated that in the free-choice paradigm, participants’ memory about their past choice is important; if participants didn’t remember a choice they had made, they didn’t show typical attitude change even after making a difficult choice. Since there is no discrepancy between attitude and behavior (thus no cognitive dissonance), if individuals don’t remember their past behaviors, the hippocampus may be necessary for cognitive dissonance.1


Although research over the past decade has identified the candidate brain regions (Figure 11.1), which seem to play pivotal roles in cognitive dissonance processes in general, it still remains largely unclear what functional role each of these regions play. For the pMFC, there are currently two ideas about the role of the pMFC in cognitive dissonance, namely conflict detection hypothesis and reinforcement learning hypothesis.

First, some researchers (e.g., Harmon-Jones, Harmon-Jones, et al., 2008; Kitayama et al., 2013; van Veen et al., 2009) have argued that the reason that the pMFC, especially the dACC, is activated by cognitive dissonance is because of its well-known conflict monitoring function. Using a variety of different tasks such as the Stroop task and Flanker task, past cognitive neuroscience studies have consistently demonstrated that the dACC is activated when there is a conflict at motor response level (i.e., two different motor responses are activated at the same time; Botvinick, Braver, Barch, Carter, & Cohen, 2001; Carter & van Veen, 2007; Mansouri, Tanaka, & Buckley, 2009). In addition to the dACC, the DLPFC is also often implicated in such tasks that require cognitive control (e.g., Kerns et al., 2004). According to cognitive control theory of dACC function (Botvinick et al., 2001; Carter & van Veen, 2007), the dACC monitors the presence of response conflict and sends conflict-related information to the DLPFC that adjusts the level of cognitive control accordingly to resolve the conflict (behavioral adjustment). As argued by Harmon-Jones, Harmon-Jones, et al. (2008), this general framework that the dACC and DLPFC play roles in conflict detection and conflict resolution, respectively, seems to fit well with possible neural processes underlying attitude change following cognitive dissonance such that the dACC monitors the presence of a cognitive type of conflict (cognitive dissonance), and the DLPFC exerts cognitive control to resolve the dissonance (attitude adjustment). However, although the close match between the neural mechanisms of response conflict vs. cognitive dissonance processing seems appealing, there is currently no direct evidence showing the link between them.

The other idea is that the reason why the dACC is activated by cognitive dissonance is because it is processed as a negative outcome, and attitude change following dissonance shares the same neural mechanisms as reinforcement learning (Izuma, 2013). Thus, just like we try to avoid choosing the same option after receiving negative monetary outcome in a simple decision-making task (behavioral adjustment), we try to avoid a negative emotional state of cognitive dissonance by adjusting our attitudes. For example, a monkey single-cell recoding study showed that monkey dACC neurons respond to negative outcome (reduced reward), which induces behavioral adjustment (Shima & Tanji, 1998). In this study, monkeys were trained to perform a simple cue-response task. In each trial, following a visual cue, monkeys either pushed or turned a handle. Importantly, monkeys could freely choose which action to take based on the amount of reward (fruit juice) they received at the end of each trial. Monkeys usually kept selecting the same action as long as the action was rewarded and changed their action when the amount of reward was reduced. The study found that neurons in the dACC responded only when monkeys received reduced reward that subsequently led monkeys to alter their action in the next trial. Similar findings were also replicated in a human fMRI study (Bush et al., 2002). Furthermore, using a variety of reward-based learning or decision-making tasks, past neuroscience research has shown that the dACC responds to negative outcomes and plays a pivotal role in subsequent behavioral adjustment (e.g., Hayden, Heilbronner, Pearson, & Platt, 2011; Matsumoto, Matsumoto, Abe, & Tanaka, 2007).

Currently, only one study has directly tested the two competing hypotheses. Izuma and Adolphs (2013) investigated the neural bases of cognitive inconsistency as defined by balance theory (Heider, 1958). Balance theory and cognitive dissonance theory share the same basic cognitive consistency principle; we prefer incoming information to be consistent with existing cognitions, beliefs and attitudes, and if there is inconsistency, people are motivated to reduce it (Abelson et al., 1968; Gawronski & Strack, 2012). Thus, just like cognitive dissonance theory, balance theory predicts that we change our attitudes following cognitive inconsistency (imbalance). While cognitive dissonance theory focuses on inconsistency between attitude and behavior, balance theory focuses on inconsistency among three attitudes; (a) one’s attitude toward another person, (b) one’s attitude toward an object, and (c) another person’s attitude toward the same object (Heider, 1958).

In Izuma and Adolphs’s study (2013), participants were first presented with a T-shirt design and asked to rate how much they liked each design. After giving their rating, they were presented with how students from the same university (liked group) or sex offenders (disliked group) rated the same item. In the second rating task, they were asked to rate the same T-shirt designs one more time, but this time no others’ rating was presented. Consistent with the balance theory, Izuma and Adolphs found that participants’ ratings were positively influenced by their fellow students’ opinions (the higher the other students’ rating for a T-shirt, the more participants increased their preference rating for the same T-shirt), while their ratings were negatively influenced by sex offenders’ opinions (the higher the sex offenders’ rating for a T-shirt, the more participants decreased their preference rating for the same T-shirt).

In addition to the T-shirt rating task, Izuma and Adolphs (2013) asked the same participants to perform two additional tasks; one is a simple reward task (the monetary incentive delay task; Knutson, Westdorp, Kaiser, & Hommer, 2000), which was intended to localize areas within the pMFC that are the most sensitive to negative outcome, and the other is a cognitive control task (the multi-source interference task; Bush & Shin, 2006), which was intended to localize pMFC areas that are the most sensitive to response conflict. The study found that just like the previous cognitive dissonance study (Izuma et al., 2010), the pMFC (especially dmPFC) tracked the degree of cognitive imbalance on a trial-by-trial basis (Izuma & Adolphs, 2013). Furthermore, interestingly, the pMFC region activated by cognitive imbalance overlapped with the area activated by negative outcome (posterior dmPFC), but not with the area activated by response conflict (pre-SMA; Izuma & Adolphs, 2013). Thus, although the activation overlap does not necessarily mean the same neural mechanisms (i.e., only indirect support for the reinforcement learning hypothesis), the study provided clear evidence against the response conflict hypothesis. Within the pMFC, the area activated by cognitive inconsistency is distinct from the area activated by response conflict.

Similarly, several studies (e.g., Jarcho et al., 2011; Kitayama et al., 2013; Qin et al., 2011; van Veen et al., 2009) interpreted the anterior insula activation in response to cognitive dissonance as reflecting the insula’s known role in representing negative emotion and physiological arousal (Calder et al., 2007; Chang, Gianaros, Manuck, Krishnan, & Wager, 2015; Damasio et al., 2000). However, at this point, this is a speculation based entirely on reverse inference (Poldrack, 2006), and the exact role played by the anterior insula in cognitive dissonance is yet to be discovered.

Finally, some researchers (Kitayama et al., 2013; Tompson et al., 2016) argued that the reason why the PCC activation is found in dissonance studies is because of its involvement in self-reference processing (Northoff et al., 2006). Although the idea that the PCC activation indexes self-relevance is interesting, there is currently no direct evidence supporting the idea (e.g., we don’t know whether the same PCC region is activated by cognitive dissonance and self-referential processing), and it is yet to be empirically tested.

To summarize, while the involvements of the brain regions (pMFC, DLPFC, anterior insula, and PCC) have been frequently reported in the past neuroimaging studies, at this point, their functional interpretations are based almost entirely on reverse inference (Poldrack, 2006), and thus remain highly speculative. As discussed in the next section, reverse inference based on the pMFC and anterior insula are especially problematic. Thus, there still remains much to be done to unveil neural mechanisms underlying cognitive dissonance and subsequent attitude change, and there are a few important avenues for future research as discussed below.


A Methodological Artifact in the Free-Choice Paradigm

As reviewed above, the majority of past social neuroscience studies on cognitive dissonance used the free-choice paradigm (Brehm, 1956). In fact, out of 13 neuroimaging and brain-stimulation studies discussed above, only three studies (Harmon-Jones, Gerdjikov, et al., 2008; Harmon-Jones et al., 2011; van Veen et al., 2009) used paradigms other than the free-choice.2 A main reason why most of the studies used the free-choice paradigm is probably because it allows researchers to manipulate cognitive dissonance on a within-subject basis (i.e., within-subject design), which is often preferred for a neuroimaging study due to large individual difference in brain activation (another practical reason is because an fMRI study is more costly than a behavioral study, so less participants [less scan time] are preferred).

However, importantly, a serious methodological flaw in the free-choice paradigm has been pointed out (Chen & Risen, 2010). Chen and Risen (2010) argued that the free-choice paradigm could measure attitude changes even in the complete absence of cognitive dissonance. The critical point is that, even if a participant gave the same ratings to two items in the first rating task, his/her preferences for the items are likely to differ at least slightly. Thus, it is likely that participant’s choice between the two items reflects this subtle difference in his/her preference. Thus, the higher preference ratings in the second rating task do not necessarily mean that the act of making a choice changed the true preference—the higher preference ratings in the second rating task may simply reflect the pre-existing preference, which was not evident in the first rating task (see Izuma & Murayama, 2013, for more detailed discussions on this issue). Thus, attitude changes measured in the free-choice paradigm could be entirely due to the statistical artifact. Chen and Risen’s claim has been empirically tested by several studies (e.g., Izuma et al., 2010; Koster, Duzel, & Dolan, 2015; Salti et al., 2014; Sharot, Fleming, Yu, Koster, & Dolan, 2012), and they demonstrated that significant attitude changes could be observed even in the condition where cognitive dissonance can never influence an individual’s attitude.3 The finding indicates that the effect of the methodological artifact is not negligible. Furthermore, while a few recent studies have demonstrated the effect of cognitive dissonance on attitude change even after controlling for the artifact (Izuma et al., 2010, 2015; Koster et al., 2015; Salti et al., 2014; Sharot et al., 2012), the meta-analysis by Izuma and Murayama (2013) showed that the magnitude of the effect is substantially smaller than what was previously reported (d = 0.61 vs. 0.26).

Despite its importance, most of the past social neuroscience studies that used the free-choice paradigm failed to address the problem (Harmon-Jones, Harmon-Jones, et al., 2008; Jarcho et al., 2011; Kitayama et al., 2013; Mengarelli, Spoglianti, Avenanti, & di Pellegrino, 2015; Qin et al., 2011; Sharot et al., 2009; Tompson et al., 2016). For example, although a few studies (Jarcho et al., 2011; Kitayama et al., 2013; Qin et al., 2011; Tompson et al., 2016) found that preference changes were correlated with activities in the dissonance network (Figure 11.1) and other regions, since the effect of cognitive dissonance is confounded with the effect purely explained by the artifact, it remains unclear whether the results reported in these studies hold after controlling for the artifact (see Izuma & Murayama, 2013). Recently, Tompson et al. (2016) argued that based on their finding that the mPFC—PCC connectivity was significantly correlated with attitude change, the attitude change they observed in the conventional free-choice paradigm is unlikely to be explained by the artifact. Although the idea of testing the validity of the paradigm using neuroimaging data is interesting, their claim is based on circular logic (a single analysis is used for testing the link between the mPFC—PCC connectivity and attitude change as well as the validity of the free-choice paradigm; see Amodio, 2010), and this idea needs to be tested in an independent study (i.e., after independently establishing the construct validity of the neural data). Thus, findings reported in those studies that didn’t address the artifact need to be interpreted with caution, and the artifact should be addressed in any future studies that use the free-choice paradigm (see Izuma & Murayama, 2013, for more discussion on how to control the artifact).

Reverse Inference Problem

In addition to the statistical artifact inherent in the free-choice paradigm, another major challenge that applies to all studies regardless of experimental paradigms is how we can go beyond reverse inference to better understand an exact functional role played by each region in the dissonance network (Figure 11.1) and other regions in cognitive dissonance. This is especially important because two regions in the network, namely the pMFC and anterior insula, are known to be two of the most functionally heterogeneous regions in the brain, and thus functional interpretations of these regions based on reverse inference tend to be unreliable (see Poldrack, 2006). Yarkoni, Poldrack, Nichols, Van Essen, and Wager (2011) analyzed the large dataset that include a total of 3,489 past neuroimaging studies and found that the pMFC and anterior insula (and DLPFC) are the most frequently activated regions across all studies (regardless of tasks; Yarkoni et al., 2011), and two meta-analyses showed that the pMFC is involved in a variety of cognitive and emotional processes (Shackman et al., 2011; Torta & Cauda, 2011). For example, although Kitayama et al. (2013) showed that the pMFC is activated in the difficult choice condition compared to the easy choice condition, the pMFC is known to play a role in a value comparison process during a simple binary choice (Hare, Schultz, Camerer, O’Doherty, & Rangel, 2011) and processing conflict at the decision level (so-called decision conflict; Izuma et al., 2013; Pochon, Riis, Sanfey, Nystrom, & Cohen, 2008; Shenhav, Straccia, Cohen, & Botvinick, 2014), and both accounts predicted and observed higher pMFC activity during difficult choices rather than easy choices. Thus, it is unclear if the dACC activity while making difficult choices is related to cognitive dissonance. Similarly, the functional specificity of the insula, PCC and DLPFC are also limited (Cieslik et al., 2013; Craig, 2009; Leech & Sharp, 2014). For example, although insula activity was often interpreted as reflecting negative emotion in past studies (Jarcho et al., 2011; Kitayama et al., 2013; Qin et al., 2011; van Veen et al., 2009), the insula is also consistently activated by reward (Sescousse, Caldú, Segura, & Dreher, 2013). Thus, given the highly limited functional specificity of these regions, the conclusions based on reverse inference (e.g., pMFC = conflict, anterior insula = negative affect, DLPFC = cognitive control [or approach motivation], and PCC = self-reference) are at best suggestive and need to be rigorously tested in future research.

The first step toward this goal may be to use functional localizer tasks just like Izuma and Adolphs’s (2013) study mentioned above. By asking the same sample of individuals to perform a cognitive dissonance task as well as localizer tasks such as a self-reference task (PCC) and a task that induces negative affect (insula), we are able to test whether areas activated by the cognitive dissonance task actually overlap with areas activated by the localizer tasks within each of the PCC and insula regions. Since a functional dissociation within each of the dissonance-related brain regions (pMFC, anterior insula, DLPFC, and PCC) are demonstrated by previous research (e.g., Cieslik et al., 2013; de la Vega, Chang, Banich, Wager, & Yarkoni, 2016; Deen, Pitskel, & Pelphrey, 2011; Leech & Sharp, 2014), it may be that areas activated by dissonance are different from areas activated by a localizer task within each anatomical region. It should be noted that, although meta-analyses of neuroimaging studies provide us with information about which brain regions are consistently activated by a certain psychological or cognitive process, and the comparison between a cognitive dissonance neuroimaging study and a meta-analysis can give us a useful insight into the functional role of a specific brain region, spatial information from a meta-analysis tends to be limited because different studies use different participants, different normalization algorithms, etc. Thus, simply comparing results from an fMRI study with a past meta-analysis might lead us to a spurious conclusion (see Deen, Koldewyn, Kanwisher, & Saxe, 2015, for an fMRI study that highlights the importance of asking the same sample of individuals to perform multiple tasks).

Importantly, even if activation overlaps are found, the overlaps between two different tasks cannot be taken as strong evidence for the shared neural mechanism. It is still possible that the same area is activated for different reasons. It may be that distinct populations of neurons specialized for different cognitive processes are located in close proximity within the same region. Recently, a neuroimaging data analysis technique called multi-voxel pattern analysis (MVPA) has been proven to be useful to interpret activation overlaps (Peelen & Downing, 2007). For example, although it was previously observed that physical pain and social pain (rejection) activated the same area within the dACC (Kross, Berman, Mischel, Smith, & Wager, 2011), a recent MPVA study showed that neural representations within the dACC are distinct (Woo et al., 2014), suggesting that largely distinct populations of neurons encode physical and social pain (see Iannetti, Salomons, Moayedi, Mouraux, & Davis, 2013). Thus, the use of functional localizer tasks and MVPA (when overlapping activations were found) will provide much stronger evidence for the neural mechanisms underlying cognitive dissonance.

How Can Neuroscience Methods Contribute to the Psychological Understanding of Cognitive Dissonance?

As we gain more knowledge about the neural basis of cognitive dissonance, or social cognition in general, it may be possible to use neuroscience methods to gain unique insights into psychological mechanisms underlying cognitive dissonance in a way that would never be possible with currently existing behavioral methods. For example, works by Izuma and his colleagues (Izuma & Adolphs, 2013; Izuma et al., 2010) demonstrated that the same pMFC region tracks the degree of cognitive dissonance and cognitive imbalance on a trial-by-trial basis (see Izuma, 2013, 2015). Thus, it may be possible to use the activity in the pMFC as a neural index of cognitive dissonance (although the construct validity of such a neural measure need to be established first with a carefully designed experimental paradigm). In past behavioral studies, the existence of cognitive dissonance was only inferred from attitude change. While it is known that skin conductance response (SCR) increases when dissonance is aroused (Croyle & Cooper, 1983; Harmon-Jones, Brehm, Greenberg, Simon, & Nelson, 1996), the sensitivity of SCR to cognitive dissonance is likely to be limited (e.g., SCR is affected by factors other than cognitive dissonance such as general arousal and fear). In contrast, pMFC activity seems to be sensitive to the degree of cognitive dissonance as demonstrated by the two fMRI studies (Izuma & Adolphs, 2013; Izuma et al., 2010). Thus, an independent and accurate neural measure of cognitive dissonance has a great potential to advance our psychological understanding of cognitive dissonance.

Another possible future contribution of neuroscience methods is the controversy between cognitive dissonance theory and self-perception theory (Bem, 1967). Self-perception theory posits that individuals come to know their internal state (e.g., attitude) by observing their own behavior just like we infer other’s internal state by observing their behavior. The theory has posed an important and serious challenge to cognitive dissonance theory; typical behavioral findings demonstrated in the past cognitive dissonance research could be explained by this self-perception process rather than cognitive dissonance (e.g., since I rejected an item, I must not like it). Despite the long history of dissonance research, the controversy has not been fully resolved (Greenwald, 2012; Harmon-Jones, Amodio, & Harmon-Jones, 2009; Olson & Stone, 2005). In all the experimental paradigms used in previous cognitive dissonance research, the two theories predict the same pattern of behavior (attitude change). Thus, it has been a major challenge to design an experimental paradigm that crucially tests the predictions from the theories. However, although the two theories predict the same behavior, underlying psychological processes are quite different between the theories, and if so, neural responses are likely to be differ as well. Thus, we may be able to distinguish these two theories from brain activations. The attempt to distinguish two different psychological processes (e.g., motivations) which lead to the same observed behavior from neural signals has already started in research on prosocial behavior. For example, individuals help others because of a purely altruistic motivation (e.g., empathy-based altruism) or a selfish motivation (e.g., reciprocity concern). Two recent fMRI MVPA studies demonstrated that we could distinguish different motivations for altruistic behaviors based on neural signals (Hein, Morishima, Leiberg, Sul, & Fehr, 2016; Tusche, Böckler, Kanske, Trautwein, & Singer, 2016). Since the brain has rich information on psychological processes underlying a behavior, psychological studies on dissonance (or any psychological topics) can be greatly benefitted by using neuroscience methods.


More than a half-century after the original conceptualization of cognitive dissonance theory in the 1950s, dissonance researchers have finally started looking at brain activations while an individual experiences cognitive dissonance. Although neuroimaging and brain-stimulation studies provided initial evidence for candidate brain regions and their possible functional roles, the endeavor is still very much in progress, and our understanding of the neural mechanisms underlying cognitive dissonance is still limited. Nonetheless, although there are important methodological and conceptual challenges we have to overcome, there is a good reason to believe that future studies will bring a lot of interesting findings. Furthermore, neuroscience methods have a great potential to help us test psychological hypotheses and further refine and advance the theory of cognitive dissonance in a way that was never even imagined by early researchers.


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1It should be noted that a previous study (Lieberman, Ochsner, Gilbert, & Schacter, 2001) demonstrated that amnesic patients also showed attitude change following a difficult choice, indicating that memory is not necessary for cognitive dissonance. However, this study didn’t address the artifact pointed out by Chen & Risen (2010; see below), and accordingly, it is unclear if the result holds after controlling for the artifact (see Izuma & Murayama, 2013).

2In addition to these three studies, de Vries et al. (2015) used scenarios to induce cognitive dissonance. Although the approach is interesting, there was no behavioral evidence suggesting that cognitive dissonance was actually induced, which makes interpretation of their fMRI results difficult.

3While the typical free-choice paradigm follows the rate-choose-rate procedure, these studies also employed a condition where participants follow the rate-rate-choose procedure (see Izuma & Murayama, 2013). In this control condition, since choices are made at the end of the experiment, choices can never affect preference change, while the effect of the statistical artifact is still present. Thus, it is possible to test the effect of the artifact using this rate-rate-choose condition, and the five studies consistently found the significant preference change even in this rate-rate-choose condition.