A Systematic Review of Research on Conformity

Online Conformity

The internet has opened up novel modes of communication, facilitating individual participation in social networks, forums, and web platforms. These digital environments offer fertile terrain for investigating social influence. The advent of these novel communication channels has prompted inquiries into the influence of majority opinion within such domains. Is conformity discernible in online settings? Do conformity rates vary across different online environments? Consequently, methodologies for assessing conformity have evolved in tandem with technological progress over the past two decades. Advances in technology have made it possible to overcome some of the limitations inherent in the previous methods.

Cinnirella and Green (2007) investigated conformity utilizing computer-mediated communication (CMC). The study replicated Asch’s stick discrimination experiment under two conditions: face-to-face (similar to Asch’s original study) and completely online CMC. Participants were led to believe they were concurrently performing the task with others, whereas in reality, they completed it individually, with computer-programmed responses. Each participant sequentially indicated their answer, with the naive participant consistently responding last. Results indicated participants conformed to majority influence in both face-to-face and CMC conditions, albeit with significantly lower conformity rates observed in the CMC condition. In alignment with this investigation, Aramovich et al. (2012) employed a CMC approach to explore the influence of the majority on participants’ moral beliefs. Despite morality’s significance to individuals, findings revealed that 80% of participants expressed reduced opposition to torture compared to their initial declarations in the pre-test, indicative of majority influence. To further investigate majority influence in a digital environment, Kyrlitsias et al. (2020) replicated Asch’s study using virtual reality. They also noted that their participants conformed to the virtual agents, highlighting that their high conformity rate (63.16% compared to Asch’s result of 75%) could be elucidated by various factors, including the level of immersion or anonymity.

Depending on the research question, there are a variety of methods for studying conformity (Garcia et al. 2021; Ivanchei et al. 2019; Pinel et al. 2010; Sah & Peng 2022; Täuber & Sassenberg, 2012). The emergence of these novel methods allows for the study of conformity without an experimenter, the adaptation of procedures to the population being studied (e.g., children), or the study of conformity in online environments. These methods raise questions about the emergence of novel conformity. Are the factors influencing susceptibility to conformity before the 2000s comparable in online environments? Do motivations for conformity remain consistent across contexts? Moreover, these new methodologies have revealed that conformity rates in online environments parallel those observed in traditional Asch paradigm studies with human confederates, underscoring the persistent nature of conformity even in virtual environments. Future research could address the impact of AI and virtual reality on individuals’ online behavior, as their growing prevalence requires new investigations. The rapid integration of new technologies into everyday life, exemplified by the increasing ubiquity of AI (such as ChatGPT) and virtual reality headsets, presents methodological opportunities for researchers to deepen their understanding of the factors contributing to conformity, an area where consensus remains limited.

Development of a New Conformity Scale

While the primary approach to measuring conformity typically entails creating situations of majority influence through exposure to an influencing source or employing confederates or fictitious majorities, some researchers have adopted scales to assess the degree of conformity, treating it as a trait. This method effectively simulates real-life scenarios and directly gauges individuals’ conforming (versus non-conforming) responses. Brügger et al. (2019) propose a 33-item conformity measurement scale aimed at evaluating variations in individuals’ conformity levels. This innovative scale seeks to address limitations associated with existing scales (Comrey 1970; Mehrabian & Stefl 1995; Schwartz 1992), which rely on evaluative statements or introspective self-reflection items that may inadvertently introduce measurement error or social desirability bias. The authors of this scale assert several advantages. First, it is based on only two parameters: the individual’s level of conformity and behavioral difficulty. Moreover, it assesses past activities rather than relying on self-evaluations, thereby mitigating methodological challenges associated with evaluating abstract concepts (Brügger et al. 2019). Furthermore, the Campbell paradigm employed in developing the scale ensures its psychometric robustness. Nevertheless, Brügger et al. (2019) acknowledge certain limitations of the scale. Firstly, it was calibrated on a sample that predominantly comprised individuals with higher levels of education compared to a representative sample of the general population. Similarly, the perceived difficulty of the items may have varied across demographic groups, leading the researchers to suggest that further testing is needed to determine the adaptability of the scale to diverse populations. In addition, they suggest that the reliability of the Rasch separation should be improved to better distinguish between medium and high levels of conformity, thus providing more reliable results. While Brügger et al.’s (2019) scale holds promise as an effective tool for measuring conformity, as posited by the authors, its validity and reliability necessitate confirmation through future research. Additionally, it could be explored as a moderator, but this requires further investigation.

In this section, we have examined the methods employed for observing and measuring individual conformity since 2004. Technological advancements have facilitated overcoming some of the costly limitations of the initial task. However, they have also introduced new challenges, such as the potential exclusion of a significant portion of the sample or the physical absence of the majority, potentially diminishing normative influence. Nonetheless, these adapted paradigms afford researchers the flexibility to choose the most suitable procedure for their specific objectives, technical capabilities, and target population, thereby constituting a notable advantage.

Age and Gender

Since Asch’s seminal studies in the 1950s, researchers have endeavored to elucidate the factors underlying individual conformity by exploring personality traits and inter-individual differences. However, no definitive effect of traits has been established thus far. Consequently, scholars have persisted in their investigation of this question. These factors are theorized to influence individuals’ susceptibility to conform to majority opinions.

Recent studies on conformity have revealed diverse effects. This section examines the complex interactions between factors that contribute to susceptibility to conform. Asch’s (1956) study concentrated on a sample comprising 123 men aged between 17 and 25. Subsequent studies have explored age and gender as potential moderators of conformity. Consistent with prior research findings, most recent studies suggest that conformity is a behavior exhibited by both males and females, with no significant disparity in conformity scores (Bos et al. 2015; Garcia et al. 2021; Hanayama & Mori 2011; Haun & Tomasello 2011; Hellmer et al. 2018; Kim et al. 2016; Lisciandra et al. 2013; Schreuter et al. 2021; Ušto et al. 2019). However, some studies have indicated potential gender differences in conformity, suggesting that women may be more inclined to conform than men. For instance, Sibilsky et al. (2021) conducted a study on conformity rates among children aged 5 to 11 (N = 125, 59 boys) across eight communities in Vanuatu. Employing Haun and Tomasello’s (2011) procedure adapted for children, they observed that girls exhibited higher levels of conformity compared to boys. Moreover, it was observed that conformity decreased as boys aged, whereas girls’ conformity remained relatively stable across different age groups. Additionally, Griskevicius and colleagues (2006) conducted a study aiming to elucidate the gender disparity in majority influence by investigating the impacts of two primary motives: partner attraction and self-protection. The study employed a method involving the priming of self-protection versus partner attraction motives through imaginative scenarios. Participants were asked to evaluate a painting (Study 1), responding to subjective versus objective questions (Study 2), or answering subjective versus objective questions with unanimous majority versus split opinions (Study 3). The discrepancy in responses before and after being influenced by majority ratings was utilized to evaluate participants’ conformity levels. Their findings suggest that men are less inclined to conform when endeavoring to attract a partner, while women exhibit higher levels of conformity in this context. No gender differences were observed when the focus was on the self-protection goal. This set of results suggest that motivations for conformity among men and women may evolve over time, potentially accounting for these findings. Similar findings were reported by Zhang et al. (2016) in a sample comprising 152 adolescents aged 10 to 16, utilizing a modified Asch task featuring figures. Mori et al. (2014) also documented analogous results among both 13–14-year-old adolescents and undergraduates (Mori & Arai 2010). These findings contrast with those observed in children aged 6–7 (Hanayama & Mori 2011). Upon comparison of these studies, it becomes evident that only a minority of them demonstrate a gender effect, and gender is not consistently considered as a potential moderator of conformity across the majority of studies examined (out of 78 studies reviewed, 64 did not mention gender). The findings suggest that conformity is a behavior observable from an early age (Haun & Tomasello 2011; Pham & Buchsbaum 2020; Yafai et al. 2014). Botto and Rochat (2018) demonstrated that children as young as two years old were sensitive to the evaluations of others and could adapt their behavior based on the attention and feedback received from the experimenter. This sensitivity increased with age, as children gradually comprehended the significance of conforming to their peers’ expectations for social acceptance. Kim et al. (2019) observed a positive correlation between age and conformity rates among children aged 3–6 years in their food preferences for vegetables, noting that older children exhibited greater conformity than younger ones. Corriveau and Harris (2010) found that children aged 3–4 years displayed lower conformity rates (20% in Study 1 and 26% in Study 2) compared to adults in Asch’s experiment (33%). Hence, as children mature, they come to understand the importance of conforming to their peers’ expectations for social acceptance. According to Cordonier et al. (2018), children as young as five years old begin to grasp the significance of adhering to peer expectations for social acceptance, a developmental milestone that younger children have yet to attain. This fosters the acceptance of conformity as a social strategy. As children progress in age, they may employ conformity as a means to avoid social exclusion, becoming more inclined to conform to their peers’ choices as they grow. However, the strength of this positive correlation between age and conformity largely depends on the specific object chosen to study conformity.

Contradictory results observed in other studies suggest that children may become less likely to conform as they grow older. However, it should be noted that these conclusions were drawn from various types of inter-group comparisons. No longitudinal study has demonstrated that individuals develop resistance to majority influence throughout their lives. Kim et al. (2016) examined the conformity of preschool children, approximately three years old, using moral, social-conventional, and visual perception tasks adapted for children (Corriveau & Harris 2010). The study presented four moral and four social transgressions to the child on a computer. A video featuring the response of the majority, consisting of two children of the same age as the participants, was shown to the child. Subsequently, the experimenter in the video asked the child whether it is acceptable to transgress the convention, whether moral or social. The results indicated that older children were less likely to conform with moral conventions (e.g., hitting another child and shoving another child) and the visual perception task than with social conventions (e.g., a boy wearing nail polish). The researchers suggest that this discrepancy may be attributed to the nature of the studied objects. Moral conventions become integrated into an individual’s value system during development and tend to remain stable over time. According to Kim et al. (2016), transgressing moral conventions typically entails more severe consequences for others compared to transgressing social conventions, which are characterized by greater flexibility and variability in norms. As children develop, they acquire an understanding of the differing importance of norms based on their nature (moral versus social), potentially accounting for their decreasing likelihood to conform as they mature. Children grasp that conformity is valued in situations governed by social norms. Conversely, regarding moral norms, children understand that conformity involves adhering to behaviors that may have negative repercussions, such as social sanctions or exclusion from the group. Similar findings were reported regarding a visual perception task in the study conducted by Sibilsky et al. (2021) among children aged 5 to 11.

Recent studies investigating the influence of age and gender on conformity have failed to identify significant effects attributable to these variables. These recent findings have not presented any novel insights that contradict previous conclusions. A meta-analysis of the most recent studies could be conducted to substantiate the diversity of effects associated with these variables.

Culture and Conformity

Similar to gender or age, the demographic specificity of Asch’s study, which concentrated on a population of Western men in the 1950s, prompted researchers to explore the influence of culture on conformity. Conformity has been investigated across various countries to determine the extent to which this behavior can be generalized across different cultures.

Of the 45 experimental articles selected for this review, studies were conducted across diverse countries, including Germany, Switzerland, England, Bosnia-Herzegovina, Canada, China, Japan, Singapore, the United States, and Vanuatu. Although most studies do not explicitly mention a cultural effect, some interpret their findings in terms of cultural factors, such as tendencies towards individualism or collectivism. Bond and Smith (1996) conducted the latest meta-analysis of the impact of culture on studies utilizing Asch’s paradigm (1952b, 1956). It included 133 studies from 17 different countries and was particularly interested in the cultural values of individualism and collectivism (between-culture level), measured in these studies by three different scales (Hofstede 1983; Schwartz 1994; Trompenaars 1993). Their results showed that conformity appears to be higher in collectivist cultures than in so-called individualist cultures. More importantly, the impact of these cultural values was greater than other situational variables considered key moderators, such as majority size (Bond & Smith 1996). However, conclusions regarding the link between culture and conformity depend on whether the study is conducted at an intercultural or intracultural level. For instance, Tu and Fischbach (2015) investigated the intercultural level by replicating one of their studies in China (n = 84), Korea (n = 102), and the United States (n = 57). The study demonstrated that there was no difference in conformity among the three samples, suggesting that cross-cultural factors do not significantly influence conformity. These findings contradict those of Bond and Smith (1996), but could be explained by the disproportionate or overly homogeneous nature of the samples, such as a predominantly student population. The comparison drawn from the study is limited in its ability to draw conclusions on the effect of culture. This limitation also arises from the fact that in most of the studies (Cinnirella & Green 2007; Corriveau & Harris 2010; Kim et al. 2016), only the socio-demographic variable of the country of birth and/or residence was measured, and no other cultural variables were considered. Consequently, certain dimensions of culture suggested by Triandis (1996) remain unexplored in these studies. These dimensions include tightness, which pertains to deviations from norms and the sanctions/punishments that may ensue, and cultural complexity, which encompasses the multitude of cultural elements differing from one culture to another, such as religious, political, or social norms. Furthermore, the studies do not address the importance of hierarchy in groups, as defined by the terms Vertical and Horizontal Relationships (Triandis 1996). This hierarchy can be critical in elucidating certain social behaviors in specific countries. Consequently, the literature fails to examine the significance of adhering to norms and tolerating deviations from these norms. It could be posited that in cultures where tightness is valued, conformity would be more emphasized. Culture encompasses more complexity than merely the collectivist versus individualist dimension, and future studies must explore these other dimensions. This restricted conclusion on the effect of culture, often reduced to a demographic variable of the country of residence, may distort the overall conclusions on the subject. To effectively conclude on the effect of culture on conformity, further research is warranted. It is noteworthy that many tools exist in order to measure various cultural dimensions. However, the use of multiple measures can be a source of confusion between the concepts (Taras et al. 2009). Therefore, for future studies to yield more robust conclusions regarding the impact of culture, it is imperative to validate and employ diverse measures to ensure accurate assessment of the dimension(s) of interest. This necessitates the utilization of large, representative samples from the population, which can pose financial and logistical challenges for researchers. The hypotheses could be tested through the development of multi-site distributive studies. Nonetheless, such studies are indispensable for attaining a deeper comprehension of the phenomenon.

Personality Traits

While age, gender, and culture have been explored as factors influencing susceptibility to conformity, the consideration of personality variables in this context remains an intriguing avenue for exploration. Kosloff et al. (2017) conducted an examination of these matters by investigating the correlation between two distinct dimensions of the Big Five personality traits, as measured by the NEO Five Factor Inventory-3 (McCrae & Costa 2010): Stability (reversed neuroticism, higher agreeableness, and conscientiousness) and Plasticity (openness and extroversion), and rates of conformity in women. Their findings revealed a positive correlation between Stability—portrayed as a potential precursor to adherence to social norms—and conformist behavior. Conversely, they found no association between Plasticity and conformity (Franzen & Mader 2023).²

These findings align with those of Hellmer et al. (2018), who explored the influence of personality traits on 3.5-year-old children (n = 59). The parents of the children first completed online questionnaires to assess their own and their child’s personalities. Next, the child completed an age-appropriate version of the Asch (1956) task in a laboratory setting. The task involved watching a video and determining which of two animals had the most dots. While watching the video, the child observed four adults (2 female and 2 male) fail to answer some of the questions. The researchers used an eye-tracking tool to observe the participants’ private responses and distinguish between two motivations to conform. If a child gave an explicit response that was incorrect, but their hidden belief was correct (i.e., looking in direction of correct answer), the researchers considered that the child had conformed normatively. When a child provided an incorrect response that aligned with their incorrect hidden belief (i.e., looking at the wrong answer), researchers labeled this as informational motivation. The study found that children of parents who self-identify as extroverts are less likely to conform to incorrect responses shown in the video. Furthermore, children rated as more extroverted by their parents were more likely to conform due to normative reasons, while those rated at higher levels of openness tended to conform for informational reasons. Current research has still not definitively and consistently demonstrated the impact of personality traits on conformity, indicating that further studies are required.

Overall, these results suggest that conformity is a complex behavior observable across various ages and irrespective of participants’ gender, culture, or personality traits. Longitudinal studies tracking changes in conformity across age and gender could offer valuable insights. Moreover, future research could systematically explore the gender effect, facilitating meta-analytical investigations on the topic. More comprehensive research on culture is warranted, avoiding simplistic comparisons, to yield more robust conclusions. However, based on the studies reviewed herein, individual factors seem to have limited moderating effects on conformity. The study by Kosloff et al. (2017) shows an effect of Stability on conformity (r = .34), and Hellmer et al. (2018) suggests an effect of openness and extraversion in children, but the results for other personality traits are inconclusive. Recent research affirms the findings of studies conducted in the latter half of the 20th century, indicating that conformity is predominantly influenced by external factors. This aspect will be further discussed in the subsequent section.

The Power of Norms

The impact of majority pressure has frequently been examined in contexts involving matters of minimal personal value or significance to individuals, such as visual perception tasks akin to Asch’s 1951 study or expressing opinions on musical choices (Egermann et al. 2013). The findings of such studies often lead to superficial and transient changes in opinions or behaviors, referred to as public conformity (Deutsch & Gérard 1955). Researchers have endeavored to ascertain whether majority influence extends to attitudes, opinions, or deeply ingrained norms within individuals’ value systems, such as moral beliefs. Preliminary findings suggest that the inclination to conform diminishes in relation to the values or norms at stake.

One type of norms investigated for susceptibility to conformity are moral norms. Moral norms tend to remain relatively stable over time and are internalized from an early age, encompassing principles such as refraining from stealing or being violent towards others (Kim et al. 2016). In a task involving moral dilemmas, Kundu and Cummins (2013) demonstrated that individuals conformed to the majority opinion. Goodmon et al. (2020) asked participants to assess the appropriateness of three different sanctions in a sexual harassment scenario, revealing an overall agreement rate of 46% for sanctions considered inappropriate. However, recent research has supported the conclusion that moral norms are more resistant to transgression than others. Consequently, it would be more challenging for individuals to comply with a majority that contradicts their moral values. Aramovich et al. (2012) conducted a study focusing on attitudes toward supporting or opposing torture, both before and after individuals were exposed to a majority viewpoint facilitated through an online chat platform. Their findings indicated that the level of moral conviction serves as a predictive variable for resistance to the majority and, consequently, the degree of conformity. Individuals with higher levels of moral conviction demonstrate greater resistance to the majority (i.e., lower conformity). Lisciandra et al. (2013) conducted a normative judgment procedure on scenarios involving violations of moral, social, and decency norms to investigate differences in susceptibility to conform to these types of norms. The study found that individuals are more likely to conform to the majority on social norms and conventions than on moral norms. Similar results were observed in four-year-olds (Kim et al. 2016), who tended to conform less to moral issues (e.g., teasing another child and calling another child names) than to issues related to social conventions (e.g., wearing a bathing suit to day care, standing during story time). Individuals perceive fewer inter-individual benefits of conforming if it involves transgressing a moral norm, unlike in other contexts such as social norms. Researchers agree that transgressing moral standards has more negative consequences on relationships with others and the group. While these findings indicate that conformity can indeed extend to moral issues, most studies imply that conformity to moral norms is less pronounced than conformity to social norms. The significance of moral values subject to group influence emerges as a moderating factor in the manifestation of conformity. These results suggest that norms are powerful factors influencing conformity. The aim of future studies will be to determine whether these norms have more weight than other factors such as the size of the majority or the anonymity of responses in susceptibility to conform. Future research on the impact of standards will investigate the effects of different standards and assess the extent of conformity based on these variations. Indeed, the multiplicity of factors at play in situations of social pressure makes it difficult to draw conclusions about the weight of particular factors (e.g., individual vs. situational). The normative nature of conformity can also be explored. Is it the norm to conform in one situation or another? Are there differences in the perception of normativity between individuals or groups?

Who Is the Majority?

In addition to norms, the characteristics of the majority group, which serve as a source of influence, may also have an effect on the rate of conformity. In this section, we will discuss recent findings that have highlighted specific group attributes that have the potential to increase or decrease control over individuals, thus acting as moderators of conformity.

As early as 1958, Kelman highlighted the different ways in which the majority exerted pressure on individuals to conform. More precisely, he identified three levels of conformism resulting from these different forms of influence, which themselves depend on certain characteristics of the majority group. When the majority has control over the means and individuals are under its control, individuals will conform with the aim of obtaining a favorable response from the group or avoiding social sanctions, a type of influence that Kelman (1958) calls compliance. On the other hand, if the majority is attractive and the relationship between individuals and the group is made salient, then individuals will conform to maintain a satisfactory relationship by identifying with the majority and adopting its point of view in a process called identification. A third level of conformism (known as internalization) is linked to the influence exerted by a majority perceived as a credible and relevant source, with a behavior consistent with the individual’s value system. These three forms of conformity described by Kelman clearly show that the characteristics of the majority and the relationships the individual has with it lead to changes in conformity.

The characteristics of the majority group, such as group size or the distinction between out-group and in-group members, have been extensively studied to determine their potential influence on conformity (Bond 2005). Recently, Ušto and colleagues (2019) replicated Asch’s (1956) experiment in Bosnia-Herzegovina and demonstrated that individuals conform more to in-group members than to out-group members. Specifically, individuals were more inclined to conform to in-group members when their group identity was prominently highlighted. Empirical studies indicate that the stronger individuals identify with the majority, feeling akin or closely connected to this majority (e.g., friends, family), the more inclined they are to conform (Tu & Fishbach 2015; Ušto et al. 2019). This phenomenon is notably congruent with the theoretical principles of social identity theory (Tajfel 1982), wherein maintaining a positive group image necessitates alignment with in-group members. Additionally, according to self-categorization theory (Turner 1991), agreement with in-group members serves as a marker of subjective validity, indicative of shared norms. Thus, individuals are more inclined to share a social reality with in-group members and, consequently, foster a greater propensity for conformity (Ušto et al. 2019). Identification with the group emerges as a pivotal determinant of conformity pressure: the less salient or important the group is, the less likely conformity becomes. This attenuation in conformity underscores the centrality of group identification as a potent moderator, highlighting that individuals are less susceptible to influence exerted by a majority with which they lack identification. Moreover, the signals sent by this majority are real clues guiding individuals to adapt their behavior in line with it. Previous studies have primarily examined the emotions experienced by individuals under the influence of the majority and motivated to conform. The emotions conveyed by members of the majority toward an individual subject to influence have garnered considerable attention in the study of conformity, notably emphasized by Heerdink et al. (2013). These emotions are considered indicators that enable individuals to assess the alignment of their behavior with prevailing situational norms, thereby influencing the extent of their conformity. When the majority expresses anger, individuals tend to experience heightened feelings of rejection by the group and are consequently more likely to conform and affiliate with the group (Heerdink et al. 2013). This effect is amplified when the group is less familiar to the individual. Moreover, in instances when the group has a cooperative objective and expresses anger directed toward the minority, individuals are more inclined to conform (Heerdink et al. 2013). Conversely, the expression of positive emotions by the majority carries distinct implications for conformity dynamics. The majority’s expression of joy fosters a heightened sense of acceptance within individuals, serving as an implicit signal that their behavior aligns with prevailing group norms, thereby encouraging further conformity. On the individual side, experiencing positive emotions such as gratitude or joy when confronted with a dissenting majority promotes conformity, especially in private contexts. In this specific context, the experience of gratitude fortifies social bonds with others, thereby contributing positively to an individual’s social integration within the group (Ng et al. 2017).

The results of studies into the effect of non-human peers (robots, virtual assistants, etc.) on conformity are consistent with the above conclusions. From chatbots on online shopping platforms to conversational assistants, technology is increasingly integrated into daily life. To what extent do individuals allow themselves to be influenced by these robots? Over the first half of the 21st century, research sought answers to this question, revealing that robots wield weaker influence over individuals compared to their human peers. A study by Beckner et al. (2016) involved exposing individuals to a majority viewpoint espoused by robots in a task reminiscent of Asch’s conformity experiments. The results showed that this robotic majority exerted no influence on the responses of participants, who steadfastly maintained their independence. These findings align with a study by Schreuter et al. (2021), demonstrating that people are more amenable to the human voice of a conversational assistant compared to that of a robot. Given the evolution of artificial intelligence (AI) and the increasing humanoid features of technological assistants—such as voice and language capabilities—it is plausible that these entities may emerge as more potent sources of conformity in the future. Consequently, future research exploring the dynamics between individuals and AI holds considerable promise in discerning their impact.

Recent studies on conformity indicate that external factors, such as the type of standards or the relationship with the majority group (in-group vs. out-group), strongly influence conformity. Manipulating these factors could lead to more or less conformity. Future studies could investigate whether varying characteristics of the majority promote different levels of conformity. This could raise significant questions regarding the promotion of pro-social behaviors such as eco-friendly behavior, healthier eating, sustainable consumption, or adopting a healthy lifestyle, which need to be addressed in studies on majority influence.

Neurocognitive Mechanisms

The question of why individuals conform has also captured the interest of researchers in the field of neuroscience. In particular, they seek to understand the neurological processes that occur when individuals are subject to the influence of the majority. Advances in imaging technologies have facilitated progress in understanding the neural basis of conformity. Various imaging techniques such as functional magnetic resonance imaging (fMRI), event-related potentials (ERP), or even transcranial magnetic stimulation (TMS) have been used to study the neurocognitive mechanisms underlying conformity (see Schnuerch & Gibbons 2014). These studies show that brain regions such as the posterior medial frontal cortex, which is involved in error detection and the need for behavioral adaptation, and the ventral striatum, which is responsible for the regulation of motivation and impulses, are activated in situations that induce conformity. These findings suggest that conformity is primarily based on an error-based reinforcement learning mechanism (Schnuerch & Gibbons 2014). Specifically, when individuals experience conflict between their judgments and those of the majority, this conflict generates an error or even a reward/punishment signal. Furthermore, these studies have shown that such conflict can induce negative affect, which conformity serves to mitigate. Previous research has linked activation of the dorsal medial prefrontal cortex (dmPFC), a region responsible for processing fear and anxiety-related information, to cognitive dissonance (Izuma et al. 2010). Thus, during conflict with a majority group, individuals may experience cognitive inconsistency, which they alleviate by conforming. These findings shed light on the neural processes involved in conformity and provide insights into the associated affective, social, and cognitive conflict.

From an evolutionary perspective on comparative cognition, Claidière and Whiten (2012) examine conformity in humans compared to animals. It appears that animals such as rats or fish can exhibit ‘conformist’ behaviors in foraging strategies. When learning new skills, chimpanzees tend to choose one behavior by imitating their peers when given a choice between two behaviors. Similarly, sparrows choose to emit the sound most commonly used by other birds. These studies suggest that conformity can be observed in animals and that the behaviors demonstrated by conspecifics serve as an informational basis for individuals to guide their own conduct. However, this conclusion remains interpretative, and there is no empirical support for similar influence processes in humans.

These findings provide insight into how the brain processes information received from the majority and how conformity plays a role in this process. Recent neuroscientific studies have provided new insights into the mechanisms of conformity and support the experimental findings of previous studies.