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The neuroscience of social conformity: implications for fundamental and applied research

Mirre stallen, alan g sanfey.

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Edited by: Hauke R. Heekeren, Freie Universität Berlin, Germany

Reviewed by: Mauricio R. Delgado, Rutgers-Newark: The State University of New Jersey, USA; Ulf Toelch, Humboldt University Berlin, Germany

*Correspondence: [email protected]

Received 2015 May 29; Accepted 2015 Sep 7; Collection date 2015.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

The development of closer ties between researchers and practitioners in the domain of behavior and behavioral change offers useful opportunities for better informing public policy campaigns via a deeper understanding of the psychological processes that operate in real-world decision-making. Here, we focus on the domain of social conformity, and suggest that the recent emergence of laboratory work using neuroscientific techniques to probe the brain basis of social influence can prove a useful source of data to better inform models of conformity. In particular, we argue that this work can have an important role to play in better understanding the specific mechanisms at work in social conformity, in both validating and extending current psychological theories of this process, and in assessing how behavioral change can take place as a result of exposure to the judgments of others. We conclude by outlining some promising future directions in this domain, and indicating how this research could potentially be usefully applied to policy issues.

Keywords: social conformity, decision making, policy implications, functional magnetic resonance imaging, behavioral change

Introduction

Recent innovative work in applied psychology has established that making people aware of the behavior of others is a useful technique for inducing positive behavioral change on a societal level. For example, taxpayers are more likely to pay what they owe when knowing that others do (Coleman, 2007 ; Cabinet Office UK Behavioural Insights Team, 2012 ), householders decrease their energy use when informed that they use more power than their neighbors (Schultz et al., 2007 ; Slemrod and Allcott, 2011 ), and people are more likely to give to a charity if it is viewed as the social norm (Alpizar et al., 2008 ; Smith et al., 2015 ). Many of these strategies have been successfully applied in recent years, albeit on a somewhat ad hoc basis. However, a better understanding of the mechanisms of social influence and conformity , both cognitively and neurally, is important in extending these techniques into other domains of interest to policy-makers.

Over the course of the last decade, a growing body of work has examined the neurocognitive correlates of social influence (for reviews see Falk et al., 2012 ; Morgan and Laland, 2012 ; Izuma, 2013 ; Schnuerch and Gibbons, 2014 ; Cascio et al., 2015 ). These studies have focused on diverse aspects of social influence, ranging from how the opinion of others affects the valuation and perception of simple stimuli (Berns et al., 2005 ; Mason et al., 2009 ; Chen et al., 2012 ; Stallen et al., 2013 ; Tomlin et al., 2013 ; Trautmann-Lengsfeld and Herrmann, 2013 ) to more complex, realistic, choice options (Klucharev et al., 2009 ; Berns et al., 2010 ; Campbell-Meiklejohn et al., 2010 ; Zaki et al., 2011 ; Huber et al., 2015 ), and finally, to what brain mechanisms underlie long-term conformity, how the mere presence of peers impacts brain activity and leads to changes in risk-taking and trust decisions (Steinberg, 2007 ; Chein et al., 2011 ; Fareri et al., 2012 , 2015 ), and how the brain reconciles misleading influence (Edelson et al., 2011 , 2014 ; Izuma, 2013 ). The goal of this Focused Review is not to re-summarize this work, but rather to explore to what extent these neuroimaging studies can contribute to our understanding of the psychology of social influence, and what promising directions lie ahead in the future. Specifically, while social influence is a broad term describing the impact of others on our behavior and opinions, we here focus on studies on conformity, with conformity referring to the actual alignment of people's opinions or behaviors with those of others. This review is structured around three ways in which neuroimaging has been suggested to contribute to psychology (Moran and Zaki, 2013 ), namely the role of neuroimaging in (i) identifying the fundamental mechanisms that underlie behavior, (ii) dissociating between psychological theories that make similar behavioral predictions, and (iii) using brain activity to predict subsequent behavioral change.

KEY CONCEPT 1. Social influence.

The influence of others on our attitudes, opinions, and behaviors. Social influence can take many forms, including conformity (see Key concept 2), reactance (deliberately adopting a view contrary to that of others), persuasion (changing one's view based on appeals to reason or emotion), and minority influence (when an individual or small group exerts influence on the majority).

KEY CONCEPT 2. Conformity.

Aligning one's attitude, opinion or behavior to those of others. Social psychology distinguishes between two reasons for conformity. Informational conformity occurs when one adopts the view of others because others are assumed to possess more knowledge about the situation. Normative conformity refers to the act of conforming to the positive expectations of others in order to be liked and accepted by them.

Mechanisms of conformity

A growing number of neuroscientific studies suggest that conformity recruits neural signals that are similar to those involved in reinforcement learning (Klucharev et al., 2009 ; Campbell-Meiklejohn et al., 2010 ; Kim et al., 2012 ; Shestakova et al., 2013 ). For example, in the study by Klucharev et al. ( 2009 ), participants were asked to rate female faces and then saw the purported aggregate judgments of other raters. Upon seeing those faces a second time, participants' ratings were shown to shift in the direction of the group judgments. Neuroimaging results demonstrated that when individual ratings differed from those of the group, activity in the rostral cingulate zone, an area in the medial prefrontal cortex and involved in the processing of conflict (Ridderinkhof et al., 2004 ), increased, while activity in the nucleus accumbens, an area associated with the expectation of reward (Knutson et al., 2005 ), decreased. Interestingly, the amplitude of these signals predicted conformity, such that when this incongruence was large (although exactly what magnitude this discrepancy should be to trigger conformity is still undetermined), people then adjusted their behavior and aligned their opinion with that of the group (Klucharev et al., 2009 ). Similar neural discrepancy signals reflecting the deviation of one's own assessment and a salient external opinion have been reported by other studies as well (Campbell-Meiklejohn et al., 2010 ; Deuker et al., 2013 ; Izuma and Adolphs, 2013 ; Lohrenz et al., 2013 ).

KEY CONCEPT 3. Reinforcement learning.

Reinforcement learning is learning about the environment by trial and error. By encountering positive and negative outcomes, individuals learn over time what action to select to maximize reward. In conformity research, acceptance by the group is typically seen as the reward and matching one's attitude, opinion or behavior with those of others as the means to achieve this outcome.

Consistent with previous work showing that regions in the medial prefrontal cortex are associated with behavioral adjustment following both positive/negative or unexpected outcomes (Ridderinkhof et al., 2004 ), activity in this region, slightly more anterior than the medial frontal activity reported by Klucharev et al. ( 2009 ), has been found to encode not only conformity toward the liked group, but has also been shown to correlate with behavioral adjustments away from the disliked group (Izuma and Adolphs, 2013 , and see Izuma, 2013 for an overview of medial frontal activations in social conformity studies). To test the causal role of the medial frontal cortex in conformity, researchers used transcranial magnetic stimulation (TMS) to temporarily down-regulate this area in order to examine whether this interfered with behavioral adjustments to group opinions (Klucharev et al., 2011 ). Indeed, transient down-regulation of this region appeared to reduce behavioral change, confirming the critical involvement of the posterior medial prefrontal cortex in conformity. We believe that this research demonstrates a clear role for functional neuroimaging in better elucidating the precise systems that underpin social conformity. While we have used the mechanism of reinforcement learning here as an example of how we can better understand complex social behavior by examining basic processes, future investigations are required to gain more insight into the exact processes underlying conformity. For instance, it is unknown to date whether deviation from the group opinion triggers actual dopamine-dependent reward prediction error signals, or whether conformity is processed in different ways.

Validating psychological theories

In addition to identifying more precisely the neural mechanisms of conformity, neuroscience can help to adjudicate between competing psychological theories that make similar behavioral predictions with regard to the reason why people conform. For instance, one of the first neuroimaging studies on social influence aimed to ascertain whether conformity is a function of an explicit decision to match the choices of others, or whether the presence of others actually changes individuals' true perception or attentional focus (Berns et al., 2005 ). By using fMRI and a mental rotation task, the authors examined the neural correlates of conformity in the face of incorrect peer feedback regarding the degree of rotation of an abstract figure. Conforming to incorrect feedback altered activity within visual cortical and parietal regions that were involved in performance of the mental rotation task itself. Based on the involvement of these regions in perception and based on the absence of activity in frontal decision-making regions the authors concluded that behavioral change in this study was due to a modification of low-level perceptual processes as opposed to a decision to conform taken at an executive level. Though caution is warranted when using these types of reverse inference techniques to establish knowledge of precise cognitive processes (Poldrack, 2006 ), additional support for the hypothesis that social conformity can affect basic cognitive processing comes from electroencephalography (EEG) work showing that deviation from the norm of a peer group can impact early visual brain signals (Trautmann-Lengsfeld and Herrmann, 2013 , 2014 ).

Another focus of neuroimaging research has been to investigate whether viewing the opinion of others can actually change individuals' true preferences, testing social psychological theories which distinguish genuine attitude modifications from mere public compliance in which people conform without changing their true attitude (Cialdini and Goldstein, 2004 ). This direction has shown promise, demonstrating that social influence moderates activity in the striatum and ventromedial prefrontal cortex. These two brain areas are known to be involved in the processing of rewards, and are believed to work in concert to encode subjective value (Bartra et al., 2013 ). Signal across these areas was enhanced when participants viewed simple, abstract symbols that had been rated in popularity by peers (Mason et al., 2009 ), in addition to when participants were presented with actual concrete stimuli such as faces and songs that were liked by others (Klucharev et al., 2009 ; Campbell-Meiklejohn et al., 2010 ; Zaki et al., 2011 ). Together, these findings suggest that the behavior and opinion of others can in fact directly impact the neural representation of value associated with particular stimuli, and demonstrate how neuroimaging can help in disentangling true conformity from simple public compliance. As such, this approach provides valuable information in validating and extending psychological theories of conformity.

KEY CONCEPT 4. Compliance.

Compliance refers to a superficial form of conformity when individuals express the same opinion or behavior as the group but do not change their actual underlying attitude or belief. Compliance is also known as public conformity and is the opposite of private conformity, or internalization, when people truly believe the group is right and actual preference change occurs.

Predicting behavioral change

A third way by which neuroscience research may contribute to a better understanding of social influence is in its ability to use brain data to directly predict behavior. For example, the strength of the discrepancy signal in response to a conflict between one's own judgment and that of a group not only predicted subsequent conformity, but activity within the striatum also correlated with individual differences, with participants who adjusted their opinion in response to group disagreement showing lower activations in this area than participants who did not adjust their views (Klucharev et al., 2009 ). Individual differences in the tendency to align one's behavior with the group have also been associated with functional and structural differences in the orbitofrontal cortex (Campbell-Meiklejohn et al., 2012a ; Charpentier et al., 2014 ). Additionally, these tendencies can be modulated by administration of oxytocin (Stallen et al., 2012 ), a hormone involved in a wide range of social behaviors, as well as methylphenidate, an indirect dopamine and noradrenalin agonist (Campbell-Meiklejohn et al., 2012b ).

An interesting extension to this laboratory research, and one that received relatively little attention to date, is to what extent neural activity can predict actual long-term behavioral change, as measured in real-world decisions. One study showed that the discrepancy signal in the medial frontal cortex could predict preference change several months later (Izuma and Adolphs, 2013 ). However, this finding could potentially be explained by the general tendency to be consistent with one's own previous behavior, since participants had already explicitly rated the stimuli once before in this experiment. A follow-up study that circumvented this issue demonstrated robust conformity effects whereby judgments of facial attractiveness were altered by knowing the opinions of others, with this effect lasting up to 3 days (Huang et al., 2014 ). Persistent conformity effects were also found in a study examining the impact of social pressure on memory change (Edelson et al., 2011 ). Participants in this study were exposed to incorrect recollections of other co-observers while being asked questions about a documentary they had viewed. After a week's delay they were tested again, and though they were informed that the answers they had heard before were actually determined randomly, participants nonetheless still showed a strong tendency to conform to the erroneous recollections of the group, with, importantly, neuroimaging data indicating that social influence modified the neural representation of the memories. Specifically, both activity in the amygdala at the time of exposure to social influence, as well as the strength of connectivity between this area and the hippocampus, predicted long-lasting, persistent memory errors. Future progress in this field could usefully focus on how this work extends to the public health arena, as discussed in the following section.

Conclusion and future directions

Though in its relative infancy in terms of a substantive body of experimental research, neuroscience, and in particular functional neuroimaging, has a great deal to offer the study of social influence. Knowledge of the neural mechanisms underlying conformity can be used to constrain existing psychological theories, as well as to construct novel ones, and can help in understanding what precise cognitive processes are engaged. To achieve this, a productive next step is to better understand how to interpret brain activity. For instance, does the discrepancy signal in the medial frontal cortex in response to a conflict between one's own opinion and that of a group reflect the process of cognitive reappraisal and subsequent attitude adjustment, or rather does it indicate an increase in negative affect which in turn can motivate behavioral change? Other interpretations are also possible, for example theories that medial frontal activity reflects recruitment of theory of mind processes (Gallagher and Frith, 2003 ), the experience of conflict (Pochon et al., 2002 ; Klucharev et al., 2009 ), or, more generally, a violation of expectations (Chang and Sanfey, 2013 ). Of course, brain areas are typically not selectively engaged in a single psychological process but rather are implicated in multiple computations, and therefore the interpretation of brain activity based solely on the findings from the research outlined here is challenging. Naturally, the increasing number of studies in this area will help in delineating the precise processes involved, and converging methodological approaches also have promise in this regard. For example, additional data from independent localizer tasks within the same participants can be helpful in determining the psychological process in which a brain area is engaged (Zaki et al., 2011 ; Izuma and Adolphs, 2013 ), and the use of meta-analyses, functional connectivity approaches assessing neural network computations, and large-scale databases can also help reduce the potential pool of hypotheses (Poldrack, 2011 ). One useful online meta-analysis database is the platform Neurosynth, which allows for large-scale automated meta-analyses of functional magnetic resonance imaging (fMRI) data (Yarkoni et al., 2011 ).

We suggest that one specific promising future direction for neuroscience to contribute to the understanding of social influence is to further investigate the emotions that drive behavioral adjustments due to conformity. For instance, people may align their preferences with others because they affiliate and thereby feel a need to belong to a group (Tafarodi et al., 2002 ; Cialdini and Goldstein, 2004 ). However, negative emotions, such as the fear of social exclusion or a sense of shame or guilt in having differing opinions, could also be drivers of conformity (Janes and Olson, 2000 ; Berns et al., 2010 ; Yu and Sun, 2013 ). Combining neuroscientific methodologies with clever behavioral paradigms can provide substantially greater insight into the specific emotions that underlie conformity in a given context, as accumulating evidence suggests that neuroimaging data can support inferences about affective states (Knutson et al., 2014 ). The use of innovative methods, including multivariate brain imaging techniques, can be expected to improve the mapping of brain activity onto both affective experience and behavior in the near future (Formisano and Kriegeskorte, 2012 ).

The accumulating laboratory evidence allied with these aforementioned likely future developments demonstrates great promise in constructing improved neural and psychological models of social conformity. A better understanding of the processes that drive conformity is not only interesting from a scientific perspective, but also provides relevant practical insights for social policy. Policy campaigns often attempt to motivate behavioral change by the use of social influence, such as programs discouraging smoking among adolescents by emphasizing peer disapproval, or reducing alcohol consumption at schools by correcting prevalent, though false, beliefs about the behavior of others (Neighbors et al., 2004 ; Youth smoking prevention: truth campaign USA 1 ). Although social influence campaigns such as these can sometimes be effective, there are also many cases in which they fail (Clapp et al., 2003 ; Granfield, 2005 ). Deeper understanding of the processes that both facilitate and prevent social conformity will undoubtedly help to predict when, and how, behavioral change can occur, and has the potential to provide useful hypotheses that can be tested in real-world field experiments.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

This work was supported by grants from the European Research Council (ERC313454) and the Donders Institute for Brain, Cognition, and Behaviour, Nijmegen, the Netherlands (FOCOM).

1 http://www.legacyforhealth.org

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Open Access

Peer-reviewed

Research Article

The effects of information and social conformity on opinion change

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation School of Public Affairs, The Pennsylvania State University - Harrisburg, Middletown, Pennsylvania, United States of America

Roles Conceptualization, Data curation, Methodology, Supervision, Validation, Writing – original draft, Writing – review & editing

Affiliations Department of Biochemistry, The Pennsylvania State University, University Park, Pennsylvania, United States of America, Department of Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America, Department of Political Science, The Pennsylvania State University, University Park, Pennsylvania, United States of America

• Daniel J. Mallinson, 
• Peter K. Hatemi

• Published: May 2, 2018
• https://doi.org/10.1371/journal.pone.0196600
• Reader Comments

18 Mar 2020: Mallinson DJ, Hatemi PK (2020) Correction: The effects of information and social conformity on opinion change. PLOS ONE 15(3): e0230728. https://doi.org/10.1371/journal.pone.0230728 View correction

Extant research shows that social pressures influence acts of political participation, such as turning out to vote. However, we know less about how conformity pressures affect one’s deeply held political values and opinions. Using a discussion-based experiment, we untangle the unique and combined effects of information and social pressure on a political opinion that is highly salient, politically charged, and part of one’s identity. We find that while information plays a role in changing a person’s opinion, the social delivery of that information has the greatest effect. Thirty three percent of individuals in our treatment condition change their opinion due to the social delivery of information, while ten percent respond only to social pressure and ten percent respond only to information. Participants that change their opinion due to social pressure in our experiment are more conservative politically, conscientious, and neurotic than those that did not.

Citation: Mallinson DJ, Hatemi PK (2018) The effects of information and social conformity on opinion change. PLoS ONE 13(5): e0196600. https://doi.org/10.1371/journal.pone.0196600

Editor: Yong Deng, Southwest University, CHINA

Received: August 17, 2017; Accepted: April 16, 2018; Published: May 2, 2018

Copyright: © 2018 Mallinson, Hatemi. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data are available from the corresponding author’s Harvard Dataverse ( http://dx.doi.org/10.7910/DVN/YVCPDT ).

Funding: This project was supported by a $1,000 internal grant from the Penn State Department of Political Science (awarded to DJM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Information and persuasion are perhaps the most important drivers of opinion and behavioral changes. Far less attention, however, has been given to the role of social pressure in opinion change on politically-charged topics. This lacuna is important because humans have a demonstrated proclivity to conform to their peers when faced with social pressure. Be it in the boardroom or on Facebook, Solomon Asch and Muzafer Sherif’s classic studies hold true today. Individuals conform based on a desire to be liked by others, which Asch [ 1 , 2 ] called compliance (i.e., going along with the majority even if you do not accept their beliefs because you want to be accepted), or a desire to be right, which Sherif et al. [ 3 ] termed private acceptance (i.e., believing that the opinions of others may be more correct or informed than their own). These two broad schemas encompass many specific mechanisms, including, motivated reasoning, cognitive dissonance, utility maximization, conflict avoidance, and pursuit of positive relationships, among others. Information-based social influence and normative social influence (i.e., conformity pressure) both play important, albeit distinct, roles in the theories of compliance and private acceptance (see [ 4 ]). In both cases, humans exhibit conformity behavior; however only in private acceptance do they actually update their beliefs due to the social delivery of new information.

Extensions of Asch and Sherif’s path-breaking works have been widely applied across a number of behavioral domains [ 5 – 9 ], to include political participation. For example, significant attention has been focused on the import of conformity on voter turnout and participatory behaviors [ 10 ], including the effects of social pressure on the electoral behavior of ordinary citizens [ 11 – 15 ]. This body of work points to both the subtle and overt power of social influence on electoral behavior, yet little is known about the import of social conformity for politically charged topics in context-laden circumstances, particularly those that challenge one’s values and opinions.

Testing conformity pressure in the ideological and political identity domain may explicate whether the pressure to align with an otherwise unified group is different when dealing with politically charged topics versus context-free topics such as the size of a line or the movement of a ball of light [ 2 , 16 ]. Opinions on politically charged topics are complex, value laden, aligned with cultural norms, and not easily changed [ 17 – 21 ]. It remains unknown if the effects of social conformity pressures on political opinions are conditioned by the personal nature of the locus of pressure. To be sure, social conformity is a difficult concept to measure without live interaction. An observational approach makes it difficult to untangle if or how social pressure independently affects behaviors given these variegated casual mechanisms, and whether changes in opinion that result from social interaction are due to compliance or private acceptance. Nevertheless, experiments provide one means to gain insight into how and why opinion change occurs. Here, we undertake an experiment to test the extent to which opinion change is due to persuasion through new information, social conformity pressure, or a combination of the two in a more realistic extended discussion environment.

Conformity and political behavior

Both observational and experimental research has addressed different aspects of the impact of socially-delivered information on individual behavior. Observational analyses of social networks form the backbone of much of the recent research on social influence and political behavior. Sinclair [ 22 ], for instance, demonstrates that citizen networks convey a bounded set of political information. Individuals may turn to highly informed peers [ 23 ] or aggregate information from trusted friends and family [ 24 ] in order to reduce the cost of gathering the information required to engage in political behavior (e.g., voting). In turning to their network, they are open to privately accepting this useful information. Political information, however, is not the only type of information transmitted through personal networks. Social pressure helps the network induce compliance with desired social norms [ 25 – 27 ]. In this case, members of the network provide information regarding the group’s expectations for appropriate engagement in politics. Individuals that are concerned about whether or not the group will continue to accept them therefore conform out of a desire to be liked, broadly defined. Norms are often self-enforcing, with merely the perceived threat of potential sanctions being enough to regulate behavior through compliance and self-sanctioning [ 28 , 29 ].

The debate over the practicality and reality of deliberative democracy further highlights the importance of understanding the role of political conformity in public and elite discourse. Scholars and theorists argue that political decisions are improved and legitimized under a deliberative process [ 30 – 34 ], even though deliberation does not necessarily result in consensus [ 35 ]. The crux of democratic deliberation is that participants are engaging in a rational discussion of a political topic, which provides the opportunity for each to learn from the others and thus privately update their preferences (i.e., out of a desire to be right). It results in a collectively rational enterprise that allows groups to overcome the bounded rationality of individuals that would otherwise yield suboptimal decisions [ 36 ]. This requires participants to fully engage and freely share the information that they have with the group.

Hibbing and Theiss-Morse [ 37 ], however, raise important questions about the desirability of deliberation among the public. Using focus groups, they find that citizens more often than not wish to disengage from discussion when they face opposition to their opinions. Instead, they appear averse to participation in politics and instead desire a “stealth democracy,” whereby democratic procedures exist, but are not always visible. In this view, deliberative environments do not ensure the optimal outcome, and can even result in suboptimal outcomes. In fact, the authors point directly to the issue of intra-group conformity due to compliance as a culprit for this phenomenon. The coercive influence of social pressure during deliberation has been further identified in jury deliberations [ 38 , 39 ] and other small group settings [ 40 ].

Beyond politics, there is experimental evidence of the propensity to conform out of a desire to either be liked or to be right [ 25 , 41 – 45 ]. Using a simple focus group format and pictures of lines, Asch [ 1 , 2 ] demonstrated that individuals would comply with the beliefs of their peers due to a desire to be accepted by the group, even if they disagree and even when they believe the group opinion does not match reality. To do this, Asch asked eight members of a group to evaluate two sets of lines. The lines were clearly either identical or different and group members were asked to identify whether there was a difference. Unknown to the participant, the seven other group members were confederates trained to act in concert. At a given point in the study, the confederates began choosing the wrong answer to the question of whether the lines were equal. Consequently, the participant faced social pressure from a unified group every time they selected their answer. Asch varied the behavior of the group, including the number of members and number of dissenting confederates. Participants often exhibited stress and many eventually complied with the group consensus, even though the group was objectively wrong and participants did not agree with them privately.

Using a much more complex and context-laden format—a youth summer camp with real campers—Sherif et al. [ 3 ] demonstrated private acceptance whereby humans internalize and conform to group norms because consensus suggests that they may have converged on a right answer. In this case, the boys in the camp quickly coalesced into competing factions and initial outliers in the groups conformed out of a desire to win competitions (i.e., be right). While the groundbreaking Robbers Cave experiments revealed a great deal about group behavior well beyond conformity, we focus specifically on this particular aspect of the findings, which have stood the test of time in numerous replications and extensions across a wide variety of social domains [ 46 – 52 ].

Replication of Asch’s experimental work, in particular, has met varying degrees success. Lalancette and Standing [ 53 ]found that Asch’s results were mixed when using a prompt more ambiguous than unequal lines. Further, Hock [ 54 ] critiques the Asch design for not replicating a real life situation. Focusing on divorce attitudes, Kenneth Hardy provided an early application of Asch’s public compliance and Sherif’s private acceptance theories to political opinions using a similar small-group format with six confederates and one participant. Confederates offered not only their opinions, but also reasons for their opinions, which provided a methodological innovation by introducing more information than just the confederates’ votes. Hardy’s work provided an important starting point for identifying the process of conformity in the political realm, but it remains limited. He only utilized men in his study and did not allow for repeated discussion to assess how long participants hold up to conformity pressure. In a more recent study, Levitan and Verhulst [ 55 ]found persistence in political attitude change after interaction with a unanimously-opposing group, but they did not incorporate any discussion.

Our experiment builds on these works by examining the micro-process underlying opinion change for a politically charged topic discussed in a real context. We bridge between studies that allow for no discussion with those that study day-long deliberations in order to determine if group influence has a stronger effect, even when the discussion centers on an attitude closely tied with social identity. Our interaction of about an hour simulates a likely real-world example of dialogue. More importantly, our design allows us to speak to the debate over social influence by pulling apart the desires to be right (private acceptance) and liked (compliance). Our main goal is not to completely predict the general public’s behavior, but rather to identify the independent causal role of social pressure on opinion change, given the known import of information effects. We expect conformity pressure and information to have joint and independent effects on opinion change.

Variation in conformity behavior

While our primary interest is in identifying the average effects of information and conformity pressure on opinion change, we nevertheless recognize that there is variation in humans’ responses to social pressure, depending on observed and unobserved individual characteristics. Thus the average treatment effect recovered can mask substantively important heterogeneity [ 56 , 57 ]. For instance, not all of Asch’s or Hardy’s subjects complied with group opinion and there was a great deal of variation in how willing Sherif et al.’s campers coalesced into cohesive and functioning groups. In order to address this possibility we test three factors that have been previously identified as covarying with the average propensity to conform: personality traits, self-esteem, and ideology. The most consistent evidence points towards those who change their opinions as being generally more agreeable, neurotic, and having lower self-esteem [ 58 ].

Generating hypotheses regarding the import of other personality and ideological dispositions on opinion change for political, moral and identity-laden topics is more complicated. Extant research indicates support for both stability and change for these traits and differs in the source of that change, i.e., whether it is informational or social. For example, on the one hand we might expect those who are more politically conservative to be more likely to conform to the group overtly, given extant studies showing conservatives think less negatively toward conformity and comply more often to group pressure and norms [ 59 – 61 ]. In addition, conservatives are also higher on the Conscientiousness personality trait, and this trait both reflects and is related to more conformist behavior [ 62 – 64 ].

On the other hand, conservatism, by definition, advocates the status quo and is related to resistance to change and greater refusal to privately accept new information, specifically if that information contradicts one’s values [ 65 , 66 ], leading to a greater likelihood of internal stability. In a similar manner, those high in openness and more politically liberal, while more likely to take in new information, and thus possibly more likely to privately accept it, are also less prone to restrictive conformity, and thus possibly less likely to conform publicly [ 67 ]. We treat these propositions as secondary hypotheses, and explore their import in a limited manner given restrictions in the data.

Materials and methods

In order to explicate the independent and joint effects of compliance and private acceptance, we designed an experiment, conducted at the Pennsylvania State University from May to December of 2013, which placed participants in a deliberative environment where they faced unified opposition to their expressed opinion on a political topic that is relevant to their local community. We assessed participants’ privately-held opinions, absent the group, before and after the treatment in order to determine whether those who expressed a change in opinion during the discussion only did so verbally in order to comply with the group and gain acceptance or if they privately accepted the group’s opinion and truly updated their own values. The group discussed the topic openly, for approximately 30–45 minutes, also allowing us to assess participant behavior throughout the discussion. We discuss the specifics in more detail below.

In designing the experiment, we leveraged a unique time in Penn State’s history, the aftermath of the Jerry Sandusky child abuse scandal and the firing of longtime Head Coach Joe Paterno. The firing provided an ideal topic of discussion and a hard test of conformity pressure given the fact that it exhibited high salience on campus, was politically charged, and connected to the participants’ identities as Penn State students. The question posed to our participants was whether or not they felt that Coach Paterno should have been fired by the University’s Board of Trustees in November 2011. Previous research demonstrates that undergraduates may not have as clearly defined political attitudes as older adults on many topics and thus may be more susceptible to conformity pressure from peers due to non-attitudes [ 68 ]. This informed our choice of the discussion topic, as Paterno’s role in the abuse was not only highly salient on the Penn State campus, but typically invoked strong and diametrically opposed opinions in the undergraduate population and the general Penn state community. We begin by providing some background on this issue and its connection to identity and politics.

Firing of Penn State football Head Coach Joe Paterno

The first week of November 2011 was a whirlwind for students at Penn State. Police arrested former defensive coach Jerry Sandusky on charges of child sexual abuse following the release of a grand jury report by the Pennsylvania Office of the Attorney General. In the midst of a national media firestorm and with evidence mounting that the University President, Athletic Director and Head football Coach had been aware of Sandusky’s activities, Penn State President Graham Spanier resigned and the Board of Trustees relieved Paterno of his duties. They also placed the Athletic Director, Tim Curley, and Vice President, Gary Schultz, on administrative leave after being indicted for perjury regarding their testimony about their knowledge of Sandusky’s sexual assaults of young boys. Immediately after the firings and suspensions, students poured into campus and downtown State College, causing damage and flipping a news van [ 69 ]. Various student protests persisted for weeks. The following summer brought Sandusky’s conviction, but controversy has not subsided, especially in Pennsylvania. The firing is continually alive at Penn State, as lawsuits against the university and the trials of Spanier, Curley, and Shultz continue to progress as Paterno’s family and supporters seek to restore his legacy.

While the real-life context of our design adds to its external validity, the discussion topic’s high salience and likelihood of evoking a strong opinion also improves the internal validity of the experiment. Paterno was more than an employee; he was the image of Penn State, “an extension of [the students’ and alumni’s] collective self” ([ 70 ], 154), and thus tied to students’ identities as members of the community [ 71 ]. As reported at the time of the scandal:

“More than any other man, Mr. Paterno is Penn State–the man who brought the institution national recognition… Paterno is at the core of the university’s sense of identity.” [ 72 ].

Given the emotion surrounding this issue, it is not unlike morality policies that evoke strong responses from individuals [ 73 ], thereby providing a hard test of conformity pressure on value- and identity-laden opinions. There is no better example of this than the ongoing pursuit of justice by the children subjected to abuse by Catholic priests and the mounting evidence of systematic concealment and enablement of such abuse by the Catholic Church. The similarities between Penn State and the Church persist on nearly every level, including the scandals threatening an important aspect of its members’ identities. In this way, the experience of students following the child abuse scandal at Penn State generalizes to politically relevant circumstances where organizational power and personal identities are challenged.

In addition to being a highly salient and identity-laden topic of discussion, the Paterno firing is a social and political issue. It weighed heavily on the 2012 Board of Trustees elections, when many candidates campaigned on their support for Paterno. Furthermore, Pennsylvania Governor Tom Corbett was a de facto member of the Board and originally launched the Sandusky investigation while serving as the state Attorney General. As a board member, Corbett advocated for Paterno’s firing and faced both praise and criticism across the Commonwealth. As a result of the scandal, Pennsylvania passed legislation that clarifies responsibilities for reporting child abuse and heightens penalties for failures to report. The abuse received national recognition. When asked for his reaction to the firing, President Obama called on Americans to search their souls and to take responsibility for protecting children [ 74 ]. Thus, there is recognition by elites, the public, the media, and the academy that Paterno’s firing is an inherently political issue. Furthermore, the topic has personal importance to the participants, is identity laden, and relevant at the local, state, and national-levels. Having described the context of the topic of discussion, we now turn to describing the experimental protocol.

Participant recruitment

The experiment was advertised as a study on political discussion in upper- and lower-level social science courses, as well as through campus fliers and a university research website. As an incentive, participants were entered into a raffle for one of eight $25 gift cards to Amazon. The first participants completed the study in May 2013 and data collection closed in December 2013. There were no major developments in the Sandusky scandal during our data collection phase, thus we believe that no outside events threaten the validity of the study. The firing of the four university officials, Joe Paterno’s death, Jerry Sandusky’s conviction, issuance of the Freeh Report, and the National Collegiate Athletic Association’s sanctions all occurred prior to the start of data collection. This study was approved by the Pennsylvania State University Office for Research Protections Institutional Review Board (Study# 41536) on February 20, 2013. All participants in the treatment group signed a written informed consent form prior to participating in the study. Participants in the control group supplied implied consent by completing the online survey after reading an informed consent document on the first web page of the survey. Penn State’s IRB approved both methods of consent. Consent materials can be found with other study reproduction materials at the corresponding author’s dataverse ( http://dx.doi.org/10.7910/DVN/YVCPDT ). Thus, all participants provided informed consent and all procedures contributing to this work complied with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975.

A total of 58 students participated in either the treatment or control groups. Compared to observational studies, this may appear a small number, but it comports with current research norms that require high participant involvement and a substantial amount of their time [ 75 , 76 ] and is consistent with the sample sizes for the foundational work in this area [ 2 , 6 ]. The pre- and post-test, discussion session and debriefing required at least 1.5 hours of each participant’s time. Researchers spent, on average, at least eight hours per participant recruiting, coordinating, and scheduling discussion groups, running discussion sessions, and coding behavioral data. The study generally targeted current undergraduates, but three graduate students and one recent graduate also participated. Upon volunteering to take part in the study, participants were randomly assigned to either the treatment (n = 34) or control (n = 24) group using a coin flip. The total sample includes an un-randomized 16 person pilot of the experimental protocol. See S3 File for additional information on this pilot group, its characteristics, and analyses showing their inclusion does not affect the main findings.

Pre-test survey

Fig 1 presents the study design including information provided to the treatment and control groups (in black) and the points at which we measured their opinion regarding Paterno’s firing (in red). Both groups were administrated a pre-test survey using Qualtrics. The treated group completed this survey before attending a discussion session. In addition to basic demographic characteristics, we collected a number of psychological and behavioral traits for every participant. Ideology was measured by an attitudinal measurement of ideology, a Liberalism-Conservatism scale [ 77 ] widely used to prevent measurement error that arises from the difficulty in accurately collapsing a complex view of politics into a single dimension. This measure of ideology is well validated (e.g., Bouchard et al. 2003) and serves as the basis for modern definitions of ideology across disciplines [ 78 , 79 ]. The measure relies on respondents simply agreeing or disagreeing with a broad range of political and social topics, from evolution to taxes. In this case, we used 48 different topics, which generate an additive scale of conservatism ranging from 0 (very low) to 48 (very high). In addition to measuring our participant’s political ideology, we assessed their self-esteem using Rosenberg’s [ 80 ] scale and personality using McCrae and John’s [ 81 ] 44-question Big 5 dimensions of personality: openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism.

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This figure presents each phase of the study, including information provided to treated and control groups (in black) and the points at which we measured their opinion of the Paterno firing (in red).

https://doi.org/10.1371/journal.pone.0196600.g001

Finally, all participants were asked their opinion on five policies that affect undergraduates at Penn State: alcohol possession on campus; government oversight of academic performance; the firing of Paterno; prevention of State Patty’s Day celebrations; and use of the student activities fee. Participants recorded their opinion using a five-point Likert scale from “strongly agree” to “strongly disagree.” We included five different topics on the survey so that treatment group participants would be unsure as to which topic they would be discussing.

Discussion group

After completion of the online survey, participants in the treatment group were scheduled individually for a discussion session. Each discussion group was comprised of a single participant and two to four trained confederates (we compare differences in the number of experimenters and find no effects; for more information see S4 File ). A total of five unique confederates, three females and two males, were used across the length of the study. Among them were four political science Ph.D. candidates of varying experience and one recent graduate who majored in political science. The confederates looked young and dressed informally, and were not distinguishable from our undergraduate students. In terms of training, the confederates were not strictly scripted so that the discussion would not appear forced or scripted. Instead, the experimenter and other volunteers took part in pre-experiment tests as mock participants so that the confederates could argue both sides of the Paterno firing and develop the consistent points they used for the duration of the study (see S2 File ). Fig 2 shows a typical discussion session. Discussion sessions were held in a conference room with all of the group members sitting around a table. There was no fixed seating arrangement.

Clockwise from bottom left: Experimenter, confederate, confederate, participant, and confederate. Note the participant’s seemingly disengaged body language. This participant ultimately changed their opinion.

https://doi.org/10.1371/journal.pone.0196600.g002

At the beginning of each discussion session, the experimenter reminded the group that the general purpose of the experiment is to understand political decision-making and how individuals form political opinions. They were told that a topic was randomly selected for each discussion group from the five included in the pre-test survey, with their topic being the firing of Paterno. Prior to the start of open discussion, group members were provided a sheet of excerpts from the Freeh Report [ 82 ] regarding Paterno’s involvement in the Sandusky scandal at Penn State (see S1 File ). They were told that the information was drawn from independent investigations and was meant to refresh their memories, given that two years had passed since the firing.

After providing time to read the information sheet, the group was polled verbally regarding whether or not they believed Paterno should have been fired (yes or no). The participant was always asked to answer first. This allowed the confederates to subsequently express the opposite opinion throughout the discussion. Though very little time passed between completion of the pre-test surveys and participation in the discussion groups, we did not rely on the opinions expressed in the pre-test surveys as the basis of our confederates’ opinion. We recorded and used the verbal response as the respondents’ opinion. This also ensures that our confederates were responding to the precise opinion held by the participant at the start of the discussion session. This way we could track the effect of conformity pressure on their opinion throughout the session.

The group was then provided 30 minutes for open discussion; however, discussion was allowed to go beyond 30 minutes in order allow participants to finish any thoughts and reflect a more natural interaction. During this discussion, up to four confederates argued the opposition position to greater or lesser degrees depending on the confederate, including responding to and interacting with the participant and even agreeing with the participant on certain points. At the conclusion of the discussion time, group members were told that researchers wished to understand their true opinion at that moment and that we would be aggregating the individual opinions from our groups in order to gain a sense of overall student opinion on each of the five topics. Thus, they were instructed to complete an anonymous ballot with their final opinion. The anonymous ballot allowed us to measure whether their opinion had actually changed during the discussion, conforming to other people’s behavior due to private acceptance that what they are saying is right, or were only publicly complying with other people’s behavior, without necessarily believing in what they are doing or saying.

Each discussion session was video recorded for the purposes of coding both verbal and non-verbal indications of their opinion. Two coders were hired to review each discussion session video and record a series of behavioral characteristics of the participants (not reported in this paper) as well as their impression regarding whether the participants verbally changed their opinion during the course of the discussion (a binary yes/no). The principal investigators also coded each video. We used the modal code from all four coders, with the principal investigators re-reviewing the videos to break six ties. Fleiss’s Kappa [ 83 ] indicates moderate agreement among raters on the verbally expressed opinion (0.54, p < 0.001).

The combination of anonymous balloting and video recording for verbal cues is an important aspect of the study design that allows us to pull apart whether participants conformed out of a desire to be right, liked, or a combination of the two. Finally, we debriefed each participant to explain the full purpose of the study, including any and all possible points of deception, and to gather information about their personal feelings on being in the minority during the discussion.

Control group

We utilized a control group in order to identify the independent effect of social pressure on opinion change. Their behavior established a baseline expectation for the amount of opinion change we could expect with just the introduction of new information and no interpersonal interaction. This baseline then allows us to compare the two groups, social influence treatment and control, in order to tease apart the independent and joint effects of social conformity pressure and information on opinion change.

To this end, the control group took the same pre-test survey as the treatment group. However, after completion of the survey, instead of being in a deliberative session, control group participants read additional information on a topic that was “randomly” selected from the five opinion questions. Based on their opinion regarding the firing of Paterno, we presented them with the same sheet of information provided to the treated as well as a summary of the same pro- and counter-arguments used by the actual confederates during the discussion group sessions (see S1 and S2 Files). After reading these, control group participants were asked whether they believe Paterno should have been fired (yes or no) and the strength of that opinion (very strongly, somewhat strongly, neutral). If they changed their opinion at this juncture, we consider they did so only because of the introduction of new information, as there was an absence of social pressure. Thus, our design allows us to parse out the effect of the discussion group and the social pressure emerging from an unanimity of opinion opposite to the participants.

Results and discussion

The core finding of this study revolves around the question to what extent will people conform to an opposing opinion on a topic that is salient, politically charged, and informs some aspect of their identity? Furthermore, can we evoke deviation rates similar to the foundational studies that relied on less complex aspects of one’s psychology [ 1 ]? And most important, what type of change is occurring? For those participants who changed their opinions, was it due to new information (i.e., private acceptance), social pressure (i.e., public compliance), or some combination of the two? To answer these questions, we first examined the degree of opinion change in both the treatment and control groups. For the control group, we compared their initial opinion from the pre-test survey with the opinion they provided after reading the information sheet and counter-arguments. Fig 3 displays the percentage of each group that did and did not change their opinion. Within the control group, which received the same information as the discussion group, but had no social interaction, only 8 percent of the participants changed their opinion. The information-based change we observed is consistent with extant research [ 84 , 85 ]. In addition, though a large proportion of the control group did not change their opinion, some did moderate it (i.e., strengthened or weakened) based on the receipt of new information alone. See S5 File for a further breakdown of these changes.

https://doi.org/10.1371/journal.pone.0196600.g003

Turning to the treatment group, 38 percent of our treated participants changed their opinion between the initial vote (after receiving information and prior to the discussion) and the final secret ballot. Our complex, identity, and value-laden topic returned findings that comport remarkably close to the deviation rates of Asch [ 2 ] and those that follow (for a meta-analysis, see [ 6 ]). If we consider all other things equal, the 30 percent increase in opinion change is dependent on the treatment of participating in the group discussion (χ 2 = 5.094, p < 0.05). This finding remains unchanged if the 16 non-randomized members of the pilot study are removed from the treatment group (though the p-value of the chi-square declines to 0.10, due to the smaller n, see S3 File ). As further evidence, Table 1 presents logistic regression results demonstrating the treatment effect. Namely, being in the treatment condition increases the odds of opinion change by 581 percent. Meaning, social pressure and/or the personal delivery of information, as opposed to simple exposure to new information, had a profound influence on either true opinion change through private acceptance or conformity through public compliance. Due to the small sample size, we are hesitant to include additional covariates in this model, but instead use t-tests below to examine differences in the characteristics of participants who changed their opinion and those who did not.

https://doi.org/10.1371/journal.pone.0196600.t001

Sources of change

Moving to our secondary analyses, the research design also allowed us to parse out the specific sources of change within the treatment group. Recall we accounted for both true opinion change (i.e., the anonymous ballot at the end of discussion) and verbal opinion change (i.e., declared opinion change during group discussion captured in video and coded by independent raters) for those in the treatment condition. Therefore, we divided those in the treatment group into four subgroups in order to better understand why they changed their opinion. Table 2 shows the percentages of participants in the treatment group who changed their opinion overtly, covertly, or not at all. In sum, 47 percent did not change their opinion between the start and end of the discussion session. A total of 33 percent changed both overtly and covertly, meaning they verbally expressed an opinion change and wrote a changed opinion on their secret ballot. We argue that this group responded to a combination of the desires to be right and liked. Of the remaining participants, 10 percent changed due to a desire to be liked (overtly, but not covertly) and 10 percent due to a desire to be right (covertly, but not overtly). Though only anecdotal, one of the participants in the desire to be right category went so far as to tell the experimenter that he agreed with the group but adamantly refused to agree openly. Such participants were swayed by the introduction of new information out of a strong desire to be right, but apparently did not want to look like they were changing their opinion. Thus, our first set of analyses confirms that information plays an important role in opinion change, but social pressure also has a substantive and, at least in this context, a larger effect. For even a topic so important to one’s identity, participants changed their previously held opinions.

https://doi.org/10.1371/journal.pone.0196600.t002

Psychological differences

Having established the main findings of our study and the relative import of the two causal mechanisms for why participants changed their opinion, we now turn to examining how underlying traits, including ideology, personality, age and sex, differ between those that changed their opinion and those that did not. Demographic differences are included for descriptive purposes. First, we assessed differences between pro- and anti-firing participants. Second, we examined the relationship between direction of opinion change and trait differences between participants that changed their opinion and those that held firm. Due the nature of the experiment and specific focus on the question of causality, these tests are secondary to the main findings in the paper. For the following analyses, the sample sizes are small and in some cases and the findings only speculative.

Across both the treatment and control groups, the pre-test survey showed almost two-to-one support for Paterno keeping his job (i.e., against the firing). As mentioned earlier, “JoePa” was not only a symbol of Penn State, but also an icon to its students, and to some degree seen as a reflection of them. Table 3 displays the average demographic and psychological measures for those for and against the firing, based on the pre-test survey. The only statistically significant difference between the groups is their political ideology. The group opposed to Paterno’s firing is, on average, more conservative in their attitude positions than those that called for his firing. It is important to note that these are college students, and thus the overall distribution of ideology exhibits a liberal skew. However, Fig 4 demonstrates that the pro-firing group is not only less conservative, on average, but is also more ideologically narrow, whereas those that did not support the firing are more conservative, but also drawn from a wider ideological span. This finding suggests that ideology is a substantial factor for individuals that supported the firing. Whereas support for Paterno may have a less pronounced ideological dimension, those supporting his firing may focus more narrowly on the issue of child abuse and the responsibility of those in leadership to protect vulnerable citizens. Given that ideology is the only difference we could identify among participants’ opinions prior to the start of the experiment, we next examined whether there were differences between participants who changed their opinion and those that did not in both the treatment and control conditions.

https://doi.org/10.1371/journal.pone.0196600.t003

https://doi.org/10.1371/journal.pone.0196600.g004

Tables 4 and 5 provide a sense of how demographic and psychological characteristics differ between participants who changed their opinion and those who did not. Table 4 includes both treatment and control participants, whereas Table 5 focuses solely on the treatment group. We found evidence both supporting and refuting our hypotheses presented above. There were consistent significant differences ( p < 0.05) in conservatism and conscientiousness. Namely, participants who changed their opinion are less conservative and less conscientious. Given the reported relationships between these two traits, this finding makes sense. Additionally, when all subjects are pooled ( Table 4 ), there is also a significant difference in neuroticism, with opinion changers registering higher on this scale. Both suggest that political and psychological traits may play a role in the mean shift demonstrated above. There were no differences based on the number of confederates. Meaning, participants were no more or less effected by social pressures from greater (4) or fewer (2) opponents in the discussion environment. These results demonstrate that individual differences exist across individuals that change their opinion and those that do not. Additional research will be required to both confirm and expand upon these findings. What we do find, however, is in line with expectations derived from past research and points to useful areas of future inquiry.

https://doi.org/10.1371/journal.pone.0196600.t004

https://doi.org/10.1371/journal.pone.0196600.t005

All participants were debriefed upon completion of the discussion and informed to all aspects of the study. Participants were asked during the debriefing how they felt about being the only dissenting voice. Forty-seven percent of the treatment group participants freely offered that they felt pressured or intimidated. Twenty-nine percent also freely said that they felt like they had to dig in and defend their position during the discussion. This included six people that ultimately changed their minds. One said, “I’m not getting any support in this room. Alright I’ll defend my own position.” Another said, “I feel extra pressure to explain myself.” For some, their defensiveness continued into the debriefing. In particular, some students that did not change their opinion continued defending themselves when talking one-on-one with the experimenter, even after it was explained no matter which position they took, they would face opposition. This demonstrates that some participants are put on the defensive when faced with a unified opposition. Of those that expressed feeling defensive, some dug-in deeply and did not budge at all, while others opened up to the influence of their peers as the discussion progressed. This behavior comports the foundational work of Asch [ 1 , 2 ] and Milgram [ 86 ] and strongly suggests that our participants indeed experienced social pressure in the treatment condition, but differs in that it highlights the variance in how individual’s react to such pressure.

Limitations

We wish to call attention to two specific limitations of this study that are discussed above and in the supplementary materials, but warrant further mention. The first limitation is the inclusion of a meaningful, relative to the overall sample size, non-randomized pilot of the treatment condition. While this had no substantive effect on the results, it is important to recognize and we discuss this in more detail in the S3 File . Second, Fig 1 makes apparent that we use two similar, but slightly different scales for opinion throughout the study. Namely, pre-test opinion is measured on a five-point Likert scale and the remaining opinion measures are dichotomous (yes/no), with an additional strength question for the control group. Our primary analyses, however, rely on the comparison of the two yes/no answers in the treatment group; the verbal designation of yes/no at the beginning of the discussion section and the yes/no in the post discussion ballot. We further discuss this in the S5 File .

Finally, to some the small sample size of the study may be a limitation, especially those concerned about a replication crisis in Social Psychology [ 87 ]. We would respond, however, that the intensive nature of this study in terms of researcher hours and treatment condition makes it difficult to scale-up. Thus, a multi-site replication is likely the best approach to assessing the veracity of these findings [ 88 , 89 ]. We encourage such replication and have provided all materials necessary on the corresponding author’s Dataverse ( http://dx.doi.org/10.7910/DVN/YVCPDT ). Additional lessons relevant to replication work and laboratory experiments in political science can be found in Mallinson (2018) [ 90 ].

Conclusions

While researchers have examined the roles of social influence (public compliance) and new information (private acceptance) on opinion change, the two are less often examined concurrently and the explicit causal arrows are more often assumed than tested through an experiment. Furthermore, social conformity is a complex concept to measure through surveys or interviews alone. Live interaction provides an optimal means to understand social pressures. Our experiment was designed specifically to further unpack the causal mechanisms underlying opinion change and test whether a person’s values and identity are subject to social pressure. Furthermore, the selection of the topic of study, the firing of an important symbol of Penn State, also allowed us to explicate the extent to which information and social pressure challenge a person’s deeply held values and identity. We find that while information has an important role in changing people’s opinions on a highly salient topic that is attached to a group identity, the social delivery of that information plays a large and independent role. Most individuals that changed their opinion did so out of some combination of the two forces, but there were people who only changed their opinion overtly in order to gain social acceptance as well as those who did not want to give the appearance of changing their mind, but still wanted to be right.

These findings have important implications for research on social and political behavior. They reinforce the understanding that citizens and elites cannot be simply viewed as rational utility maximizers independent of group dynamics. Yet, at the same time, the desire to be right and information remain critical components of opinion change. Furthermore, there are important individual differences such as ideology, self-esteem, and personality that appear to have a role in conformity. Exposure to politics and political discussion are fundamentally social, and therefore behavior is conditioned on the combination of the information one receives, and the social influence of the person or group providing that information interacting with one’s disposition. All should be considered when examining any inter-personal, social or political outcome. Be it a deliberative setting like a jury or a town hall meeting or informal gatherings of citizens, or political elites for that matter, changes in behavior are not simply due to rational information-driven updating, and even when they are, that updating may be pushed by the social forces that we experience in our interactions with other humans in variegated ways dependent upon the characteristics of the individual (for example, see [ 91 ]). This was the case for simple and objective stimuli, like Asch’s lines, and it is also the case in our context-laden experiment that focuses on the complexities of personal identity and opinion. That is, the conformity of social and political values relies on the same psychological mechanisms underlying general conformity.

Beyond theoretical and empirical importance for the study of social and political behavior, these findings also hold normative importance for democratic society. The normative implications are perhaps best exemplified by the organizational and personal turmoil that followed the revelation of child abuse by priests in the Catholic Church. Politics forms important aspects of the social and personal identities of elites and citizens, more so today than ever before [ 92 , 93 ]. People include their political party, positions on particular issues (e.g., environmentalism), and membership in political, religious, social and academic organizations, among other things, as key aspects of their identities. Our experiment helps us better understand how individuals behave when part of that identity is challenged.

That being said, no design is perfect, and this experiment only unpacks part of the causal mechanism. Like the early work on social conformity, it serves as a foundation for future studies to extend upon and further explicate the causal mechanism. For example, an extension on this design, such as controlling variation in the type and number of confederates [ 44 , 94 ], could help us better understand the nature and amount of pressure necessary to induce conformity across a variety of individual characteristics. For example, a potentially fruitful avenue of extension would be to provide the participant with one supportive confederate who verbally changes their opinion during the discussion. Having support reduces conformity pressure, but deviation by that support should increase it. Additionally, while we identify individuals whose behavior was prompted by either social pressure or information, the largest group responded to a combination of the two. Further parsing out the interaction between information, persuasion, pressure and the complexity of human dynamics will require an even more complex research design on a larger scale. The numerous extensions of Asch’s original experiment demonstrate the wealth of potential extensions of this design that can help unpack this black box. Doing so requires an incremental approach that will be time and resource intensive. This study provides the foundation for those next steps.

Supporting information

S1 file. information sheet provided to both treatment and control groups..

https://doi.org/10.1371/journal.pone.0196600.s001

S2 File. Confederate talking points.

https://doi.org/10.1371/journal.pone.0196600.s002

S3 File. Randomization.

https://doi.org/10.1371/journal.pone.0196600.s003

S4 File. Use of deception in the study design.

https://doi.org/10.1371/journal.pone.0196600.s004

S5 File. Breakdown of opinion change in the treatment and control groups.

https://doi.org/10.1371/journal.pone.0196600.s005

Acknowledgments

An earlier version of this paper was presented at the 2013 American Political Science Association Annual Meeting in Chicago, Illinois, and the April 2014 Center for American Political Responsiveness Brown Bag in State College, Pennsylvania. We would like to thank the editor, the anonymous reviewer, Ralf Kurvers, Rose McDermott, and conference attendees for their helpful comments and suggestions on this manuscript. We are also grateful to Ralf Kurvers for providing Fig 1 . We would like to thank our research assistants, Ronald Festa, Emilly Flynn, Christina Grier, Christopher Ojeda, Kimberly Seufer, and Matthew Wilson, that helped make this experimental protocol a success.

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International Review of Social Psychology

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Research article

A systematic review of research on conformity.

• Carla Capuano
• Peggy Chekroun

This systematic review offers a comprehensive overview of conformity research conducted since 2004. Adhering to the PRISMA guidelines, the review identified 48 relevant articles from a substantial pool (literature review conducted between January and April 2023), systematically extracting valuable insights into key findings, methodologies, and future research directions. While recent studies confirm the prevalence of conformity across diverse contexts, echoing Asch’s seminal findings (1951), the review emphasizes the need for a unified understanding of influencing factors, including age, gender, and culture, with contextual variables playing a central role. Advances in digital technology have expanded research possibilities, enabling investigations across diverse digital contexts. Researchers employ innovative methods such as computer-mediated communication (Cinnirella & Green 2007) and virtual reality (Kyrlitsias et al. 2020) to explore conformity within digital spaces that closely mirror real online interactions.

Given the evolving landscape of conformity research, this review advocates for further interdisciplinary and intercultural investigations, comprehensive meta-analyses, and replications to deepen our understanding of this multifaceted phenomenon.

• systematic review
• majority influence
• social influence

Introduction

Conformity denotes the process whereby individuals adjust their behavior, opinions, and attitudes to accord with those prevailing among the majority, even in cases where they hold dissenting views ( Asch 1956 ). This phenomenon, initially elucidated by Asch in the 1950s, has since become a focal point of extensive inquiry within the realm of social psychology. Asch employed an original methodology during the 1950s to gauge conformity, employing a visual perception task. In this task, participants were required to verbally identify which of three lines, displayed on the left side of a screen, corresponded in length to a standard line presented on the right side. This visual perception task was characterized by its simplicity and lack of ambiguity. Participants undertook this task alongside individuals who were actually confederates of the experimenter, deliberately providing unanimous incorrect responses on certain trials. The naïve participant would then deliver their response last, enabling observation of the potential influence exerted by this unanimous majority. As of September 18, 2023, the original article by Asch ( 1951 ) has garnered 7962 citations on Google Scholar, and Asch’s line judgment paradigm has been replicated numerous times thereafter. Despite individuals occasionally portraying themselves as less conformist than their peers ( Pronin et al. 2007 ), conformity has consistently manifested across diverse contexts and modalities ( Bolderdijk et al. 2022 ; Bond 2005 ; Cress & Kimmerle 2007 ; Galinsky et al. 2008 ; Mori & Arai 2010 ).

According to the most recent meta-analysis encompassing 125 Asch-type conformity studies, conformity emerges as a robust behavior, exhibiting a weighted average effect size of 0.89 ( Bond 2005 ). Recent investigations have indeed reported conformity rates closely resembling those observed by Asch in the 1950s, exemplified by the replication conducted by Franzen and Mader ( 2023 ), 1 which observed a conformity rate of 33%, mirroring Asch’s rates ( 1951 , 1956 ). For instance, Goodmon et al. ( 2020 ) discovered that 82.67% of their participants conformed to the majority at least once. In Ušto et al.’s ( 2019 ) replication of Asch ( 1956 ), the conformity rate reached 59.2% (compared to Asch’s 75%). Recent replications and meta-analyses on conformity underscore the robustness of this effect, obviating the necessity to continually assess its existence as it persistently manifests. The processes and factors hypothesized to underlie the phenomenon of conformity are diverse and extensive, and previous research substantiates the pressing need for further inquiry to elucidate definitive explanations regarding this phenomenon.

This systematic review provides an overview of conformity studies conducted since 2004, with the previous literature review focusing on studies concerning conformity and compliance ( Cialdini & Goldestein 2004 ). Furthermore, our investigation indicates a notable absence of systematic reviews addressing conformity to date. The primary objective of this review is to elucidate the latest insights regarding the methodologies employed to investigate conformity and its associated influencing factors. Notably, seven decades have elapsed since Asch’s seminal work on conformity in 1951. Given the societal transformations since those initial studies, it prompts the question: do contemporary individuals exhibit conformity to the same extent as their counterparts 70 years ago? Are behaviors of conformity still as prevalent? Observations on social media platforms suggest a culture that encourages individuals to assert and uphold their opinions, often valuing non-conformity to majority views if in disagreement. This raises the question: have individuals become less inclined towards conformity in reality?

The review is divided into four main sections. The first section outlines the specifics of our PRISMA methodology used for article selection. The next section discusses the methodologies used since 2004, including the various modified paradigms derived from the Asch task. The third section of this review focuses on the results of the included studies and the multitude of factors identified as either influencing conformity or having a negligible impact. Finally, the review concludes with a discussion of the contributions of these recent studies to the field of social influence research, as well as their limitations.

The method used in this study is based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines: a sequence of 27 steps accompanied by a flowchart ( Page et al. 2021 ). The subsequent section presents the procedures undertaken and the resulting outcomes.

Eligibility Criteria

In January 2023, we conducted a systematic literature search characterized by a broad inclusion criteria framework. In order to include all pertinent literature, we encompassed not only experimental articles but also literature reviews and meta-analyses.

Sources of Information and Research

Given the extensive body of research on conformity, spanning several decades, we conducted our investigation using the EBSCOhost platform. Specifically, we selected APA PsycArticles, APA PsycInfo, and APA PsycExtra as primary sources. The search period covered publications from January 2004 to December 2022 (publication date). We employed specific keywords to identify literature on conformity, specifically those relating to Asch’s research. The search query was: “conformi*” in the title AND “Asch” in the title OR “Asch” in the abstract OR “Asch” in the text. This search yielded a total of 1406 articles retrieved from APA PsycArticles (n = 883), APA PsycInfo (n = 479), and APA PsycExtra (n = 44) databases. Following the elimination of duplicates (n = 21), we compiled a final list of 1385 articles.

Selection of Studies

Two independent researchers analyzed the titles and abstracts of 1385 articles. The inclusion criteria for the titles were: any article mentioning conformity or majority influence within the field of psychology (social, developmental, cognitive, clinical). Subsequently, during the abstract screening phase, articles that explicitly measured conformity and/or referenced the utilization of Asch’s paradigm (or any variation) were included. We also considered meta-analyses and literature reviews addressing conformity or majority influence. Following the initial screening of titles and abstracts, 47 and 66 articles were retained by the two reviewers, respectively. A secondary selection process was conducted, ultimately yielding a total of 48 articles for inclusion in this review (41 experimental or quasi-experimental articles, 6 literature reviews, and 1 meta-analysis). Subsequent procedural steps were executed by one of the researchers. A graphical representation of the selection process is presented in Figure 1 , following the PRISMA guidelines.

From: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71 . For more information, visit: http://www.prisma-statement.org/ .

*Consider, if feasible to do so, reporting the number of records identified from each database or register searched (rather than the total number across all databases/registers).**If automation tools were used, indicate how many records were excluded by a human and how many were excluded by automation tools.

Data extraction

Given our principal objective of evaluating research on conformity since 2004, we systematically extracted various elements from the selected articles (see Table 1 ). We developed a comprehensive table including authors’ names, publication dates, pivotal theoretical concepts introduced in the articles, research methods employed, target populations, key findings, as well as limitations and prospective avenues for future research. This comprehensive data extraction process provides a perspective on the extant literature and its advancements, while striving for complete comprehensiveness.

Methods and main results of the 41 experimental articles included in the systematic review.

Developments in Methods for Measuring Conformity

Conformity, initially elucidated by Asch in 1951 through his seminal paradigm involving the comparison of lines to a standard line in the presence of confederates, has undergone methodological refinements and adaptations over time. While Asch’s methodology continues to yield robust results and remains pertinent in contemporary research ( Franzen & Mader 2023 ; Qin et al. 2022 ; Ušto et al. 2019 ), innovations have emerged to address practical limitations and better align with evolving societal contexts.

Despite the enduring relevance of Asch’s paradigm, it is constrained by material requirements, particularly the necessity of multiple physical partners. To mitigate this limitation and render the procedure more economical or suitable for diverse populations, researchers have developed alternative paradigms.

Several adaptations have emerged, including the fMORI technique introduced by Mori and Arai ( 2010 ). This approach utilizes deceptive eyeglasses to present static stimuli in varied formats. Participants engage in a line discrimination task akin to Asch’s ( 1956 ) while wearing special glasses. The glasses worn by the designated minority participant are manipulated to perceive lines of differing lengths from those perceived by the majority. Meanwhile, majority participants wearing unaltered glasses serve as confederates to the experimenter, furnishing discrepant responses to the final participant. This method facilitates the replication of Asch’s experiment without the necessity of recruiting confederates. Study findings suggest that minority group participants, wearing manipulated glasses, provided a greater number of incorrect responses compared to majority participants. It is important to note the limited sample size of the study, comprising only 26 minority participants (10 men and 16 women), with not all confederates included in the analysis ( N = 78). The reduced sample size may have influenced these results, alongside the heightened task complexity relative to Asch’s original experiment ( Mori & Arai 2010 ). While this technique proves beneficial in circumventing the need for confederates, it necessitates a notably extensive participant pool, as only the responses of the minority participant are considered. Nevertheless, replications are imperative to corroborate the obtained findings and ascertain the validity of the fMORI technique. Hanayama and Mori ( 2011 ) conducted a replication of Mori and Arai’s ( 2010 ) study with children aged 6–7. Their findings revealed that girls exhibited conformity levels comparable to those of women in the prior study. However, notably, boys demonstrated conformity, contrasting with the absence of conformity observed among adult males in Mori and Arai ( 2010 ). Another adaptation of Asch’s paradigm has been utilized to investigate conformity among children as young as four years old. Haun and Tomasello ( 2011 ) adapted Asch’s paradigm by replacing lines with animals of various sizes, such as a father, mother, and baby lion, aiming to render the task more engaging and comprehensible for young participants. Furthermore, they devised a back-to-back configuration, eliminating the necessity for human peers, which proves to be a practical approach, particularly when working with children. Each child received a book containing three animals of differing sizes on the left and a control animal on the right. The task entailed identifying whether the control animal corresponded to the father, mother, or baby depicted on the left. One child received a different book from the rest, akin to Mori and Arai’s ( 2010 ) experiment employing manipulated glasses, thereby serving as the minority participant. Haun and Tomasello ( 2011 ) yielded findings akin to those of Mori and Arai ( 2010 ) in children. Their modified paradigm successfully evoked conformity in 18 out of 24 minority children, who provided more incorrect responses compared to their peers. The methodology employed in this study, tailored for children, shares a common limitation with the fMORI technique, necessitating a large sample with only minority children’s results being included in analyses. However, this limitation persists even with a majority physically present alongside the participant. To mitigate these challenges, online procedures have been developed.

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.

Is There a (Non)Conformist Profile?

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.

Motivations to Conform

As elaborated in the preceding section, a comparative analysis of recent research findings on conformity indicates that it is a behavior evident in both men and women, irrespective of age or cultural background. In the following section, we will examine how conformity is primarily shaped by the motivations of individuals, depending on the stimuli used to assess conformity. The challenge of reaching definitive conclusions about conformity stems in part from the diversity of observed behaviors and the existence of different processes that lead to conformity. Building upon Asch’s seminal work ( 1951 ), Deutsch and Gerard ( 1955 ) suggested two explanatory processes for the phenomenon of conformity: normative influence, wherein individuals conform in response to social pressure, and informational influence, wherein individuals conform due to cognitive uncertainty. These processes are driven by two primary motivations: the desire for social acceptance and the pursuit of accuracy ( Cialdini & Goldstein 2004 ). Asch’s seminal research on conformity commenced with a simple and unequivocal visual perception task. The study found that individuals often conformed to the majority opinion, even when they held dissenting views and were aware of the correctness of their own response. Various external factors have been identified as significant determinants of conformity, including the size of the majority and the nature of the task ( Bond & Smith 1996 ). Recent investigations have demonstrated that conformity levels fluctuate depending on the stimuli under examination or the characteristics of the majority. This implies that external influences predominantly shape individuals’ levels of conformity, and that individuals exhibit varying motivations to conform to different extents based on these influences.

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.

Are There New Theoretical Explanations to Conformity?

Over the past few decades, research on conformity has aimed to understand the relationship between various factors, such as majority size or response type, and conformity. In his meta-analysis, Bond ( 2005 ) suggests that the multitude of variables involved in conformity makes it challenging to isolate some of these variables and infer their moderating role. In particular, he concludes that future research should examine the different motivations that lead to conformity, as well as how the characteristics of the task or context can lead to different motivations ( Bond 2005 ).

Asch’s ( 1951 ) seminal work has been widely replicated; however, despite the robustness of conformity as a phenomenon, there are ongoing debates, even in more recent research, about the interpretation of his findings. In their review, Spillane and Jouillié ( 2022 ) refer to Friedrich’s theory of authority ( Friedrich 1958 ) to explain Asch’s findings. The observed conformist behavior in laboratory settings results from participants’ perception of the experimenter’s authority. Participants conform to the expectations of this authority within the specific confines of a laboratory setting, without necessarily displaying similar behavior in ‘real life’ situations.

Conversely, Hodges and Geyer ( 2006 ) argue that Asch’s experiments demonstrate that individuals do conform, but not consistently, and instead seek consensus. Consequently, conformity rates never exceed 75% for ‘typical’ participants (i.e., excluding those who consistently conform or never conform). According to pragmatic value theory ( Hodges & Geyer 2006 ), Asch’s scenarios represent dilemmas in which individuals balance between truth (implying nonconformity) and consensus (implying conformity). Thus, instances of conformity reflect the individuals’ desire to fit in with the group, whereas maintaining one’s position is an expression of disagreement. This theory suggests that individuals are more likely to conform in the presence of strangers than among friends because it is more difficult and less straightforward to express dissent when dealing with unfamiliar individuals, where trust and honesty have not yet been established ( Hodges & Geyer 2006 ). In contrast, theories of conformity (e.g., Cialdini & Trost 1998 ; Graham 1962 ), as well as research on the effects of group formation discussed above, anticipate the opposite trend: increased identification and cohesion are expected to exert stronger normative and informational pressures. These theories agree that conformity is a socially motivated behavior, either to acquiesce to the authority of an experimenter or to conform to group norms. Furthermore, Cialdini and Goldstein ( 2004 ) suggest in their literature review, that conformity serves three fundamental goals: accuracy, belonging, and maintaining a positive self-image. These goals are aligned with the theoretical considerations mentioned above. Individuals conform to satisfy their need for group affiliation, avoid appearing deviant and thereby promote a positive self-image, and to respond appropriately to group-derived information (i.e., accuracy). These theoretical propositions are broadly consistent with the initial concepts of Deutsch and Gerard ( 1955 ) or Kelman ( 1958 ), who distinguished between social and cognitive motivations supporting two pathways to conformity: informational influence and normative influence. Over the years, research has primarily focused on understanding the underlying processes of conformity, with less emphasis on interpreting results related to individual independence. Griggs ( 2015 ) investigated the prevalence of mentions of nonconformity results in 30 introductory psychology and social psychology textbooks, and found that only 15% of the selected textbooks referred to the proportion of independent responses (i.e., nonconformity). This finding highlights the authors’ preoccupation with conformist responses, leaving the exploration of non-conformity as an avenue for future research.

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.

The range of articles examined in this review underscores that conformity remains a domain of research yet to be exhaustively explored. This behavioral phenomenon is multifaceted, influenced by a multitude of factors, each with varying degrees of significance in shaping susceptibility to conformity. Moreover, the influence of these factors fluctuates contingent upon the subject under investigation, the demographic characteristics of the population, and the methodological approaches adopted. Studies that examine conformity suffer from several limitations, predominantly stemming from methodological constraints.

To experimentally investigate conformity, the utilization of confederates to induce majority influence was imperative in the initial studies on this subject. The recruitment of confederates presents challenges for researchers, both financially and logistically. Consistency is crucial as the same confederates must be employed for all participants and they must be adequately trained to provide incorrect responses at the designated junctures. In response to these challenges, researchers have leveraged technological advancements in recent decades to develop new online methodologies, circumventing the need for confederates. While online procedures are gaining traction among researchers, they entail notable limitations when examining conformity. Firstly, the absence of physical presence in online settings may diminish the impact of majority influence. This visual anonymity may attenuate feelings of accountability and anxiety associated with evaluation, resulting in reduced levels of conformity compared to traditional Asch-type tasks ( Cinnirella & Green 2007 ). Additionally, the design of online chat platforms varies across studies, potentially influencing perceptions of majority influence. Thus, it is imperative to replicate studies utilizing these novel online methodologies, such as computer-mediated communication (CMC) or online chat, to ensure the reliability and specificity of findings. Furthermore, as evidenced in this review, conformity has been explored across a spectrum of topics including moral dilemmas, visual tasks, and problem-solving tasks. Despite the diversity in the objects of study and task types, a notable takeaway from this review is the resilience of conformity as a behavioral phenomenon.

The proliferation of new technologies since the 2000s has facilitated the expansion of social networks, online chat platforms, forums, and other virtual spaces. Consequently, individuals now dedicate a significant portion of their time to these digital environments, prompting inquiries into the social influences to which they are exposed. As individuals become increasingly and readily exposed to the opinions and attitudes of a vast number of peers, it becomes crucial to evaluate the ramifications of this continual exposure on their own beliefs and values. Does this heightened exposure lead to greater conformity among individuals? Do they integrate the information disseminated by the majority into their personal value systems? Can such conformity precipitate genuine changes in individual behavior? The examination of online conformity necessitates contextualization within the broader societal issues prevalent in these digital environments, including online harassment, misinformation dissemination, and radicalization. Therefore, future studies should explore these multifaceted issues in greater depth to elucidate their complexities and implications.

In addition to methodological limitations, studies often explore cultural differences as potential moderators of conformity, particularly regarding the collectivist-individualist dimensions. However, conclusions regarding the impact of culture have primarily stemmed from studies that compare results across various countries, neglecting to investigate culture’s effect on other dimensions. Further research is warranted to determine whether culture moderates conformity and which specific dimensions of culture are implicated, or if factors such as norms contribute to the observed differences.

This systematic review assesses recent studies on conformity and sheds light on a relatively neglected aspect: new research practices (e.g., open data, pre-registration), and ethics. Out of the 48 articles included in the review, only six underwent ethical review by a committee, with four involving minors as participants ( Bolderdijk & Cornelissen 2022 ; Ivanchei et al. 2019 ; Pham & Buchsbaum 2020 ; Sibilsky et al. 2021 ; Yafai et al. 2014 ; Zhang et al. 2016 ). The ethical implications of conformity studies are notable, particularly given that researchers often need to deceive participants about the true purpose of the study to observe genuine influence. To align with the Ethical Principles of Psychologists and the American Psychological Association Code of Conduct ( APA Code of Ethics 2016 ), conformity researchers must adhere to rigorous protocols. This includes obtaining informed consent from participants, either in written or verbal form, before the experiment, and providing comprehensive debriefing afterward. Moreover, data must be fully anonymized and made accessible for replication studies. These steps are essential for upholding the ethical standards integral to the study of conformity.

Among the 41 experimental articles, only 10 provided access to study materials or data ( Bolderdijk & Cornelissen 2022 ; Brügger et al. 2019 ; Egermann et al. 2013 ; Garcia et al. 2021 ; Kim et al. 2019 ; Kyrlitsias et al. 2020 ; Pham & Buchsbaum 2020 ; Qin et al. 2022 ; Schreuter et al. 2021 ; Sibilsky et al. 2021 ). Notably, none of the articles mentioned pre-registration. However, it is plausible that data accessibility and open science practices are increasingly adopted, given the recency of the cited articles. This underscores the critical need for rigorous ethical considerations in a field as socially significant as conformity research. One possible answer to these limitations could be to conduct multi-site distributional studies, holding potential for yielding more robust results.

Several factors influencing susceptibility to conformity have been identified, and replications conducted over recent decades have shed light on this phenomenon. However, conformity remains a subject of ongoing study due to lingering ambiguities. While certain determinants of conformity have received considerable attention (e.g., type of paradigm, type of response, characteristics of the majority), others, such as the influence of psychological needs like the need for uniqueness or need to belong, remain relatively unexplored but may significantly contribute. Additionally, while there is some understanding of why individuals conform, strategies for resisting majority influence remain unclear. One of the most important factors in conformity is whether the majority is unanimous. In the articles included in this review, only one article investigates and mentions a minority effect ( Qin et al. 2022 ). Furthermore, the results of this study suggest that the minority (in this case robot-induced) was successful in distracting the participant from the majority response. This finding is consistent with the work of Moscovici ( 1980 ) and his conversion theory, which postulates that under certain conditions individuals can also conform to a minority ( Moscovici & Naffrechoux 1969 ). Moscovici ( 1980 ) posits that the motivation to conform or not is a function of two factors: the consistency of the source of influence and the individual’s confidence and attachment to their own judgments ( Martin & Hewstone 2012 ; Mugny 1975 ). Thus, it is the type of behavior of the source of influence, particularly the consistency of responses, that enables conformity and not dependence on this majority, as suggested by Asch’s ( 1951 ) approach. This interpretation of majority/minority influence seems to have been set aside in the articles selected for this review in favor of Asch’s ( 1951 ) interpretation. Further work focusing on Moscovici’s proposed interpretation could help to enrich the literature, particularly in online conformity, where behavior type, such as response consistency, could play an important role. Future studies could investigate whether there are effective strategies for resisting conformity. Although conformity is predominantly influenced by external factors, in-depth examinations of inter-individual variables may offer insights into resisting social influence.

This systematic review offers a comprehensive overview of advancements in conformity research since the 2000s. Over the past two decades, studies have consistently demonstrated the robust nature of conformity. Methodologies for measuring conformity have diversified, particularly with the emergence of digital technology, enabling investigations across various contexts (e.g., online, with real/fictional acquaintances, with robots, or artificial intelligence). Despite the prominence of conformity as a research topic, the literature still lacks a definitive understanding of the underlying factors driving this behavior. Presently, no consensus exists regarding the influence of age, gender, or culture on conformity. The determinants of conformity vary depending on specific situational contexts within studies.

Consequently, future research should aim to provide more precise insights into the processes operating under specific contextual conditions, as well as the object of conformity (e.g., moral dilemmas, musical preferences, logic or visual perception tasks). Further studies, including intercultural investigations, meta-analyses, and replications, are necessary to expand and refine our understanding of this multifaceted phenomenon.

This article is not included in our systematic review as it was published after the review was realized, on 29 November 2023.  

Franzen and Mader ( 2023 ) found that only openness had an effect, while the other measured traits, such as intelligence, self-esteem, and need for social approval, did not.  

Acknowledgements

We would like to thank the anonymous reviewers for their very helpful comments on the initial version of this paper.

Competing Interests

The authors have no competing interests to declare.

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The neuroscience of social conformity: implications for fundamental and applied research

Affiliations.

• 1 Department of Psychology, Stanford University Stanford, CA, USA.
• 2 Behavioural Science Institute, Radboud University Nijmegen Nijmegen, Netherlands ; Donders Institute for Brain, Cognition and Behaviour Nijmegen, Netherlands.
• PMID: 26441509
• PMCID: PMC4585332
• DOI: 10.3389/fnins.2015.00337

The development of closer ties between researchers and practitioners in the domain of behavior and behavioral change offers useful opportunities for better informing public policy campaigns via a deeper understanding of the psychological processes that operate in real-world decision-making. Here, we focus on the domain of social conformity, and suggest that the recent emergence of laboratory work using neuroscientific techniques to probe the brain basis of social influence can prove a useful source of data to better inform models of conformity. In particular, we argue that this work can have an important role to play in better understanding the specific mechanisms at work in social conformity, in both validating and extending current psychological theories of this process, and in assessing how behavioral change can take place as a result of exposure to the judgments of others. We conclude by outlining some promising future directions in this domain, and indicating how this research could potentially be usefully applied to policy issues.

Keywords: behavioral change; decision making; functional magnetic resonance imaging; policy implications; social conformity.

Publication types