social conformity experiments

Social Psychology Experiments: 10 Of The Most Famous Studies

Ten of the most influential social psychology experiments explain why we sometimes do dumb or irrational things. 

Ten of the most influential social psychology experiments explain why we sometimes do dumb or irrational things.

“I have been primarily interested in how and why ordinary people do unusual things, things that seem alien to their natures. Why do good people sometimes act evil? Why do smart people sometimes do dumb or irrational things?” –Philip Zimbardo

Like famous social psychologist Professor Philip Zimbardo (author of The Lucifer Effect: Understanding How Good People Turn Evil ), I’m also obsessed with why we do dumb or irrational things.

The answer quite often is because of other people — something social psychologists have comprehensively shown.

Each of the 10 brilliant social psychology experiments below tells a unique, insightful story relevant to all our lives, every day.

Click the link in each social psychology experiment to get the full description and explanation of each phenomenon.

1. Social Psychology Experiments: The Halo Effect

The halo effect is a finding from a famous social psychology experiment.

It is the idea that global evaluations about a person (e.g. she is likeable) bleed over into judgements about their specific traits (e.g. she is intelligent).

It is sometimes called the “what is beautiful is good” principle, or the “physical attractiveness stereotype”.

It is called the halo effect because a halo was often used in religious art to show that a person is good.

2. Cognitive Dissonance

Cognitive dissonance is the mental discomfort people feel when trying to hold two conflicting beliefs in their mind.

People resolve this discomfort by changing their thoughts to align with one of conflicting beliefs and rejecting the other.

The study provides a central insight into the stories we tell ourselves about why we think and behave the way we do.

3. Robbers Cave Experiment: How Group Conflicts Develop

The Robbers Cave experiment was a famous social psychology experiment on how prejudice and conflict emerged between two group of boys.

It shows how groups naturally develop their own cultures, status structures and boundaries — and then come into conflict with each other.

For example, each country has its own culture, its government, legal system and it draws boundaries to differentiate itself from neighbouring countries.

One of the reasons the became so famous is that it appeared to show how groups could be reconciled, how peace could flourish.

The key was the focus on superordinate goals, those stretching beyond the boundaries of the group itself.

4. Social Psychology Experiments: The Stanford Prison Experiment

The Stanford prison experiment was run to find out how people would react to being made a prisoner or prison guard.

The psychologist Philip Zimbardo, who led the Stanford prison experiment, thought ordinary, healthy people would come to behave cruelly, like prison guards, if they were put in that situation, even if it was against their personality.

It has since become a classic social psychology experiment, studied by generations of students and recently coming under a lot of criticism.

5. The Milgram Social Psychology Experiment

The Milgram experiment , led by the well-known psychologist Stanley Milgram in the 1960s, aimed to test people’s obedience to authority.

The results of Milgram’s social psychology experiment, sometimes known as the Milgram obedience study, continue to be both thought-provoking and controversial.

The Milgram experiment discovered people are much more obedient than you might imagine.

Fully 63 percent of the participants continued administering what appeared like electric shocks to another person while they screamed in agony, begged to stop and eventually fell silent — just because they were told to.

6. The False Consensus Effect

The false consensus effect is a famous social psychological finding that people tend to assume that others agree with them.

It could apply to opinions, values, beliefs or behaviours, but people assume others think and act in the same way as they do.

It is hard for many people to believe the false consensus effect exists because they quite naturally believe they are good ‘intuitive psychologists’, thinking it is relatively easy to predict other people’s attitudes and behaviours.

In reality, people show a number of predictable biases, such as the false consensus effect, when estimating other people’s behaviour and its causes.

7. Social Psychology Experiments: Social Identity Theory

Social identity theory helps to explain why people’s behaviour in groups is fascinating and sometimes disturbing.

People gain part of their self from the groups they belong to and that is at the heart of social identity theory.

The famous theory explains why as soon as humans are bunched together in groups we start to do odd things: copy other members of our group, favour members of own group over others, look for a leader to worship and fight other groups.

8. Negotiation: 2 Psychological Strategies That Matter Most

Negotiation is one of those activities we often engage in without quite realising it.

Negotiation doesn’t just happen in the boardroom, or when we ask our boss for a raise or down at the market, it happens every time we want to reach an agreement with someone.

In a classic, award-winning series of social psychology experiments, Morgan Deutsch and Robert Krauss investigated two central factors in negotiation: how we communicate with each other and how we use threats.

9. Bystander Effect And The Diffusion Of Responsibility

The bystander effect in social psychology is the surprising finding that the mere presence of other people inhibits our own helping behaviours in an emergency.

The bystander effect social psychology experiments are mentioned in every psychology textbook and often dubbed ‘seminal’.

This famous social psychology experiment on the bystander effect was inspired by the highly publicised murder of Kitty Genovese in 1964.

It found that in some circumstances, the presence of others inhibits people’s helping behaviours — partly because of a phenomenon called diffusion of responsibility.

10. Asch Conformity Experiment: The Power Of Social Pressure

The Asch conformity experiments — some of the most famous every done — were a series of social psychology experiments carried out by noted psychologist Solomon Asch.

The Asch conformity experiment reveals how strongly a person’s opinions are affected by people around them.

In fact, the Asch conformity experiment shows that many of us will deny our own senses just to conform with others.

Author: Dr Jeremy Dean

Psychologist, Jeremy Dean, PhD is the founder and author of PsyBlog. He holds a doctorate in psychology from University College London and two other advanced degrees in psychology. He has been writing about scientific research on PsyBlog since 2004. View all posts by Dr Jeremy Dean

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• v.17(10); 2021 Oct

How conformity can lead to polarised social behaviour

Folco panizza.

1 Molecular Mind Laboratory, IMT School for Advanced Studies Lucca, Italy

2 Center for Mind/Brain Sciences, University of Trento, Mattarello (TN), Italy

Alexander Vostroknutov

3 Department of Economics (MPE), Maastricht University, Maastricht, the Netherlands

Giorgio Coricelli

4 Department of Economics, University of Southern California, Los Angeles, California, United States of America

5 LaPsyDÉ, UMR CNRS 8240, La Sorbonne, Paris, France

Associated Data

Behavioural and computational data, as well as a live version of the code used for the analysesare available via the Open Science Framework (osf.io/p5xq3).

Learning social behaviour of others strongly influences one’s own social attitudes. We compare several distinct explanations of this phenomenon, testing their predictions using computational modelling across four experimental conditions. In the experiment, participants chose repeatedly whether to pay for increasing (prosocial) or decreasing (antisocial) the earnings of an unknown other. Halfway through the task, participants predicted the choices of an extremely prosocial or antisocial agent (either a computer, a single participant, or a group of participants). Our analyses indicate that participants polarise their social attitude mainly due to normative expectations. Specifically, most participants conform to presumed demands by the authority (vertical influence), or because they learn that the observed human agents follow the norm very closely (horizontal influence).

Author summary

What drives people to extreme acts of generosity? What causes behaviour that is unduly spiteful? This study explored how our social decisions polarise. Participants chose whether to spend money to increase or decrease the earnings of an unknown person. Halfway through this task, they observed another agent playing. The agent took participants’ choices to the extremes: if for instance the participant was moderately generous, it spent considerable sums to help the other. Participants conformed regardless of whether the agent was a computer algorithm, a person, or a group of people. We tested several competing explanations of why this happened with the help of cognitive modelling. Our analyses identify two factors behind polarisation: willingness to comply with the experimenter expectations (social desirability), and concern about appropriate behaviour (norm conformity). Our approach provided insight into how social choices are influenced by others, and could be applied in the study of conformity in other types of decisions.

Introduction

Recent years have seen a growing concern with online discourse promoting violence, such as cyber-bullying or hate speech [ 1 ]. Increasing exposure to uncivil commenting, besides taking substantial psychological and societal toll [ 2 ], is thought to reinforce users’ toxic behaviours [ 3 , 4 ], political polarisation [ 5 ], or their perception of political divide [ 6 ]. Conversely, viral trends can also lead to pro-social outcomes: learning about others’ donation choices increases individuals’ willingness to give to charity [ 7 , 8 ]. Evidence suggests that fund-raising success of charitable initiatives is predicted by how much they are shared by social network users [ 9 ], or by how concerted the network structure is [ 10 ]. If people’s attitude becomes more charitable or more malevolent in these contexts, this is at least partly due to social conformity [ 11 – 13 ].

Insights on the cognitive mechanisms behind anti- and prosocial conformity come from the literature on attitude alignment and preference learning [ 14 , 15 ]. These studies have spanned a variety of domains such as attractiveness ratings [ 16 ], food [ 17 ], risk preferences [ 18 – 20 ], moral behaviour [ 21 ], effort [ 20 ], and inter-temporal decisions [ 20 , 22 – 25 ]. At a brain level, learning about others’ attitudes or preferences appears to alter the value representation of choices [ 16 , 21 , 22 , 24 ] or even reward signals [ 19 ], while not necessarily affecting one’s private preferences [ 18 ]. In addition, features such as choice variability [ 23 ] or attitude extremeness [ 25 ] seem to be significant predictors of conformity.

Recent studies have also brought attention towards the behavioural aspects of social conformity [ 8 , 26 – 29 ]. The work by Dimant and colleagues shows for instance how anti-social models beget a higher degree of conformity compared to pro-social models, and that social proximity to the model is also a strong predictor of conformity.

While this research helps untangling the brain bases and behavioural ramifications of preference conformity, it remains largely unclear why exactly people shift their attitude in the direction of others’ behaviour in general, and their social attitude towards other individuals in particular. In this preregistered study ( osf.io/th6wp ; changes to the original protocol: S1 Methods ) we test several competing mechanisms that were proposed as explanations of attitude conformity. We consider five competing hypotheses. The time-dependence hypothesis predicts that people change their social attitude even in the absence of any observation. Indeed, there is preliminary evidence that during strategic interactions, participants’ behaviour becomes more self-oriented with time [ 30 – 33 ]. The contagion hypothesis [ 19 , 34 ] posits that attitude conformity is the result of some kind of automatic imitation of an agent’s behaviour, irrespective of its nature or relationship with the observer. This hypothesis predicts that conformity will occur regardless of whether the observed agent is human or non-human. The compliance hypothesis states that participants could change attitude due to the mere presence of an authority, in our case the experimenter [ 35 ]. This hypothesis predicts that a portion of participants would change their attitude in any context where they think they are expected to, rather than actually reacting to others’ behaviour. The preference learning hypothesis [ 23 ] posits that people are unsure about what their own preferences are, but they can learn them from the behaviour of others, assuming that the agent’s and observer’s preferences come from a common distribution. Other people’s choices can thus be used to learn how one wants to behave, rather than how one ought to behave. Since preference learning should decrease in the process of learning others’ choices, a second prediction of this hypothesis is that participants’ behaviour should become more consistent after learning.

The last hypothesis that we test, norm learning , states that attitude conformity stems from learning what behaviour is socially appropriate or how much social appropriateness matters in a given context. We conjecture that in many real life situations there is a considerable amount of uncertainty about what constitutes a social norm or how salient it is [ 36 ]. Furthermore, many studies have shown that people have a strong preference to follow norms conditional on others following them as well [ 37 , 38 ]. Thus, observing other people’s behaviour should reveal either information about what others believe is “the right thing to do” or at least how frequent or infrequent deviations from the norm are [ 33 , 39 ]. This hypothesis makes two separate predictions: that participants conform after changing their beliefs about which norms are in place ( norm uncertainty ), or rather that participants are aware of the existing norms, but conform after learning how strictly the norm is followed ( norm salience ).

To our knowledge of the various literatures surveyed, these five hypotheses exhaust the list of tested explanations of conformity in decision making. Therefore, our approach is to falsify as many hypotheses as we can, and attribute the behaviour to the hypotheses that we cannot reject. To disentangle the predictions of these five hypotheses, we use a series of between-subjects experimental conditions. In all conditions participants play a resource-allocation game where in each round they choose between two money allocations to themselves and another unknown participant. Halfway through the game, participants are asked to predict and learn the choices made by another agent in the same task. Depending on the condition, the agent is either a computer, a previous participant, or a group of previous participants (in the Baseline condition participants do not observe anyone). After the main part of the experiment, we administer another task measuring the normative beliefs of participants.

We use a series of cognitive models of participants’ decisions to analyse behaviour in the resource-allocation game. These models link behaviour to the mental processes associated with the different hypotheses. Testing of the competing mechanisms behind social conformity is then performed by comparing social attitudes before and after the manipulation phase, and using additional evidence collected during and after the main task. Model estimation is essential for distinguishing different sources of attitude variability, as for instance participants’ own variability in behaviour and attitude changes induced by learning.

Ethics statement

The local Ethical Committee of the University of Trento approved the study and subjects provided written informed consent prior to their inclusion.

Participants

Participants were recruited through the recruitment system of the Cognitive and Experimental Economics Laboratory (CEEL) at the University of Trento and contacted via e-mail. No particular exclusion criteria were defined, with the only exception that participants should not have taken part in other experiments involving a similar task. Payments were made in cash and varied depending on participants’ choices.

To determine the sample size necessary to detect a change in social attitude, we conducted a power analysis using G*Power [ 40 ] aiming to obtain.95 power, .05 α probability, and at least a small-to-medium effect size (Cohen’s d = 0.35 for all tests). This effect size figure was recently proposed as a plausible mean prior for experiments in social psychology [ 41 ]. As the original hypotheses were directional (i.e., participants’ attitudes shift towards the agent’s attitude), tests considered were one-tailed one-sample t -tests against constant, and two-tailed two-sample t -tests (for post-hoc pairwise comparisons between conditions, uncorrected). Calculation yielded a sample size of 90 participants in order to achieve the required power across all conditions. Due to an unforeseen limit in the size of the recruitment pool however, the samples of the last two conditions in order of acquisition were smaller than this pre-specified size (74 and 66 participants). Unbalance in sample size across experimental conditions is not particularly concerning as the tests used to compare conditions (see Attitude convergence) are non-parametric and therefore do not require the assumptions typically achieved with samples of similar size such as homoscedasticity.

376 participants (age M = 22, SD = 2, 167 males) took part in the experiment. Data from four participants had to be excluded due to failures in the software, as well as the data from three other participants who already participated in a pilot version of the study. Analyses were thus conducted on 369 participants.

Resource-allocation game

During each trial of the task, participants observed an allocation of points (1 point = 0.10€) distributed between themselves and an unknown other participant, the recipient. Participants were then asked whether they preferred the current allocation of points or a default allocation (100 points to oneself, 50 points to the recipient, Fig 1B ). Participants played the game twice, with different allocations, before and after the manipulation phase. At the end of the experiment, participants were randomly paired, and one participant in each pair was randomly selected: one of the selected participant’s decisions was randomly sampled and implemented for payment (i.e. the selected participant earned the points for herself and the non-selected participant received the points for the other).

The complete list of trials is available at osf.io/th6wp . A : Participants chose between a default allocation (black rhombus) and an alternative allocation, which could be either prosocial (light blue) or antisocial (orange). Allocations were limited to a certain arc of the circumference and to a certain range, with the exception of 9 allocations giving more points to the participant (not shown in the figure but included in S1 File ). B : resource-allocation game. Participants observed the current alternative allocation and had a maximum of 10 seconds to respond. Decision cues (‘yes’/‘no’) indicated which button to press for each decision (up/down arrows). Points for self and for the other were colour-coded (points for self: red; points for the other: blue) and were presented on the left and on the right of the decision cues. Both cues and points (self/other) switched position randomly across trials. If participants did not answer within 10 seconds, the trial ended, and they were automatically assigned the default allocation. Unanswered trials were considered missing data. After the decision, an inter-stimulus interval of 1 second divided the decision and the feedback. Feedback lasted for 1.5 seconds and displayed the allocation preferred by the participant. The trial ended with an inter-trial interval of 2 seconds. C : manipulation phase. Participants were presented with an alternative allocation, and indicated whether they believed the agent preferred the alternative over the default allocation (‘yes’) or not (‘no’). The choice could be made within 10 seconds, after which it would no longer be valid. After an inter-stimulus interval of 1 second, participants received feedback about their answer. If the prediction was correct, the feedback message ‘correct prediction’ appeared on the screen for approximately 1.5 seconds (minimum 1, maximum 2). If the prediction was wrong or not given in time, a similar feedback message (‘wrong prediction’ or ‘no answer’) appeared on the screen for about the same time, followed by the actual choice of the agent, lasting 1.5 seconds. The name of the agent varied between conditions (Group condition: ‘majority’; Individual condition: ‘participant’; Computer condition: ‘computer’). After the feedback, the trial ended with an inter-trial interval of 2 seconds.

101 alternative allocations (102 for Baseline condition) and the default allocation were drawn from the set of integer allocations closest to the circumference of radius 50 centred at (50, 50) ( Fig 1A ). Compared to the default allocation, these alternatives provided less points to the participant, but could in exchange affect the recipient’s payoffs: half of the alternative allocations were more advantageous for the recipient than the default allocation (“prosocial” trials), whereas the other half left the recipient worse off (“antisocial” trials). In addition, 9 alternative allocations were more profitable for the participant (making her earn more than 100 points) whereas the recipient gained 50 points, as in the default allocation.

To define the allocations around the circumference, we first considered all integer coordinates within one point tolerance from the circumference (i.e., all values between a circumference of radius 49 and a circumference of radius 51). Second, only points between 112.5° and −112.5° were included for the analyses; this range excluded allocations that were too extreme (e.g. (15, 15) or (0, 50)). Third, we excluded allocations with more points to oneself than the default option, because gains for the player risk to overshadow the difference in points for the other. Likewise, we excluded allocations with the same points to the other as the default option. Finally, we eliminated allocations that give more than 100 or less than 0 to the other player. This procedure yielded 406 allocations in total; these were then divided in four subsets, two of 101 and two of 102 trials, all evenly distributed around the arc of the circle. The two subsets of 102 trials were used in the Baseline condition (in the choice parts of the task), whereas the remaining subsets of 101 trials were used in the other conditions ( S1 File contains a full list of trials).

The use of the circumference as a way to select trials was based on two considerations. First, the circumference has been used in the previous literature as a measure of social orientation ([ 42 ] is the seminal paper), and should yield comparable results. Second, many models have been adopted in the literature to describe how people value options in social decision-making (e.g., [ 43 – 46 ]); by using allocations around a circumference, most of these models make the same predictions. Agreement in model predictions allowed us to use a very simple utility model ( Eq 1 ), with only one variable defining the attitude of the decision-maker. A simple model greatly simplifies computations and thus helps testing our cognitive predictions concerning attitude conformity and choice consistency.

To estimate attitude towards others, we assume that participants can attribute to each allocation of points a unique subjective value. Value of an allocation is computed according to Eq 1 :

where π y and π o are respectively the amount of points for oneself (you) and for the other, and α represents the “social value orientation” or social attitude of the participant [ 47 , 48 ]. The attitude defines how much and in what way the amount of points for the other plays a role in the participant’s decisions; in fact, tan( α ) represents how much one point for the other person is worth in terms of one’s own points (e.g., when α = 30° one point for the other is roughly equal to 0.58 points for oneself). If α is positive (negative), then the higher (smaller) amount of points for the other makes the player better off. A participant with a positive α is said to be prosocial , whereas a participant with a negative α is said to be antisocial .

Social attitude—together with other parameters relevant to the decision process—is estimated twice, for choices before ( α before ) and choices after ( α after ) the manipulation phase. A separate estimation allows measuring any change in attitude that ensues from the manipulation phase (Cognitive modelling and Model comparison).

Manipulation phase

In the Computer, Individual, and Group conditions, after the first part of the resource-allocation game, participants were asked to predict the choices of an agent in a different set of alternative allocations ( Fig 1C ). Participants played 63 trials of the manipulation phase in all conditions except Baseline. Correct predictions were incentivised to ensure that participants paid attention to the task. Participants received immediate feedback after each prediction, so that they could correctly learn about the agent’s attitude.

The attitude of the observed agent ( α obs ) was controlled experimentally unbeknownst to participants. Specifically, if participants displayed a prosocial attitude in the first part of the game ( α before > 0), they observed an agent with an extremely prosocial attitude ( α obs ≈ 45°, one point for the other equals one point for the self); and vice versa: if participants displayed an antisocial attitude ( α before < 0), they observed an agent with an extremely antisocial attitude ( α obs ≈ −45°, one point for the other equals negative one point for the self).

The behaviour of the observed agents was based on real choices of participants in the Baseline condition taken from either the first or second part of the resource-allocation game. In the Individual condition, the agent was a single previous participant, with an estimated α value close to 45° (prosocial agent) or −45° (antisocial agent). In the Group condition, the agent consisted of a group of five previous participants (the size of the group was not mentioned in the instructions). The choices shown to participants referred to the allocation preferred by the majority of the group, that is the modal response. Lastly, in the Computer condition, participants were told that the agent was a computer selecting options according to a predefined criterion. The criterion of the computer agent was in fact to choose exactly as the group in the Group condition.

We chose to display extreme agents in order to distinguish attitude conformity from a gradual increase in selfishness that was observed by [ 30 ] and [ 32 ]. Hence, if participants with a moderate attitude conformed to the agent’s attitude, this change could not be attributed to an increase in selfishness. In addition, if participants did learn about decision makers who were less extreme than themselves, the attitude change would push them in the same direction as the regression to the mean. Any movement of the attitude away from the mean cannot then be obfuscated by this effect. In addition to experimentally manipulating agents’ attitudes, we also carefully calibrated the consistency of choices. This procedure ensured that agents displayed consistent patterns of choice, so that their attitudes could be easily predicted by participants.

We calibrated α obs based on the participant’s attitude in the first part of the resource-allocation game, before the manipulation phase ( α before ). Since we could not tell a priori which cognitive model would fit participants’ data best (Cognitive modelling), we determined whether participants had an α before greater or less than zero (prosocial or antisocial) using the following formula 2 :

where π yt and π ot are the points in the alternative allocation for self (you) and the other in trial t of the resource-allocation game, and I t , A is an indicator variable that equals 1 when the participant preferred the alternative allocation in trial t and 0 when the participant preferred the default allocation in trial t .

The dependent variable that we use to measure conformity is attitude convergence , denoted by δ diff . To compute its value, we use Eq 3 (coincidentally similar to the Contagion Gap of [ 26 ]):

where δ before and δ after are the distances between the attitude of the observed agent α obs and participant’s attitude estimated respectively before and after the manipulation phase. In order to have comparable results with the other conditions, we use this measure also for Baseline participants as if they were predicting choices of an agent from the Group or the Computer condition. As an exercise of parameter recovery for this variable, see S2 Analyses .

We have chosen δ diff as a measure of attitude conformity because it has two critical advantages over previous measures used in the literature [ 22 , 24 ]. First, δ diff depends on the original distance from the model: if a participant’s starting attitude is very close to that of the observed agent, then δ diff can only be small. As this is a conservative measure, it prevents close participants from biasing the estimate at sample level. Second, by taking into account the attitude distances from α obs of both α before and α after , δ diff differentiates between participants who shift attitude closer to the agent, and those who overshoot and become more extreme than the agent. It is indispensable to distinguish between these two types of attitude change, as the hypotheses that we test–with the exception of time-dependence–are concerned only with the former kind (moving closer to the agent).

Attitude convergence unambiguously predicts participants’ attitude to converge towards the observed agent’s attitude, hence this measure also penalises attitude changes that lead the participant to become more extreme than the observed agent. As a robustness check, we show that all the main results hold using an alternative measure that accounts for polarisation (see S6 Analyses ):

Predictions regarding attitude convergence for each of the five hypotheses are summarised in the left part of Table 1 . Notice that the predictions of the time-dependence hypothesis have an opposite direction compared to all other hypotheses. Moreover, the two versions of the norm learning hypothesis (norm uncertainty and norm salience) make no specific prediction about attitude change in the Individual condition.

“↑” refers to increasing extremeness of the attitudes. “–” means no change predicted. “↓” refers to the shift towards selfishness.

Other measures

While attitude convergence is the main measure that we use to distinguish among the hypotheses, we also need to test ancillary predictions that these hypotheses make to distinguish between the contagion and compliance hypotheses, and between the preference learning and norm learning hypotheses. For this purpose, we adopt a series of additional measures. The right-hand side of Table 1 summarises the related predictions.

The contagion and compliance hypotheses make identical predictions in terms of attitude change. To distinguish between them, we assess participants’ tendency to comply with the experimenter’s expectations [ 49 , 50 ]. If the contagion hypothesis is true, we should observe attitude convergence in the Computer condition even after controlling for participants’ compliance tendencies. If instead attitude convergence in the Computer condition depends on compliance tendency, this result should support the compliance hypothesis.

Compliance to experimenter demand in standard Dictator Games has been associated with an increase in prosocial behaviour (see for instance [ 51 ]). In the resource-allocation game, however, participants can make both prosocial and antisocial decisions, making prosocial behaviour a less obvious choice to appease the experimenter [ 52 ]. Moreover, such demand by the experimenter is explicitly ruled out in the instructions, where we specify that we do not expect any particular behaviour, neither prosocial nor antisocial. Evidence for what might constitute compliance in the resource-allocation game comes from an experiment adopting a similar paradigm [ 53 ]. This study suggests that when presented with conflicting choices during a task, such as behaving prosocially and antisocially, complying participants think they should demonstrate both types of behaviour to meet the experimenter’s expectations, even if these choices yield paradoxical outcomes. Critically, such pattern of behaviour has been associated with compliance with experimenter expectations and a personality index measuring social desirability [ 53 , 54 ]. If a participant displays such behaviour in the resource-allocation game, then it is plausible that authority compliance—rather than conformity to the observed agent—explains her attitude change. To measure authority compliance, we consider separately the proportion of prosocial alternatives and the proportion of antisocial alternatives chosen over the default allocation: we define our index of compliance as the smallest of these two numbers in percentage terms.

To distinguish between compliant and non-compliant participants, we use a preregistered threshold set to 25% ( osf.io/th6wp ; see S1 Table for the robustness of results adopting different thresholds). In other words, a participant is said to be compliant if she chose both prosocial and antisocial alternatives at least once out of every four choices made. We use the compliance index to test attitude convergence in the Computer condition. If compliant participants change attitude but non-compliant participants do not, we interpret this evidence in favour of the compliance hypothesis. If instead participants change attitude regardless of the compliance index, we interpret this evidence in favour of the contagion hypothesis. As an exploratory analysis, we also treat compliance index as a continuous variable, to test whether attitude convergence is linearly associated with compliance.

Preference learning

The preference learning hypothesis predicts that participants change their attitude because they learn their own social preferences from others. Since learning in this case should reduce participants’ uncertainty about how they want to behave, we should observe a corresponding increase in choice consistency after the manipulation phase in human (Individual, Group) relative to non-human (Baseline, Computer) conditions. In other words, consistency (variability) between choices should reflect how certain (uncertain) a person is about her social attitude, and consistency should increase after learning about the preferences of others.

While increased consistency is a precondition for preference learning, participants might also become more consistent if the norm learning hypothesis is true. Contrary to preference learning, however, norm learning does not exclude that participants already follow a social norm even before observing the agent. If this is the case, the information obtained from the observed agent might add knowledge about the norm without necessarily increasing choice consistency. Therefore, if our analyses fail to confirm a differential increase in consistency between human and non-human conditions, we will interpret this evidence as being against the preference learning hypothesis but not against the norm learning hypothesis.

We can test for changes in consistency by looking at the cognitive model used to understand participants’ choices, and in particular at the parameter representing variability in participants’ choices. Depending on the winning cognitive model, this parameter is either τ or σ (see see Variability parameters τ vs. σ ): a small τ ( σ ) corresponds to very consistent choices and vice versa. Hence, to test the preference learning hypothesis we measure whether participants’ variability decreases after the manipulation phase (e.g., for σ , σ after < σ before ). If participants do become more consistent after the manipulation, we will further test whether consistency increase is significantly different between conditions, and particularly between the human and non-human conditions.

Norm following

The norm learning hypothesis assumes that participants’ behaviour is influenced by beliefs about what constitutes a socially appropriate or inappropriate action. Accordingly, we should expect that prosocial and antisocial participants have different beliefs about what choices are considered appropriate in the resource-allocation game. To measure appropriateness perception, participants in the Computer, Individual, and Group conditions completed the norm elicitation task [ 55 ] at the end of the experiment. In this task, participants rated on a 4-point Likert scale the degree of social appropriateness of choosing the alternative allocation over the default option in a selection of choices from the resource-allocation game. If one of these ratings, randomly chosen, matched that of the majority of other participants in the experimental session, then the participant was rewarded with 3.00€. This procedure ensured that participants reported their true beliefs about what the majority thought was socially appropriate, namely what constituted a social norm. Using the norm elicitation task, we test whether prosocial and antisocial participants have different perceptions of the social norms in the game. This is done in the Computer condition where no social information can be acquired from the agent. We expect that any difference in appropriateness ratings is due to participants’ original beliefs before the task. If prosocial and antisocial participants do indeed report different normative beliefs, this could explain their differences in social attitudes, in accordance to the norm learning hypothesis.

Norm uncertainty and norm salience

If the results of the elicitation task support the hypothesis of norm learning, we also use appropriateness ratings to explore what kind of information participants learn about the norm. Indeed, observing a social agent allows learning distinct features of a social norm. First, if participants are uncertain about what is appropriate and what is not, observation can provide useful information about the norm itself ( norm uncertainty ). If there is norm uncertainty, we expect observation of a human agent with very prosocial or antisocial behaviour to polarise the perception of what constitutes a right or wrong choice. Polarisation in turn should lead to more extreme appropriateness ratings in the Group and Individual conditions than in the Computer condition, where participants simply predict the choice patterns of a computer (and therefore learn nothing about social norms).

In addition to learning about the norm itself, observing the behaviour of others reveals whether a norm is actually followed or not ( norm salience ). The norm elicitation task, however, is not designed to measure norm salience, and we are unaware of any other task that could possibly elicit this feature of a norm. We therefore test for norm uncertainty by computing differences in appropriateness ratings between the Computer and human conditions, for prosocial and antisocial participants separately. If the ratings differ between the conditions, we interpret this as evidence of norm uncertainty. If ratings do not differ between conditions, we conjecture that norm learning occurred through a change in norm salience.

Cognitive modelling

Bias parameter κ.

We associate participants’ choices to their social attitude via Eq 1 . Yet this estimate or that of other parameters could be biased by the participant’s tendency to comply to authority (see Other measures). To control for compliance, we allow for the possibility that participants prefer an alternative allocation even when it should be on a par with the default allocation, or vice versa. To represent this change in subjective value of the alternative allocation, we define for each participant a bias parameter κ 5 :

where κ is equivalent to an amount of penalty or bonus points for the default allocation: The higher (lower) κ is, the higher (lower) the propensity to choose the alternative over the default allocation. In other words parameter κ captures the bias, unexplained by other parameters, according to which participants choose between the alternative allocation and the default allocation as if the default allocation was missing or having additional κ points. Although we acknowledge that this parameter could capture phenomena other than authority compliance, we were unable to provide any other interpretation of κ . In addition, κ and the original compliance index strongly correlate (see Cognitive modelling), suggesting a common variance between the two measures.

Variability parameters τ vs. σ

During the allocation game, participants might show variability in the way they choose, such as being more or less prosocial (or antisocial) from choice to choice. Not accounting for this variability within each part of the game (before or after prediction) could bias the estimation of the change in attitude due to the manipulation. To estimate choice variability, we compare two types of cognitive models that also give different interpretations about the nature of social attitude.

The first model type, Stable Attitude, assumes that attitudes are a stable personal trait, and that any variability in participants’ choices is due to cognitive mistakes when comparing different options. If for instance a person occasionally shows a more prosocial (or more antisocial) attitude than usual, this fluctuation is interpreted by the model as a miscalculation on how to behave. Comparisons errors are modelled through the parameter τ : the smaller (larger) τ is, the higher (lower) the probability of choosing consistently with one’s own attitude. Stable Attitude models compare alternatives using a softmax function ( Eq 6 , [ 56 ]):

where Λ is the logit link function, Pr( D = 1) is the probability of choosing the default allocation, V D and V A are the estimated values for the default and alternative allocations, as in Eqs 1 and 5 .

The second model type, Variable Attitude, assumes instead that attitude is a variable mental state. If for instance people behave more or less prosocially, this is interpreted as a natural fluctuation of attitude. Participants’ choices are modelled using random preference [ 57 – 59 ]: every time the participant has to make a decision, her social attitude α is sampled from a normal distribution with centre μ and standard deviation σ . The parameter σ represents variability in the way participants behave: the smaller (larger) σ is, the more (less) consistent the participant will be across her choices. The model is defined as 7 :

where Φ is the probit link function, Pr( D = 1) is the probability of choosing the default allocation, and threshold T α is the value of α for which the default and alternative allocations have equal subjective value ( V D = V A , Eq 8 ):

If the sampled α > T α , an allocation is preferred and consequently taken, otherwise the other option is chosen.

Error parameter ε

The error parameter ε defines the probability with which participants make a mistake in implementing their choice (e.g., mistyping or inattention). The probability of choosing the default allocation is expressed as Eq 9 :

where Pr model represents the probability of choosing the default allocation according to the model under consideration ( Eq 6 for Stable Attitude or Eq 7 for Variable Attitude). The error parameter thus allows to assume that participants’ answers are a mixture between model-based choices and random errors.

Model estimation

We estimate three versions of each model type. In the full version of a model, all parameters are estimated twice, before and after the manipulation phase. A second, simpler version of the models assumes that social attitude α is fixed for the whole task, as if it could not change with the manipulation; α is thus estimated only once across all choices. In the third version of the models instead, it is the variability parameter ( σ for Variable Attitude or τ for Stable Attitude) to be estimated once for the whole task, as if participants could not get more or less self-consistent in their choices after the manipulation phase. Models thus vary based on two factors: 2 (Stable Attitude / Variable Attitude) × 3 (fixed attitude / fixed variability / both vary). Consequently, we estimate and compare 6 unique models.

Models are estimated in JAGS [ 60 ] using the rjags [ 61 ] and R2jags [ 62 ] packages. Parameters are fitted using Hierarchical Bayesian Analysis (HBA, [ 63 ]) on two levels: a sample level (by subject) and a subject level (by time: before/after prediction). For each model, we ran 4 Markov chains for 100,000 iterations, with a burn-in period of 5,000 iterations and a thinning rate of 4. The model with the lowest Deviance Information Criterion (DIC) is selected and used for the statistical analyses. We use the maximum a posteriori (MAP) estimate to derive the most likely value for each parameter, including attitude convergence δ diff .

Model comparison

The cognitive model that describes participants’ behaviour best is the full version of the Variable Attitude model in which both α and σ vary before/after the manipulation phase ( DIC VA = 32997.4, Fig 2 ). Model comparison thus suggests that both attitude and attitude variability change after the manipulation phase, and that α varies across trials rather than being stable. This latter finding is further supported by the generally lower DIC values of Variable Attitude models as compared to all Stable Attitude models.

The difference Δ DIC between the Deviance Information Criterion (DIC) of a model and the full version of the winning Variable Attribute model. The Variable Attitude models are in blue, and the Stable Attitude models are in red.

Despite our original attitude categorisation of participants was agnostic with respect to the winning cognitive model, 346 participants out of 369 (93.8%) were classified in the same categories when using α before estimates from the Variable Attitude model. Mismatch affects only participants with a moderate social attitude (mean | α before | = 2.12°, max = 12.91°, N Baseline = 10, N Computer = 5, N Individual = 3, N Group = 5).

Compliance and κ relation

We test whether the bias parameter κ before estimated before the manipulation phase correlates with the compliance index. If correct, the bias parameter could be then used as an improved measure of authority compliance, in that it could integrate compliance effects directly within the computation of the decision process, and improve the estimates of other parameters. Splitting participants below and above the compliance threshold, we observe that the two groups have significantly different estimated values of κ before (Wilcoxon rank-sum test with continuity correction, log( V ) = 7.65, p <.001, r = .51[.42, .61]), with participants below threshold with average κ before = 1.14[0.85, 1.43] and participants above threshold with average κ before = 11.48[8.78, 14.19]. We then measure the association between the two measures using a Spearman’s rank correlation, and find a significant association ( ρ = .62[.55, .67], p <.001). These results support the hypothesis that the bias parameter κ in our model also captures a significant portion of the effect of authority on participants.

Attitude convergence

Preliminary results.

Based on choices before the manipulation phase, 75% (25%) of participants were categorised as having a prosocial (antisocial) attitude. When presented with prosocial alternatives, prosocial participants chose them over the default allocation 40% of the time, while antisocial participants did the same with antisocial alternatives around 54% of the time. According to our model estimates, mean social attitude before prediction α before was 20° ( SD = 14°) for prosocial and −22° ( SD = 20°) for antisocial participants (see panel A of S1 Fig for a comparison across experimental conditions). However, for a significant portion of participants (21% of the sample), 1 point for the other player was worth less than one tenth of a point for oneself (−5° < α before < 5°), meaning that many participants showed a moderate, if not selfish, social attitude.

In the manipulation phase, prediction accuracy was relatively high: the average number of correct predictions in the last 20 trials was 18.6 ( SD = 2.2; Computer: 19.1, SD = 1.8, Individual: 17.9, SD = 2.8, Group: 18.7, SD = 1.8). This result suggests that participants had successfully learned the attitude of the observed agent.

Time dependence

If the time-dependence hypothesis is true, participants should become more selfish in all experimental conditions. We first test whether participants chose more often the default, selfish allocation in the second part of the resource-allocation game (after and despite the manipulation phase) than in the first part. Contrary to this prediction, prosocial (antisocial) participants chose more prosocial (antisocial) alternatives when learning about the agent’s choices (Baseline (no agent): -4.0%, Computer: +0.7%, Individual: +5.3%, Group: +6.9%; S5 Analyses ).

These results are mirrored by changes in social attitude, where we find that α after was on average more polarised than α before (Baseline: 0°, Computer: +4°, Individual: +7°, Group: +5°). Bayes Factor analyses suggest that the data are overwhelmingly more likely under the alternative hypothesis ( H 1 : δ α ≠ 0) than under the null hypothesis (all BF 10 > 100) with the exception of Baseline, where there is substantial to strong evidence in favour of the null (BF 01 = 10.2; see S5 Analyses for a full report of the analyses). These results suggest that time dependence is not present even in the Baseline condition, indicating that this mechanism is not an important factor at play.

If the contagion hypothesis is true, attitude convergence δ diff should be significant and positive in all conditions excluding Baseline. Given that the normality assumption does not hold (Shapiro-Wilk test, all p < .001), we test this prediction using a one-tailed Wilcoxon signed-rank test. Indeed, participants, in all conditions except Baseline shifted attitude towards that of the observed agent ( Fig 3 ; Baseline: log( V ) = 8.40, p = .457, δ diff = −1°[−2°, 0°], r = .01[−.17, .18], BF 0+ = 12.32; Computer: log( V ) = 7.64, p < .001, δ diff = 4°[2°, 6°], r = .43[.23, .63], BF +0 = 786.20; Individual: log( V ) = 7.51, p < .001, δ diff = 6°[3°, 8°], r = .57[.41, .75], BF +0 > 10000; Group: log( V ) = 8.28, p < .001, δ diff = 5°[4°, 7°], r = .58[.43, .72], BF +0 > 10000).

Error bars indicate t -adjusted, 95% Gaussian confidence intervals. *: p < .05; **: p < .01; ***: p < .001.

We also measure the difference in convergence across conditions. A Kruskal-Wallis test reveals that there is a significant effect of condition on attitude convergence ( χ 2 (3) = 42.22, p < .001, ε 2 = .11[.07, .19]). Post-hoc pairwise comparisons reveal that attitude convergence differs between the Baseline and Group conditions ( z = 5.87, p < .001, r = −.39[−.51, −.27], BF 10 > 10000), between the Baseline and Individual conditions ( z = 4.68, p < .001, r = −.33[−.46, −.17], BF 10 = 717.97), and between the Baseline and Computer conditions ( z = 3.56, p = .005, r = −.24[−.38, −.10], BF 10 = 33.55). Bayes Factor analyses additionally suggest no difference in attitude convergence between the Group and Individual conditions (BF 01 = 5.78, substantial evidence).

The above results are compatible with the contagion hypothesis, but also with the compliance hypothesis. To study the influence of the experimenter on attitude convergence, we categorise participants using the compliance index. One participant did not respond in any antisocial trial before manipulation: without these responses we could not compute a compliance index and the participant was consequently excluded for analyses about compliance. We find that 63 participants (Baseline: 23, Computer: 13, Individual: 16, Group: 11), around 17% of the sample, are above the 25% preregistered threshold ( Fig 4A ). We thus focus on the remaining participants below threshold ( N = 305). While the results in the Group and Individual conditions hold, attitude convergence in the Computer condition is still significant but weakened (Baseline: log( V ) = 8.06, p = .323, δ diff = 0°[−1°, 1°], r = .04[−.15, .23], BF 0+ = 7.16, n obs = 109; Computer: log( V ) = 7.11, p = .015, δ diff = 3°[0°, 5°], r = .30[.06, .53], BF +0 = 6.27, n obs = 60; Individual: log( V ) = 6.96, p < .001, δ diff = 5°[2°, 7°], r = .57[.36, .77], BF +0 = 958.61, n obs = 50; Group: log( V ) = 8.02, p < .001, δ diff = 5°[3°, 7°], r = .55[.39, .71], BF +0 = 6652.22, n obs = 86).

A: Distribution of the compliance index across all participants. The vertical line shows the threshold value (25%) beyond which a participant is considered to be susceptible to authority compliance. B: Consistency increase across conditions. Participants become more consistent after the manipulation phase ( σ after < σ before ), but the increase is not significantly different across conditions. Error bars indicate t -adjusted, 95% Gaussian confidence intervals. Arrows indicate outliers. *: p < .05; **: p < .01; ***: p < .001.

In support of this observation, we find that Baseline and Computer conditions are no more significantly different ( z = 2.06, p = .058, r = −.15[−.30, .01], BF 10 = 1.08; Kruskal-Wallis test: χ 2 (3) = 31.72, p < .001, ε 2 = .10[.05, .19], n obs = 305). Instead, Baseline and Group conditions are still significantly different ( z = 5.20, p < .001, r = −.37[−.51, −.24], BF 10 = 2117.44), and so are Baseline and Individual conditions ( z = 3.90, p = .001, r = −.31[−.45, −.15], BF 10 = 54.87).

To explore the relation between compliance and attitude convergence, we run a robust linear regression [ 64 ] with attitude convergence as dependent variable, and with experimental condition and the interaction between compliance and experimental condition as predictor variables ( Fig 5A ). We compare the deviance of the regression against a simpler model using only experimental condition as an independent variable: the full model has a better fit on the data (( χ 2 (4) = 19.70, p < .001, adjusted R 2 = .241). In support of the compliance hypothesis, we find that the weights for the main effect of Group and Individual conditions are significant, whereas that of the Computer condition is not ( Fig 5B ; Baseline: β = .170[−1.02, 1.36], t = .279, p = .780; Computer: β = .176[−1.50, 1.85], t = .206, p = .837; Individual: β = 3.69[1.89, 5.50], t = 4.011, p < .001; Group: β = 4.67[3.29, 6.03], t = 6.67, p < .001). Furthermore, the interaction term with compliance is only significant in the Computer condition ( β = 25.31[14.75, 35.87], t = 4.70, p < .001) and not in the other experimental conditions (all p >.05). These findings suggest that attitude convergence in the Computer condition is mainly driven by experimenter compliance, rather than contagion.

A: Robust regression on attitude convergence with experimental condition and the interaction between compliance and experimental condition as predictor variables. Shaded areas indicate 95% confidence intervals. B: Coefficients of the regression. Labels report unstandardised effect size, t -value, and p -value. Error bars indicate t -adjusted, 95% Gaussian confidence intervals.

If compliance could explain attitude convergence in the Computer condition, and given that we tend to exclude the presence of contagion in our experiment, attitude convergence in the Group and Individual conditions could be explained instead by either the preference learning or the norm learning hypotheses. We first test the second prediction of the preference learning hypothesis, namely that learning about others’ attitude should significantly increase participants’ consistency. We thus test whether choice consistency increased, and if this increase is higher after observing a human agent (Individual, Group conditions) than after predicting a computer’s choices or nothing at all (Computer and Baseline conditions). Shapiro-Wilk test for normality is significant (all p < .001), therefore we adopt non-parametric tests. Wilcoxon signed-rank tests show that σ after was indeed significantly smaller than σ before in all conditions ( p < .001; Fig 4B ).

When we compare consistency increase across conditions, we find that participants become more consistent in the Group condition than in the Baseline condition ( z = 2.85, p = .026, r = .20[.06, .33], BF 10 = 6.83; Kruskal-Wallis test: χ 2 (3) = 8.50, p = .037, ε 2 = .02[0, .07]). Despite this significant result, Bayes Factor analyses for most comparisons favour the null hypothesis (no differential increase; Baseline-Computer: BF 01 = 6.07; Baseline-Individual: BF 01 = 4.48; Computer-Individual: BF 01 = 4.53; Individual-Group: BF 01 = 3.44). We question then that the preference learning hypothesis does adequately explain our data.

Norm learning

We use the data from the norm elicitation task to test the plausibility of the norm learning hypothesis and to distinguish between norm uncertainty and norm salience.

We first compare appropriateness ratings between prosocial and antisocial participants in the Computer condition using a series of Kruskal-Wallis tests (one test for each rating; Fig 6 , top). Appropriateness ratings are statistically different for every rating, even after correcting for multiple comparisons (all p < .004). These ratings link norm perception to social attitude: prosocial participants seem to consider it very appropriate to give money to the other and very inappropriate to take money, while the opposite is true for antisocial participants. Thus, it appears that participants’ social attitudes are influenced by normative beliefs.

Appropriateness ratings for prosocial and antisocial participants in the Computer (top), Individual (centre), and Group (bottom) conditions. Only participants below threshold are plotted. Square size is proportional to the number of participants, whereas the lines connect the median ratings for each alternative allocation.

Given this evidence in support of the Norm Learning hypothesis, we proceed to testing norm uncertainty. To check this, we test whether the distribution of appropriateness ratings differ across conditions by participant type, which would happen if norm uncertainty was present. Two Kruskal-Wallis tests out of twenty-four are statistically significant, but do not survive the correction for multiple comparisons (all p >.153). We also perform Bayes Factor analyses, but given the large number of pairwise comparisons we choose to adopt a wider Cauchy prior, with spread r = 2 . Results show that the null hypothesis is favoured by 67 tests out of 72 ( Fig 7 ). Similar ratings in human (Group/Individual) and Computer conditions are thus not compatible with the norm uncertainty hypothesis, leaving norm salience as the only available explanation. Thus, authority compliance and norm salience are the only hypotheses that we failed to reject.

Histogram of Bayes Factor values for the 72 pairwise comparisons of appropriateness ratings for each allocation, for each pair of experimental conditions, binned by strength of evidence. Evidence in favour of the null hypothesis ( H 0 ) increases from right to left, and vice versa.

Drivers of social conformity

In this study, we identified and estimated the contributions of several competing explanations to attitude conformity in social decision making. Attitude conformity was assessed using a series of cognitive models coupled with several experimental conditions and complementary measures, which helped to test the predictions of these hypotheses. Participants’ attitude became more prosocial or antisocial when they learned about the choices of an extremely prosocial or antisocial agent, regardless of whether the agent was a group of people, one person, or a computer. We found, however, that attitude compliance in the Computer condition was primarily driven by those participants who were more likely to conform to authority demands, suggesting that attitude change in this condition was primarily driven by compliance with the experimenter’s expectations rather than conformity to the observed agent. Once we had accounted for authority compliance, computational modelling helped us to disentangle the surviving hypotheses, preference learning and norm learning. We first tested the prediction of the preference learning hypothesis that participants should have become more self-consistent in their choices after observing human agents. Since results disconfirmed this prediction, we proceeded to test one prediction of the norm learning hypothesis, namely that the behaviour of participants in the game is reflected by their beliefs about what is considered appropriate or not. Results from a norm elicitation task [ 55 ] confirmed this prediction, adding evidence in favour of the hypothesis that participants conform mainly because of social expectations. As an exploratory analysis, we additionally tested the norm uncertainty hypothesis, which posits that participants are uncertain about the norm underlying the game and learn about it by observing other human agents. Results from the norm elicitation task however suggest that participants do not change their norm perceptions upon observing human agents. Based on this finding, we speculate that social conformity in the experiment occurs because participants learn how salient following the norm is (i.e. how unlikely it is for someone to deviate from the norm). Given the exploratory nature of this result, we cannot exclude other interpretations: a broader set of responses in the norm elicitation task paired with a properly powered study design should be able to assess what mechanisms underlie norm learning.

A number of findings support the idea that norms and the beliefs related to them are at the basis of social attitudes. Social appropriateness has been shown to play a role in decisions in various economic games [ 38 , 55 , 65 – 68 ]. More relevantly to our study, it was found that anonymity, and therefore reduced accountability, appears to have clear effect on allocation choices. Experiments with increased anonymity—also with respect to the experimenter, i.e. double blind paradigms—show plummeting contributions in economic games such as the Dictator game [ 51 , 69 ]. At the same time, even subtle cues of being observed seem to increase contributions [ 70 ] (although see [ 71 ] for a recent failed replication). The impact of reputation can also account for attitude change driven by compliance. Authority compliance is indeed a phenomenon analogous to conformity, as it links attitude change to vertical influences, as opposed to peer observation. Participants who have a strong tendency to choose the alternative option, regardless of whether it is beneficial or detrimental for the other and regardless of the identity of the observed agent, may think that this is what authority wants, and that this is the norm in the experiment [ 38 , 72 , 73 ]. The complementary result, that participants who are not influenced by authority only change their attitude when learning about other humans’ behaviour, works in a similar fashion. By learning to predict the agent’s behaviour, participants deduce how salient following the norm is for others, and change their behaviour to be more consistent with them. Therefore, we can conclude that the two effects that we observe—authority compliance and attitude conformity in human conditions—are both in line with the general social norms explanation.

The results of this study prompt some additional thoughts about the process of learning social norms. First, we observe that information about norms can spread through indirect transmission [ 74 ]. During the experiment participants cannot interact in any way with the observed human agent—who is not physically present—but participants can nevertheless extract some information about the norm from the observed behaviour in the manipulation phase. Indirect transmission thus highlights how adherence to social norms can be pervasive in dispersed and loosely regulated groups such as online communities. Second, the fact that participants conform by learning how salient a norm is implicates that if a norm is already salient among a group of individuals then such group should be more resilient to conformity influences. If future studies do confirm that norm perception prior to observation does predict conformity, this could suggest new measures to countervail polarisation in social discourse.

Our contribution not only fosters and provides better characterisation of the norm learning hypothesis, but also systematically devalues the several competing explanations that we tested, that to our knowledge were not yet properly compared in one framework. These non-social hypotheses include time-dependence, contagion, and preference learning. The social/non-social distinction is crucial here as it gives an insight into how to interpret conformity dynamics in interpersonal relations: if a person changes her attitude we suggest that this change has to be primarily social in nature, and linked to the changes in social context in which the decision maker is placed. This idea can have profound implications for studying any social learning mechanisms and social decision making in general. Specifically, many non-social explanations of the change in behaviour can be ruled out.

Whereas the experimental design focused on some of the most prominent hypotheses in the literature, it is also possible that further mechanisms may guide participants’ social conformity. Social mimicry for instance suggests that participants change attitude in order to increase ties within a group [ 14 , 75 , 76 ]. The need to belong [ 77 ] could represent an alternative explanation to norm compliance, although it is not able to explain why prosocial and antisocial participants display different appropriateness beliefs. It is also by all means possible that several mechanisms contribute together to the present results; our main conclusion is that they probably operate on a social level. Concurrently, we do not claim that results on social behaviour directly apply to other domains of decision-making, such as risk or temporal preferences. Our research method, however, could be applied to other types of preferences to test whether these results extend to other types of choices.

Cognitive modelling does not only play a fundamental role in testing the predictions of the different hypotheses, but is also inherently connected to two additional contributions of this paper. First, we add to the series of studies challenging the conceptualisation of preference as a stable trait of people, and thus the use of the softmax function as the privileged method to model value-based choices. Studies on both risk [ 78 , 79 ] and inter-temporal preferences [ 23 , 80 ] have in fact highlighted how choice variability can be better explained by fluctuations of subjective preferences rather than “errors” in comparing different alternatives. This is in line with our finding that the Variable Attitude model explains behavioural data better than the Stable Attitude model. While we do not claim that computational distortions are absent during the estimation of value, we nonetheless support the idea that this mechanism cannot be the only one, nor can it be the main cause for choice inconsistencies in value-based decision making.

This interpretation finds additional support in recent perspectives on brain architecture, which hold that value representation is less specifically defined and is more distributed than current thinking suggests [ 81 – 84 ]. Assuming that preferences vary across contexts and across time requires a network of resources that not only keeps track of the current internal state, but that takes also into account the situational factors and the different scopes within which the choice is considered. For instance, a decision to act prosocially would require the integration of the tendency of an individual to help others, considerations related to the nature of the interaction and of the other person, the general goals of the decision maker, as well as the history of choices preceding that particular choice. Considering the complexity of a choice and of the neural substrates that make it possible, it seems hard to postulate the stability of subjective value as a justifiable premise for studying personal preferences and attitudes.

While we stand by the current findings, future research could improve the Variable Attitude model by accounting for some of its limitations. One way to do this could be to integrate both types of choice variability (errors in comparison and variability in attitude) under a common cognitive model to test whether these mechanisms co-exist and what are their individual contributions (see for example [ 85 – 88 ]). Such a model, however, requires either a prohibitive number of trials per participant, or the integration of some other type of information. This problem could possibly be overcome by integrating temporal information to simple choice data: several studies have successfully analysed subjective choices with this method before using so-called sequential sampling models (SSM, see for instance [ 21 , 89 ]). While this approach would require challenging improvements, such as disentangling variability both within and between trials, it could also promote the analysis of other decision components, such as the trade-off between fidelity with one’s preferences and speed in making a decision.

A second contribution of cognitive modelling is the use of a computational parameter to directly measure the impact of authority compliance on the decision process (Bias parameter κ and Compliance and κ relation). This parameter correlates with the compliance index that we used in the present study to categorise participants. We propose that this parameter can be used independently to measure compliance to authority demands. Directly including the effect of compliance in the computational model has the advantage that other estimates, such as the person’s attitude or its choice consistency, are corrected for the presence of this effect. We also consider this estimation procedure as more reliable than alternatives in the literature: while other methods indeed exist, they are based on ad hoc tasks to quantify authority demand (e.g., [ 50 , 90 ]), whereas the measures we use work within the main task of the experiment, thus reducing the risk that results in one task do not extend to another. As a limitation of our approach, it could be argued that using a default option might seem too unequivocal; we argue however that this feature of the task design actually simplifies the expression of attitude by participants as it makes value comparison less challenging also from a computational point of view (see for instance [ 91 , 92 ]). We thus think that our computational parameter could be of value to researchers who need to control for the influence of the experimenter when fitting decision models.

Limitations

Our study does not come without some limitations. The experimental design is between-subjects, and it is thus not possible to compare directly the effect of the various manipulations, nor does it allow to exclude the possibility that multiple mechanisms are at work simultaneously. While this weakness does not fundamentally challenge the reported findings, implementing an intermixed design such as the ones proposed in [ 18 , 27 ] or [ 19 ] could yield more powerful predictions and interpretations. A second constraint of our experiment design is that in some conditions we could not reach the pre-determined sample size necessary to achieve the power 1− β = .95. We note however that our findings seem robust, even when applying design changes such as different ways to account for the influence of compliance ( S1 Table ) or changing the dependent variable ( S6 Analyses ), suggesting that this problem might be not too concerning.

Another limitation of the design is that, given that the attitude of the observed agent was fixed, social distance from the agent and attitude change are correlated. This means that we may be missing a connection between how close one’s initial attitude is to the observed agent’s and how much she will conform after learning. Indeed, recent research suggests that similarity with the observed agent influences the effect of conformity [ 21 ]. To solve this problem, in future experiments we propose to dynamically adjust the attitude of the agent depending on participants’ own attitude. This design can also help to understand what happens when prosocial participants observe an antisocial agent and vice versa. We have deliberately excluded this question from consideration in our experiment because we were not sure ex ante if we would manage to separate the effect of learning about a very socially distant agent from the drift of attitudes towards selfishness (though, ex post we know that such time-dependence is not a likely explanation of the findings, which should make it straightforward to test hypotheses about observing others with very different attitudes). Previous results suggest however that, at least concerning antisocial participants, cross-type conformity should be less pronounced than same-type conformity [ 26 ].

We would like to note that, contrary to the predictions of the norm learning hypothesis, attitude change in the Individual condition was not significantly smaller than attitude change in the Group condition. This unexpected result could be linked to the fact that participants were not informed about the size of the group, which in turn could have influenced their representation of the agent. A key direction for the future research will be to explore the relationship between group size and attitude change (e.g., [ 93 ]). Another possible explanation for the lack of the difference between the Individual and Group conditions could be that participants in the Group condition were not connected in any way with the group of people whose behaviour they observed, and that they would have conformed more on average had they identified more with the group. This scenario could be compatible with recent findings suggesting that norms are stronger when there is a stronger group identification [ 27 , 94 ]. Testing this idea would require more rigorous control of the perception of the group by participants.

Finally, we would like to comment on the implicit assumption that we make when analysing responses in the norm elicitation task. Specifically, we assume that the norms elicited in the Computer condition were not influenced by the predicting of computer’s behaviour in the second task, and thus these norms are those that participants had in mind while choosing in the first part of the resource-allocation game. It can be argued that learning about the computer’s “attitude” can change the perception of norms and that our assumption is therefore incorrect. We disagree with this opinion on the following grounds. The computer is not a social agent, so whatever it is doing should not, by definition, change the perception of the social environment that participants are in. This is evident from the fact that attitude conformity is not significant in the Computer condition after regressing compliance to authority against attitude convergence. Moreover, in a post-experimental questionnaire, 66 of the 74 participants in the Computer condition reported that, in their opinion, the computer was acting either according to a mathematical rule (e.g., addition or subtraction between the players’ payoffs; N = 62), or randomly (N = 4). It is thus unlikely that these participants ‘humanised’ the choices made by the computer agent. The fact that we do not find any differences in the elicited norms across the three conditions is much more likely to reflect the stability of normative beliefs across conditions rather than beliefs that change in all three conditions in exactly the same way. It can nonetheless still be argued that the mere experience of the task influences norm perception. This idea has been directly tested by [ 95 ] who found no evidence to support it. Overall, we believe therefore that our treatment of norms in the Computer condition is legitimate.

In conclusion, we find that compliance to authority and learning how consistently others follow social norms are the most likely explanations behind prosocial and antisocial conformity. We hope that these findings will shed some light on the polarisation and viral diffusion of information online, that it will push towards a similar systematic exploration of preferences across other domains, and to a renewed interest in the cognitive and brain processes underlying these changes.

Supporting information

Individual parameters’ distribution before manipulation, by condition. Each vertical line represents a participant, jittered for illustration purposes. Parameter α represents participants’ estimated social attitude; σ estimates participants consistency across choices, κ indicates the penalty points of the default allocation with respect to the alternative allocations; ε indicates the percentage of trials in which there was likely a response mistake by the participant.

An.xlsx file containing all the allocations used in the task: osf.io/y8v63 .

S1 Analyses

S2 analyses, s3 analyses, s4 analyses, s5 analyses, s6 analyses, s7 analyses, acknowledgments.

We would like to thank Dimitris Katsimpokis, Stephan Nebe, and the members of the Learning and Decision Making group at University of Trento for invaluable comments. All mistakes are our own.

Funding Statement

GC acknowledges the financial support of the European Research Council (ERC Consolidator Grant 617629; https://erc.europa.eu/funding/consolidator-grants ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability

• PLoS Comput Biol. 2021 Oct; 17(10): e1009530.

Decision Letter 0

Dear Dr. Panizza,

Thank you very much for submitting your manuscript "How Conformity Can Lead to Polarised Social Behaviour" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by two independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

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Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The present study investigated the social conformity of other-regarding preference in humans. Specifically, the authors examined conformity in the domain of social behaviour and tested the underlying psychological mechanism by combining behavioural experiments with computational modelling. While we know that people exhibit social conformity, its computational basis remains elusive. In this sense, I believe this study will advance our understanding of human social conformity. I have reviewed the manuscript for another journal, and the authors have already addressed most of my concerns. My comments are therefore relatively minor.

Does contagion or automatic imitation imply conformity occur regardless of whether the observed agent is human or non-human? How about human-specific automatic imitation reported in psychological literature?

Are the groups of participants matched in terms of sex, age, baseline social preference and other characteristics? In between-participant design, this issue should be carefully checked.

Reviewer #2: In this manuscript, Panizza and colleagues investigate how learning social behaviour of others influences one’s own social attitudes. They propose to disentangle 6 different hypothesis to account for the past empirical observations of such attitude alignment: Time-dependence, Contagion, Compliance, Preference learning, Norm uncertainty and Norm salience. To tease apart those hypotheses, they leverage a between-subjects experimental design of a “standard” attitude alignment experiment (own-preference elicitation – observation/prediction of other preferences - own-preference elicitation) with different treatments, which are defined by a manipulation of the observation/prediction phase: baseline (no observation), computer (predicting a computer behavior), individual (predicting preferences of one individual) and group (predicting preferences of a group of individuals). The different hypotheses make different qualitative prediction wrt to attitude alignment in the different treatments.

There is a lot to like in the manuscript: the topic is clearly interesting and important, the authors put a real effort to propose an exhaustive test of multiple credible hypotheses, the design is clever/elegant and suited to address the question(s) at hand, the manuscript features a very transparent and exhaustive reporting of the results (with Bayesian statistics), a solid preregistration (with transparent reporting of deviations from original plan) and open research practices.

I only have a few suggestions that I hope the authors will find useful to improve the manuscript.

First, I find the modelling part quite under-exploited which I suspect is due to the fact that the manuscript was initially formatted for another journal. For PLoScb, I suggest a stronger focus on the modelling approach, with modelling methods (Supp. Mat. A7) and modelling results (Supp. Mat. B1) incorporated in the main text. Also, because a lot of analyses apply to attitude alignment (d_diff), which is a model parameter, the other would need to provide a parameter recovery exercise (see e.g. Wilson and Collins, eLife 2019). A model identification would also be needed to support the model comparison results.

To provide a simple, first overview of the main effect of interest, I suggest that the authors also include a Figure (before the current Figure 2) which simply display the attitude convergence in the different conditions

Some effects size/direction are missing (e.g. paragraph about attitude convergence differences between conditions p.9)

Fig 3B : Unless I’m missing something, there seems to be an inconsistency between the significance stars and the CI (and the results reported in the Main text)

P9: Paragraph head “consistency increase” should be “preference learning” for clarity/consistency

Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: None

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Reviewer #2: No

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References:

Review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

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Author response to Decision Letter 0

30 Jul 2021

Submitted filename: Panizza_reviewers_response.docx

Decision Letter 1

Thank you very much for submitting your manuscript "How Conformity Can Lead to Polarised Social Behaviour" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

Reviewer #1: The authors have adequately address all the concerns.

Reviewer #2: It seems that the authors have satisfactorily addressed my previous comments. I am therefore happy to recommend the manuscript for publication, conditional on the minor issue/question below being addressed as well. Congratulations to the authors for this very fine piece of work.

Minor issue:

I am surprised about the reported effect-size confidence intervals and how they relate to p-values e.g. lines 521-522: “Baseline and Group conditions are still significantly different (z = 5:20, p < :001, 520 r = -.37[-.51; .24], BF10 = 2117:44), and so are Baseline and Individual conditions (z = 3:90, p = :001, r = -:31[-:45; :15], BF10 = 54:87).” Aren’t some “minus” signs missing from the second terms in the brackets (to exclude 0 from the CI and reach high significance levels of P<0.001)? Also would better correspond to the reported mean the center of the CI. Please check throughout the manuscript.

Author response to Decision Letter 1

Decision letter 2.

We are pleased to inform you that your manuscript 'How Conformity Can Lead to Polarised Social Behaviour' has been provisionally accepted for publication in PLOS Computational Biology.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

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Acceptance letter

15 Oct 2021

PCOMPBIOL-D-21-00918R2

How Conformity Can Lead to Polarised Social Behaviour

Dear Dr Panizza,

I am pleased to inform you that your manuscript has been formally accepted for publication in PLOS Computational Biology. Your manuscript is now with our production department and you will be notified of the publication date in due course.

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The Power of Social Conformity: Insights from The Asch Conformity Experiment

By declan fitzpatrick, this article is divided into the following sections:.

The Asch Conformity Experiment, conducted by social psychologist Solomon Asch in the 1950s, remains one of the most influential studies in understanding social influence and the power of conformity. Through a series of controlled experiments, Asch sought to investigate the extent to which social pressure from a majority group could affect an individual’s judgment.

By examining the methodology, findings, and implications of the Asch Conformity Experiment, we can gain insights into the mechanisms of social influence and the impact of conformity on human behavior.

Methodology and Design

Solomon Asch designed the experiment to explore how individuals would respond to group pressure in a controlled setting. Participants were asked to take part in what they believed was a visual perception test. They were shown a series of lines and asked to identify which line matched the length of a reference line. Each participant was placed in a group with confederates—individuals who were aware of the true nature of the experiment and instructed to give predetermined incorrect answers.

The critical aspect of the design was that the real participant was unaware of the confederates’ role and believed them to be fellow participants. As the group members gave their answers aloud, the real participant was exposed to the unanimous but incorrect responses of the confederates. This setup allowed Asch to observe whether the participant would conform to the group’s incorrect judgment or rely on their own perception.

Key Findings

The results of the Asch Conformity Experiment were both revealing and unsettling. A significant number of participants conformed to the incorrect majority at least once, demonstrating the powerful influence of social pressure. On average, about one-third of the participants conformed to the group’s incorrect answer in critical trials. When interviewed afterward, many participants admitted that they knew the group’s answer was wrong but chose to conform to avoid standing out or being ridiculed.

These findings illustrated the strong impact of social influence on individual behavior, even in clear-cut situations where the correct answer was obvious. The Asch Conformity Experiment provided empirical evidence showing that people are willing to ignore their own perceptions and judgment to align with the majority, highlighting the pervasive nature of conformity.

Psychological Mechanisms and Implications

The Asch Conformity Experiment revealed several psychological mechanisms underlying conformity. One key factor is normative social influence, where individuals conform to gain acceptance and avoid rejection or disapproval from the group. The desire to belong and be liked often outweighs the need to be correct, leading individuals to conform to group norms and opinions.

Another mechanism identified is informational social influence, where individuals look to the group for guidance when they are uncertain or lack confidence in their own judgment. In ambiguous or challenging situations, people are more likely to rely on the perceived expertise or consensus of the group, leading to conformity.

The implications of these findings extend beyond academic discourse, offering insights into various spheres of life. In social settings, the pressure to conform can influence attitudes, and decision-making processes. Understanding the dynamics of conformity can help address issues such as peer pressure, bullying, and groupthink, where the desire for consensus overrides critical thinking and dissent.

Ethical Considerations

While the Asch Conformity Experiment yielded valuable insights, it also raised ethical concerns regarding the use of deception and the potential psychological impact on participants. The real participants were misled about the true purpose of the study and experienced confusion and discomfort during the experiment. Modern ethical standards emphasize the importance of informed consent, debriefing, and minimizing harm to participants.

Despite these concerns, the ethical guidelines developed in response to studies like Asch’s have significantly improved the conduct of psychological research. These guidelines ensure that participants are treated with respect and dignity, balancing the pursuit of knowledge with ethical responsibility.

Broader Societal Impact

The Asch Conformity Experiment has had a profound impact on various domains, including education, organizational behavior, and public policy. In educational settings, awareness of conformity’s influence can inform teaching strategies that encourage critical thinking and independent judgment. Educators can create environments that value diverse perspectives and foster open discussions, reducing the pressure to conform and promoting intellectual growth.

In organizational contexts, understanding the dynamics of conformity can help leaders and managers create a culture that encourages innovation and dissent. By valuing diverse viewpoints and creating safe spaces for expression, organizations can mitigate the risks of groupthink and make more informed decisions. The insights from the Asch Conformity Experiment can guide efforts to build inclusive and collaborative workplaces that harness the strengths of individual contributions.

In public policy, the principles derived from the Asch Conformity Experiment can inform strategies to address social issues such as prejudice, discrimination, and political polarization. By promoting awareness of conformity’s impact on behavior and attitudes, policymakers can develop interventions that encourage critical thinking and reduce the influence of harmful social norms.

Theoretical Contributions

The Asch Conformity Experiment has made significant contributions to social psychology theories, particularly in understanding social influence and group dynamics. It provided empirical support for the concepts of normative and informational social influence, illustrating how group pressure can shape individual behavior.

Asch’s work laid the groundwork for subsequent research on conformity and obedience, influencing notable studies such as Stanley Milgram’s obedience experiments and Philip Zimbardo’s Stanford prison experiment. These studies collectively deepened our understanding of the complex interplay between individual autonomy and social influence, shaping the field of social psychology.

The Asch Conformity Experiment remains a cornerstone in the study of social influence and conformity. Through its innovative design and rigorous methodology, the experiment revealed the powerful impact of social pressure on individual judgment and behavior. The findings underscored the pervasive nature of conformity, demonstrating that individuals often prioritize group acceptance over personal accuracy.

As we reflect on the legacy of the Asch Conformity Experiment, its lessons resonate in various domains, from education to organizational management to public policy. The study highlights the importance of fostering environments that encourage critical thinking, independent judgment and diverse perspectives.

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Key Insights from Asch’s Conformity Experiments: A Deep Dive

Table of Contents

Have you ever found yourself agreeing with a group even when you secretly felt they were wrong? Why do we sometimes conform to the opinions of others, even against our better judgment? The answers lie in a series of groundbreaking experiments conducted by psychologist Solomon Asch in the 1950s that uncovered the underpinnings of conformity in social situations. Let’s dive into Asch’s conformity experiments and unearth the key insights that reveal much about the social fabric of human behavior.

The setup of Asch’s experiments

Asch’s experiments were deceptively simple. He placed a participant in a room with a group of actors , all privy to the true nature of the experiment except for the unsuspecting subject. The task was straightforward: participants were shown a series of lines and had to match one line with a standard line in length. The catch? The actors, posing as fellow participants, were instructed to give incorrect answers out loud before the actual participant responded. The real focus of the study was not on visual perception but on whether the participant would conform to the group’s incorrect consensus.

Public announcements and their influence

One of the most striking features of Asch’s experiment was the requirement for participants to announce their judgment publicly. This aspect of the study highlighted the power of public commitment . When we vocalize our agreement or disagreement, we not only communicate our stance but also align ourselves with a particular group or opinion, making the situation ripe for the influence of conformity. Asch found that the pressure to conform was so strong that a significant number of participants conformed at least once, even when the group’s answer was clearly wrong.

The impact of immediate group pressure

Immediate group pressure was a pivotal factor in Asch’s findings. The presence of a unanimous group often swayed participants into conforming, even when they knew the group’s consensus was incorrect. The tension between maintaining personal integrity and the desire to belong was palpable in the experiment’s atmosphere. This pressure to conform speaks volumes about our innate social nature and the lengths we might go to avoid social ridicule or isolation.

Insights into social environments and perceptions

The social environment created within Asch’s experiments was a microcosm of larger societal dynamics. Participants’ perceptions were not just about line lengths but were shaped by the social context and the real-time reactions of those around them. This demonstrates how our environment can dramatically influence what we see, how we think, and the decisions we make. The experiments highlight the social nature of human perception, showing that what we believe to be objective can be surprisingly malleable.

The complexity of conformity and independence

Not all participants conformed in Asch’s experiments. There were those who consistently gave correct answers, despite the group’s incorrect consensus. These instances of independence illuminate the complex interplay between the individual and the group. They suggest that while social forces are powerful, personal factors such as confidence, self-concept, and perhaps prior experiences also play crucial roles in the decision to conform or stand firm on one’s own perceptions.

Conformity across cultures and time

Asch’s experiments have been replicated across various cultures and decades, revealing interesting variations in levels of conformity. Some cultures with a high value on social harmony and group cohesion, such as collectivist societies , exhibit higher rates of conformity. This suggests that cultural norms and values are integral in shaping the social behaviors of conformity and independence. Over time, shifts in societal values and the rise of individualism in certain cultures may also influence the degree to which people conform.

Modern applications of Asch’s findings

The insights from Asch’s experiments extend beyond the psychology laboratory and into the realms of education, business, and politics. Understanding the mechanisms of conformity can help educators foster critical thinking and individuality among students. In business, it can illuminate the dynamics of group decision-making and the importance of dissenting voices. Politically, it reminds us of the power of propaganda and the dangers of groupthink .

Asch’s conformity experiments have left an indelible mark on social psychology, offering profound insights into the ways our social environments shape us. From the influence of public commitment to the impact of immediate group pressure, these experiments teach us about the delicate balance between conformity and independence and how deeply our perceptions and decisions are embedded in the social fabric.

What do you think? How does understanding the dynamics of conformity influence your view on daily social interactions? Can you recall a time when you stood your ground against group pressure, and what was the outcome?

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Social Psychology

1 Definition, Concept and Research Methods in Social Psychology

• Definition and Concept of Social Psychology
• Research Methods in Social Psychology
• Experimental Methods
• Non-Experimental Methods
• Other Research Methods
• Research Ethics

2 Historical Perspective of Social Psychology, Social Psychology and Other Related Disciplines

• Historical Perspective
• Landmarks in the History of Social Psychology
• Social Psychology and Other Related Disciplines
• Significance of Social Psychology Today

3 Social and Person Perception – Definition, Description and Functional Factors

• Social Cognition – Description and Nature
• Social Perception – Definition
• Understanding Temporary States
• Understanding of the Most Permanent or Lasting Characteristics – Attributions
• Impression Formation
• Implicit Personality Theory
• Person Perception
• Social Categorisation

4 Cognitive Basis and Dynamics of Social Perception and Person Perception

• Cognitive and Motivational Basis of Social and Person Perception
• Bias in Attribution
• Role of Emotions and Motivation in Information Processing
• Motivated Person Perception
• Effect of Cognitive and Emotional States

5 Definition, Concept, Description, Characteristic of Attitude

• Defining Attitudes
• Attitudes, Values, and Beliefs
• Formation of Attitudes
• Functions of Attitudes

6 Components of Attitude

• ABCs of Attitudes
• Properties of Attitudes

7 Predicting Behaviour from Attitude

• Relationship between Attitude and Behaviour
• Attitudes Predict Behaviour
• Attitudes Determine Behaviour?
• Behaviour Determine Attitudes

8 Effecting Attitudinal Change and Cognitive Dissonance Theory, Compliance of Self-perception Theory, Self-affirmation

• Self Presentation
• Cognitive Dissonance
• Self Perception
• Self Affirmation

9 Introduction to Groups- Definition, Characteristics and Types of Groups

• Groups-Definition Meaning and Concepts
• Characteristics Features of Group
• Types of Group
• The Role of Groups

10 Group Process- Social Facilitation, Social Loafing, Group Interaction, Group Polarization and Group Mind

• Social Facilitation
• Social Loafing
• Group Interaction
• Group Polarization

11 Group Behaviour- Influence of Norms, Status and Roles; Introduction to Crowd Behavioural Theory, Crowd Psychology (Classical and Convergence Theories)

• Human Behaviour in Groups
• Influence of Norms Status and Roles
• Crowd Behavioural Theory
• Crowd Psychology

12 Crowd Psychology- Collective Consciousness and Collective Hysteria

• Crowd: Definition and Characteristics
• Crowd Psychology: Definition and Characteristics
• Collective Behaviour
• Collective Hysteria

13 Definition of Norms, Social Norms, Need and Characteristics Features of Norms

• Meaning of Norms
• Types of Norms
• Violation of Social Norms
• Need and Importance of Social Norms
• Characteristic Features of Social Norms

14 Norm Formation, Factors Influencing Norms, Enforcement of Norms, Norm Formation and Social Conformity

• Norm Formation
• Factors Influencing Norm Formation
• Enforcement of Norms
• Social Conformity

15 Autokinetic Experiment in Norm Formation

• Autokinetic Effect
• Sherif’s Experiment
• Salient Features of Sherif’s Autokinetic Experiments
• Critical Appraisal
• Related Latest Research on Norm Formation

16 Norms and Conformity- Asch’s Line of Length Experiments

• Solomon E. Asch – A Leading Social Psychologist
• Line and Length Experiments
• Alternatives Available with Probable Consequences
• Explanation of the Yielding Behaviour
• Variants in Asch’s Experiments
• Salient Features
• Related Research on Asch’s Findings

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Social Conformity and Group Pressure

Looking at social, psychological, and cultural explanations..

Posted May 21, 2023 | Reviewed by Jessica Schrader

• Solomon Asch is considered the pioneer of experiments related to the impact of social pressure on conformity.
• The extent of conformity has been found to be influenced by factors like culture.
• Awareness of when conformity can be optimal or detrimental can protect individuals from group pressure.

By Varun Ramgopal, Research Affiliate at the Department of Psychology, Monk Prayogshala

What causes individuals to conform to the opinions and judgments of others? Why do they need to? When is it an advantage to merge with the group and when is it not? Investigating the social, psychological, and cultural characteristics of conformity is critical to understanding the extent to which group pressure can modify and distort individual judgment and decision-making . Substantial research, starting with Solomon Asch’s line judgment experiments, has been pursued over the last seven decades to arrive at potential explanations for people’s urge to conform to the majority, while ignoring the unambiguity of their individual perceptual judgments.

In an Asch-style social conformity experiment, seven or eight participants are seated in a room, required to perform a line-judgment task. This task entails reporting which line out of three lines shown on Card 2 is of the same length as that of a single line shown on Card 1. The correct response on each trial is meant to be unambiguous to the normal eye such that the absence of any group pressure would typically result in minimal to no erroneous responses. However, in order to test the influence of social pressure on people’s tendency to conform, these experiments typically involve a single real participant with all the other participants being instructed by the experimenter to play confederates. These confederates unanimously and deliberately disagree with the original participant on certain trials, causing the participant to be torn between their perceptual evidence and the unanimous dissent of the group.

Asch found that an appreciable percentage of individuals submitted to the majority. While interviewed following the experiment, participants who predominantly conformed indicated their lack of confidence in the evidence of their senses and greater confidence in the claims that others made. Surprisingly, there were participants who reported that they yielded to the majority despite believing with certainty that the majority dissenting was incorrect. A meta-analytic review revealed that a larger size of the majority, similarity of the respondent with the majority, a higher proportion of female participants, and a smaller average discrepancy between the original participant’s response and that of the majority were significantly and positively associated with conformity. The (greater) effort that non-conformity might involve with respect to forming individual opinions and convincing others in the group about the validity of one’s judgment could also lead to higher levels of conformity.

Cultural differences in social conformity

Studies aimed at investigating the relationship between culture and conformity have primarily focussed on making comparisons between individualistic and collectivist societies. According to Hofstede , individualistic structures entail ascribing greater importance to individual goals , self-actualization, autonomy, and uniqueness. Collectivism, on the other hand, focuses on the well-being and interdependence of social entities such as families and friendships. In a collectivist culture, the individual strives for the benefit and welfare of the social unit rather than just focusing on individual achievement. Examples of countries that have a collectivist structure in place could include Asian countries such as China, India, Japan, and South Korea, among others (MasterClass, 2022a). Countries such as the United States, the United Kingdom, Australia, and New Zealand, among other countries, are considered to have individualistic cultures (MasterClass, 2022b).

With regard to the association between culture and conformity, the hypothesis would be that higher levels of social conformity would be found in collectivist structures compared to conformity in individualistic structures. A rationale for this hypothesis would be that individuals coming from collectivist countries could be more inclined to ascribe greater importance to social opinion and blend with the majority while those from individualistic countries would be more willing to establish their autonomic decision-making abilities and uniqueness.

A meta-analysis undertaken on the replications of Asch’s line-judgment task across 17 countries confirms the hypothesis stated above: individualistic cultures exhibit lower levels of conformity than collectivist cultures. From a cultural perspective, by relating this finding to some of the moderator variables that have been found to impact conformity, one could argue that individuals from geographies that prioritize social unity may feel a greater need to conform when the size of the majority increases. Similarly, with respect to similarities between the focal participant and the majority, individuals coming from the same cultural background (in-group) would be more likely to conform with each other than with members from another cultural context (out-group).

Other studies that have not used Asch’s line judgment experiment to investigate the cultural element of conformity also report similar findings. Comparing the choices of East Asian and British students in a lottery choice task, a study found that British students were more likely to deviate from the choices made by the majority, comprising peers. Findings from the same study also revealed that with an increase in the size of the majority, East Asian students were more likely to conform in comparison to the choices made by Australians. In another analysis using archival data sources, the authors found that the online review ratings of restaurants provided by individuals from collectivist backgrounds were in concurrence with prior ratings compared to the ratings of those coming from individualistic backgrounds.

Considering individuals’ general tendency to submit to the majority in the group pressure experiments discussed above, with cultural differences in the inclination to conform, there is one question that demands attention : Is it always detrimental to conform to social opinions? For example, in the context of health care, one would be better off consciously discerning, conforming to, and spreading information on the effectiveness of preventive health-related behaviours and vaccines rather than conforming to conspiracy theories that emerge against these beneficial preventive measures. In social contexts such as the one created in the Asch line judgment task, it may pay off to consciously remind oneself of the fact that just because one is part of the minority, it does not necessarily imply that they are incorrect. Such explicit awareness could help individuals to make rational decisions and enhance their individual fitness in an evolutionary sense.

MasterClass. (2022a, November 9). Collectivist Culture: Pros and Cons of a Collectivist Culture - 2023 - MasterClass . https://www.masterclass.com/articles/collectivist-culture

MasterClass. (2022b, November 13). Individualistic Culture Explained: Pros and Cons of Individualism - 2023 - MasterClass . https://www.masterclass.com/articles/individualistic-culture

Monk Prayogshala Research Institution is a not-for-profit academic research institution in Mumbai, India.

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Asch Study Reimagined: Navigating the Labyrinth of Conformity in the Contemporary Mind

Summary: A recent study replicates and extends Solomon Asch’s famous conformity experiments, revealing intriguing insights about human behavior.

The research demonstrates that monetary incentives reduce conformity errors in line-judging tasks, though social influence remains a factor. It also extends Asch’s findings to political opinions, showing a significant rate of conformity.

Interestingly, the study finds that openness, but not other personality traits like intelligence or self-esteem, is inversely related to conformity, challenging long-held assumptions about social influence.

• The study replicated Asch’s experiment with 210 participants, finding a 33% error rate in standard line-judging tasks and a 25% error rate when monetary incentives were involved.
• When examining political opinions, the conformity rate was 38%, suggesting that social influence extends beyond simple perceptual tasks to more complex beliefs.
• Among various personality traits studied, only ‘openness’ from the Big Five was found to be significantly related to lower susceptibility to conformity, contrary to expectations about traits like intelligence or self-esteem.

Source: Neuroscience News

In the realm of social psychology, few experiments have garnered as much attention and debate as Solomon Asch’s conformity experiments from the 1950s. These groundbreaking studies highlighted the compelling power of social influence, showing how individuals could be swayed by group opinions even against their own senses.

Fast-forward to the present, a recent study seeks to revisit these pivotal experiments, offering fresh insights into the dynamics of conformity in today’s context.

The primary aim of the new research was fourfold: to replicate Asch’s original experiment with a contemporary cohort, to assess the impact of monetary incentives on conformity, to extend the exploration of conformity to the domain of political opinions, and to investigate the relationship between various personality traits and the propensity to conform.

Conducted with 210 participants, the study meticulously replicated Asch’s original line-judging task, while introducing nuanced variations to probe deeper into the psychological underpinnings of conformity.

One of the study’s most compelling findings was the persistent influence of social pressure, even when monetary incentives were introduced. While financial rewards did reduce the error rate from 33% to 25% in line-judging tasks, the fact that a significant proportion of participants still conformed to the group’s incorrect judgment underscores the robustness of social influence.

This finding adds a new layer to our understanding of conformity, suggesting that human behavior in group contexts is not solely driven by rational, self-interested calculations but is also significantly influenced by the desire to align with social norms.

Expanding the scope of Asch’s work, the study also ventured into the realm of political opinions. The researchers found a conformity rate of 38%, indicating that social influence extends beyond simple perceptual tasks to the more complex territory of beliefs and opinions.

This extension is particularly relevant in our current era, where political discourse is increasingly polarized and influenced by group dynamics. The findings suggest that the social environment can significantly shape political views, raising important questions about the formation of public opinion and the role of social conformity in political decision-making.

Another intriguing aspect of the study was its exploration of the relationship between personality traits and susceptibility to conformity. Contrary to what one might expect, the research found that traits like intelligence, self-esteem, and the need for social approval were not convincingly related to conformity. The only exception was ‘openness’ from the Big Five personality traits, which showed an inverse relationship with conformity.

This challenges some traditional assumptions about the types of personalities that are more likely to conform and suggests that a willingness to entertain new ideas and experiences might actually buffer against social pressure.

The study also raises critical questions about the universality of Asch’s findings. While the original experiments were predominantly conducted with American student samples, this research, along with other international studies, suggests that the influence of groups on individual judgment is a universal phenomenon, prevalent across different cultures and contexts.

This universality speaks to the fundamental nature of social influence in human psychology and underscores its relevance across diverse social and cultural landscapes.

However, the study is not without limitations. The sample, composed predominantly of students, highlights the need for further research with more diverse participant pools.

Additionally, the study’s participants were strangers, leaving open the question of whether group pressure would be stronger or weaker among acquaintances or friends.

Furthermore, the study’s use of relatively moderate and general political statements raises the question of whether the findings would hold true for more extreme or divisive opinions.

Despite these limitations, the study offers a compelling modern reexamination of Asch’s conformity experiments. It not only reaffirms the enduring power of social influence but also extends our understanding of how this influence manifests in the context of political opinions and the role of personality traits in susceptibility to conformity.

The findings have far-reaching implications for various domains, from understanding group dynamics in organizational settings to the shaping of public opinion in the political arena.

In conclusion, this study not only pays homage to a classic experiment but also propels it into the contemporary era, offering new insights and raising intriguing questions for future research.

It serves as a reminder of the complex interplay between individual psychology and social dynamics, a relationship that continues to fascinate and challenge researchers in the field of social psychology.

About this conformity and social neuroscience research news

Author: Neuroscience News Communications Source: Neuroscience News Contact: Neuroscience News Communications – Neuroscience News Image: The image is credited to Neuroscience News

Original Research: Open access. “ The power of social influence: A replication and extension of the Asch experiment ” by Axel Franzen et al. PLOS ONE

The power of social influence: A replication and extension of the Asch experiment

In this paper, we pursue four goals: First, we replicate the original Asch experiment with five confederates and one naïve subject in each group (N = 210).

Second, in a randomized trial we incentivize the decisions in the line experiment and demonstrate that monetary incentives lower the error rate, but that social influence is still at work.

Third, we confront subjects with different political statements and show that the power of social influence can be generalized to matters of political opinion.

Finally, we investigate whether intelligence, self-esteem, the need for social approval, and the Big Five are related to the susceptibility to provide conforming answers.

We find an error rate of 33% for the standard length-of-line experiment which replicates the original findings by Asch (1951, 1955, 1956). Furthermore, in the incentivized condition the error rate decreases to 25%.

For political opinions we find a conformity rate of 38%. However, besides openness, none of the investigated personality traits are convincingly related to the susceptibility of group pressure.

One thing I think is worth looking into is of those who showed the trait of openness, what percentage of them were ADHD or Autistic? It seems many people with ADHD or Autism exhibit openness, so I think it would be nice if it could be confirmed that these groups of people actually act as protection against society fully conforming to destructive ideas, because they are less likely to conform.

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FOCUSED REVIEW article

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

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Peer influence: neural mechanisms underlying in-group conformity

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• 1 Department of Psychology, Stanford University, Stanford, CA, USA
• 2 Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, Netherlands
• 3 Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands

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.

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

Citation: Stallen M and Sanfey AG (2015) The neuroscience of social conformity: implications for fundamental and applied research. Front. Neurosci . 9:337. doi: 10.3389/fnins.2015.00337

Received: 29 May 2015; Accepted: 07 September 2015; Published: 28 September 2015.

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Copyright © 2015 Stallen and Sanfey. 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.

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What Is Conformity? Definition, Types, Psychology Research

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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On This Page:

Conformity is a type of social influence involving a change in belief or behavior in order to fit in with a group.

This change is in response to real (involving the physical presence of others) or imagined (involving the pressure of social norms/expectations) group pressure.

Conformity can also be simply defined as “ yielding to group pressures ” (Crutchfield, 1955).  Group pressure may take different forms, for example bullying, persuasion, teasing, criticism, etc.  Conformity is also known as majority influence (or group pressure).

The term conformity is often used to indicate an agreement to the majority position, brought about either by a desire to ‘ fit in ’ or be liked (normative) or because of a desire to be correct (informational), or simply to conform to a social role (identification).

Jenness (1932) was the first psychologist to study conformity.  His experiment was an ambiguous situation involving a glass bottle filled with beans.

He asked participants individually to estimate how many beans the bottle contained.  Jenness then put the group in a room with the bottle and asked them to provide a group estimate through discussion.

Participants were then asked to estimate the number on their own again to find whether their initial estimates had altered based on the influence of the majority.

Jenness then interviewed the participants individually again and asked if they would like to change their original estimates or stay with the group’s estimate.  Almost all changed their individual guesses to be closer to the group estimate.

However, perhaps the most famous conformity experiment was by Solomon Asch (1951) and his line judgment experiment.

Types of Conformity

Kelman (1958) distinguished between three different types of conformity:

Compliance (or group acceptance)

This occurs “when an individual accepts influence because he hopes to achieve a favorable reaction from another person or group. He adopts the induced behavior because….he expects to gain specific rewards or approval and avoid specific punishment or disapproval by conformity” (Kelman, 1958, p. 53).

In other words, conforming to the majority (publicly) in spite of not really agreeing with them (privately). This is seen in Asch’s line experiment .

Compliance stops when there are no group pressures to conform and is, therefore, a temporary behavior change.

Internalization (genuine acceptance of group norms)

This occurs “when an individual accepts influence because the content of the induced behavior – the ideas and actions of which it is composed – is intrinsically rewarding . He adopts the induced behavior because it is congruent [consistent] with his value system” (Kelman, 1958, p. 53).

Internalization always involves public and private conformity. A person publicly changes their behavior to fit in with the group while also agreeing with them privately.

This is the deepest level of conformity, where the beliefs of the group become part of the individual’s own belief system. This means the change in behavior is permanent. This is seen in Sherif’s autokinetic experiment.

This is most likely to occur when the majority has greater knowledge and members of the minority have little knowledge to challenge the majority’s position.

Identification (or group membership)

This occurs “when an individual accepts influence because he wants to establish or maintain a satisfying self-defining relationship to another person or group” (Kelman, 1958, p. 53).

Individuals conform to the expectations of a social role, e.g., nurses and police officers.

It is similar to compliance as there does not have to be a change in private opinion. A good example is Zimbardo’s Prison Study .

Ingratiational

This is when a person conforms to impress or gain favor/acceptance from other people.

It is similar to normative influence but is motivated by the need for social rewards rather than the threat of rejection, i.e., group pressure does not enter the decision to conform.

Why Do People Conform?

Deutsch and Gerrard (1955) identified two reasons why people conform :

Normative Conformity

• Yielding to group pressure because a person wants to fit in with the group. E.g., Asch Line Study.
• Conforming because the person is scared of being rejected by the group.
• This type of conformity usually involves compliance – where a person publicly accepts the views of a group but privately rejects them.

Informational Conformity

• This usually occurs when a person lacks knowledge and looks to the group for guidance.
• Or when a person is in an ambiguous (i.e., unclear) situation and socially compares their behavior with the group. E.g., Sherif’s Study.
• This type of conformity usually involves internalization – where a person accepts the views of the groups and adopts them as an individual.

Conformity Examples

Sherif (1935) autokinetic effect experiment.

Aim : Sherif (1935) conducted an experiment with the aim of demonstrating that people conform to group norms when they are put in an ambiguous (i.e., unclear) situation.

Method : Sherif used a lab experiment to study conformity.  He used the autokinetic effect – this is where a small spot of light (projected onto a screen) in a dark room will appear to move even though it is still (i.e., it is a visual illusion).

It was discovered that when participants were individually tested, their estimates of how far the light moved varied considerably (e.g., from 20cm to 80cm).

The participants were then tested in groups of three.  Sherif manipulated the composition of the group by putting together two people whose estimate of the light movement when alone was very similar and one person whose estimate was very different.  Each person in the group had to say aloud how far they thought the light had moved.

Results : Sherif found that over numerous estimates (trials) of the movement of light, the group converged to a common estimate.  The person whose estimate of movement was greatly different from the other two in the group conformed to the view of the other two.

Sherif said that this showed that people would always tend to conform.  Rather than make individual judgments, they tend to come to a group agreement.

Conclusion : The results show that when in an ambiguous situation (such as the autokinetic effect), a person will look to others (who know more / better) for guidance (i.e., adopt the group norm).  They want to do the right thing but may lack the appropriate information.  Observing others can provide this information.  This is known as informational conformity.

Non Conformity

Not everyone conforms to social pressure.  Indeed, there are many factors that contribute to an individual’s desire to remain independent of the group.

For example, Smith and Bond (1998) discovered cultural differences in conformity between western and eastern countries.  People from Western cultures (such as America and the UK) are more likely to be individualistic and don’t want to be seen as being the same as everyone else.

This means that they value being independent and self-sufficient (the individual is more important than the group) and, as such, are more likely to participate in non-conformity.

In contrast, eastern cultures (such as Asian countries) are more likely to value the needs of the family and other social groups before their own.  They are known as collectivist cultures and are more likely to conform.

Asch, S. E. (1951). Effects of group pressure upon the modification and distortion of judgments. In H. Guetzkow (Ed.), Groups, leadership and men . Pittsburg, PA: Carnegie Press.

Crutchfield, R. (1955). Conformity and Character. American Psychologist , 10, 191-198.

Deutsch, M., & Gerard, H. B. (1955). A study of normative and informational social influences upon individual judgment . The journal of abnormal and social psychology, 51(3) , 629.

Jenness, A. (1932). The role of discussion in changing opinion regarding a matter of fact.  The Journal of Abnormal and Social Psychology , 27, 279-296.

Kelman, H. C. (1958). Compliance, identification, and internalization: three processes of attitude change . Journal of Conflict Resolution, 2, 51–60.

Mann, L (1969). Social Psychology . New York: Wiley.

Sherif, M. (1935). A study of some social factors in perception. Archives of Psychology , 27(187) .

Smith, P. B., & Bond, M. H. (1993). Social Psychology Across Cultures: Analysis and Perspectives . Hemel Hempstead: Harvester Wheatsheaf.

# 5 Famous Social Psychology Experiments

There are countless social psychology experiments that have been influential. Here, we highlight five powerful experiments in social psychology that have shaped the development of the field.

# 1. Solomon Asch’s Experiments on Conformity

Solomon Asch carried out a series of psychological tests known as the Asch Conformity Experiments in the 1950s to find out how much social pressure from the majority group could persuade a person to conform. Asch’s experimental hypothesis was centered around how people gave in to peer pressure and whether they would disregard their own opinions in order to fit in with the group. The experiment summary of the Asch conformity studies is that several lines with different heights are presented and the participant is challenged by the confederate’s answers to either agree or disagree.

The Asch experiment's basic design comprised a subject and a cohort of accomplices. The participants were informed that they would be performing a visual perception task in which they would need to match a given line's length to one of three comparison lines.

Out of all the participants in each group, only one was truly ‘naïve’; the others were ‘confederates’ who were told to provide false answers on purpose for specific trials. Thus, the ‘naive’ participant would be challenged by the ‘confederates’ who provided wrong answers. This would essentially place the ‘naive’ participant in a challenging position to be in.

An example of the experimental procedure from Solomon Asch’s experiment on conformity in 1955. There are 6 confederates pictures and the 1 real participant, sitting in the second to last seat, who are looking at the trial stimuli at the front of the room. Image copyright: Cara Flanagan.

Throughout the trials, the confederates would intentionally select the incorrect response. The crucial query was whether the ‘naive’ participant would follow their own accurate assessment or adhere to the false majority opinion. The results and findings demonstrated that even in cases where the right response was evident, a sizable portion of the ‘naive’ participants would agree with the confederate group's inaccurate responses.

The degree of conformity was influenced by several factors:

• Group Size: Up to a certain point, conformity grew in proportion to the size of the majority. The rate of conformity did not significantly increase after a certain number of confederates.
• Unanimity: A participant was far less likely to comply if even one other person in the group provided the right response. The pressure to fit in was significantly lessened when there was a dissident voice.
• Task Difficulty: Participants found it more difficult to trust their own judgment when the task was more ambiguous or difficult, ie. when the comparison lines were more similar in size, leading to an increase in conformity.
• Response Type - Public vs. Private: When participants were required to provide their answers in public, they were more likely to comply, as opposed to when providing answers privately. Thus, one factor that clearly affected conformity was the fear of social rejection.

In summary, the Asch Conformity Experiment results emphasize the strong influence of social pressure on individual behavior and the propensity to conform even in the face of clear evidence to the contrary, have become classic studies in social psychology.

Try it out in Labvanced:

A preview of the data and Asch Conformity Experiment results recorded in Labvanced can be seen in the image below, such as the values for the presented line heights, choices, and reaction times:

View of the data collected from an online version of the Asch Conformity Experiment conducted with Labvanced.

open in new window

# 2. Bobo Doll Experiment by Albert Bandura: Social Learning Theory

Frames from a video and images shown to the children who participated in the Bobo doll experiment. Copyright owner: Albert Bandura.

Social psychologist Albert Bandura carried out a groundbreaking study in 1961 called the Bobo Doll experiment, which made a substantial contribution to our understanding of children's social learning and aggression. Bandura was curious about how children learn to pick up new behaviors by imitation and observation.

In this experiment, children interacted with a life-sized inflatable doll called Bobo while being exposed to adult models who were aggressive and non-aggressive. The conditions of the study were as follows:

• Aggressive Model Condition: Children witnessed a role model act violently against the Bobo doll. Along with hitting and kicking, the aggressive actions included verbal abuse.
• Non-Aggressive Model Condition: Children witnessed a role model who did not act aggressively toward the Bobo doll.
• Control Group: No adult role model was seen interacting with the Bandura Bobo doll.

Children were placed in a room with the Bobo doll and other toys after looking at the conditions / models. The purpose of the study was to determine whether the children would imitate the violent acts they witnessed.

The Bobo Doll study produced some fascinating results. Compared to the control group and the non-aggressive model, children who watched the aggressive model were more likely to act aggressively toward the Bobo doll. This finding aligned with Albert Bandura's social learning theory which postulates that people learn new abilities through observing and imitating the behaviors of others. The girls in the aggressive model condition also reacted more physically aggressive when the model was male, but they responded more verbally when the model was female. The observation of how frequently they punched Bobo broke the general pattern of gender-inverted effects. It was also found that boys were more likely than girls to imitate same-sex models.

Our knowledge of the roles that imitation and observational learning play in children's development of aggressive behaviors has greatly increased as a result of Bandura’s Bobo doll study.

# 3. Stanford Prison Experiment by Philip Zimbardo

Experiment participants who had the role of a ‘guard’, pictured walking in the prison yard.

Social psychologist Philip Zimbardo carried out a study at Stanford University in 1971 that is known as the Stanford Prison Experiment. The experiment's goal was to find out how people would act in a prison simulation if they were in positions of power or powerlessness.

Out of the 75 volunteers, Zimbardo and his colleagues chose 24 male college students to take part in the study. The participants were divided into two groups at random and placed in a mock prison located in the Stanford psychology building's basement: guards or inmates.

The participants were completely absorbed in their parts; guards were deindividualized by being outfitted in sunglasses and uniforms, and inmates were given numbers rather than names. The guards started acting abusively and authoritarian toward the inmates as a result of the authority that had been bestowed upon them. In response, the inmates displayed symptoms of severe stress and emotional collapse.

The experiment was supposed to last two weeks, but because of the participants' severe psychological distress, it was called off after just six days! The experiment's inherent ethical issues surfaced as a result of the situation getting worse. The study has sparked ethical questions due to issues like incomplete debriefing, intense simulation, and incompletely informed consent. Because the participants' psychological well-being was compromised, Philip Zimbardo’s Stanford Prison Experiment has come under fire on a number of occasions.

In summary, the results for Philip Zimbardo’s Stanford experiment shed a light on how even ordinary beings can quickly adopt harmful and dangerous behaviors just because of their environment or roles. The Stanford Prison Experiment is frequently brought up in conversations concerning how circumstances can affect behavior and how people can misuse their power when they are in positions of authority.

# 4. Obedience Experiment by Stanley Milgram

The study setup of the Obedience experiment where the experimenter and student are confederates and the teacher who is the participant is instructed to administer shocks.

In the early 1960s, social psychologist Stanley Milgram carried out a number of contentious studies on submission to authority figures and the Milgram Experiment is the most well-known of these studies.

For the Obedience Experiment, three people were involved in the basic setup of the experiment: the learner (an associate of the experimenter), the teacher (a participant), and the experimenter (an authority figure). The ‘teacher’ participant was informed that the overall aim of the study was to examine the impact of punishment on learning and was directed to shock the student with progressively stronger electric shocks each time they erred on a memory task. The teacher participants were led to believe that the shocks were real (even though they weren't). Thus this setup was a mask for the real aim of the study: to assess to what extent an individual will be obedient to an authority figure, even in the case where their obedience is causing severe harm to others.

As the experiment went on, the experimenter (ie. the authority figure) would give the participant instructions to intensify the shocks while the learner, or confederate, made deliberate mistakes. Voltage levels ranging from mild to severe were labeled on the shocks, with the highest level indicating possible danger from 15 volts to 450 (danger – severe shock). Thus, the teacher could see how dangerous the high shock levels were and know they were ‘inflicting’ pain (even though the shocks were not real).

In summary, the key discovery of Milgram's Obedience to Authority experiment was that a sizable fraction of participants kept shocking the confederate even after they showed signs of distress, objected, and finally fell silent. The experiment result showed that a significant number of participants used the shock generator to its maximum capacity, demonstrating a high degree of submission to authority.

Because Stanley Milgram's Obedience study caused participants psychological distress, criticism and questions were raised pointing to its ethical issues. However, the study still managed to shed light on how common people might act dubiously or immorally when directed by an authority figure, offering insightful information about the influence of authority and social conformity.

# 5. The Hawthorne Effect by Henry A. Landsberger

A phenomenon known as the Hawthorne Effect occurs when people adjust their behavior when they become aware that they are being watched or observed by others. A set of experiments carried out at the Western Electric Hawthorne Works in Chicago in the 1920s and 1930s led to the naming of this effect. The initial purpose of the studies was to look into how worker productivity and lighting conditions relate to one another. Elton Mayo also studied in this context how work structure changes (like rest periods) influenced worked outcomes at the factory.

The data from the Hawthorne studies were later reanalyzed and interpreted in the 1950s by social scientist Henry A. Landsberger. His work, especially the 1958 paper "Hawthorne Revisited," was instrumental in making the Hawthorne Effect concept widely known.

Landsberger came to the conclusion that it was the workers' awareness of being observed/studied that actually explained the observed changes in worker productivity, rather than the lighting conditions as first believed. The workers' motivation and performance improved as a result of the researchers' interest and attention.

From then, the results from Hawthorne Effect study has gained widespread acceptance in organizational behavior psychology and social science. It emphasizes how crucial social and psychological elements are in shaping behavior, especially in settings like research or in the workplace where people may behave differently because they are aware that they are being watched or studied. The Hawthorne Effect is frequently brought up when talking about the difficulties in using human subjects in experiments and research because it can be difficult to identify and comprehend the underlying causes of observed behavior when subjects are aware they are being observed.

# Social Psychology Experiments Today

While these classic experiments helped establish the field of social psychology by studying complex topics like obedience and conformity, today there are more ethical guidelines that researchers must follow.

Furthermore, due to the digitization of the 21st century, online experiments are becoming more and more popular which allow for participants to complete tasks together using their computers or smartphones.

# References

• Asch, S. E. (1952). Group forces in the modification and distortion of judgments. In S. E. Asch, Social psychology (pp. 450–501). Prentice-Hall, Inc.
• Asch, S. E. (1953). Effects of group pressure upon the modification and distortion of judgements. Group dynamics. Asch, S. E. (1956). Studies of independence and conformity: I. A minority of one against a unanimous majority. Psychological monographs: General and applied, 70(9), 1.
• Bandura, A. (1965). Influence of models' reinforcement contingencies on the acquisition of imitative responses. Journal of personality and social psychology, 1(6), 589.
• Bandura, A., Ross, D., & Ross, S. A. (1961). Transmission of aggression through imitation of aggressive models. The Journal of Abnormal and Social Psychology, 63(3), 575.
• Bandura, A., Ross, D., & Ross, S. A. (1963). Imitation of film-mediated aggressive models. The Journal of Abnormal and Social Psychology, 66(1), 3.
• Bandura, A., & Walters, R. H. (1977). Social learning theory(Vol. 1). Prentice Hall: Englewood cliffs.
• Landsberger, H. A. (1958). Hawthorne Revisited: Management and the Worker, Its Critics, and Developments in Human Relations in Industry.
• Milgram, S. (1963). Behavioral study of obedience. The Journal of abnormal and social psychology, 67(4), 371.
• Milgram, S. (1965). Some conditions of obedience and disobedience to authority. Human relations, 18(1), 57-76.
• Zimbardo, P. G. (1973). On the ethics of intervention in human psychological research: With special reference to the Stanford prison experiment. Cognition, 2(2), 243–256.
• Zimbardo, P. G. (1995). The psychology of evil: A situationist perspective on recruiting good people to engage in anti-social acts. Japanese Journal of Social Psychology, 11(2), 125-133.

The Asch Conformity Experiments

What Solomon Asch Demonstrated About Social Pressure

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The Asch Conformity Experiments, conducted by psychologist Solomon Asch in the 1950s, demonstrated the power of conformity in groups and showed that even simple objective facts cannot withstand the distorting pressure of group influence.

The Experiment

In the experiments, groups of male university students were asked to participate in a perception test. In reality, all but one of the participants were "confederates" (collaborators with the experimenter who only pretended to be participants). The study was about how the remaining student would react to the behavior of the other "participants."

The participants of the experiment (the subject as well as the confederates) were seated in a classroom and were presented with a card with a simple vertical black line drawn on it. Then, they were given a second card with three lines of varying length labeled "A," "B," and "C." One line on the second card was the same length as that on the first, and the other two lines were obviously longer and shorter.

Participants were asked to state out loud in front of each other which line, A, B, or C, matched the length of the line on the first card. In each experimental case, the confederates answered first, and the real participant was seated so that he would answer last. In some cases, the confederates answered correctly, while in others, the answered incorrectly.

Asch's goal was to see if the real participant would be pressured to answer incorrectly in the instances when the Confederates did so, or whether their belief in their own perception and correctness would outweigh the social pressure provided by the responses of the other group members.

Asch found that one-third of real participants gave the same wrong answers as the Confederates at least half the time. Forty percent gave some wrong answers, and only one-fourth gave correct answers in defiance of the pressure to conform to the wrong answers provided by the group.

In interviews he conducted following the trials, Asch found that those that answered incorrectly, in conformance with the group, believed that the answers given by the Confederates were correct, some thought that they were suffering a lapse in perception for originally thinking an answer that differed from the group, while others admitted that they knew that they had the correct answer, but conformed to the incorrect answer because they didn't want to break from the majority.

The Asch experiments have been repeated many times over the years with students and non-students, old and young, and in groups of different sizes and different settings. The results are consistently the same with one-third to one-half of the participants making a judgment contrary to fact, yet in conformity with the group, demonstrating the strong power of social influences.

Connection to Sociology

The results of Asch's experiment resonate with what we know to be true about the nature of social forces and norms in our lives. The behavior and expectations of others shape how we think and act on a daily basis because what we observe among others teaches us what is normal , and expected of us. The results of the study also raise interesting questions and concerns about how knowledge is constructed and disseminated, and how we can address social problems that stem from conformity, among others.

Updated  by Nicki Lisa Cole, Ph.D.

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6 Shocking Social Psychology Experiments That Show How Far People Go to Fit in

• Post author: Janey Davies, B.A. (Hons)
• Post published: June 20, 2017
• Reading time: 7 mins read
• Post category: Psychology & Mental Health / Uncommon Science

Social psychology experiments can give us great insight into how we think, behave and act.

They help us to explain how our thoughts are influenced by others, how group dynamics work, and how we perceive others.

Here are six of the most important social psychology experiments:

1. The Milgram Experiment

After the atrocities of WW2, scientists wanted to know why a race of people did not speak out, and moreover, why they carried out tasks that were deemed to go against the very fabric of society.

Stanley Milgram (1963) set up an experiment in which participants were told to apply electrical shocks to another participant in another room. What the participants did not know was the person in the other room was in on the experiment and told to scream when the higher levels of power were applied.

Milgram wanted to know how far people would go in obeying an instruction if that instruction meant hurting another person.

Results showed that 65% of participants continued to the highest level of 450 volts . Milgram surmised that people will obey orders if they perceive these orders to be from someone in authority and they can relinquish their responsibility.

2. The Conformity Experiment

The Conformity experiment (1951), one of the most important social psychology experiments, took male students and put them in a room with eight other participants.

These eight were in on the experiment, unbeknown to the male students. The tests were simple enough; three lines of differing lengths were compared to a reference line and the whole group had to pick the line that was the same length.

The right answer was obvious but as the researcher went down the group, all those in on the experiment chose the wrong line . So would the student go along with the group or would they be assertive and chose the correct line?

The results showed that 50% followed the group and gave the wrong answer . Only 25% went against the group and over all the trials the average conformity rate was 33%. This appears to show that our willingness to fit in will override our wish to stand out .

3. The Halo Effect

The ‘ halo effect ’ is a kind of bias where our evaluation of the person leads us to make assumptions about the rest of their character .

One good example of the halo effect is how we perceive celebrities. They are often portrayed as beautiful, handsome, and wealthy. Because of these characteristics, we are more likely to think they are also funny, intelligent, and kind.

One favourable judgement about a person’s personality tends to bleed over into other judgments that are also favourable.

4. Sherif Robbers Cave Experiment

Muzafer Sherif’s most famous experiment is the ‘Robber’s Cave, 1954’, in which he wanted to understand group dynamics, in particular – conflict , negative prejudices, and stereotypes that people experience when groups are competing for resources.

Twenty-two boys were split randomly into two groups and transported to a summer camp, where they were separated with no knowledge of the other group. The boys chose names for their groups; the Rattlers and the Eagles. They spent a week bonding with the members of their group, then a competition stage was introduced.

The two groups met for the first time and competed for resources, prizes, and trophies. Despite the groups having only spent a week together, they were solid in their bonds and immediately started to show prejudice against the other group . At first, it was verbal assaults, then the abuse grew physical.

In the end, the two groups were so aggressive towards each other that researchers had to step in. Even after a two-day cooling off period, the boys were still describing their group in favourable terms and the others in less so terms.

Results suggest that we have an innate need to be in a group and will behave favourably to our group against others.

5. Stanford Prison Experiment

One of the most well-known social psychology experiments, the Stanford Prison Experiment was devised by Philip Zimbardo in 1971. It was focused on  the effects of perceived power , in particular, the struggle between guards and prisoners.

In the experiment, young men were given roles as either guards or prisoners and moved to a prison-like environment in the basement of Stanford University.

It soon became clear that the men given the roles as guards took their roles very seriously and began to abuse the prisoners , both verbally and psychologically. The prisoners appeared to accept their role and accepted the abuse without question. After only six days the situation was so intense that it had to be called off.

The researchers decided that it was the situation we are in that determines our behaviour, and not our individual personalities.

6. How stereotypes affect our judgement

Do we make instant judgements based on stereotypes? In one test, John Bargh (1996) divided 34 participants into 3 groups and subconsciously ‘programmed’ these groups into a different state; rude, polite, and neutral .

In order to do this, the participants were given word puzzles to work out. To install the different states into the three groups, each word puzzle’s answers related to words that defined that particular state, for instance for polite, words used were ‘courteous’, ‘patiently’ and ‘behaved’.

When they had finished, they were asked to talk to the lead experimenter, who was spotted in deep in conversation with someone. They had a choice whether to interrupt his conversation, wait for him to finish, or walk away.

Of the group that had been programmed with rude words , 64% interrupted the experimenter , compared to just 18% of participants programmed with polite words. The neutral condition recorded 36% interrupting.

The results showed that unconscious cues can lead to a change in our behaviour.

As you see from the above social psychology experiments, human nature is so susceptible to social conditioning that it sometimes makes people do truly crazy things.

References:

• https://web.stanford.edu/dept/spec_coll/uarch/exhibits/Narration.pdf
• https://nature.berkeley.edu/ucce50/ag-labor/7article/article35.htm
• https://psycnet.apa.org/record/1952-00803-001
• https://muse.jhu.edu/book/1107
• https://psycnet.apa.org/record/1996-06400-003

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What Is Conformity?

Social pressure can sway your behavior—for good and for bad

What Causes Conformity?

Famous experiments on conformity, types of conformity, factors that can influence conformity.

• Potential Pitfalls

Frequently Asked Questions

Social pressure can sometimes lead us to change our behavior, a process known as conformity. This can sometimes be overt, like being pressured to behave in a certain way, or a more subtle influence that causes you go along with the rest of the group.

Conformity is the act of changing your behaviors to fit in or go along with the people around you.

In some cases, this social influence might involve agreeing with or acting like the majority of people in a specific group, or it might involve behaving in a particular way in order to be perceived as "normal" by the group. Essentially, conformity involves giving in to group pressure.

Keep reading to learn more about how conformity works, how different types of conformity can influence your behavior, and what you can do to resist giving in to social pressure.

Each situation is different, but researchers suggest that there are many reasons why people conform. It isn't always a bad thing. Consider this: in many cases, looking to the rest of the group for clues about how we should behave can be helpful.

Other people might have greater knowledge or experience than we do, so following their lead can actually be instructive.

In some instances, we conform to the group's expectations to avoid looking foolish. This tendency can become particularly strong in situations where we are not quite sure how to act or where the expectations are ambiguous.

In 1955, Deutsch and Gerard identified two key reasons why people conform: informational influence and normative influence.

Informational Influence

Informational influence happens when people change their behavior to be correct. In situations where we are unsure of the correct response, we often look to others who are better informed and more knowledgeable and use their lead as a guide for our own behaviors.

In a classroom setting, for example, this might involve agreeing with the judgments of another classmate you perceive as highly intelligent.

Normative Influence

Normative influence stems from a desire to avoid punishments (such as going along with the rules in class even though you don't agree with them) and gain rewards (such as behaving in a certain way in order to get people to like you).

Conformity is something that happens regularly in our social worlds. Sometimes we are aware of our behavior, but in many cases, it happens without much thought or awareness on our parts.

While conformity isn't always a negative influence, sometimes it causes us to go along with things that we disagree with or behave in ways that we know we shouldn't.

Some of the best-known experiments on the psychology of conformity deal with people going along with the group, even when they know the group is wrong.

Jenness's 1932 Experiment

In one of the earliest experiments on conformity, Jenness asked participants to estimate the number of beans in a bottle. They first estimated the number individually and then later as a group.

After being asked as a group, they were asked again individually. The experimenter found that their estimates shifted from their original guess to closer to what other group members had guessed.

Sherif's Autokinetic Effect Experiments

In a series of experiments, Muzafer Sherif asked participants to estimate how far a dot of light in a dark room moved. In reality, the dot was static, but it appeared to move due to something known as the autokinetic effect. Essentially, tiny movements of the eyes make it appear that a small spot of light is moving in a dark room.

When asked individually, the participants' answers varied considerably. When asked as part of a group, however, Sherif found that the responses converged toward a central mean.

Sherif's results, published in 1935, demonstrated that in an ambiguous situation, people will conform to the group, an example of informational influence.

Asch's Conformity Experiments

In this series of famous experiments , conducted in the 1950s, psychologist Solomon Asch asked participants to complete what they believed was a simple perceptual task. They were asked to choose a line that matched the length of one of three different lines.

When asked individually, participants would choose the correct line. When asked in the presence of confederates who were in on the experiment and who intentionally selected the wrong line, around 75% of participants conformed to the group at least once.

This experiment is a good example of normative influence. Participants changed their answer and conformed to the group in order to fit in and avoid standing out.

Stanford Prison Experiment

In this controversial experiment , conducted in 1971, Philip Zimbardo simulated a prison setting to see how people's behavior would change according to the role they were given (prisoner or prison guard). It showed that behavior was affected by the expectations of the role.

While this is one of the most famous psychology experiments on conformity, it is important to note that it has been criticized extensively, and its results have been questioned. In addition to the ethical issues with the study, recent examination of the research methods and procedures has cast serious doubts on the study's findings, validity, and authenticity.

Normative and informational influences are two important types of conformity, but there are also a number of other reasons why we conform.

Normative Conformity

This type of conformity involves changing one's behavior in order to fit in with a group. For example, a teenager might dress in a certain style because they want to look like their peers who are members of a particular group.

Informational Conformity

In this case, conformity is looking to the group for information and direction (this happens when a person lacks knowledge). Think of attending your first class at a new yoga studio. You would probably watch what others were doing to see where you should hang your coat, stow your shoes, unroll your mat, and so on.

Identification

Identification is conforming based on social roles. In other words, a person might change their behavior to fit with what might be expected of a person in that specific role. The Stanford Prison Experiment is an example of this type of conformity.

Compliance is changing one's behavior while still internally disagreeing with the group. For example, you might read a book for your book club and really enjoy it. But at your meeting, you learn that the other members all disliked the book. Rather than go against the group opinion, you might simply agree that the book was terrible.

Internalization

This type of conformity involves changing one's behavior to be like another person. You might notice this in a friend who's taste in music or movies shifts to match that of their romantic partner.

Conformity doesn't happen in every situation. Some people might resist conformity while being more susceptible to these influences in others. It's important to remember that human behavior and psychology are complex. People may conform in some situations and not in others, depending on factors including:

• The difficulty of the task : Difficult tasks can lead to increased and decreased conformity. Not knowing how to perform a difficult task makes people more likely to conform, but increased difficulty can also make people more accepting of different responses, leading to less conformity.
• Individual differences : Personal characteristics, such as motivation to achieve and strong leadership abilities , are linked with a decreased tendency to conform.
• Group size : People are more likely to conform in situations that involve between three and five other people.
• Situation : People are more likely to conform in ambiguous situations where they are unclear about how they should respond.
• Cultural differences : People from collectivist cultures are more likely to conform.

Potential Pitfalls of Conformity

While fitting in with a group is often beneficial, conformity can sometimes have undesirable consequences. For example:

• Feeling like you have to change your appearance or personality to be a member of a group might lower your self-esteem .
• Succumbing to peer pressure could lead to risky or illegal behavior, such as underage drinking.
• Conformity might also lead to a bystander effect , in which going along with the group means failing to act when someone is in need.
• The desire to conform might also limit your openness to new ideas or arguments.
• And conforming with a group could even result in feelings or acts of prejudice .

Understanding conformity can help you make sense of the reasons why some people go along with the crowd, even when their choices seem out of character for them. It can also help you see how other people's behavior may influence the choices you make.

Compliance is changing one's behavior in response to a request to do so, such as a friend asking you to give them a ride. It's not the same as obedience (for example, a student following a school rule) because the request came from someone who doesn't have authority over you. Conformity is more subtle. It is when you change your behavior (consciously or unconsciously) not based on a request, but based on a perceived need to fit in with those around you.

Research shows that conformity to peers peaks in mid-adolescence, around age 14. At this age, children spend more time with peers and their influence is strongest.

In more individualistic cultures, people are less likely to conform. In collectivist cultures, conformity is more valued.

Conformity bias is the tendency to make decisions or judgments based on other people's behavior. Once one person in a class cheats on a test, for example, others may be more willing to cheat because they see that it is acceptable to the group.

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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."