psychology behind social experiment

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

Join the free PsyBlog mailing list. No spam, ever.

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

The Most Famous Social Psychology Experiments Ever Performed

Social experiments often seek to answer questions about how people behave in groups or how the presence of others impacts individual behavior. Over the years, social psychologists have explored these questions by conducting experiments .

The results of some of the most famous social psychology experiments remain relevant (and often quite controversial) today. Such experiments give us valuable information about human behavior and how group influence can impact our actions in social situations.

At a Glance

Some of the most famous social psychology experiments include Asch's conformity experiments, Bandura's Bobo doll experiments, the Stanford prison experiment, and Milgram's obedience experiments. Some of these studies are quite controversial for various reasons, including how they were conducted, serious ethical concerns, and what their results suggested.

The Asch Conformity Experiments

What do you do when you know you're right but the rest of the group disagrees with you? Do you bow to group pressure?

In a series of famous experiments conducted during the 1950s, psychologist Solomon Asch demonstrated that people would give the wrong answer on a test to fit in with the rest of the group.

In Asch's famous conformity experiments , people were shown a line and then asked to select a line of a matching length from a group of three. Asch also placed confederates in the group who would intentionally choose the wrong lines.

The results revealed that when other people picked the wrong line, participants were likely to conform and give the same answers as the rest of the group.

What the Results Revealed

While we might like to believe that we would resist group pressure (especially when we know the group is wrong), Asch's results revealed that people are surprisingly susceptible to conformity .

Not only did Asch's experiment teach us a great deal about the power of conformity, but it also inspired a whole host of additional research on how people conform and obey, including Milgram's infamous obedience experiments.

The Bobo Doll Experiment

Does watching violence on television cause children to behave more aggressively? In a series of experiments conducted during the early 1960s, psychologist Albert Bandura set out to investigate the impact of observed aggression on children's behavior.

In his Bobo doll experiments , children would watch an adult interacting with a Bobo doll. In one condition, the adult model behaved passively toward the doll, but in another, the adult would kick, punch, strike, and yell at the doll.

The results revealed that children who watched the adult model behave violently toward the doll were likelier to imitate the aggressive behavior later on.​

The Impact of Bandura's Social Psychology Experiment

The debate over the degree to which violence on television, movies, gaming, and other media influences children's behavior continues to rage on today, so it perhaps comes as no surprise that Bandura's findings are still so relevant.

The experiment has also helped inspire hundreds of additional studies exploring the impacts of observed aggression and violence.

The Stanford Prison Experiment

During the early 1970s, Philip Zimbardo set up a fake prison in the basement of the Stanford Psychology Department, recruited participants to play prisoners and guards, and played the role of the prison warden.

The experiment was designed to look at the effect that a prison environment would have on behavior, but it quickly became one of the most famous and controversial experiments of all time.

Results of the Stanford Prison Experiment

The Stanford prison experiment was initially slated to last a full two weeks. It ended after just six days. Why? Because the participants became so enmeshed in their assumed roles, the guards became almost sadistically abusive, and the prisoners became anxious, depressed, and emotionally disturbed.

While the Stanford prison experiment was designed to look at prison behavior, it has since become an emblem of how powerfully people are influenced by situations.  

Ethical Concerns

Part of the notoriety stems from the study's treatment of the participants. The subjects were placed in a situation that created considerable psychological distress. So much so that the study had to be halted less than halfway through the experiment.

The study has long been upheld as an example of how people yield to the situation, but critics have suggested that the participants' behavior may have been unduly influenced by Zimbardo himself in his capacity as the mock prison's "warden."  

Recent Criticisms

The Stanford prison experiment has long been controversial due to the serious ethical concerns of the research, but more recent evidence casts serious doubts on the study's scientific merits.

An examination of study records indicates participants faked their behavior to either get out of the experiment or "help" prove the researcher's hypothesis. The experimenters also appear to have encouraged certain behaviors to help foster more abusive behavior.

The Milgram Experiments

Following the trial of Adolph Eichmann for war crimes committed during World War II, psychologist Stanley Milgram wanted to better understand why people obey. "Could it be that Eichmann and his million accomplices in the Holocaust were just following orders? Could we call them all accomplices?" Milgram wondered.

The results of Milgram's controversial obedience experiments were astonishing and continue to be both thought-provoking and controversial today.

What the Social Psychology Experiment Involved

The study involved ordering participants to deliver increasingly painful shocks to another person. While the victim was simply a confederate pretending to be injured, the participants fully believed that they were giving electrical shocks to the other person.

Even when the victim was protesting or complaining of a heart condition, 65% of the participants continued to deliver painful, possibly fatal shocks on the experimenter's orders.

Obviously, no one wants to believe that they are capable of inflicting pain or torture on another human being simply on the orders of an authority figure. The results of the obedience experiments are disturbing because they reveal that people are much more obedient than they may believe.

Controversy and Recent Criticisms

The study is also controversial because it suffers from ethical concerns, primarily the psychological distress it created for the participants. More recent findings suggest that other problems question the study's findings.

Some participants were coerced into continuing against their wishes. Many participants appeared to have guessed that the learner was faking their responses, and other variations showed that many participants refused to continue the shocks.

What This Means For You

There are many interesting and famous social psychology experiments that can reveal a lot about our understanding of social behavior and influence. However, it is important to be aware of the controversies, limitations, and criticisms of these studies. More recent research may reflect differing results. In some cases, the re-evaluation of classic studies has revealed serious ethical and methodological flaws that call the results into question.

Jeon, HL.  The environmental factor within the Solomon Asch Line Test .  International Journal of Social Science and Humanity.  2014;4(4):264-268. doi:10.7763/IJSSH.2014.V4.360 

Bandura and Bobo . Association for Psychological Science.

Zimbardo, G. The Stanford Prison Experiment: a simulation study on the psychology of imprisonment .

Le Texier T.  Debunking the Stanford Prison Experiment.   Am Psychol.  2019;74(7):823-839. doi:10.1037/amp0000401

Blum B.  The lifespan of a lie .  Medium .

Baker PC. Electric Schlock: Did Stanley Milgram's famous obedience experiments prove anything? Pacific Standard .

Perry G.  Deception and illusion in Milgram's accounts of the obedience experiments .  Theory Appl Ethics . 2013;2(2):79-92.

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

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

The PMC website is updating on October 15, 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Front Neurosci

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

Mirre stallen.

1 Department of Psychology, Stanford University, Stanford, CA, USA

Alan G. Sanfey

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

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 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

• Alpizar F., Carlsson F., Johansson-Stenman O. (2008). Anonymity, reciprocity, and conformity: evidence from voluntary contributions to a national park in Costa Rica . J. Public Econ. 92 , 1047–1060. 10.1016/j.jpubeco.2007.11.004 [ CrossRef ] [ Google Scholar ]
• Bartra O., McGuire J. T., Kable J. W. (2013). The valuation system: a coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value . Neuroimage 76 , 412–427. 10.1016/j.neuroimage.2013.02.063 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Berns G. S., Capra C. M., Moore S., Noussair C. (2010). Neural mechanisms of the influence of popularity on adolescent ratings of music . Neuroimage 49 , 1–24. 10.1016/j.neuroimage.2009.10.070.Neural [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Berns G. S., Chappelow J., Zink C. F., Pagnoni G., Martin-Skurski M. E., Richards J. (2005). Neurobiological correlates of social conformity and independence during mental rotation . Biol. Psychiatry 58 , 245–253. 10.1016/j.biopsych.2005.04.012 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cabinet Office UK Behavioural Insights Team (2012). Applying Behavioural Insights to Reduce Fraud, Error and Debt. London: UK Government. [ Google Scholar ]
• Campbell-Meiklejohn D. K., Bach D. R., Roepstorff A., Dolan R. J., Frith C. D. (2010). How the opinion of others affects our valuation of objects . Curr. Biol. 20 , 1165–1170. 10.1016/j.cub.2010.04.055 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Campbell-Meiklejohn D. K., Kanai R., Bahrami B., Bach D. R., Dolan R. J., Roepstorff A., et al.. (2012a). Structure of orbitofrontal cortex predicts social influence . Curr. Biol. 22 , R123–R124. 10.1016/j.cub.2012.01.012 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Campbell-Meiklejohn D. K., Simonsen A., Jensen M., Wohlert V., Gjerløff T., Scheel-Kruger J., et al.. (2012b). Modulation of social influence by methylphenidate . Neuropsychopharmacology 37 , 1517–1525. 10.1038/npp.2011.337 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cascio C. N., Scholz C., Falk E. B. (2015). Social influence and the brain: persuasion, susceptibility to influence and retransmission . Curr. Opin. Behav. Sci. 3 , 51–57. 10.1016/j.cobeha.2015.01.007 [ CrossRef ] [ Google Scholar ]
• Chang L. J., Sanfey A. G. (2013). Great expectations: neural computations underlying the use of social norms in decision-making . Soc. Cogn. Affect. Neurosci. 8 , 277–284. 10.1093/scan/nsr094 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Charpentier C. J., Moutsiana C., Garrett N., Sharot T. (2014). The brain's temporal dynamics from a collective decision to individual action . J. Neurosci. 34 , 5816–5823. 10.1523/JNEUROSCI.4107-13.2014 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chein J., Albert D., O'Brien L., Uckert K., Steinberg L. (2011). Peers increase adolescent risk taking by enhancing activity in the brain's reward circuitry . Dev. Sci. 14 , 1–16. 10.1111/j.1467-7687.2010.01035.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chen J., Wu Y., Tong G., Guan X., Zhou X. (2012). ERP correlates of social conformity in a line judgment task . BMC Neurosci. 13 :43. 10.1186/1471-2202-13-43 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cialdini R. B., Goldstein N. J. (2004). Social influence: compliance and conformity . Annu. Rev. Psychol. 55 , 591–621. 10.1146/annurev.psych.55.090902.142015 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Clapp J. D., Lange J. E., Russell C., Shillington A., Voas R. (2003). A failed norms social marketing campaign . J. Stud. Alcohol 64 , 409–414. 10.15288/jsa.2003.64.409 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Coleman S. (2007). The Minnesota income tax compliance experiment: replication of the social norms experiment , in MPRA Working Paper , 1–6. 10.2139/ssrn.1393292 [ CrossRef ] [ Google Scholar ]
• Deuker L., Müller A. R., Montag C., Markett S., Reuter M., Fell J., et al.. (2013). Playing nice: a multi-methodological study on the effects of social conformity on memory . Front. Hum. Neurosci. 7 : 79 . 10.3389/fnhum.2013.00079 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Edelson M. G., Dudai Y., Dolan R. J., Sharot T. (2014). Brain substrates of recovery from misleading influence . J. Neurosci. 34 , 7744–7753. 10.1523/JNEUROSCI.4720-13.2014 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Edelson M., Sharot T., Dolan R. J., Dudai Y. (2011). Following the crowd: brain substrates of long-term memory conformity . Science 333 , 108–111. 10.1126/science.1203557 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Falk E. B., Way B. M., Jasinska A. J. (2012). An imaging genetics approach to understanding social influence . Front. Hum. Neurosci. 6 : 168 . 10.3389/fnhum.2012.00168 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fareri D. S., Chang L. J., Delgado M. R. (2015). Computational substrates of social value in interpersonal collaboration . J. Neurosci. 35 , 8170–8180. 10.1523/JNEUROSCI.4775-14.2015 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fareri D. S., Niznikiewicz M., Lee V. K., Delgado M. R. (2012). Social network modulation of reward-related signals . J. Neurosci. 32 , 9045–9052. 10.1523/JNEUROSCI.0610-12.2012 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Formisano E., Kriegeskorte N. (2012). Seeing patterns through the hemodynamic veil - The future of pattern-information fMRI . Neuroimage 62 , 1249–1256. 10.1016/j.neuroimage.2012.02.078 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gallagher H. L., Frith C. D. (2003). Functional imaging of “theory of mind.” Trends Cogn. Sci. 7 , 77–83. 10.1016/S1364-6613(02)00025-6 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Granfield R. (2005). Alcohol use in college: limitations on the transformation of social norms . Addict. Res. Theory 13 , 281–292. 10.1080/16066350500053620 [ CrossRef ] [ Google Scholar ]
• Huang Y., Kendrick K. M., Yu R. (2014). Conformity to the opinions of other people lasts for no more than 3 days . Psychol. Sci. 25 , 1388–1393. 10.1177/0956797614532104 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Huber R. E., Klucharev V., Rieskamp J. (2015). Neural correlates of informational cascades: brain mechanisms of social influence on belief updating . Soc. Cogn. Affect. Neurosci. 10 , 589–597. 10.1093/scan/nsu090 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Izuma K. (2013). The neural basis of social influence and attitude change . Curr. Opin. Neurobiol. 23 , 456–462. 10.1016/j.conb.2013.03.009 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Izuma K., Adolphs R. (2013). Social manipulation of preference in the human brain . Neuron 78 , 563–573. 10.1016/j.neuron.2013.03.023 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Janes L. M., Olson J. M. (2000). Jeer pressure: the behavioral effects of observing ridicule of others . Pers. Soc. Psychol. Bull. 26 , 474–485. 10.1177/0146167200266006 [ CrossRef ] [ Google Scholar ]
• Kim B.-R., Liss A., Rao M., Singer Z., Compton R. J. (2012). Social deviance activates the brain's error-monitoring system . Cogn. Affect. Behav. Neurosci. 12 , 65–73. 10.3758/s13415-011-0067-5 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Klucharev V., Hytönen K., Rijpkema M., Smidts A., Fernández G. (2009). Reinforcement learning signal predicts social conformity . Neuron 61 , 140–151. 10.1016/j.neuron.2008.11.027 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Klucharev V., Munneke M. A. M., Smidts A., Fernández G. (2011). Downregulation of the posterior medial frontal cortex prevents social conformity . J. Neurosci. 31 , 11934–11940. 10.1523/JNEUROSCI.1869-11.2011 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Knutson B., Katovich K., Suri G. (2014). Inferring affect from fMRI data . Trends Cogn. Sci. 18 , 422–428. 10.1016/j.tics.2014.04.006 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Knutson B., Taylor J., Kaufman M., Peterson R., Glover G. (2005). Distributed neural representation of expected value . J. Neurosci. 25 , 4806–4812. 10.1523/JNEUROSCI.0642-05.2005 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lohrenz T., Bhatt M., Apple N., Montague P. R. (2013). Keeping up with the Joneses: interpersonal prediction errors and the correlation of behavior in a tandem sequential choice task . PLoS Comput. Biol. 9 :e1003275. 10.1371/journal.pcbi.1003275 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mason M. F., Dyer R., Norton M. I. (2009). Neural mechanisms of social influence . Organ. Behav. Hum. Decis. Process. 110 , 152–159. 10.1016/j.obhdp.2009.04.001 [ CrossRef ] [ Google Scholar ]
• Moran J. M., Zaki J. (2013). Functional neuroimaging and psychology: what have you done for me lately? J. Cogn. Neurosci. 25 , 834–842. 10.1162/jocn_a_00380 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Morgan T. J. H., Laland K. N. (2012). The biological bases of conformity . Front. Neurosci. 6 : 87 . 10.3389/fnins.2012.00087 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Neighbors C., Larimer M. E., Lewis M. A. (2004). Targeting misperceptions of descriptive drinking norms: efficacy of a computer-delivered personalized normative feedback intervention . J. Consult. Clin. Psychol. 72 , 434–447. 10.1037/0022-006X.72.3.434 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pochon J. B., Levy R., Fossati P., Lehericy S., Poline J. B., Pillon B., et al.. (2002). The neural system that bridges reward and cognition in humans: an fMRI study . Proc. Natl. Acad. Sci. U.S.A. 99 , 5669–5674. 10.1073/pnas.082111099 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Poldrack R. A. (2006). Can cognitive processes be inferred from neuroimaging data? Trends Cogn. Sci. 10 , 59–63. 10.1016/j.tics.2005.12.004 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Poldrack R. A. (2011). Inferring mental states from neuroimaging data: from reverse inference to large-scale decoding . Neuron 72 , 692–697. 10.1016/j.neuron.2011.11.001 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ridderinkhof K. R., Ullsperger M., Crone E. A., Nieuwenhuis S. (2004). The role of the medial frontal cortex in cognitive control . Science 306 , 443–447. 10.1126/science.1100301 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schnuerch R., Gibbons H. (2014). A review of neurocognitive mechanisms of social conformity . Soc. Psychol. 45 , 466–478. 10.1027/1864-9335/a000213 [ CrossRef ] [ Google Scholar ]
• Schultz P. W., Nolan J. M., Cialdini R. B., Goldstein N. J., Griskevicius V. (2007). The constructive, destructive, and reconstructive power of social norms . Psychol. Sci. 18 , 429–434. 10.1111/j.1467-9280.2007.01917.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shestakova A., Rieskamp J., Tugin S., Ossadtchi A., Krutitskaya J., Klucharev V. (2013). Electrophysiological precursors of social conformity . Soc. Cogn. Affect. Neurosci. 8 , 756–763. 10.1093/scan/nss064 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Slemrod J., Allcott H. (2011). Social norms and energy conservation . J. Public Econ. 95 , 1082–1095. 10.1016/j.jpubeco.2011.03.003 [ CrossRef ] [ Google Scholar ]
• Smith S., Windmeijer F., Wright E. (2015). Peer effects in charitable giving: evidence from the (running) field . Econ. J. 125 , 1053–1071. 10.1111/ecoj.12114 [ CrossRef ] [ Google Scholar ]
• Stallen M., De Dreu C. K. W., Shalvi S., Smidts A., Sanfey A. G. (2012). The herding hormone: oxytocin stimulates in-group conformity . Psychol. Sci. 23 , 1288–1292. 10.1177/0956797612446026 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Stallen M., Smidts A., Sanfey A. G. (2013). Peer influence: neural mechanisms underlying in-group conformity . Front. Hum. Neurosci. 7 : 50 . 10.3389/fnhum.2013.00050 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Steinberg L. (2007). Risk taking in adolesence - new perspectives from brain and behavioral science . Curr. Dir. Psychol. Sci. 16 , 55–59. 10.1111/j.1467-8721.2007.00475.x [ CrossRef ] [ Google Scholar ]
• Tafarodi R. W., Kang S.-J., Milne A. B. (2002). When different becomes similar: compensatory conformity in bicultural visible minorities . Pers. Soc. Psychol. Bull. 28 , 1131–1142. 10.1177/01461672022811011 [ CrossRef ] [ Google Scholar ]
• Tomlin D., Nedic A., Prentice D., Holmes P., Cohen J. D. (2013). The neural substrates of social influence on decision making . PLoS ONE 8 :e52630. 10.1371/journal.pone.0052630 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Trautmann-Lengsfeld S. A., Herrmann C. S. (2013). EEG reveals an early influence of social conformity on visual processing in group pressure situations . Soc. Neurosci. 8 , 75–89. 10.1080/17470919.2012.742927 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Trautmann-Lengsfeld S. A., Herrmann C. S. (2014). Virtually simulated social pressure influences early visual processing more in low compared to high autonomous participants . Psychophysiology 51 , 124–135. 10.1111/psyp.12161 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Yarkoni T., Poldrack R., Nichols T. E., Van Essen D. C., Wager T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data . Nat. Methods 8 , 665–670. 10.1038/nmeth.1635 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Yu R., Sun S. (2013). To conform or not to conform: spontaneous conformity diminishes the sensitivity to monetary outcomes . PLoS ONE 8 :e64530. 10.1371/journal.pone.0064530 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zaki J., Schirmer J., Mitchell J. P. (2011). Social influence modulates the neural computation of value . Psychol. Sci. 22 , 894–900. 10.1177/0956797611411057 [ PubMed ] [ CrossRef ] [ Google Scholar ]

• Skip to main content
• Skip to primary sidebar

IResearchNet

Social Psychology Experiments

Social psychology experiments have played a pivotal role in unraveling the intricate tapestry of human behavior, cognition, and emotions within the social context. These experiments represent more than just scientific inquiries; they serve as windows into the fundamental aspects of human nature and the ways in which we interact with others. This article delves into a selection of famous experiments in social psychology, each a milestone in understanding the complexities of human social behavior.

Thesis Statement: The significance of these famous experiments extends far beyond the realm of academia, shaping our understanding of conformity, obedience, group dynamics, morality, and the subconscious biases that influence our decisions and actions. Through these groundbreaking studies, we gain valuable insights into the human condition, prompting us to question, explore, and reflect upon the intricate web of social interactions that define our lives.

Famous Experiments in Social Psychology

The Bennington College study was conducted by sociologist Theodore Newcomb from 1935 until 1939. The study examined the attitudes of students attending the then all-female Bennington College early in the college’s history; indeed, the study began during the first year that the college had a senior class.

Solomon Asch’s Conformity experiments in the 1950s starkly demonstrated the power of conformity on people’s estimation of the length of lines. On over a third of the trials, participants conformed to the majority, even though the majority judgment was clearly wrong. Seventy-five percent of the participants conformed at least once during the experiment.

In Muzafer Sherif ’s Robbers Cave experiment (1954) boys were divided into two competing groups to explore how much hostility and aggression would emerge. It is also known as realistic group conflict theory, because the intergroup conflict was induced through competition over resources.

Leon Festinger’s Cognitive Dissonance experiment subjects were asked to perform a boring task. They were divided into two groups and given two different pay scales. At the end of the study, participants who were paid $1 to say that they enjoyed the task and another group of participants were paid $20 to say the same lie. The first group ($1) would later believe that they like the task better than the second group ($20). People justified the lie by changing their previously unfavorable attitudes about the task (Festinger and Carlsmith 1959).

Stanley Milgram’s Obedience to Authority experiment has shown how far people would go to obey an authority figure. Following the events of the Holocaust in World War II Stanley Milgram’s experiments of the 1960s/1970s showed that normal American citizens were capable of following orders to the point of causing extreme suffering in an innocent human being.

Albert Bandura’s Bobo Doll experiment has demonstrated how aggression is learned by imitation (Bandura et al. 1961). Bandura’s experimental work was one of the first studies in a long line of research showing how exposure to media violence leads to aggressive behavior in the observers.

In Philip Zimbardo’s Stanford Prison experiment a simulated exercise between student prisoners and guards showed how far people would follow an adopted role. This was an important demonstration of the power of the immediate social situation, and its capacity to overwhelm normal personality traits (Haney et al. 1973).

The Milgram Experiment

Background and Context

The Milgram Experiment, conducted by psychologist Stanley Milgram in the early 1960s, arose in a climate of post-World War II questions about obedience, authority, and moral responsibility. Inspired by the Nuremberg Trials and the revelation of the atrocities committed by Nazi personnel who claimed to be “just following orders,” Milgram sought to explore the extent to which individuals would obey authority figures, even when it conflicted with their own moral beliefs.

Experiment Setup and Procedure

The experiment involved three key roles: the experimenter (authority figure), the teacher (participant), and the learner (an actor). Participants believed they were assisting in a study examining the effects of punishment on learning. The teacher was instructed to administer increasingly severe electric shocks to the learner for incorrect responses in a word-pair memory test. Unbeknownst to the teacher, the learner did not actually receive shocks, but their responses were scripted to simulate distress and pain.

Ethical Concerns and Criticisms

The Milgram Experiment has been widely criticized for its ethical implications. Participants were exposed to significant psychological stress and believed they were causing harm to another person, potentially leading to long-lasting emotional trauma. Critics argue that the experiment lacked proper informed consent, and the debriefing process may not have been sufficient to alleviate the distress experienced by participants.

Major Findings and Their Impact

The Milgram Experiment revealed astonishing results. Contrary to expectations, a significant proportion of participants, under the pressure of the authority figure’s commands, continued to administer shocks up to potentially lethal levels, even when they were aware of the learner’s distress. This demonstrated the profound influence of authority figures on individual behavior.

The study shed light on the psychology of obedience and the potential for ordinary people to engage in harmful actions under the guise of following orders. Milgram’s findings raised ethical and moral questions about blind obedience and individual responsibility in the face of authority.

The Stanford Prison Experiment

The Stanford Prison Experiment, conducted by psychologist Philip Zimbardo in 1971, stands as one of the most notorious and influential studies in social psychology. Emerging during a tumultuous period in American history marked by social unrest and the questioning of authority, the experiment sought to investigate the psychological dynamics of power, authority, and the consequences of perceived roles within a simulated prison environment.

Description of the Experiment

The experiment involved the transformation of the basement of Stanford University’s psychology department into a mock prison. Volunteers were randomly assigned to play the roles of either guards or prisoners in a simulated prison environment. The participants quickly adapted to their roles, with guards displaying authoritarian behaviors, and prisoners experiencing psychological distress and rebellion. The study was originally intended to last two weeks but was terminated after only six days due to the alarming and unethical behaviors exhibited by both guards and prisoners.

Ethical Controversies

The Stanford Prison Experiment has been mired in ethical controversies. Critics argue that the psychological harm inflicted upon participants was severe, and the lack of proper oversight allowed the study to veer into dangerous territory. Questions have also been raised regarding the informed consent process, as participants were not fully aware of the potential psychological consequences of their involvement.

Key Findings and Implications

Despite its ethical shortcomings, the Stanford Prison Experiment yielded valuable insights into the malleability of human behavior in response to situational factors. It demonstrated how ordinary individuals could quickly adopt abusive and authoritarian roles when placed in positions of power. The study underscored the importance of ethical considerations in psychological research and prompted discussions about the responsibility of researchers to ensure the well-being of participants.

The implications of the study extend beyond academia, offering a cautionary tale about the potential for abuses of power and authority. It has influenced discussions on ethics in research, the psychology of group dynamics, and the understanding of how situational factors can shape behavior.

The Asch Conformity Experiment

Introduction and Historical Context

The Asch Conformity Experiment, conducted by Solomon Asch in the 1950s, remains a seminal study in the field of social psychology. Emerging in the post-World War II era, this experiment aimed to investigate the extent to which individuals conform to group norms and the impact of social pressure on individual decision-making.

Experiment Design and Methodology

In the Asch Conformity Experiment, participants were placed in a group of individuals, with the participant being the only true subject. The group was presented with a simple perceptual task: comparing the length of lines. Participants were asked to state which of several lines was of equal length to a reference line. Unknown to the participant, the other group members were confederates who had been instructed to give incorrect answers in some trials.

During the critical trials, the confederates deliberately provided incorrect answers that contradicted the obvious correct response. The participant, seated at the end of the row, faced the dilemma of whether to conform to the group’s incorrect consensus or assert their own judgment.

Conformity Results and Interpretations

The results of the Asch Conformity Experiment were striking. Despite the obvious correctness of their own judgments, participants frequently succumbed to group pressure and provided incorrect responses to match the consensus of the group. On average, about one-third of participants conformed to the group’s incorrect answers in the face of social pressure.

Asch’s findings underscored the potent influence of social conformity and the willingness of individuals to abandon their own perceptions and judgment in favor of group consensus. He also identified several factors that influenced the likelihood of conformity, such as the size of the majority and the unanimity of the group.

Influence on Social Psychology and Beyond

The Asch Conformity Experiment significantly impacted social psychology by highlighting the powerful role of social influence on human behavior. It prompted further research into group dynamics, conformity, and the psychology of social norms. Asch’s work laid the foundation for studies on topics such as groupthink, normative influence, and the conditions under which individuals are more likely to resist social pressure.

Beyond social psychology, the experiment has practical implications for understanding how conformity operates in everyday life, from peer pressure among adolescents to decision-making in organizations. The study has also been instrumental in discussions about individual autonomy and the tension between conforming to societal expectations and asserting one’s independent judgment.

The Asch Conformity Experiment remains a timeless exploration of the human propensity to conform and the psychological mechanisms at play when individuals navigate the tension between individuality and social cohesion.

The Robbers Cave Experiment

Background and Purpose of the Study

The Robbers Cave Experiment, conducted by psychologist Muzafer Sherif and his colleagues in 1954, was designed to investigate intergroup conflict and cooperation among children. The study emerged during a time when Cold War tensions and conflicts between nations were a prominent backdrop, prompting Sherif to explore the dynamics of group conflict on a smaller scale.

The central purpose of the study was to understand how group identities, competition, and cooperation could influence the attitudes and behaviors of individuals within groups and across groups. It sought to shed light on the origins of intergroup hostility and the potential for reconciliation.

Experimental Design and Procedures

The study took place at Robbers Cave State Park in Oklahoma and involved two phases.

• Group Formation : In the first phase, a group of 22 boys was divided into two groups, the Rattlers and the Eagles, with no prior knowledge of each other. The boys formed strong group identities through team-building activities and bonding experiences.
• Intergroup Competition : In the second phase, the two groups were introduced to each other and engaged in competitive activities, such as sports and contests, where rivalries quickly developed. The competition intensified intergroup conflicts, leading to name-calling, vandalism, and hostility.
• Intervention and Cooperation : To address the escalating conflict, the researchers initiated activities that required the groups to collaborate, such as solving common problems and working together towards common goals. These cooperative experiences aimed to reduce intergroup tensions.

Notable Findings and Insights on Intergroup Conflict

The Robbers Cave Experiment yielded several important findings:

• Intergroup conflict emerged swiftly when groups were formed and exposed to competition, even among previously unacquainted individuals.
• The competition exacerbated stereotypes and prejudices between the groups.
• Cooperation between groups, when introduced strategically, had the potential to reduce hostilities and foster intergroup harmony.
• The study illustrated the role of superordinate goals (common objectives that transcended group boundaries) in promoting cooperation and reducing conflict.

Practical Applications and Contributions

The Robbers Cave Experiment has had lasting implications in the fields of social psychology and conflict resolution. It provided valuable insights into the dynamics of intergroup conflict and cooperation, shedding light on the processes by which hostility between groups can be both fueled and mitigated.

The concept of superordinate goals, derived from the study, has been widely applied in conflict resolution efforts. By identifying shared objectives that require collaboration across group lines, individuals and societies have been able to bridge divides and work together toward common aims. The study’s lessons have informed strategies for reducing prejudice, improving intergroup relations, and fostering peace in various contexts, including education, organizational management, and international diplomacy.

The Robbers Cave Experiment remains a classic illustration of how group identities and competition can lead to conflict, while also highlighting the potential for cooperation and reconciliation when shared goals and positive intergroup interactions are promoted.

The Zimbardo Stanford Prison Experiment

Overview of the Experiment

The Zimbardo Stanford Prison Experiment, conducted by psychologist Philip Zimbardo in 1971, is a widely recognized and controversial study in the realm of social psychology. The experiment was designed to investigate the psychological effects of perceived power and authority within a simulated prison environment.

In this study, participants were randomly assigned to play the roles of either guards or prisoners in a mock prison set up in the basement of Stanford University’s psychology department. The experiment aimed to explore how individuals, when placed in positions of power or vulnerability, would react and adapt to their roles.

Ethical Considerations and Criticisms

The Zimbardo Stanford Prison Experiment has been marred by significant ethical concerns and criticisms. The study generated intense psychological distress among participants, with the guards exhibiting abusive and authoritarian behaviors, and the prisoners experiencing emotional and psychological harm. The experiment’s duration, initially planned for two weeks, was terminated after only six days due to the extreme and unethical behaviors displayed by participants.

Critics argue that the study lacked proper informed consent, as participants were not fully aware of the potential psychological consequences of their involvement. The absence of proper oversight and safeguards to protect the well-being of participants has been a focal point of ethical critique.

Psychological Effects on Participants

The Zimbardo Stanford Prison Experiment had profound psychological effects on its participants. Guards, assigned to positions of power, quickly adopted authoritarian roles, displaying abusive behaviors toward the prisoners. Prisoners, on the other hand, experienced distress, humiliation, and a sense of powerlessness.

The psychological effects on participants were so severe that the study was terminated prematurely to prevent further harm. Post-experiment interviews revealed that some participants struggled to differentiate between their roles and their true identities, emphasizing the significant impact of situational factors on individual behavior.

Enduring Influence on Social Psychology

Despite its ethical controversies, the Zimbardo Stanford Prison Experiment had a lasting influence on the field of social psychology. It highlighted the malleability of human behavior in response to situational factors and the potential for ordinary individuals to engage in abusive actions when placed in positions of authority.

The study contributed to discussions on ethics in research and the responsibility of researchers to prioritize the well-being of participants. It also prompted further investigations into the psychology of power, authority, and obedience, leading to a deeper understanding of the complexities of human behavior within social contexts.

The Zimbardo Stanford Prison Experiment remains a cautionary tale in the annals of psychology, reminding researchers of the ethical imperative to protect participants and the enduring influence of situational factors on human behavior.

The Little Albert Experiment

Introduction to the Study

The Little Albert Experiment is a classic and ethically controversial study conducted by behaviorist John B. Watson and his graduate student Rosalie Rayner in 1920. The experiment aimed to investigate the process of classical conditioning, particularly the acquisition of phobias and emotional responses in humans.

The study is named after its subject, a 9-month-old boy known as “Little Albert.” It remains a notable case study in the field of psychology due to its ethical concerns and contributions to the understanding of learned behaviors.

Experiment Details and Ethical Concerns

In the Little Albert Experiment, Little Albert was exposed to a white rat, a rabbit, a dog, a monkey, and other stimuli. Initially, he displayed no fear or aversion to these objects. However, Watson and Rayner sought to condition an emotional response in Little Albert by pairing the presentation of these stimuli with a loud, frightening noise (produced by striking a suspended steel bar with a hammer). As a result of this pairing, Little Albert began to exhibit fear and distress in response to the previously neutral stimuli, particularly the white rat.

The ethical concerns surrounding this experiment are significant. Little Albert was not provided with informed consent, and his emotional well-being was disregarded. The study also lacked proper debriefing, and the long-term consequences of Little Albert’s conditioning were not addressed. The ethical standards of today would prohibit such a study from being conducted.

Conditioning Process and Long-Term Implications

The Little Albert Experiment demonstrated the principles of classical conditioning in humans. It illustrated how conditioned emotional responses, such as fear and anxiety, could be acquired through association with previously neutral stimuli. In this case, Little Albert learned to fear the white rat because it had been consistently paired with a loud, frightening noise.

The long-term implications of the study are less clear due to a lack of follow-up research on Little Albert. It remains unknown whether his conditioned fears persisted or how they may have impacted his later development. The study’s ethical shortcomings prevent a comprehensive assessment of its long-term effects.

Contemporary Perspectives on the Study

The Little Albert Experiment is viewed with skepticism and ethical concern from contemporary perspectives. It serves as a reminder of the importance of informed consent, debriefing, and the ethical treatment of research participants in psychological research. Ethical standards in research have evolved significantly since the time of the experiment, emphasizing the need to prioritize the well-being and rights of participants.

While the Little Albert Experiment contributed to the understanding of classical conditioning, it also serves as a cautionary tale about the ethical boundaries of research and the potential consequences of disregarding the psychological well-being of participants. Modern research ethics prioritize the protection and respect of individuals involved in psychological studies, ensuring that similar experiments would not be conducted today.

The Blue-Eyes/Brown-Eyes Exercise

Historical Context and Significance

The Blue-Eyes/Brown-Eyes Exercise is a landmark social experiment conducted by educator and activist Jane Elliott in the late 1960s. The experiment was born out of the civil rights movement in the United States and sought to address issues of racism, discrimination, and prejudice. Against the backdrop of racial tensions and the struggle for civil rights, Elliott designed the exercise to provide a firsthand experience of the effects of discrimination.

Experiment Design and Outcomes

In the Blue-Eyes/Brown-Eyes Exercise, Elliott divided her third-grade students into two groups based on eye color, designating one group as “superior” (those with blue eyes) and the other as “inferior” (those with brown eyes). Over the course of the exercise, Elliott systematically treated the two groups differently, providing privileges to the superior group while subjecting the inferior group to discrimination and negative stereotypes.

The results of the experiment were profound. Children in the inferior group quickly internalized their assigned role and began to exhibit lower self-esteem, diminished academic performance, and a range of negative emotional responses. On the other hand, those in the superior group displayed increased arrogance and a sense of entitlement.

Elliott conducted the exercise over multiple days, reversing the roles on the second day to provide a taste of both sides of discrimination. The exercise aimed to create empathy and understanding among participants by allowing them to personally experience the emotional and psychological impact of discrimination.

Broader Societal Impact and Implications

The Blue-Eyes/Brown-Eyes Exercise had a significant societal impact. It garnered attention in the media and brought issues of racism and discrimination to the forefront of public consciousness. Elliott’s work challenged prevailing beliefs about the nature of prejudice and discrimination, highlighting the role of societal conditioning in perpetuating such attitudes.

The exercise also emphasized the importance of empathy and perspective-taking in combatting racism and prejudice. By allowing participants to experience discrimination firsthand, Elliott aimed to foster greater empathy and understanding among individuals of different racial backgrounds.

Experimentation in Social Psychology

Experimentation definition.

Experimentation, in its simplest form, is a research method used to investigate the presence or absence of a causal relationship between two variables. This method involves systematically manipulating one variable, known as the independent variable, and then assessing the impact or effect of this manipulation on another variable, referred to as the dependent variable. Through experimentation, researchers aim to discern whether changes in the independent variable cause changes in the dependent variable, providing insights into causal relationships within a given phenomenon or context. This systematic and controlled approach allows for rigorous testing of hypotheses and the establishment of cause-and-effect relationships in scientific inquiry.

Importance and Consequences of Experiments

The importance and consequences of experiments in research are closely tied to their unique ability to establish causal relationships. Here are key features of experiments that facilitate the ability to draw causal conclusions and their implications:

• Establishing Causality: Experiments are highly valuable because they allow researchers to make statements about causality. By systematically manipulating the independent variable and assessing its impact on the dependent variable, researchers can infer that changes in the independent variable cause changes in the dependent variable. This cause-and-effect relationship is central to scientific inquiry and helps uncover the mechanisms underlying various phenomena.
• Directionality of Relationship: Experiments provide a clear temporal sequence where changes in the independent variable precede the assessment of the dependent variable. This temporal order is crucial for determining the directionality of the relationship between variables. In causal relationships, the cause must precede the effect. Experiments ensure that this criterion is met, enabling researchers to infer the causal direction.
• Random Assignment: In experiments, participants are randomly assigned to different experimental groups. Random assignment ensures that each participant has an equal chance of being assigned to any experimental condition, creating equivalent groups at the outset. This eliminates the possibility that pre-existing differences between participants could account for observed differences in the dependent variable. Random assignment strengthens the validity of causal claims by minimizing confounding variables.
• Isolation of Effects: Experiments enable researchers to isolate the effects of the independent variable by controlling all other aspects of the environment. This control ensures that all participants have a similar experience, except for the experimental manipulation. By eliminating extraneous variables, researchers can attribute any observed differences in the dependent variable solely to the independent variable. This isolation of effects enhances the internal validity of the study.

In summary, experiments are a powerful research method that allows for the establishment of causal relationships in scientific inquiry. Their ability to establish causality, ensure temporal precedence, employ random assignment, and isolate the effects of the independent variable makes experiments a cornerstone of empirical research. Researchers must adhere to these principles to draw valid and reliable conclusions about the causal relationships between variables, advancing our understanding of various phenomena in social psychology and other fields.

Some scholars have questioned the utility of experimentation, noting that the experiments which researchers design sometimes do not resemble the circumstances that people encounter in their everyday lives. However, experimentation is the only research method that allows one to definitively establish the existence of a causal relationship between two or more variables.

References:

• Goodwin, C. J. (2003). Research methods in psychology: Methods and design. New York: Wiley.
• Pelham, B. W. (1999). Conducting research in psychology: Measuring the weight of smoke. Pacific Grove,CA: Brooks/Cole.

We're sorry, your browser is not supported. Please update to a modern browser to view this page. Learn More

The Biggest Psychological Experiment in History Is Running Now

What can the pandemic teach us about how people respond to adversity?

• Research has shown that when faced with potentially traumatic events, about two thirds of people show psychological resilience.
• But the mental health toll of the pandemic may not fit this paradigm.
• Life has been upended at an unprecedented scope and speed, and researchers see an opportunity to investigate the science of resilience in new ways.

T he impact of ­COVID-19 on the physical health of the world's citizens is extraordinary. By mid-May there were upward of four million cases spread across more than 180 countries. The pandemic's effect on mental health could be even more far-reaching. At one point roughly one third of the planet's population was under orders to stay home. That means 2.6 billion people--more than were alive during World War II--were experiencing the emotional and financial reverberations of this new coronavirus. "[The lockdown] is arguably the largest psychological experiment ever conducted," wrote health psychologist Elke Van Hoof of Free University of Brussels-VUB in Belgium. The results of this unwitting experiment are only beginning to be calculated.

The science of resilience, which investigates how people weather adversity, offers some clues. A resilient individual, wrote Harvard University psychiatrist George Vaillant, resembles a twig with a fresh, green living core. "When twisted out of shape, such a twig bends, but it does not break; instead it springs back and continues growing." The metaphor describes a surprising number of people: As many as two thirds of individuals recover from difficult experiences without prolonged psychological effects, even when they have lived through events such as violent crime or being a prisoner of war. Some even go on to grow and learn from what happened to them. But the other third suffers real psychological distress--some people for a few months, others for years.

Even if most individuals prove resilient, the toll of the ­COVID-19 disruptions and the sheer numbers involved have experts warning of a mental illness "tsunami." People face a multiple wallop: the threat of disease, loneliness of isolation, loss of loved ones , repercussions of job loss and ongoing uncertainty about when the pandemic will end . Depression, anxiety and post-traumatic stress will undoubtedly follow for some. Mental health hotlines are reporting surges in calls, and early surveys have found high levels of concern. "This pandemic just ticks all the boxes in terms of the kinds of stressors that are going to be difficult," says psychologist Anita DeLongis of the University of British Columbia, who studies psychosocial responses to disease. The deaths by suicide of health care professionals who had been on the medical front lines are powerful reminders of the risks .

Individual resilience is further complicated by the fact that this pandemic has not affected each person in the same way. For all that is shared--the coronavirus has struck every level of society and left few lives unchanged-- there has been tremendous variation in the disruption and devastation experienced . Consider Brooklyn, just one borough in hard-hit New York City. Residents who started the year living or working within a few miles of one another have very different stories of illness, loss and navigating the challenges of social distancing. How quickly and how well individuals, businesses and organizations recover will depend on the jobs, insurance and health they had when this started, on whether they have endured hassle or heartbreak, and on whether they can tap financial resources and social support.

The pandemic has laid bare the inequities in the American health care system and economic safety net. Black and Latino Americans are dying at much higher rates than white Americans . "When we talk about preexisting conditions, it isn't just if I'm obese, it's our society's preexisting condition," says medical anthropologist Carol Worthman of Emory University, an expert in global mental health.

Fortunately, the unprecedented pandemic is leading to unprecedented science not just in virology but on mental health and resilience. Behavioral scientists are measuring the psychological toll in real time and striving to identify what helps people cope. Unlike, say, the September 11 terrorist attacks or Hurricane Katrina, which occurred over a finite period even though their effects were drawn out, the open-ended time frame for ­COVID-19 allows for new kinds of longitudinal studies and research directions. The sudden mass switch to virtual forms of working and socializing is expected to jump-start more nuanced investigations into what makes social interaction satisfying--or stultifying. If researchers meet the challenge of ­COVID-19, says psychiatrist Dennis Charney of the Icahn School of Medicine at Mount Sinai, "there will be a whole new science of resilience. We could learn how to help people become more resilient before these things happen."

Rafael Hasid arrived in New York City from his native Israel in 2000 to attend the French Culinary Institute. In 2005 he opened a restaurant called Miriam in Brooklyn that became a neighborhood favorite. In the first weeks of March Hasid could see what was coming. "I was following the news in Israel," he says. "We were two weeks behind in every respect. I was saying, 'This is going to happen here.'" When Miriam's popular weekend brunch attracted a third of the usual crowd, Hasid did not spend much time wondering what to do: he gave away all of the restaurant's perishable food to the neighbors. By the time the city required all restaurants to shut down, Miriam had already closed.

Faced with potentially traumatic events, "about 65 percent of people are going to show minimal psychological symptoms," says clinical psychologist George Bonanno of Teacher's College at Columbia University. Bonanno, who is an expert on resiliency, studies the aftermath of hurricanes, terrorist attacks, life-threatening injuries and epidemics such as the 2003 SARS outbreak. His research and that of others consistently show three common psychological responses to hardship. Two thirds of people follow a resilience trajectory and maintain relatively stable psychological and physical health. About 25 percent struggle temporarily with psychopathology such as depression or post-traumatic stress disorder and then recover--a pattern known as the recovery trajectory. And 10 percent suffer lasting psychological distress. These results hold true across diverse populations and socioeconomic statuses. "We're talking about everybody," Bonanno says. On the other hand, the risk of psychiatric disorders is twice as high for people on the lowest economic rungs.

But the mental health effects of a crisis so sweeping and insidious may not adhere to this paradigm. Studies show that strict quarantine can lead to negative psychological effects such as PTSD, although few of us have been under true quarantine, which refers to isolating after a possible exposure to infection. Instead much of the world is living with restrictions that Bonanno suspects amount to something more like managing constant stress. "This is the first time in living history we've had a global lockdown that's gone on for such a long time," says epidemiologist Daisy Fancourt of University College London. "We simply don't know how people are going to react to this."

The potential scope of the impact is considerable. "This is different from other forms of stress because it's not just one domain of your life," says health psychologist Nancy Sin of the University of British Columbia. "People are dealing with relationship or family challenges, with financial and work challenges, with health."

Early reports are already showing clear effects. The first nationwide large-scale survey in China, where the crisis hit earliest, found that almost 35 percent reported psychological distress. In the U.S., rising fear and anxiety about ­COVID have been found in people who already suffer from anxiety. Another study captured worrisome findings in older adults. This is surprising because previous research shows that, for the most part, older adults have better emotional well-being. "During this pandemic, older adults don't have those age-related strengths in emotions that we would typically expect," says Sin, who studies aging and is collaborating with DeLongis in an ongoing ­COVID-19 study of 64,000 individuals worldwide. "They are reporting just as much stress as middle-aged and younger people."

Sin is still analyzing the causes of the stress but suspects it is caused by older adults' higher likelihood of getting sick and of losing loved ones. Older people are coping with their stress better than younger people, however, and reporting less depression or anxiety. They may be benefiting from the perspective that comes with having lived through more than younger people, Sin says. Adults older than 65 have also had more time to develop skills for dealing with stress, and many have retired and so are less likely to be concerned about work.

Fancourt began a study in mid-March that grew to include more than 85,000 U.K. residents. It is tracking depression, anxiety, stress and loneliness week by week. "We need to know in real time what's happening," Fancourt says. Six weeks in, they found that levels of depression were significantly higher than before the pandemic.

Generally, those with previously diagnosed mental health illnesses, those who live alone and younger people were reporting the highest levels of depression and anxiety. On the positive side, there was a slight decrease in anxiety levels once the lockdown was declared. "Uncertainty tends to make things worse," Fancourt says. Some are frozen by not knowing what is to come, whereas others find ways to carry on.

After Hasid's restaurant had been closed for three weeks, he had not yet received any of the government payments meant to protect small businesses. While his situation was rife with uncertainty, "I was thinking that we have to continue creating business for ourselves," he says. When a few customers e-mailed to inquire if he would consider catering their Passover seders, Hasid developed a prix fixe holiday menu for delivery. Before the pandemic, Hasid was planning to open a delicatessen that would be located in an adjacent storefront. Instead of renovating the new space, he opened the deli inside the restaurant. His biggest worry was whether employees would feel safe. To reassure them, in addition to social distancing, he requires masks and gloves and has someone come in to bleach the restaurant morning and night. Hasid is looking into other sanitizing strategies involving blowers and alcohol that he heard have been used in Singapore.

Hasid recognizes that his ability to adapt is not something every business can do, especially many restaurants that run on tight margins. The new operation is using minimal staff, but Hasid continues to pay--out of his own pocket--any employees who have not been able to get through to unemployment. Serving food via delivery brings in less than a third of Miriam's former income, but he says it is better than nothing. The restaurant is also preparing a weekly meal for a local hospital. "It is not a money maker, but it's the least we can do." Hasid is pleased with Miriam's reinvention and optimistic that the restaurant will ultimately survive. "We are in a much better situation than a lot of other places in New York," he says.

When Brooklyn resident Tom Inck developed a persistent fever and dry cough in the middle of March, the psychotherapist and management consultant feared he had ­COVID-19. Because of the shortage of tests at the time, Inck's doctor first screened for every other known virus (Inck paid for the test panel). Then doctor and patient met on the streets of Manhattan. Standing on Madison Avenue in full protective equipment, the doctor administered the test, which came back positive six days later.

Successfully coping in a crisis means continuing to function and engaging in day-to-day activities. One must solve problems (whether that means getting groceries or a virus test), regulate emotions and manage relationships. There are factors that predict resilience such as optimism, the ability to keep perspective, strong social support and flexible thinking. People who believe they can cope do, in fact, tend to cope better.

During nine days of isolation in a spare room, Inck filled the time with meditating and reading. In some ways, things were harder for his wife, Wendy Blattner, who was managing her husband's care, the transition of her marketing agency to remote work, and the emotions of the couple's two college-aged daughters, who were upset at the loss of their semesters and anxious about their father. Blattner left meals outside her husband's door and got up every three hours throughout the night to record his temperature and blood oxygen level. She was scared but resolute. "I felt like he had excellent care, even though it was remote, and that I had the resources within myself and the support I needed," she says. "That's what I told my kids and what I told myself--that it might get rough, but it was going to be okay."

Most people's coping skills can be strengthened. Several of the new studies are designed to identify successful strategies that buffer the effects of the stress. So far, Fancourt says, people are encouraged to follow classic mental health strategies: getting enough sleep, observing a routine, exercising, eating well and maintaining strong social connections. Spending time on projects, even small ones, that provide a sense of purpose also helps.

In previous work, DeLongis has shown that those who are high in empathy are more likely to engage in appropriate health behaviors such as social distancing and to have better mental health outcomes than people who are low in empathy. But her earlier studies of diseases such as SARS and West Nile were cross-sectional and captured only a moment in time. Her ­COVID-19 study will follow people's behavior and attitudes for months to capture changes in empathy and coping over time. "This isn't just about a trait of empathy," DeLongis says. Empathetic responses can be learned and encouraged with proper messaging, and her hunch is that increases or decreases in empathetic responding over weeks and months will be associated with shifts in health behaviors and coping mechanisms.

As part of DeLongis's study, Sin is having people record their daily activities and emotions for a week. "So far the picture is that life is really challenging, but people are finding ways to meet that challenge," she says. Many report a great deal of positive social interactions, many of them remote. Older adults are reporting the highest levels of positive experiences in their daily lives, often through providing support to others.

It is striking that remote connections are proving satisfying. Previous research on the effects of digital technology and media focused on the association between time spent on screens and psychological well-being but revealed little about the worth of different kinds of online interaction. Now that the world is relying on the Internet to socialize, investigating those nuances is crucial. Should social media closely mimic face-to-face interaction or can less intense forms of communication leave people feeling connected? We do not know yet, but it is likely those studies will now get funded when previously they weren't. "I think we just skipped a decade of conversation in a month," says psychologist Amy Orben of the University of Cambridge, who studies adolescent mental health and technology use.

Social media is a factor in other kinds of research as well. Psychologist Roxane Cohen Silver of the University of California, Irvine, is assessing the impact of media exposure on people's well-being. "Those who consume a great deal of news about a community-wide crisis are more distressed," she says. Computational social scientist Johannes Eichstaedt of Stanford University is combining large-scale analyses of Twitter with machine learning to capture levels of depression, loneliness and joy during the pandemic.

As Blattner feared, things did get rough for their family. On nights seven and eight, when Inck's fever hovered around 103 and his blood oxygen levels dropped to 93, his doctor (via Zoom) said if the levels stayed there or got worse, Inck should go to the hospital. "I'm not going to have a patient who dies at home," he said, a statement that alarmed the children. "The toughest thing for us was the fear," Inck says. But Tylenol kept the fever in check, and short, shallow breaths kept Inck's blood oxygen level in the safety zone. After 10 days, he began to feel better.

The experience left Inck grateful and energized. He threw himself back into work counseling others who were sick and signed up to be a plasma donor for critical patients. But, unlike others who recovered, he did not initially venture out much. "The world felt like a vulnerable place," he says.

Even those brimming with personal resilience need outside help if they face challenges on multiple fronts. As executive director of IMPACCT Brooklyn, a community development corporation that serves the historically black neighborhoods of Brooklyn, Bernell K. Grier sees just how hard the pandemic has hit the African-American community. "Daily, I'm hearing of people who are either ­COVID-positive, recovering from it or have died from it," she says. Three of those deaths occurred in apartments that Grier manages and required her to organize deep-cleaning services. Still, she pressed on. "Seniors are fearful of going out, fearful of anyone coming to their front door," Grier says. "They also are not tech-savvy. A lot of things where they're being told to go on the computer, they need someone to hold their hand and help them through the process."

The pandemic, Fancourt says, "is going to exacerbate the social gradient that we're used to seeing across society. It's crucial that [people] have interventions at a national level that can support [them]." In the U.K., such interventions include the National Health Service and a furlough program that pays up to 80 percent of the salaries of millions of Britons who could not work because of the pandemic. In the U.S., paycheck-protection packages and unemployment exist but proved difficult to access quickly.

Grier's organization provides a variety of services around housing, small business advocacy, and interaction with financial and government institutions. As soon as the pandemic hit, her staff distributed information about public health and economic resources. They introduced webinars to help businesses apply for loans. As of late April, "none of the ones that we helped got anything," Grier says. "It's not reaching our businesses." Only 70 percent of Grier's tenants were able to pay rent in April. "We still have to pay the supers, the porters, the heat and electricity, the taxes and everything else," Grier says. "It's a domino effect. If the residents can't pay, we can't pay."

Worthman, the Emory anthropologist, says the ability to cope with the pandemic's reverberations is not just an individual issue but a societal one. It is also an opportunity. "People have pointed to periods of disaster in American history, after World War I and the Depression, that led to real structural change that benefited people."

Grier is advocating for positive change for her community. In her talks with public health and elected officials, she points out disparities such as the fact that the first test centers were not located in poor neighborhoods. "This is a spotlight on what has existed for too long," she says. "When you're looking at [solutions], make sure that income equality and a racial-equity lens is a filter for everything that's put in place." As Brooklyn reemerges from social isolation, Grier knows the critical role groups like hers play. "We will continue to be here to be that liaison, that credit counselor, that navigator."

Cultivating resilience though community support appears to be more important than ever. As a school nurse in Brooklyn, Marilyn Howard, who immigrated from Guyana as a teenager, worked through the early weeks of March until the public schools closed. She got sick the day after she left work. It took 10 days to get the test results that confirmed she had ­COVID-19. By then Howard thought she was on the road to recovery. But on Saturday, April 4, she awoke with labored breathing that rapidly worsened. Her brother Nigel Howard, with whom she shared an apartment, called an ambulance. But April 4 was near the peak of the pandemic in Brooklyn, and there was no ambulance available. Nigel drove them to the nearest hospital, but Marilyn's breathing deteriorated on the way. Less than a minute before they arrived, her heart stopped, and she could not be revived. She was 53.

"A couple of simple things could have saved my sister's life," says Haslyn Howard, the youngest of Marilyn's five brothers. If schools had closed earlier or her colleague could have taken a sick day, she might not have gotten sick. If someone had recommended a pulse oximeter, she would have known to go to the hospital sooner. If an ambulance had been available ... The Howard brothers arranged a viewing at a Long Island funeral home to provide some closure. Haslyn permitted only three people in the room at a time, but a simultaneous virtual service allowed more than 250 people to celebrate Marilyn's life.

Nigel has since tested positive for ­­COVID-19 and has been isolated at home. "My brothers and I are in the initial phases of trying to plan an organization that targets efforts to help the black and brown community, poor communities, address some of these [issues] on a local and tangible level," Haslyn says. It is something they can do in memory of their sister that would have made her proud. "That's one of the ways that we're coping," he adds. "How do we turn tragedy into triumph?"

Read more about the coronavirus outbreak from Scientific American here . And read coverage from our international network of magazines here .

• How the COVID-19 Pandemic Could End Recent epidemics provide clues to ways the current crisis could stop By Lydia Denworth
• How Doctors and Nurses Manage Coronavirus Grief In their own voices, health care workers from across the country reflect on coping with the pandemic Interviews by Jillian Mock and Jen Schwartz
• Genetic Engineering Could Make a COVID-19 Vaccine in Months Rather Than Years Candidates are speeding toward human trials By Charles Schmidt
• Virus Mutations Reveal How COVID-19 Really Spread Globe-trotting humans were the culprits By Mark Fischetti, Martin Krzywinski

Support science journalism.

Thanks for reading Scientific American. Knowledge awaits.

Already a subscriber? Sign in.

Thanks for reading Scientific American. Create your free account or Sign in to continue.

See Subscription Options

Continue reading with a Scientific American subscription.

You may cancel at any time.

Stanley Milgram Shock Experiment

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.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Stanley Milgram, a psychologist at Yale University, carried out one of the most famous studies of obedience in psychology.

He conducted an experiment focusing on the conflict between obedience to authority and personal conscience.

Milgram (1963) examined justifications for acts of genocide offered by those accused at the World War II, Nuremberg War Criminal trials. Their defense often was based on obedience  – that they were just following orders from their superiors.

The experiments began in July 1961, a year after the trial of Adolf Eichmann in Jerusalem. Milgram devised the experiment to answer the question:

Could it be that Eichmann and his million accomplices in the Holocaust were just following orders? Could we call them all accomplices?” (Milgram, 1974).

Milgram (1963) wanted to investigate whether Germans were particularly obedient to authority figures, as this was a common explanation for the Nazi killings in World War II.

Milgram selected participants for his experiment by newspaper advertising for male participants to take part in a study of learning at Yale University.

The procedure was that the participant was paired with another person and they drew lots to find out who would be the ‘learner’ and who would be the ‘teacher.’  The draw was fixed so that the participant was always the teacher, and the learner was one of Milgram’s confederates (pretending to be a real participant).

The learner (a confederate called Mr. Wallace) was taken into a room and had electrodes attached to his arms, and the teacher and researcher went into a room next door that contained an electric shock generator and a row of switches marked from 15 volts (Slight Shock) to 375 volts (Danger: Severe Shock) to 450 volts (XXX).

The shocks in Stanley Milgram’s obedience experiments were not real. The “learners” were actors who were part of the experiment and did not actually receive any shocks.

However, the “teachers” (the real participants of the study) believed the shocks were real, which was crucial for the experiment to measure obedience to authority figures even when it involved causing harm to others.

Milgram’s Experiment (1963)

Milgram (1963) was interested in researching how far people would go in obeying an instruction if it involved harming another person.

Stanley Milgram was interested in how easily ordinary people could be influenced into committing atrocities, for example, Germans in WWII.

Volunteers were recruited for a controlled experiment investigating “learning” (re: ethics: deception). 

Participants were 40 males, aged between 20 and 50, whose jobs ranged from unskilled to professional, from the New Haven area. They were paid $4.50 for just turning up.

At the beginning of the experiment, they were introduced to another participant, a confederate of the experimenter (Milgram).

They drew straws to determine their roles – learner or teacher – although this was fixed, and the confederate was always the learner. There was also an “experimenter” dressed in a gray lab coat, played by an actor (not Milgram).

Two rooms in the Yale Interaction Laboratory were used – one for the learner (with an electric chair) and another for the teacher and experimenter with an electric shock generator.

The “learner” (Mr. Wallace) was strapped to a chair with electrodes.

After he has learned a list of word pairs given to him to learn, the “teacher” tests him by naming a word and asking the learner to recall its partner/pair from a list of four possible choices.

The teacher is told to administer an electric shock every time the learner makes a mistake, increasing the level of shock each time. There were 30 switches on the shock generator marked from 15 volts (slight shock) to 450 (danger – severe shock).

The learner gave mainly wrong answers (on purpose), and for each of these, the teacher gave him an electric shock. When the teacher refused to administer a shock, the experimenter was to give a series of orders/prods to ensure they continued.

There were four prods, and if one was not obeyed, then the experimenter (Mr. Williams) read out the next prod, and so on.

Prod 1 : Please continue. Prod 2: The experiment requires you to continue. Prod 3 : It is absolutely essential that you continue. Prod 4 : You have no other choice but to continue.

These prods were to be used in order, and begun afresh for each new attempt at defiance (Milgram, 1974, p. 21). The experimenter also had two special prods available. These could be used as required by the situation:

• Although the shocks may be painful, there is no permanent tissue damage, so please go on’ (ibid.)
• ‘Whether the learner likes it or not, you must go on until he has learned all the word pairs correctly. So please go on’ (ibid., p. 22).

65% (two-thirds) of participants (i.e., teachers) continued to the highest level of 450 volts. All the participants continued to 300 volts.

Milgram did more than one experiment – he carried out 18 variations of his study.  All he did was alter the situation (IV) to see how this affected obedience (DV).

Conclusion 

The individual explanation for the behavior of the participants would be that it was something about them as people that caused them to obey, but a more realistic explanation is that the situation they were in influenced them and caused them to behave in the way that they did.

Some aspects of the situation that may have influenced their behavior include the formality of the location, the behavior of the experimenter, and the fact that it was an experiment for which they had volunteered and been paid.

Ordinary people are likely to follow orders given by an authority figure, even to the extent of killing an innocent human being.  Obedience to authority is ingrained in us all from the way we are brought up.

People tend to obey orders from other people if they recognize their authority as morally right and/or legally based. This response to legitimate authority is learned in a variety of situations, for example in the family, school, and workplace.

Milgram summed up in the article “The Perils of Obedience” (Milgram 1974), writing:

“The legal and philosophic aspects of obedience are of enormous import, but they say very little about how most people behave in concrete situations. I set up a simple experiment at Yale University to test how much pain an ordinary citizen would inflict on another person simply because he was ordered to by an experimental scientist. Stark authority was pitted against the subjects’ [participants’] strongest moral imperatives against hurting others, and, with the subjects’ [participants’] ears ringing with the screams of the victims, authority won more often than not. The extreme willingness of adults to go to almost any lengths on the command of an authority constitutes the chief finding of the study and the fact most urgently demanding explanation.”

Milgram’s Agency Theory

Milgram (1974) explained the behavior of his participants by suggesting that people have two states of behavior when they are in a social situation:

• The autonomous state – people direct their own actions, and they take responsibility for the results of those actions.
• The agentic state – people allow others to direct their actions and then pass off the responsibility for the consequences to the person giving the orders. In other words, they act as agents for another person’s will.

Milgram suggested that two things must be in place for a person to enter the agentic state:

• The person giving the orders is perceived as being qualified to direct other people’s behavior. That is, they are seen as legitimate.
• The person being ordered about is able to believe that the authority will accept responsibility for what happens.

According to Milgram, when in this agentic state, the participant in the obedience studies “defines himself in a social situation in a manner that renders him open to regulation by a person of higher status. In this condition the individual no longer views himself as responsible for his own actions but defines himself as an instrument for carrying out the wishes of others” (Milgram, 1974, p. 134).

Agency theory says that people will obey an authority when they believe that the authority will take responsibility for the consequences of their actions. This is supported by some aspects of Milgram’s evidence.

For example, when participants were reminded that they had responsibility for their own actions, almost none of them were prepared to obey.

In contrast, many participants who were refusing to go on did so if the experimenter said that he would take responsibility.

According to Milgram (1974, p. 188):

“The behavior revealed in the experiments reported here is normal human behavior but revealed under conditions that show with particular clarity the danger to human survival inherent in our make-up.

And what is it we have seen? Not aggression, for there is no anger, vindictiveness, or hatred in those who shocked the victim….

Something far more dangerous is revealed: the capacity for man to abandon his humanity, indeed, the inevitability that he does so, as he merges his unique personality into larger institutional structures.”

Milgram Experiment Variations

The Milgram experiment was carried out many times whereby Milgram (1965) varied the basic procedure (changed the IV).  By doing this Milgram could identify which factors affected obedience (the DV).

Obedience was measured by how many participants shocked to the maximum 450 volts (65% in the original study). Stanley Milgram conducted a total of 23 variations (also called conditions or experiments) of his original obedience study:

In total, 636 participants were tested in 18 variation studies conducted between 1961 and 1962 at Yale University.

In the original baseline study – the experimenter wore a gray lab coat to symbolize his authority (a kind of uniform).

The lab coat worn by the experimenter in the original study served as a crucial symbol of scientific authority that increased obedience. The lab coat conveyed expertise and legitimacy, making participants see the experimenter as more credible and trustworthy.

Milgram carried out a variation in which the experimenter was called away because of a phone call right at the start of the procedure.

The role of the experimenter was then taken over by an ‘ordinary member of the public’ ( a confederate) in everyday clothes rather than a lab coat. The obedience level dropped to 20%.

Change of Location:  The Mountain View Facility Study (1963, unpublished)

Milgram conducted this variation in a set of offices in a rundown building, claiming it was associated with “Research Associates of Bridgeport” rather than Yale.

The lab’s ordinary appearance was designed to test if Yale’s prestige encouraged obedience. Participants were led to believe that a private research firm experimented.

In this non-university setting, obedience rates dropped to 47.5% compared to 65% in the original Yale experiments. This suggests that the status of location affects obedience.

Private research firms are viewed as less prestigious than certain universities, which affects behavior. It is easier under these conditions to abandon the belief in the experimenter’s essential decency.

The impressive university setting reinforced the experimenter’s authority and conveyed an implicit approval of the research.

Milgram filmed this variation for his documentary Obedience , but did not publish the results in his academic papers. The study only came to wider light when archival materials, including his notes, films, and data, were studied by later researchers like Perry (2013) in the decades after Milgram’s death.

Two Teacher Condition

When participants could instruct an assistant (confederate) to press the switches, 92.5% shocked to the maximum of 450 volts.

Allowing the participant to instruct an assistant to press the shock switches diffused personal responsibility and likely reduced perceptions of causing direct harm.

By attributing the actions to the assistant rather than themselves, participants could more easily justify shocking to the maximum 450 volts, reflected in the 92.5% obedience rate.

When there is less personal responsibility, obedience increases. This relates to Milgram’s Agency Theory.

Touch Proximity Condition

The teacher had to force the learner’s hand down onto a shock plate when the learner refused to participate after 150 volts. Obedience fell to 30%.

Forcing the learner’s hand onto the shock plate after 150 volts physically connected the teacher to the consequences of their actions. This direct tactile feedback increased the teacher’s personal responsibility.

No longer shielded from the learner’s reactions, the proximity enabled participants to more clearly perceive the harm they were causing, reducing obedience to 30%. Physical distance and indirect actions in the original setup made it easier to rationalize obeying the experimenter.

The participant is no longer buffered/protected from seeing the consequences of their actions.

Social Support Condition

When the two confederates set an example of defiance by refusing to continue the shocks, especially early on at 150 volts, it permitted the real participant also to resist authority.

Two other participants (confederates) were also teachers but refused to obey. Confederate 1 stopped at 150 volts, and Confederate 2 stopped at 210 volts.

Their disobedience provided social proof that it was acceptable to disobey. This modeling of defiance lowered obedience to only 10% compared to 65% without such social support. It demonstrated that social modeling can validate challenging authority.

The presence of others who are seen to disobey the authority figure reduces the level of obedience to 10%.

Absent Experimenter Condition 

It is easier to resist the orders from an authority figure if they are not close by. When the experimenter instructed and prompted the teacher by telephone from another room, obedience fell to 20.5%.

Many participants cheated and missed out on shocks or gave less voltage than ordered by the experimenter. The proximity of authority figures affects obedience.

The physical absence of the authority figure enabled participants to act more freely on their own moral inclinations rather than the experimenter’s commands. This highlighted the role of an authority’s direct presence in influencing behavior.

A key reason the obedience studies fascinate people is Milgram presented them as a scientific experiment, contrasting himself as an “empirically grounded scientist” compared to philosophers. He claimed he systematically varied factors to alter obedience rates.

However, recent scholarship using archival records shows Milgram’s account of standardizing the procedure was misleading. For example, he published a list of standardized prods the experimenter used when participants questioned continuing. Milgram said these were delivered uniformly in a firm but polite tone.

Analyzing audiotapes, Gibson (2013) found considerable variation from the published protocol – the prods differed across trials. The point is not that Milgram did poor science, but that the archival materials reveal the limitations of the textbook account of his “standardized” procedure.

The qualitative data like participant feedback, Milgram’s notes, and researchers’ actions provide a fuller, messier picture than the obedience studies’ “official” story. For psychology students, this shows how scientific reporting can polish findings in a way that strays from the less tidy reality.

Critical Evaluation

Inaccurate description of the prod methodology:.

A key reason the obedience studies fascinate people is Milgram (1974) presented them as a scientific experiment, contrasting himself as an “empirically grounded scientist” compared to philosophers. He claimed he systematically varied factors to alter obedience rates.

However, recent scholarship using archival records shows Milgram’s account of standardizing the procedure was misleading. For example, he published a list of standardized prods the experimenter used when participants questioned continuing. Milgram said these were delivered uniformly in a firm but polite tone (Gibson, 2013; Perry, 2013; Russell, 2010).

Perry’s (2013) archival research revealed another discrepancy between Milgram’s published account and the actual events. Milgram claimed standardized prods were used when participants resisted, but Perry’s audiotape analysis showed the experimenter often improvised more coercive prods beyond the supposed script.

This off-script prodding varied between experiments and participants, and was especially prevalent with female participants where no gender obedience difference was found – suggesting the improvisation influenced results. Gibson (2013) and Russell (2009) corroborated the experimenter’s departures from the supposed fixed prods. 

Prods were often combined or modified rather than used verbatim as published.

Russell speculated the improvisation aimed to achieve outcomes the experimenter believed Milgram wanted. Milgram seemed to tacitly approve of the deviations by not correcting them when observing.

This raises significant issues around experimenter bias influencing results, lack of standardization compromising validity, and ethical problems with Milgram misrepresenting procedures.

Milgram’s experiment lacked external validity:

The Milgram studies were conducted in laboratory-type conditions, and we must ask if this tells us much about real-life situations.

We obey in a variety of real-life situations that are far more subtle than instructions to give people electric shocks, and it would be interesting to see what factors operate in everyday obedience. The sort of situation Milgram investigated would be more suited to a military context.

Orne and Holland (1968) accused Milgram’s study of lacking ‘experimental realism,”’ i.e.,” participants might not have believed the experimental set-up they found themselves in and knew the learner wasn’t receiving electric shocks.

“It’s more truthful to say that only half of the people who undertook the experiment fully believed it was real, and of those two-thirds disobeyed the experimenter,” observes Perry (p. 139).

Milgram’s sample was biased:

• The participants in Milgram’s study were all male. Do the findings transfer to females?
• Milgram’s study cannot be seen as representative of the American population as his sample was self-selected. This is because they became participants only by electing to respond to a newspaper advertisement (selecting themselves).
• They may also have a typical “volunteer personality” – not all the newspaper readers responded so perhaps it takes this personality type to do so.

Yet a total of 636 participants were tested in 18 separate experiments across the New Haven area, which was seen as being reasonably representative of a typical American town.

Milgram’s findings have been replicated in a variety of cultures and most lead to the same conclusions as Milgram’s original study and in some cases see higher obedience rates.

However, Smith and Bond (1998) point out that with the exception of Jordan (Shanab & Yahya, 1978), the majority of these studies have been conducted in industrialized Western cultures, and we should be cautious before we conclude that a universal trait of social behavior has been identified.

Selective reporting of experimental findings:

Perry (2013) found Milgram omitted findings from some obedience experiments he conducted, reporting only results supporting his conclusions. A key omission was the Relationship condition (conducted in 1962 but unpublished), where participant pairs were relatives or close acquaintances.

When the learner protested being shocked, most teachers disobeyed, contradicting Milgram’s emphasis on obedience to authority.

Perry argued Milgram likely did not publish this 85% disobedience rate because it undermined his narrative and would be difficult to defend ethically since the teacher and learner knew each other closely.

Milgram’s selective reporting biased interpretations of his findings. His failure to publish all his experiments raises issues around researchers’ ethical obligation to completely and responsibly report their results, not just those fitting their expectations.

Unreported analysis of participants’ skepticism and its impact on their behavior:

Perry (2013) found archival evidence that many participants expressed doubt about the experiment’s setup, impacting their behavior. This supports Orne and Holland’s (1968) criticism that Milgram overlooked participants’ perceptions.

Incongruities like apparent danger, but an unconcerned experimenter likely cued participants that no real harm would occur. Trust in Yale’s ethics reinforced this. Yet Milgram did not publish his assistant’s analysis showing participant skepticism correlated with disobedience rates and varied by condition.

Obedient participants were more skeptical that the learner was harmed. This selective reporting biased interpretations. Additional unreported findings further challenge Milgram’s conclusions.

This highlights issues around thoroughly and responsibly reporting all results, not just those fitting expectations. It shows how archival evidence makes Milgram’s study a contentious classic with questionable methods and conclusions.

Ethical Issues

What are the potential ethical concerns associated with Milgram’s research on obedience?

While not a “contribution to psychology” in the traditional sense, Milgram’s obedience experiments sparked significant debate about the ethics of psychological research.

Baumrind (1964) criticized the ethics of Milgram’s research as participants were prevented from giving their informed consent to take part in the study. 

Participants assumed the experiment was benign and expected to be treated with dignity.

As a result of studies like Milgram’s, the APA and BPS now require researchers to give participants more information before they agree to take part in a study.

The participants actually believed they were shocking a real person and were unaware the learner was a confederate of Milgram’s.

However, Milgram argued that “illusion is used when necessary in order to set the stage for the revelation of certain difficult-to-get-at-truths.”

Milgram also interviewed participants afterward to find out the effect of the deception. Apparently, 83.7% said that they were “glad to be in the experiment,” and 1.3% said that they wished they had not been involved.

Protection of participants 

Participants were exposed to extremely stressful situations that may have the potential to cause psychological harm. Many of the participants were visibly distressed (Baumrind, 1964).

Signs of tension included trembling, sweating, stuttering, laughing nervously, biting lips and digging fingernails into palms of hands. Three participants had uncontrollable seizures, and many pleaded to be allowed to stop the experiment.

Milgram described a businessman reduced to a “twitching stuttering wreck” (1963, p. 377),

In his defense, Milgram argued that these effects were only short-term. Once the participants were debriefed (and could see the confederate was OK), their stress levels decreased.

“At no point,” Milgram (1964) stated, “were subjects exposed to danger and at no point did they run the risk of injurious effects resulting from participation” (p. 849).

To defend himself against criticisms about the ethics of his obedience research, Milgram cited follow-up survey data showing that 84% of participants said they were glad they had taken part in the study.

Milgram used this to claim that the study caused no serious or lasting harm, since most participants retrospectively did not regret their involvement.

Yet archival accounts show many participants endured lasting distress, even trauma, refuting Milgram’s insistence the study caused only fleeting “excitement.” By not debriefing all, Milgram misled participants about the true risks involved (Perry, 2013).

However, Milgram did debrief the participants fully after the experiment and also followed up after a period of time to ensure that they came to no harm.

Milgram debriefed all his participants straight after the experiment and disclosed the true nature of the experiment.

Participants were assured that their behavior was common, and Milgram also followed the sample up a year later and found no signs of any long-term psychological harm.

The majority of the participants (83.7%) said that they were pleased that they had participated, and 74% had learned something of personal importance.

Perry’s (2013) archival research found Milgram misrepresented debriefing – around 600 participants were not properly debriefed soon after the study, contrary to his claims. Many only learned no real shocks occurred when reading a mailed study report months later, which some may have not received.

Milgram likely misreported debriefing details to protect his credibility and enable future obedience research. This raises issues around properly informing and debriefing participants that connect to APA ethics codes developed partly in response to Milgram’s study.

Right to Withdrawal 

The BPS states that researchers should make it plain to participants that they are free to withdraw at any time (regardless of payment).

When expressing doubts, the experimenter assured them all was well. Trusting Yale scientists, many took the experimenter at his word that “no permanent tissue damage” would occur, and continued administering shocks despite reservations.

Did Milgram give participants an opportunity to withdraw? The experimenter gave four verbal prods which mostly discouraged withdrawal from the experiment:

• Please continue.
• The experiment requires that you continue.
• It is absolutely essential that you continue.
• You have no other choice, you must go on.

Milgram argued that they were justified as the study was about obedience, so orders were necessary.

Milgram pointed out that although the right to withdraw was made partially difficult, it was possible as 35% of participants had chosen to withdraw.

Replications

Direct replications have not been possible due to current ethical standards . However, several researchers have conducted partial replications and variations that aim to reproduce some aspects of Milgram’s methods ethically.

One important replication was conducted by Jerry Burger in 2009. Burger’s partial replication included several safeguards to protect participant welfare, such as screening out high-risk individuals, repeatedly reminding participants they could withdraw, and stopping at the 150-volt shock level. This was the point where Milgram’s participants first heard the learner’s protests.

As 79% of Milgram’s participants who went past 150 volts continued to the maximum 450 volts, Burger (2009) argued that 150 volts provided a reasonable estimate for obedience levels. He found 70% of participants continued to 150 volts, compared to 82.5% in Milgram’s comparable condition.

Another replication by Thomas Blass (1999) examined whether obedience rates had declined over time due to greater public awareness of the experiments. Blass correlated obedience rates from replication studies between 1963 and 1985 and found no relationship between year and obedience level. He concluded that obedience rates have not systematically changed, providing evidence against the idea of “enlightenment effects”.

Some variations have explored the role of gender. Milgram found equal rates of obedience for male and female participants. Reviews have found most replications also show no gender difference, with a couple of exceptions (Blass, 1999). For example, Kilham and Mann (1974) found lower obedience in female participants.

Partial replications have also examined situational factors. Having another person model defiance reduced obedience compared to a solo participant in one study, but did not eliminate it (Burger, 2009). The authority figure’s perceived expertise seems to be an influential factor (Blass, 1999). Replications have supported Milgram’s observation that stepwise increases in demands promote obedience.

Personality factors have been studied as well. Traits like high empathy and desire for control correlate with some minor early hesitation, but do not greatly impact eventual obedience levels (Burger, 2009). Authoritarian tendencies may contribute to obedience (Elms, 2009).

In sum, the partial replications confirm Milgram’s degree of obedience. Though ethical constraints prevent full reproductions, the key elements of his procedure seem to consistently elicit high levels of compliance across studies, samples, and eras. The replications continue to highlight the power of situational pressures to yield obedience.

Milgram (1963) Audio Clips

Below you can also hear some of the audio clips taken from the video that was made of the experiment. Just click on the clips below.

Why was the Milgram experiment so controversial?

The Milgram experiment was controversial because it revealed people’s willingness to obey authority figures even when causing harm to others, raising ethical concerns about the psychological distress inflicted upon participants and the deception involved in the study.

Would Milgram’s experiment be allowed today?

Milgram’s experiment would likely not be allowed today in its original form, as it violates modern ethical guidelines for research involving human participants, particularly regarding informed consent, deception, and protection from psychological harm.

Did anyone refuse the Milgram experiment?

Yes, in the Milgram experiment, some participants refused to continue administering shocks, demonstrating individual variation in obedience to authority figures. In the original Milgram experiment, approximately 35% of participants refused to administer the highest shock level of 450 volts, while 65% obeyed and delivered the 450-volt shock.

How can Milgram’s study be applied to real life?

Milgram’s study can be applied to real life by demonstrating the potential for ordinary individuals to obey authority figures even when it involves causing harm, emphasizing the importance of questioning authority, ethical decision-making, and fostering critical thinking in societal contexts.

Were all participants in Milgram’s experiments male?

Yes, in the original Milgram experiment conducted in 1961, all participants were male, limiting the generalizability of the findings to women and diverse populations.

Why was the Milgram experiment unethical?

The Milgram experiment was considered unethical because participants were deceived about the true nature of the study and subjected to severe emotional distress. They believed they were causing harm to another person under the instruction of authority.

Additionally, participants were not given the right to withdraw freely and were subjected to intense pressure to continue. The psychological harm and lack of informed consent violates modern ethical guidelines for research.

Baumrind, D. (1964). Some thoughts on ethics of research: After reading Milgram’s” Behavioral study of obedience.”.  American Psychologist ,  19 (6), 421.

Blass, T. (1999). The Milgram paradigm after 35 years: Some things we now know about obedience to authority 1.  Journal of Applied Social Psychology ,  29 (5), 955-978.

Brannigan, A., Nicholson, I., & Cherry, F. (2015). Introduction to the special issue: Unplugging the Milgram machine.  Theory & Psychology ,  25 (5), 551-563.

Burger, J. M. (2009). Replicating Milgram: Would people still obey today? American Psychologist, 64 , 1–11.

Elms, A. C. (2009). Obedience lite. American Psychologist, 64 (1), 32–36.

Gibson, S. (2013). Milgram’s obedience experiments: A rhetorical analysis. British Journal of Social Psychology, 52, 290–309.

Gibson, S. (2017). Developing psychology’s archival sensibilities: Revisiting Milgram’s obedience’ experiments.  Qualitative Psychology ,  4 (1), 73.

Griggs, R. A., Blyler, J., & Jackson, S. L. (2020). Using research ethics as a springboard for teaching Milgram’s obedience study as a contentious classic.  Scholarship of Teaching and Learning in Psychology ,  6 (4), 350.

Haslam, S. A., & Reicher, S. D. (2018). A truth that does not always speak its name: How Hollander and Turowetz’s findings confirm and extend the engaged followership analysis of harm-doing in the Milgram paradigm. British Journal of Social Psychology, 57, 292–300.

Haslam, S. A., Reicher, S. D., & Birney, M. E. (2016). Questioning authority: New perspectives on Milgram’s ‘obedience’ research and its implications for intergroup relations. Current Opinion in Psychology, 11 , 6–9.

Haslam, S. A., Reicher, S. D., Birney, M. E., Millard, K., & McDonald, R. (2015). ‘Happy to have been of service’: The Yale archive as a window into the engaged followership of participants in Milgram’s ‘obedience’ experiment. British Journal of Social Psychology, 54 , 55–83.

Kaplan, D. E. (1996). The Stanley Milgram papers: A case study on appraisal of and access to confidential data files. American Archivist, 59 , 288–297.

Kaposi, D. (2022). The second wave of critical engagement with Stanley Milgram’s ‘obedience to authority’experiments: What did we learn?.  Social and Personality Psychology Compass ,  16 (6), e12667.

Kilham, W., & Mann, L. (1974). Level of destructive obedience as a function of transmitter and executant roles in the Milgram obedience paradigm. Journal of Personality and Social Psychology, 29 (5), 696–702.

Milgram, S. (1963). Behavioral study of obedience . Journal of Abnormal and Social Psychology , 67, 371-378.

Milgram, S. (1964). Issues in the study of obedience: A reply to Baumrind. American Psychologist, 19 , 848–852.

Milgram, S. (1965). Some conditions of obedience and disobedience to authority . Human Relations, 18(1) , 57-76.

Milgram, S. (1974). Obedience to authority: An experimental view . Harpercollins.

Miller, A. G. (2009). Reflections on” Replicating Milgram”(Burger, 2009), American Psychologis t, 64 (1):20-27

Nicholson, I. (2011). “Torture at Yale”: Experimental subjects, laboratory torment and the “rehabilitation” of Milgram’s “obedience to authority”. Theory & Psychology, 21 , 737–761.

Nicholson, I. (2015). The normalization of torment: Producing and managing anguish in Milgram’s “obedience” laboratory. Theory & Psychology, 25 , 639–656.

Orne, M. T., & Holland, C. H. (1968). On the ecological validity of laboratory deceptions. International Journal of Psychiatry, 6 (4), 282-293.

Orne, M. T., & Holland, C. C. (1968). Some conditions of obedience and disobedience to authority. On the ecological validity of laboratory deceptions. International Journal of Psychiatry, 6 , 282–293.

Perry, G. (2013). Behind the shock machine: The untold story of the notorious Milgram psychology experiments . New York, NY: The New Press.

Reicher, S., Haslam, A., & Miller, A. (Eds.). (2014). Milgram at 50: Exploring the enduring relevance of psychology’s most famous studies [Special issue]. Journal of Social Issues, 70 (3), 393–602

Russell, N. (2014). Stanley Milgram’s obedience to authority “relationship condition”: Some methodological and theoretical implications. Social Sciences, 3, 194–214

Shanab, M. E., & Yahya, K. A. (1978). A cross-cultural study of obedience. Bulletin of the Psychonomic Society .

Smith, P. B., & Bond, M. H. (1998). Social psychology across cultures (2nd Edition) . Prentice Hall.

Further Reading

• The power of the situation: The impact of Milgram’s obedience studies on personality and social psychology
• Seeing is believing: The role of the film Obedience in shaping perceptions of Milgram’s Obedience to Authority Experiments
• Replicating Milgram: Would people still obey today?

Learning Check

Which is true regarding the Milgram obedience study?

• The aim was to see how obedient people would be in a situation where following orders would mean causing harm to another person.
• Participants were under the impression they were part of a learning and memory experiment.
• The “learners” in the study were actual participants who volunteered to be shocked as part of the experiment.
• The “learner” was an actor who was in on the experiment and never actually received any real shocks.
• Although the participant could not see the “learner”, he was able to hear him clearly through the wall
• The study was directly influenced by Milgram’s observations of obedience patterns in post-war Europe.
• The experiment was designed to understand the psychological mechanisms behind war crimes committed during World War II.
• The Milgram study was universally accepted in the psychological community, and no ethical concerns were raised about its methodology.
• When Milgram’s experiment was repeated in a rundown office building in Bridgeport, the percentage of the participants who fully complied with the commands of the experimenter remained unchanged.
• The experimenter (authority figure) delivered verbal prods to encourage the teacher to continue, such as ‘Please continue’ or ‘Please go on’.
• Over 80% of participants went on to deliver the maximum level of shock.
• Milgram sent participants questionnaires after the study to assess the effects and found that most felt no remorse or guilt, so it was ethical.
• The aftermath of the study led to stricter ethical guidelines in psychological research.
• The study emphasized the role of situational factors over personality traits in determining obedience.

Answers : Items 3, 8, 9, and 11 are the false statements.

Short Answer Questions

• Briefly explain the results of the original Milgram experiments. What did these results prove?
• List one scenario on how an authority figure can abuse obedience principles.
• List one scenario on how an individual could use these principles to defend their fellow peers.
• In a hospital, you are very likely to obey a nurse. However, if you meet her outside the hospital, for example in a shop, you are much less likely to obey. Using your knowledge of how people resist pressure to obey, explain why you are less likely to obey the nurse outside the hospital.
• Describe the shock instructions the participant (teacher) was told to follow when the victim (learner) gave an incorrect answer.
• State the lowest voltage shock that was labeled on the shock generator.
• What would likely happen if Milgram’s experiment included a condition in which the participant (teacher) had to give a high-level electric shock for the first wrong answer?

Group Activity

Gather in groups of three or four to discuss answers to the short answer questions above.

For question 2, review the different scenarios you each came up with. Then brainstorm on how these situations could be flipped.

For question 2, discuss how an authority figure could instead empower those below them in the examples your groupmates provide.

For question 3, discuss how a peer could do harm by using the obedience principles in the scenarios your groupmates provide.

Essay Topic

• What’s the most important lesson of Milgram’s Obedience Experiments? Fully explain and defend your answer.
• Milgram selectively edited his film of the obedience experiments to emphasize obedient behavior and minimize footage of disobedience. What are the ethical implications of a researcher selectively presenting findings in a way that fits their expected conclusions?

• History & Society
• Science & Tech
• Biographies
• Animals & Nature
• Geography & Travel
• Arts & Culture
• Games & Quizzes
• On This Day
• One Good Fact
• New Articles
• Lifestyles & Social Issues
• Philosophy & Religion
• Politics, Law & Government
• World History
• Health & Medicine
• Browse Biographies
• Birds, Reptiles & Other Vertebrates
• Bugs, Mollusks & Other Invertebrates
• Environment
• Fossils & Geologic Time
• Entertainment & Pop Culture
• Sports & Recreation
• Visual Arts
• Demystified
• Image Galleries
• Infographics
• Top Questions
• Britannica Kids
• Saving Earth
• Space Next 50
• Student Center

• When did science begin?
• Where was science invented?

Milgram experiment

Our editors will review what you’ve submitted and determine whether to revise the article.

• SimplyPsychology - Stanley Milgram Shock Experiment: Summary, Results, & Ethics
• Plos Journals - PLOS ONE - A Virtual Reprise of the Stanley Milgram Obedience Experiments
• University of California - College of Natural Resources - Milgrams Experiment on Obedience to Authority
• Social Science LibreTexts - The Milgram Experiment- The Power of Authority
• Verywell Mind - What was the Milgram Experiment?
• BCcampus Open Publishing - Ethics in Law Enforcement - The Milgram Experiment
• Nature - Modern Milgram experiment sheds light on power of authority
• Open University - OpenLearn - Psychological research, obedience and ethics: 1 Milgram’s obedience study

Milgram experiment , controversial series of experiments examining obedience to authority conducted by social psychologist Stanley Milgram . In the experiment, an authority figure, the conductor of the experiment, would instruct a volunteer participant, labeled the “teacher,” to administer painful, even dangerous, electric shocks to the “learner,” who was actually an actor. Although the shocks were faked, the experiments are widely considered unethical today due to the lack of proper disclosure, informed consent, and subsequent debriefing related to the deception and trauma experienced by the teachers. Some of Milgram’s conclusions have been called into question. Nevertheless, the experiments and their results have been widely cited for their insight into how average people respond to authority.

Milgram conducted his experiments as an assistant professor at Yale University in the early 1960s. In 1961 he began to recruit men from New Haven , Connecticut , for participation in a study he claimed would be focused on memory and learning . The recruits were paid $4.50 at the beginning of the study and were generally between the ages of 20 and 50 and from a variety of employment backgrounds. When they volunteered, they were told that the experiment would test the effect of punishment on learning ability. In truth, the volunteers were the subjects of an experiment on obedience to authority. In all, about 780 people, only about 40 of them women, participated in the experiments, and Milgram published his results in 1963.

Volunteers were told that they would be randomly assigned either a “teacher” or “learner” role, with each teacher administering electric shocks to a learner in another room if the learner failed to answer questions correctly. In actuality, the random draw was fixed so that all the volunteer participants were assigned to the teacher role and the actors were assigned to the learner role. The teachers were then instructed in the electroshock “punishment” they would be administering, with 30 shock levels ranging from 15 to 450 volts. The different shock levels were labeled with descriptions of their effects, such as “Slight Shock,” “Intense Shock,” and “Danger: Severe Shock,” with the final label a grim “XXX.” Each teacher was given a 45-volt shock themselves so that they would better understand the punishment they believed the learner would be receiving. Teachers were then given a series of questions for the learner to answer, with each incorrect answer generally earning the learner a progressively stronger shock. The actor portraying the learner, who was seated out of sight of the teacher, had pre-recorded responses to these shocks that ranged from grunts of pain to screaming and pleading, claims of suffering a heart condition, and eventually dead silence. The experimenter, acting as an authority figure, would encourage the teachers to continue administering shocks, telling them with scripted responses that the experiment must continue despite the reactions of the learner. The infamous result of these experiments was that a disturbingly high number of the teachers were willing to proceed to the maximum voltage level, despite the pleas of the learner and the supposed danger of proceeding.

Milgram’s interest in the subject of authority, and his dark view of the results of his experiments, were deeply informed by his Jewish identity and the context of the Holocaust , which had occurred only a few years before. He had expected that Americans, known for their individualism , would differ from Germans in their willingness to obey authority when it might lead to harming others. Milgram and his students had predicted only 1–3% of participants would administer the maximum shock level. However, in his first official study, 26 of 40 male participants (65%) were convinced to do so and nearly 80% of teachers that continued to administer shocks after 150 volts—the point at which the learner was heard to scream—continued to the maximum of 450 volts. Teachers displayed a range of negative emotional responses to the experiment even as they continued to obey, sometimes pleading with the experimenters to stop the experiment while still participating in it. One teacher believed that he had killed the learner and was moved to tears when he eventually found out that he had not.

Milgram included several variants on the original design of the experiment. In one, the teachers were allowed to select their own voltage levels. In this case, only about 2.5% of participants used the maximum shock level, indicating that they were not inclined to do so without the prompting of an authority figure. In another, there were three teachers, two of whom were not test subjects, but instead had been instructed to protest against the shocks. The existence of peers protesting the experiment made the volunteer teachers less likely to obey. Teachers were also less likely to obey in a variant where they could see the learner and were forced to interact with him.

The Milgram experiment has been highly controversial, both for the ethics of its design and for the reliability of its results and conclusions. It is commonly accepted that the ethics of the experiment would be rejected by mainstream science today, due not only to the handling of the deception involved but also to the extreme stress placed on the teachers, who often reacted emotionally to the experiment and were not debriefed . Some teachers were actually left believing they had genuinely and repeatedly shocked a learner before having the truth revealed to them later. Later researchers examining Milgram’s data also found that the experimenters conducting the tests had sometimes gone off-script in their attempts to coerce the teachers into continuing, and noted that some teachers guessed that they were the subjects of the experiment. However, attempts to validate Milgram’s findings in more ethical ways have often produced similar results.

Experimental Psychology Studies Humans and Animals

Understanding Experimental Psychology

Our personalities, and to some degree our life experiences, are defined by the way we behave. But what influences the way we behave in the first place? How does our behavior shape our experiences throughout our lives? 

Experimental psychologists are interested in exploring theoretical questions, often by creating a hypothesis and then setting out to prove or disprove it through experimentation. They study a wide range of behavioral topics among humans and animals, including sensation, perception, attention, memory, cognition and emotion.

Experimental Psychology Applied

Experimental psychologists use scientific methods to collect data and perform research. Often, their work builds, one study at a time, to a larger finding or conclusion. Some researchers have devoted their entire career to answering one complex research question. 

These psychologists work in a variety of settings, including universities, research centers, government agencies and private businesses. The focus of their research is as varied as the settings in which they work. Often, personal interest and educational background will influence the research questions they choose to explore. 

In a sense, all psychologists can be considered experimental psychologists since research is the foundation of the discipline, and many psychologists split their professional focus among research, patient care, teaching or program administration. Experimental psychologists, however, often devote their full attention to research — its design, execution, analysis and dissemination. 

Those focusing their careers specifically on experimental psychology contribute work across subfields . For example, they use scientific research to provide insights that improve teaching and learning, create safer workplaces and transportation systems, improve substance abuse treatment programs and promote healthy child development.

Related books

Deliberate Practice in Interpersonal Psychotherapy

Psychological Assessment of Emotional ...

APA Handbook of Health Psychology

Methodological Issues and Strategies, 5e

Essentials of Constructivist Critical Incident ...

Psychology subfields

Cover Story

The truth about lying.

• APS 28th Annual Convention (2016)
• Behavioral Economics
• Experimental Psychology

In one of his many experiments designed to measure people’s rationalization of cheating, Dan Ariely rigged a vending machine to return both candy and the customer’s money. Although people could have filled their pockets with candy without paying a cent, on average they took no more than three or four items, he says. “Nobody took five because [they thought] five would be stealing,” he adds.

God goes to Sarah and says, “You’re going to have a child.” Sarah laughs and responds, “How can I have a child when my husband is so old?” God then goes to Abraham and tells him, “You’re going to have a child.” Abraham responds, “What did Sarah say?” And God lies: “Sarah wondered how [she can] have a child when she is so old.”

The moral of the story: It’s okay to lie for peace at home.

“When you think about it, that’s what dishonesty is all about,” Ariely said in his Fred Kavli Keynote Address at the 2016 APS Annual Convention in Chicago.

Ariely, the James B. Duke Professor of Psychology and Behavioral Economics at Duke University, points out that how we think we would act often strays far from how we actually act in the real world.

“Just to be clear, the prevalent theory of dishonesty from a legal perspective is the idea of cost–benefit analysis,” said Ariely. “It says that when people think about being dishonest, they think about ‘What can I gain? What can I lose?’ and figure out if this is a worthwhile act of dishonesty. If there’s a big cost, we’re not going to be dishonest.”

The idea of cost–benefit analysis does not describe our personal experiences, though. For instance, the theory behind the death penalty is that people considering whether to murder someone will think ahead and realize that committing that crime could result in a death sentence, so they won’t kill. But this is not how people actually function in the real world, Ariely said.

“If we have the wrong theory, our solutions are going to be ineffective,” he added.

Ariely joked that it is difficult to get people to steal millions of dollars to fund studies on dishonesty. So he has employed several different strategies, such as running task-based experiments and conducting qualitative research with criminals.

In one experiment, he and his colleagues had participants roll a die for a monetary reward corresponding to the number on the die. If the die landed on the number 5, the individual was paid $5, for example. Before rolling, though, participants decided which side of the die — top or bottom — determined the dollar amount they were to receive. Participants were instructed not to tell the researcher but to mark “top” or “bottom” on a sheet of paper.

For instance, a die might land with 5 on the bottom and 2 on top. Ariely asked the participant who rolled the die, “Which side did you pick?” If the participant had picked “bottom,” no problem, but if they had picked “top,” they faced a dilemma — should they lie to make more money or tell the truth and make less money?

“When people did this 20 times, we found that they were incredibly lucky,” said Ariely. “Not lucky 100 percent of the time, but maybe 13 or 14 times.”

In another die experiment conducted at Duke University, researchers presented participants with the following situation: You can earn either $4 or $40 depending on where the die lands. In every scenario, the experimenter said, “Sorry, you landed on the $4 one.” Then, the experimenter told the participant, “My boss isn’t here, so if you give me the $3 you received just for coming in, I’ll pretend you landed on the $40 one.” Ninety percent of students took the bribe.

In another study, Ariely utilized a vending machine. The machine was set up to say that bags of candy cost 75 cents on the outside, but its mechanism on the inside was set to zero cents. So when people put money in the vending machine, they would get extra bags of candy, and all of their money back. A big sign on the vending machine read, “If there’s something wrong with this machine, please call this number”— in this instance, Ariely’s cell phone number. Nobody called, but nobody took more than four bags of candy.

“The majority took three or four, but nobody took five because five would be stealing,” Ariely said, drawing laughs. “And you think about how people might rationalize this decision: ‘This other vending machine took my money and didn’t give me candy, and this vending machine must be a close relative of that one.’ We’re just sorting out the vending karma in the world.”

In another experiment, participants performed some tasks and then told the experimenter how much money they earned and immediately received that amount. In a variation of the same experiment, they came and asked for tokens instead, then walked 12 feet and exchanged those tokens for money. Participants were twice as likely to cheat when they requested tokens compared with when they asked for money.

“As a society, we’re moving away from tangible representations of money,” said Ariely. “Could it be that, as psychological distance increases, people behave in a worse way but still feel good about themselves? If it does, what are the precautions we should have under those systems?”

Additionally, Ariely talked about the role that conflicts of interest and dishonesty can play in the academic world. In an experiment conducted at Harvard University, the pattern of results confirmed the researchers’ hypothesis except for one outlier. Researchers recalled that the man who represented the outlier was 20 years older than the other participants and also had been intoxicated. When they pulled out his data point, the data was much more uniform.

About 1 week later, one of Ariely’s students asked, “What if that drunk person had fit into the mean average and wasn’t an outlier?”

“We probably would have never looked,” said Ariely. “There was a particular version of reality that we wanted to see, and we were using our creativity to justify setting this path. We were cheating ourselves.”

To better understand cheating and dishonesty, Ariely also took an anthropological approach to his research and spoke with various criminals. He tells the story of Joe Papp, an Olympic cyclist who went back to school to complete his undergraduate education. When Papp returned to cycling, he felt like he was performing as well as he had before college but that other cyclists were faster. One of Papp’s friends recommended that he see a physician, who wrote Papp a prescription for erythropoietin (EPO), a cancer treatment that increases the production of red blood cells. Papp gave himself the injections, but when there was a shortage of EPO, he imported and distributed EPO for his team and for other teams. He essentially became a drug dealer.

“When you look at crimes, in a lot of the cases, it’s about the slippery slope,” explained Ariely. “You say to yourself, ‘I can’t imagine being a drug dealer.’ But ask yourself, when would you have stopped? Because of the commonality and danger of the first step, what is the difference between people who commit crimes and those who don’t? Is it just missed opportunity? We find that it’s all about the ability to rationalize dishonesty.”

But Ariely also shared results of experiments that, by priming people to think about their personal morals or ethics, tilt their behaviors in a more honest direction. He recounted experiments in which he and his colleagues asked one group of study participants to recall the Ten Commandments, and the other group to recall 10 books they had read in high school. The latter group largely engaged in widespread but moderate cheating when given subsequent reward-based tasks designed to measure honesty. But the group that recalled the Ten Commandments didn’t cheat at all. The result was the same when they reran the experiment on a group of self-declared atheists who were asked to swear on the Bible.

Those findings have plenty of real-world applications, some of which already are being tested or implemented. One of the most noteworthy, Ariely pointed out, is having people put their signature at the top rather than the bottom of various documents (e.g., insurance forms); they’re essentially verifying that the information they’re providing is true before they have a chance to fudge it.

“So there is hope,” he says, “and I think as long as we understand where dishonesty comes from, we can do something about it.”

Im interested in using this as an external source for an essay. Is there any information, such as the author and publication date, available. Please let me know . Thank you for your time.

So if we are required to leave our name and email are we more likely to make our comments honest and objective?

FYI – Dan does an excellent documentary on Netflix on this topic.

I need the citations to this article. By the way, is the spelling correct for “die” or is it actually spell as “dice”. Thank you.

Please have Dan Ariely read Genesis 18 properly. God never went to Sarah but to Abraham. Sarah eavesdropped and laughed at God’s promise of a son. God asked Abraham why Sarah laughed and SARAH lied!

No point in starting your entire speech with a lie. It discredits everything else you have to say.

your story about Abramham and sarah is incorrect here, Sarah over heard the comment from three strangers who Abraham had invited to a meal, that she would be in child within the next year when they return, she laughed while behind the tent due to her old age and her husbands age, it says the Lord asked why she had laughed in private behind her tent/ she denied laughing and Lied, You

APS regularly opens certain online articles for discussion on our website. Effective February 2021, you must be a logged-in APS member to post comments. By posting a comment, you agree to our Community Guidelines and the display of your profile information, including your name and affiliation. Any opinions, findings, conclusions, or recommendations present in article comments are those of the writers and do not necessarily reflect the views of APS or the article’s author. For more information, please see our Community Guidelines .

Please login with your APS account to comment.

Careers Up Close: Joel Anderson on Gender and Sexual Prejudices, the Freedoms of Academic Research, and the Importance of Collaboration

Joel Anderson, a senior research fellow at both Australian Catholic University and La Trobe University, researches group processes, with a specific interest on prejudice, stigma, and stereotypes.

Experimental Methods Are Not Neutral Tools

Ana Sofia Morais and Ralph Hertwig explain how experimental psychologists have painted too negative a picture of human rationality, and how their pessimism is rooted in a seemingly mundane detail: methodological choices. 

APS Fellows Elected to SEP

In addition, an APS Rising Star receives the society’s Early Investigator Award.

Privacy Overview

The Psychology of Social Media

In today’s cultural and technological climate, everyone uses some sort of social media. According to a monumental 2018 Pew Research Center study, 88% of respondents between the ages of 18 to 29 reported using some kind of social media. Seventy-eight percent of 30- to 49-year-olds said the same.

The number of reported users dips for the next age group but not as much as you may think. A stunning 64% of people between the ages of 50 and 64 use social media on a frequent basis. For a generation that didn’t grow up with the internet or social media, this statistic is surprising and helps explain the prevalence of social media use in our culture.

With the pervasiveness of social media across all ages, more attention needs to be given to what it’s doing to us as individual users. The endless stream of communication and connection provided by social media is changing the way we think and absorb information. As it is, people are currently developing social media habits that can simultaneously benefit and harm their mental health. The question being, what does social media do to your brain?

Because this trend of extended social media use will only continue, more researchers are joining the field to analyze and understand the psychology of social media in our constantly connected culture.

Social Media and The Brain

From a neurological perspective, social media affects different brain functions in unique ways. It contains many combinations of stimuli that can trigger different reactions, and because of this, social media’s effects on the brain appear in a variety of ways.

Positive attention on social media, for example, affects multiple parts of the brain. According to an article in Social Cognitive and Affective Neuroscience , accruing likes on Facebook, Twitter, or Instagram causes “activation in brain circuity implicated in reward , including the striatum and ventral tegmental area, regions also implicated in the experience of receiving Likes from others.” This sounds really complicated and involved, but when approached from a different perspective, it becomes a little more digestible.

The ventral tegmental area (VTA) is one of the primary parts responsible for determining the rewards system in people’s bodies. When social media users receive positive feedback (likes), their brains fire off dopamine receptors, which is facilitated in part by the VTA.

Another study that employed the use of MRI technology to monitor brain activity found similar results. As researchers analyzed the brains of adolescents browsing Instagram, they found that “viewing photos with many (compared with few) likes was associated with greater activity in neural regions implicated in reward processing, social cognition, imitation, and attention.”

Again, with social media so tightly connected to individuals’ rewards systems, users should realize the power – and possibility for abuse – of the platforms they use. Things like gambling and narcotic drugs have the power to rule over the brain’s rewards system in a similar capacity. Social media users should be aware of these parallels to avoid potential pitfalls.

Outside of the rewards systems, social media stimuli can affect the brain’s decision-making and emotional processing functions. In yet another study that observed the brain activity in adolescents, researchers found that parts of the brain that deal with emotional and sensory processing reacted noticeably when participants felt excluded . This study highlighted the effects of “online social exclusion” on the developing brains of adolescents. What this means is that when social media users are excluded from online groups, chats, or events, the brain reacts in these specific regions directly.

The research on social media and how the parts of the brain react to it is still in the early stages. While these studies reflect an effort toward better understanding the effects of social media on different parts of the brain, there’s still a lot of progress to be made. Social media is growing by leaps and bounds and these studies are helping us identify more about why people post on social media.

Why We Post on Social Media

People choose different material to post on different social media platforms. When people want to post pictures, they tend to pick Instagram. When they want to post short bits of text like jokes, they go to Twitter. So much goes into deciding what to post where, and that’s not even including the psychological factors that determine what gets posted and what doesn’t.

Psychological Motivations for Posting

Pinpointing exactly why people post is an impossible exercise. However, by understanding some significant social media behaviors, it becomes easier to grasp general motivations for posting.

A recent Medium article titled “The Psychology of Social Sharing” helped articulate the different tiers of posting motivations. While the writers for this article approached the psychology of posting on social media from a marketing perspective, they tapped into clear psychological incentives for sharing content. They even cleverly adapt noted psychologist Abraham Maslow’s hierarchy of human needs to reasons why people post and consume updates. They are:

• Physiological needs: People sometimes post to benefit the health or well-being of their friends and family.
• Safety: Physical, mental, and financial security are important for people when they choose to post some material on their social media.
• Love/belonging: Users generally want to post to feel some kind of social acceptance from a group or a particular individual.
• Esteem: People want to quell the rewards-oriented parts of their brains, which helps explain why people post “me-centric” content regularly.
• Self-actualization: As the most important facet of the human needs hierarchy, this aspect of social media posting manifests when people share their successes – getting a new job, completing an arduous project, or graduating from school, to name a few examples.

The psychological world has only recently begun to confront the motivations for posting material on social media. An article titled “Why We Share: A Study of Motivations for Mobile Media Sharing” posed an actual experiment where respondents were asked to record their posting habits and corresponding feelings in a diary and then participate in post-study interviews. After monitoring the media sharing behavior, the researchers found “that social and emotional influences played an important role in media sharing behavior.”

Some researchers have looked toward the ways social media has affected the psychological development in children. The article “Growing Up Wired: Social Networking Sites and Adolescent Psychosocial Development” stated that some reasons people share is because they have been reared since childhood to post. The researchers said:

Overall, adolescents and young adults’ stated motivations for using (social networking sites) are quite similar to more traditional forms of communication – to stay in touch with friends, make plans, get to know people better, and present oneself to others.

Moreover, the researchers in the study explained that children and adolescents are beginning to have their identities shaped by posting on and engaging with social media.

One reason people post on social media, according to an article in the Journal of Experimental Social Psychology , is because social media sharing can link to positive social media feedback and self-esteem. More directly, the quest for likes or follows on social media heavily influences why people post. The positive attention some users receive for posting inspires more and more social sharing in many users.

In sum, people generally post from some kind of emotional position that seeks a response. Since the very nature of social media centers on communication, it makes sense that the primary motivation for posting comes from a psychological point to connect with others. But this constant quest for acceptance and exposure on social media can lead to major psychological problems for some.

Interested in finding out more about how social media affects brain function? Want to know more about the positive and negative effects of social media on mental health? Download our free guide to start learning about social media psychology today!

When Social Media Habits Turn to Social Media Addictions

Social media dependency has received more and more consideration in the last five years. The boom of social networking applications has caused many researchers to explore not only why people post the content they choose to share, but also the addictive tendencies in some users.

Specifically, the article “Social Networking Sites and Addiction” pinpointed some reasons people become addicted to social networking sites (SNS). These reasons include lower self-esteem and a general anxiety about being excluded.

The authors were quick to make the distinction between social networking and social media, though, since “social networking is a way of being” while “individuals can become addicted to using social networking sites.” They extend social media addiction to connect more clearly to smartphone addiction, and that levels of addiction may depend of sociodemographic information. Further, the researchers conclude that the fear of missing out (FOMO) “may be part of SNS addiction.” These are all significant features of how people are more and more inclined to post on or consume social media because of an underlying addictive behavior problem.

Social media addiction is gaining traction in the academic world because a growing number of people are reporting problems of dependence. The article “The Relations Among Social Media Addiction, Self-Esteem, and Life Satisfaction in University Students” detailed a study that explored the consequences of excessive social media use. In it, respondents who reported a moderate use of social media had a much more positive outlook on their social positions. Other participants overwhelmingly reported “addictive use of social media had a negative association with self-esteem.” These same respondents in the survey said they lacked satisfaction with their lives, which they directly linked to their lowered self-esteem.

Moreover, the chapter “Social Networking Addiction” from Behavioral Addictions contextualized the significance of social media addiction in a world where it hasn’t been researched as much as it should . The chapter explored more directly ways that mental health professionals can conduct effective screening and treatment processes in response to users suspected of having addictive tendencies.

Though this chapter does a good job of providing impressive prospective frameworks for screening and treatment responses, a lot more work needs to be done to confront the problem directly. In order to unpack the psychology of social media more comprehensively, a closer look into preventative measures needs to be taken.

Understanding the Change in Self Concept from Using Social Media

Social media allows users to express their personalities in unique ways. But the ability to create multiple accounts and to curate the material on their profiles has given users an unprecedented opportunity to develop new personae. These new digital identities can align with, be a complement to, or conflict with users’ real personalities.

How Social Media Shapes Identity

In order to understand more clearly how social media shapes individuals’ identities, it’s necessary first to look at the landscape of social media.

The article “Psychology of Social Media: From Technology to Identity” stated the spatial makeups of hybridized social media networks has given a “rise to ‘interreality,’ a new social space, more malleable and dynamic than preceding social networks.” Out of these new frameworks, people now:

• Alter their own social identities.
• Change the ways others perceive them through curated social media profiles.
• Use social media tools to expand their own social connections.
• Have their real identities concealed by virtual ones.

As a result of this power through new social media technology, users are in some ways able to have much greater control over their identity formations. The researchers warned, however, that social media tools should be used by older, more mature people because, when “it’s used in an irresponsible way by people who are too young, they can cause problems and difficulties that in some cases even time cannot erase.”

From a social media psychology standpoint, this new ability to control one’s own identity formation is as empowering as it is alarming. Users can build their identities on social media as honest representations of their personalities and traits, and at the same time, they can also create entirely new social media personae. This power has impressive advantages and severe consequences.

The Effects of Self-Perception on Social Media

Social media users’ self-image is put under a microscope when they constantly compare their situations with others. And these comparisons happen frequently when they engage with each other. Matthew Pittman and Brandon Reich, both media specialists and academics, have found that people can sharpen their own identities when they engage in intimate, image-oriented social media platforms like Instagram and Pinterest.

They stated that “quantitative results suggest that loneliness may decrease , while happiness and satisfaction with life may increase, as a function of image-based social media use. In contrast, text-based media use appears ineffectual.” As a result, some users have greater confidence and a stronger self-perception on social media in image-oriented environments.

On the other hand, social media can also motivate people who view themselves negatively to build entirely new identities. The thinking here, though not always malicious, is to trick others into thinking they’re someone else. In the context of social media dating services, this practice is known as catfishing. According to Scientific American, “Users may feel pressured to alter (height, weight, and age) information to present what they perceive is their ideal self and maximize their attractiveness .” Social media has created an environment where users feel pressured to either lie or fabricate their physical and psychological identities to become more desirable.

These pressures extend far beyond the dating world and into many other facets of social media interactions. The vulnerabilities of some social media users more generally can lead to a “ false Facebook -self.” According to the study “The ‘Facebook-self’: characteristics and psychological predictors of false self-presentation on Facebook,” researchers were able to highlight that people with low self-esteem on social media were much more likely to create alternate, sometimes conflicting Facebook personas.

Social Pressures to Fit into Social Media Groups

A huge incentive to use social media stems from the acceptance users can receive from various groups. As with practically all aspects of social media, this group-focused direction of social media has benefits and drawbacks.

One major benefit for social media users is they can reach out to and connect with groups of people with similar interests across the planet. People can find more information about niche hobbies, popular pastimes, and general interests. This ability to belong to different groups is excellent for people coming from smaller or distant communities, and the psychological advantages for those individuals are immense.

According to Art Markman of Psychology Today, belonging to a group can dramatically improve a person’s drive to complete tasks. Specifically, he stated “that even a simple relationship between people based on arbitrary reasons, like sharing a birthday or being randomly assigned to a group, is enough to increase feelings of warmth and motivation.” Social media, thus, offers opportunities for people to form groups for both general and specific interests, which can help improve their overall productivity.

On the other hand, belonging to a group too closely or intimately can change the way the typical social media user thinks and behaves. The academic journal Media Psychology recently published a study that found that when users keep to their social media groups, they begin to mimic the behaviors of those groups. This mimicry results in a social media identity bubble that’s reinforced by prolonged engagement with the group.

Social Media and Mental Health

Outside of the ability to dominate emotional and mental states, social media platforms have the power to influence, either positively or negatively, the psychological behaviors of people. Social media can dramatically help to improve users’ mental health, but at the same time, it can negatively impact people’s psychological well-being.

Mental Health Benefits from Social Media

Though many researchers focus on the cons of social media use, there are several potential mental wellness advantages. The advantages extend across demographics and appear in unexpected ways.

For millennials, who tend to dominate some spheres of social media consumption, the digital world of social sharing poses several mental health and relationship benefits. Psychologists Adriana M. Manago and Lanen Vaughn found there are ample opportunities for friendship and happiness as younger people transition to adulthood. Specifically, they said younger social media users can now create stronger bonds with friends because of the easy access to friends’ information and interests.

Further, they found these connections give users an opportunity for greater independence and autonomy, which increases their critical thinking and decision-making abilities. These feelings of community and self-worth will palpably improve the mental health of users over the course of time.

The organization Painted Brain, which combats mental health hardships through advocacy, artistic expression, and business, outlined the ways social media can positively affec t the mental health of users. From a psychological standpoint, they found many positive effects of social media on mental health, such as:

• Social integration with similar interest groups.
• Healthy and body-positive lifestyle motivations.
• The availability of support groups.
• Maintaining and building new relationships.
• An introduction into new modes of thinking.

Mental Health Consequences from Social Media Use

While there certainly are tangible benefits to social media consumption and engagement, it’s been rightly critiqued for its tendency to have toxic effects on users’ mental health.

This kind of anxiety manifests much more severely in teens. As licensed clinical social worker Katie Hurley found, teens online must “confront cyberbullying, trolls, toxic comparisons, sleep deprivation, and less frequent face-to-face interactions.” In a cultural moment that stresses the importance of staying online all the time, these seemingly disconnected issues can overwhelm users and result in profound anxiety. These negative effects on teens’ mental health illustrates the need for parents, educators, and other role models to build better models for social media engagement.

Further, according to a scholarly article published in the Journal of Social and Clinical Psychology , higher levels of depression correlate with Facebook use. The study found the subjects’ mental health suffered with the more time they spent on Facebook, causing users to feel worse about their own positions when they compared their profiles with others.

Another article by medical doctor and cyberpsychologist Igor Pantic echoed the finding . He stated that “prolonged use of social networking sites, such as Facebook, may be related to signs and symptoms of depression.” As people compare their lives to so many others, they will only find their mental health continue to deteriorate.

The Implications of Social Media Psychology

The field of social media psychology has only existed for the past 10 to 15 years, which coincides directly with the rise of social media. As a result, the research being conducted is still in its early stages. In nearly all the scholarly articles featured in this guide, researchers mentioned on the limitations of their own methods so that future studies could explore them further.

Because there are so many gaps in the existing research, new perspectives need to join the field. According to Atlantic contributor and psychologist of 20 years Jean M. Twenge, people need to become much more aware of the consequences of social media dependence for the sake of our children’s future . “What’s at stake isn’t just how kids experience adolescence,” she said. “The constant presence of smartphones is likely to affect them well into adulthood.”

You can respond to and help solve this overarching problem by continuing your education in the field. There are many levels of career paths in psychology that offer different research opportunities, depending on your own professional and personal preferences. An online B.S. in Psychology will prepare you to analyze and understand the psychological effects of social media on users by studying social psychology , group dynamics, and more.

Gain greater insight on how social media influences, both positively and negatively, the psychology of users with King University Online’s psychology degree. Our program is taught by trained and decorated faculty who will prepare you for a successful future. With year-round course availability and a generous credit transfer policy, you may be able to earn your degree in as little as 16 months.

• Open access
• Published: 27 September 2024

Evaluation of emotion classification schemes in social media text: an annotation-based approach

• Fa Zhang 1 ,
• Jian Chen 1 ,
• Qian Tang 1 &
• Yan Tian 1  

BMC Psychology volume  12 , Article number:  503 ( 2024 ) Cite this article

Metrics details

Emotion analysis of social media texts is an innovative method for gaining insight into the mental state of the public and understanding social phenomena. However, emotion is a complex psychological phenomenon, and there are various emotion classification schemes. Which one is suitable for textual emotion analysis?

We proposed a framework for evaluating emotion classification schemes based on manual annotation experiments. Considering both the quality and efficiency of emotion analysis, we identified five criteria, which are solidity, coverage, agreement, compactness, and distinction. Qualitative and quantitative factors were synthesized using the AHP, where quantitative metrics were derived from annotation experiments. Applying this framework, 2848 Sina Weibo posts related to public events were used to evaluate the five emotion schemes: SemEval’s four emotions, Ekman’s six basic emotions, ancient China’s Seven Emotions, Plutchik’s eight primary emotions, and GoEmotions’ 27 emotions.

The AHP evaluation result shows that Ekman’s scheme had the highest score. The multi-dimensional scaling (MDS) analysis shows that Ekman, Plutchik, and the Seven Emotions are relatively similar. We analyzed Ekman’s six basic emotions in relation to the emotion categories of the other schemes. The correspondence analysis shows that the Seven Emotions’ joy aligns with Ekman’s happiness, love demonstrates a significant correlation with happiness, but desire is not significantly correlated with any emotion. Compared to Ekman, Plutchik has two more positive emotions: trust and anticipation. Trust is somewhat associated with happiness, but anticipation is weakly associated with happiness. Each emotion of Ekman’s corresponds to several similar emotions in GoEmotions. However, some emotions in GoEmotions are not clearly related to Ekman’s, such as approval, love, pride, amusement, etc.

Ekman’s scheme performs best under the evaluation framework. However, it lacks sufficient positive emotion categories for the corpus.

Peer Review reports

In the age of Web 2.0, many people use online social media. Social media reflects the emotions, attitudes, and opinions of Internet users. Sentiment analysis, the basic task of which is to determine the polarity of a text, such as positive, negative, or neutral [ 1 ], has been widely used in social media [ 2 ]. Beyond polarity, emotion analysis could identify the types of emotions such as joy, anger, sadness, and fear, helping to understand the mental state more accurately. Emotion analysis is an important topic that has received wide attention [ 3 ]. Emotion analysis of social media also has a wide range of applications [ 4 ]. For instance, during the COVID-19 epidemic, emotion analysis was used to understand people’s emotions and assess policy effects [ 5 ]. Some companies conduct emotion analysis on online reviews to understand the user experience and to enhance product development [ 6 ].

A fundamental part of emotion analysis is the selection of an emotion model. An emotion model is a theoretical framework for describing, explaining, or predicting human emotions and affective processes. There are many emotion models, roughly categorized as discrete and dimensional [ 7 ]. The discrete approach suggests that humans have discrete, distinguishable emotions. A component of discrete emotion models is how to categorize emotions, which in this paper is called an emotion classification scheme. If a discrete emotion approach is adopted, emotion analysis is a multi-classification problem [ 4 ]. Lexicon-based methods and machine learning methods are commonly used for emotion classification. The lexicon-based method depends mainly on the lexicon and rules. The machine learning method can obtain better classification results, but it needs to be trained with a large corpus. Regardless of which method is used, the problem of choosing an emotion scheme is faced. There are various emotion classification schemes. Which one is appropriate for emotion analysis?

A systematic approach is required for selecting an emotion classification scheme. The emotion schemes have complex effects on the quality and efficiency of emotion analysis. For example, a suitable scheme can enhance the performance of machine learning models and achieve better application results [ 8 ]. In supervised learning, annotated datasets are required, and the emotion scheme has an impact on annotation efficiency.

In this paper, we propose an Analytic Hierarchy Process (AHP) evaluation framework based on annotation experiments. This framework used five criteria, which are solidity, coverage, agreement, compactness, and distinction, to evaluate the emotion schemes. This framework could combine qualitative and quantitative factors. Applying this framework, we collected Sina Weibo posts related to public events, conducted annotation experiments, and evaluated five emotion classification schemes. After evaluating the five schemes, we analyzed their differences and associations.

The rest of the paper is organized as follows: Sect. 2 introduces the emotion schemes. Section 3 proposes the evaluation framework. Section 4 conducts an annotation experiment and evaluates the five emotion schemes. Section 5 explores the differences and associations among these schemes. Section 6 is a discussion, and finally, Sect. 7 concludes the article.

Literature review

Emotion models.

Emotion is a psychological phenomenon and has been studied from a variety of perspectives [ 9 ]. There are still many divergences in the understanding of emotion [ 10 , 11 ]. Researchers have proposed a variety of emotion models. The discrete approaches propose that there are emotions that can be distinguished and that different types of emotions are independent. Dimensional approaches suggest that emotional states do not exist independently. There are multiple dimensions that make up the emotional space with smooth transitions between different emotions.

There are various discrete emotion models. Ekman identified that there are six basic emotions [ 12 ]. Scherer and Wallbott used seven major emotions in their cross-cultural questionnaire studies [ 13 ]. In ancient China, the theory of the “Seven Emotions” suggested that there are seven emotions [ 14 , 15 ]. In Plutchik’s wheel of emotions model [ 16 ], there are eight (four pairs) primary emotions, and each primary emotion is subdivided into three categories based on intensity. The combination of two neighboring primary emotions produces a complex emotion. There are also more refined emotion models, such as the OCC model, which adds 16 emotions to Ekman’s six basic emotions for a total of 22 categories of emotions [ 17 ]. Parrott’s three-layer structured emotion model has six primary emotions. Primary emotions are subdivided into secondary emotions, and secondary emotions are subdivided into tertiary emotions [ 18 ]. Cowen et al. found that there are 27 distinct varieties of emotional experience based on the self-reported method [ 19 ]. The classification of emotions by these models is shown in Table  1 .

There are also various dimensional models. Russell’s circumplex model has two dimensions: valence and arousal [ 20 , 21 ]. Another important dimensional model is the PAD [ 22 ]. It has three dimensions: pleasure-displeasure, arousal-nonarousal, and dominance-submissiveness. Later, the PAD was extended to a valence-arousal-dominance (VAD) one [ 23 ]. There are also higher-dimensional models, such as the four-dimensional model [ 24 ] and the six-dimensional model [ 25 ].

Application of emotion models in text mining

The emotion lexicon and emotion-labeled dataset are the essential resources for textual emotion analysis. They use a wide variety of emotion models. The principles and process of emotion model selection are not described in detail in these resources. An emotion lexicon contains many emotion-related words, each assigned one or more emotion labels. There are many emotion lexicons. LIWC divides words into positive and negative and identifies three negative emotions: anxiety, anger, and sadness [ 26 ]. LIWC has been widely used in psychology and sociology but the categorization of emotions is not refined enough. The NRC lexicon uses Plutchik’s eight emotions [ 27 ]. It provides fine-grained emotion with a limited ability to recognize polysemous words and implied emotions. WordNet-Affect adds hierarchically arranged emotion tags such as positive, negative, neutral, and ambiguous, and each tag is subdivided into a variety of emotions [ 28 ]. It performs fine-grained annotation of emotions and can capture the nuances of emotions. However, lexicon construction requires significant expertise.

An emotion-labeled dataset is a collection of data where each entry is labeled with the emotion category or affective state associated with it. There are many emotion-labeled datasets that use different emotion models. Bostan and Klinger surveyed those datasets [ 29 ] and found that most of them adopt discrete models, of which Ekman’s and Plutchik’s are the most frequent [ 4 ]. Some datasets use dimensional models, with VAD being the most popular. Others employ hybrid models [ 30 ], which label both emotion class and VAD scores.

Instead of using emotion models from the field of psychology, some datasets have customized emotion categories. For example, Grounded-Emotions uses only two emotions: happy and sad [ 31 ]. It has computational efficiency but affects the accuracy of emotion recognition. SemEval 2018 Task 1 uses four basic emotions: joy, sadness, fear, and anger [ 32 ]. EmoInt also follows the same four emotions [ 33 ]. The SemEval’s four basic emotions have the advantages of simplicity, broad applicability, ease of assessment and comparison. However, this categorization method suffers from limited emotion categories. GoEmotions, which is a large emotion-labeled dataset, uses 27 emotions [ 34 ]. The use of 27 emotions has the advantage of fine-grained emotion categorization. However, it also increases annotation complexity, model complexity, and usage complexity.

Comparison of emotion classification schemes

If discrete emotion viewpoints are adopted for emotion analysis, it is necessary to choose an emotion classification scheme, which is needed for corpus annotation and model training and evaluation. Different emotion classification schemes vary in terms of psychological basis, number of emotions, set of emotions, etc. It has an impact on annotation as well as machine learning models in many aspects. Choosing an emotion classification scheme is not simple and deserves systematic study.

Some researchers have compared different models of emotion. Power et al. designed a questionnaire containing 30 emotion terms. A group of participants were asked how much in general they experienced each of the emotions. A confirmatory factor analysis was conducted to compare six different models of emotion with “goodness of fit” [ 35 ]. The purpose of the article was to analyze the quantities and relationships of basic emotions and not compare the commonly used emotion models.

A few studies have evaluated emotion classification schemes. Williams et al. compared six emotion classification schemes based on the ease of use of manual annotation and supervised machine learning performance [ 36 ]. The corpus was annotated separately using different emotion schemes, and then the six schemes were ranked using Inter-Annotator Agreement (IAA). Wood et al. conducted annotation experiments on tweets, comparing different annotation methods and emotion representation schemes [ 37 ]. They also use IAA as an evaluation indicator. Bruyne et al. conducted annotation experiments on tweets using the VAD model and compared different annotation methods (rating scales, pairwise comparison, and best-worst scaling) [ 38 ]. They evaluated the annotation methods based on the criterion of inter-annotator agreement. They noticed the effects of different annotation methods on the time-consuming, complexity of affective judgments, but did not perform a comprehensive assessment of multiple metrics.

The use of an emotion classification scheme has important implications for the quality and efficiency of emotion analysis. How does one evaluate an emotion classification scheme? IAA is only an indicator of annotation reliability, and there are other aspects of annotation quality such as accuracy and coverage. For large corpus, annotation efficiency also needs to be considered. Therefore, a systematic assessment of emotion schemes from multiple perspectives is needed for emotion analysis.

This study developed a framework for evaluating emotion classification schemes. The framework can be applied to the evaluation of discrete emotion schemes. A good emotional scheme should lead to a balance between annotation quality and efficiency. Five criteria are proposed for this goal, which are solidity, coverage, agreement, compactness, and distinction. For the quantitative metrics, we designed a computational method based on annotation experiments. AHP was used to calculate the composite score. As an application of this framework, we collected Sina Weibo posts related to public events, evaluated five emotion classification schemes, ranked them, and analyzed the differences and associations of these schemes. This framework can evaluate emotion schemes from multiple aspects. It may be helpful to determine the emotion classification scheme in emotion analyses.

The evaluation framework

Choosing an emotion classification scheme suitable for the annotation of posts involves many qualitative and quantitative factors that need to be synthesized. AHP is an evaluation method that is capable of coping with both the rational and the intuitive to select the best candidates [ 39 ]. The elements of AHP include goals, criteria, metrics, and candidates. A goal is what is expected to be achieved. Criteria are refined based on the goal and then transformed into computable metrics. Candidates are the objects being evaluated.

What kind of emotion the text conveys is subjective, vague, and ambiguous. We conducted annotation experiments and obtained quantitative metrics from the annotation results. For the qualitative factors, pairwise comparisons were used to construct judgment matrices to quantify the qualitative issues. Finally, top-down weighting and addition were performed to obtain a composite score for each candidate.

The goal is to choose a suitable emotion classification scheme from a set of candidates, which should balance the quality and efficiency of annotation. A suitable scheme is expected to achieve a high quality of annotation. In addition, the efficiency of annotation also needs to be considered. Efficiency is the output that can be achieved with a given resource investment (time, manpower, budget, etc.). Text annotation is a resource-intensive and time-consuming task; efficient annotation can significantly reduce the time and cost.

Emotion classification schemes may affect annotation quality and efficiency in many ways. Based on the goal of the evaluation, we identified five criteria as follows:

There are many emotion models, which may differ in their solidity. Solidity refers to their tightness and robustness in terms of logical structure, empirical validation, explanatory and predictive power, etc. For example, some models, such as Ekman’s six emotions, have a great deal of scientific research, and some models have less scientific research. The use of a more solid model with accurate categorization of emotions, appropriate emotion granularity, and ease of understanding facilitates better annotation quality.

The categories in an emotion model should cover as many of the emotions in the corpus as possible. Coverage refers to how likely it is that a piece of text in the corpus contains a class of emotion that belongs to the emotion model. Insufficient coverage may cause some of the posts to lack appropriate emotion labels, resulting in mislabeling and lower annotation quality. Posts that are not labeled with emotion become ineffective outputs and reduce efficiency.

Posts are often labeled with multiple annotators. Different annotators may have different judgments about the emotions embedded in the post. Agreement refers to the degree of consistency between the annotation results of annotators. If multiple annotators select the same label for a post, the results are more reliable. Inter-annotator agreement is an important aspect of quality [ 40 ]. If the consistency is too low, the post will be difficult to use as ground truth, thus reducing the efficiency of the annotation.

Compactness

Each scheme contains a number of emotion categories. Compactness refers to the number of emotions contained in an emotion scheme. Using a scheme with fewer emotion categories, the annotator uses less time and effort to make choices and is more efficient. If there are too many emotion categories, some may overlap, the annotator is prone to misuse. The burden of annotation work is greater.

Distinction

It is crucial that each emotion category can be easily differentiated. Distinction refers to whether there is a clear distinction between the various emotions in the scheme. If there is a clear distinction, it is beneficial to reduce the cognitive load on the annotator and improve annotation efficiency.

Assuming there are \(\:n\) posts in the corpus, each post is annotated by \(\:m\) annotators. The number of emotion schemes to be evaluated is \(\:v\) , each scheme contains \(\:{k}_{j},\:j=\text{1,2},\dots\:,v\) categories of emotions.

Metric of solidity

Solidity is subjective in nature. Obtaining quantized values of solidity is a complex issue. We used subjective judgments by consulting with people familiar with emotion models. Applying the AHP method to calculate the metric based on the pairwise comparisons. The elements of the judgment matrix are scaled from 1 to 9, and the computed solidity for each scheme ranges from 0 to 1.

Metric of coverage

When using the emotion scheme \(\:\:j\) , each post was provided with \(\:{k}_{j}\) emotions and “neutral”, “no suitable emotion”, and “undistinguishable” options. If there are \(\:{x}_{i}\) posts labeled by annotator \(\:i\) with “no suitable emotion”, the coverage is:

The coverage ranges from 0 to 1.

Metric of agreement

IAA is an important measure of reliability [ 41 ]. The IAA is used as a measure to show how much the coders agree. Common metrics used include Scott’s pi [ 42 ], Cohen’s kappa [ 43 ], Fleiss’ kappa [ 44 ], and Krippendorff’s alpha [ 45 ]. Krippendorff’s alpha is useful for multiple categories, multiple coders, can handle missing values, and corrects for randomness [ 46 ]. We employed Krippendorff’s alpha (k-alpha) to assess agreement. We utilized Real Statistics software to compute both k-alpha and confidence intervals [ 47 ]. The k-alpha ranges from 0 to 1.

Metric of compactness

The smaller the number of emotion categories contained in a scheme, the more compact it is. Among all the emotion schemes, the minimum number of categories is denoted as \(\:{s}_{min}\) and the maximum number of categories is denoted as \(\:{s}_{max}\) , the scheme \(\:j\) has \(\:{k}_{j}\) categories, its compactness is:

The Laplace correction is used, with the numerator added 1 to avoid a compactness of 0. \(\:v\) is the number of emotion schemes, with the denominator adding \(\:v\) to avoid compactness > 1. The compactness ranges from 0 to 1.

Metric of distinction

In the annotation process, the annotator is asked to give a unique emotion, and if he or she is not sure, he or she chooses the option “undistinguishable”. If the annotator \(\:i\) judges that there are \(\:{y}_{i}\) posts “undistinguishable”, then the distinction is:

The distinction ranges from 0 to 1.

Researchers can choose any emotion classification scheme to evaluate for their own needs. In this paper, we chose five emotion schemes to demonstrate the application of the evaluation framework.

SemEval has four basic emotions: joy, sadness, anger, and fear. These four emotions are common to many basic emotion models.

Ekman’s six basic emotions are happiness, sadness, anger, fear, disgust, and surprise. Ekman’s six basic emotions have had a wide impact on psychology.

The Seven Emotions of China: joy, anger, sadness, fear, love, disgust, and desire. It has a long history and a wide influence in Eastern societies.

Plutchik’s eight primary emotions are joy, sadness, anger, fear, trust, disgust, surprise, and anticipation. Pluchik’s model of emotions has had a wide-ranging influence in psychology.

GoEmotions 27 emotions: admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, and surprise. GoEmotions is a recent large-scale emotion labeling dataset that attempts to cover the diverse types of emotions in web texts.

Based on the analysis above, the AHP model is shown in Fig.  1 .

The hierarchy model for the evaluation of the five emotion schemes

There are six judgment matrices in the model. The importance of each criterion to the goal is subjective, and the goal-criterion matrix was obtained by pairwise comparisons expressed on a 1–9 scale. Similarly, quantitative comparison of candidates in terms of solidity is a complex problem. Pairwise comparison was also used to obtain the solidity-candidates matrix. The remaining four matrices were obtained by quantitative calculation. The coverage-candidates matrix was calculated based on the coverage of each scheme, with matrix element \(\:{a}_{ij}=coverag{e}_{i}/coverag{e}_{j}\) , i.e., the ratio of coverage of scheme \(\:i\) to scheme \(\:j\) . Similarly, the agreement-candidates matrix used the ratio of k-alpha, the compactness-candidates matrix used the ratio of compactness, and the distinction-candidates matrix used the ratio of distinction.

After the judgment matrix passed the consistency test, the priority vector of schemes was computed. The optimal scheme was selected based on their scores.

Data collection and cleaning

We used Octopus crawler to search for social event posts from Sina Weibo, with keywords including post-COVID-19 economy, health care reform, influenza A, negative population growth, college student employment, Russian-Ukrainian conflict, earthquakes, and GPT. The time range of microblog posting is 2022.12.1-2023.5.31, and a total of 15,098 microblogs were obtained.

A random sample of 3,000 posts was manually inspected to remove posts unrelated to social events. These include deleting posts with advertisement links, deleting posts with less than 5 words, and some posts that do not reflect the search intent, such as some posts under the keyword “earthquake” that have nothing to do with earthquakes, such as “pupil quake”, which is just an Internet buzzword expressing surprise. After cleaning, we got 2,848 posts as a corpus. A few samples were taken from the corpus, as shown in Table  2 .

We counted the number of words in each post, with a minimum of seven words, a maximum of 4743 words, and an average of 153 words. The distribution of word counts is shown in Fig.  2 . Here, 58.4% of the posts had no more than 100 words, 88.5% had no more than 300 words, and 94.8% had no more than 500 words. Overall, while a small number of posts were long, most were short.

Word count distribution of 2848 posts

We also analyzed the number of sentences in each post. The mean of the number of sentences is 3.89, and the mode is 1. Here, 38.5% had only 1 sentence, 67.3% had no more than 3 sentences, 81.8% had no more than 5 sentences, and 91.2% had no more than 8 sentences. Most posts have a low number of sentences.

Manual annotation

We recruited five college students as annotators. They are all Chinese and have no religious beliefs. We provided an annotation guide, which includes an introduction to the annotation task, an introduction to the five emotion schemes, the meanings of various labels, and operation methods. The guide makes it clear that the emotions to be annotated are those of the post writers.

The annotators were first trained. The researchers explained the annotation guide to the annotators. Discussions were held with the annotator to solve their queries. Then 50 randomly selected posts from the corpus were pre-annotated, and the annotation results were discussed until a consensus understanding was achieved.

The labeling of each post includes the following 4 items:

Select the emotion label. The options include all emotions in the current scheme, “neutral” means no emotion, “no suitable emotion” means there is no suitable emotion, and " undistinguishable” means that more than two emotions are difficult to distinguish.

Degree of confidence, including ‘sure’ and ‘not sure’.

If ‘not sure’, explain why.

Memo, describing what needs to be clarified in the labeling process.

Each post was annotated by the five annotators. The labeling process took one month and was divided into five phases. Five sequences were randomly generated for the five emotion schemes and assigned to annotators 1 ~ 5. For each phase, each annotator chose a particular emotion scheme to annotate according to the assigned sequence. At the end of each phase, the annotators had a three-day rest period to dilute the effect of the previous annotations on the subsequent annotations.

After the annotation was completed, the annotation results were collected and organized, the omissions and errors were manually checked, and the annotator was asked to correct them. Finally, the annotation results of the five emotion schemes were obtained.

Annotation results

Distribution of emotions.

The corpus contains 2,848 posts, and each post was labeled by five annotators. The labels include all the emotions in the current scheme, “neutral”, “no suitable emotion”, and “undistinguishable”. All the labeling results of the 5 annotators were counted, and the percentage of each label was calculated. The percentages of each label under each emotion scheme are shown in Figs.  3 , 4 , 5 , 6 and 7 .

Distribution of emotions based on the SemEval scheme

Distribution of emotions based on the Ekman scheme

Distribution of emotions based on the Seven Emotions scheme

Distribution of emotions based on the Plutchik scheme

Distribution of emotions based on the GoEmotions scheme

The distribution of emotions shows that the four basic emotions, sadness, joy (happiness), fear, and anger, have a relatively large share of the corpus, and their proportions are close to each other in each scheme, making them the main emotions in the corpus. However, GoEmotions disperses these four emotions into a variety of similar emotions due to the fine-grained division of emotions. In addition to the four main emotions, some emotions specific to each scheme, such as surprise and disgust in Ekman, disgust, love, and desire in Seven Emotions, and trust, anticipation, surprise, and disgust in Plutchik, although accounting for a relatively small proportion, are also present, indicating that the corpus covers all emotions.

Coverage and distinction

When the annotator cannot find an appropriate emotion in the current scheme, he or she selects “no suitable emotion”, which is related to the metric of coverage. The other option, “undistinguishable”, is used to compute the metric of distinction. Table  3 shows both options’ percentages in each scheme.

The percentage of “no suitable emotion” varies significantly between the schemes, suggesting that the use of different schemes has a significant effect on coverage. While the percentage of “undistinguishable” is generally lower across all schemes, there are still some differences, suggesting that the differentiation may be slightly varied.

Labeling a post by many annotators is like the voting process. The labels are candidates, and each label will have some votes, where at least one label has the largest number of votes, called the maximum number of votes (denoted \(\:max\_votes\) ), which can be used to simply reflect consistency. If every annotator chooses a different label, we denote it as NA (No Agreement), and the \(\:max\_votes\) is 1. If \(\:max\_votes\ge\:\:3\) , it can be considered a majority vote and achieve consensus. We calculated the percentages of posts with \(\:max\_votes=1\) and the ones with \(\:max\_votes\ge\:3\) for each scheme to indicate consistency. Krippendorff’s alpha (k-alpha) was also used to measure the IAA. Table  4 shows the consistency of each scheme.

The data for NA ( \(\:max\_votes=1\) ) show that there are a small number of posts that cannot be agreed upon in all schemes. The percentage of \(\:max\_votes\ge\:3\) is greater than 70% in all schemes, indicating that the annotators agree on the emotion embedded in most of the posts. The k-alpha of each scheme ranges from 0.33 to 0.41. In textual emotion labeling, k-alpha may be lower than the threshold of reliability [ 48 ]. Williams et al. reported that the range of k-alpha is 0.202 to 0.483 in their annotation work [ 36 ]. Despite the low k-alpha, there are some differences in k-alpha across the five schemes, implying that different schemes have an impact on consistency.

Evaluation results

We constructed all judgment matrices based on the AHP model. The importance of each criterion is subjective, and AHP supports the quantification of subjective judgments. By comparing the five criteria pairwise, the goal-criteria matrix was obtained, and weights were calculated, as shown in Table  5 . The pairwise comparison was made on a scale of 1–9, with 1 indicating equal importance, 3 indicating moderate importance, 5 indicating strong importance, 7 indicating very strong importance, and 9 indicating extreme strong importance.

To obtain the exact value of the vector of weights \(\:W={\left({w}_{1},{w}_{2},\dots\:,{w}_{n}\right)}^{T}\) , one needs to solve for \(\:AW={\lambda\:}_{max}W\) , where \(\:A\) is the judgment matrix and \(\:{\lambda\:}_{max}\) is the largest eigenvalue. However, approximation methods are generally used. Here we used one of the common approximation algorithms: first normalizing the elements in each column of the judgment matrix, then averaging over each row, and finally normalizing.

Pairwise comparisons of the five schemes according to the solidity criterion yielded the solidity-candidates matrix, as shown in Table  6 .

The other four matrices were obtained based on the metrics of coverage, agreement, compactness, and distinction, respectively. Coverage is equal to 1 minus the percentage of “no suitable emotion”. Agreement is measured using the k-alpha. Compactness is obtained by substituting the number of categories in each scheme into Eq. ( 2 ). Distinction is equal to 1 minus the percentage of “undistinguishable”. The four metrics of each scheme is shown in Table  7 .

In the coverage-candidates matrix, the element \(\:{a}_{ij}\) is equal to the ratio of the coverage of scheme \(\:\:i\) to the coverage of scheme \(\:\:j\) . Similarly, the other three matrices are also calculated using the ratio of corresponding metrics. The four matrices are shown in Tables  8 , 9 , 10 and 11 .

According to the five criteria-candidates’ matrices, the performance of each scheme under each criterion can be calculated, as shown in Table  12 .

To obtain the score of each scheme, we multiplied each weight of a scheme by the weight of its corresponding criterion, then added over all the criteria. Based on the scores, in descending order, the rankings are Ekman, Plutchik, GoEmotions, Seven Emotions, and SemEval, as shown in Fig.  8 .

The scores of the five emotion schemes

Each scheme has its own strengths and weaknesses, and they differ significantly in terms of solidity, coverage, agreement, and compactness. The performance of each scheme under each criterion was visually compared in Fig.  9 .

Performance of the five emotion schemes under the five criteria

As shown in Fig.  9 , Ekman and Plutchik are better at solidity, GoEmotions and Plutchik are better at coverage, SemEval and GoEmotions are better at agreement, and SemEval and Ekman are better at compactness. There is very little difference in distinction, as the percentage of “undistinguishable” posts is close across each scheme.

Sensitivity analysis

Criteria weights were determined based on subjective judgment, with ambiguity and randomness. Do changes in the criteria weights have a significant impact on the scoring results? We performed a sensitivity analysis of the weights of the criteria. The sum of the weights of each criterion was set to 1, and the weights of only one criterion were adjusted individually at a time, while the weights of the remaining criteria were distributed in equal proportions to the initial weights.

The initial weight of solidity is 0.4020, and when it varies within [0.0375, 1], Ekman and Plutchik remain in the 1st and 2nd places, and the ranking results are basically unchanged. When the coverage varies within [0, 0.9016], the ranking result is basically unchanged. When agreement varies within [0, 0.8313], the result is basically unchanged. Compactness varies within [0, 0.8750], and the results are almost the same. Ranking results are not sensitive to changes in distinction.

When the weights of each criterion change in a wide range, the ranking results remain essentially unchanged, indicating that the results are robust. Of course, only the change of single criterion weights was discussed here, and the case of multiple weights changing at the same time was not discussed.

Comparison of different schemes

According to the evaluation results, the Ekman scheme scored the highest. It is better in terms of solidity and compactness but not in terms of coverage and agreement. Whether it is ideal and how similar or different it is from others needs to be analyzed in depth.

Similarities

Each of the five schemes has pros and cons; which of them is more similar? Firstly, the results of the five annotators are aggregated by majority vote. For a post, if \(\:max\_votes<3\) , the consensus cannot be achieved by a majority vote, it is denoted as NC (No Consensus). For two emotion schemes, the NC co-occurrence of all posts was used to measure the similarity between them. The NC co-occurrence matrix is shown in Table  13 , where the diagonal elements \(\:{a}_{ii}\) are the proportion of NC posts under scheme \(\:i\) , and the other elements \(\:{a}_{ij},\:i\ne\:j\) are the proportion of posts that are NC in both scheme \(\:i\) and scheme \(\:j\) .

The similarity of the five schemes cannot be directly observed from the NC co-occurrence matrix. Multidimensional scaling (MDS) is a dimensionality reduction and visualization method that could map high-dimensional data to lower dimensions, while keeping the distance relationship between data points, and facilitating observation of patterns in the data. We employed PROXSCAL for MDS to demonstrate the similarity of the schemes in 2D space, as shown in Fig.  10 . Here, stress = 0.0305 and D.A.F = 0.99844, lower stress (to a minimum of 0) and higher D.A.F (to a maximum of 1) indicate that the fit is good in two dimensions. The depiction is highly explanatory. In 2D space, the more similar the schemes, the closer they are to each other.

Multidimensional scaling of the five emotion schemes

In Fig.  10 , GoEmotions is separated from the other schemes by dimension 1, while dimension 2 separates SemEval and GoEmotions from the remaining three schemes. According to spatial proximity, the five schemes can be grouped into three: Ekman, Plutchik, and the Seven Emotions form a group; SemEval is one group; and GoEmotions is also a group. This suggests that Ekman, Plutchik, and Seven Emotions are more alike. The similarities here are only those analyzed from the perspective of NC co-occurrence.

Correspondence analysis

While the Ekman scheme scored the highest, it was not the most dominant in terms of coverage and agreement. Correspondence analysis was conducted between Ekman’s and others to examine the association between emotion categories in different schemes.

Ekman and SemEval.

These posts with \(\:max\_votes\ge\:3\) could get the final label by majority vote. The number of posts in the two schemes was counted to get the contingency table, as shown in Table  14 .

The correspondence analysis result is shown in Fig.  11 , where the explained variance of the two dimensions is 62.5%. Fear, sadness, and anger are consistently linked, while joy correlates closely with happiness. The emotions of surprise and disgust in Ekman do not have a strong association with any emotion in SemEval, indicating their necessity.

Ekman (red dot) versus SemEval (blue triangle)

(2)Ekman and the Seven Emotions.

The correspondence analysis result is presented in Fig.  12 . The explained variance of the two dimensions is 60.2%. Dimension 1 separates positive emotions (joy, love, and desire) from negative emotions (anger, sadness, fear, and disgust).

The four synonymous emotions (fear, sadness, disgust, and anger) are highly consistent. In Seven Emotions, joy is strongly linked to happiness in Ekman’s model, while love is strongly linked to happiness, and desire has no obvious counterpart in Ekman. On the other hand, Seven Emotions does not have a clear corresponding emotion for surprise as in Ekman.

Ekman (red dot) versus Seven Emotions (blue triangle)

(3)Ekman and Plutchik.

The correspondence analysis result is shown in Fig.  13 . The five synonymous emotions (sadness, disgust, anger, fear, and surprise) are highly consistent. Plutchik’s joy is highly consistent with Ekman’s happiness. Compared to Ekman, Plutchik has two more positive emotions: trust and anticipation, trust is somewhat associated with happiness, and anticipation is weakly associated with happiness.

Ekman (red dot) versus Plutichik (blue triangle)

(4)Ekman and GoEmotions.

The correspondence analysis result is shown in Fig.  14 . Each emotion of Ekman corresponds to a number of similar emotions in GoEmotions, such as: happiness corresponds to joy, admiration, excitement, and gratitude; anger corresponds to anger; and annoyance; sadness corresponds to sadness, grief, and remorse; fear corresponds to fear and nervousness; disgust corresponds to disgust; and surprise corresponds to surprise and curiosity.

However, some emotions in GoEmotions are not clearly related to Ekman, such as approval, disapproval, love, pride, amusement, embarrassment, desire, and caring. This indicates the independence of these emotions.

Ekman (red dot) versus GoEmotions (blue triangle)

The above analyses show that the correspondence between emotions in each scheme is complex. Ekman is more similar to Seven Emotions and Plutchik, and their synonymous emotions, sadness, fear, anger, and disgust, are highly consistent. However, the only positive emotion in Ekman is happiness, which corresponds to joy in Seven Emotions and Plutchik, and there are more positive emotions that are not expressed in Ekman. These positive emotions in Seven Emotions and Plutchik have a non-negligible proportion in the corpus. For the current corpus, there is a lack of sufficient positive emotion categories in the Ekman scheme.

Discussions

How to choose an appropriate one from the many emotion schemes is an important problem in emotion analysis. This paper makes two contributions to the selection of discrete emotion schemes. First, an evaluation framework was proposed to provide an integrated assessment of multiple factors, which helps to select an overall better scheme and overcome the shortcomings of a single indicator. Second, five commonly used emotion schemes were evaluated to select the scheme with the highest score. These five schemes were analyzed for similarities and differences, which helped to provide insight into the strengths and weaknesses of each scheme.

The evaluation framework consists of five criteria, including solidity, coverage, agreement, compactness, and distinction. Solidity reflects the credibility of the emotion scheme. Coverage reflects the completeness of emotional categories. Agreement reflects the consistency of the annotators. Compactness reflects the degree of non-redundancy in emotion categories. Distinction reflects whether there are significant differences between emotions. These criteria are key factors that affect the quality and efficiency of emotion analysis. Previous studies [ 36 , 37 ] have used a single indicator, IAA, which is the agreement criterion in the framework. The IAA is an indicator of annotation reliability. It reflects only one aspect of annotation quality and cannot cover other quality indicators, nor does it reflect annotation efficiency. This is supported by the results of our evaluation. If only the IAA was used for ranking, the priority would be SemEval, GoEmotions, Plutchik, Seven Emotions, and Ekman. However, this rank is not consistent with the other four criteria, and the final scores differ. This implies that agreement cannot cover the other four criteria.

We evaluated the five commonly used emotion schemes and found that Ekman scored the highest. This is close to the actual adoption in the field of textual emotion analysis [ 4 ]. According to our evaluation, the Ekman scheme has stronger evidence and the highest solidity score. It also performed better in terms of compactness. However, the Ekman scheme has limited coverage, which is supported by findings from Williams [ 36 ]. The reason may be the absence of positive emotion categories, and some studies have argued that there may be more than six basic emotions [ 49 ]. The addition of emotion categories does not always decrease agreement. For example, GoEmotions includes 27 emotions and still has a high level of agreement.

This study has limitations. Many factors may impact emotion annotation, including emotion classification schemes, annotators, the annotation process, emotion ontology, single or multi-labels, etc. This study only considered the effects of the emotion classification scheme. We selected only five emotion schemes for the evaluation, not all of them, and the ranking results are limited. In addition, textual emotion is domain specific. Emotional distribution may vary in different corpora. This study used Weibo posts focused on public events. Using a different corpus, the evaluation results may vary.

Conclusions and future works

Emotion analysis of text requires the selection of an emotion model. There are many discrete emotion schemes that need to be evaluated from multiple perspectives. This paper proposed an evaluation framework with the goal of achieving a balance between quality and efficiency in emotion analysis, for which five criteria are identified, which are solidity, coverage, agreement, compactness, and distinction. Indicators were designed for each criterion, with quantitative indicators calculated from the results of annotated experiments and qualitative indicators using pairwise comparisons. The AHP method was used to realize the combination of qualitative and quantitative metrics. As an application of this framework, Weibo posts in the domain of public events were collected, and five emotion classification schemes were evaluated. The results of the evaluation show that the Ekman scheme is the best, but it is deficient in coverage and agreement. The Ekman scheme has only one positive emotion, happiness, which may lead to less accurate labeling results for positive emotion texts.

In recent years, deep learning has developed rapidly and has advantages in emotion analysis. Commonly used deep learning models include CNN, LSTM, Bi-LSTM, GRU, and transformer-based models. CNNs can efficiently capture local features and are suitable for emotion analysis of short texts. LSTMs retain sequential information through a gating mechanism, and Bi-LSTM goes a step further by processing sequential and reverse-ordered contexts through bi-directional propagation, which enhances the comprehensive understanding of emotion. GRU serves as a variant of LSTM that reduces computational complexity while retaining similar performance. Transformer-based models such as BERT, XLM, and GPT capture long-distance dependencies through a self-attentive mechanism. BERT performs well in multiple emotion categorization tasks, while XLM demonstrates its cross-linguistic power in multilingual emotion analysis, and GPT performs well in emotion generation and comprehension tasks.

Deep learning models perform well in emotion analysis, but manual selection of an emotion classification scheme is still crucial. This is because emotion is a complex psychological phenomenon that is not fully understood. The appropriate emotion model needs to be selected based on the purpose of application. Different scenarios have specific needs for emotion classification and manually determining the emotion scheme allows for customized adjustments. Emotions in text are implicit and models need to be trained with annotated data. The emotion classification scheme needs to be determined while annotating the data. Therefore, the emotion scheme evaluation method still has value.

In future research, the proposed framework will be expanded to integrate both discrete and continuous emotion schemes. This expansion will likely require modifications to the existing metrics and their corresponding computational methodologies. Furthermore, we will investigate the quantification of some criteria within the framework. For instance, we will explore the use of bibliometric techniques to measure the solidity of emotion schemes. Finally, we aim to extend the framework’s applicability by evaluating a broader range of emotion schemes across various domains. This will enable a comprehensive analysis of how domain-specific characteristics influence the selection of emotion schemes.

Data availability

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Mäntylä MV, Graziotin D, Kuutila M. The evolution of sentiment analysis—A review of research topics, venues, and top cited papers. Comput Sci Rev. 2018;27:16–32.

Article   Google Scholar  

Rodríguez-Ibánez M, Casánez-Ventura A, Castejón-Mateos F, Cuenca-Jiménez. P-M. A review on sentiment analysis from social media platforms. Expert Syst Appl. 2023;223:119862.

Deng J, Ren F. A survey of textual emotion recognition and its challenges. IEEE Trans Affect Comput. 2023;14:49–67.

Acheampong FA, Wenyu C, Nunoo-Mensah H. Text‐based emotion detection: advances, challenges, and opportunities. Eng Rep. 2020;2.

Zhang F, Tang Q, Chen J, Han N. China public emotion analysis under normalization of COVID-19 epidemic: using Sina Weibo. Front Psychol. 2023;13:1066628.

Article   PubMed   PubMed Central   Google Scholar  

Xu C, Zheng X, Yang F. Examining the effects of negative emotions on review helpfulness: the moderating role of product price. Comput Hum Behav. 2023;139:107501.

Harmon-Jones E, Harmon-Jones C, Summerell E. On the importance of both dimensional and discrete models of emotion. Behav Sci. 2017;7:66.

Beck J. Quality aspects of annotated data. AStA Wirtsch Sozialstat Arch. 2023;17:331–53.

Lerner JS, Li Y, Valdesolo P, Kassam KS. Emotion and decision making. Annu Rev Psychol. 2015;66:799–823.

Article   PubMed   Google Scholar  

Kuppens P. Improving theory, measurement, and reality to advance the future of emotion research. Cognition Emot. 2019;33:20–3.

Brady M, Précis. Emotions: the basics. J Philos Emot. 2021;3:1–4.

Ekman P. An argument for basic emotions. Cognition Emot. 1992;6:169–200.

Scherer KR, Wallbott HG. Evidence for universality and cultural variation of differential emotion response patterning. J Pers Soc Psychol. 1994;66:310–28.

Confucius. The Book of rites (Li Ji). Createspace Independent Pub; 2013.

Wang Y. The three-character classic. People’s Literature Publishing House; 2020.

Plutchik R. The nature of emotions: human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice. Am Sci. 2001;89:344.

Ortony A, Clore GL, Collins A. The cognitive structure of emotions. Cambridge, MA: Cambridge University Press; 1990.

Google Scholar  

Parrott WG. Emotions in social psychology: key readings. Psychology; 2001.

Cowen AS, Keltner D. Self-report captures 27 distinct categories of emotion bridged by continuous gradients. Proc Natl Acad Sci. 2017;114:E7900–9.

Russell JA. A circumplex model of affect. J Pers Soc Psychol. 1980;39:1161–78.

Posner J, Russell JA, Peterson BS. The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev Psychopathol. 2005;17:715–34.

Russell JA, Mehrabian A. Evidence for a three-factor theory of emotions. J Res Pers. 1977;11:273–94.

Bradley MM, Lang PJ. Measuring emotion: the self-assessment manikin and the semantic differential. J Behav Ther Exp Psychiatry. 1994;25:49–59.

Izard CE. The psychology of emotions. Springer Science & Business Media; 1991.

Frijda NH. The emotions. Cambridge University Press; 1986.

Tausczik YR, Pennebaker JW. The psychological meaning of words: LIWC and computerized text analysis methods. J Lang Soc Psychol. 2010;29:24–54.

Mohammad SM, Turney PD. Crowdsourcing a word–emotion association lexicon. Comput Intell. 2013;29:436–65.

Strapparava C, Valitutti A. WordNet-Affect: an Affective Extension of WordNet. In: Proceedings of the 4th International Conference on Language Resources and Evaluation. Lisbon; 2004. pp. 1083–6.

Bostan L-A-M, Klinger R. An analysis of annotated corpora for emotion classification in text. In: Proceedings of the 27th International Conference on Computational Linguistics. 2018. pp. 2104–2119.

Park EH, Storey VC. Emotion ontology studies: a framework for expressing feelings digitally and its application to sentiment analysis. ACM Comput Surv. 2023;55:1–38.

Liu V, Banea C, Mihalcea R, Grounded. emotions. 2017 Seventh Int Conf Affect Comput Intell Interact (ACII). 2017;:477–83.

Mohammad S, Bravo-Marquez F, Salameh M, Kiritchenko S. SemEval-2018 task 1: affect in tweets. Proc 12th Int Work Semantic Evaluation. 2018;:1–17.

Mohammad S, Bravo-Marquez F. WASSA-2017 shared task on emotion intensity. In: Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. 2017. pp. 34–49.

Demszky D, Movshovitz-Attias D, Ko J, Cowen A, Nemade G, Ravi S. GoEmotions: a dataset of fine-grained emotions. Proc 58th Annu Meet Assoc Comput Linguistics. 2020;:4040–54.

Power MJ. The structure of emotion: an empirical comparison of six models. Cogn Emot. 2006;20:694–713.

Williams L, Arribas-Ayllon M, Artemiou A, Spasic I. Comparing the utility of different classification schemes for emotive language analysis. J Classif. 2019;36:619–48.

Wood ID, McCrae JP, Andryushechkin V, Buitelaar P. A comparison of emotion annotation approaches for text. Information. 2018;9:117.

Bruyne LD, Clercq OD, Hoste V. Annotating affective dimensions in user-generated content. Lang Resour Eval. 2021;55:1017–45.

Saaty TL, Vargas LG, Models, Methods C. Applications of the Analytic Hierarchy process. Int Ser Oper Res Manag Sci. 2012. https://doi.org/10.1007/978-1-4614-3597-6 .

Braylan A, Alonso O, Lease M. Measuring annotator agreement generally across complex structured, multi-object, and free-text annotation tasks. In: Proceedings of the ACM Web Conference 2022. 2022. pp. 1720–30.

Krippendorff K. Reliability in content analysis: some common misconceptions and recommendations. Hum Commun Res. 2004;30:411–33.

Scott WA. Reliability of content analysis: the case of nominal scale coding. Public Opin Q. 1955;19:321.

Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20:37–46.

Fleiss JL. Measuring nominal scale agreement among many raters. Psychol Bull. 1971;76:378–82.

Krippendorff K. Bivariate agreement coefficients for reliability of data. Sociol Methodol. 1970;2:139.

Krippendorff K. Content analysis: An Introduction to its methodology. 4th Edition. Sage publications; 2019.

Zaiontz. Real Statistics using Excel. 2020. www.real-statistics.com

Antoine J-Y, Villaneau J, Lefeuvre A. Weighted Krippendorff’s alpha is a more reliable metrics for multi-coders ordinal annotations: experimental studies on emotion, opinion and coreference annotation. Proc 14th Conf Eur Chapter Assoc Comput Linguistics. 2014;:550–9.

Keltner D, Sauter D, Tracy J, Cowen A. Emotional expression: advances in basic emotion theory. J Nonverbal Behav. 2019;43:133–60.

Download references

This research was funded by the National Natural Science Foundation of China (Grant No. 71571190), the Guangdong Province Key Research Base of Humanities and Social Sciences (Grant No. 2022WZJD012), and Key Issues on High-Quality Development of the Guangdong-Hong Kong-Macao Greater Bay Area (Grant No. XK-2023-007).

Author information

Authors and affiliations.

Beijing Institute of Technology, Zhuhai School, No.6, JinFeng Road, Zhuhai, Guangdong Province, 519088, China

Fa Zhang, Jian Chen, Qian Tang & Yan Tian

You can also search for this author in PubMed   Google Scholar

Contributions

FZ contributed to the conception, design, analysis of the manuscript. JC contributed to data collection and analysis. QT contributed the results and discussions. YT contributed to the evaluation of emotion schemes. All authors contributed to the article and approved the submitted version.

Corresponding author

Correspondence to Fa Zhang .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Zhang, F., Chen, J., Tang, Q. et al. Evaluation of emotion classification schemes in social media text: an annotation-based approach. BMC Psychol 12 , 503 (2024). https://doi.org/10.1186/s40359-024-02008-w

Download citation

Received : 27 May 2024

Accepted : 17 September 2024

Published : 27 September 2024

DOI : https://doi.org/10.1186/s40359-024-02008-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Emotion classification scheme
• Social media

BMC Psychology

ISSN: 2050-7283

• General enquiries: [email protected]

Conspiracy Theories

Personality and belief in online conspiracy theories, narcissism and anxious attachment predict conspiracy theory belief differently..

Posted September 27, 2024 | Reviewed by Monica Vilhauer

Online conspiracy theories are often taken to be a serious problem and critical challenge to democracy. Indeed, so important is the perceived problem that the education minister of the new UK government suggested that children will be taught to recognise and evaluate such online content: “ …our curriculum review will develop plans to embed critical skills in lessons to arm our children against the disinformation, fake news and putrid conspiracy theories awash on social media . ” 1 Whether or not this gets done, always a major "if" with governments, such views are premised on the suggestion that believing conspiracy theories results from a disorder of thinking: believers are intellectually impaired, and their thinking style needs correction. However, remedial approaches based on changing cognition are doomed to failure, as they misunderstand the aetiologies of conspiracy theory belief. Those believing conspiracy theories are not necessarily intellectually challenged, but rather can be damaged or dangerous or both – the problem, primarily, is emotional-social, not intellectual.

Understanding and tackling online conspiracy theory beliefs requires understanding three overlapping issues: (1) the psychology of those promulgating such views, whose motivations may be nefarious, monetary, or simply malcontented; 2 (2) the nature of the social media platforms allowing the rapid dissemination of such views; 3,4 and (3) the social-emotional capacities of those exposed to such views. 5,6,7 The latter is the focus, here, as a plethora of recent studies imply both narcissism and anxious attachment are associated with belief in online conspiracy theories. These studies suggest narcissism and attachment problems are associated with perceived challenges to the self, which believing in conspiracy theories helps to overcome. 5 Without tackling the issues underlying these problems, belief in, frankly, weird online theories cannot be tackled – and certainly not by cognitive means alone.

Mirroring the broader social and political concerns, 1 research into online conspiracy theories has more than tripled over the last five years 8 – some of it is very good, and almost none impacts policy. A recurring theme is that conspiracy theories postulate the existence of a secret plot between powerful people, belief in which reduces the complexities of experience, and makes a frightening world more easily understandable. 3,8 However, there is a wide variation in the content and nature of conspiracy theories, 9,10 and which ones are promulgated and believed depends on the digital platforms accessed, 8 and personalities of the receipients. 7

It has long been thought that belief in conspiracy theories reflects a delusional, schizoid, or paranoid personality style 7 – a view dating back to Hofstader in 1964. 11 However, many recent meta-analyses show that many aspects of personality do not predict conspiracy theory belief as strongly as might be expected. 12 For example, in terms of the Big-5 personality traits: extroversion , conscientiousness , and neuroticism have negligible relationships with believing in conspiracy theories; while agreeableness has a small negative, and openness a small positive, correlation with conspiracy theory belief. 12

It is when research moves beyond the Big-5 traits that the personality predictors of conspiracy theory belief get important. 6,12 One factor reliably related to not believing in conspiracy theories is intellectual humility. 12 There may be reasons why intellectual humility protects against being conned by a conspiracy theory. This trait means people are less likely to rush to judgement, less likely to adopt an all-or-nothing position with certainty, and are more likely to question themselves and take personal responsibility for any position (which is at odds with a prime conspiracy theory tenet that others are to blame).

As intellectual humility is typically not displayed by narcissists, narcissism may mediate the negative relationship between intellectual humility and belief in conspiracy theories. 12 This raises the question of why narcissists believe conspiracy theories. In fact, the adoption of conspiracy theories by narcissists may offer them various means by which to protect the self. 5 Consideration of drives to protect the self, in all its incarnations – the individual (the drive to maintain uniqueness); the relational (the drive to form significant interpersonal bonds); and the collective (the drive to belong) – can help to understand proneness to conspiracy theory belief, and why narcissism potentiates this belief.

Belief in a conspiracy theory can protect the individual-self in two ways that may be important for narcissists. Grandiose narcissists can protect or bolster the individual-self by accepting theories that blame or criticise others for problems; while vulnerable narcissists can protect or bolster the individual-self through signalling their uniqueness by adhering to unusual conspiracy theories. Similarly, the collective-self may be protected through strengthening intra-group bonds by pathologising non-members as responsible for the ills of the world. In fact, this behaviour is highly indicative of "collective narcissism."

The role of the relational-self in believing conspiracy theories, however, involves consideration of attachment problems, rather than narcissism. The relational-self can be protected by adopting the views of others in the group surrounding the person, no matter how odd they may be, to reduce fears of potential rejection. 5,6 Some variants of attachment disorder are implicated in creating the rejection-avoidance need. Those displaying avoidant attachment are not vulnerable: they do not care what the others believe; and, unlike narcissists, do not need to put the others down to protect their ego, as the status of others is irrelevant to the status of the avoidantly attached individual’s ego. In contrast, the anxiously attached are not immune to the need to protect the relational-self. 6 This could be considered as a core feature of anxious attachment. In fact, there is a reliable and very consistent relationship between anxious attachment and belief in, and promulgation of, conspiracy theories, which is not the case for those showing avoidant attachment. 13

These drivers of the need to believe in conspiracy theories have nothing to do with a lack of intellectual capacity. Indeed, given the nature of some conspiracy theories, it would take a very intellectually able person to defend them! Thus, schemes to remediate such beliefs by teaching "appropriate" thinking styles may have little impact. These approaches may reflect a collective narcissism of politicians, who suggest we need protection from ideas that are not theirs, or more likely not their parties’, with reasoning something like: people not believing what I believe are dangerous to my group (and, therefore, to me), must be wrong, and must be demonised as deficient. Rather, what seems implicated in guarding against divisive conspiracy theories is understanding the factors leading to the psychological styles associated with belief in them – such as the social and emotional conditions in which children are raised. Of course, this places the problem within the remit of politicians, and does not allow politicians’ externality onto teachers. Given this, and the complexities involved, gaining protection may be a long road to travel.

1. ITV News (11.9.24). Children to be taught how to spot fake news and 'putrid' conspiracy theories. ITV News . Children to be taught how to spot fake news and 'putrid' conspiracy theories | ITV News

2. Reed, P. (2020). The motivations of malcontents. Psychology Today . The Motivations of Malcontents | Psychology Today United Kingdom

3. Reed, P. (2022). Doom-scrolling and the manipulation of anxiety. Psychology Today . Doom-Scrolling and the Manipulation of Anxiety | Psychology Today

4. Reed, P. (2023). Can social media help search for the truth. Psychology Today . Can Social Media Help the Search for Truth? | Psychology Today

5. Biddlestone, M., Green, R., Cichocka, A., Douglas, K., & Sutton, R. (2022). A systematic review and meta-analytic synthesis of the motives associated with conspiracy beliefs. https://www.researchgate.net/profile/Mikey-Biddlestone/publication/359841963_A_systematic_review_and_meta-analytic_synthesis_of_the_motives_associated_with_conspiracy_beliefs/links/625fc7ec1c096a380d12cb3e/A-systematic-review-and-meta-analytic-synthesis-of-the-motives-associated-with-conspiracy-beliefs.pdf

6. Loria, E., & Meini, C. (2022). Uncertainty, attachment, and narcissism, but most of all vulnerability: the perfect recipe for conspiracy therapy. Rivista Italiana di Filosofia del Linguaggio .187-200

7. Stasielowicz, L. (2022). Who believes in conspiracy theories? A meta-analysis on personality correlates. Journal of Research in Personality , 98 , 104229.

8. Mahl, D., Schäfer, M. S., & Zeng, J. (2023). Conspiracy theories in online environments: An interdisciplinary literature review and agenda for future research. New Media & Society , 25 (7), 1781-1801.

9. Introne, J., Korsunska, A., Krsova, L., & Zhang, Z. (2020). Mapping the narrative ecosystem of conspiracy theories in online anti-vaccination discussions. In International Conference on Social Media and Society , July, 2020 , 184-192.

10. Heft, A., & Buehling, K. (2022). Measuring the diffusion of conspiracy theories in digital information ecologies. Convergence , 28 (4), 940-961.

11. Hofstadter, R. (1964). The paranoid style in American politics. Harper’s Magazine , November , 77-96.

12. Bowes, S.M., Costello, T.H., Ma, W., & Lilienfeld, S.O. (2021). Looking under the tinfoil hat: Clarifying the personological and psychopathological correlates of conspiracy beliefs. Journal of Personality , 89 (3), 422-436.

13. Marchlewska, M., Górska, P., Green, R., Szczepańska, D., Rogoza, M., Molenda, Z., & Michalski, P. (2024). From individual anxiety to collective narcissism? Adult attachment styles and different types of national commitment. Personality and Social Psychology Bulletin , 50 (4), 495-515.

Phil Reed, Ph.D., is a professor of psychology at Swansea University.

• Find a Therapist
• Find a Treatment Center
• Find a Psychiatrist
• Find a Support Group
• Find Online Therapy
• United States
• Brooklyn, NY
• Chicago, IL
• Houston, TX
• Los Angeles, CA
• New York, NY
• Portland, OR
• San Diego, CA
• San Francisco, CA
• Seattle, WA
• Washington, DC
• Asperger's
• Bipolar Disorder
• Chronic Pain
• Eating Disorders
• Passive Aggression
• Personality
• Goal Setting
• Positive Psychology
• Stopping Smoking
• Low Sexual Desire
• Relationships
• Child Development
• Self Tests NEW
• Therapy Center
• Diagnosis Dictionary
• Types of Therapy

It’s increasingly common for someone to be diagnosed with a condition such as ADHD or autism as an adult. A diagnosis often brings relief, but it can also come with as many questions as answers.

• Emotional Intelligence
• Gaslighting
• Affective Forecasting
• Neuroscience