Social Psychology: How Groups, Identity, and Influence Shape Human Behavior

Last Updated May 21, 2026

Social psychology appears here not only as an experimental and theoretical science, but also as a quantitative, computational, institutional, cultural, ethical, and systems-oriented one. The aim of this article map is to preserve the conceptual richness of social psychology while showing how contemporary social science increasingly relies on measurement, experimental design, network thinking, reproducible workflows, computational simulation, and formal models of social judgment.

Social Psychology as a Foundational Science

Social psychology occupies a foundational place within psychological science because it explains how human thought, emotion, judgment, identity, and behavior are shaped by social context. Individuals do not perceive, decide, or act in isolation. They interpret the world through relationships, group memberships, norms, social identities, cultural expectations, institutional roles, perceived audiences, and systems of power.

This foundational role does not mean that social psychology replaces cognitive psychology, personality psychology, positive psychology, political psychology, behavioral economics, sociology, anthropology, or institutional analysis. Rather, it provides an integrating framework for understanding how individual minds are socially situated. Cognitive psychology explains attention, perception, memory, and reasoning. Social psychology asks how those processes change when the object of thought is another person, a group, a social norm, an authority figure, a moral conflict, or an institution.

Social psychology also provides a bridge between individual behavior and collective life. It explains why people conform, obey, resist, cooperate, stereotype, persuade, trust, blame, help, exclude, rationalize harm, identify with groups, and mobilize for collective action. It therefore connects laboratory psychology to public life, institutions, politics, technology, organizational behavior, education, conflict, civic trust, and social change.

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Context, Identity, and Influence

Social psychology may be understood as one of the great sciences of context, identity, and influence. It asks how situations alter behavior, how groups shape self-understanding, how norms guide action, how authority structures compliance, how identity changes interpretation, and how social meaning affects judgment.

This makes social psychology different from a purely individual account of behavior. People often explain actions in terms of personality, intention, or moral character, but social psychology shows that behavior is deeply sensitive to context. A person may cooperate in one setting and compete in another. They may resist persuasion from an out-group source but accept the same message from an in-group source. They may judge identical behavior differently depending on identity, status, framing, perceived motive, social role, or institutional setting.

Social psychology is therefore a systems-level psychology of human behavior in social environments. It studies the interaction between mind and situation, person and group, belief and norm, identity and institution. It explains why social behavior is patterned, why conflict persists, why trust is fragile, why group boundaries matter, and why human judgment is often shaped by factors people do not consciously recognize.

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Quantitative and Computational Social Psychology

Modern social psychology is increasingly quantitative. Social processes are not only described conceptually; they are measured, modeled, compared, experimentally manipulated, statistically analyzed, and simulated. Attitudes can be measured through survey scales and implicit tasks. Attribution can be studied through experimental vignettes. Conformity can be measured through group-pressure designs. Persuasion can be analyzed through message framing, source credibility, and attitude change. Social networks can be modeled through ties, centrality, diffusion, clustering, and influence.

This does not mean that social psychology becomes a purely computational field. Rather, it means that serious social explanation often requires moving across modes of inquiry. A researcher may design an experiment, collect participant-level data, analyze mixed effects, model social influence, compare groups, examine network structure, store trial-level data in SQL, document assumptions in a notebook, and interpret results through theories of identity, attribution, norm enforcement, persuasion, or social cognition.

For that reason, this series treats mathematics, statistics, computational modeling, experimental design, reproducible notebooks, SQL metadata, and open code repositories as increasingly important parts of social-psychological literacy. Some articles remain primarily conceptual, historical, ethical, or theoretical. Others naturally require statistical workflows, network analysis, simulations, survey models, attribution models, social-cognition experiments, or reproducible code. The aim is not to reduce social life to numbers, but to build a Social Psychology series that reflects how the field is practiced when it is methodologically serious.

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What Social Psychology Studies

Social psychology studies how people interpret, evaluate, and respond to other people and social situations. At the cognitive level, it studies social cognition, schemas, heuristics, attribution, impression formation, stereotyping, prejudice, social memory, and judgment under uncertainty. At the interpersonal level, it studies attraction, relationships, helping, aggression, cooperation, trust, conflict, persuasion, communication, and moral judgment.

At the group level, social psychology studies conformity, obedience, leadership, social identity, group norms, groupthink, group polarization, deindividuation, social loafing, collective behavior, intergroup relations, status, power, and institutional legitimacy. At the applied level, it studies organizations, politics, health behavior, education, technology, misinformation, public trust, diversity, inclusion, justice, and social change.

Social psychology further studies the tension between individual agency and social influence. People are not passive products of their environments, but neither are they independent from context. Social psychology examines how behavior emerges from the interaction between persons, situations, identities, norms, institutions, histories, media systems, and material conditions.

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What This Series Covers

This series brings together the major domains through which social psychology interprets human behavior in social context. It includes social cognition, attribution, heuristics, self-serving bias, social comparison, attitudes, persuasion, cognitive dissonance, conformity, obedience, social norms, group identity, groupthink, group polarization, intergroup relations, stereotypes, prejudice, discrimination, implicit bias, prosocial behavior, altruism, bystander behavior, diffusion of responsibility, moral disengagement, cooperation, social dilemmas, tragedy of the commons, collective action, social facilitation, social loafing, deindividuation, institutions, organizations, public trust, technology, misinformation, and mediated social life.

These domains differ in method, emphasis, and scale, but together they form a coherent intellectual project: the attempt to understand how human beings make meaning, assign responsibility, interpret others, coordinate with groups, respond to power, and act inside social systems. Social psychology is therefore not only a body of knowledge about interpersonal behavior. It is also a way of asking how minds become social, how groups become meaningful, and how social contexts shape what people experience as true, fair, threatening, legitimate, or possible.

The series also treats social psychology as a field that links the individual and the institutional. Social-psychological knowledge informs education, leadership, organizational behavior, conflict resolution, public policy, civic trust, health communication, technology design, misinformation research, social justice, and democratic governance. For that reason, the article map is designed not only to introduce social-psychological concepts, but to clarify why social-psychological reasoning remains indispensable for understanding the contemporary world.

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Mathematics, Computation, and Modeling in Social Psychology

Mathematics provides part of the formal language through which social psychology understands inference, influence, attribution, identity, network structure, trust, cooperation, and collective dynamics. Probability supports theories of social inference, uncertainty, risk, and expectation. Statistics supports experimental analysis, survey measurement, psychometrics, group comparison, causal inference, and model evaluation. Network analysis supports the study of influence, diffusion, centrality, clustering, contagion, and group structure. Computational models support the study of attitude change, cooperation, polarization, social learning, and identity dynamics.

Social cognition can be formalized as inference under uncertainty. Let \(x\) represent observed social cues, and let \(h\) represent a hypothesis about a person or situation, such as “cooperative,” “threatening,” “competent,” or “constrained by context.” A Bayesian framing expresses social judgment as:

Attribution can also be represented as a weighted comparison between dispositional and situational explanations. Let \(D\) represent dispositional inference and \(S\) situational inference. Then one may write:

Impression formation can be represented as an update process over trait estimates:

Identity-shaped evaluation can also be expressed formally:

Social influence can be represented as a networked update process:

Trust can be represented as an accumulative process shaped by competence, fairness, reliability, shared identity, and institutional performance:

Computation is especially valuable where social processes become too complex for simple verbal explanation. R supports mixed models, survey analysis, psychometrics, experimental statistics, visualization, and reproducible reports. Python supports social simulation, network analysis, natural language processing, machine learning, behavioral data pipelines, and agent-based modeling. Julia supports high-performance simulation and dynamic social models. SQL supports structured survey data, participant metadata, trial-level records, network ties, message exposures, and reproducible provenance. C++, Fortran, C, Rust, and Go support performance-sensitive simulation, command-line utilities, embedded research tools, and reproducible computational infrastructure.

Used carefully, mathematics and computation clarify social-psychological assumptions rather than replacing human interpretation. They make it possible to ask how attribution weights shift, how social influence diffuses through networks, how identity salience changes judgment, how group polarization emerges, how misinformation spreads, how trust erodes, and how norms stabilize or collapse.

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Why Social Psychology Matters

Social psychology matters because many of the most important human problems are social in structure. People must decide whom to trust, whom to blame, whom to help, whom to follow, which groups to identify with, which institutions to accept, and which messages to believe. These decisions are rarely made in isolation. They are shaped by norms, identities, emotions, social cues, power, authority, group membership, institutional context, and perceived legitimacy.

Social psychology also matters because modern societies are increasingly shaped by mediated social environments. Social media, digital platforms, algorithmic feeds, political messaging, organizational dashboards, remote work, online communities, and AI systems all structure how people encounter social information. These environments can amplify trust or suspicion, cooperation or polarization, inclusion or exclusion, civic knowledge or misinformation. Social psychology provides tools for understanding how social meaning is produced in these systems.

Finally, social psychology matters because it reveals that many social harms are not simply the result of individual ignorance or bad character. Stereotyping, conformity, obedience, prejudice, polarization, institutional mistrust, moral disengagement, diffusion of responsibility, and collective inaction often emerge from ordinary social processes operating under particular conditions. That insight is ethically important because it opens the possibility of redesigning contexts, institutions, incentives, norms, and communication systems rather than blaming individuals alone.

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Social Psychology and Human Self-Understanding

Social psychology changes how human beings understand themselves because it reveals that the self is socially situated. People experience themselves as individuals, but identity, belief, judgment, emotion, and behavior are shaped by social relationships, groups, norms, histories, roles, media systems, and institutional contexts. The mind is not sealed off from the social world. It is formed through it and continually responsive to it.

Yet social psychology also complicates simple accounts of conformity or manipulation. Social influence is not only a threat to autonomy. It is also the basis of learning, cooperation, morality, trust, belonging, language, culture, and social order. Human beings need social influence in order to become human, but they also need the capacity to question destructive norms, unjust hierarchies, misleading group pressures, and institutional failures.

For that reason, social psychology has philosophical as well as scientific significance. It raises enduring questions about agency, responsibility, freedom, morality, identity, prejudice, justice, cooperation, authority, and the conditions under which social life becomes humane or destructive. A serious Social Psychology knowledge series should therefore not end with experiments alone. It should clarify the wider implications of social science for institutions, democracy, technology, ethics, and human self-understanding.

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Social Psychology Article Map

The map below organizes the Social Psychology knowledge series around the articles already published in the current article list. Published articles are linked to their actual URLs. Planned articles are left unlinked and marked as planned so the section remains complete without creating broken links.

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Measurement, Experiment, and Social-Psychological Practice

One of social psychology’s enduring contributions is its insistence that social processes can be studied scientifically. Social judgments, attitudes, norms, prejudice, conformity, persuasion, trust, identity, and responsibility are familiar from everyday life, but they are not transparent. Social psychology uses experiments, surveys, vignettes, behavioral tasks, implicit measures, response times, network data, qualitative interpretation, field studies, and statistical modeling to make social processes measurable under controlled or interpretable conditions.

This matters because social explanations are easy to overpersonalize. People often explain behavior by appealing to character, intention, intelligence, or moral failing. Social psychology shows that behavior often depends on context, identity, group pressure, power, norms, authority, framing, and perceived legitimacy. Measurement and experiment therefore help reveal causes that ordinary intuition may miss.

Modern social-psychological practice increasingly depends on reproducible workflows. Studies generate participant-level data, trial-level responses, survey scales, manipulation checks, model outputs, and uncertainty estimates. A serious Social Psychology knowledge series should therefore treat data quality, statistical modeling, research ethics, replication, preregistration, transparent reporting, and reproducibility as central to the scientific study of social life.

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Social Psychology, Technology, and the Modern World

Social psychology has become increasingly important because modern social life is mediated by technologies that shape identity, attention, comparison, persuasion, trust, and belonging. Social media platforms, recommender systems, search engines, online communities, organizational software, messaging platforms, AI assistants, and digital public spaces all structure how people encounter one another.

The connection between social psychology and technology is especially visible in misinformation, polarization, online harassment, status competition, social comparison, algorithmic amplification, parasocial influence, digital persuasion, and trust in institutions. Technologies do not merely transmit social information. They organize social visibility, reward attention, rank credibility, create feedback loops, and alter the perceived norms of communities.

At the same time, technology can support cooperation, learning, civic participation, mutual aid, and institutional transparency. A mature social psychology of technology must therefore ask not only how platforms influence behavior, but how digital environments can be designed to support trust, dignity, agency, inclusion, and public accountability.

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Social Psychology, Computation, and Social Simulation

Computation has become central to social psychology because many social processes unfold dynamically across people, groups, networks, and institutions. Attitudes spread. Norms stabilize. Trust accumulates or erodes. Misinformation diffuses. Groups polarize. Identity becomes salient. Cooperation emerges or collapses. Responsibility diffuses. Collective action becomes possible or fails. These processes often cannot be understood through individual-level analysis alone.

Social simulation allows researchers to formalize assumptions about agents, identities, networks, norms, thresholds, and feedback. A model can test how small changes in network structure affect diffusion, how identity weighting affects judgment, how repeated interaction changes trust, or how misinformation spreads under different credibility assumptions. These models do not replace empirical social psychology, but they can clarify mechanisms and generate hypotheses.

For that reason, this series treats computation as a supporting discipline of social psychology, not as a substitute for human interpretation. Models must remain transparent, ethically grounded, empirically informed, and attentive to power, inequality, and context. The strongest form of computational social psychology is therefore not technocratic reduction, but auditable social reasoning in service of better explanations of collective life.

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R Section: Modeling Social Influence, Attribution, and Trust

For analytical readers, R is useful for estimating how identity salience, perceived credibility, group membership, situational cues, dispositional inference, social pressure, and institutional fairness shape social judgment. The example below creates a synthetic dataset and models trust as a function of competence, fairness, shared identity, source credibility, prior belief, perceived threat, social pressure, and institutional harm. It is not real research data. It is a reproducible scaffold for thinking clearly about social-psychological measurement.

# Synthetic social psychology model in R
# Educational example only.
# This script simulates participant-level social judgment data and models trust.

# install.packages(c("tidyverse", "broom", "scales"))

library(tidyverse)
library(broom)
library(scales)

set.seed(42)

n <- 900

social_data <- tibble(
  participant_id = 1:n,
  competence = runif(n, 0.10, 1.00),
  fairness = runif(n, 0.10, 1.00),
  shared_identity = runif(n, 0.00, 1.00),
  source_credibility = runif(n, 0.10, 1.00),
  prior_belief_alignment = runif(n, 0.00, 1.00),
  perceived_threat = runif(n, 0.00, 1.00),
  social_pressure = runif(n, 0.00, 1.00),
  institutional_harm = runif(n, 0.00, 1.00)
) |>
  mutate(
    latent_trust =
      -0.25 +
      1.20 * competence +
      1.45 * fairness +
      0.90 * shared_identity +
      1.10 * source_credibility +
      0.65 * prior_belief_alignment -
      1.25 * perceived_threat -
      0.55 * social_pressure -
      1.35 * institutional_harm,

    probability_trust = 1 / (1 + exp(-latent_trust)),

    trust_binary = rbinom(n, size = 1, prob = probability_trust),

    trust_score = pmin(
      pmax(
        50 +
          18 * competence +
          22 * fairness +
          14 * shared_identity +
          16 * source_credibility +
          10 * prior_belief_alignment -
          20 * perceived_threat -
          12 * social_pressure -
          24 * institutional_harm +
          rnorm(n, mean = 0, sd = 8),
        0
      ),
      100
    )
  )

# Linear model for continuous trust score
trust_score_model <- lm(
  trust_score ~ competence + fairness + shared_identity +
    source_credibility + prior_belief_alignment +
    perceived_threat + social_pressure + institutional_harm,
  data = social_data
)

# Logistic model for high trust as a binary outcome
trust_binary_model <- glm(
  trust_binary ~ competence + fairness + shared_identity +
    source_credibility + prior_belief_alignment +
    perceived_threat + social_pressure + institutional_harm,
  data = social_data,
  family = binomial()
)

trust_score_summary <- tidy(trust_score_model, conf.int = TRUE)
trust_binary_summary <- tidy(trust_binary_model, conf.int = TRUE, exponentiate = TRUE)

print(trust_score_summary)
print(trust_binary_summary)

# Summarize trust by fairness band
fairness_summary <- social_data |>
  mutate(fairness_band = cut(
    fairness,
    breaks = c(0, 0.33, 0.66, 1),
    labels = c("Low fairness", "Moderate fairness", "High fairness"),
    include.lowest = TRUE
  )) |>
  group_by(fairness_band) |>
  summarise(
    mean_trust_score = mean(trust_score),
    mean_probability_trust = mean(probability_trust),
    mean_shared_identity = mean(shared_identity),
    .groups = "drop"
  )

print(fairness_summary)

ggplot(fairness_summary, aes(x = fairness_band, y = mean_probability_trust)) +
  geom_col() +
  scale_y_continuous(labels = percent_format()) +
  labs(
    title = "Synthetic Trust Probability by Perceived Fairness",
    x = "Perceived fairness band",
    y = "Mean probability of trust"
  ) +
  theme_minimal()

This workflow models one core social-psychological intuition: trust is not produced by information alone. It is shaped by perceived competence, fairness, shared identity, source credibility, prior belief, social pressure, threat, and institutional history. In real research, such models require careful measurement, sampling, ethics, and interpretation. In a pillar-level context, the value of the workflow is conceptual clarity: it shows how social-psychological claims can be translated into explicit variables, assumptions, and model outputs.

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Python Section: Simulating Social Networks, Influence, and Polarization

Python is useful for simulating social processes that unfold over networks. Beliefs, attitudes, trust, suspicion, misinformation, and norms do not spread evenly across society. They move through relationships, communities, authority structures, media systems, group identities, and repeated exposures. The example below creates a simple network simulation in which individuals update beliefs based on their neighbors, identity similarity, and susceptibility to influence.

# Synthetic social influence simulation in Python
# Educational example only.
# This script simulates belief updating in a small social network.

import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt

np.random.seed(42)

n_agents = 80
n_steps = 60

# Create a small-world social network.
graph = nx.watts_strogatz_graph(n=n_agents, k=6, p=0.12, seed=42)

# Initial beliefs range from 0 to 1.
belief = np.random.beta(a=2.0, b=2.0, size=n_agents)

# Identity groups: 0 or 1.
identity = np.random.binomial(1, 0.5, size=n_agents)

# Susceptibility determines how strongly each agent updates.
susceptibility = np.random.uniform(0.05, 0.28, size=n_agents)

# Store belief history.
history = np.zeros((n_steps, n_agents))
history[0, :] = belief

for t in range(1, n_steps):
    new_belief = belief.copy()

    for i in range(n_agents):
        neighbors = list(graph.neighbors(i))

        if not neighbors:
            continue

        neighbor_beliefs = belief[neighbors]
        neighbor_identities = identity[neighbors]

        # Identity similarity increases influence weight.
        similarity_weight = np.where(neighbor_identities == identity[i], 1.25, 0.75)

        weighted_neighbor_mean = np.average(
            neighbor_beliefs,
            weights=similarity_weight
        )

        # Update toward weighted neighbor belief.
        new_belief[i] = (
            (1 - susceptibility[i]) * belief[i] +
            susceptibility[i] * weighted_neighbor_mean
        )

    # Add small random environmental noise.
    belief = np.clip(new_belief + np.random.normal(0, 0.01, n_agents), 0, 1)
    history[t, :] = belief

simulation = pd.DataFrame(history)
simulation["step"] = np.arange(n_steps)

mean_belief = history.mean(axis=1)
belief_variance = history.var(axis=1)

summary = pd.DataFrame({
    "step": np.arange(n_steps),
    "mean_belief": mean_belief,
    "belief_variance": belief_variance
})

print(summary.head())
print(summary.tail())

plt.figure(figsize=(10, 6))
plt.plot(summary["step"], summary["mean_belief"])
plt.xlabel("Simulation step")
plt.ylabel("Mean belief")
plt.title("Synthetic Social Influence Simulation: Mean Belief Over Time")
plt.tight_layout()
plt.show()

plt.figure(figsize=(10, 6))
plt.plot(summary["step"], summary["belief_variance"])
plt.xlabel("Simulation step")
plt.ylabel("Belief variance")
plt.title("Synthetic Social Influence Simulation: Belief Variance Over Time")
plt.tight_layout()
plt.show()

# Visualize final network beliefs.
final_belief = history[-1, :]

plt.figure(figsize=(9, 7))
positions = nx.spring_layout(graph, seed=42)

nx.draw_networkx_nodes(
    graph,
    positions,
    node_size=80,
    node_color=final_belief,
    cmap="viridis"
)

nx.draw_networkx_edges(
    graph,
    positions,
    alpha=0.25,
    width=0.8
)

plt.title("Synthetic Network Beliefs After Social Influence")
plt.axis("off")
plt.tight_layout()
plt.show()

This simulation is deliberately modest. It does not claim that belief formation, group polarization, misinformation, or trust can be explained by network averaging alone. Its value is that it makes assumptions visible. Network structure matters. Identity similarity matters. Susceptibility matters. Repeated exposure matters. Social influence is not merely an individual cognitive event; it is a dynamic process distributed across relationships and groups.

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Interpretive Limits and Social-Psychological Cautions

Social psychology is powerful because it shows how behavior is shaped by context, identity, norms, power, institutions, relationships, media systems, and collective meaning. Yet the same strength can become a weakness when social explanation becomes too sweeping. A situational explanation does not erase responsibility. A group-level pattern does not fully describe an individual. A statistical effect does not reveal every motive. A laboratory experiment does not automatically generalize to every culture, institution, or historical moment.

Analysts and readers should therefore avoid confusing social influence with determinism, model fit with truth, experimental control with universal validity, or group averages with individual experience. Social psychology can reveal the hidden power of situations, but it must remain attentive to agency, moral responsibility, cultural variation, structural inequality, historical context, and the lived experience of people affected by the processes being studied.

The field is strongest when it combines experimental discipline with ethical humility. It should not be used to manipulate publics, flatten cultures, pathologize disagreement, or treat people as predictable behavioral objects. Its better purpose is explanatory and civic: to understand how social environments shape human conduct so that institutions, technologies, organizations, and communities can be made more just, trustworthy, humane, and accountable.

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Social Psychology in a Wider Intellectual Context

Social psychology belongs not only to psychology, but to the broader history of human thought about society, morality, identity, power, cooperation, conflict, and social order. Philosophers, historians, theologians, political theorists, artists, and sociologists have long asked why people obey authority, identify with groups, fear outsiders, seek belonging, trust leaders, punish wrongdoing, rationalize harm, and build institutions. Social psychology brings experimental and empirical discipline to those questions.

The field changes the imagination of human behavior. It shows that social life is not merely a collection of individual choices. It is a structured environment of cues, norms, meanings, roles, identities, expectations, and institutions. Human beings are not isolated atoms. They are social interpreters whose perceptions and actions are shaped by the worlds they inhabit together.

For that reason, social psychology should be understood as both a scientific and civic achievement. It brings together experiment, theory, computation, ethics, public life, technology, organizational analysis, and social critique in a sustained effort to understand how people live with, judge, help, harm, persuade, trust, and organize one another. It remains indispensable for any serious framework concerned with cooperation, conflict, justice, public accountability, technology, institutional legitimacy, and social change.

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Related Reading

• Psychology
• Cognitive Psychology
• Developmental Psychology
• Personality Psychology
• Positive Psychology
• Organizational Psychology
• Institutional Psychology
• Behavioral Economics
• Systems Thinking
• Data Systems & Analytics
• Artificial Intelligence Systems
• Institutions & Governance

Further Reading

• American Psychological Association (n.d.) Social Psychology. Available at: https://dictionary.apa.org/social-psychology.
• American Psychological Association (n.d.) Social Cognition. Available at: https://dictionary.apa.org/social-cognition.
• Allport, G.W. (1954) The Nature of Prejudice. Cambridge, MA: Addison-Wesley.
• Aronson, E., Wilson, T.D., Akert, R.M. and Sommers, S.R. (2022) Social Psychology. 11th edn. New York: Pearson.
• Cialdini, R.B. (2021) Influence: The Psychology of Persuasion. New and expanded edn. New York: Harper Business.
• Fiske, S.T. and Taylor, S.E. (2020) Social Cognition: From Brains to Culture. 4th edn. London: Sage.
• Gilovich, T., Griffin, D. and Kahneman, D. (eds.) (2002) Heuristics and Biases: The Psychology of Intuitive Judgment. Cambridge: Cambridge University Press.
• Heider, F. (1958) The Psychology of Interpersonal Relations. New York: Wiley.
• Milgram, S. (1974) Obedience to Authority: An Experimental View. New York: Harper & Row.
• Tajfel, H. and Turner, J.C. (1979) ‘An integrative theory of intergroup conflict’, in Austin, W.G. and Worchel, S. (eds.) The Social Psychology of Intergroup Relations. Monterey, CA: Brooks/Cole, pp. 33–47.

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References

• American Psychological Association (n.d.) Social Psychology. Available at: https://dictionary.apa.org/social-psychology.
• American Psychological Association (n.d.) Social Cognition. Available at: https://dictionary.apa.org/social-cognition.
• Allport, G.W. (1954) The Nature of Prejudice. Cambridge, MA: Addison-Wesley.
• Aronson, E., Wilson, T.D., Akert, R.M. and Sommers, S.R. (2022) Social Psychology. 11th edn. New York: Pearson.
• Cialdini, R.B. (2021) Influence: The Psychology of Persuasion. New and expanded edn. New York: Harper Business.
• Fiske, S.T. and Taylor, S.E. (2020) Social Cognition: From Brains to Culture. 4th edn. London: Sage.
• Gilovich, T., Griffin, D. and Kahneman, D. (eds.) (2002) Heuristics and Biases: The Psychology of Intuitive Judgment. Cambridge: Cambridge University Press.
• Heider, F. (1958) The Psychology of Interpersonal Relations. New York: Wiley.
• Kahneman, D. (2011) Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.
• Milgram, S. (1974) Obedience to Authority: An Experimental View. New York: Harper & Row.
• Tajfel, H. and Turner, J.C. (1979) ‘An integrative theory of intergroup conflict’, in Austin, W.G. and Worchel, S. (eds.) The Social Psychology of Intergroup Relations. Monterey, CA: Brooks/Cole, pp. 33–47.
• Taylor, S.E., Peplau, L.A. and Sears, D.O. (2006) Social Psychology. 12th edn. Upper Saddle River, NJ: Pearson.

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