what is social context essay

Frontiers for Young Minds

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How Does Social Context Influence Our Brain and Behavior?

When we interact with others, the context in which our actions take place plays a major role in our behavior. This means that our understanding of objects, words, emotions, and social cues may differ depending on where we encounter them. Here, we explain how context affects daily mental processes, ranging from how people see things to how they behave with others. Then, we present the social context network model. This model explains how people process contextual cues when they interact, through the activity of the frontal, temporal, and insular brain regions. Next, we show that when those brain areas are affected by some diseases, patients find it hard to process contextual cues. Finally, we describe new ways to explore social behavior through brain recordings in daily situations.

Introduction

Everything you do is influenced by the situation in which you do it. The situation that surrounds an action is called its context. In fact, analyzing context is crucial for social interaction and even, in some cases, for survival. Imagine you see a man in fear: your reaction depends on his facial expression (e.g., raised eyebrows, wide-open eyes) and also on the context of the situation. The context can be external (is there something frightening around?) or internal (am I calm or am I also scared?). Such contextual cues are crucial to your understanding of any situation.

Context shapes all processes in your brain, from visual perception to social interactions [ 1 ]. Your mind is never isolated from the world around you. The specific meaning of an object, word, emotion, or social event depends on context ( Figure 1 ). Context may be evident or subtle, real or imagined, conscious or unconscious. Simple optical illusions demonstrate the importance of context ( Figures 1A,B ). In the Ebbinghaus illusion ( Figure 1A ), rings of circles surround two central circles. The central circles are the same size, but one appears to be smaller than the other. This is so because the surrounding circles provide a context. This context affects your perception of the size of the central circles. Quite interesting, right? Likewise, in the Cafe Wall Illusion ( Figure 1B ), context affects your perception of the lines’ orientation. The lines are parallel, but you see them as convergent or divergent. You can try focusing on the middle line of the figure and check it with a ruler. Contextual cues also help you recognize objects in a scene [ 2 ]. For instance, it can be easier to recognize letters when they are in the context of a word. Thus, you can see the same array of lines as either an H or an A ( Figure 1C ). Certainly, you did not read that phrase as “TAE CHT”, correct? Lastly, contextual cues are also important for social interaction. For instance, visual scenes, voices, bodies, other faces, and words shape how you perceive emotions in a face [ 3 ]. If you see Figure 1D in isolation, the woman may look furious. But look again, this time at Figure 1E . Here you see an ecstatic Serena Williams after she secured the top tennis ranking. This shows that recognizing emotions depends on additional information that is not present in the face itself.

• Figure 1 - Contextual affects how you see things.
• A,B. The visual context affects how you see shapes. C. Context also plays an important role in object recognition. Context-related objects are easier to recognize. “THE CAT” is a good example of contextual effects in letter recognition (reproduced with permission from Chun [ 2 ]). D,E. Context also affects how you recognize an emotion [by Hanson K. Joseph (Own work), CC BY-SA 4.0 ( http://creativecommons.org/licenses/by-sa/4.0 ), via Wikimedia Commons].

Contextual cues also help you make sense of other situations. What is appropriate in one place may not be appropriate in another. Making jokes is OK when studying with your friends, but not OK during the actual exam. Also, context affects how you feel when you see something happening to another person. Picture someone being beaten on the street. If the person being beaten is your best friend, would you react in the same way as if he were a stranger? The reason why you probably answered “no” is that your empathy may be influenced by context. Context will determine whether you jump in to help or run away in fear. In sum, social situations are shaped by contextual factors that affect how you feel and act.

Contextual cues are important for interpreting social situations. Yet, they have been largely ignored in the world of science. To fill this gap, our group proposed the social context network model [ 1 ]. This model describes a brain network that integrates contextual information during social processes. This brain network combines the activity of several different areas of the brain, namely frontal, temporal, and insular brain areas ( Figure 2 ). It is true that many other brain areas are involved in processing contextual information. For instance, the context of an object that you can see affects processes in the vision areas of your brain [ 4 ]. However, the network proposed by our model includes the main areas involved in social context processing. Even contextual visual recognition involves activity of temporal and frontal regions included in our model [ 5 ].

• Figure 2 - The parts of the brain that work together, in the social context network model.
• This model proposes that social contextual cues are processed by a network of specific brain regions. This network is made up of frontal (light blue), temporal (orange), and insular (green) brain regions and the connections between these regions.

How Does Your Brain Process Contextual Cues in Social Scenarios?

To interpret context in social settings, your brain relies on a network of brain regions, including the frontal, temporal, and insular regions. Figure 2 shows the frontal regions in light blue. These regions help you update contextual information when you focus on something (say, the traffic light as you are walking down the street). That information helps you anticipate what might happen next, based on your previous experiences. If there is a change in what you are seeing (as you keep walking down the street, a mean-looking Doberman appears), the frontal regions will activate and update predictions (“this may be dangerous!”). These predictions will be influenced by the context (“oh, the dog is on a leash”) and your previous experience (“yeah, but once I was attacked by a dog and it was very bad!”). If a person’s frontal regions are damaged, he/she will find it difficult to recognize the influence of context. Thus, the Doberman may not be perceived as a threat, even if this person has been attacked by other dogs before! The main role of the frontal regions is to predict the meaning of actions by analyzing the contextual events that surround the actions.

Figure 2 shows the insular regions, also called the insula, in green. The insula combines signals from within and outside your body. The insula receives signals about what is going on in your guts, heart, and lungs. It also supports your ability to experience emotions. Even the butterflies you sometimes feel in your stomach depend on brain activity! This information is combined with contextual cues from outside your body. So, when you see that the Doberman breaks loose from its owner, you can perceive that your heart begins to beat faster (an internal body signal). Then, your brain combines the external contextual cues (“the Doberman is loose!”) with your body signals, leading you to feel fear. Patients with damage to their insular regions are not so good at tracking their inner body signals and combining them with their emotions. The insula is critical for giving emotional value to an event.

Lastly, Figure 2 shows the temporal regions marked with orange. The temporal regions associate the object or person you are focusing on with the context. Memory plays a major role here. For instance, when the Doberman breaks loose, you look at his owner and realize that it is the kind man you met last week at the pet shop. Also, the temporal regions link contextual information with information from the frontal and insular regions. This system supports your knowledge that Dobermans can attack people, prompting you to seek protection.

To summarize, combining what you experience with the social context relies on a brain network that includes the frontal, insular, and temporal regions. Thanks to this network, we can interpret all sorts of social events. The frontal areas adjust and update what you think, feel, and do depending on present and past happenings. These areas also predict possible events in your surroundings. The insula combines signals from within and outside your body to produce a specific feeling. The temporal regions associate objects and persons with the current situation. So, all the parts of the social context network model work together to combine contextual information when you are in social settings.

When Context Cannot be Processed

Our model helps to explain findings from patients with brain damage. These patients have difficulties processing contextual cues. For instance, people with autism find it hard to make eye contact and interact with others. They may show repetitive behaviors (e.g., constantly lining up toy cars) or excessive interest in a topic. They may also behave inappropriately and have trouble adjusting to school, home, or work. People with autism may fail to recognize emotions in others’ faces. Their empathy may also be reduced. One of our studies [ 6 ] showed that these problems are linked to a decreased ability to process contextual information. Persons with autism and healthy subjects performed tasks involving different social skills. Autistic people did poorly in tasks that relied on contextual cues—for instance, detecting a person’s emotion based on his gestures or voice tone. But, autistic people did well in tasks that didn’t require analyzing context, for example tasks that could be completed by following very general rules (for example, “never touch a stranger on the street”). Thus, the social problems that we often see in autistic people might result from difficulty in processing contextual cues.

Another disease that may result from problems processing contextual information is called behavioral variant frontotemporal dementia . Patients with this disease exhibit changes in personality and in the way they interact with others, after about age 60. They may do improper things in public. Like people with autism, they may not show empathy or may not recognize emotions easily. Also, they find it hard to deal with the details of context needed to understand social events. All these changes may reflect general problems processing social context information. These problems may be caused by damage to the brain network described above.

Our model can also explain patients with damage to the frontal lobes or those who have conditions such as schizophrenia or bipolar disorder [ 7 ]. Schizophrenia is a mental disorder characterized by atypical social cognition and inability to distinguish between real and imagined world (as in the case of hallucinations). Similar but milder problems appear in patients with bipolar disorder, which is another psychiatric condition mainly characterized by oscillating periods of depression and periods of elevated mood (called hypomania or mania).

In sum, the problems with social behavior seen in many diseases are probably linked to poor context processing after damage to certain brain areas, as proposed by our model ( Figure 2 ). Future research should explore how correct this model is, adding more data about the processes and regions it describes.

New Techniques to Assess Social Behavior and Contextual Processing

The results mentioned above are important for scientists and doctors. However, they have a great limitation. They do not reflect how people behave in daily life! Most of the research findings came from tasks in a laboratory, in which a person responded to pictures or videos. These tasks do not really represent how we act every day in our lives. Social life is much more complicated than sitting at a desk and pressing buttons when you see images on a computer, right? Research based on such tasks doesn’t reflect real social situations. In daily life, people interact in contexts that constantly change.

Fortunately, new methods allow scientists to assess real-life interactions. Hyperscanning is one of these methods. Hyperscanning allows measurement of the brain activity of two or more people while they perform activities together. For example, each subject can lie inside a separate scanner (a large tube containing powerful magnets). This scanner can detect changes in blood flow in the brain while the two people interact. This approach is used, for example, to study the brains of a mother and her child while they are looking at each other’s faces ( Figure 3A ).

• Figure 3 - New techniques to study processing of contextual cues.
• A. A mother and her infant look at each others’ facial expression while their brain activity is recorded (reproduced with permission from Masayuki et al. [ 8 ]). B. Hyperscanning of people interacting with each other during a game of Jenga (reproduced with permission from Liu et al. [ 9 ]). C. A new method of studying brain activity, called mobile brain/body imaging (MoBI) (reproduced with permission from Makeig et al. [ 10 ]). D. Virtual reality simulations of a virtual train at the station and a virtual train carriage (reproduced with permission from Freeman et al. [ 11 ]).

Hyperscanning can also be done using electroencephalogram equipment. Electroencephalography measures the electrical activity of the brain. Special sensors called electrodes are attached to the head. They are hooked by wires to a computer which records the brain’s electrical activity. Figure 3B shows an example of the use of electroencephalogram hyperscanning. This method has been used to measure the brain activity in two individuals while they are playing Jenga. Future research should apply this technique to study the processing of social contextual cues.

One limitation of hyperscanning is that it typically requires participants to remain still. However, real-life interactions involve many bodily actions. Fortunately, a new method called mobile brain/body imaging (MoBI, Figure 3C ) allows the measurement of brain activity and bodily actions while people interact in natural settings.

Another interesting approach is to use virtual reality . This technique involves fake situations. However, it puts people in different situations that require social interaction. This is closer to real life than the tasks used in most laboratories. As an example, consider Figure 3D . This shows a virtual reality experiment in which participants traveled through an underground tube station in London. Our understanding of the way context impacts social behavior could be expanded in future virtual reality studies.

In sum, future research should use new methods for measuring real-life interactions. This type of research could be very important for doctors to understand what happens to the processing of social context cues in various brain injuries or diseases. These realistic tasks are more sensitive than most of the laboratory tasks that are usually used for the assessment of patients with brain disorders.

Empathy : ↑ The ability to feel what another person is feeling, that is, to “place yourself in that person’s shoes.”

Autism : ↑ A general term for a group of complex disorders of brain development. These disorders are characterized by repetitive behaviors, as well as different levels of difficulty with social interaction and both verbal and non-verbal communications.

Behavioral Variant Frontotemporal Dementia : ↑ A brain disease characterized by progressive changes in personality and loss of empathy. Patients experience difficulty in regulating their behavior, and this often results in socially inappropriate actions. Patients typically start to show symptoms around age 60.

Hyperscanning : ↑ A novel technique to measure brain activity simultaneously from two people.

Virtual Reality : ↑ Computer technologies that use software to generate realistic images, sounds, and other sensations that replicate a real environment. This technique uses specialized display screens or projectors to simulate the user’s physical presence in this environment, enabling him or her to interact with the virtual space and any objects depicted there.

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. The authors declare no competing financial interests.

Acknowledgements

This study was supported by grants from CONICYT/FONDECYT Regular (1170010), FONDAP 15150012, and the INECO Foundation.

[1] ↑ Ibanez, A., and Manes, F. 2012. Contextual social cognition and the behavioral variant of frontotemporal dementia. Neurology 78(17):1354–62. doi:10.1212/WNL.0b013e3182518375

[2] ↑ Chun, M. M. 2000. Contextual cueing of visual attention. Trends Cogn. Sci. 4(5):170–8. doi:10.1016/S1364-6613(00)01476-5

[3] ↑ Barrett, L. F., Mesquita, B., and Gendron, M. 2011. Context in emotion perception. Curr. Direct Psychol. Sci. 20(5):286–90. doi:10.1177/0963721411422522

[4] ↑ Beck, D. M., and Kastner, S. 2005. Stimulus context modulates competition in human extrastriate cortex. Nat. Neurosci. 8(8):1110–6. doi:10.1038/nn1501

[5] ↑ Bar, M. 2004. Visual objects in context. Nat. Rev. Neurosci. 5(8):617–29. doi:10.1038/nrn1476

[6] ↑ Baez, S., and Ibanez, A. 2014. The effects of context processing on social cognition impairments in adults with Asperger’s syndrome. Front. Neurosci. 8:270. doi:10.3389/fnins.2014.00270

[7] ↑ Baez, S, Garcia, A. M., and Ibanez, A. 2016. The Social Context Network Model in psychiatric and neurological diseases. Curr. Top. Behav. Neurosci. 30:379–96. doi:10.1007/7854_2016_443

[8] ↑ Masayuki, H., Takashi, I., Mitsuru, K., Tomoya, K., Hirotoshi, H., Yuko, Y., and Minoru, A. 2014. Hyperscanning MEG for understanding mother-child cerebral interactions. Front Hum Neurosci 8:118. doi:10.3389/fnhum.2014.00118

[9] ↑ Liu, N., Mok, C., Witt, E. E., Pradhan, A. H., Chen, J. E., and Reiss, A. L. 2016. NIRS-based hyperscanning reveals inter-brain neural synchronization during cooperative Jenga game with face-to-face communication. Front Hum Neurosci 10:82. doi:10.3389/fnhum.2016.00082

[10] ↑ Makeig, S., Gramann, K., Jung, T.-P., Sejnowski, T. J., and Poizner, H. 2009. Linking brain, mind and behavior: The promise of mobile brain/body imaging (MoBI). Int J Psychophys 73:985–1000

[11] ↑ Evans, N., Lister, R., Antley, A., Dunn, G., and Slater, M. 2014. Height, social comparison, and paranoia: An immersive virtual reality experimental study. Psych Res 218(3):348–52. doi:10.1016/j.psychres.2013.12.014

Doc’s Things and Stuff

social context | Definition

Social context refers to the environment of people, relationships, and culture that surrounds and influences an individual’s behavior and experiences.

Understanding Social Context

Definition and importance.

Social context is the setting in which social interactions occur, encompassing the cultural, economic, political, and social conditions that influence people’s lives. It plays a crucial role in shaping individuals’ attitudes, behaviors, and perceptions. Understanding social context helps explain why people act the way they do and how various factors influence human behavior.

Elements of Social Context

Cultural norms and values.

Cultural norms and values are the shared beliefs and practices that guide behavior within a society. These norms and values shape how individuals interact with each other and understand the world. For example, cultural norms around family structure, work ethic, and social roles influence how people behave and make decisions.

Social Structures

Social structures refer to the organized patterns of relationships and institutions that make up society. These include family, education systems, religious institutions, and economic systems. Social structures provide a framework for social behavior and influence individuals’ opportunities and constraints.

Social Roles and Status

Social roles are the expected behaviors associated with particular positions in society, such as being a student, parent, or employee. Social status refers to the prestige or social value assigned to these roles. Both roles and status influence how individuals interact with others and navigate their social world. For example, a person’s status as a doctor may command respect and influence their interactions with patients and colleagues.

Economic Conditions

Economic conditions, such as wealth distribution, employment rates, and access to resources, significantly impact social context. Economic stability or instability can influence individuals’ stress levels, opportunities for education and employment, and overall quality of life. For instance, economic downturns often lead to increased job insecurity and can affect family dynamics and mental health.

Political Environment

The political environment includes the laws, policies, and government structures that regulate society. Political conditions can affect individuals’ rights, access to services, and participation in civic life. For example, policies on healthcare, education, and immigration directly impact people’s lives and social interactions.

The Role of Social Context in Shaping Behavior

Influence on identity.

Social context plays a vital role in shaping individual identity. Through interactions with family, peers, and broader society, people develop their sense of self and their place in the world. For example, a person’s cultural background, social class, and community can influence their values, aspirations, and self-perception.

Behavior and Decision-Making

Individuals make decisions and behave in ways that are influenced by their social context. For example, peer pressure can affect teenagers’ choices, such as engaging in risky behaviors or conforming to group norms. Similarly, cultural expectations around gender roles can influence career choices and family dynamics.

Socialization Process

Socialization is the process by which individuals learn and internalize the norms, values, and behaviors appropriate to their society. This process occurs throughout life, beginning in childhood and continuing through adulthood. Social institutions like family, schools, and media play significant roles in socialization, transmitting cultural norms and expectations.

Social Context and Social Change

Impact of social movements.

Social movements can challenge and change the existing social context. Movements for civil rights, gender equality, and environmental sustainability have reshaped societal norms and policies. For example, the women’s rights movement has significantly altered norms around gender roles and workplace equality.

Technological Advancements

Technological advancements can also transform social context by changing how people communicate, work, and access information. The rise of social media, for instance, has created new forms of social interaction and influenced cultural norms around communication and self-presentation.

Challenges and Opportunities

Navigating diverse social contexts.

In increasingly multicultural societies, individuals often navigate multiple social contexts simultaneously. This can create challenges in balancing different cultural expectations and norms. However, it also presents opportunities for learning, growth, and greater cultural understanding.

Addressing Social Inequality

Understanding social context is crucial for addressing social inequalities. By recognizing how factors like economic conditions, social structures, and cultural norms contribute to disparities, policymakers and activists can develop more effective strategies for promoting social justice and equity.

Social context is a multifaceted concept that encompasses the various environments influencing individuals’ behaviors, attitudes, and experiences. By examining the elements and impacts of social context, we gain a deeper understanding of human behavior and the complex interplay of social forces. This understanding is essential for fostering more inclusive and equitable societies.

References and Further Reading

• Dornbusch, S. M. (1989). The sociology of adolescence .  Annual review of sociology ,  15 (1), 233-259.

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• Fundamentals of Sociology

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Social Context

‘Social Context’ refers to the immediate physical and social setting in which people live or in which something happens or develops. This includes the culture that the individual was educated or lives in, and the people and institutions with whom they interact. Social context influences and, to some extent, determines thought, feeling, and action. It ranges from a brief interaction with a stranger to broad societal and cultural forces.

Understanding Social Context

To grasp the concept of social context, we must delve into its components and the influence it exerts on individuals and societies.

Social context can be broken down into various elements, including cultural norms, social structures (like family or community), and the specific situation in which an individual finds themselves.

Influence on Behavior and Perceptions

Social context significantly impacts individual behavior, perceptions, and interactions. It can shape an individual’s values, beliefs, and expectations.

The Role in Different Fields

Social context plays a pivotal role across various disciplines, from psychology to sociology and beyond.

In Psychology

In psychology, social context is used to understand individual behavior in social situations and the influences of societal norms and structures.

In Sociology

Sociologists study social context to comprehend societal patterns, trends, and structures, helping them understand social phenomena and changes over time.

Understanding the social context offers valuable insights into various aspects of life and society.

Enhances Communication

By understanding the social context of a situation, we can communicate more effectively, considering cultural norms, values, and expectations.

Guides Policy and Decision-Making

Social context is crucial in informing policy-making, ensuring that decisions consider societal norms, values, and structures.

To further illuminate the concept, let’s consider some examples of social context.

Example 1: Educational Settings

The social context of a classroom— including its cultural norms, student-teacher dynamics, and broader school environment— can influence students’ learning and engagement.

Example 2: Online Communities

Online communities, like those on social media platforms, have their unique social contexts that impact user behavior, interactions, and content creation.

Recognizing and Analyzing Social Context

Being able to recognize and analyze social context is a valuable skill. Here are some tips to help.

Be Observant

Pay attention to the physical and social environment, the individuals involved, and the cultural and societal norms at play.

Keep an Open Mind

Maintain an open mind and be sensitive to cultural differences, acknowledging that social context can differ greatly between societies and groups.

In essence, social context is a crucial factor that shapes our behaviors, interactions, perceptions, and the world around us. By understanding and considering social context, we can communicate more effectively, make informed decisions, and appreciate the complexity and diversity of human societies.

Social Psychology: Definition, Theories, Scope, & Examples

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:

Social psychology is the scientific study of how people’s thoughts, feelings, beliefs, intentions, and goals are constructed within a social context by the actual or imagined interactions with others.

It, therefore, looks at human behavior as influenced by other people and the conditions under which social behavior and feelings occur.

Baron, Byrne, and Suls (1989) define social psychology as “the scientific field that seeks to understand the nature and causes of individual behavior in social situations” (p. 6).

Topics examined in social psychology include the self-concept , social cognition, attribution theory , social influence, group processes, prejudice and discrimination , interpersonal processes, aggression, attitudes , and stereotypes .

Social psychology operates on several foundational assumptions. These fundamental beliefs provide a framework for theories, research, and interpretations.

• Individual and Society Interplay : Social psychologists assume an interplay exists between individual minds and the broader social context. An individual’s thoughts, feelings, and behaviors are continuously shaped by social interactions, and in turn, individuals influence the societies they are a part of.
• Behavior is Contextual : One core assumption is that behavior can vary significantly based on the situation or context. While personal traits and dispositions matter, the circumstances or social environment often play a decisive role in determining behavior.
• Objective Reality is Difficult to Attain : Our perceptions of reality are influenced by personal beliefs, societal norms, and past experiences. Therefore, our understanding of “reality” is subjective and can be biased or distorted.
• Social Reality is Constructed : Social psychologists believe that individuals actively construct their social world . Through processes like social categorization, attribution, and cognitive biases, people create their understanding of others and societal norms.
• People are Social Beings with a Need to Belong : A fundamental assumption is the inherent social nature of humans. People have an innate need to connect with others, form relationships, and belong to groups. This need influences a wide range of behaviors and emotions.
• Attitudes Influence Behavior : While this might seem straightforward, it’s a foundational belief that our attitudes (combinations of beliefs and feelings) can and often do drive our actions. However, it’s also understood that this relationship can be complex and bidirectional.
• People Desire Cognitive Consistency : This is the belief that people are motivated to maintain consistency in their beliefs, attitudes, and behaviors. Cognitive dissonance theory , which posits that people feel discomfort when holding conflicting beliefs and are motivated to resolve this, is based on this assumption.
• People are Motivated to See Themselves in a Positive Light : The self plays a central role in social psychology. It’s assumed that individuals are generally motivated to maintain and enhance a positive self-view.
• Behavior Can be Predicted and Understood : An underlying assumption of any science, including social psychology, is that phenomena (in this case, human behavior in social contexts) can be studied, understood, predicted, and potentially influenced.
• Cultural and Biological Factors are Integral : Though earlier social psychology might have been criticized for neglecting these factors, contemporary social psychology acknowledges the roles of both biology (genes, hormones, brain processes) and culture (norms, values, traditions) in shaping social behavior.

Early Influences

Aristotle believed that humans were naturally sociable, a necessity that allows us to live together (an individual-centered approach), whilst Plato felt that the state controlled the individual and encouraged social responsibility through social context (a socio-centered approach).

Hegel (1770–1831) introduced the concept that society has inevitable links with the development of the social mind. This led to the idea of a group mind, which is important in the study of social psychology.

Lazarus & Steinthal wrote about Anglo-European influences in 1860. “Volkerpsychologie” emerged, which focused on the idea of a collective mind.

It emphasized the notion that personality develops because of cultural and community influences, especially through language, which is both a social product of the community as well as a means of encouraging particular social thought in the individual. Therefore Wundt (1900–1920) encouraged the methodological study of language and its influence on the social being.

Early Texts

Texts focusing on social psychology first emerged in the 20th century. McDougall published the first notable book in English in 1908 (An Introduction to Social Psychology), which included chapters on emotion and sentiment, morality, character, and religion, quite different from those incorporated in the field today.

He believed social behavior was innate/instinctive and, therefore, individual, hence his choice of topics.  This belief is not the principle upheld in modern social psychology, however.

Allport’s work (1924) underpins current thinking to a greater degree, as he acknowledged that social behavior results from interactions between people.

He also took a methodological approach, discussing actual research and emphasizing that the field was a “science … which studies the behavior of the individual in so far as his behavior stimulates other individuals, or is itself a reaction to this behavior” (1942: p. 12).

His book also dealt with topics still evident today, such as emotion, conformity, and the effects of an audience on others.

Murchison (1935) published The first handbook on social psychology was published by Murchison in 1935.  Murphy & Murphy (1931/37) produced a book summarizing the findings of 1,000 studies in social psychology.  A text by Klineberg (1940) looked at the interaction between social context and personality development. By the 1950s, several texts were available on the subject.

Journal Development

• 1950s – Journal of Abnormal and Social Psychology

• 1963 – Journal of Personality, British Journal of Social and Clinical Psychology

• 1965 – Journal of Personality and Social Psychology, Journal of Experimental Social Psychology

• 1971 – Journal of Applied Social Psychology, European Journal of Social Psychology

• 1975 – Social Psychology Quarterly, Personality and Social Psychology Bulletin

• 1982 – Social Cognition

• 1984 – Journal of Social and Personal Relationships

Early Experiments

There is some disagreement about the first true experiment, but the following are certainly among some of the most important.

Triplett (1898) applied the experimental method to investigate the performance of cyclists and schoolchildren on how the presence of others influences overall performance – thus, how individuals are affected and behave in the social context.

By 1935, the study of social norms had developed, looking at how individuals behave according to the rules of society. This was conducted by Sherif (1935).

Lewin et al. then began experimental research into leadership and group processes by 1939, looking at effective work ethics under different leadership styles.

Later Developments

Much of the key research in social psychology developed following World War II, when people became interested in the behavior of individuals when grouped together and in social situations. Key studies were carried out in several areas.

Some studies focused on how attitudes are formed, changed by the social context, and measured to ascertain whether a change has occurred.

Amongst some of the most famous works in social psychology is that on obedience conducted by Milgram in his “electric shock” study, which looked at the role an authority figure plays in shaping behavior.  Similarly,  Zimbardo’s prison simulation notably demonstrated conformity to given roles in the social world.

Wider topics then began to emerge, such as social perception, aggression, relationships, decision-making, pro-social behavior, and attribution, many of which are central to today’s topics and will be discussed throughout this website.

Thus, the growth years of social psychology occurred during the decades following the 1940s.

The scope of social psychology is vast, reflecting the myriad ways social factors intertwine with individual cognition and behavior.

Its principles and findings resonate in virtually every area of human interaction, making it a vital field for understanding and improving the human experience.

• Interpersonal Relationships : This covers attraction, love, jealousy, friendship, and group dynamics. Understanding how and why relationships form and the factors that contribute to their maintenance or dissolution is central to this domain.
• Attitude Formation and Change : How do individuals form opinions and attitudes? What methods can effectively change them? This scope includes the study of persuasion, propaganda, and cognitive dissonance.
• Social Cognition : This examines how people process, store, and apply information about others. Areas include social perception, heuristics, stereotypes, and attribution theories.
• Social Influence : The study of conformity, compliance, obedience, and the myriad ways individuals influence one another falls within this domain.
• Group Dynamics : This entails studying group behavior, intergroup relations, group decision-making processes, leadership, and more. Concepts like groupthink and group polarization emerge from this area.
• Prejudice and Discrimination : Understanding the roots of bias, racism, sexism, and other forms of prejudice, as well as exploring interventions to reduce them, is a significant focus.
• Self and Identity : Investigating self-concept, self-esteem, self-presentation, and the social construction of identity are all part of this realm.
• Prosocial Behavior and Altruism : Why do individuals sometimes help others, even at a cost to themselves? This area delves into the motivations and conditions that foster cooperative and altruistic behavior.
• Aggression : From understanding the underlying causes of aggressive behavior to studying societal factors that exacerbate or mitigate aggression, this topic seeks to dissect the nature of hostile actions.
• Cultural and Cross-cultural Dimensions : As societies become more interconnected, understanding cultural influences on behavior, cognition, and emotion is crucial. This area compares and contrasts behaviors across different cultures and societal groups.
• Environmental and Applied Settings : Social psychology principles find application in health psychology, environmental behavior, organizational behavior, consumer behavior, and more.
• Social Issues : Social psychologists might study the impact of societal structures on individual behavior, exploring topics like poverty, urban stress, and crime.
• Education : Principles of social psychology enhance teaching methods, address issues of classroom dynamics, and promote effective learning.
• Media and Technology : In the digital age, understanding the effects of media consumption, the dynamics of online communication, and the formation of online communities is increasingly relevant.
• Law : Insights from social psychology inform areas such as jury decision-making, eyewitness testimony, and legal procedures.
• Health : Concepts from social psychology are employed to promote health behaviors, understand doctor-patient dynamics, and tackle issues like addiction.

Example Theories

Allport (1920) – social facilitation.

Allport introduced the notion that the presence of others (the social group) can facilitate certain behavior.

It was found that an audience would improve an actor’s performance in well-learned/easy tasks but leads to a decrease in performance on newly learned/difficult tasks due to social inhibition.

Bandura (1963) Social Learning Theory

Bandura introduced the notion that behavior in the social world could be modeled. Three groups of children watched a video where an adult was aggressive towards a ‘bobo doll,’ and the adult was either just seen to be doing this, was rewarded by another adult for their behavior, or was punished for it.

Children who had seen the adult rewarded were found to be more likely to copy such behavior.

Festinger (1950) –  Cognitive Dissonance

Festinger, Schacter, and Black brought up the idea that when we hold beliefs, attitudes, or cognitions which are different, then we experience dissonance – this is an inconsistency that causes discomfort.

We are motivated to reduce this by either changing one of our thoughts, beliefs, or attitudes or selectively attending to information that supports one of our beliefs and ignores the other (selective exposure hypothesis).

Dissonance occurs when there are difficult choices or decisions or when people participate in behavior that is contrary to their attitude. Dissonance is thus brought about by effort justification (when aiming to reach a modest goal), induced compliance (when people are forced to comply contrary to their attitude), and free choice (when weighing up decisions).

Tajfel (1971) –  Social Identity Theory

When divided into artificial (minimal) groups, prejudice results simply from the awareness that there is an “out-group” (the other group).

When the boys were asked to allocate points to others (which might be converted into rewards) who were either part of their own group or the out-group, they displayed a strong in-group preference. That is, they allocated more points on the set task to boys who they believed to be in the same group as themselves.

This can be accounted for by Tajfel & Turner’s social identity theory, which states that individuals need to maintain a positive sense of personal and social identity: this is partly achieved by emphasizing the desirability of one’s own group, focusing on distinctions between other “lesser” groups.

Weiner (1986) – Attribution Theory

Weiner was interested in the attributions made for experiences of success and failure and introduced the idea that we look for explanations of behavior in the social world.

He believed that these were made based on three areas: locus, which could be internal or external; stability, which is whether the cause is stable or changes over time: and controllability.

Milgram (1963) – Shock Experiment

Participants were told that they were taking part in a study on learning but always acted as the teacher when they were then responsible for going over paired associate learning tasks.

When the learner (a stooge) got the answer wrong, they were told by a scientist that they had to deliver an electric shock. This did not actually happen, although the participant was unaware of this as they had themselves a sample (real!) shock at the start of the experiment.

They were encouraged to increase the voltage given after each incorrect answer up to a maximum voltage, and it was found that all participants gave shocks up to 300v, with 65 percent reaching the highest level of 450v.

It seems that obedience is most likely to occur in an unfamiliar environment and in the presence of an authority figure, especially when covert pressure is put upon people to obey. It is also possible that it occurs because the participant felt that someone other than themselves was responsible for their actions.

Haney, Banks, Zimbardo (1973) – Stanford Prison Experiment

Volunteers took part in a simulation where they were randomly assigned the role of a prisoner or guard and taken to a converted university basement resembling a prison environment. There was some basic loss of rights for the prisoners, who were unexpectedly arrested, and given a uniform and an identification number (they were therefore deindividuated).

The study showed that conformity to social roles occurred as part of the social interaction, as both groups displayed more negative emotions, and hostility and dehumanization became apparent.

Prisoners became passive, whilst the guards assumed an active, brutal, and dominant role. Although normative and informational social influence played a role here, deindividuation/the loss of a sense of identity seemed most likely to lead to conformity.

Both this and Milgram’s study introduced the notion of social influence and the ways in which this could be observed/tested.

Provides Clear Predictions

As a scientific discipline, social psychology prioritizes formulating clear and testable hypotheses. This clarity facilitates empirical testing, ensuring the field’s findings are based on observable and quantifiable phenomena.

The Asch conformity experiments hypothesized that individuals would conform to a group’s incorrect judgment.

The clear prediction allowed for controlled experimentation to determine the extent and conditions of such conformity.

Emphasizes Objective Measurement

Social psychology leans heavily on empirical methods, emphasizing objectivity. This means that results are less influenced by biases or subjective interpretations.

Double-blind procedures , controlled settings, and standardized measures in many social psychology experiments ensure that results are replicable and less prone to experimenter bias.

Empirical Evidence

Over the years, a multitude of experiments in social psychology have bolstered the credibility of its theories. This experimental validation lends weight to its findings and claims.

The robust body of experimental evidence supporting cognitive dissonance theory, from Festinger’s initial studies to more recent replications, showcases the theory’s enduring strength and relevance.

Limitations

Underestimates individual differences.

While social psychology often looks at broad trends and general behaviors, it can sometimes gloss over individual differences.

Not everyone conforms, obeys, or reacts in the same way, and these nuanced differences can be critical.

While Milgram’s obedience experiments showcased a startling rate of compliance to authority, there were still participants who resisted, and their reasons and characteristics are equally important to understand.

Ignores Biology

While social psychology focuses on the social environment’s impact on behavior, early theories sometimes neglect the biological underpinnings that play a role.

Hormones, genetics, and neurological factors can influence behavior and might intersect with social factors in complex ways.

The role of testosterone in aggressive behavior is a clear instance where biology intersects with the social. Ignoring such biological components can lead to an incomplete understanding.

Superficial Snapshots of Social Processes

Social psychology sometimes offers a narrow view, capturing only a momentary slice of a broader, evolving process. This might mean that the field fails to capture the depth, evolution, or intricacies of social processes over time.

A study might capture attitudes towards a social issue at a single point in time, but not account for the historical evolution, future shifts, or deeper societal underpinnings of those attitudes.

Allport, F. H. (1920). The influence of the group upon association and thought. Journal of Experimental Psychology , 3(3), 159.

Allport, F. H. (1924). Response to social stimulation in the group. Social psychology , 260-291.

Allport, F. H. (1942). Methods in the study of collective action phenomena. The Journal of Social Psychology , 15(1), 165-185.

Bandura, A., Ross, D., & Ross, S. A. (1963). Vicarious reinforcement and imitative learning. The Journal of Abnormal and Social Psychology , 67(6), 601.

Baron, R. A., Byrne, D., & Suls, J. (1989). Attitudes: Evaluating the social world. Baron et al, Social Psychology . 3rd edn. MA: Allyn and Bacon, 79-101.

Festinger, L., Schachter, S., & Back, K. (1950). Social processes in informal groups .

Haney, C., Banks, W. C., & Zimbardo, P. G. (1973). Study of prisoners and guards in a simulated prison. Naval Research Reviews , 9(1-17).

Klineberg, O. (1940). The problem of personality .

Krewer, B., & Jahoda, G. (1860). On the scope of Lazarus and Steinthals “Völkerpsychologie” as reflected in the. Zeitschrift für Völkerpsychologie und Sprachwissenschaft, 1890, 4-12.

Lewin, K., Lippitt, R., & White, R. K. (1939). Patterns of aggressive behavior in experimentally created “social climates”. The Journal of Social Psychology , 10(2), 269-299.

Mcdougall, W. (1908). An introduction to social psychology . Londres: Methuen.

Milgram, S. (1963). Behavioral study of obedience. The Journal of Abnormal and Social Psychology , 67(4), 371.

Murchison, C. (1935). A handbook of social psychology .

Murphy, G., & Murphy, L. B. (1931). Experimental social psychology .

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

Tajfel, H., Billig, M. G., Bundy, R. P., & Flament, C. (1971). Social categorization and intergroup behavior. European journal of social psychology , 1(2), 149-178.

Triplett, N. (1898). The dynamogenic factors in pacemaking and competition. American journal of Psychology , 9(4), 507-533.

Weiner, B. (1986). An attributional theory of motivation and emotion . New York: Springer-Verlag.

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Decision-Making Processes in Social Contexts

Elizabeth bruch, fred feinberg.

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Please direct correspondence to Elizabeth Bruch at: 500 S. State Street, Department of Sociology, University of Michigan, Ann Arbor, MI, 48104

Issue date 2017 Jul.

Over the past half-century, scholars in the interdisciplinary field of Judgment and Decision Making have amassed a trove of findings, theories, and prescriptions regarding the processes ordinary people enact when making choices. But this body of knowledge has had little influence on sociology. Sociological research on choice emphasizes how features of the social environment shape individual behavior, not people’s underlying decision processes. Our aim in this article is to provide an overview of selected ideas, models, and data sources from decision research that can fuel new lines of inquiry on how socially situated actors navigate both everyday and major life choices. We also highlight opportunities and challenges for cross-fertilization between sociology and decision research that can allow the methods, findings, and contexts of each field to expand their joint range of inquiry.

Keywords: heuristics, decision making, micro-sociology, discrete choice

INTRODUCTION

Over the past several decades, there has been an explosion of interest in, and recognition of the importance of, how people make decisions. From Daniel Kahneman’s 2002 Nobel Prize for his work on “Heuristics and Biases,” to the rise in prominence of Behavioral Economics, to the burgeoning policy applications of behavioral “nudges” ( Kahneman 2003 ; Camerer & Loewenstein 2004 ; Shafir 2013 ), both scholars and policy makers increasingly focus on choice processes as a key domain of research and intervention. Researchers in the interdisciplinary field of Judgment and Decision Making (JDM)—which primarily comprises cognitive science, behavioral economics, academic marketing, and organizational behavior—have generated a wealth of findings, insights, and prescriptions regarding how people make choices. In addition, with the advent of rich observational data from purchase histories, a related line of work has revolutionized statistical models of decision making that aim to represent underlying choice process.

But for the most part these models and ideas have not penetrated sociology. 1 We believe there are several reasons for this. First, JDM research has largely been focused on contrasting how a fully informed, computationally unlimited (i.e., “rational”) person would behave to how people actually behave, and pointing out systematic deviations from this normative model ( Loewenstein 2001 ). Since sociology never fully embraced the rational choice model of behavior, debunking it is less of a disciplinary priority. 2 Second, JDM research is best known for its focus on problems that involve risk , where the outcome is probabilistic and the payoff probabilities are known ( Kahneman & Tversky 1979 , 1982 , 1984 ), and ambiguity , where the outcome is probabilistic and the decision-maker does not have complete information on payoff probabilities ( Ellsberg 1961 ; Einhorn & Hogarth 1988 ; Camerer & Weber 1992 ). In both these cases, there is an optimal choice to be made, and the research explores how people’s choices deviate from that answer. But most sociological problems—such as choosing a romantic partner, neighborhood, or college—are characterized by obscurity : there is no single, obvious, optimal, or correct answer.

Perhaps most critically, the JDM literature has by and large minimized the role of social context in decision processes. This is deliberate. Most experiments performed by psychologists are designed to isolate processes that can be connected with features of decision tasks or brain functioning; it is incumbent on researchers working in this tradition to “de-socialize” the environment and reduce it to a single aspect or theoretically predicted confluence of factors. Although there is a rich body of work on how heuristics are matched to particular decision environments ( Gigerenzer & Gaissmaier 2011 ), these environments are by necessity often highly stylized laboratory constructs aimed at exerting control over key features of the environment. 3 This line of work intentionally de-emphasizes or eliminates aspects of realistic social environments, which limits its obvious relevance for sociologists.

Finally, there is the challenge of data availability: sociologists typically do not observe the intermediate stages by which people arrive at decision outcomes. For example, researchers can fairly easily determine what college a person attended, what job they chose, or whom they married, but they rarely observe how they got to that decision—that is, how people learned about and evaluated available options, and which options were excluded either because they were infeasible or unacceptable. But such process data can be collected in a number of different ways, as detailed later in this article. Moreover, opportunities to study sociologically relevant decision processes are rapidly expanding, owing to the advent of disintermediated sources like the Internet and smart phones, which allow researchers to observe human behavior at a much finer level of temporal and geographic granularity than ever before. Equally important, these data often contain information on which options people considered , but ultimately decided against. Such activity data provide a rich source of information on sociologically relevant decision processes ( Bruch et al. 2016 ).

We believe that the time is ripe for a new line of work that draws on insights from cognitive science and decision theory to examine choice processes and how they play out in social environments. As we discuss in the next section, sociology and decision research offer complementary perspectives on decision-making and there is much to be gained from combining them. One benefit of this union is that it can deepen sociologists’ understanding of how and why individual outcomes differ across contexts. By leveraging insights on how contextual factors and aspects of choice problems influence decision strategies, sociologists can better pinpoint how, why, and when features of the social environment trigger and shape human behavior. This also presents a unique opportunity for cross-fertilization. While sociologists can draw from the choice literature’s rich understanding of and suite of tools to probe decision processes, work on decision-making can also benefit from sociologists’ insights into how social context enables or constrains behavior.

The literature on judgment and decision-making is enormous; our goal here is to offer a curated introduction aimed at social scientists new to this area. In addition to citing recent studies, we deliberately reference the classic and integrative literature in this field so that researchers can acquaint themselves with the works that introduced these ideas, and gain a comfortable overview to it. We highlight empirical studies of decision making that help address how people make critical life decisions, such as choosing a neighborhood, college, life partner, or occupation. Thus, our focus is on research that is relevant for understanding decision processes characterized by obscurity, where there is no obvious correct or optimal answer. Due to its selective nature, our review does not include a discussion of several major areas of the JDM literature, most notably Prospect Theory, which focuses on how people can distort both probabilities and outcome values when these are known (to the researcher) with certainty; we also do not discuss the wide range of anomalies documented in human cognition, for example mental accounting, the endowment effect, and biases such as availability or anchoring ( Tversky & Kahneman 1973 ; Kahneman & Tversky 1973 , 1981 ; Kahneman et al. 1991 ).

The balance of the article is as follows. We first explain how decision research emerged as a critique of rational choice theory, and show how these models of behavior complement existing work on action and decision-making in sociology. The core of the paper provides an overview of how cognitive, emotional, and contextual factors shape decision processes. We then introduce the data and methods commonly used to study choice processes. Decision research relies on a variety of data sources, including results from lab and field experiments, surveys, brain scans, and observations of in-store shopping and other behavior. We discuss their relative merits, and provide a brief introduction to statistical modeling approaches. We close with some thoughts about opportunities and challenges for sociologists wanting to incorporate insights and methods from the decision literature into their research programs.

SOCIOLOGICAL AND PSYCHOLOGICAL PERSEPCTIVES ON DECISION PROCESSES

To understand how sociology and psychology offer distinct but complementary views of decision processes, we begin with a brief introduction to the dominant model of human decision-making in the social sciences: rational choice theory. This model, endemic to neoclassical economic analyses, has permeated into many fields including sociology, anthropology, political science, philosophy, history, and law ( Coleman 1991 ; Gely & Spiller 1990 ; Satz & Ferejohn 1994 ; Levy 1997 ). In its classic form, the rational choice model of behavior assumes that decision makers have full knowledge of the relevant aspects of their environment, a stable set of preferences for evaluating choice alternatives, and unlimited skill in computation ( Samuelson 1947 ; Von Neumann & Morgenstern 2007 ; Becker 1993 ). Actors are assumed to have a complete inventory of possible alternatives of action; there is no allowance for focus of attention or a search for new alternatives ( Simon 1991 , p. 4). Indeed, a distinguishing feature of the classic model is its lack of attention to the process of decision-making. Preference maximization is a synonym for choice ( McFadden 2001 , p. 77).

Rational choice has a long tradition in sociology, but its popularity increased in the 1980s and 1990s, partly as a response to concern within sociology about the growing gap between social theory and quantitative empirical research ( Coleman 1986 ). Quantitative data analysis, despite focusing primarily on individual-level outcomes, is typically conducted without any reference to—let alone a model of—individual action ( Goldthorpe 1996 ; Esser 1996 ). Rational choice provides a theory of action that can anchor empirical research in meaningful descriptions of individuals’ behavior ( Hedström & Swedberg 1996 ). Importantly, the choice behavior of rational actors can also be straightforwardly implemented in regression-based models readily available in statistical software packages. Indeed while some scholars explicitly embrace rational choice as a model of behavior ( Hechter and Kanazawa 1997 ; Kroneberg and Kalter 2012 ), many others implicitly adopt it in their quantitative models of individual behavior.

Beyond Rational Choice

Sociologists have critiqued and extended the classical rational choice model in a number of ways. They have observed that people are not always selfish actors who behave in their own best interests ( England 1989 ; Margolis 1982 ), that preferences are not fixed characteristics of individuals ( Lindenberg and Frey 1993 ; Munch 1992 ), and that individuals do not always behave in ways that are purposive or optimal ( Somers 1989 ; Vaughan 1998 ). Most relevant to this article, sociologists have argued that the focus in classical rational choice on the individual as the primary unit of decision-making represents a fundamentally asocial representation of behavior. In moving beyond rational choice, theories of decision-making in sociology highlight the importance of social interactions and relationships in shaping behavior ( Pescosolido 1992 ; Emirbayer 1997 ). A large body of empirical work reveals how social context shapes people’s behavior across a wide range of domains, from neighborhood and school choice to decisions about friendship and intimacy to choices about eating, drinking, and other health-related behaviors ( Carrillo et al. 2016 ; Perna and Titus 2005 ; Small 2009 ; Pachucki et al. 2011 ; Rosenquist et al. 2010 ).

But This focus on social environments and social interactions has inevitably led to less attention being paid to the individual-level processes that underlie decision-making. In contrast, psychologists and decision theorists aiming to move beyond rational choice have focused their attention squarely on how individuals make decisions. In doing so, they have amassed several decades of work showing that the rational choice model is a poor representation of this process. 4 Their fundamental critique is that decision-making, as envisioned in the rational choice paradigm, would make overwhelming demands on our capacity to process information ( Bettman 1979 ; Miller 1956 ; Payne 1976 ). Decision-makers have limited time for learning about choice alternatives, limited working memory, and limited computational capabilities ( Miller 1956 ; Payne et al. 1993) As a result, they use heuristics that keep the information-processing demands of a task within the bounds of their limited cognitive capacity. 5 It is now widely recognized that the central process in human problem solving is to apply heuristics that carry out highly selective navigations of problem spaces ( Newell & Simon 1972 ).

However, in their efforts to zero in on the strategies people use to gather and process information, psychological studies of decision-making have focused largely on individuals in isolation. Thus, sociological and psychological perspectives on choice are complementary in that they each emphasize a feature of decision-making that the other field has left largely undeveloped. For this reason, and as we articulate further in the conclusion, we believe there is great potential for cross-fertilization between these areas of research. Because our central aim is to introduce sociologists to the JDM literature, we do not provide an exhaustive discussion of sociological work relevant to understanding decision processes. Rather, we highlight studies that illustrate the fruitful connections between sociological concerns and JDM research.

In the next sections, we discuss the role of different factors—cognitive, emotional, and contextual—in heuristic decision processes.

THE ROLE OF COGNITIVE FACTORS IN DECISION PROCESSES

There are two major challenges in processing decision-related information: first, each choice is typically characterized by multiple attributes, and no alternative is optimal on all dimensions; and, second, more than a tiny handful of information can overwhelm the cognitive capacity of decision makers ( Cowan 2010 ). Consider the problem of choosing among three competing job offers. Job 1 has high salary, but a moderate commuting time and a family-unfriendly workplace. Job 2 offers a low salary, but has a family-friendly workplace and short commuting time. Job 3 has a family-friendly workplace but a moderate salary and long commuting time. This choice would be easy if one alternative clearly dominated on all attributes. But, as is often the case, they all involve making tradeoffs and require the decision maker to weigh the relative importance of each attribute. Now imagine that, instead of three choices, there were ten, a hundred, or even a thousand potential alternatives. This illustrates the cognitive challenge faced by people trying to decide among neighborhoods, potential romantic partners, job opportunities, or health care plans.

We focus in this section on choices that involve deliberation, for example deciding where to live, what major to pursue in college, or what jobs to apply for. 6 (This is in contrast to decisions that are made more spontaneously, such as the choice to disclose personal information to a confidant [ Small & Sukhu 2016 ].) Commencing with the pioneering work of Howard & Sheth (1969) , scholars have accumulated substantial empirical evidence for the idea that such decisions are typically made sequentially , with each stage reducing the set of potential options ( Swait 1984 ; Roberts & Lattin 1991 , 1997 ). For a given individual, the set of potential options can first be divided into the set that he or she knows about, and those of which he or she is unaware. This “awareness set” is further divided into options the person would consider, and those that are irrelevant or unattainable. This smaller set is referred to as the consideration set , and the final decision is restricted to options within that set.

Research in consumer behavior suggests that the decision to include certain alternatives in the consideration set can be based on markedly different heuristics and criteria than the final choice decision (e.g., Payne 1976 ; Bettman & Park 1980 ; Salisbury & Feinberg 2012 ). In many cases, people use simple rules to restrict the energy involved in searching for options, or to eliminate options from future consideration. For example, a high school student applying to college may only consider schools within commuting distance of home, or schools where someone she knows has attended. Essentially, people favor less cognitively taxing rules that use a small number of choice attributes earlier in the decision process to eliminate almost all potential alternatives, but take into account a wider range of choice attributes when evaluating the few remaining alternatives for the final decision ( Liu & Dukes 2013 ).

Once the decision maker has narrowed down his or her options, the final choice decision may allow different dimensions of alternatives to be compensatory; in other words, a less attractive value on one attribute may be offset by a more attractive value on another attribute. However, a large body of decision research demonstrates that strategies to screen potential options for consideration are non-compensatory ; a decision-maker’s choice to eliminate from or include for consideration based on one attribute will not be compensated by the value of other attributes. In other words, compensatory decision rules are “continuous,” while non-compensatory decision rules are discontinuous or threshold ( Swait 2001 ; Gilbride & Allenby 2004 ).

Compensatory Decision Rules

The implicit decision rule used in statistical models of individual choice and the normative decision rule for rational choice is the weighted additive rule. Under this choice regime, decision-makers compute a weighted sum of all relevant attributes of potential alternatives. Choosers develop an overall assessment of each choice alternative by multiplying the attribute weight by the attribute level (for each salient attribute), and then sum over all attributes. This produces a single utility value for each alternative. The alternative with the highest value is selected, by assumption. Any conflict in values is assumed to be confronted and resolved by explicitly considering the extent to which one is willing to trade off attribute values, as reflected by the relative importance or beta coefficients ( Payne et al. 1993 , p. 24). Using this rule involves substantial computational effort and processing of information.

A simpler compensatory decision rule is the tallying rule, known to most of us as a “pro and con” list ( Alba & Marmorstein 1987 ). This strategy ignores information about the relative importance of each attribute. To implement this heuristic, a decision maker decides which attribute values are desirable or undesirable. Then she counts up the number desirable versus undesirable attributes. Strictly speaking, this rule forces people to make trade-offs among different attributes. However, it is less cognitively demanding than the weighted additive rule, as it does not require people to specify precise weights associated with each attribute. But both rules require people to examine all information for each alternative, determine the sums associated with each alternative, and compare those sums.

Non-Compensatory Decision Rules

Non-compensatory decision rules do not require decision makers to explicitly consider all salient attributes of an alternative, assign numeric weights to each attribute, or compute weighted sums in one’s head. Thus they are far less cognitively taxing than compensatory rules. The decision maker need only examine the attributes that define cutoffs in order to make a decision (to exclude options for a conjunctive rule, or to include them for a disjunctive one). The fewer attributes that are used to evaluate a choice alternative, the less taxing the rule will be.

Conjunctive rules require that an alternative must be acceptable on one or more salient attributes. For example, in the context of residential choice, a house that is unaffordable will never be chosen, no matter how attractive it is. Similarly, a man looking for romantic partners on an online dating website may only search for women who are within a 25-mile radius and do not have children. Potential partners who are unacceptable on either dimension are eliminated from consideration. So conjunctive screening rules identify “deal-breakers”; being acceptable on all dimensions is a necessary but not sufficient criterion for being chosen.

A disjunctive rule dictates that an alternative is considered if at least one of its attributes is acceptable to chooser i. For example, a sociology department hiring committee may always interview candidates with four or more American Journal of Sociology publications, regardless of their teaching record or quality of recommendations. Similarly (an especially evocative yet somewhat fanciful example), a disjunctive rule might occur for the stereotypical “gold-digger” or “gigolo,” who targets all potential mates with very high incomes regardless of their other qualities. Disjunctive heuristics are also known as “take-the-best” or “one good reason” heuristics that base their decision on a single overriding factor, ignoring all other attributes of decision outcomes ( Gigerenzer & Goldstein 1999 ; Gigerenzer & Gaissmaier 2011 ; Gigerenzer 2008 ).

While sociologists studying various forms of deliberative choice do not typically identify the decision rules used, a handful of empirical studies demonstrate that people do not consider all salient attributes of all potential choice alternatives. For example, Krysan and Bader (2009) find that white Chicago residents have pronounced neighborhood “blind spots” that essentially restrict their knowledge of the city to a small number of ethnically homogeneous neighborhoods. Daws and Brown (2002 Daws and Brown (2004) find that, when choosing a college, UK students’ awareness and choice sets differ systematically by socioeconomic status. Finally, in a recent study of online mate choice, Bruch and colleagues (2016) build on insights from marketing and decision research to develop a statistical model that allows for multistage decision processes with different (potentially noncompensatory) decision rules at each stage. They find that conjunctive screeners are common at the initial stage of online mate pursuit, and precise cutoffs differ by gender and other factors.

THE ROLE OF EMOTIONAL FACTORS IN DECISION PROCESSES

Early decision research emphasized the role of cognitive processes in decision-making (e.g., Newell & Simon 1972 ). But more recent work shows that emotions—not just strong emotions like anger and fear, but also “faint whispers of emotions” known as affect ( Slovic et al. 2004 , p. 312)—play an important role in decision-making. Decisions are cast with a certain valence, and this shapes the choice process on both conscious and unconscious levels. In other words, even seemingly deliberative decisions, like what school to attend or job to take, may be made not just through careful processing of information, but based on intuitive judgments of how a particular outcome feels ( Loewenstein & Lerner 2003 ; Lerner et al. 2015 ). This is true even in situations where there is numeric information about the likelihood of certain events ( Denes-Raj & Epstein 1994 ; Windschitl & Weber 1999 ; Slovic et al. 2000 ). This section focuses on two topics central to this area: first, that people dislike making emotional tradeoffs, and will go to great lengths to avoid them; and second, how emotional factors serve as direct inputs into decision processes. 7

Emotions Shape Strategies for Processing Information

In the previous section, we emphasized that compensatory decision rules that involve tradeoffs require a great deal of cognitive effort. But there are other reasons why people avoid making explicit tradeoffs on choice attributes. For one, some tradeoffs are more emotionally difficult than others, for example the decision whether to stay at home with one’s children or put them in day care. Some choices also involve attributes that are considered sacred or protected ( Baron & Spranca 1997 ). People prefer not to make these emotionally difficult tradeoffs, and that shapes decision strategy selection ( Hogarth 1991 ; Baron 1986 ; Baron & Spranca 1997 ). Experiments on these types of emotional decisions have shown that, when facing emotionally difficult decisions, decision-makers avoid compensatory evaluation and instead select the alternative that is most attractive on whatever dimension is difficult to trade off ( Luce et al. 2001 ; Luce et al. 1999 ). Thus, the emotional valence of specific options shapes decision strategies.

Emotions concerning the set of all choice alternatives—specifically, whether they are perceived as overall favorable or unfavorable—also affects strategy selection. Early work with rats suggests that decisions are relatively easy when choosing between two desirable options with no downsides ( Miller 1959 ). However, when deciding between options with both desirable and undesirable attributes, the choice becomes harder. When deciding between two undesirable options, the choice is hardest of all. Subsequent work reveals that this finding extends to human choice. For instance, people invoke different choice strategies when forced to choose “the lesser of two evils.” In their experiments on housing choice, Luce and colleagues (2000) found that when faced with a set of substandard options, people are far more likely to engage in “maximizing” behavior and select the alternative with the best value on whatever is perceived as the dominant substandard feature. In other words, having a suboptimal choice set reduces the likelihood of tradeoffs on multiple attributes. Extending this idea to a different sociological context, a woman confronted with a dating pool filled with what she perceives as arrogant men may focus her attention on selecting the least arrogant of the group.

Emotions as Information

Emotions also serve as direct inputs into the decision process. A large body of work on perceptions of risk shows that a key way people evaluate the risks and benefits of a given situation is through their emotional response ( Slovic et al. 2004 ; Slovic and Peters 2006 ; Loewenstein et al. 2001 ). In a foundational and generative study, Fischhoff et al. (1978) discovered that people’s perceptions of risks decline as perceived benefits increase. This is puzzling, because risks and benefits tend to be positively correlated. The authors also noted that the attribute most highly correlated with perceived risk was the extent to which the item in question evoked a feeling of dread. This finding has been confirmed in many other studies (e.g., McDaniels et al. 1997 ). Subsequent work also showed that this inverse relationship is linked to the strength of positive or negative affect associated with the stimulus. In other words, stronger negative responses led to perception of greater risk and lower benefits ( Alhakami & Slovic 1994 ; Slovic & Peters 2006 ).

This has led to a large body of work on the affect heuristic , which is grounded in the idea that people have positive and negative associations with different stimuli, and they consult this “affect pool” when making judgments. This shortcut is often more efficient and easier than cognitive strategies such as weighing pros and cons or even disjunctive rules for evaluating the relative merits of each choice outcome ( Slovic et al. 2004 ). Affect— particularly how it relates to decision-making—is rooted in dual process accounts of human behavior. The basic idea is that people experience the world in two different ways: one that is fast, intuitive, automatic, and unconscious, and another that is slow, analytical, deliberate, and verbal ( Evans 2008 ; Kahneman 2011 ). A defining characteristic of the intuitive, automatic system is its affective basis ( Epstein 1994 ). Indeed, affective reactions to stimuli are often the very first reactions people have. Having determined what is salient in a given situation, affect thus guides subsequent processes, such as information processing, that are central to cognition ( Zajonc 1980 ).

Over the past two decades, sociologists—particularly in the study of culture—have incorporated insights from dual process theory to understand how actions may be both deliberate and automatic (e.g., Vaisey 2009 ). Small and Sukhu (2016) argue that dual processes may play an important role in the mobilization of support networks. Kroneberg and Esser ( Kroneberg 2014 ; Esser and Kroneberg 2015 ) explore how automatic and deliberative processes shape how people select the “frame” for making sense of a particular situation. Although some scholars debate whether automatic and deliberative processes are more like polar extremes or a smooth spectrum (for an example of this critique within sociology, see Leschziner and Green 2013 ), the dual process model remains a useful framework for theorizing about behavior.

THE ROLE OF CONTEXTUAL FACTORS IN DECISION PROCESSES

Sociologists have long been interested in how social environments—for example, living in a poor neighborhood, attending an affluent school, or growing up in a single-parent household—shape life outcomes such as high school graduation, non-marital fertility, and career aspirations ( Sharkey and Faber 2014 ; Lee et al. 1993 ; Astone and McLanahan 1991 ). Social environments shape behavior directly through various forms of influence such as peer pressure and social learning, and indirectly by dictating what opportunities or social positions are available ( Blalock 1984 ; Manski 2000 ; Schelling 1971 ). But while the sociological literature on contextual effects is vast, the subset of that work which focuses on decisions emphasizes the causes or consequences of those decisions more than the processes through which they are made.

Decision researchers devote considerable attention to contextual effects, but typically “context” in this field refers to architectural features of choice environments such as the number of alternatives; whether time pressures limit the effort that can be put into a decision; and what option is emphasized as the default. (In the world, of course, these features are socially determined. But this is less emphasized in decision research, much of which occurs in a laboratory setting.) The overwhelming finding from these studies is that people’s choices are highly sensitive to context. This insight has led to an influential literature on the “Construction of Preferences” (see Sidebar) as well as a great deal of interest in policy interventions that manipulate features of choice environments ( Thaler and Sunstein 2008 ; Shafir 2013 ). Recently, decision researchers have begun to look at how decisions are shaped by more explicitly social environments such as poverty (e.g., Mullainathan and Shafir 2013 ). In this section, we discuss how four aspects of social context—what opportunities are available, the importance of the “default” option, time pressure and constrained resources, and the choices of others—shape decision processes.

Choice Sets and Defaults

A classic assumption of conventional choice models is that the ratio of choice probabilities for any two options is independent of what other options are available ( Luce 1959 ). (In the literature on statistical models of choice, this is known as the principle of Independence of Irrelevant Alternatives [IIA].) But it is well established that people’s choices depend heavily on the relative merits of a particular set of options rather than their absolute values ( Tversky & Simonson 1993 ). For example, people tend to avoid more extreme values in alternatives (the “compromise effect”); thus, adding a new option to the mix can lead choosers to shift their views about what constitutes a reasonable choice ( Simonson 1989 ; Simonson & Tversky 1992 ). In a similar vein, a robust finding is that adding a new “asymmetrically dominated” alternative – one dominated by some items in the set but not by others – can actually increase the choice probabilities of the items that dominate it. Such a “decoy effect” ( Huber et al. 1982 ) should be impossible under IIA, and in fact violates regularity (i.e., new items cannot increase probabilities of existing ones). Both of these effects have been attributed to the fact that people making choices are trying to justify them based on reasons ( Simonson 1989 ; Dhar et al. 2000 ); changing the distribution of options may alter how compelling a particular reason might be.

Choice outcomes are also highly influenced by what option is identified as the “default.” Defaults are whatever happens in a decision if the chooser “decides not to decide” ( Feinberg & Huber 1996 ). Defaults exert a strong effect on people’s choices, even when the stakes of the decision are high ( Johnson et al. 1993 ; Johnson & Goldstein 2013 ). Defaults also tap into other, well-established features of human decision making: procrastination, bias for the status quo, and inertial behavior ( Samuelson & Zeckhauser 1988 ; Kahneman et al. 1991 ). In recent years, manipulating the default option—for example making retirement savings or organ donation something people opt out of rather than opt into—has been identified as a potentially low cost, highly impact policy intervention ( Shafir 2013 ). Defaults are also of potentially great sociological interest. A number of sociological studies have theorized about how people “drift” into particular outcomes or situations (e.g., Matza 1967 ); defaults exist in part because choices are embedded in specific environments that emphasize one set of options over others. 8

Scarcity and Social Influence

A number of recent studies examine how conditions of scarcity— with regard to time, resources, and energy—shape decision-making. Consistent with studies of cognitive effort and decision-making, a variety of experimental results demonstrate that time pressure reduces people’s tendency to make tradeoffs ( Wright & Weitz 1977 ; Edland 1989 ; Rieskamp & Hoffrage 2008 ). This finding is especially interesting in light of recent line of work by Shah et al. (2015) , who show via experiments that resource scarcity forces people to make tradeoffs, e.g., “If I buy dessert, I can’t afford to take the bus home.” Weighing tradeoffs is cognitively costly; they deplete people’s resources for other decision tasks, which overall reduces their ability to engage in deliberative decision-making ( Pocheptsova et al. 2009 ). Given the fact that people living in conditions of extreme scarcity are typically limited in both time and financial resources, a cognitive perspective suggests that they are “doubly taxed” in terms of cognitive effort, and offers important insights in understanding how conditions of poverty shape, and are shaped by, people’s choices ( Bertrand et al. 2004 ). In short, the context of poverty depletes people’s cognitive resources in an environment where mistakes are costly ( Mullainathan & Shafir 2013 , Gennetian & Shafir 2015 ).

There are also a small number of studies that examine how the actions of other people influence decision processes. They focus on the role of descriptive norms ( Cialdini 2003 ; Cialdini & Goldstein 2004 ), which are information about what other people are doing. The key finding is that people are more likely to adopt a particular behavior—such as conserving energy in one’s home or avoiding changing sheets and towels at a hotel—if they learn that others are doing the same ( Goldstein et al. 2008 ; Nolan et al. 2008 ). The more similar the comparison situation is to one’s own, the more powerful the effect of others’ behavior on one’s own. For instance, people are more likely to be influenced if the descriptive norm references their neighbors or others who share social spaces ( Schultz et al. 2007 ). The finding that descriptive norms are most powerful when they are immediate is reinforced by a study of charitable giving, which shows that people’s behavior is disproportionately influenced by information about what others have given, especially the most recent, non-specified donor ( Shang & Croson 2009 ).

This work on social influence is consistent with a classic literature in sociology that emphasizes how people’s beliefs about the sort of situation they are in shape their behavior (e.g., Thomas & Znaniecki 1918 ; Goffman 1974 ). Social cues provide information about what choices are consistent with desired or appropriate behavior. For example, a classic study demonstrates that whether a Prisoner’s dilemma game was presented to research subjects as a simulated “Wall Street” or “Community” shaped subsequent playing decisions ( Liberman et al. 2004 ; Camerer & Thaler 1993 ). In other words, there is an interpretive component to decision-making that informs one’s views about the kind of response that is appropriate.

STUDYING DECISION PROCESSES

Psychologists have devised a number of techniques to shed light on human decision processes in conjunction with targeted stimuli. Process tracing is a venerated suite of methods broadly aimed at extracting how people go about acquiring, integrating, and evaluating information, as well as physiological, neurological, and concomitants of cognitive processes ( Svenson 1979 ; Schulte-Mecklenbeck et al. 2010 ). (Note that this approach is quite different from what political scientists and sociologists typically refer to as process tracing [ Mahoney 2012 ; Collier 2011 ].) In a classic study, Lohse & Johnson (1996) examine individual-level information acquisition using both computerized tracing and eye-tracking across multiple process measures (e.g., total time, number of fixations, accuracy, etc.)

More recently, the use of unobtrusive eye-trackers has allowed researchers to discern which information is being sought and assimilated in the sort of stimuli-rich environments that typify online interactions, without querying respondents’ knowledge or intermediate goals ( Duchowski 2007 , Wedel & Pieters 2008 ). Also, it has recently become possible to use neuroimaging techniques like PET, EEG, and fMRI ( Yoon et al. 2006 ) to observe decision processes in vivo , although at a high cost of invasiveness. Such studies offer the benefit of sidestepping questions about, for example, the emotional reactions experienced by decision-makers, by observing which portions of the brain are active when information is being accessed and processed, as well as final decisions arrived at.

Stated and Revealed Preferences

While not directly focused on the process of decision-making (i.e., in terms of identifying decision strategies), a large literature assumes a linear compensatory model and aims to capture the weights people ascribe to different choice attributes (see Louviere et al. 1999 for a broad overview; also Train 2009 ). These methods, long known to social scientists ( Samuelson 1948 ; Manski and McFadden 1981 ; Bruch and Mare 2012 ), rely on both field data—where the analyst records decision-makers’ “revealed preferences” as reflected in their actions—and choice experiments, where analysts enact control over key elements of the decision environment through vignettes. Stated choice experiments have two advantages that are relevant in modeling choice processes: their ability to (1) present decision-makers with options unavailable in practice or outside their usual purview; and (2) record multiple (hypothetical) choices for each decision-maker, even for scenarios like mate choice or home purchase that are made few times in a lifespan. The downside is that they are difficult to fully contextualize or make incentive-compatible; for example, experiment participants are routinely more willing to spend simulated experimental money than their own hard-won cash ( Carlsson & Martinsson 2001 ).

Among the main statistical methods for enacting choice experiments is conjoint analysis ( Green and Srinivasan 1978 , Green et al. 2001 ), a broad suite of techniques, implemented widely in dedicated software (e.g., Sawtooth), to measure stated preferences and how they vary across a target population. Conjoint works by decomposing options into their attributes , each of which can have several levels. For example, housing options each provide cooking facilities, sleeping quarters, bathrooms, among other attributes, and each of these varies in terms of their quality levels (e.g., larger vs. smaller; number overall; and categorical attributes like type of heating, color, location, etc.) The goal of conjoint is to assign a utility or part-worth to each level of each attribute, with higher numbers representing more preferred attribute levels; for example, one might say that, for families with several children, a home with four bedrooms has a much higher utility than one with two. Conjoint approaches can be—and have been ( Wittink & Cattin 1989 ; Wittink et al. 1994 )—applied to a wide range of settings where it is useful to measure the importance of specific choice attributes.

Field data such as residential, work, or relationship histories have the advantage of reflecting actual choices made in real contexts ( Louviere et al. 1999 ). However, such data suffer from several drawbacks: (1) variables necessarily covary (i.e., higher neighborhood prices correlate with levels of many attributes at once); (2) we cannot infer how people would respond to possible novel options, like affordable, diverse neighborhoods that may not yet exist; and (3) each person typically makes just one choice. Such problems are exacerbated when researchers cannot in principle know the entire “consideration set” of options available and actively mulled over by each decision-maker, which is typically the case in sociological applications. 9 When such confounds are present in field data, conjoint and other experimental methods allow researchers to control and “orthogonalize” the attributes and levels used in choice experiments, to achieve maximal efficiency and avoid presenting each participant with more than a couple of dozen hypothetical choice scenarios ( Chrzan & Orme 2000 ). 10

Two other choice assessment methods deserve brief mention, as they leverage some of the best features of experimental and field research: natural experiments and field experiments. In a natural experiment, an event exogenous to the outcome of interest affects only certain individuals, or affects different individuals to varying degrees. One example is the effect of natural disasters, like the 2005 flooding of New Orleans in the aftermath of Hurricane Katrina, on migration decisions (Kirk 2009). In field experiments, researchers exert control over focal aspects of people’s choice environments. Although it may seem difficult to study sociologically relevant choice processes this way, the use of web sites as search tools enables a nontrivial degree of experimental control over choice environments (see, for example, Bapna et al. 2016).

Statistical Models of Choice Processes

The bedrock formulation of statistical analyses of choice is the random utility model , which posits that each option available to a decision-maker affords a certain value (“utility”), which can be decomposed into that explicable by the analyst and a (“random”) error ( Ben-Akiva & Lerman 1985 ; Train 2009 ). 11 The former can be related through regression-based techniques to other observed covariates on the options, decision-maker, and environment, whereas the latter can be due to systematically unavailable information (e.g., income or education) or intrinsic unobservables (e.g., the mood of the decision-maker). Of particular importance is McFadden’s conditional logit model ( McFadden 1973 ), 12 which allows attributes of options to vary across choice occasions (e.g., prices or waiting times for different transportation modes; neighborhoods or room sizes for renting vs. buying a home; etc.) Because this model is supported in much commercial statistical software, and converges rapidly even for large data sets, it is by far the most widely deployed in choice applications.

Statistical models of choice have been grounded primarily in rational utility theory, with concessions towards efficient estimation. As such, the utility specifications underlying such models have tended to be linear, to include all available options, and incorporate full information on those options. Much research reveals not only the psychological process accuracy, but the statistical superiority, of relaxing these assumptions by incorporating limits on information, cognitive penalties, and nonlinear/noncompensatory utility. For example, the formal incorporation of consideration sets into choice modeling ( Horowitz, & Louviere 1995 ; Louviere, Hensher, & Swait 2000 , section 10.3) has demonstrated superior fit and predictive accuracy for explicit models of exclusion of certain options from detailed processing, with some analyses (see Hauser & Wernerfelt 1990 ) attributing in excess of 75% of the choice model’s fit to such restrictions. Similarly, lab studies have confirmed that decision-makers wish to conserve cognitive resources, and formal statistical models (e.g., Shugan 1980 , Roberts & Lattin 1991 ) have attempted to account for and measure a “cost of thinking” or consideration from real-world data.

However, despite a decades-deep literature on noncompensatory evaluation processes ( Einhorn 1970 ), the practical estimation of such models is largely in its infancy, due to complexities of specification and data needs (e.g., Elrod et al. 2004 ). Although nonlinearity can be captured using polynomial functions of covariates, these impose smoothness of response; by contrast, conjunctive and disjunctive processes impose cutoffs beyond which evaluation is either wholly positive or insurmountably negative ( Bruch et al. , 2016 ). We view the development of this area as critical to the widespread acceptance of formal choice models to social scientists and sociologists in particular, who typically wish to know which neighborhoods we would never live in, jobs we would never take, etc.

CHALLENGES AND OPPORTUNITIES FOR FUTURE RESEARCH

Sociology has long been interested in individuals’ choices and their implications for social life, and there is renewed interest in theories that can explain human action (e.g., Kroneberg 2014 ; Gross 2009 ). Our hope is that this review enables interested scholars to pursue a more nuanced and structurally accurate representation of the choice process. Greater insight into human behavior will also allow for greater insights into the dynamic relationship between micro- and meso-level processes and their larger-scale implications ( Hedstrom and Bearman 2009 ). Consider how choice sets are constructed in the first place. People may rule out certain options due to preferences, affordability and/or time constraints—classic cognitive, temporal, and economic variables—but also the anticipation of unfair treatment or discrimination. For example, a high school student searching for colleges may eliminate those that are too expensive, too far from home, or are perceived as unwelcoming. Identifying the criteria through which people rule themselves (or others) out can illuminate more precise mechanisms through which people’s actions shape, and are shaped by, larger-scale inequality structures.

We also believe that many of the data limitations that hampered sociologists’ ability to study decision processes in the past are becoming far less of an issue. This is due at least in part to increasingly available data sources that can aid sociologists in studying choice processes. These so-called “big data” are often behavioral data: specific actions taken by individuals that can shed light on processes of information search, assimilation, and choice. Moreover, the online environments through which we increasingly communicate and interact enable not only observational “field” data, but also targeted, unobtrusive experiments where the decision environment is directly altered. The latter possibility offers greater precision than ever before in isolating both idiosyncratic and potentially universal features of behavior, as well as better understanding the interplay between context and human action.

However, existing approaches from marketing and psychology are often not suited to sociological inquiry directly out of the box, creating both challenges and opportunities for future work. For example, most extant models were designed to capture more prosaic decisions, like supermarket shopping, where attributes are known, options are stable, and “stakes” are modest. Although some choices of sociological interest may fit this pattern, most do not. For example, many pivotal life decisions—purchasing a home (where sellers and buyers must agree on terms); college admissions; dating and marriage decisions; employment offers—require a partnership , wherein each decision maker must “choose you back.” Sociologists must therefore be circumspect in applying models developed for choice among “inert” options (such as flavors of yogurt) to the data they most commonly analyze.

The fact that so many sociological choices are characterized by obscurity (i.e., there is no obvious or single optimal choice to be made) is also a good reason to proceed with caution in applying ideas from JDM to sociological research. Take, for example, the literature discussed earlier on interventions that manipulate people’s default option. It is one thing to encourage healthier eating by putting apples before chocolate cake in the cafeteria line; it is quite another to nudge someone towards a particular neighborhood or school. Sociologists rarely have a clear sense of what choice will be optimal for a group of people, let alone particular individuals. On the other hand, few would argue that growing up surrounded by violence isn’t universally harmful (Harding 2009; Sharkey et al. 2013). Policies can only be improved by a more nuanced understanding of how choices unfold in particular environments, and how the default options are shaped by contextual factors.

Thus, this challenge also creates the opportunity to build knowledge on how heuristics operate under conditions of obscurity. JDM research has largely focused on documenting whether and how heuristics fall short of some correct or optimal answer. In sociology, by contrast, we typically lack a defensible metric for the “suboptimality” of decisions. While some decisions may be worse than others, it is impossible to know, for example, whether one has chosen the “right” spouse or peer group, or how to set up appropriate counterfactual scenarios as yardsticks against which specific decisions could be dispassionately assessed. By branching into decision domains where the quality or optimality of outcomes cannot be easily quantified (or in some cases even coherently conceptualized), sociologists can not only actively extend the range of applications of decision research, but also break new theoretical ground.

SIDEBAR 1: THE CONSTRUCTION OF PREFERENCES.

Decision researchers have amassed several lines of evidence to suggest that, rather than being stable constructs that are retrieved on demand, preferences are constructed in the moment of elicitation ( Lichtenstein & Slovic 2006 ; Bettman, Luce, and Payne 1998 ). This finding is echoed in studies of judgments and attitudes, which are also sensitive to contextual cues ( Ross and Nisbett 1991 ; Schwarz 1999 , 2007 ). Preference variation can be generated from simple anchors or changes in question wording (e.g., Mandel & Johnson 2002 ). A classic finding is that people exhibit preference reversals when making multiattribute choices; in one context they indicate a preference of A over B, but in another context they indicate they prefer B over A (e.g., Cox & Grether 1996 ; Seidl 2002 ). Several theories have been put forward to explain this effect (see Lichtenstein & Slovic 2006 , Chapter 1 for a review and synthesis). One explanation is that people’s preferences, attitudes, judgments reflect what comes to mind; and what is salient at one time point may not be salient at another ( Higgins 1996 ; Schwarz, Strack, and Mai 1991 ).

Acknowledgments

We are grateful to Scott Rick Mario Small, and an anonymous reviewer for helpful feedback on this manuscript. This work was supported by a training grant (K01-HD-079554) from the National Institute of Child and Human Developent

A notable exception is Herb Simon’s concept of “satisficing,” which many influential sociological works—especially in the subfield of economic sociology—have incorporated into models of action and behavior (e.g., Baker 1984 ; Granovetter 1985 ; Uzzi 1997 ; Beckert 1996 ).

Although rational choice has had a strong influence sociological research—for example, Coleman’s (1994) Foundations of Social Theory has almost 30,000 citations and there is a journal, Rationality and Society , devoted to related topics—this framework never overtook the discipline as it did economics.

While this is accurate as a broad characterization, there are studies that examine decision-making “in the wild” through observation or field experiments (e.g., Camerer 2004 ; Barberis 2013 ).

The best-known critique of the rational choice model within JDM comes from the “Heuristics and Biases” school of research (Tversky 1972; Kahneman & Tversky 1979 ; Tversky & Kahneman 1981 ). Their studies show that decision makers: (1) have trouble processing information; (2) use decision-making heuristics that do not maximize preferences; (3) are sensitive to context and process; and (4) systematically misperceive features of their environment. Since then, a large body of work provides convincing evidence that individuals are “limited information processing systems” ( Newell & Simon 1972 ; Kahneman 2003 ).

Heuristics are “problem-solving methods that tend to produce efficient solutions to difficult problems by restricting the search through the space of possible solutions, on the basis of some evaluation of the structure of the problem” ( Braunstein 1972 , p. 520).

The decision strategies presented in this section are sometimes known as “reasons” heuristics (c.f., Gigerenzer 2004 ) because they are the reasons people give for why they chose the way that they did. As such, they are less applicable in situations where people have few if any options. For example, a person evicted from their home may have a single alternative to homelessness: staying with a family member. In this case, the difficulty of the decision is not information processing.

There is a rich literature in sociology on how emotions are inputs to and outcomes of social processes (e.g., Hochschild 1975; Scheff 1988 ). While this work has not historically been integrated with psychology ( Turner and Stets 2014 , p. 2), this may be a fruitful direction for future research.

We are grateful for conversations with Rob Sampson and Mario Small that led to this insight.

Newer online data sources—for example, websites for housing search and dating—generate highly granular, intermediate data on consideration sets. This parallels a revolution that occurred among choice modelers in marketing 30 years ago: the introduction of in-store product code scanners and household panels whose longitudinal histories provided information not only on which options were eventually chosen, but also which were actually available at the time of purchase, but rejected.

A review of the vast choice experiment design literature is beyond the scope of this article, but turnkey solutions for designing, deploying, and estimating discrete choice models for online panels are commercially available (e.g., Sawtooth Software’s “Discover”).

There is an enormous literature on the analysis of discrete choice data—items chosen from a known array with recorded attributes. Our treatment here is necessarily brief and highly selective. For more comprehensive introductions to this topic, we refer the reader to the many treatments available (e.g., Ben-Akiva & Lerman 1985 , Hensher et al. 2005 , Louviere et al. 2000 , Train 2009 ).

Empirical work typically refers to this as the multinomial logit model, although economists often distinguish the latter as applicable to when attributes change for the decision-maker (e.g., age, income), not the options themselves.

Contributor Information

Elizabeth Bruch, Department of Sociology and Complex Systems.

Fred Feinberg, Ross School of Business and Statistics.

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31 Thinking in a Social Context: A Gricean Perspective

Norbert Schwarz, Provost Professor, Dornsife Department of Psychology and Marshall School of Business, University of Southern California

• Published: 21 August 2024
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People do much of their thinking in a social context by drawing on information provided by others and sharing their own judgments with others. This information exchange is guided by the tacit assumptions underlying the conduct of conversations in everyday life. This chapter reviews these assumptions and their implications for social cognition research. It highlights that many familiar biases and shortcomings of human judgment reflect, in part, a basic misunderstanding about the nature of communication in research situations. Whereas participants assume that researchers are cooperative communicators, whose contributions are informative, relevant, and clear, the researchers may (deliberately or inadvertently) present information that does not meet these criteria. When this misconception is avoided, many familiar biases are attenuated or eliminated, suggesting that they are the result of faulty communication rather than faulty judgment.

Introduction

A truism of psychology holds that people respond to the world as they see it. The processes underlying this construction of reality are the core issue of social psychology. In the 1970s, the emerging field of social cognition turned to this topic with the conceptual repertoire of the information processing paradigm ( Lachman et al., 1979 ; Strack, 1988 ; for a history, see Moskowitz & Okten , this volume). Drawing on models of encoding, storage, and retrieval, social cognition researchers highlighted the importance of inferential processes in phenomena that had traditionally been described in terms of social “perception” ( Hastie et al., 1980 ; Wyer & Carlston, 1979 ). This development aligned research in social psychology with emerging theorizing in the cognitive sciences. Less than a decade later, the contributions to Wyer and Srull’s (1984) three-volume Handbook of Social Cognition illustrated the rapid progress made. Within a few more years, the social cognition approach became the dominant conceptual framework of social psychology.

But as historians of science are aware, every paradigm has its blind spots because the guiding metaphors focus attention on some questions at the expense of others ( Gigerenzer, 1991 ; Kuhn, 1962 ; Root-Bernstein, 1989 ). In the case of social cognition, the computer metaphor of the information processing paradigm highlighted “cold” processes of encoding, storage, and retrieval, while clouding the importance of other processes. Some of these oversights were quickly corrected as social cognition researchers turned to the synergism of motivation and cognition (for early discussions, see the contributions in Sorrentino & Higgins, 1986 ) and the interplay of feeling and thinking (for an early review, see Clore et al., 1994 ). Another oversight has been more persistent. As many observers noted, the information processing paradigm fostered a focus on individuals as isolated information processors, prompting Schneider (1991 , p. 553) to ask, “Where, oh where, is the social in social cognition?” With few exceptions, a field studying social inference processes neglected the social and inferential nature of communication and instead treated the verbal materials it presented to participants simply as “inputs” for other inference processes. This chapter addresses this oversight. The next section introduces the core assumptions of cooperative conversational conduct and previews their implications for social cognition research.

Cooperative Communication

Communication is a cooperative endeavor centered around intentions and inferences. Speakers make an utterance with the intention to convey something, and recipients attempt to infer what it is that the speaker wants to convey (Clark, 1996 , 2004 ). Both interlocutors draw on the context of their exchange as well as assumptions about one another to achieve their goals. When the communication is synchronous, the interlocutors can engage in collaborative efforts to arrive at a mutually agreed-on understanding ( Clark & Henetz, 2014 ). This collaboration is precluded when the communication is asynchronous and the speaker is absent when the recipient processes the message; it is also precluded when synchronous communication is highly standardized and the speaker merely repeats the same, or a highly similar, utterance. Both conditions apply to most research situations, where instructions, questions, and messages are standardized and increasingly presented in the absence of any individual who could provide clarification ( Schwarz, 1996 ; Strack, 1994 ). Under these conditions, recipients’ only option to make (some) sense of the speaker’s utterances is to rely on contextual information and background knowledge. Theories of conversational pragmatics can shed light on how they do that. These theories focus on verbal communication in oral or written modes and pay limited attention to nonverbal elements of communication, although they provide a useful framework for the conceptualization of gestures and facial expressions as communicative tools ( Bavelas & Chovil, 2018 ; Clark, 1996 , 1997 ).

The Logic of Conversation

Paul Grice ( 1975 , 1978 ) conceptualized conversations as social cooperation tasks. People are most likely to succeed at such tasks when they observe a few basic rules of cooperative conduct, which Grice sometimes illustrated with tongue-in-cheek examples (e.g., Grice, 1989 , p. 28). For example, when mixing the ingredients for a cake, we do not expect to be handed a good book—whatever the partner contributes should be relevant to what we are doing ( maxim of relation ). Nor do we want to receive salt when we ask for sugar or be told that the salt is, in fact, sugar—the contributions to the task should be not only relevant, but also honestly presented ( maxim of quality ). We also don’t want to be handed four spoons, or none, when we need one—a partner’s contributions should provide what we need, not more and not less ( maxim of quantity ). And when we ask how much sugar is needed, we expect a quantitative answer, not a riddle—the partner’s contributions should be clear and avoid ambiguity ( maxim of manner ). Together, these four maxims constitute a cooperative principle that is central to conversational conduct. While Grice’s (1975) maxims seem utterly obvious in such examples, their contribution to our conversations in everyday life and research situations is often overlooked—with unfortunate consequences for social cognition research, as later sections will illustrate.

Grice’s analysis of conversations assumes that participants proceed as if the speaker adhered to the above maxims. The maxim of manner asks speakers to make their contribution such that it can be understood by their audience. A central element of audience design is the speaker’s assumptions about the information that they share with recipients, that is, the common ground ( Schiffer, 1972 ). Listeners, in turn, assume that the speaker observes this maxim and interpret the speaker’s utterance against what they assume to constitute the common ground ( Clark et al., 1983 ). Each successful contribution to the conversation extends the cumulative common ground of the participants.

This cumulative nature of the common ground is consistent with the maxim of relation, which asks speakers to make all contributions relevant to the aims of the ongoing conversation. This entitles listeners to use the context of an utterance to disambiguate its meaning by making bridging inferences ( Clark & Clark, 1977 ). Hence, speakers are unlikely to assume that a contribution is irrelevant to the goal of the conversation, unless it is marked as such. As Sperber and Wilson (1986 , p. vi) put it, “communicated information comes with a guarantee of relevance.” If in doubt, it is the listener’s task to determine the intended meaning of the utterance by referring to the common ground or by asking for clarification. As discussed in the sections titled “The Conversational Relevance of Irrelevant Information” and “Conversational Implications of Methodological Choices,” these conversational dynamics contribute to many context effects that social cognition researchers often attribute solely to knowledge accessibility.

In addition, the maxim of quantity requires speakers to make their contribution as informative as is required, but not more informative than is required. That is, speakers should respect the established, or assumed, common ground by providing the information that recipients need, without reiterating what the recipient already knows or may take for granted ( Clark & Haviland, 1977 ; Prince, 1981 ). This aspect of conversational conduct can profoundly influence the use and disuse of accessible information, as discussed in the section “Giving Informative Answers.”

Finally, a maxim of quality enjoins speakers not to say anything they believe to be false or lack adequate evidence for. This contributes to the common observation that recipients are more likely to accept a speaker’s utterance than to question it. As discussed in the section “The Default Is Acceptance, Not Scrutiny,” closer scrutiny of others’ contributions is most likely when background knowledge or concurrent signals call the speaker’s cooperativeness into question.

While speakers do not always obey these maxims, listeners usually proceed as if speakers did. This is apparent in what listeners infer from utterances that violate the maxims. Grice refers to these inferences as conversational implicatures , that is, inferences that go beyond the semantic meaning of what is being said by determining the pragmatic meaning of the utterance. Suppose A asks, “Where is Bill?” and B responds, “There’s a yellow VW outside Sue’s home” ( Levinson, 1983 , p. 102). If taken literally, B’s contribution fails to answer A’s question, thus violating (at least) the maxims of relation and quantity. Nevertheless, most people infer that Bill has a yellow VW and that its location suggests that Bill may be at Sue’s home. In contrast to logical implications, conversational implicatures are “inferences based on both the content of what has been said and some specific assumptions about the cooperative nature of ordinary verbal interaction” ( Levinson, 1983 , p. 104). The distinction between logical implications and conversational implicatures has received insufficient attention in social cognition research and contributes to the emergence and size of many apparent shortcomings and biases of human judgment, as this chapter will illustrate.

Relevance Theory

Building on Grice’s (1975) seminal theorizing, Sperber and Wilson (1986) traced Grice’s maxims to two more general principles. Their relevance theory assumes that humans’ cognitive system has evolved to track what is most relevant in a given context and to do so with the least effort. Because this holds for the producer as well as the recipient of the utterance, the most accessible interpretation of the utterance can be assumed to be the intended interpretation. From this perspective, listeners follow the “path of least effort” by testing “interpretive hypotheses in the order of accessibility” and stop when their “expectations of relevance are satisfied” ( Wearing, 2015 , p. 90). These assumptions are compatible with several robust observations in social cognition research. For example, people do not consider all potentially relevant information when forming a judgment but truncate their information search after a few highly accessible pieces have come to mind (for reviews, see Bodenhausen & Wyer, 1987 ; Higgins, 1996 ). Easily processed information is also considered more credible and trustworthy and fosters inferences that are consistent with the information’s declarative implications. In contrast, disfluent processing—from perception and interpretation to recall—is a marker that something may be wrong and can foster inferences that are opposite to the information’s declarative implications (for reviews, see Schwarz, 2015 ; Schwarz et al., 2003 , 2021 ).

Relevance theory can also handle phenomena that are cumbersome to conceptualize within Grice’s maxims, such as the use of figurative language and irony ( Wilson, 2006 ) or the observation that what counts as “true” depends on the recipient’s needs. For example, when asked at 3:13 p.m. what the time is, responding “3:15 p.m.” is an acceptable answer, unless the questioner asks because she needs to reset her watch, in which case more precision is needed ( Van der Henst et al., 2002 ). People sometimes even prefer incorrect but useful information over more correct but less useful alternatives. In December 2019, Chinese doctors noticed that a disease was being transmitted person to person in Wuhan, China. Prior to any government action, they announced on social media that seven cases of SARS had been confirmed and urged the public to protect themselves. The disease was COVID-19, not SARS (despite similarities), and the Chinese government punished the doctors for “spreading rumors.” In contrast, the public appreciated the warning because the factually false reference to SARS tapped into action-relevant knowledge acquired during earlier episodes of SARS to offer correct recommendations for protective behavior. To capture this difference, Zhang and Schwarz (2021) suggested distinguishing between factually true utterances and pragmatically true utterances, which provide correct action-relevant information while erring on the facts. A series of experiments confirmed that people prioritize pragmatic truth over factual truth when it is beneficial for the recipient. They also showed that what counts as pragmatically true depends on the needs of the recipient. Such observations are difficult to reconcile with Grice’s (1975) maxim of manner, but compatible with Sperber and Wilson’s (1986) relevance theory. For the purposes of the present chapter, I will nevertheless discuss conversational pragmatics primarily in a Gricean language, which provides more concrete representations of the conversational principles that require attention in social cognition research.

In sum, “communicated information comes with a guarantee of relevance” ( Sperber & Wilson, 1986 , p. vi) and listeners are entitled to assume that the speaker tries to be informative, truthful, relevant, and clear ( Grice, 1975 ). Listeners interpret the speakers’ utterances “on the assumption that they are trying to live up to these ideals” ( Clark & Clark, 1977 , p. 122).

Implications for Social Cognition Research

A conversational analysis suggests that many standard findings reported in the social cognition literature may reflect, in part, a basic misunderstanding about the nature of communication in research situations. Whereas research participants assume that researchers are cooperative communicators, whose contributions are informative, relevant, and clear, the researchers may (knowingly or unknowingly) present information that does not meet these criteria. This disconnect gives rise to errors of judgment that are usually interpreted as violations of rational inference rather than the result of failed, or deliberatively misleading, communication. This chapter addresses this possibility.

One implication of the default assumption of conversational cooperativeness is that recipients focus more on making sense of a speaker’s utterance than on testing whether the utterance is truthful. Hence, message acceptance and confirmatory elaboration is the default, unless there is reason to doubt that the speaker is a cooperative communicator, as discussed in the next section. The same tacit assumptions also make it difficult to assess how well people can discern what is or is not relevant to a task. The sheer fact that information is communicated favors its acceptance as relevant—or why else would the speaker contribute it? This is particularly likely in situations that people perceive as task oriented, such as research interviews and experiments. Little do participants know that the researchers may violate all maxims of conversation by providing information that is neither relevant nor truthful, informative, and clear—while carefully designing the situation to suggest otherwise. Once the cooperativeness assumption is called into question, familiar shortcomings and biases of human judgment are attenuated or eliminated, as reviewed in the section “The Conversational Relevance of Irrelevant Information.” Unfortunately, researchers are rarely aware of these conversational dynamics and conclude that participants lack the ability to distinguish relevant from irrelevant information.

Going beyond explicitly communicated claims, the maxim of relation licenses the use of context information to make sense of an utterance and to tailor one’s own contributions to meet the inferred intent of the speaker. This has implications for meaning making in everyday life as well as research settings. Even researchers who pay close attention to the specific wording of their core questions and materials are usually insensitive to what they “leak” through other aspects of their procedures. Because the communicative constraints of standardized research situations do not allow for clarification or negotiation of a shared meaning, participants treat all aspects of the research procedures as part of the researcher’s contribution to the conversation. This endows many tangential and supposedly “irrelevant” features-–from the letterhead of the invitation to the informed consent form and the format of the rating scale—with the potential to shape participants’ interpretation of the researcher’s utterances. I review examples in the section “Conversational Implications of Methodological Choices.”

Cooperative communicators are expected to provide information that is relevant to the recipient. They are not supposed to offer information that “goes without saying,” to repeat what the recipient already knows, or to emphasize things that are tangential to the recipient’s epistemic interest. This encourages research participants to take the full exchange of information into account when they design their answers to the researcher’s questions. Whereas many researchers hope that each question and task is treated as a separate event, research participants are likely to treat the whole interaction as an extended conversation and assume that questions and answers build on one another. This has implications for participants’ construction of an informative answer, as discussed in the section “Giving Informative Answers.”

As will become apparent, the conversational dynamics of research situations exacerbate and sometimes create the shortcomings of human judgment that social cognition research has identified. They are less pronounced, or even absent, in situations where people are aware that their interlocutor may not be a cooperative communicator. Unless we take this into account, we run the risk of painting an unduly negative picture of human judgment. I return to this issue in a plea for Gricean charity that concludes this chapter.

The Default Is Acceptance, Not Scrutiny

The tacit assumptions of cooperative conduct entail that communicated information is likely to be truthful, relevant, and clear. When a statement is ambiguous, it is the listener’s task to ask for clarification or to draw on the context of the utterance to make sense of it. It is therefore not surprising that people are more likely to consider how an utterance may make sense in light of the context than to consider whether it makes sense at all, unless they suspect that the speaker is not cooperative.

Misleading Questions

Attempts to find meaning in ambiguous utterances are particularly likely when the utterance is a question that demands an answer. To provide a cooperative answer, one needs to determine which information the questioner is asking for. This makes presuppositions that are slipped into (mis)leading questions highly influential, especially when the question is asked in an apparently cooperative context—and less so when cooperativeness is not assumed, as the case of misleading questions illustrates.

For example, public opinion researchers often worry that self-presentation concerns induce “respondents to conjure up opinions even when they had not given the particular issue any thought prior to the interview” ( Erikson et al., 1988 , p. 44). Indeed, around 30% of survey respondents offer opinions on highly obscure or completely fictitious issues that they have never heard about (e.g., Bishop et al., 1986 ; Schuman & Presser, 1981 ). Their answers are presumably meaningless and based on a “mental flip of coin” ( Converse, 1964 ). A Gricean perspective suggests otherwise. The sheer fact that a question about an issue is asked presupposes that the issue exists. If clarifications are not provided or a well-trained interviewer merely repeats the same question, respondents are likely to turn to the context of the question to determine its meaning, as they would be expected to do in other conversations. Testing this possibility, Strack et al. (1991 , Experiment 1) asked German college students about their attitude toward a fictitious “educational contribution” law, allegedly discussed in state parliament. Depending on condition, the question was preceded by a question about the average tuition students pay in the United States (in contrast to Germany, where university education is free) or a question about the stipend Swedish students receive from their government as financial support. The German students were more supportive of an “educational contribution” when the earlier question pertained to students receiving a stipend than to students paying tuition. When later asked (in an open response format) what the alleged “educational contribution” would entail, students’ answers confirmed that they interpreted the fictitious law consistent with the common ground established by the preceding question, allowing them to provide a meaningful answer.

Similar effects have been observed in memory and eyewitness research. For example, Loftus (1975 , Experiment 4) showed participants a brief film clip and subsequently asked them questions about what they saw. For some participants, the questions included, “Did you see the children getting on the school bus?” although no school bus was shown in the film. One week later, these participants were more likely to erroneously remember having seen a school bus than those who were not exposed to the misleading question. Such findings are usually interpreted as indicating that “a presupposition of unknown truthfulness will likely be treated as fact, incorporated into memory, and subsequently ‘known’ to be true” ( Dodd & Bradshaw, 1980 , p. 695). However, later studies suggest that the observed effects are most pronounced in supposedly cooperative research situations. Observers of legal proceedings, for example, may suspect that communicators try to introduce information that favors their own side. Testing whether this awareness limits the impact of leading questions, Dodd and Bradshaw (1980) found biasing effects of questions about an observed car accident when the source of the question was the researcher, but not when the source was said to be the defendant’s lawyer (Experiment 1) or the driver of the car who caused the accident (Experiment 2). The biasing influence of leading questions was “canceled by attributing the verbal material to a source that may be presumed to be biased” ( Dodd & Bradshaw, 1980 , p. 701). Similarly, V. Smith and Ellsworth (1987) only found biasing effects of leading questions when the questioner was assumed to be familiar with the event that participants had witnessed, but not otherwise.

Research on impression formation reiterates this theme. Studying incrimination through innuendo (Bell, 1997 ), Wegner and colleagues (1981) observed that many readers infer from media questions of the type “Is Jane using drugs?” that there must be some reason to suspect so. Similarly, learning that someone asked John, “What would you do to liven things up at a party?” becomes conjectural evidence that John is probably an extrovert ( Swann et al., 1982 ). However, people’s judgment is not affected by the question asked when they learn that the question has been drawn from a fishbowl, thus undermining the implicit guarantee of relevance.

These illustrative findings from attitude research, memory research, legal psychology, and person perception converge on the conclusion that theoretical accounts of the impact of leading questions need to take the assumed cooperativeness of the questioner into account. By itself, the information conveyed by the leading question is not sufficient to affect people’s judgments or recollections. People draw on the presuppositions if they can assume that the speaker has access to the relevant knowledge and is a cooperative communicator who provides information that is informative, truthful, relevant, and clear. Hence, leading questions are more influential in research settings than in settings where people suspect “that the interrogator does not know the facts and is likely to have reasons to mislead” ( Dodd & Bradshaw, 1980 , p. 696). This suspicion can even attenuate the impact of misleading information when it is evoked after the message has already been processed (e.g., Echterhoff et al., 2005 ), in contrast to the more common observation that corrections after encoding are relatively futile (for a review, see Lewandowski et al., 2012 ).

Social Orientations: Trust and Distrust

Not surprisingly, people are more likely to cooperate when they trust their interaction partner than when they do not ( Christakis, 2019 ). Even minor manipulations of distrust, such as exposure to an incidental fishy smell, can reduce cooperation in economic trust games ( S. W. S. Lee & Schwarz, 2012 ; for a review of the link between smell and trust, see Schwarz & Lee, 2019 ). If the assumption of cooperative conversational conduct is essential for the influence of misleading questions, incidental experiences of distrust may mirror the above observation that awareness of an adversarial relationship curtails the impact of misleading information. Empirically, this is the case. For example, when asked, “How many animals of each kind did Moses take on the Ark?,” most American participants answer “two,” despite knowing that the biblical actor was Noah and despite being instructed to mark the question as faulty when there’s something wrong with it (e.g., Song & Schwarz, 2008 ). Inducing suspicion through exposure to a fishy smell increases error detection in this paradigm from 16.7% under neutral conditions to 41.9% under fishy conditions ( D. Lee et al., 2015 , Experiment 1). Testing the influence of distrust in Loftus and colleagues’ (1978) memory paradigm, Sheaffer et al. (2021) had participants witness a target event before they were exposed to misleading questions under conditions of a pleasant or a fishy smell. Forty-eight hours later, they observed a biasing effect on memory when the questions had been asked in the presence of a pleasant smell, but not when they had been asked in the presence of a fishy smell. These findings are consistent with the assumption that distrust invites an exploration of how things may be different than they seem (for a review, see Mayo, 2015 ), which favors disconfirmatory over confirmatory reasoning ( Mayo et al., 2014 ; D. Lee et al., 2015 , Experiment 2). Future research may fruitfully explore to what extent temporary feelings of distrust moderate other aspects of cooperative conversational conduct.

Conclusions

The reviewed examples show that people are more likely to draw on contextual information when it comes with the guarantee of relevance that characterizes cooperative conversational conduct. Under these conditions, it is the recipient’s task to make sense of an utterance and to draw on its implications for the ongoing exchange, which fosters reliance on misleading implicatures (as in the above examples) and the acceptance of meaningless claims (e.g., Lin et al., 2022 ). When recipients doubt the communicator’s cooperativeness, the influence of potentially misleading contextual information is attenuated or eliminated. This contingency implies that participants’ inferences involve consideration of the interlocutor’s intentions and credibility, in contrast to what their more common characterization as mindless “top-of-the-head” ( Taylor & Fiske, 1978 ) phenomena would suggest.

The Conversational Relevance of Irrelevant Information

People’s attempts to make sense of their interlocutor’s utterances also contribute to another troublesome aspect of human judgment: our apparently poor ability to distinguish relevant from irrelevant information. As numerous experiments illustrate, people rely on nondiagnostic individuating information at the expense of more diagnostic base rate information, ignore situational influences in explaining social behavior, are unduly influenced by surface characteristics of the tasks presented to them, and are easily misled by suggestive questions (for reviews, see Gilovich et al., 2002 ; Ross & Nisbett, 1991 ; Sudman et al., 1996 ). A conversational analysis again suggests that these findings are, in part, driven by the research procedures used. By presenting logically irrelevant information in a context that suggests otherwise, researchers render the information conversationally relevant, and participants do their best to make sense of it. When this misunderstanding is avoided, the otherwise obtained errors and biases are attenuated or eliminated, again indicating that they are not top-of-the-head phenomena but based on extensive inferences about the interlocutor’s intentions.

Reliance on Nondiagnostic Information

Kahneman and Tversky’s (1973) classic experiments on base rate neglect provide a prime example of procedures that generate dramatic effects by rendering normatively irrelevant information conversationally relevant. In one task, they told participants that Jack “shows no interest in political and social issues and spends most of his free time on his many hobbies which include home carpentry, sailing, and mathematical puzzles.” Based on this description, their participants predicted that Jack is most likely an engineer, independent of whether the base rate probability for any person being an engineer was .30 or .70. To warrant this insensitivity to base rate information, the person information would need to be highly diagnostic. Hence, a common conclusion from this line of work is that people do not understand probability, neglect base rates, and fail to consider the diagnosticity of information. A closer look at Kahneman and Tversky’s instructions suggests another possibility (emphases added):

A panel of psychologists have interviewed and administered personality tests to 30 (resp., 70) engineers and 70 (resp. 30) lawyers, all successful in their respective fields. On the basis of this information, thumbnail descriptions of the 30 engineers and 70 lawyers have been written. You will find on your forms five descriptions, chosen at random from the 100 available descriptions. For each description, please indicate your probability that the person described is an engineer, on a scale from 0 to 100. The same task has been performed by a panel of experts who were highly accurate in assigning probabilities to the various descriptions. You will be paid a bonus to the extent that your estimates come close to those of the expert panel .

These instructions inform participants that the person description was prepared by psychologists, based on standard tools of their trade. Moreover, other experts—most likely psychologists as well—are said to be highly accurate in making the prediction and participants will receive a bonus when their judgment approximates that of those experts. Next, participants are asked to judge five target persons for whom different person descriptions are presented, whereas the base rate information remains the same. This further suggests that the person information is relevant, because without it, all tasks would have the same solution. These elements of the instructions and procedures converge on rendering the normatively irrelevant person information conversationally relevant.

When the conversational relevance assumption is called into question, the classic finding of base rate neglect is attenuated or eliminated. To vary the conversational relevance of the person information, Schwarz, Strack, et al. (1991 , Experiment 1) provided some participants with the original instructions but told others that the (identical) person description was compiled by a computer that drew a random sample of information from the database that the psychologists had compiled. Replicating the classic finding, those who received the original instructions estimated the likelihood of the target being an engineer as .76, despite a low base rate of .30. But when the same information was allegedly selected by a computer, the likelihood estimate dropped to .40. These findings highlight that participants’ reliance on individuating information at the expense of base rate information reflects their assumption that the experimenter is a cooperative communicator who presents information that is relevant to the task at hand. When that assumption is called into question, base rate neglect is strongly attenuated.

In related work, Krosnick et al. (1990) observed that participants were more likely to use base rate information when this information was presented after rather than before the individuating information. They suggested that those who first receive base rate information and are subsequently provided with individuating information may reason, “The first piece of information I was given (i.e., the base rate) has clear implications for my judgment, so it was sufficient. A speaker should only give me additional information if it is highly relevant and informative, so the experimenter must believe that the individuating information should be given special weight in my judgment” ( Krosnick et al., 1990 , p. 1141). Measures of participants’ reasoning process supported this interpretation. Other work identified numerous additional pragmatic (e.g., Macchi, 1995 ; Zukier & Pepitone, 1984 ) and procedural complexities of the literature on base rate neglect, leading Koehler (1996 , p. 1) to conclude after an extensive review that “we have been oversold on the base-rate fallacy in probabilistic judgment from an empirical, normative, and methodological standpoint.”

Violations of cooperative conversational conduct also contribute to other classic findings of the heuristics-and-biases tradition, including the conjunction fallacy ( Kahneman & Tversky, 1982 ). In the famous Linda problem, participants are provided with individuating information about Linda’s personality and engagement with social justice issues before they are asked what is more likely: that Linda is a bank teller or that Linda is a bank teller and active in the feminist movement. Logically, the unconstrained category “bank teller” has a higher probability than the conjunction “bank teller” + “active in the feminist movement”—feminist bank tellers are a subset of bank tellers. But if the answer is obvious on logical grounds, why was detailed information about Linda provided in the first place? Paralleling the above discussion of base rates, providing extensive individuating information about Linda’s personal history indicates that the experimenter considers it important. By drawing on that information, participants fall prey to the conjunction fallacy and select “Linda is a bank teller and active in the feminist movement” as the more likely outcome (for related discussions, see Adler, 1984 ; Dulany & Hilton, 1991 ; Hertwig & Gigerenzer, 1999 ; C. Lee, 2006 ; Politzer & Macchi, 2000 ; Politzer & Noveck, 1991 ).

Similar considerations apply to many classic findings outside the heuristics-and-biases tradition. For example, numerous studies suggest that people account for an actor’s behavior in terms of the actor’s dispositions, even under conditions where the actor had no choice and merely complied with situational constraints (for reviews, see Jones, 1990 ; Ross & Nisbett, 1991 ). This “fundamental attribution error” ( Ross, 1977 ) again seems to illustrate insufficient attention to the diagnosticity of information. But when asked, people are aware that situational constraints render behaviors uninformative for inferences about the actor. For example, Miller and colleagues’ (1984) participants reported that the position taken in an essay tells us little about the author’s opinion when the position was assigned by the experimenter—and right after acknowledging this ambiguity, they proceeded to base dispositional inferences on that very essay. They presumably assumed that “the experimenter believes that the essay has some diagnostic value (otherwise, why were they given the essay?),” as Wright and Wells (1988 , p. 184) put it. Dispositional inferences are attenuated when participants are told that their information packages were randomly selected from a larger pool and may not always include information pertinent to the specific questions asked ( Wright & Wells, 1988 ). Other research showed that adding nondiagnostic information dilutes the impact of diagnostic information, indicating that people consider both types of information and fail to appropriately weight the inputs by their diagnosticity ( Nisbett et al., 1981 ; Zukier, 1982 ). Again, this dilution effect is attenuated or eliminated when the conversational guarantee of relevance is called into question ( Igou & Bless, 2005 ; Tetlock et al., 1996 ). In the absence of such manipulations, merely motivating people to think more carefully by making them accountable for their judgments increases the dilution effect (Tetlock et al., 1996 ).

As these examples illustrate, some of the more dramatic shortcomings of human judgment reflect, in part, a violation of conversational norms by the researcher, rather than inherently flawed reasoning. Participants have no reason to assume that the researcher would intentionally provide information that is uninformative and irrelevant to the task at hand; hence, they try to make sense of it. Once the implicit guarantee of relevance is called into question, the impact of normatively irrelevant information is reduced.

The Conversational Relevance of Terminological Choices

On logical grounds, substantively equivalent information should result in substantively equivalent inferences, independent of the form in which the information is presented. Violating this principle of rationality, the form in which equivalent information is presented frequently moderates its impact (see also the section “Conversational Implications of Methodological Choices”). From a conversational perspective, this presumably reflects that people consider their interlocutors’ choice of expression informative (for a related discussion, see Sher & McKenzie, 2006 ). If so, the influence of differential expressions should be attenuated or eliminated when this assumption is called into question. Inferences from quantitative expressions can illustrate this principle.

Quantities can be expressed at different levels of granularity; for example, “1 year” can also be described as “12 months,” “52 weeks,” or “365 days.” Cooperative speakers should choose the granularity that is relevant to the purpose of the conversation (maxim of relation) and easy to understand (maxim of manner) while providing the relevant level of detail, that is, neither more nor less detail than needed (maxim of quantity). Empirically, communicators as well as recipients observe these tacit norms. Compared to coarse expressions, fine-grained expressions are more likely to be used when communicators have the relevant detailed knowledge and are confident in what they convey ( Yaniv & Foster, 1995 ). Recipients, in turn, consider fine-grained expressions more precise and are more likely to rely on the information they convey in judgment and choice ( Zhang & Schwarz, 2012 ). For example, consumers are more likely to believe that a gadget’s battery charge will last for the promised duration when the promise is made in fine-grained (“120 min”) rather than coarse units (“2 hr”; Zhang & Schwarz, 2012 , Experiment 4). Such inferences from granularity are eliminated when the cooperativeness of the communicator is called into question, for example, because the communicator lacks the relevant knowledge ( Zhang & Schwarz, 2012 , Experiment 2) or is not trustworthy (Experiment 3).

Just as the choice of a more granular unit conveys higher precision, a more precise numerical value entails higher granularity. For example, a time measurement of 2 hr 48 min 2.92 s is more precise and more granular than a measurement of 2 hr 48 min. As Janiszewski and Uy (2008) observed, more precise numbers elicit stronger anchoring effects on subsequent estimates (see also, M. Thomas et al., 2010 ). Again, the influence of numerical precision is eliminated when the communicator’s cooperativeness ( Zhang & Schwarz, 2013 ) or knowledge ( Loschelder et al., 2016 ) is called into question.

Two aspects are worth noting. First, the granularity of a quantitative expression can influence a host of different variables, from the perceived reliability of the number to the perceived credibility and expertise of the communicator, even when the actual quantity remains the same. Experimenters sometimes attempt to reduce eye-catching similarities in the description of choice alternatives by varying the units in which attributes are described. By doing so, they inadvertently also vary the perceived relative diagnosticity of the descriptions. Second, the influence of numerical expressions on judgment and choice is usually explained in terms of theories of numerical cognition, which focus on the representation of numbers (for a review, see M. Thomas & Morwitz, 2009 ). But numerical expressions are chosen and conveyed by a communicator and their impact depends on whether that communicator is perceived as observing the norms of cooperative conversational conduct. We cannot understand how numerical expressions influence the mind without taking these communicative dynamics into account.

The lesson from these and related studies is simple: Presenting normatively irrelevant information makes it conversationally relevant. People try to make sense of the information when they can assume that the communicator is cooperative, but discount it otherwise. While the resulting judgment phenomena are robust under most laboratory conditions, their observation does not necessarily imply a cognitive shortcoming in the form of poor discrimination between relevant and irrelevant information—we cannot tell without attending to the communicative component of the process. The methodological implications depend on what we want to know. If we want to assess whether people can assess the diagnosticity of information, conditions under which cooperative communication is not assumed will provide more insight. If we want to assess when people do attend to the diagnosticity of information, variations in perceived cooperativeness are a crucial variable. As is usually the case, the relationship is likely to be bidirectional, and blatantly nondiagnostic information may itself call the communicator’s cooperativeness into question.

Conversational Implications of Methodological Choices: What Participants Learn From Research Procedures

In the examples discussed so far, researchers presented normatively irrelevant information in a context that rendered it conversationally relevant. Researchers’ insensitivity to conversational processes extends to many other aspects of the research situation, with numerous unintended consequences. I review some illustrative examples.

Informed Consent

The first thing research participants encounter is an invitation to participate in a study. It usually includes a carefully worded description of the study that aims to convey that it is important research, conducted at a credible institution, without disclosing any details that may influence participants’ self-selection into the study or their later answers. It will also include information mandated by the institutional review board that informs participants that their participation is voluntary and can be terminated whenever they feel uncomfortable. They are further assured that only the researchers will have access to their data and that the data’s confidentiality will be protected to the limits allowed by the law.

Researchers usually pay little attention to their institutional review board’s boilerplate text. But from a Gricean perspective, communicators should only provide information that is relevant to the task, not more and not less. Hence, the boilerplate implies that there is some reason for all these assurances or they would not be made. If all questions were innocuous and I would not mind if others learned what I said, there would be little reason to assure me that the privacy of my answers will be protected. Hence, routine assurances of privacy, confidentiality, and low risk may prompt concerns rather than reduce them. Singer et al. (1992) found that this is the case: The more the informed consent form assured confidentiality, the more potential participants expected that the study to which they were invited would include highly personal questions that could make them uncomfortable. They also worried that they might not want to disclose such information and that their answers might fall into the wrong hands. Not surprisingly, this impaired their willingness to participate.

A meta-analysis of survey recruitment procedures further showed that assuring confidentiality can attenuate concerns when a study addresses sensitive topics that raise such concerns, whereas boilerplate confidentiality assurances can be counterproductive when the study is innocuous ( Singer et al., 1995 ). The same is likely to apply to many other assurances—making them implies that there is a need for them or else their provision would violate the norms of cooperative conversational conduct.

Researcher Affiliation

In natural conversations, the inferred meaning of an utterance can change with our background knowledge about the speaker. The question “Can you read this?” requires a different response when posed by an ophthalmologist compared to a traffic cop. The same applies to research situations, where the researcher’s affiliation can provide important clues. For example, Norenzayan and Schwarz (1999) asked participants to explain a mass shooting at a Michigan post office, described in a New York Times clipping. The otherwise identical questionnaire was printed on the letterhead of an “Institute for Personality Research” or an “Institute for Social Research.” As expected, participants’ open-ended explanations entailed a greater emphasis on personality variables or on social and organizational variables depending on whether they thought the researcher was a personality psychologist or a social scientist. This effect was independent of whether participants read the news clipping before or after they were exposed to the researcher’s letterhead, indicating that it occurred when participants generated their explanations in response to the questions asked, not when they encoded the original clipping. In an extended replication, Kemmelmeier (2014 , Experiment 1) asked participants to list reasons that can explain the action or reasons that cannot explain the action. In both cases, participants tailored their answers to the researcher’s inferred epistemic interest. Moreover, the extent of tailoring was unrelated to individual differences in social desirability. Similarly, participants who were asked to complete “I am …” statements were more likely to provide social completions (e.g., ethnic identity, party affiliation) when the researcher’s affiliation was an Institute for Political Research rather than an Institute for Personality Research ( Norenzayan & Schwarz, 2006 ), whereas family-related completions dominated when the researcher was identified as a family researcher ( Kemmelmeier, 2014 , Experiment 2).

These findings reflect that participants focus on variables that seem relevant to the researcher’s inferred epistemic interest. Hence, person-related variables loom larger in explanation and self-description when a study is identified as a “psychology” study. If so, the fundamental attribution error ( Ross, 1977 ) and other markers of lay dispositionism ( Ross & Nisbett, 1991 ) may loom larger in psychology experiments than in other settings. This is particularly likely when the questions allow for many interpretations, as is the case with open-ended requests.

Open and Closed Response Formats

Question ambiguity is reduced when the question includes a list of response alternatives, which specify what the researcher is interested in. The downside of this specification is that participants will withhold answers that are not covered by the response alternatives. As an example, consider the question, “What have you done today?” Although most participants will have gotten dressed in the morning, none of them is likely to report so—it is so common that it “goes without saying” (maxim of quantity). If the response alternatives included “getting dressed,” all would check it—and if the letterhead or study introduction indicated that this is a survey of home healthcare needs, many would have volunteered it even in an open response format (for reviews of response format effects, see Schwarz & Hippler, 1991 ; Tourangeau et al., 2000 ).

That people take the recipient’s epistemic interest into account when interpreting questions and formulating answers contributes to many phenomena that social cognition researchers usually attribute solely to knowledge accessibility. While people will obviously not draw on information that is not accessible, which of several accessible pieces of information they use will often depend on their interpretation of the task. What the interlocutor probably wants to know looms large in that interpretation.

Frequency Scales and Reference Periods

Participants are often asked to report on the frequency of their behaviors, thoughts, and feelings by checking a value on a frequency scale. While the researcher considers the scale a mere measurement device, participants draw on its specifics to determine the substantive meaning of the question. Suppose people are asked how frequently they have been “really irritated” recently. To provide an answer, they need to determine what the questioner means by “really irritated”—does the question refer to major or minor annoyances? When the response alternatives present high-frequency values, they convey that the researcher has frequent events in mind, whereas low-frequency values convey an interest in infrequent events. Because major annoyances are less frequent than minor ones, the frequency values indirectly specify which kind of annoyance is of interest. Hence, participants report on substantively different events when an identically worded question is accompanied by different frequency values ( Schwarz et al., 1988 ). The same logic holds for reference periods. Asking how often someone was “really angry yesterday” conveys an interest in events that are likely to happen on a daily basis, whereas asking the same question about “last year” conveys an interest in relatively rare events. Not surprisingly, people attempt to retrieve more extreme (and less frequent) events in the latter than in the former case ( Winkielman et al., 1998 ).

This also sheds light on the familiar observation that frequency reports for different time periods do not “add up”—people’s estimate for a month or a year is less than what one would expect by multiplying out their estimates for a day or a week ( Larsen & Fredrickson, 1999 ; D. Thomas & Diener, 1990 ). This subadditivity is usually attributed to differences in memory ( D. Thomas & Diener, 1990 ) and estimation ( Fiedler & Armbruster, 1994 ). But it also reflects that people think that the question refers to substantively different events and behaviors as the reference period shifts. Without taking this into account, the relative contribution of memory and estimation processes remains ambiguous.

The influence of frequency alternatives extends beyond question interpretation (for a review, see Schwarz, 1999 ). Participants assume that researchers construct meaningful scales based on their knowledge of the issue under investigation. From this perspective, the middle range of the scale is likely to reflect the “usual” or “average” frequency and the extremes of the scale represent the extremes of the distribution. Based on this assumption, participants use the scale as a frame of reference in estimating their own behavioral frequencies. This results in higher estimates along high- than low-frequency scales, as has been observed for a wide range of behaviors, from media consumption ( Schwarz et al., 1985 ) to physical symptoms ( Schwarz & Scheuring, 1992 ) and sexual behaviors ( Tourangeau & Smith, 1996 ). Moreover, checking one’s own behavioral frequency on a scale amounts to determining one’s position in the distribution, which provides comparison information. This, in turn, influences judgments from health satisfaction ( Schwarz & Scheuring, 1992 ) to relationship satisfaction ( Schwarz & Scheuring, 1988 ).

Such results illustrate how much research participants consider all contributions of the researcher relevant to their task, including aspects of the research instrument that are merely intended to “record” their answers. Participants draw on these features in interpreting the question and work within the parameters set up by the research instrument. So do others who read about the research results, including fellow researchers and professional users. For example, medical doctors with an average of 8.5 years of professional experience inspected patients’ symptom reports and evaluated whether the symptom frequency was sufficiently problematic to require a doctor visit. Depending on conditions, the same absolute frequency was checked on a low-frequency scale, where it was above the midrange, or on a high-frequency scale, where it was below the midrange. The physicians were more likely to be concerned, and to send the patient for a checkup, in the former than in the latter case, indicating that they drew on comparison information conveyed by the scale ( Schwarz et al., 1991 ).

Rating Scale Formats

When designing a rating scale, most researchers pay attention to the choice of verbal endpoint labels and the number of scale points (for reviews, see Dawes & Smith, 1985 ; Krosnick & Fabrigar, 1997 ). Having decided to use a 6-point scale, for example, they are less likely to worry whether those points should be represented by unnumbered boxes, by numbers running from −3 to +3, or by numbers running from 1 to 6. For example, at the University of Michigan’s Survey Research Center the preferred presentation format changed repeatedly in response to technical convenience (Charles Cannell, personal communication, 1994). Early work followed Likert’s (1932) classic use of minus and plus signs (with scales running from “−−−” to “+++”). With the advent of punch cards, this format was replaced by numbers running from −3 to +3 to reduce transcription errors. Shortly after, someone realized that one could cut the keystrokes by half by having the numbers run from 1 to 6. At no point did the display format seem relevant to question meaning, despite extensive experimentation with the number of scale points and the extremity of endpoint labels.

Unfortunately, the format matters. Consider the question, “How successful would you say you have been in life?,” accompanied by an 11-point rating scale with the endpoints not at all successful and extremely successful . A sample of German adults was asked this question with the numbers on the rating scale running either from −5 to +5 or from 0 to 10. Whereas 34% endorsed a value between 0 and 5 on a scale of 0 to 10, only 13% endorsed a formally equivalent value between −5 and 0 on a scale of −5 to +5 ( Schwarz, Knäuper, et al., 1991 , Experiment 1). On the one hand, this may reflect a self-protective desire not to label one’s success with a negative number; on the other hand, it may reflect a change in question interpretation. After all, what does “not at all successful” mean? Does it denote the absence of noteworthy success (“I didn’t do anything great”) or the presence of failure (“I really messed up”)? Subsequent questions showed that participants’ interpretation of “not so successful” resembled “I didn’t do anything great” when combined with 0, but “I messed up” when combined with −5. In light of these interpretations, more people reported not having done anything great than reported having messed up. In a follow-up experiment, participants inferred that a student had failed on twice as many exams when he checked a −3 on a scale of −5 to +5 for academic satisfaction than when he checked a 2 on a formally equivalent scale of 0 to 10 ( Schwarz Knäuper, et al., 1991 , Experiment 3).

In general, a format with only positive numbers (or its graphical equivalent; Tourangeau et al., 2007 ) conveys that the researcher has a unipolar dimension in mind, where different values indicate different degrees of the same attribute, no matter whether this attribute is success in life, as in the above example, or the sweetness of orange juice ( Mantonakis et al., 2017 ). In contrast, a minus-to-plus format (or its graphical equivalent; Tourangeau et al., 2007 ) conveys that the researcher has a bipolar dimension in mind, where the two endpoints refer to opposite attributes. Participants are most likely to consider the scale format when the verbal labels are ambiguous and their own processing motivation is high. Hence, people who habitually engage in more thought (i.e., those high in need for cognition) are more influenced by the numeric values than those who do not ( Yan, 2006 ).

Once made, the ratings themselves can serve as inputs into subsequent judgments and tasks ( Carlston, 1980 ). For example, Haddock and Carrick (1999) found that the British prime minister Tony Blair received more favorable trait ratings along a bipolar than a unipolar scale. These favorable trait ratings then carried over to more favorable predictions of Blair’s future political performance. Similarly, Mantonakis and colleagues (2017) found that consumers rated a sample of orange juice as sweeter and fruitier on bipolar than on unipolar scales. More important, once they had given the sampled juice a “sweeter” rating, they were more likely to misidentify it when the taste test was repeated, erroneously picking a sweeter beverage as the one they had just tasted a few minutes earlier.

Several processes are likely to contribute to these downstream effects of rating scale formats. Because the scale format influences the interpretation of the initial question, it is likely to influence what people access and focus on to answer the question, resulting in differential representations of the target. Moreover, the rating they just provided remains accessible for a while and may itself serve as input into subsequent judgments without requiring changes in the target representation. Finally, once reported, the rating becomes part of the cumulative common ground, which increases the likelihood that it will be considered in interpreting and answering subsequent questions. To date, the relative contribution of these assumed component processes has not been examined.

Attention Checks

As part of many studies, research participants will also encounter a conversationally peculiar type of question known as an “attention check” or “instructional manipulation check” ( Oppenheimer et al., 2009 ). For example, a question may present a list of common sports and ask participants to check all sports they play. While the question, often printed in bold, seems straightforward, the introduction to the question asks participants to ignore its content. Instead, they are asked to check “other” and type into an answer box, “I read the instructions.” This is intended to identify participants who do not pay attention to instructions and may provide superficial answers. Inattentive participants can contribute substantial noise to a data set, which can be reduced by excluding their answers (e.g., Oppenheimer et al., 2009 ; Paolacci et al., 2010 ).

However, such attention checks also teach participants that the researcher’s questions should not be taken at face value—the researcher is presenting “trick questions” and attempting to lead them astray, which indicates that the researcher is not a cooperative communicator. This insight may increase participants’ vigilance when answering subsequent questions, paralleling the effects of distrust discussed earlier. Empirically, this is the case. Hauser and Schwarz (2015 , Experiment 1) presented participants with reasoning tasks from Frederick’s (2005) Cognitive Reflection Test, where intuitive responding leads to different answers than careful analytic thought. Depending on condition, participants answered the Cognitive Reflection Test before or after answering an attention check. As expected, participants who passed the attention check before they worked on the reasoning tasks were more likely to solve the problems correctly than participants who passed the same attention check after solving the reasoning tasks. Encountering an attention check first also improved participants’ performance on a probabilistic reasoning task ( Hauser & Schwarz, 2015 , Experiment 2). Note that these performance differences cannot be attributed to chronic differences in attention or conscientiousness—all participants included in these comparisons across task order passed the attention check, which was merely presented before versus after the dependent variable. Hence, the improvements reflect that attention checks alert participants that the researcher’s questions should not be taken at face value, which increases vigilance on subsequent tasks.

Follow-up studies explored how far this vigilance extends. On the one hand, realizing that one may encounter trick questions may increase attentiveness to other questions that fit the trick question category, which holds for the tasks used by Hauser and Schwarz (2015) . On the other hand, trick questions entail that the researcher is not a cooperative communicator, which may affect participants’ reliance on other attributes of the research instrument in determining their task. To shed light on this issue, Hauser and colleagues (2016) tested the impact of attention checks on a number of conversational phenomena observed in self-report research, including the influence of response alternatives and scale formats (discussed above) and the influence of context questions (discussed below). Their results suggest that attention checks have a negligible influence on these conversational effects and are most likely to affect performance on tasks that can be perceived as trick questions. This is the case for many reasoning and inference tasks used in judgment and decision research. To avoid the vigilance effect of trick questions, it is advisable to assess attention with study-specific factual questions that test participants’ comprehension of key elements of the materials presented to them after the dependent variables have been collected (for helpful best practice recommendations, see Hauser et al., 2019 ).

As these examples illustrate, research participants treat all contributions of the researcher as part of the ongoing conversation, including supposedly “formal” features of the research instrument. They draw on these contributions to make sense of their task, as reflected in the reviewed results and in think-aloud protocols ( Galasiński & Kozłowska, 2013 ). Reliance on this contextual information is compounded when there is little opportunity to ask for clarification, as is the case in online studies, or when a well-trained experimenter repeats the verbatim instructions. The observed effects reflect not mindless responding, but a thoughtful use of contextual information to provide an informative answer. Hence, the influence of formal characteristics of research instruments increases with the relevance of the task and participants’ sophistication and thoughtfulness.

Giving Informative Answers: Conversational Determinants of Information Use

A core principle of social cognition holds that people truncate an information search as soon as enough information has come to mind to form a judgment with sufficient certainty ( Bodenhausen & Wyer, 1987 ). Hence, the most accessible information exerts a disproportionate influence. Because a key determinant of accessibility is recency of use, information that has just been brought to mind by a preceding task is particularly likely to influence judgment on subsequent tasks (for reviews, see Fὅrster & Liberman, 2007 ; Higgins, 1996 ). What is often overlooked is the role of conversational processes. As discussed in the section “Cooperative Communication,” cooperative conversational conduct entails that the contributions to an ongoing conversation are cumulative and should be meaningfully related to one another. Accordingly, cooperative communicators deliberately draw on their own and their interlocutor’s previous utterances when tailoring the next contribution. Social cognition researchers have neglected this element of deliberate information use in favor of an emphasis on unintentional information use. This emphasis appropriately captures the influence of incidental exposure to information, for example, in an allegedly unrelated experiment (e.g., Higgins et al., 1977 ) or through television news (e.g., Iyengar, 1990 ). But as seen in the preceding sections, much of what happens in experiments and surveys resembles conversations in natural contexts, where each turn in a conversation contributes to a shared common ground. Hence, the use and disuse of information that was rendered accessible by an earlier contribution is not necessarily a function of its mere accessibility and applicability. It is also driven by the logic of cooperative communication. This is most apparent when conversational influences discourage the use of accessible information and hence attenuate or eliminate the effects that models of information accessibility would predict.

One condition that reduces interlocutors’ reliance on previously used information is a change in topic that signals that the common ground established with regard to the previous topic may no longer be applicable. Hence, explicitly drawing attention to changes in topic (“Now let’s turn to something else”) has been found to attenuate question order effects (for a discussion, see Sudman et al., 1996 ; Wänke & Schwarz, 1997 ). Another condition that attenuates reliance on previously shared information is the expectation that interlocutors’ contributions should provide the information needed and neither more nor less. Hence, they should not reiterate information that the recipient already has or may take for granted ( Clark & Clark, 1977 ). In everyday life, we commonly observe this norm of nonredundancy. Suppose a resident of Manhattan Beach, a little town in Southern California, is asked, “Where do you live?” When asked in Paris, “In the United States” is a perfectly fine answer; when asked in New York, that answer would be odd and “California” will seem more appropriate; when asked in Los Angeles, the answer may be “Manhattan Beach”; and when asked in Manhattan Beach, it may be the street address—but answering “328 Strand” would be inappropriate when asked in any other location. That such examples seem trivial illustrates the routine observation of Gricean maxims in daily life. Unfortunately, this observance is not routine in research situations, where the same process produces “anomalies” from the perspective of information processing models.

Repeated Questions and Changing Interpretations

The expectation that speakers provide new information and refrain from reiterating information the recipient already has has important implications for the interpretation of questions that are highly similar in content or even repeated literally. Unless there is reason to believe that the questioner did not understand the answer already given, the person asked is likely to interpret the second question as a request for new information. As an example, consider the procedures used to assess whether children have mastered number conservation ( Piaget, 1952 ). In a typical study, a child is shown two rows of objects, equal in number and aligned in one-to-one correspondence. When asked, “Is there more here or more here, or are both the same number?,” most children answer that both rows are the same in number. Next, the experimenter rearranges the objects in one of the rows to extend the row’s length. Following this transformation, the previously asked question is repeated. Many young children now respond that there are more objects in the longer row, suggesting that they did not master number conservation. Given that only the perceptual configuration of the objects has changed, the children’s answers are assumed to show their susceptibility to perceptual influences.

A conversational analysis suggests a different account. When initially asked, most children answered the experimenter’s number question correctly. Next, the experimenter herself changed the perceptual configuration without changing the number of objects. Surely the experimenter is aware that she did not add or take any objects—so what can the repeated question refer to? Perhaps she is talking about whether one row looks larger? Testing this possibility, Rose and Blank (1974) asked some children the number question before and after the experimenter changed the arrangement, whereas other children received the same question only once, after having watched the experimenter change the arrangement. As usual, many of the children in the standard condition reported that the number is the same when the rows were of equal length but changed their answer after the experimenter manipulated the appearance of the rows. In contrast, most of the children who were only asked after the experimenter had changed the appearance of the rows reported that the number is the same (see also Siegal et al., 1988 ). Such findings show that it is not children’s confusion of number and length that leads them astray. Having already answered the number question, the children assume that the experimenter must have something else in mind when the question is reiterated—apparently, the experimenter wants to talk about what she did, thus changing the reference of the question from number to length. This shift in question interpretation should not occur under conditions where repeating the same question makes sense. Testing this prediction, McGarrigle and Donaldson (1974) introduced a “naughty teddy bear,” who tried to “spoil the game” by rearranging the objects. This allowed the experimenter to repeat the previously asked question to learn what the “naughty teddy” had done. Whereas only 16% of the children showed number conservation when the experimenter herself manipulated the length of the row, 62% did when the change was caused by “naughty teddy” (see also Dockrell et al., 1980 ; Light et al., 1979 ).

Adults have similarly been observed to change, or not to change, their interpretation of highly similar questions as a function of the conversational context. For example, Strack et al. (1991 , Experiment 2) asked German students to rate their happiness and satisfaction with life as a whole along 11-point scales (11 = very happy or very satisfied , respectively). In one condition, both questions were presented at the end of the same questionnaire and were introduced by a joint lead-in that read, “Now, we have two questions about your life.” In the other condition, only the happiness question was presented at the end of the questionnaire, introduced by a parallel lead-in: “Now, we have a question about your life.” The subsequent rating of life satisfaction, however, was presented as the first question in a new and ostensibly unrelated questionnaire about characteristics of research participants, attributed to a different researcher.

People usually perceive happiness and satisfaction as closely related concepts and both judgments are affected by the same variables in studies of subjective well-being (for a review, see Schwarz & Strack, 1999 ). But when both questions are presented as part of the same conversational context, interpreting them as (nearly) identical in meaning would result in considerable redundancy. Hence, participants may infer that the researcher intends both questions to tap different aspects of their subjective well-being and may draw on different aspects of their lives in making their judgments. This redundancy concern does not arise when the questions are asked by two different communicators, who may simply use somewhat different words to refer to the same thing. Moreover, providing the same answer to two different recipients would not violate the norm of nonredundancy. Accordingly, the answers to the happiness and satisfaction questions should be more similar when the questions are asked by different researchers than when they are asked by the same researcher. Empirically, this was the case. When both questions were asked in ostensibly unrelated questionnaires, the mean reports of happiness ( M = 8.0) and satisfaction ( M = 8.2) did not differ and both measures correlated, r = .96. But when both questions were presented as part of the same conversation, participants differentiated between happiness ( M = 8.1) and satisfaction ( M = 7.4) and the correlation dropped to r = .75.

Conversational Redundancy and the Use of Accessible Information: Specific–General Question Sequences

Redundancy concerns can also arise when questions of differential generality are posed. Suppose, for example, that you have just reported on your marital satisfaction. Immediately afterward, you are asked how satisfied you are with your life as a whole. Should you include the quality of your marriage in your overall assessment, or should you disregard that aspect of your life, given that you just reported on it? Several studies show that the answer depends on whether both questions are perceived as part of the same conversational context.

Strack et al. (1988 , Experiment 2) asked American college students to report their general life satisfaction as well as their dating frequency. When the life satisfaction question preceded the dating frequency question, the correlation was weak, r = −.12, and not significant. Reversing the question order, however, increased the correlation to r = .66. This presumably reflects that the dating frequency question increased the accessibility of dating-related information, which was then used in evaluating one’s life in general. In a third condition, the two questions were introduced with a joint lead-in: “Now we would like to know about two aspects of life that may be important for students’ overall wellbeing: (a) dating and (b) happiness with life in general.” With this introduction, the correlation in the dating–life order dropped from r = .66 to a nonsignificant r = .15, suggesting that participants ignored the information they had already provided when a joint lead-in evoked the Gricean norm of nonredundancy.

A follow-up study with German participants provided more direct support for this interpretation. Schwarz, Strack, and Mai (1991) asked a community sample to report their satisfaction with their marriage or romantic relationship as well as their general life satisfaction, varying the order in which both questions were asked. When the life satisfaction question preceded the marital satisfaction question, both measures were moderately correlated, r = .32. Reversing the question order increased the correlation to r = .67. Explicitly asking participants to consider their marriage when reporting on their lives in general resulted in a comparable correlation of r = .61. In a fourth condition, both questions were introduced by a joint lead-in that read, “We now have two questions about your life. The first pertains to your marital satisfaction and the second to your general life satisfaction.” Under this condition, the same question order produced a low and nonsignificant correlation of r = .18, suggesting that the participants deliberately ignored marriage-related information. If so, explicitly asking participants how satisfied they are with “other aspects” of their lives, “aside from their marriage,” should lead to a similarly low correlation. This was the case, r = .20. In addition, respondents who were induced to disregard their marriage in evaluating their life as a whole, either by the conversational context manipulation or by explicit instructions, reported higher mean life satisfaction when they were unhappily married and lower mean life satisfaction when they were happily married compared to respondents who were not induced to exclude their marriage from consideration.

Because individuals in a temporary or chronically interdependent mindset attend more to the communicative context than individuals in a temporary or chronically independent mindset ( Adair et al., 2016 ; Gudykunst et al., 1996 ; Wolgast & Oyserman, 2020 ), the observed diverging influence of information accessibility and conversational redundancy can result in pronounced cultural differences that invite potentially misleading substantive interpretations of participants’ reports. For example, Haberstroh and colleagues (2002 , Experiment 2) asked students in Germany and China how satisfied they are with their academic lives and with their lives as a whole. When the general life satisfaction question preceded the academic satisfaction question, the correlation was r = .53 in Germany and r = .50 in China, suggesting that academic satisfaction is similarly important to students in both countries. But when the question order was reversed, correlations of r = .78 in Germany and r = .36 in China emerged, apparently suggesting that academic satisfaction is more important for German students’ than for Chinese students’ well-being. From a conversational perspective, the German students failed to notice the redundancy issue in the absence of a joint lead-in and included the accessible information about their academic lives in their general judgment, whereas the Chinese students noticed the redundancy and excluded the information they had already provided. Compatible with this interpretation, interdependence priming increased German students’ sensitivity to conversational redundancy ( Haberstroh et al., 2002 , Experiment 1), paralleling Wolgast and Oyserman’s (2020) finding that interdependence priming improves German participants’ ability to take the perspective of their interlocutor when talking about visual stimuli. Finally, individuals who habitually enjoy engaging in thought are more likely to adjust their answers than those who do not, resulting in higher responsiveness to the conversational context among participants high in need for cognition ( McCabe & Brannon, 2004 ).

The Case of Multiple Specific Questions

Importantly, the applicability of the norm of nonredundancy varies as a function of the amount of specific information that has already been exchanged. In the case of question order effects, information that has been provided in response to a single specific question is excluded from consideration when answering a subsequent general question. Suppose, however, that several specific questions precede the general one. For example, participants may be asked to report on their marriage, their job, and their leisure time before reporting on their general life satisfaction. In that case, two alternative interpretations are compatible with the norm of nonredundancy. One interpretation treats the general question as a request to consider still other aspects of their life, much as if it were worded, “Aside from what you already told us, …” Another interpretation treats the general question as a request to integrate the previously reported aspects into an overall judgment, much as if it were worded, “Taking these aspects together, how satisfied are you with your life as a whole?” This interpretational ambiguity of the general question does not arise if only one specific question was asked—“taking all aspects together” makes little sense when only one aspect was addressed. If several specific questions have been asked, however, an integrative judgment is informative because it does provide “new” information about the relative importance of the respective domains, which are in the focus of the conversation. Moreover, “summing up” at the end of a series of related thoughts is acceptable conversational practice, whereas there is little to sum up if only one thought was offered.

To address these possibilities, some participants in Schwarz, Strack, and Mai’s (1991) study were asked three specific questions, pertaining to their leisure time satisfaction, their job satisfaction, and, finally, their marital satisfaction. In this condition, the correlation between marital satisfaction and life satisfaction increased from r = .32 to r = .46 when answering the specific questions first brought information about one’s marriage to mind. More important, introducing the three specific and the general question with a joint lead-in did not reduce the emerging correlation, r = .48, indicating that participants adopted a “taking-all-aspects-together” interpretation. This conclusion is supported by a highly similar correlation of r = .53 when the general question was reworded to request an integrative judgment, compared to r = .11 when the reworded question required the consideration of other aspects of one’s life.

The reviewed research has important implications for the use of accessible information. Because information search is truncated when “enough” information has come to mind, the information that is most accessible at the time usually exerts a disproportionate influence, independent of whether it comes to mind because it was addressed in earlier questions (as in the above examples), mentioned in the study’s introduction ( D. M. Smith et al., 2006 ), is a focal attribute of the target ( Schkade & Kahneman, 1998 ), or is particularly salient in the current situation ( Oishi et al., 2003 ). The impact of a given input decreases as the amount and accessibility of competing information increases (for reviews, see Bless & Schwarz, 2010 ; Bless et al., 2003 ), for example, because multiple aspects were addressed in earlier questions ( Schwarz et al, 1991 ; S. M. Lee et al., 2016 ) or are chronically accessible because of personality characteristics or enduring life concerns ( Schimmack & Oishi, 2005 ). However, highly accessible information is not always used, even when it is applicable ( Higgins, 1996 ) and relevant to the judgment at hand. This is the case when the accessible input has already been reported on in the same conversation, thus rendering its repeated use redundant with what has been conveyed earlier (e.g., Schwarz et al., 1991 ; Strack et al., 1988 , 1991 ). People who are chronically or temporarily in an interdependent mindset are more sensitive to the conversational context than people in an interdependent mindset ( Lin et al., 2022 ; Wolgast & Oyserman, 2020 ), which increases their compliance with the norm of nonredundancy ( Haberstroh et al., 2002 ). In addition, telling participants explicitly that all questions are unrelated and taken from different studies may impair the development of a cumulative common ground that takes preceding information into account. For example, question order effects were not observed in a multilab study that included 28 different tasks along with an introduction that assured participants that all questions asked are unrelated ( Klein et al., 2018 ). This possibility deserves systematic investigation.

A Plea for Gricean Charity

At first glance, many of the reviewed findings fit a familiar theme of social cognition theorizing: they look like “mindless” ( Langer et al., 1978 ), “top-of-the-head” ( Taylor & Fiske, 1978 ) responses that reflect superficial reasoning ( Nisbett & Ross, 1980 ) and satisficing ( Krosnick, 1991 ). But in contrast to top-of-the-head phenomena, effects that are driven by conversational inferences increase with the importance of the task and the judge’s accountability and need for cognition. The more people are motivated to arrive at the “correct” answer, the more they will attempt to determine the speaker’s likely intended meaning by paying close attention to contextual information and the more they will strive to make sense of all the (useless) information that the speaker apparently deemed relevant. Little do they know that speakers who routinely observe the conventions of cooperative conversational conduct in everyday life will happily violate them in their professional role as researchers by presenting information that is neither relevant nor truthful, informative, and clear. Given this, incentives and other motivational interventions “are not going to make respondents drop a conversationally rational interpretation in favor of a less plausible one in the context” ( Hilton, 1995 , p. 265).

The reviewed phenomena also differ from psychology’s common understanding of “artifacts” in behavioral research, which portrays experiments as unique endeavors that differ in important ways from everyday social interactions. Whereas the seminal work of Orne (1962 ; see also Kruglanski, 1975 ) assumed unique motivations linked to the role of research participant, a conversational analysis suggests the opposite: When things go wrong in experiments, it is often because participants do exactly what they would be expected to do in daily life. A key difference between experiments and conversations in natural settings is merely that the experimenter is less likely to comply with conversational rules in conducting an experiment than in conducting any other conversation, while participants have no reason to suspect so. As a result, they apply the tacit assumptions that usually govern the conduct of conversation to the research setting and go beyond the literal information provided to them by drawing inferences based on the conversational context.

The apparent biases and errors participants commit by doing so are less likely to result in mistakes in everyday contexts, where communicators try to conform to conversational norms, provide information that is relevant to the judgment at hand, and make the task one that is clear rather than ambiguous—and where recipients are indeed expected to use contextual cues to disambiguate the communication, should the communicator not live up to the ideal. In contrast to many other forms of “charitable” interpretations of human judgmental errors (for discussions, see C. Lee, 2006 ; Stich, 1984 ; Thagard & Nisbett, 1983 ), a Gricean analysis does not entail a presumption of rationality; it merely entails that one should not infer a violation of rationality when a lack of cooperative communication allows for diverging construals of the task. As Asch (1952) emphasized, differences in judgment often reflect that people are evaluating different things, rather than that they arrive at different evaluations of the same thing. Although this construal principle is a core tenet of social psychology, we routinely fail to apply it to our own instructions, experimental materials, and questionnaires. When we do, it becomes apparent that many familiar biases and shortcomings are driven by faulty communication rather than faulty judgment.