research on theory of

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

4 Theories in scientific research

As we know from previous chapters, science is knowledge represented as a collection of ‘theories’ derived using the scientific method. In this chapter, we will examine what a theory is, why we need theories in research, the building blocks of a theory, how to evaluate theories, how can we apply theories in research, and also present illustrative examples of five theories frequently used in social science research.

Theories are explanations of a natural or social behaviour, event, or phenomenon. More formally, a scientific theory is a system of constructs (concepts) and propositions (relationships between those constructs) that collectively presents a logical, systematic, and coherent explanation of a phenomenon of interest within some assumptions and boundary conditions (Bacharach 1989). [1]

Theories should explain why things happen, rather than just describe or predict. Note that it is possible to predict events or behaviours using a set of predictors, without necessarily explaining why such events are taking place. For instance, market analysts predict fluctuations in the stock market based on market announcements, earnings reports of major companies, and new data from the Federal Reserve and other agencies, based on previously observed correlations . Prediction requires only correlations. In contrast, explanations require causations , or understanding of cause-effect relationships. Establishing causation requires three conditions: one, correlations between two constructs, two, temporal precedence (the cause must precede the effect in time), and three, rejection of alternative hypotheses (through testing). Scientific theories are different from theological, philosophical, or other explanations in that scientific theories can be empirically tested using scientific methods.

Explanations can be idiographic or nomothetic. Idiographic explanations are those that explain a single situation or event in idiosyncratic detail. For example, you did poorly on an exam because: you forgot that you had an exam on that day, you arrived late to the exam due to a traffic jam, you panicked midway through the exam, you had to work late the previous evening and could not study for the exam, or even your dog ate your textbook. The explanations may be detailed, accurate, and valid, but they may not apply to other similar situations, even involving the same person, and are hence not generalisable. In contrast, nomothetic explanations seek to explain a class of situations or events rather than a specific situation or event. For example, students who do poorly in exams do so because they did not spend adequate time preparing for exams or because they suffer from nervousness, attention-deficit, or some other medical disorder. Because nomothetic explanations are designed to be generalisable across situations, events, or people, they tend to be less precise, less complete, and less detailed. However, they explain economically, using only a few explanatory variables. Because theories are also intended to serve as generalised explanations for patterns of events, behaviours, or phenomena, theoretical explanations are generally nomothetic in nature.

While understanding theories, it is also important to understand what theories are not. A theory is not data, facts, typologies, taxonomies, or empirical findings. A collection of facts is not a theory, just as a pile of stones is not a house. Likewise, a collection of constructs (e.g., a typology of constructs) is not a theory, because theories must go well beyond constructs to include propositions, explanations, and boundary conditions. Data, facts, and findings operate at the empirical or observational level, while theories operate at a conceptual level and are based on logic rather than observations.

There are many benefits to using theories in research. First, theories provide the underlying logic for the occurrence of natural or social phenomena by explaining the key drivers and outcomes of the target phenomenon, and the underlying processes responsible for driving that phenomenon. Second, they aid in sense-making by helping us synthesise prior empirical findings within a theoretical framework and reconcile contradictory findings by discovering contingent factors influencing the relationship between two constructs in different studies. Third, theories provide guidance for future research by helping identify constructs and relationships that are worthy of further research. Fourth, theories can contribute to cumulative knowledge building by bridging gaps between other theories and by causing existing theories to be re-evaluated in a new light.

However, theories can also have their own share of limitations. As simplified explanations of reality, theories may not always provide adequate explanations of the phenomenon of interest based on a limited set of constructs and relationships. Theories are designed to be simple and parsimonious explanations, while reality may be significantly more complex. Furthermore, theories may impose blinders or limit researchers’ ‘range of vision’, causing them to miss out on important concepts that are not defined by the theory.

Building blocks of a theory

David Whetten (1989) [2] suggests that there are four building blocks of a theory: constructs, propositions, logic, and boundary conditions/assumptions. Constructs capture the ‘what’ of theories (i.e., what concepts are important for explaining a phenomenon?), propositions capture the ‘how’ (i.e., how are these concepts related to each other?), logic represents the ‘why’ (i.e., why are these concepts related?), and boundary conditions/assumptions examines the ‘who, when, and where’ (i.e., under what circumstances will these concepts and relationships work?). Though constructs and propositions were previously discussed in Chapter 2, we describe them again here for the sake of completeness.

Constructs are abstract concepts specified at a high level of abstraction that are chosen specifically to explain the phenomenon of interest. Recall from Chapter 2 that constructs may be unidimensional (i.e., embody a single concept), such as weight or age, or multi-dimensional (i.e., embody multiple underlying concepts), such as personality or culture. While some constructs, such as age, education, and firm size, are easy to understand, others, such as creativity, prejudice, and organisational agility, may be more complex and abstruse, and still others such as trust, attitude, and learning may represent temporal tendencies rather than steady states. Nevertheless, all constructs must have clear and unambiguous operational definitions that should specify exactly how the construct will be measured and at what level of analysis (individual, group, organisational, etc.). Measurable representations of abstract constructs are called variables . For instance, IQ score is a variable that is purported to measure an abstract construct called ‘intelligence’. As noted earlier, scientific research proceeds along two planes: a theoretical plane and an empirical plane. Constructs are conceptualised at the theoretical plane, while variables are operationalised and measured at the empirical (observational) plane. Furthermore, variables may be independent, dependent, mediating, or moderating, as discussed in Chapter 2. The distinction between constructs (conceptualised at the theoretical level) and variables (measured at the empirical level) is shown in Figure 4.1.

Propositions are associations postulated between constructs based on deductive logic. Propositions are stated in declarative form and should ideally indicate a cause-effect relationship (e.g., if X occurs, then Y will follow). Note that propositions may be conjectural but must be testable, and should be rejected if they are not supported by empirical observations. However, like constructs, propositions are stated at the theoretical level, and they can only be tested by examining the corresponding relationship between measurable variables of those constructs. The empirical formulation of propositions, stated as relationships between variables, are called hypotheses . The distinction between propositions (formulated at the theoretical level) and hypotheses (tested at the empirical level) is depicted in Figure 4.1.

The third building block of a theory is the logic that provides the basis for justifying the propositions as postulated. Logic acts like a ‘glue’ that connects the theoretical constructs and provides meaning and relevance to the relationships between these constructs. Logic also represents the ‘explanation’ that lies at the core of a theory. Without logic, propositions will be ad hoc, arbitrary, and meaningless, and cannot be tied into the cohesive ‘system of propositions’ that is the heart of any theory.

Finally, all theories are constrained by assumptions about values, time, and space, and boundary conditions that govern where the theory can be applied and where it cannot be applied. For example, many economic theories assume that human beings are rational (or boundedly rational) and employ utility maximisation based on cost and benefit expectations as a way of understand human behaviour. In contrast, political science theories assume that people are more political than rational, and try to position themselves in their professional or personal environment in a way that maximises their power and control over others. Given the nature of their underlying assumptions, economic and political theories are not directly comparable, and researchers should not use economic theories if their objective is to understand the power structure or its evolution in an organisation. Likewise, theories may have implicit cultural assumptions (e.g., whether they apply to individualistic or collective cultures), temporal assumptions (e.g., whether they apply to early stages or later stages of human behaviour), and spatial assumptions (e.g., whether they apply to certain localities but not to others). If a theory is to be properly used or tested, all of the implicit assumptions that form the boundaries of that theory must be properly understood. Unfortunately, theorists rarely state their implicit assumptions clearly, which leads to frequent misapplications of theories to problem situations in research.

Attributes of a good theory

Theories are simplified and often partial explanations of complex social reality. As such, there can be good explanations or poor explanations, and consequently, there can be good theories or poor theories. How can we evaluate the ‘goodness’ of a given theory? Different criteria have been proposed by different researchers, the more important of which are listed below:

Logical consistency: Are the theoretical constructs, propositions, boundary conditions, and assumptions logically consistent with each other? If some of these ‘building blocks’ of a theory are inconsistent with each other (e.g., a theory assumes rationality, but some constructs represent non-rational concepts), then the theory is a poor theory.

Explanatory power: How much does a given theory explain (or predict) reality? Good theories obviously explain the target phenomenon better than rival theories, as often measured by variance explained (R-squared) value in regression equations.

Falsifiability: British philosopher Karl Popper stated in the 1940s that for theories to be valid, they must be falsifiable. Falsifiability ensures that the theory is potentially disprovable, if empirical data does not match with theoretical propositions, which allows for their empirical testing by researchers. In other words, theories cannot be theories unless they can be empirically testable. Tautological statements, such as ‘a day with high temperatures is a hot day’ are not empirically testable because a hot day is defined (and measured) as a day with high temperatures, and hence, such statements cannot be viewed as a theoretical proposition. Falsifiability requires the presence of rival explanations, it ensures that the constructs are adequately measurable, and so forth. However, note that saying that a theory is falsifiable is not the same as saying that a theory should be falsified. If a theory is indeed falsified based on empirical evidence, then it was probably a poor theory to begin with.

Parsimony: Parsimony examines how much of a phenomenon is explained with how few variables. The concept is attributed to fourteenth century English logician Father William of Ockham (and hence called ‘Ockham’s razor’ or ‘Occam’s razor’), which states that among competing explanations that sufficiently explain the observed evidence, the simplest theory (i.e., one that uses the smallest number of variables or makes the fewest assumptions) is the best. Explanation of a complex social phenomenon can always be increased by adding more and more constructs. However, such an approach defeats the purpose of having a theory, which is intended to be a ‘simplified’ and generalisable explanation of reality. Parsimony relates to the degrees of freedom in a given theory. Parsimonious theories have higher degrees of freedom, which allow them to be more easily generalised to other contexts, settings, and populations.

Approaches to theorising

How do researchers build theories? Steinfeld and Fulk (1990) [3] recommend four such approaches. The first approach is to build theories inductively based on observed patterns of events or behaviours. Such an approach is often called ‘grounded theory building’, because the theory is grounded in empirical observations. This technique is heavily dependent on the observational and interpretive abilities of the researcher, and the resulting theory may be subjective and non-confirmable. Furthermore, observing certain patterns of events will not necessarily make a theory, unless the researcher is able to provide consistent explanations for the observed patterns. We will discuss the grounded theory approach in a later chapter on qualitative research.

The second approach to theory building is to conduct a bottom-up conceptual analysis to identify different sets of predictors relevant to the phenomenon of interest using a predefined framework. One such framework may be a simple input-process-output framework, where the researcher may look for different categories of inputs, such as individual, organisational, and/or technological factors potentially related to the phenomenon of interest (the output), and describe the underlying processes that link these factors to the target phenomenon. This is also an inductive approach that relies heavily on the inductive abilities of the researcher, and interpretation may be biased by researcher’s prior knowledge of the phenomenon being studied.

The third approach to theorising is to extend or modify existing theories to explain a new context, such as by extending theories of individual learning to explain organisational learning. While making such an extension, certain concepts, propositions, and/or boundary conditions of the old theory may be retained and others modified to fit the new context. This deductive approach leverages the rich inventory of social science theories developed by prior theoreticians, and is an efficient way of building new theories by expanding on existing ones.

The fourth approach is to apply existing theories in entirely new contexts by drawing upon the structural similarities between the two contexts. This approach relies on reasoning by analogy, and is probably the most creative way of theorising using a deductive approach. For instance, Markus (1987) [4] used analogic similarities between a nuclear explosion and uncontrolled growth of networks or network-based businesses to propose a critical mass theory of network growth. Just as a nuclear explosion requires a critical mass of radioactive material to sustain a nuclear explosion, Markus suggested that a network requires a critical mass of users to sustain its growth, and without such critical mass, users may leave the network, causing an eventual demise of the network.

Examples of social science theories

In this section, we present brief overviews of a few illustrative theories from different social science disciplines. These theories explain different types of social behaviors, using a set of constructs, propositions, boundary conditions, assumptions, and underlying logic. Note that the following represents just a simplistic introduction to these theories. Readers are advised to consult the original sources of these theories for more details and insights on each theory.

Agency theory. Agency theory (also called principal-agent theory), a classic theory in the organisational economics literature, was originally proposed by Ross (1973) [5] to explain two-party relationships—such as those between an employer and its employees, between organisational executives and shareholders, and between buyers and sellers—whose goals are not congruent with each other. The goal of agency theory is to specify optimal contracts and the conditions under which such contracts may help minimise the effect of goal incongruence. The core assumptions of this theory are that human beings are self-interested individuals, boundedly rational, and risk-averse, and the theory can be applied at the individual or organisational level.

The two parties in this theory are the principal and the agent—the principal employs the agent to perform certain tasks on its behalf. While the principal’s goal is quick and effective completion of the assigned task, the agent’s goal may be working at its own pace, avoiding risks, and seeking self-interest—such as personal pay—over corporate interests, hence, the goal incongruence. Compounding the nature of the problem may be information asymmetry problems caused by the principal’s inability to adequately observe the agent’s behaviour or accurately evaluate the agent’s skill sets. Such asymmetry may lead to agency problems where the agent may not put forth the effort needed to get the task done (the moral hazard problem) or may misrepresent its expertise or skills to get the job but not perform as expected (the adverse selection problem). Typical contracts that are behaviour-based, such as a monthly salary, cannot overcome these problems. Hence, agency theory recommends using outcome-based contracts, such as commissions or a fee payable upon task completion, or mixed contracts that combine behaviour-based and outcome-based incentives. An employee stock option plan is an example of an outcome-based contract, while employee pay is a behaviour-based contract. Agency theory also recommends tools that principals may employ to improve the efficacy of behaviour-based contracts, such as investing in monitoring mechanisms—e.g. hiring supervisors—to counter the information asymmetry caused by moral hazard, designing renewable contracts contingent on the agent’s performance (performance assessment makes the contract partially outcome-based), or by improving the structure of the assigned task to make it more programmable and therefore more observable.

Theory of planned behaviour. Postulated by Azjen (1991), [6] the theory of planned behaviour (TPB) is a generalised theory of human behaviour in social psychology literature that can be used to study a wide range of individual behaviours. It presumes that individual behaviour represents conscious reasoned choice, and is shaped by cognitive thinking and social pressures. The theory postulates that behaviours are based on one’s intention regarding that behaviour, which in turn is a function of the person’s attitude toward the behaviour, subjective norm regarding that behaviour, and perception of control over that behaviour (see Figure 4.2). Attitude is defined as the individual’s overall positive or negative feelings about performing the behaviour in question, which may be assessed as a summation of one’s beliefs regarding the different consequences of that behaviour, weighted by the desirability of those consequences. Subjective norm refers to one’s perception of whether people important to that person expect the person to perform the intended behaviour, and is represented as a weighted combination of the expected norms of different referent groups such as friends, colleagues, or supervisors at work. Behavioural control is one’s perception of internal or external controls constraining the behaviour in question. Internal controls may include the person’s ability to perform the intended behaviour (self-efficacy), while external control refers to the availability of external resources needed to perform that behaviour (facilitating conditions). TPB also suggests that sometimes people may intend to perform a given behaviour but lack the resources needed to do so, and therefore posits that behavioural control can have a direct effect on behaviour, in addition to the indirect effect mediated by intention.

TPB is an extension of an earlier theory called the theory of reasoned action, which included attitude and subjective norm as key drivers of intention, but not behavioural control. The latter construct was added by Ajzen in TPB to account for circumstances when people may have incomplete control over their own behaviours (such as not having high-speed Internet access for web surfing).

Innovation diffusion theory. Innovation diffusion theory (IDT) is a seminal theory in the communications literature that explains how innovations are adopted within a population of potential adopters. The concept was first studied by French sociologist Gabriel Tarde, but the theory was developed by Everett Rogers in 1962 based on observations of 508 diffusion studies. The four key elements in this theory are: innovation, communication channels, time, and social system. Innovations may include new technologies, new practices, or new ideas, and adopters may be individuals or organisations. At the macro (population) level, IDT views innovation diffusion as a process of communication where people in a social system learn about a new innovation and its potential benefits through communication channels—such as mass media or prior adopters— and are persuaded to adopt it. Diffusion is a temporal process—the diffusion process starts off slow among a few early adopters, then picks up speed as the innovation is adopted by the mainstream population, and finally slows down as the adopter population reaches saturation. The cumulative adoption pattern is therefore an s-shaped curve, as shown in Figure 4.3, and the adopter distribution represents a normal distribution. All adopters are not identical, and adopters can be classified into innovators, early adopters, early majority, late majority, and laggards based on the time of their adoption. The rate of diffusion also depends on characteristics of the social system such as the presence of opinion leaders (experts whose opinions are valued by others) and change agents (people who influence others’ behaviours).

At the micro (adopter) level, Rogers (1995) [7] suggests that innovation adoption is a process consisting of five stages: one, knowledge : when adopters first learn about an innovation from mass-media or interpersonal channels, two, persuasion : when they are persuaded by prior adopters to try the innovation, three, decision : their decision to accept or reject the innovation, four,: their initial utilisation of the innovation, and five, confirmation : their decision to continue using it to its fullest potential (see Figure 4.4). Five innovation characteristics are presumed to shape adopters’ innovation adoption decisions: one, relative advantage : the expected benefits of an innovation relative to prior innovations, two, compatibility : the extent to which the innovation fits with the adopter’s work habits, beliefs, and values, three, complexity : the extent to which the innovation is difficult to learn and use, four, trialability : the extent to which the innovation can be tested on a trial basis, and five, observability : the extent to which the results of using the innovation can be clearly observed. The last two characteristics have since been dropped from many innovation studies. Complexity is negatively correlated to innovation adoption, while the other four factors are positively correlated. Innovation adoption also depends on personal factors such as the adopter’s risk-taking propensity, education level, cosmopolitanism, and communication influence. Early adopters are venturesome, well educated, and rely more on mass media for information about the innovation, while later adopters rely more on interpersonal sources—such as friends and family—as their primary source of information. IDT has been criticised for having a ‘pro-innovation bias’—that is for presuming that all innovations are beneficial and will be eventually diffused across the entire population, and because it does not allow for inefficient innovations such as fads or fashions to die off quickly without being adopted by the entire population or being replaced by better innovations.

Elaboration likelihood model . Developed by Petty and Cacioppo (1986), [8] the elaboration likelihood model (ELM) is a dual-process theory of attitude formation or change in psychology literature. It explains how individuals can be influenced to change their attitude toward a certain object, event, or behaviour and the relative efficacy of such change strategies. The ELM posits that one’s attitude may be shaped by two ‘routes’ of influence: the central route and the peripheral route, which differ in the amount of thoughtful information processing or ‘elaboration required of people (see Figure 4.5). The central route requires a person to think about issue-related arguments in an informational message and carefully scrutinise the merits and relevance of those arguments, before forming an informed judgment about the target object. In the peripheral route, subjects rely on external ‘cues’ such as number of prior users, endorsements from experts, or likeability of the endorser, rather than on the quality of arguments, in framing their attitude towards the target object. The latter route is less cognitively demanding, and the routes of attitude change are typically operationalised in the ELM using the argument quality and peripheral cues constructs respectively.

Whether people will be influenced by the central or peripheral routes depends upon their ability and motivation to elaborate the central merits of an argument. This ability and motivation to elaborate is called elaboration likelihood . People in a state of high elaboration likelihood (high ability and high motivation) are more likely to thoughtfully process the information presented and are therefore more influenced by argument quality, while those in the low elaboration likelihood state are more motivated by peripheral cues. Elaboration likelihood is a situational characteristic and not a personal trait. For instance, a doctor may employ the central route for diagnosing and treating a medical ailment (by virtue of his or her expertise of the subject), but may rely on peripheral cues from auto mechanics to understand the problems with his car. As such, the theory has widespread implications about how to enact attitude change toward new products or ideas and even social change.

General deterrence theory. Two utilitarian philosophers of the eighteenth century, Cesare Beccaria and Jeremy Bentham, formulated general deterrence theory (GDT) as both an explanation of crime and a method for reducing it. GDT examines why certain individuals engage in deviant, anti-social, or criminal behaviours. This theory holds that people are fundamentally rational (for both conforming and deviant behaviours), and that they freely choose deviant behaviours based on a rational cost-benefit calculation. Because people naturally choose utility-maximising behaviours, deviant choices that engender personal gain or pleasure can be controlled by increasing the costs of such behaviours in the form of punishments (countermeasures) as well as increasing the probability of apprehension. Swiftness, severity, and certainty of punishments are the key constructs in GDT.

While classical positivist research in criminology seeks generalised causes of criminal behaviours, such as poverty, lack of education, psychological conditions, and recommends strategies to rehabilitate criminals, such as by providing them job training and medical treatment, GDT focuses on the criminal decision-making process and situational factors that influence that process. Hence, a criminal’s personal situation—such as his personal values, his affluence, and his need for money—and the environmental context—such as how protected the target is, how efficient the local police are, how likely criminals are to be apprehended—play key roles in this decision-making process. The focus of GDT is not how to rehabilitate criminals and avert future criminal behaviours, but how to make criminal activities less attractive and therefore prevent crimes. To that end, ‘target hardening’ such as installing deadbolts and building self-defence skills, legal deterrents such as eliminating parole for certain crimes, ‘three strikes law’ (mandatory incarceration for three offences, even if the offences are minor and not worth imprisonment), and the death penalty, increasing the chances of apprehension using means such as neighbourhood watch programs, special task forces on drugs or gang-related crimes, and increased police patrols, and educational programs such as highly visible notices such as ‘Trespassers will be prosecuted’ are effective in preventing crimes. This theory has interesting implications not only for traditional crimes, but also for contemporary white-collar crimes such as insider trading, software piracy, and illegal sharing of music.

• Bacharach, S.B. (1989). Organizational theories: some criteria for evaluation. Academy of Management Review , 14(4), 496-515. ↵
• Whetten, D. (1989). What constitutes a theoretical contribution? Academy of Management Review , 14(4), 490-495. ↵
• Steinfield, C.W. and Fulk, J. (1990). The theory imperative. In J. Fulk & C.W. (Eds.), Organizations and communications technology (pp. 13–26). Newsburt Park, CA: Sage Publications. ↵
• Markus, M.L. (1987). Toward a ‘critical mass’ theory of interactive media: universal access, interdependence and diffusion. Communication Research , 14(5), 491-511. ↵
• Ross, S.A. (1973). The economic theory of agency: The principal’s problem. American Economic , 63(2), 134-139 ↵
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes , (50), 179–211. ↵
• Rogers, E. (1995). Diffusion of innovations (4th ed.). New York: Free Press. ↵
• Petty, R.E. and Cacioppo, J.T. (1986). C ommunication and persuasion: Central and peripheral routes to attitude change . New York: Springer-Verlag. ↵

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

• - Google Chrome

Intended for healthcare professionals

• Access provided by Google Indexer
• My email alerts
• BMA member login
• Username * Password * Forgot your log in details? Need to activate BMA Member Log In Log in via OpenAthens Log in via your institution

Search form

• Advanced search
• Search responses
• Search blogs
• Why use theories in...

Why use theories in qualitative research?

• Related content
• Peer review
• Scott Reeves , associate professor 1 ,
• Mathieu Albert , assistant professor 2 ,
• Ayelet Kuper , assistant professor 3 ,
• Brian David Hodges , associate professor and vice-chair (education) 2
• 1 Department of Psychiatry, Li Ka Shing Knowledge Institute, Centre for Faculty Development, and Wilson Centre for Research in Education, University of Toronto, 200 Elizabeth Street, Eaton South 1-565, Toronto, Ontario, Canada M5G 2C4
• 2 Department of Psychiatry and Wilson Centre for Research in Education
• 3 Department of Medicine, Sunnybrook Health Sciences Centre, and Wilson Centre for Research in Education
• Correspondence to: S Reeves scott.reeves{at}utoronto.ca

Theories such as interactionism, phenomenology, and critical theory can be used to help design a research question, guide the selection of relevant data, interpret the data, and propose explanations of causes or influences

Previous articles in this series have addressed several methodologies used in qualitative research. Qualitative researchers also rely heavily on theories drawn from the social sciences and humanities to guide their research process and illuminate their findings. This article discusses the role and use of three theoretical approaches commonly used by qualitative researchers in health domains: interactionism, phenomenology, and critical theory. It also explains why such theories are important for clinicians, for health policy, and for patient care.

Why is theory useful?

Theories provide complex and comprehensive conceptual understandings of things that cannot be pinned down: how societies work, how organisations operate, why people interact in certain ways. Theories give researchers different “lenses” through which to look at complicated problems and social issues, focusing their attention on different aspects of the data and providing a framework within which to conduct their analysis.

Just as there is no one way to understand why, for instance, a culture has formed in a certain way, many lenses can be applied to a problem, each focusing on a different aspect of it. For example, to study doctor-nurse interactions on medical wards, various theories can provide insights into different aspects of hospital and ward cultures. Box 1 indicates how each of the theories discussed in this paper could be used to highlight different facets of this research problem.

Box 1 How different theories help illuminate the culture of doctor-nurse interactions on a medical ward

Phenomenology.

A researcher using phenomenology would approach the study of doctor-nurse interprofessional interactions by exploring how individual doctors and nurses made sense of their ward-based interprofessional experiences. Such a study would aim to elicit, through interviews, the meanings each individual attached to their interactions and the classifications they employed to make sense of their working lives within this context. Data would be analysed inductively, focusing on allowing meanings to emerge from the interviews. Specifically, this process would entail examining statements from the interviews and clustering them to form common themes linked to understanding the meanings that doctors and nurses each individually attached to their interactions.

Interactionism

Interactionist theory would be used to explore how the interprofessional relations within a medical ward context were created and modified during the daily interactions of doctors and nurses. Researchers in such a study would observe how doctors and nurses interacted (both verbally and non-verbally) in their shared clinical work; they would also interview both groups to understand the meanings they attached to their differing interprofessional interactions. Data would be analysed inductively by examining observational field notes and interviews to identify and explore the different elements which contributed to the nature of doctor-nurse interactions within a particular context. For example, researchers might examine differences between formal interactions (in front of patients) and informal interactions (in more private hospital settings).

Critical theory

A researcher employing critical theory would approach a study of doctor-nurse interactions by asking how power is related to characteristics of individuals or groups (for example, gender, race, culture). For example, critical scholars such as Anne Witz have shown that professions form hierarchies in which the dominant ones are predominately male (doctors), the first subordinate profession is largely female (nurses) and the most subordinate are often members of ethic minorities (nursing assistants). 14 Data analysis would be informed by the specific critical theoretical lens selected by the researcher. For example, data could be filtered through a feminist lens to help understand how patriarchy operates through doctor-nurse interactions within medical ward settings.

What are examples of theories used by qualitative researchers?

This theory was originally developed by Edmund Husserl to explain how individuals give meanings to social phenomena in their everyday lives. The role of phenomenology was therefore to explore “the essence of consciousness as experienced from the first-person point of view.” 1 Studies that draw upon this theoretical perspective concentrate on exploring how individuals make sense of the world in terms of the meanings and classifications they employ. As such, phenomenology aims to provide accounts that offer an insight into the subjective “lived” experience of individuals. 2 Given the emphasis, phenomenological studies do not attempt to generate wider explanations; rather their focus is on providing research accounts for individuals in a specific setting.

In general, studies that draw upon a phenomenological approach gather data in the form of in-depth semistructured or unstructured interviews and personal documents such as diaries. For example, Porter and colleagues used in-depth individual interviews to understand the meanings people living in residential homes held about their caregivers, 3 whereas Mitchell gathered the meanings of being a senior from narrative stories written by older people about their personal experiences in later life. 4 Theories that privilege understanding of human experience in terms of individual consciousness, such as phenomenology, share links to the work of the German philosopher Georg Wilhelm Friedrich Hegel and French existentialists such as Jean-Paul Sartre. However, phenomenology, as a result of its specific, empirical focus on the individual experience, is an example of a micro level theory within this philosophical orientation.

In contrast to the phenomenological focus on understanding individual perceptions, interactionism concentrates on exploring collective (group or team) behaviours and perceptions. Originally developed by George Mead, this approach aimed to provide an understanding of individuals’ interactions by examining the symbols, especially the language, they use in their daily encounters. In particular, interactionism is an approach that aims to elicit an understanding of how meaning is created and modified by individuals through their social actions, interactions, and reactions. Herbert Blumer outlined interactionism’s three guiding assumptions: that human beings act towards things on the basis of the meanings that these things have for them; that the meaning of such things is derived from, and arises out of, social interaction with one’s fellows; that these meanings are handled in, and modified through, an interpretative process used by the person in dealing with the things he or she encounters. 5

Given the emphasis on understanding the processes of social interactions, interactionist research studies often draw on methods of data collection such as participant observation and interviews to capture these elements of social action. For example, Goffman found that individuals’ interactions are largely dependent on whether they are interacting in a “front stage” (a hospital ward, for example) or a “backstage” (private office, for example) setting. 6 More recent research on the socialisation of medical students has indicated the significance of front and backstage performances in their socialisation. 7

Several theories conceptualise reality as a social or collective construction, and these have roots in the work of European writers such as Émile Durkheim and Lev Vygotsky and of Americans Peter Berger and Thomas Luckman. Interactionism attempts to generalise beyond the individual experience but retains a mid-range focus on local systems and contexts within this broader social constructivist school.

Critical theory is oriented towards critiquing and changing society as a whole. 8 With roots in the work of Marx on production and capitalism, it was further developed at the Institute for Social Research of the University of Frankfurt in the 1930s. More recently, this tradition has been carried on by social scientists such as Pierre Bourdieu and Michel Foucault.

Critical theorists study how the construction of knowledge and the organisation of power in society generally, and in institutions such as schools, hospitals, and governments specifically, can lead to the subjugation or oppression of particular individuals, groups, or perspectives. Critical theorists are concerned with equity and justice in relation to issues such as race, socioeconomic status, religion, and sexuality. 9 For example, Battiste studied how Euro-American dominated health care, pharmaceutical research, and educational institutions marginalise indigenous knowledge, and how both endangered certain populations and marginalised important knowledge about health and the environment. 10 Muzzin used critical theory in her study of how education of health professionals has come to reflect corporate interests, thereby reproducing gender and class inequity, as universities developed “academic capitalism.” 11

Critical theory is not tied to one specific methodology and can be applied at the micro (individual), macro (local systems and contexts), or macro (societal) level.

Aren’t there a lot more theories?

The three theories we have discussed so far in this paper are examples of the possible theories a qualitative researcher might use. Box 2 provides some further examples of other theories that have been used in qualitative research studies in domains related to medicine.

Box 2 More examples of theories used in qualitative research

Professionalisation theory.

Elliot Freidson developed his theory of professionalisation in response to previous explanations that had considered only the range of positive traits of professional groups. 15 Freidson argued that occupational groups, such as medicine, had previously engaged in a process of professionalisation to secure exclusive ownership of specific areas of knowledge and expertise. In obtaining exclusivity, occupational groups secure autonomy of practice, which leads to economic rewards and enhanced status. To protect the gains obtained from professionalisation, occupations guard their areas of knowledge and expertise through strict regulation of entry and the maintenance of professional standards. More recently, this theory has been questioned because of the increasing influence of clinical management on medicine, which Haug argued had resulted in a “deprofessionalisation” process, whereby some of the professional gains described by Freidson have been undermined. 16

Labelling theory

Originating in the sociology of deviance, labelling theory focuses on how society can negatively label a group whose behaviour is deemed as deviating from the norm. The theory was applied in a healthcare context by Scheff to help understand the nature of mental illness. 17 Scheff argued that mental illness is essentially generated as a result of societal influence. To understand deviant actions, individuals often place the label “mental illness” on those who show such actions. Certain expectations are then placed on these individuals and, over time, they unconsciously change their behaviour to fulfil them (a notion termed self fulfilling prophesy). Empirical work by Link et al has shown how influential labelling can be for mentally ill patients: once they are labelled as having this type of illness, people may withdraw from society. 18

Negotiated order theory

This theory was developed by Strauss et al to advance thinking about the way social order is maintained in organisations. 19 Previous explanations of social order within organisations tended to emphasise formal structures and rules and to neglect the influence of negotiations at the micro level. For Strauss and his colleagues, negotiation between individuals (through bargaining, compromising, and mediating) creates and shapes organisational rules and structures. Consequently, micro level negotiation contributes to the development and maintenance of the social order that exists within an organisation. This theory has been used in various organisational settings, including health care, where it indicated that informal negotiation was key in nurse-doctor decisions on patient care. 20

Following work by Merton, such theories can usefully be grouped into a taxonomy to guide novice researchers as to which theories are likely to be helpful in dealing with a particular research problem (table ⇓ ). 12 As shown in the table, grand or “macro” theories are non-specific and constructed from relatively abstract concepts. As a result of their wide ranging nature, these types of theories are difficult to operationalise and verify on an empirical basis. Mid-range theories consider specific phenomena and involve a small number of concepts relating to a restricted range of contexts. “Micro” or practice theories have the narrowest range of interest and are focused on specific phenomena and contexts. Box 3 addresses further frequently asked questions about theories in qualitative research.

Types of theory, with examples from related domains

• View inline

Box 3 Frequently asked questions

How is the term “theory” defined.

A theory is an organised, coherent, and systematic articulation of a set of issues that are communicated as a meaningful whole.

How are theories generated?

Theories are usually generated deductively, from an empirically informed act of creativity, then empirically verified. In this sense, theories result from an ongoing process of deduction and induction.

How can theories be used?

Theories are usually used to help design a research question, guide the selection of relevant data, interpret the data, and propose explanations of the underlying causes or influences of observed phenomena.

Can theories be used to predict research findings and generate hypotheses?

In general, theories in the natural sciences are used to generate predictions about the relation between two or more different variables in order to generate universal laws. In contrast, social scientists assume that social reality is too complex to consider variables in isolation in order to test their causal relationship. In addition, social scientists view universal laws as being unable to explain the complex interrelated functions of societies, thus making it impossible to draw on evidence for prediction. So, for social scientists, a theory is first and foremost a conceptual tool useful in making sense of a complex social reality.

How are theories and methodologies related?

Some theories and methodologies are historically related—that is, they both derive from the same discipline or school, and although they are sometimes used separately they are often taught and used together. The classic example of this is the link between interactionism (theory) and ethnography. Other theories (or families of theories) link well with multiple methodologies. For example, critical theories have been used to varying effect with almost every available methodology (both qualitative and quantitative).

Are these all the possible theories?

No—we have just scratched the surface. Theories have been developed and modified over several hundred years and have dialectically informed each other’s changes over time. The theories we have mentioned in this paper are those that readers are likely to encounter in the health domain. Other important theories used often in the social sciences and humanities, but only occasionally in health related research, include (but are not limited to) marxism and its descendants, feminism, hermeneutics, and the post-modernist family of theories.

Box 4 Further reading

Crotty M. Foundations of social research: meaning and perspective in the research process . London: Sage, 1998.

Denzin NK, Lincoln YS, eds. Handbook of qualitative research . 2nd ed. Thousand Oaks, CA: Sage, 2000.

Layder D. Understanding social theory . London: Sage, 1994.

Ritzer G, Smart B, eds. Handbook of social theory . London: Sage, 2003.

Roberts B. Micro social theory . Basingstoke: Palgrave, 2006.

Scamber G, ed. Sociological theory and medical sociology . London: Tavistock, 1987.

Stones R, ed. Key sociological thinkers . Basingstoke: Macmillan, 1998.

Why is theory important to health policy and patient care?

Theories such as those described above are important to health policy and the delivery of patient care, as the insights they provoke enable research that provides practitioners with a broader understanding of the situations they face in their daily working lives. The use of theory makes it possible for researchers to understand, and to translate for policy makers and healthcare providers, the processes that occur beneath the visible surface and so to develop knowledge of underlying (generating) principles. Importantly, theory can help people move beyond individual insights gained from their professional lives to a situation where they can understand the wider significance and applicability of these phenomena. Good theory based research is immediate, insightful, and applicable in practice; in the words of Kurt Lewin, “there’s nothing so practical as a good theory.” 13

Summary points

Different theories provide different lenses through which to analyse research problems

Various theories are currently used within health related research

Theories can be divided into macro (or grand) theories, mid-range theories, and micro (or practice) theories.

Theories can arise from, or be used within, different research domains (for example, biomedical domain, psychological domain, social domain)

The insights derived from theories are important for guiding health policy and informing the delivery of patient care

Cite this as: BMJ 2008;337:a949

• Related to doi: , 10.1136/bmj.a288
• doi: , 10.1136/bmj.39602.690162.47
• doi: , 10.1136/bmj.a1020
• doi: , 10.1136/bmj.a879
• doi: 10.1136/bmj.a1035

This is the fifth in a series of six articles that aim to help readers to critically appraise the increasing number of qualitative research articles in clinical journals. The series editors are Ayelet Kuper and Scott Reeves.

For a definition of general terms relating to qualitative research, see the first article in this series.

Funding: None.

Competing interests: None declared.

Provenance and peer review: Commissioned; externally peer reviewed.

• ↵ Smith D. Husserl . London: Routledge, 2007 .
• ↵ Schutz A. The phenomenology of the social world . New York: Northwestern University Press, 1967 .
• ↵ Porter E, Ganong H, Drew N, Lanes T. A new typology of home-care helpers. Gerontologist 2004 ; 44 : 750 -9. OpenUrl Abstract / FREE Full Text
• ↵ Mitchell G. The meaning of being a senior. Nurs Sci Q 1994 ; 7 : 70 -9. OpenUrl Abstract / FREE Full Text
• ↵ Blumer H. Symbolic interactionism: perspective and method . Englewood Cliffs, NJ: Prentice-Hall, 1969 .
• ↵ Goffman E. The presentation of self in everyday life . London: Penguin, 1963 .
• ↵ Sinclair S. Making doctors: an institutional apprenticeship . Oxford: Berg, 1997 .
• ↵ Calhoun C. Critical social theory: culture, history and the challenge of difference . Oxford: Blackwell, 1995 .
• ↵ Kinchloe JL, McLaren P. Rethinking critical theory and qualitative research. In: Denzin NK, Lincoln YS, eds. Handbook of qualitative research . 2nd ed. Thousand Oaks, CA: Sage, 2000 .
• ↵ Battiste M. You can’t be the global doctor if you’re the colonial disease. In: Tripp P, Muzzin L, eds. Teaching as activism: equity meets environmentalism . Montreal and Kingston: McGill-Queen’s University Press, 2005 :121-133
• ↵ Muzzin L. The brave new world of professional education. In: Tripp P, Muzzin L, eds. Teaching as activism: equity meets environmentalism . Montreal and Kingston: McGill-Queen’s University Press, 2005 :149-166
• ↵ Merton R. Social theory and social structure . New York: Free Press, 1968 .
• ↵ Lewin K. Field theory in social science: selected theoretical papers . New York: Harper & Row, 1951 .
• ↵ Witz A. Professions and patriarchy . London: Macmillan, 1992 .
• ↵ Freidson E. Profession of medicine: a study of the sociology of applied knowledge. New York: Harper & Row, 1970 .
• ↵ Haug M. De-professionalisation: an alternative hypothesis for the future. Sociol Rev Monograph 1993 ; 20 : 195 -211. OpenUrl
• ↵ Scheff T. Being mentally ill: a sociological theory . 3rd ed. New York: Transaction, 1999 .
• ↵ Link B, Phelan J. The labelling theory of mental disorder: The consequences of labelling. In: Horwitz A, Scheid T, eds. A handbook for the study of mental health . New York: Cambridge University Press, 1999 :361-76.
• ↵ Strauss A, Schatzman D, Ehrlich R, Bucher M, Sabshin C. The hospital and its negotiated order. In: Freidson E, ed. The hospital in modern society . New York: Free Press, 1963 :147-69.
• ↵ Svensson R. The interplay between doctors and nurses—a negotiated order perspective. Sociol Health Illness 1996 ; 18 : 379 -98. OpenUrl CrossRef Web of Science

• Library databases
• Library website

Theories and Frameworks: Introduction

Theoretical & conceptual frameworks.

The terms theoretical framework and conceptual framework are often used interchangeably to mean the same thing. Although they are both used to understand a research problem and guide the development, collection, and analysis of research, it's important to understand the difference between the two. When working on coursework or dissertation research, make sure to clarify what is being asked and any specific course or program requirements. 

Theoretical framework 

A theoretical framework is a single formal theory. When a study is designed around a theoretical framework, the theory is the primary means in which the research problem is understood and investigated. Although theoretical frameworks tend to be used in quantitative studies, you will also see this approach in qualitative research.  

Conceptual framework

A conceptual framework includes one or more formal theories (in part or whole) as well as other concepts and empirical findings from the literature. It is used to show relationships among these ideas and how they relate to the research study. Conceptual frameworks are commonly seen in qualitative research in the social and behavioral sciences, for example, because often one theory cannot fully address the phenomena being studied.

Investigate theory

Identifying and learning about theories requires a different search strategy than other types of research. Even though the steps are different, you will still use many of the same skills and tools you’ve used for other library research.

• psychology:  human development, cognition, personality, motivation
• sociology:  social change, race, class, gender
• business:  leadership, management
• health:  patient care, well-being, environment
• course textbooks
• encyclopedias and handbooks
• credible websites

Theory in doctoral research

Identifying a theory that aligns with your dissertation or doctoral study takes time. It’s never too early to start exploratory research. The process of identifying an appropriate theory can seem daunting, so try breaking down the process into smaller steps.

• your theory courses
• completed dissertations and doctoral studies
• the scholarly literature on your topic
• Keep a list of theories and take notes on how and why they were used.
• Identify and learn more about relevant theories.
• Locate influential and seminal works  related to those theories.
• Next Page: Discover Theories
• Office of Student Disability Services

Walden Resources

Departments.

• Academic Residencies
• Academic Skills
• Career Planning and Development
• Customer Care Team
• Field Experience
• Military Services
• Student Success Advising
• Writing Skills

Centers and Offices

• Center for Social Change
• Office of Academic Support and Instructional Services
• Office of Degree Acceleration
• Office of Research and Doctoral Services
• Office of Student Affairs

Student Resources

• Doctoral Writing Assessment
• Form & Style Review
• Quick Answers
• ScholarWorks
• SKIL Courses and Workshops
• Walden Bookstore
• Walden Catalog & Student Handbook
• Student Safety/Title IX
• Legal & Consumer Information
• Website Terms and Conditions
• Cookie Policy
• Accessibility
• Accreditation
• State Authorization
• Net Price Calculator
• Contact Walden

Walden University is a member of Adtalem Global Education, Inc. www.adtalem.com Walden University is certified to operate by SCHEV © 2024 Walden University LLC. All rights reserved.

The use of theory in research

Affiliations.

• 1 School of Pharmacy and Life Sciences, Robert Gordon University, Riverside East, Garthdee Road, Aberdeen, UK. [email protected].
• 2 Faculty of Health and Social Care, Robert Gordon University, Riverside East, Garthdee Road, Aberdeen, UK.
• PMID: 26511946
• DOI: 10.1007/s11096-015-0216-y

All researchers should consider the theoretical basis for their studies very early on in the planning stage. The aim of this paper is to describe and discuss how theory (a 'comprehensive explanation of some aspect of nature that is supported by a body of evidence') can inform and improve the quality and relevance of pharmacy-based research. Theories can be applied at many stages of quantitative and qualitative (and mixed) research processes, including: providing rationale for the study; defining the aim and research questions; considering the methodological stance; developing data collection and generation tools; providing a framework for data analysis, and interpretation. The focus of the paper is on the use of theoretical lenses, their selection and application. Two key theoretical lenses and their potential applications are described: the Theoretical Domains Framework in studies of behavioural change, and Normalization Process Theory in implementing, embedding and integrating interventions.

Keywords: Normalization Process Theory; Qualitative; Quantitative; Theoretical Domains Framework; Theoretical lens; Theory.

• Data Collection
• Models, Theoretical
• Pharmacy Research / methods*
• Statistics as Topic

• Tools and Resources
• Customer Services
• Conflict Studies
• Development
• Environment
• Foreign Policy
• Human Rights
• International Law
• Organization
• International Relations Theory
• Political Communication
• Political Economy
• Political Geography
• Political Sociology
• Politics and Sexuality and Gender
• Qualitative Political Methodology
• Quantitative Political Methodology
• Security Studies
• Share This Facebook LinkedIn Twitter

Article contents

What is theory.

• Patrick Thaddeus Jackson Patrick Thaddeus Jackson School of International Service, American University
• https://doi.org/10.1093/acrefore/9780190846626.013.361
• Published in print: 01 March 2010
• Published online: 11 January 2018

The concept of theory takes part in a conceptual network occupied by some of the most common subjects of European Enlightenment, such as “science” and “reason.” Generally speaking, a theory is a rational type of abstract or generalizing thinking, or the results of such thinking. Theories drive the exercise of finding facts rather than of reaching goals. To formulate a theory, or to “theorize,” is to assert something of a privileged epistemic status, manifested in the traditional scholarly hierarchy between theorists and those who merely labor among the empirical weeds. In so doing, a theory provides a fixed point upon which analysis can be founded and action can be performed. Scholar and author Kenneth W. Thompson describes a nexus of relations between and among three different senses of the word “theory:” normative theory, a “general theory of politics,” and the set of assumptions on the basis of which a given actor is acting. These three types of theory are somehow paralleled by Marysia Zalewski’s triad of theory as “tool,” theory as “critique,” and theory as “everyday practice.” While Thompson’s and Zalewski’s interpretations of theory are each inherently consistent, both signal a different philosophical ontology. Thompson’s viewpoint is dualist, presuming the existence of a mind-independent world to which knowledge refers; while Zalewski’s is more of a monist, rejecting the mind/world dichotomy in favor of a more complex interrelationship between observers and their objects of study.

• Kenneth W. Thompson
• dualistic approach
• Marysia Zalewski
• monistic approach

You do not currently have access to this article

Please login to access the full content.

Access to the full content requires a subscription

Printed from Oxford Research Encyclopedias, International Studies. Under the terms of the licence agreement, an individual user may print out a single article for personal use (for details see Privacy Policy and Legal Notice).

date: 29 April 2024

• Cookie Policy
• Privacy Policy
• Legal Notice
• Accessibility
• [66.249.64.20|185.147.128.134]
• 185.147.128.134

Character limit 500 /500

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research Research Tools and Apps

Theoretical Research: Definition, Methods + Examples

Research is the careful study of a particular research problem or concern using the scientific method. A theory is essential for any research project because it gives it direction and helps prove or disprove something. Theoretical basis helps us figure out how things work and why we do certain things.

Theoretical research lets you examine and discuss a research object using philosophical ideas and abstract theoretical structures.

In theoretical research, you can’t look at the research object directly. With the help of research literature, your research aims to define and sketch out the chosen topic’s conceptual models, explanations, and structures.

LEARN ABOUT: Research Process Steps

This blog will cover theoretical research and why it is essential. In addition to that, we are going to go over some examples.

What is the theoretical research?

Theoretical research is the systematic examination of a set of beliefs and assumptions.

It aims to learn more about a subject and help us understand it better. The information gathered in this way is not used for anything in particular because this kind of research aims to learn more.

All professionals, like biologists, chemists, engineers, architects, philosophers, writers, sociologists, historians, etc., can do theoretical research. No matter what field you work in, theoretical research is the foundation for new ideas.

It tries to answer basic questions about people, which is why this kind of research is used in every field of knowledge.

For example , a researcher starts with the idea that we need to understand the world around us. To do this, he begins with a hypothesis and tests it through experiments that will help him develop new ideas. 

What is the theoretical framework?

A theoretical framework is a critical component in research that provides a structured foundation for investigating a specific topic or problem. It encompasses a set of interconnected theories, existing theories, and concepts that guide the entire research process. 

The theoretical framework introduces a comprehensive understanding of the subject matter. Also, the theoretical framework strengthens the research’s validity and specifies the key elements that will be explored. Furthermore, it connects different ideas and theories, forming a cohesive structure that underpins the research endeavor.

A complete theoretical framework consists of a network of theories, existing theories, and concepts that collectively shape the direction of a research study. 

The theoretical framework is the fundamental principle that will be explored, strengthens the research’s credibility by aligning it with established knowledge, specifies the variables under investigation, and connects different aspects of the research to create a unified approach.

Theoretical frameworks are the intellectual scaffolding upon which the research is constructed. It is the lens through which researchers view their subject, guiding their choice of methodologies, data collection, analysis, and interpretation. By incorporating existing theory, and established concepts, a theoretical framework not only grounds the research but also provides a coherent roadmap for exploring the intricacies of the chosen topic.

Benefits of theoretical research

Theoretical research yields a wealth of benefits across various fields, from social sciences to human resource development and political science. Here’s a breakdown of these benefits while incorporating the requested topics:

Predictive power

Theoretical models are the cornerstone of theoretical research. They grant us predictive power, enabling us to forecast intricate behaviors within complex systems, like societal interactions. In political science, for instance, a theoretical model helps anticipate potential outcomes of policy changes.

Understanding human behavior

Drawing from key social science theories, it assists us in deciphering human behavior and societal dynamics. For instance, in the context of human resource development, theories related to motivation and psychology provide insights into how to effectively manage a diverse workforce.

Optimizing workforce

In the realm of human resource development, insights gleaned from theoretical research, along with the research methods knowledge base, help create targeted training programs. By understanding various learning methodologies and psychological factors, organizations can optimize workforce training for better results.

Building on foundations

It doesn’t exist in isolation; it builds upon existing theories. For instance, within the human resource development handbook, theoretical research expands established concepts, refining their applicability to contemporary organizational challenges.

Ethical policy formulation

Within political science, theoretical research isn’t confined to governance structures. It extends to ethical considerations, aiding policymakers in creating policies that balance the collective good with individual rights, ensuring just and fair governance. 

Rigorous investigations

Theoretical research underscores the importance of research methods knowledge base. This knowledge equips researchers in theory-building research methods and other fields to design robust research methodologies, yielding accurate data and credible insights.

Long-term impact

Theoretical research leaves a lasting impact. The theoretical models and insights from key social science theories provide enduring frameworks for subsequent research, contributing to the cumulative growth of knowledge in these fields.

Innovation and practical applications

It doesn’t merely remain theoretical. It inspires innovation and practical applications. By merging insights from diverse theories and fields, practitioners in human resource development devise innovative strategies to foster employee growth and well-being.

Theoretical research method

Researchers follow so many methods when doing research. There are two types of theoretical research methods.

• Scientific methods
• Social science method 

Let’s explore them below:

Scientific method

Scientific methods have some important points that you should know. Let’s figure them out below:

• Observation: Any part you want to explain can be found through observation. It helps define the area of research.
• Hypothesis: The hypothesis is the idea put into words, which helps us figure out what we see.
• Experimentation: Hypotheses are tested through experiments to see if they are true. These experiments are different for each research.
• Theory: When we create a theory, we do it because we believe it will explain hypotheses of higher probability.
• Conclusions: Conclusions are the learnings we derive from our investigation.

Social science methods

There are different methods for social science theoretical research. It consists of polls, documentation, and statistical analysis.

• Polls: It is a process whereby the researcher uses a topic-specific questionnaire to gather data. No changes are made to the environment or the phenomenon where the polls are conducted to get the most accurate results. QuestionPro live polls are a great way to get live audiences involved and engaged.
• Documentation: Documentation is a helpful and valuable technique that helps the researcher learn more about the subject. It means visiting libraries or other specialized places, like documentation centers, to look at the existing bibliography. With the documentation, you can find out what came before the investigated topic and what other investigations have found. This step is important because it shows whether or not similar investigations have been done before and what the results were.
• Statistic analysis : Statistics is a branch of math that looks at random events and differences. It follows the rules that are established by probability. It’s used a lot in sociology and language research. 

Examples of theoretical research

We talked about theoretical study methods in the previous part. We’ll give you some examples to help you understand it better.

Example 1: Theoretical research into the health benefits of hemp

The plant’s active principles are extracted and evaluated, and by studying their components, it is possible to determine what they contain and whether they can potentially serve as a medication.

Example 2: Linguistics research

Investigate to determine how many people in the Basque Country speak Basque. Surveys can be used to determine the number of native Basque speakers and those who speak Basque as a second language.

Example 3: Philosophical research

Research politics and ethics as they are presented in the writings of Hanna Arendt from a theoretical perspective.

LEARN ABOUT: 12 Best Tools for Researchers

From our above discussion, we learned about theoretical research and its methods and gave some examples. It explains things and leads to more knowledge for the sake of knowledge. This kind of research tries to find out more about a thing or an idea, but the results may take time to be helpful in the real world. 

This research is sometimes called basic research. Theoretical research is an important process that gives researchers valuable data with insight.

QuestionPro is a strong platform for managing your data. You can conduct simple surveys to more complex research using QuestionPro survey software.

At QuestionPro, we give researchers tools for collecting data, such as our survey software and a library of insights for any long-term study. Contact our expert team to find out more about it.

FREE TRIAL         LEARN MORE

MORE LIKE THIS

NPS Survey Platform: Types, Tips, 11 Best Platforms & Tools

Apr 26, 2024

User Journey vs User Flow: Differences and Similarities

Best 7 Gap Analysis Tools to Empower Your Business

Apr 25, 2024

12 Best Employee Survey Tools for Organizational Excellence

Other categories.

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

An official website of the United States government

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

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

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Front Hum Neurosci

Theory of mind: mechanisms, methods, and new directions

Lindsey j. byom.

1 Department of Communication Sciences and Disorders, University of Wisconsin-Madison, Madison, WI, USA

Bilge Mutlu

2 Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA

Theory of Mind (ToM) has received significant research attention. Traditional ToM research has provided important understanding of how humans reason about mental states by utilizing shared world knowledge, social cues, and the interpretation of actions; however, many current behavioral paradigms are limited to static, “third-person” protocols. Emerging experimental approaches such as cognitive simulation and simulated social interaction offer opportunities to investigate ToM in interactive, “first-person” and “second-person” scenarios while affording greater experimental control. The advantages and limitations of traditional and emerging ToM methodologies are discussed with the intent of advancing the understanding of ToM in socially mediated situations.

Introduction

Every day, humans engage in a wide variety of social interactions to achieve a diverse set of social goals that include acquiring information, influencing a partner's behavior through, and maintaining emotional intimacy through sharing thoughts and feelings. Integral to an individual's success in these social encounters is his or her ability to reason about the thoughts, beliefs, and feelings of others to predict behavioral responses. This ability has been termed theory of mind (ToM; Premack and Woodruff, 1978 ; Baron-Cohen et al., 1985 ).

The social importance of ToM can be demonstrated by imagining social interaction without it. To demonstrate the difficulty of explaining human behavior without ToM, Baron-Cohen ( 1997 ) used the task of explaining the behavior of a man who walks into a room, looks around, and then simply exits the room. Plausible mentalistic explanations can be easily generated for this scenario (e.g., the man forgot why he entered the room, the man was looking for something in the room and discovered it was not there); however, developing plausible non-mentalistic explanations of the man's behavior is more difficult (Baron-Cohen, 1997 ). The difficulties in generating concise and probable non-mentalistic explanations for this set of simple behaviors illustrate, on a small scale, the confusion that might result from complex behaviors like deception, persuasion, or flirting in the absence of ToM. Indeed, recognition of ToM's importance for social functioning has sparked extensive research in clinical populations for whom challenges in social interaction are common, including individuals with autism spectrum disorders (Perner et al., 1989 ; Happé, 1994 ; Baron-Cohen et al., 1995 , 1999 , 2001 ; Hill, 2004 ; Losh et al., 2012 ), schizophrenia (Corcoran et al., 1995 ; Brüne et al., 2007 ; Champagne-Lavau and Stip, 2010 ; Couture et al., 2011 ; Hooker et al., 2011 ), and traumatic brain injury (Bibby and McDonald, 2005 ; Havet-Thomassin et al., 2006 ; Henry et al., 2006 ; Milders, 2006 ; Muller et al., 2010 ; Turkstra et al., 2004 ; Turkstra, 2008 ).

Given the importance of ToM in daily interactions and the prevalence of ToM deficits in some clinical populations, it is important for researchers to critically consider both the concept of ToM as well as tasks used to investigate it. Accordingly, the goals of this article are to (1) present a framework of mechanisms that allow humans to infer and reason about mental states in social interaction, (2) review the benefits and limitations of current behavioral tasks designed to test each mechanism, and (3) discuss potential new directions for studying and understanding ToM, with consideration of both the advantages and the limitations that these approaches offer over more traditional techniques. It is our position that the knowledge to be gained from the incorporation of these new methodologies may advance not only the understanding of how humans reason about the mental states of others, but may also further sciences devoted to improving or compensating for ToM impairments and artificial intelligence research that is focused on developing artificial models of human-like social processes.

Mechanisms of ToM

Since ToM research has been broad in terms of the interested disciplines, target populations and testing methodologies used, it is important to critically consider the concept of ToM, especially the ways in which our conceptualizations influence the course of ToM research. Developing a “working definition” of ToM will help guide research on not only the underlying network of skills that facilitate ToM, but may also provide insight into where breakdowns in ToM may occur. To begin forming such a definition, we posed the question, “How do humans accurately infer the mental states of others?” From this consideration, three components of interaction emerged as clues to ToM: (1) knowledge of the shared context, (2) perception of social cues, and (3) interpretations of actions, (See Table ​ Table1 1 and Figure ​ Figure1). 1 ). These components, and the experimental tasks developed to assess each, are reviewed below.

A summary of tasks used to test key mechanisms of Theory of Mind .

A storyboard of the Sally-Ann or Location False Belief Task (Wimmer and Perner, 1983 ) used to test attribution of mental states to others in children .

Shared world knowledge

ToM, as one manifestation of cognition, is situated in the context of the surrounding world (Wilson, 2002 ). Consider, for example, the ToM demands implicit in a typical conversation. During a conversation, individuals must quickly infer their partners' thoughts, beliefs, emotions, and goals in order to formulate an appropriate response. As with other forms of joint action, making appropriate responses in conversation also requires the integration of cues from the conversational partner and the context, including prior world knowledge (e.g., amount of personal space with which a partner might be comfortable), knowledge about the relationship between individuals (e.g., how much disclosure is appropriate with a close friend vs. a co-worker), the goal of the interaction (e.g., what information is required to complete a joint task), and the conditions under which the conversation will occur (e.g., in a group setting) to make quick, on-line guesses about a partners' mental states (for review see Sebanz et al., 2006 ; Knoblich et al., 2011 ).

To investigate how shared world knowledge might facilitate ToM, researchers commonly present participants with short descriptions or picture sets of social scenarios and then ask participants to infer the mental states of characters or to predict characters' behaviors based on these inferred mental states (Happé, 1994 ; Havet-Thomassin et al., 2006 ). Happé's Strange Stories Task (1994) is one example of this type of task. The Strange Stories Task tests the ability to use prior world knowledge in order to understand several communication acts embedded in story situations, including faux pas, persuasion, pretending, and deception, and to accurately select the intended story interpretation.

Tasks like the Strange Stories Task, designed to assess an ability to reason about mental states through integration of shared world knowledge, are valuable in that they have been found to detect ToM deficits in both individuals with autism spectrum disorders and traumatic brain injury (Happé, 1994 ; Havet-Thomassin et al., 2006 ). However, it is important to consider that these tasks impose considerable cognitive demands while testing ToM, especially demands on working memory and, in the case of verbal or text-based tasks, linguistic processing. For example, in interpreting a story describing a scenario in which a character tells a white lie, participants must process the language of the story and hold the relevant information in working memory while that information is integrated with prior knowledge and an interpretation is selected. These demands become especially critical when investigating ToM of individuals who may have language disorders or deficits in working memory. Further, many of these tasks are passive and reflective in nature (e.g., Happé, 1994 ; Brüne et al., 2007 ), which may overestimate ToM ability in daily life. For example, tasks requiring individuals to make mental state inferences from described scenarios generally allow ample response time and do not typically require individuals to formulate appropriate responses as if they themselves were in the situation.

Perceiving social cues

Another way in which humans infer the mental states of others is through the perception of various social cues. Though mental states are inherently cognitive phenomena, humans have a sophisticated repertoire of behaviors, including gaze cues, facial expressions, and vocal cues, through which they express their mental states.

The perception of gaze cues as a method in which humans infer mental states is one of the most studied mechanisms of ToM. Gaze cues signal the basic direction or object of one's attention (Bayliss et al., 2007 ; Frischen et al., 2007 ), and by following a partner's gaze, an individual is able to infer his or her partner's intentions (Baron-Cohen et al., 1995 ). An individual's ability to use gaze-based cues to infer the knowledge of others develops in childhood, and these cues have been found to outweigh deceptive verbal cues in experimental paradigms (Freire et al., 2004 ). Additionally, gaze cues in conversation allow an individual to monitor understanding of his or her message (Clark and Krych, 2004 ) and also to signal a desire for partner feedback or to surrender the speaking floor, (Kendon, 1967 ; Duncan, 1972 ; Bavelas et al., 2002 ). Further, speaker gaze cues may work to resolve linguistic ambiguities in non-literal language like sarcasm, as Williams et al. ( 2009 ) reported. For example, in Western cultures speakers tend to look away from their partners while making sarcastic comments, signaling that the speaker does not actually believe what he or she is saying (Williams et al., 2009 ).

Emotion recognition likewise has received much research attention as, like gaze cues, facial, and vocal emotional cues are valuable in the inference of mental states (De Sonneville et al., 2002 ). Typically, humans develop the ability to discriminate and perceive changes in facial expressions very early in life; however, the speed and accuracy with which children identify and match facial emotions continues to develop into adulthood (Barrera and Maurer, 1981 ; McClure, 2000 ). Both children and adults are generally quicker and more accurate in their identification of positive emotions like “happy” as compared to negative emotions like “sad” (De Sonneville et al., 2002 ). Humans also convey emotional content in aspects of vocal production including vocal intensity, prosody, quality, and speech rate.

Several tasks exist to study both gaze behavior and emotion recognition. Much has been learned about gaze behavior through the observation of dyadic interactions (e.g., Duncan, 1972 ; Clark and Krych, 2004 ) as well as through experimental manipulation of gaze cues (Baron-Cohen et al., 1995 ; Bayliss and Tipper, 2006 ). In a standard gaze perception task, individuals are shown a face with the eyes either oriented straight ahead or shifted in one direction. From these images, participants are asked to make inferences about the characters' intentions or mental states (Frischen et al., 2007 ). Similar tasks have been developed to assess comprehension of facial emotion (Ekman and Friesen, 1976 ). In one example, De Sonneville et al. ( 2002 ) presented participants with four faces, each of which portrayed a different emotion, and asked participants to determine whether or not a target emotion was demonstrated in one of the four foils. These authors also used a matching emotion recognition task, in which participants decided if two faces showed the same or different emotions (De Sonneville et al., 2002 ). Another method used to evaluate emotion recognition is to determine how accurately participants identify emotions from facial expressions with varying levels of subtlety. Thomas et al. ( 2007 ) employed a task in which participants viewed photographs of people portraying different degrees of various emotions, from very subtle anger to very obvious happiness, to detect the accuracy of participant emotion recognition. Vocal emotion recognition can also be evaluated with tasks similar to those designed to measure facial emotion. In these tasks, participants generally hear semantically neutral sentences with different forms of emotional prosody and are asked to identify the emotion of the speaker (Nowicki and Carton, 1993 ; Scherer and Scherer, 2011 ).

Like tasks requiring the use of shared world knowledge for ToM reasoning, social cue perception tasks have greatly contributed to what is understood about mental state reasoning. These tasks too, however, share limitations in their reflective, offline design, and limited ecological validity. Considering emotion recognition tasks, for example, participants are often presented with decontextualized images of faces (e.g., Bowers et al., 1999 ; Baron-Cohen et al., 2001 ) and are asked to either identify the emotion or to match it to a target (De Sonneville et al., 2002 ; Thomas et al., 2007 ). Additionally, even when stimuli are dynamic in the form of video clips (McDonald et al., 2006 ), participants are still given time to observe the stimulus, consider its properties, and make a judgment. In daily life, emotional displays, are fleeting but are rarely presented in isolation—redundant clues to mental states are presented in partners' words, faces, voices, and actions. This combination of presenting isolated social cues, which may underestimate actual abilities, and prolonged observation and thinking time, which may overestimate abilities, make it difficult to establish an accurate picture of the perception of social cues in everyday interaction. These limitations are of clinical importance because the ability to infer mental states from social cues has been commonly studied as a means to better understand the impact of social deficits on functioning in everyday life (Spell and Frank, 2000 ; Baron-Cohen et al., 2001 ; Croker and McDonald, 2005 ; Tonks et al., 2007 ; Turkstra, 2008 ; Zupan et al., 2009 ).

Interpretation of actions

Research on the development of ToM has provided evidence that children as young as 6 months of age form expectations regarding how humans interact with other humans and inanimate objects (Legerstee et al., 2000 ). As humans, we generally believe that others act in ways that are consistent with their beliefs and goals (Heider and Simmel, 1944 ; Ajzen, 1991 ). Given this assumption, passively observing behavior can offer important clues regarding the intentions or beliefs of others. Several tasks have been developed to evaluate participants' abilities to infer mental states from behavior (Wimmer and Perner, 1983 ). For example, in a standard false-belief task (e.g., Wimmer and Perner, 1983 ), participants infer a character's belief based on the observations of her actions.

Tasks requiring the interpretation of actions are frequently used in developmental literature (Baron-Cohen et al., 1985 ; Luo, 2011 ; Scott et al., 2012 ), but studies of joint action have also provided insight into how humans interpret actions to infer the mental states of others (for review see Sebanz et al., 2006 ; Knoblich et al., 2011 ). For example, Sebanz et al. ( 2006 ) suggest that using gaze cues to infer what someone is attending to, as well as knowing what task the person is engaged in, helps observers to predict others' action goals. Supporting the relationship between observing actions and inferring mental states, Ramnani and Miall ( 2004 ) trained participants to perform a button-press task in response to visual symbols, with the symbol color indicating whether the participant, a partner, or the computer should respond. Neuroimaging data from this experiment suggested that predicting another's actions (i.e., predicting when a partner should act) activated neural regions important for ToM (Ramnani and Miall, 2004 ).

While tasks designed to test ToM through the observation of actions are inherently passive in nature, joint action tasks, like that used by Ramnani and Miall, have allowed for the study of ToM abilities in simulated interactions as opposed to simply reasoning about social scenarios as third-party observers.

Emerging tools and methods to study ToM

The mechanisms discussed above allow individuals to draw on information about the actions, behaviors, and knowledge of others to make inferences about their thoughts, beliefs, feelings, and intentions. Individuals gather this information through reciprocal interactions and process it on-line to make ToM inferences and determine subsequent behavior. To study this interactive, on-line social-cognitive process, research on ToM has primarily used experimental paradigms that involve participants making ToM inferences from stimuli presented as static images (Baron-Cohen et al., 2001 ; De Sonneville et al., 2002 ; Mutlu et al., 2009 ), textual stories (Happé et al., 1998 ), or video vignettes (Turkstra, 2008 ) that provide an observation-based, reflective “third-person” understanding of ToM (Frith and Frith, 2006 ; Schilbach et al., 2012 ). Schilbach et al. ( 2012 ) argue that ToM—and social cognition in general—has fundamentally different motivational consequences and underlying neural processes when individuals are socially and emotionally engaged with others than when they are third-person observers. Interacting with others provides individuals with the ability to perform active conversational roles, which might include initiating or responding to episodes of interaction, rather than observing the interaction as a bystander. This active involvement also facilitates shared goals, intentions, and actions among the participants of the interaction, providing individuals with the ability to draw on firsthand experience in making ToM inferences.

Recent research has highlighted the limitations of experimental methods that provide “third-person” evaluations of social phenomena and has proposed a “second-person” approach to studying social cognition (Wilms et al., 2010 ; Duff et al., 2012 ; Risko et al., 2012 ; Schilbach et al., 2012 ). Risko et al. ( 2012 ) suggested that experimental paradigms designed to study social cognition form a continuum between simple, static representations of socially relevant stimuli and actual live social interaction or between “reel” and “real” instances of interaction. This continuum includes static schematic faces, dynamic schematic faces, static photographs of faces, static photographs of people in complex social scenes, dynamic images of people in complex social scenes, situations with the potential for real social interaction, and real social interactions. Studies that compare responses to stimuli from different sections of this continuum show significant differences. For instance, imaging studies show that direct gaze elicits significantly greater brain response than either gaze aversion or no gaze, but only when participants observe live stimuli and not when they observe static images (Ponkanen et al., 2010 ). Similarly, while the propensity to look toward another individual's eyes exists across the spectrum from schematic faces to dynamic social scenes, potential for actual social interaction significantly affects this propensity (Risko et al., 2012 ). These results highlight key limitations in the use of existing “third-person” paradigms in the study of ToM mechanisms and motivate the use of alternative approaches that afford studying “first-person” or “second-person” social cognition in “real” experimental paradigms.

First- and second-person approaches to studying ToM

An emerging approach that seeks to build a first- or second-person understanding of ToM mechanisms and processes is the use of simulation-based computational methods such simulated social interaction (Blascovich et al., 2002 ) and cognitive simulation (Scassellati, 2002 ). These methods draw on advancements in computer sciences to employ complex computational systems that enable the simulation of embodied, situated interactions and thus the development of protocols with great experimental control and ecological validity.

Simulated social interaction

Simulated social interaction involves generating social behavior in artificial agents such as virtual characters, which are often embedded in immersive virtual environments or as humanlike robots. Simulated social interaction offers greater experimental control and ecological validity than do traditional “reel” experimental paradigms (Loomis et al., 1999 ; Blascovich et al., 2002 ; MacDorman and Ishiguro, 2006 ; Mutlu et al., 2009 ; Wilms et al., 2010 ). In these experimental paradigms, participants interact with simulated others whose behaviors are precisely controlled to reflect experimental manipulations and who respond to changes in the participants' behaviors affording interactions that more closely resemble real-world interactions than static simuli do. These interactions might take place in immersive virtual environments, in the physical environment with a virtually simulated character (Pelphrey and Carter, 2008 ; Wilms et al., 2010 ), or in the physical environment with a humanlike robot (MacDorman and Ishiguro, 2006 ; Mutlu et al., 2009 ) (Figure ​ (Figure2 2 ).

Illustrations of three simulated experimental paradigms: (1) immersive virtual environments, (2) physical environment with a virtually simulated character, and (3) physical environment with a humanlike robot .

Simulated social interaction offers a number of advantages over traditional experimental paradigms used for the study of ToM including precise control of experimental stimuli, interactive, dynamic social interaction, on-line processing and measurement. Thus, these methods more closely approximate the ToM demands of everyday interactions. Simulations of social stimuli follow computational representations of human behavior, which provide the experimenter with control parameters for the behavior or mechanism under study and the ability to create experimental manipulations that are impossible or infeasible for human confederates to perform. In a recent study, Andrist et al. ( 2012 ) developed a parametric model of gaze shifts and precisely manipulated how much the head of a virtual character aligned with its gaze target, creating two kinds of gaze shifts: affiliative and referential. Affiliative gaze shifts involved the virtual character aligning the head more toward the participant when it shifted its gaze in the environment, while referential gaze shifts involved the character aligning its gaze more with its gaze target (Figure ​ (Figure3). 3 ). The results showed that affiliative gaze increased subjective evaluations of the character and the interaction, while referential gaze increased recall of information from the environment (Andrist et al., 2012 ).

The two types of gaze behaviors designed as experimental stimuli: referential (left) and affiliative (right) (Andrist et al., 2012 ) .

The experimental paradigms involved in simulated social situations go beyond “presenting social stimuli,” but also offer reciprocal social interaction. For instance, Wilms et al. ( 2010 ) developed an experimental paradigm in which a virtual character shifted its gaze jointly with the participant using real-time eye-tracking input. Their results showed that establishing joint attention to an object of interest elicited greater activity in the brain (medial prefrontal cortex and posterior cingulate cortex) than did attending to the object non-jointly. The simulation approach also affords on-line processing and measurement of social interaction. For instance, another imaging study showed that a virtual character elicited greater brain activation in the superior temporal sulcus when it established mutual gaze with participants than it did when it averted its gaze as it passed by participants in the virtual world (Pelphrey et al., 2004 ), allowing researchers to measure an on-line neural response that could be captured by reflective approaches.

The precise control, interactivity, and on-line processing afforded by this experimental approach offer greater ecological validity for the study of ToM and social cognition. In a study that embodies these characteristics, Mutlu et al. ( 2009 ) explored how leakage cues—non-verbal cues that individuals give off on their thoughts, beliefs, and intentions—presented by a robot might elicit ToM inferences in participants. Participants played a version of the 20-Questions guessing game with a humanlike robot in which the robot covertly picked one of the items that were laid on a table and the participants tried to guess which item the robot picked by asking questions that the robot could only answer with “Yes” and “No” (Figure ​ (Figure4). 4 ). In half of the trials, the robot produced a brief gaze shift toward the item before answering the questions, producing a leakage cue, while it did not shift its gaze in the other half. Participants were able to identify the robot's pick with fewer questions and in shorter time when the robot produced leakage cues than they did when the robot did not leak information, suggesting that the participants used the socially relevant information to make ToM inferences and to more effectively narrow down the response options. Furthermore, the majority of participants did not report noticing leakage cues or using this information in the task, suggesting an implicit processing of such cues. This experimental paradigm offers the ability to precisely control the gaze cues presented by the robot, present these stimuli in an interactive, dynamic protocol, and support on-line processing toward shaping subsequent behavior in the interaction. The experiment also contextualized ToM processes in a simulated interaction that closely resembled face-to-face interaction and captured effects of ToM processes on objective measures of task performance.

Participants interacting with two humanlike robots in an interactive protocol designed to study how individuals might use nonverbal leakage to make ToM inferences (Mutlu et al., 2009 ) .

Cognitive simulation

While simulated social interaction offers the ability to generate precisely controlled social behaviors in artificial agents and to create dynamic, interactive experimental scenarios, this approach relies primarily on pre-scripted and experimenter-controlled protocols—often referred to as Wizard-of-Oz studies (Dahlbäck et al., 1993 ). This approach does not afford the study of ToM processes in complex interactions such as joint action scenarios (Sebanz et al., 2006 ) that require all parties to engage ToM mechanisms in coordinating their actions toward a common goal. A complementary approach to simulated social interaction is cognitive simulation, which seeks to develop artificial representations of neurocognitive mechanisms such as imitation and perception of self, simulate them in artificial agents such as humanlike robots, and assess their functioning in enabling ToM inferences in human-agent interactions (Breazeal and Scassellati, 2002 ; Scassellati, 2002 ; Michel et al., 2004 ).

Building on simulation theory (Gallese and Goldman, 1998 ), cognitive simulation involves the robot establishing and maintaining representations of the mental states of its human counterparts by tracking and matching their states with resonant states of its own. These representations enable the robot to take the perspective of its human counterparts, make inferences about the human's goals, and learn from their actions. For instance, Trafton et al. ( 2005 ) developed a cognitive simulation that enabled the robot to simulate “alternative worlds” and assess propositions about these worlds in order to make inferences about the perspective of its human counterparts. A similar approach by Breazeal et al. ( 2006 ) involved the robot maintaining separate sets of “beliefs” in its belief system for itself and for its human counterparts. The separate sets of beliefs enabled the robot to identify differences in its beliefs from those of its human counterparts in order to plan actions that it might take or skills it might learn in order to establish a shared set of beliefs.

Examples of the cognitive simulation approach also include simulations of the motor-resonance mechanism (Blakemore and Decety, 2001 ) for enabling ToM inferences in artificial agents. These examples build on the finding that observing the actions of others elicits automatic activation of motor representations associated with these actions and enables predictions about action consequences. For instance, Gray et al. ( 2005 ) developed a system in which the robot parsed the actions of its human counterparts and matched them to actions in its own repertoire in order to make inferences about the goals of the participant. These inferences enabled the robot to perform task-level simulations and track the participant's progress in the task in order to anticipate the needs of its partner and offer help. A model developed by Bicho et al. ( 2011 ) extended this paradigm by including a mapping between observed actions and complementary actions required to successfully complete a task and enabled the robot to more effectively coordinate its actions with those of its human counterparts in the task.

The preceeding examples illustrate how cognitive simulation might complement the simulated interaction approach for studying ToM mechanisms by simulating ToM processes in artificial agents. When coupled, the two approaches promise two key methodological advances in the study of ToM. First, they help in assessing existing neurocognitive models of ToM mechanisms by computationally simulating them and observing system behavior in interactive situations. Second, they enable empirical studies to build new understanding of ToM processes in truly interactive protocols in which all agents—human or artificial—involved in the interaction employ ToM mechanisms. The coupling of the two approaches extends the methodological advantages of simulated social interaction by enabling not only on-line measurement of responses to social stimuli but also on-the-fly precise control over simulated cognitive mechanisms and social behaviors, thus affording even greater experimental control. The truly interactive setting of the protocols enabled by the coupling of these two approaches also improves their ecological validity.

Example paradigms for studying ToM mechanisms

Recent research across many fields of social, cognitive, and computational sciences has developed first- and second-person experimental methods to study ToM mechanisms with the goal of gaining a better understanding of these mechanisms and designing artificial agents that effectively interact with people. The paragraphs below illustrate paradigms that study the three key ToM mechanisms described earlier.

In making ToM inferences, individuals draw on a shared world knowledge to integrate information from various sources including resources in the environment, knowledge about social norms, the goals of the interaction, the relationship among interaction partners, and the participation structure of the interaction. Mumm and Mutlu ( 2011 ) investigated how the relationship between a humanlike robot and participants affected the participants' preferences for interpersonal distance, creating a “likable” or “unlikable” humanlike robot using verbal framing. Participants responded to the unlikable robot's attempts at increasing intimacy using mutual gaze by physically distancing themselves from the robot (Figure ​ (Figure5), 5 ), while they did not change their proxemic behavior with a likable robot. The results suggest that the experimental protocol successfully established different relationships between the robot and the participant across the two conditions, which in turn shaped their preferences for interpersonal distance, enabling on-line processing of gaze stimuli and measurement of preferences for interpersonal distance directly from the distance that the participants maintained with the robot.

Participants maintaining different amounts of distance between themselves and an “unlikable” robot in response to the robot's attempts to increase intimacy by following or avoiding the participants with its gaze (Mumm and Mutlu, 2011 ) .

Another study by Mutlu et al. ( 2009 ) explored how a robot might use gaze cues to signal participation structure in a three-party conversation and how this structure shaped participants' conversational behavior. There were three conditions, which differed according to the percent of time the robot gazed at each of the two participants in that condition. In one condition, the robot looked exclusively at one participant (the addressee ), signaling that the second participant had the role of overhearer . In the second condition, the robot looked mostly at the addressee and occasionally at the second participant, indicating a role of bystander . In the third condition, gaze was distributed equally between the two participants, indicating that both were addressees . The results from the study showed that the participants conformed to the norms of their signaled roles in their conversational behavior 97% of the time and rated their experience with the conversation and involvement in the group consistently with their conversational roles (e.g., feeling excluded in the overhearer condition). The simulated social interaction approach enabled precise control of the robot's gaze behaviors to signal specific participation structures and illustrated how individuals integrate a perception of the robot's behavioral cues and their world knowledge, particularly the norms associated with the participation structure of a conversation, to make ToM inferences about the conversational intentions of the robot and follow the norms associated with inferred intentions.

Perception of social cues

ToM inferences are also informed by perceptions of social cues such as gaze. The study by Mutlu et al. ( 2009 ) illustrates how ToM might be studied using simulated social interaction. Examples of simulation-based protocols also include studies that explore how the precise temporal and spatial congruency of such cues might affect outcomes such as joint attention, information recall, and task performance (Staudte and Crocker, 2011 ; Huang and Mutlu, 2012 ). Staudte and Crocker ( 2009 ) developed a video-based experimental paradigm in which a robot presented factual statements about objects in the environment such as “the sphere is next to a pyramid” and the robot looked toward the referenced objects (e.g., sphere and pyramid) 800–1000 milliseconds before the object names were spoken. The study manipulated the congruency between the robot's linguistic and gaze references and showed that participants confirmed the correctness of the statements faster when the two references were congruent. The simulation-based approach not only ensured that gaze cues were presented with precise timings but also enabled the presentation of incongruent cues in gaze and speech that is difficult to reliably produce by human confederates in an interactive protocol.

Another study by Huang and Mutlu ( 2012 ) extended these results by comparing congruent gaze and speech cues to temporally incongruent cues by introducing a delay into the robot's gaze shifts such that they were produced 500—1000 milliseconds after the onset of linguistic references. The study also contextualized these comparisons in two realistic tasks. The first task involved the robot recounting a story and referring—using linguistic and gaze cues—to a set of props. In the second task, the robot provided instructions to participants to complete a sorting task, referring to objects to be sorted using linguistic and gaze cues (Figure ​ (Figure6). 6 ). The results from the study showed that participants recalled the details of the story better in the first task than the second task, and were faster at locating to-be-sorted objects when the robot used congruent speech and gaze cues than when cues were spatially or temporally incongruent. In addition to extending the results obtained by Staudte and Crocker ( 2009 ) to a physically situated paradigm, this study demonstrated the role of temporal congruency in ToM processes. The study also illustrated the effects of perception of social cues on the outcomes of ToM processes such as information recall and task performance in joint action. When the robot used congruent gaze cues, participants established stronger associations between objects in the environment and verbal information presented by the robot, thereby showing improved task performance.

The robot is using gaze cues to establish joint attention with the participants in a storytelling task (top) and a sorting task (bottom) (Huang and Mutlu, 2012 ) .

Mechanisms such as action observation (Knoblich et al., 2011 ) also facilitate ToM processes by establishing a “procedural common ground” between interaction partners (Clark, 1996 ). Paradigms developed to study this mechanism most commonly follow the cognitive simulation approach (e.g., Trafton et al., 2005 ; Breazeal et al., 2006 ) or employ a coupling of the simulated social interaction and cognitive simulation (e.g., Gray et al., 2005 ; Bicho et al., 2011 ). A recent study used a paradigm in which a robot provided participants with instructions on how to assemble a structure and, by observing the participant's actions, maintained an internal model of the participants' progress, specifically modeling potential breakdowns in the participant's understanding or execution of the instructions (Mutlu et al., 2013 ) (Figure ​ (Figure7). 7 ). When the robot inferred misunderstandings or confusion from its observations of participant actions or lack thereof, it offered clarifications on the actions that the participant must take to successfully progress in the task using conversational repair strategies (Clark, 1994 ). Compared to the robot only providing instructions, the robot that made ToM inferences through action observation and offered clarifications based on these inferences received fewer requests for help from the participants when breakdowns occurred. This protocol illustrates how the cognitive simulation and simulated social interaction approaches might be combined to create truly interactive experimental paradigms in which participants and artificial agents employ ToM mechanisms. Cognitive simulation enabled the robot to use its observations of participant actions to infer misunderstandings and confusions and to plan appropriate actions to offer clarifications. The simulated social interaction approach established communication between the participant and the robot and enabled the robot to use conversational strategies to execute its plan to offer clarifications.

The robot combines cognitive simulation and simulated social behaviors to maintain a model of the participant's progress in the task using action observation and to provide feedback using conversational repair (Mutlu et al., 2013 ) .

Limitations of computational paradigms

Simulation-based approaches offer unprecedented methodological advantages for studying social cognition and ToM processes in particular. They offer greater experimental control, on-line processing of stimuli, a richer set of measures for ToM outcomes, and truly interactive experimental scenarios, enabling the study of complex interactions and affording more ecologically valid research. These approaches, however, build on a number of assumptions and are affected by a number of factors that limit their promise.

Differences in simulated and real interactions

A key assumption that the simulation-based approaches discussed above make is that simulations match “real” stimuli and that interactions with artificial agents are equivalent to interactions with humans. While theoretical accounts such as the mindlessness hypothesis (Langer, 1992 ; Nass and Moon, 2000 ) and the findings presented above suggest that simulated interactions closely approximate human interactions, a critical examination of the differences between these forms of interactions is essential.

Design of computational platforms

Another factor that might limit the generalizability of findings obtained in simulated approaches is the design of the simulation platform. For instance, the experimental protocol developed by Mutlu et al. ( 2009 ) to study leakage gaze cues employed two robot platforms to investigate whether the design of the specific robot platform used in the study affected these inferences. The results showed that leakage gaze cues elicited ToM inferences, thus improving participant performance in guessing the robot's pick, when presented by a highly humanlike android but not when presented by a robot with a stylized design that featured cartoon-like features (although a trend in the data toward an effect was present). This finding suggests that the physical design of the robot platform might affect social cognition and ToM inferences with artificial agents and that different designs might vary in the extent to which they activate ToM mechanisms. While the choice of experimental materials and the design of experimental stimuli has been found to affect social cognition in conventional experimental paradigms, such as the attractiveness of faces shown in stimuli interacting with gaze manipulations (Kampe et al., 2001 ), a better understanding of how design features for artificial agents affect social cognition and the development of benchmark platforms for research in social cognition might help isolate such effects.

Simplifications in simulations

Simulations of cognitive and behavioral mechanisms necessarily involve simplifications in representation and presentation due to difficulties in capturing the complexity of these mechanisms. Such simplifications might result in artificial agents displaying “robot-like” behaviors that fail in activating ToM mechanisms or eliciting feelings of eeriness or discomfort—an effect often referred to as the “uncanny valley” (Mori, 1970 ). Recent findings indicate that exposure to artificial agents results in a shift in what participants consider to be acceptable or preferred human qualities (Chen et al., 2010 ), suggesting that properly acclimating participants to simulations might alleviate such effects or that people might attune to simplified behaviors as artificial agents become commonplace.

Individual differences

A final consideration in the use of simulation-based experimental paradigms is individual differences in responses to artificial agents. Studies to date have identified individual differences based on participant sex (Mutlu et al., 2006 ), cultural background (Nomura et al., 2008 ), personality (Walters et al., 2008 ), and pet ownership (Mutlu et al., 2009 ). While such differences might also be observed in conventional experimental paradigms, whether these differences predict those in simulation-based paradigms is unknown. Simulation-based studies of ToM processes must consider demographic and attitudinal factors to establish a better understanding of their effects.

Summary and conclusions

The study of ToM has provided much knowledge regarding how the ability to reason about mental states develops in typical children and insight into impaired ToM in clinical populations. The tasks developed through this research largely measure individuals' abilities to utilize and integrate information from shared world knowledge, social cues, and physical actions to infer the mental states of others to predict future behavior. While the passive and reflective methods traditionally used to study ToM have been invaluable to understanding the mechanisms associated with the ability to infer mental states, we, with others (Risko et al., 2012 ), argue that investigations using interactive, on-line approaches to study ToM and social cognition hold great promise to enrich the established knowledge base. As described, emerging methods, including the use of virtual and robotic platforms, provide opportunities to study human behavior in social interactions while maintaining experimental control. These emerging interactive and computational methods may, in addition to extending knowledge regarding ToM processing, enable the development of innovative, technology-driven protocols for improving mentalizing in cases of disorder.

Conflict of interest statement

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

Acknowledgments

The authors wish to thank Dr. Lyn Turkstra and the anonymous reviewers for their valuable comments and feedback on this manuscript. National Science Foundation awards IIS-1017952, IIS-1149970, IIS-0121426, and ECS-0325383, JSPS Grant-in-Aid for Scientific Research (S), KAKENHI (20220002), and Mitsubishi Heavy Industries, Ltd., have provided support for the research presented as examples of simulation-based protocols. NIDCD T32DC005359 and NIDCD F31DC012481 to the University of Wisconsin-Madison Department of Communication Sciences and Disorders have provided fellowship support to the first author. The authors are also grateful for support from the University of Wisconsin-Madison Libraries.

• Ajzen I. (1991). The theory of planned behavior . Organ. Behav. Hum. Decis. Process . 50 , 179–211 10.1016/0749-5978(91)90020-T [ CrossRef ] [ Google Scholar ]
• Andrist S., Tomislav P., Mutlu B., Gleicher M. (2012). Designing effective gaze mechanisms for virtual agents , in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '12) (Austin, TX: ACM; ), 705–714 [ Google Scholar ]
• Baron-Cohen S. (1997). Mindblindness . Cambridge, MA; London: The MIT Press [ Google Scholar ]
• Baron-Cohen S., Campbell R., Karmiloff-Smith A., Grant J. (1995). Are children with autism blind to the mentalistic significance of the eyes? Br. J. Dev. Psychol . 13 , 379–398 10.1111/j.2044-835X.1995.tb00687.x [ CrossRef ] [ Google Scholar ]
• Baron-Cohen S., Leslie A. M., Frith U. (1985). Does the autistic child have a ‘theory of mind’? Cognition 21 , 37–46 [ PubMed ] [ Google Scholar ]
• Baron-Cohen S., O'Riordan M., Stone V., Jones R., Plaisted K. (1999). Recognition of faux pas by normally developing children with asperger syndrome or high-functioning autism . J. Autism Dev. Disord . 29 , 407–418 10.1023/A:1023035012436 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Baron-Cohen S., Wheelwright S., Hill J., Raste Y., Plumb I. (2001). The ‘Reading the mind in the eyes’ Test revised version: a study with normal adults, and adults with Asperger syndrome or high-functioning autism . J. Child Psychol. Psychiatry 42 , 241–251 10.1111/1469-7610.00715 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Barrera M. E., Maurer D. (1981). The perception of facial expressions by the three-month-old . Child Dev . 52 , 203–206 10.2307/1129231 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bavelas J. B., Coates L., Johnson T. (2002). Listener responses as a collaborative process: the role of gaze . J. Commun . 52 , 566–580 10.1111/j.1460-2466.2002.tb02562.x [ CrossRef ] [ Google Scholar ]
• Bayliss A. P., Frischen A., Fenske M. J., Tipper S. P. (2007). Affective evaluations of objects are influenced by observed gaze direction and emotional expression . Cognition 104 , 644–653 10.1016/j.cognition.2006.07.012 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bayliss A. P., Tipper S. P. (2006). Predictive gaze cues and personality judgments: should eye trust you? Psychol. Sci . 17 , 514–520 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bibby H., McDonald S. (2005). Theory of mind after traumatic brain injury . Neuropsychologia 43 , 99–114 10.1016/j.neuropsychologia.2004.04.027 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bicho E., Erlhagen W., Louro L., Costa e Silva E. (2011). Neuro-cognitive mechanisms of decision making in joint action: a human robot interaction study . Hum. Mov. Sci . 30 , 846–868 10.1016/j.humov.2010.08.012 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Blakemore S.-J., Decety J. (2001). From the perception of action to the understanding of intention . Nat. Rev. Neurosci . 2 , 561–567 [ PubMed ] [ Google Scholar ]
• Blascovich J., Loomis J., Beall A. C., Swinth K. R., Hoyt C. L., Bailenson J. N. (2002). Immersive virtual environment technology as a methodological tool for social psychology . Psychol. Inq . 13 , 103–124 10.1207/S15327965PLI1302_01 [ CrossRef ] [ Google Scholar ]
• Bowers D., Blonder L., Heilman K. M. (1999). Florida Affect Battery, a Manual . Gailnsville, FL: Centre for Neuropsychological Studies, Cognitive Science laboratory [ Google Scholar ]
• Breazeal C., Berlin M., Brooks A., Gray J., Thomaz A. L. (2006). Using perspective taking to learn from ambiguous demonstrations . Rob. Auton. Syst . 54 , 385–393 10.1016/j.robot.2006.02.004 [ CrossRef ] [ Google Scholar ]
• Breazeal C., Scassellati B. (2002). Robots that imitate humans . Trends Cogn. Sci . 6 , 481–487 10.1016/S1364-6613(02)02016-8 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brüne M., Abdel-Hamid M., Lehmkämper C., Sonntag C. (2007). Mental state attribution, neurocognitive functioning, and psychopathology: what predicts poor social competence in schizophrenia best? Schizophr. Res . 92 , 151–159 [ PubMed ] [ Google Scholar ]
• Carlson S. M., Moses L. J., Claxton L. J. (2004). Individual differences in executive functioning and theory of mind: an investigation of inhibitory control and planning ability . J. Exp. Child Psychol . 87 , 299–319 10.1016/j.jecp.2004.01.002 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Champagne-Lavau M., Stip E. (2010). Pragmatic and executive dysfunction in schizophrenia . J. Neurolinguist . 23 , 285–296 10.1016/j.jneuroling.2009.08.009 [ CrossRef ] [ Google Scholar ]
• Chen H., Russell R., Nakayama K., Livingstone M. (2010). Crossing the uncanny valley: adaptation to cartoon faces can influence perception of human faces . Perception 39 , 378–386 10.1068/p6492 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Clark H. H. (1994). Managing problems in speaking . Speech Commun . 15 , 243–250 10.1016/0167-6393(94)90075-2 [ CrossRef ] [ Google Scholar ]
• Clark H. H. (1996). Using Language . Cambridge: University Press; 10.1017/CBO9780511620539 [ CrossRef ] [ Google Scholar ]
• Clark H. H., Krych M. A. (2004). Speaking while monitoring addressees for understanding . J. Mem. Lang . 50 , 62–81 10.1016/j.jml.2003.08.004 [ CrossRef ] [ Google Scholar ]
• Corcoran R., Mercer G., Frith C. (1995). Schizophrenia, symptomatology and social influence: investigating ‘theory of mind’ in people with schizophrenia . Schizophr. Res . 17 , 5–13 10.1016/0920-9964(95)00024-G [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Couture S. M., Granholm E. L., Fish S. C. (2011). A path model investigation of neurocognition, theory of mind, social competence, negative symptoms and real-world functioning in schizophrenia . Schizophr. Res . 125 , 152–160 10.1016/j.schres.2010.09.020 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Croker V., McDonald S. (2005). Recognition of emotion from facial expression following traumatic brain injury . Brain Inj . 19 , 787–799 10.1080/02699050500110033 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dahlbäck N., Jönsson A., Ahrenberg L. (1993). Wizard of Oz studies—why and how . Knowl. Based Syst . 6 , 258–266 10.1016/0950-7051(93)90017-N [ CrossRef ] [ Google Scholar ]
• De Sonneville L. M. J., Vershoor C. A., Njiokiktjien C., Veld V. H. H., Toorenaar N., Vranken M. (2002). Facial identity and facial emotions: speed, accuracy and processing strategies in children and adults . J. Clin. Exp. Neuropsychol . 24 , 200–213 10.1076/jcen.24.2.200.989 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Duff M. C., Mutlu B., Byom L. J., Turkstra L. S. (2012). Beyond utterances: distributed cognition as a framework for studying discourse in adults with acquired brain injury . Semin. Speech Lang . 33 , 44–54 10.1055/s-0031-1301162 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Duncan S. (1972). Some signals and rules for taking speaking turns in conversations . J. Pers. Soc. Psychol . 23 , 283–292 10.1037/h0033031 [ CrossRef ] [ Google Scholar ]
• Ekman P., Friesen W. (1976). Pictures of Facial Affect . Palo Alto, CA: Consulting Psychologists Press [ Google Scholar ]
• Flavell J. H., Flavell E. R., Green F. L. (1983). Development of the appearance-reality distinction . Cogn. Psychol . 15 , 95–120 10.1016/0010-0285(83)90005-1 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Freire A., Eskritt M., Lee K. (2004). Are eyes windows to a deceiver's soul? children's use of another's eye gaze cues in a deceptive situation . Dev. Psychol . 40 , 1093–1104 10.1037/0012-1649.40.6.1093 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Frischen A., Bayliss A. P., Tipper S. P. (2007). Gaze cueing of attention: visual attention, social cognition, and individual differences . Psychol. Bull . 133 , 694–724 10.1037/0033-2909.133.4.694 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Frischen A., Smilek D., Eastwood J. D., Tipper S. P. (2007). Inhibition of return in response to gaze cues: the roles of time course and fixation cue . Vis. Cogn . 15 , 881–895 10.1080/13506280601112493 [ CrossRef ] [ Google Scholar ]
• Frith C. D., Frith U. (2006). The neural basis of mentalizing . Neuron 50 , 531–534 10.1016/j.neuron.2006.05.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gallese V., Goldman A. (1998). Mirror neurons and the simulation theory of mind-reading . Trends Cogn. Sci . 2 , 493–501 10.1016/S1364-6613(98)01262-5 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gray J., Breazeal C., Berlin M., Brooks A., Lieberman J. (2005). Action parsing and goal inference using self as simulator , in Proceedings of the 14th IEEE International Workshop on Robot and Human Interaction Communication (Nashville, TN: IEEE; ). [ Google Scholar ]
• Happé F. (1994). An advanced test of theory of mind: understanding of story characters' thoughts and feelings by able autistic, mentally handicapped, and normal children and adults . J. Autism Dev. Disord . 24 , 129–154 10.1007/BF02172093 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Happé F., Winner E., Brownell H. (1998). The getting of wisdom: theory of mind in old age . Dev. Psychol . 34 , 358–362 10.1037/0012-1649.34.2.358 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Havet-Thomassin V., Allain P., Etcharry-Bouyx F., Le Gall D. (2006). What about theory of mind after severe brain injury? Brain Inj . 20 , 83–91 [ PubMed ] [ Google Scholar ]
• Heider F., Simmel M. (1944). An experimental study of apparent behavior . Am. J. Psychol . 57 , 243–259 10.2307/1416950 [ CrossRef ] [ Google Scholar ]
• Henry J. D., Phillips L. H., Crawford J. R., Ietswaar M., Summers S. (2006). Theory of mind following traumatic brain injury: the role of emotion recognition and executive dysfunction . Neuropsychologia 44 , 1623–1628 10.1016/j.neuropsychologia.2006.03.020 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hill E. L. (2004). Evaluating the theory of executive dysfunction in autism . Dev. Rev . 24 , 189–233 10.1016/j.dr.2004.01.001 [ CrossRef ] [ Google Scholar ]
• Hooker C. I., Bruce L., Lincoln S. H., Fisher M., Vinogradov S. (2011). Theory of mind skills are related to gray matter volume in the ventromedial prefrontal cortex in schizophrenia . Biol. Psychiatry 70 , 1169–1178 10.1016/j.biopsych.2011.07.027 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Huang C.-M., Mutlu B. (2012). Robot behavior toolkit: generating effective social behaviors for robots , in Proceedings of the 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI '12) (Boston, MA: ACM; ), 25–32 [ Google Scholar ]
• Kampe K. K. W., Frith C., Dolan R. J., Frith U. (2001). Psychology: reward value of attractiveness and gaze . Nature 413 , 589–589 10.1038/35098149 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kendon A. (1967). Some functions of gaze-direction in social interaction . Acta Psychol . 26 , 22–63 10.1016/0001-6918(67)90005-4 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Knoblich G., Butterfill S., Sebanz N. (2011). Psychological research on joint action: theory and data , in The Psychology of Learning and Motivation , eds Ross B. (Burlington, VT: Academic Press; ), 59–101 [ Google Scholar ]
• Langer E. J. (1992). Matters of mind: mindfulness/mindlessness in perspective . Conscious. Cogn . 1 , 289–305 10.1016/1053-8100(92)90066-J [ CrossRef ] [ Google Scholar ]
• Legerstee M., Barna J., DiAdamo C. (2000). Precursors to the development of intention at 6 months: understanding people an their actions . Dev. Psychol . 36 , 627–634 10.1037/0012-1649.36.5.627 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Loomis J., Blascovich J., Beall A. (1999). Immersive virtual environment technology as a basic research tool in psychology . Behav. Res. Methods 31 , 557–564 10.3758/BF03200735 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Losh M., Martin G. E., Klusek J., Hogan-Brown A. L., Sideris J. (2012). Social communication and theory of mind in boys with autism and fragile X syndrome . Front. Psychol . 3 : 266 10.3389/fpsyg.2012.00266 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Luo Y. (2011). Do 10-month-old infants understand others' false beliefs? Cognition 121 , 289–298 [ PubMed ] [ Google Scholar ]
• MacDorman K. F., Ishiguro H. (2006). The uncanny advantage of using androids in cognitive and social science research . Interact. Stud . 7 , 297–337 10.1075/is.7.3.03mac [ CrossRef ] [ Google Scholar ]
• McClure E. B. (2000). A meta-analytic review of sex differences in facial expression processing and their development in infants, children, and adolescents . Psychol. Bull . 126 , 424–453 10.1037/0033-2909.126.3.424 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• McDonald S., Bornhofen C., Shum D., Long E., Saunders C., Neulinger K. (2006). reliability and validity of the awareness of social inference test (TASIT): a clinical test of social perception . Disabil. Rehabil . 28 , 1529–1542 10.1080/09638280600646185 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Michel P., Gold K., Scassellati B. (2004). Motion-based robotic self-recognition , in Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '04) (Sendai: IEEE; ). [ Google Scholar ]
• Milders M. C. D. (2006). Impairments in theory of mind shortly after traumatic brain injury and at 1-year follow-up . Nueropsychology 20 , 400–408 10.1037/0894-4105.20.4.400 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mori M. (1970). The uncanny valley . Energy 7 , 33–35 [ Google Scholar ]
• Muller F., Simion A., Reviriego E., Galera C., Mazaux J. M., Barat M., et al. (2010). Exploring theory of mind after severe traumatic brain injury . Cortex 46 , 1088–1099 10.1016/j.cortex.2009.08.014 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mumm J., Mutlu B. (2011). Human-robot proxemics: physical and psychological distancing in human-robot interaction , in Proceedings of the 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI '11) (Lausanne: ACM; ). [ Google Scholar ]
• Mutlu B., Forlizzi J., Hodgins J. (2006). A storytelling robot: modeling and evaluation of human-like gaze behavior , in Proceedings of the 6th IEEE International Conference on Humanoid Robots (Genova: IEEE; ). [ Google Scholar ]
• Mutlu B., Shiwa T., Kanda T., Ishiguro H., Hagita N. (2009). Footing in human-robot conversations: how robots might shape participant roles using gaze cues , in Proceedings of the 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI '09) (La Jolla, CA: ACM; ). [ Google Scholar ]
• Mutlu B., Terrell A., Huang C.-M. (2013). Coordination mechanisms in human-robot collaboration , in Proceedings of the Workshop on Collaborative Manipulation, 8th ACM/IEEE International Conference on Human-Robot Interaction (Tokyo: ). [ Google Scholar ]
• Mutlu B., Yamaoka F., Kanda T., Ishiguro H., Hagita N. (2009). Nonverbal Leakage in robots: communication of intentions through seemingly unintentional behavior , in Proceedings of the 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI '09) (La Jolla, CA: ACM; ). [ Google Scholar ]
• Nass C., Moon Y. (2000). Machines and mindlessness: social responses to computers . J. Soc. Issues 56 , 81–103 10.1111/0022-4537.00153 [ CrossRef ] [ Google Scholar ]
• Nomura T., Suzuki T., Kanda T., Han J., Hin N., Burke J., et al. (2008). What people assume about humanoid and animal-type robots: cross-cultural analysis between Japan, Korea, and the United States . Int. J. Hum. Robot . 5 , 25–46 10.1142/S0219843608001297 [ CrossRef ] [ Google Scholar ]
• Nowicki S., Carton J. (1993). The measurement of emotional intensity from facial expressions . J. Soc. Psychol . 133 , 749–750 10.1080/00224545.1993.9713934 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pelphrey K. A., Carter E. J. (2008). Brain mechanisms for social perception . Ann. N.Y. Acad. Sci . 1145 , 283–299 10.1196/annals.1416.007 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pelphrey K. A., Viola R. J., McCarthy G. (2004). When strangers pass processing of mutual and averted social gaze in the superior temporal sulcus . Psychol. Sci . 15 , 598–603 10.1111/j.0956-7976.2004.00726.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Perner J., Frith U., Leslie A. M., Leekam S. R. (1989). Exploration of the autistic child's theory of mind: knowledge, belief, and communication . Child Dev . 60 , 689–700 10.2307/1130734 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Perner J., Wimmer H. (1985). “John thinks that Mary thinks that…”: attribution of second-order beliefs by 5- to 10-year-old children . J. Exp. Child Psychol . 39 , 437–471 10.1016/0022-0965(85)90051-7 [ CrossRef ] [ Google Scholar ]
• Ponkanen L. M., Alhoniemi A., Leppanen J. M., Hietanen J. K. (2010). Does it make a difference if I have an eye contact with you or with your picture? An ERP study . Soc. Cogn. Affect. Neurosci . 6 , 486–494 10.1093/scan/nsq068 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Premack D., Woodruff G. (1978). Does the chimpanzee have a theory of mind? Behav. Brain Sci . 1 , 515–526 10.1017/S0140525X00076512 [ CrossRef ] [ Google Scholar ]
• Ramnani N., Miall R. C. (2004). A system in the human brain for predicting the actions of others . Nat. Neurosci . 7 , 85–90 10.1038/nn1168 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Risko E. F., Laidlaw K. E. W., Freeth M., Foulsham T., Kingstone A. (2012). Social attention with real versus reel stimuli: toward an empirical approach to concerns about ecological validity . Front. Hum. Neurosci . 6 : 143 10.3389/fnhum.2012.00143 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sarfati Y., Hardy-Bayle M.-C., Besche C., Widlocher D. (1997). Attribution of intentions to others in people with schizophrenia: a non-verbal exploration with comic strips . Schizophr. Res . 25 , 199–209 10.1016/s0920-9964(97)00025-x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Scassellati B. (2002). Theory of mind for a humanoid robot . Auton. Robots 12 , 13–24 10.1023/A:1013298507114 [ CrossRef ] [ Google Scholar ]
• Scherer K. R., Scherer U. (2011). Assessing the ability to recognize facial and vocal expressions of emotion: construction and validation of the Emotion Recognition Index . J. Nonverbal Behav . 35 , 305–326 10.1007/s10919-011-0115-4 [ CrossRef ] [ Google Scholar ]
• Schilbach L., Timmermans B., Reddy V., Costall A., Bente G., Schlicht T., et al. (2012). Toward a second-person neuroscience . Behav. Brain Sci . (in press). [ PubMed ] [ Google Scholar ]
• Scott R. M., He Z., Baillargeon R., Cummins D. (2012). False-belief understanding in 2.5-year-olds: evidence from two novel verbal spontaneous-response tasks . Dev. Sci . 15 , 181–193 10.1111/j.1467-7687.2011.01103.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sebanz N., Bekkering H., Knoblich G. (2006). Joint action: bodies and minds moving together . Trends Cogn. Sci . 10 , 70–76 10.1016/j.tics.2005.12.009 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sparks A., McDonald S., Lino B., O'Donnell M., Green M. J. (2010). Social cognition, empathy and functional outcome in schizophrenia . Schizophr. Res . 122 , 172–178 10.1016/j.schres.2010.06.011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Spell L. A., Frank E. (2000). Recognition of nonverbal communication of affect following traumatic brain injury . J. Nonverbal Behav . 24 , 285–300 10.1023/A:1006675230193 [ CrossRef ] [ Google Scholar ]
• Staudte M., Crocker M. (2009). The effect of robot gaze on processing robot utterances , in Proceedings of the 31th Annual Conference of the Cognitive Science Society (Amsterdam: ). [ Google Scholar ]
• Staudte M., Crocker M. (2011). Investigating joint attention mechanisms through spoken human robot interaction . Cognition 120 , 268–291 10.1016/j.cognition.2011.05.005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Thomas L. A., De Bellis M. D., Graham R., LaBar K. S. (2007). Development of emotional facial recognition in late childhood and adolescence . Dev. Sci . 10 , 547–558 10.1111/j.1467-7687.2007.00614.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tonks J., Williams W. H., Frampton I., Yates P., Slater A. (2007). Reading emotions after child brain injury: a comparison between children with brain injury and non-injured controls . Brain Inj . 21 , 731–739 10.1080/02699050701426899 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Trafton J. G., Cassimatis N. L., Bugajska M. D., Brock D. P., Mintz F. E., Schultz A. C. (2005). Enabling effective human-robot interaction using perspective-taking in robots . IEEE Trans. Syst. Man Cybern. A Syst. Hum . 35 , 460–470 10.1109/TSMCA.2005.850592 [ CrossRef ] [ Google Scholar ]
• Turkstra L. S. (2008). Conversation-based assessment of social cognition in adults with traumatic brain injury . Brain Inj . 22 , 397–409 10.1080/02699050802027059 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Turkstra L. S., Dixon T. M., Baker K. K. (2004). Theory of Mind and social beliefs in adolescents with traumatic brain injury . Neurorehabilitation 19 , 245–256 [ PubMed ] [ Google Scholar ]
• Walters M. L., Syrdal D. S., Dautenhahn K., Te Boekhorst R., Koay K. L. (2008). Avoiding the uncanny valley: robot appearance, personality and consistency of behavior in an attention-seeking home scenario for a robot companion . Auton. Robots 24 , 159–178 10.1007/s10514-007-9058-3 [ CrossRef ] [ Google Scholar ]
• White S., Hill E., Happe F., Frith U. (2009). Revisiting the strange stories: revealing mentalizing impairments in autism . Child Dev . 80 , 1097–1117 10.1111/j.1467-8624.2009.01319.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Williams J. A., Burns E. L., Harmon E. A. (2009). Insincere utterances and gaze: eye contact during sarcastic statements . Percept. Mot. Skills 108 , 565–572 10.2466/pms.108.2.565-572 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wilms M., Schilbach L., Pfeiffer U., Bente G., Fink G. R., Vogeley K. (2010). It's in your eyes—using gaze-contingent stimuli to create truly interactive paradigms for social cognitive and affective neuroscience . Soc. Cogn. Affect. Neurosci . 5 , 98–107 10.1093/scan/nsq024 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wilson M. (2002). Six views of embodied cognition . Psychon. Bull. Rev . 9 , 625–636 10.3758/BF03196322 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wimmer H., Perner J. (1983). Beliefs about beliefs: representation and constraining function of wrong beliefs in young children's understanding of deception . Cognition 13 , 103–128 10.1016/0010-0277(83)90004-5 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zupan B., Neumann D., Babbage D. R., Willer B. (2009). The importance of vocal affect to bimodal processing of emotion: implications for individuals with traumatic brain injury . J. Commun. Disord . 42 , 1–17 10.1016/j.jcomdis.2008.06.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]

Theory can be defined as “a formal logical explanation of some events that includes predictions of how things relate to one another” [1] .

Concepts are “collectives used to label certain bits of experience. In other words, they are elementary constructs by which reality is classified and categorised” [2] . Alternatively, concepts are “mental constructs or images developed to symbolise ideas, persons, things, or events” [3]

Model can be defined as “a set of ideas and numbers that describe the past, present or future state of something” [4]

A framework is “a system of rules, laws, agreements, etc. that establish the way that something operates” [5]

A tool is “a thing used to perform a job” [6] .

There are many theories, concepts, models, frameworks and tools in the area of business that you will have to use at some stage of your studies. The following are the most popular ones listed in alphabetical order:

• Kotter’s Eight Steps Model
• Lewin’s Force-Field Theory of Change
• Lewin’s Model of Change

Consumer behavior

• Behaviorist approach to consumer behavior
• Cognitive approach to consumer behavior
• Consumer buyer behavior
• Consumer decision making process
• Customer perceived value
• Economic Man approach to consumer behavior
• Model of Goal Directed Behavior
• Psychodynamic Approach to consumer behavior
• Theory of Trying

Corporate Social Responsibility

• Approaches to CSR
• Carrol’s CSR Pyramid
• Code of Ethics
• Cross-cultural competencies
• Cross-cultural management
• Elements of organizational culture
• Harrison’s Model of Culture
• Hofstede’s Cultural Dimensions
• Trompenaars and Hampden-Turner Cultural Dimensions
• Foreign Direct Investment
• Inter-industry and intra-industry trade
• Liberal and coordinated market economies
• Specialization for developing countries
• Theory of absolute advantage
• Theory of comparative advantage

Human Resources Management

• Cognitive Theory of Training Transfer
• Continuous Professional Development
• Equity Theory of Motivation
• Employee training and development
• Forms of training transfer
• Frederick Hertzberg’s Two Factor Theory
• Hertzberg’s Motivation and Hygiene Factors
• Honey and Mumford’s Learning Style
• Maslow’s Hierarchy of Needs
• McClelland’s Achievement Motivation
• Job analysis as an important HRM factor
• Recruitment and selection plan
• Stimulus Generalization Theory
• Tangible and intangible employee motivation tools
• Theory X and Theory Y
• The Principle of Identical Elements
• Three Cs of Intrinsic Motivation

Management and Leadership

• Belbin’s Team Roles Theory
• Critical path analysis
• Cycles of failure and cycles of success
• Executive information systems
• Fiedler’s Theory
• Functions of leadership
• Hersey and Blanchard’s Model of Leadership
• Leadership Continuum Theory
• Leadership vs Management
• Management information system
• Management structure
• Managerial Grid by Blake and Mouton
• Organization information needs
• Path-Goal Theory
• The Great Men Theory
• The importance of time management
• Transactional leadership style
• Tuckman Theory for Developing Teams
• Ansoff Growth Matrix
• Brand Essence Wheel
• Classification of viral marketing
• Integrated Marketing Communications
• Marketing Communication Mix
• Marketing strategy
• Porter’s Generic Strategies
• Product life cycle
• Product Placement
• Segmentation, Targeting & Positioning
• Self-Reference Criterion
• Standardization vs adaptation
• Strategic marketing process
• Viral marketing criticism
• Unique Selling Proposition
• 7 Ps of Marketing
• Gaps Model of Service Quality
• Greenfield investment
• McKinsey 7s Model
• New market entry strategies
• PESTEL analysis
• Porter’s Five Forces analysis
• Strategy as Revolution
• SWOT analysis
• “Think Globally, Act Locally”: a critical analysis
• Value chain analysis

Some of the above (hyperlinked) are explained in this portal. The portal will be expanded to include in-depth explanations of theories, concepts, models, frameworks and tools in the area of business studies in a regular manner.

[1] Zikmund, W.G., Babin, J., Carr, J. & Griffin, M. (2012) “Business Research Methods: with Qualtrics Printed Access Card” Cengage Learning  p.38

[2] Fox, W. & Bayat, M.S. (2007) “A Guide to Managing Research” Juta Publications  p.45

[3] Monette, D.R., Gullivan, T.J. & DeJong, C.R. (2011) “Applied Social Research: A Tool for the Human Resources” 8 th edition, Cengage Learning p.30

[4] Merriam-Webster (2015) Available at: http://www.merriam-webster.com/dictionary/model

[5] Macmillan Dictionary (2015) Available at: http://www.macmillandictionary.com/dictionary/british/framework

[6] Oxford Dictionaries (2015) Available at: http://www.macmillandictionary.com/dictionary/british/framework

• Privacy Policy

Home » Theory – Definition, Types and Examples

Theory – Definition, Types and Examples

Table of Contents

Definition:

Theory is a set of ideas or principles used to explain or describe a particular phenomenon or set of phenomena. The term “theory” is commonly used in the scientific context to refer to a well-substantiated explanation of some aspect of the natural world that is based on empirical evidence and rigorous testing.

Types of Theories

Types of Theories are as follows:

Scientific Theories

These are theories that explain natural phenomena and are based on empirical evidence. Examples include the theory of evolution, the germ theory of disease, and the theory of relativity.

Social Theories

These are theories that attempt to explain social phenomena, such as human behavior, culture, and society. Examples include social learning theory, structural functionalism, and feminist theory.

Psychological Theories

These are theories that attempt to explain human behavior and mental processes. Examples include behaviorism, cognitive psychology, and psychoanalysis.

Economic Theories

These are theories that attempt to explain economic phenomena, such as the behavior of markets, businesses, and consumers. Examples include supply and demand theory, Keynesian economics, and game theory.

Political Theories

These are theories that attempt to explain political phenomena, such as the behavior of governments, political systems, and international relations. Examples include liberalism, conservatism, and Marxism.

Philosophical Theories

These are theories that attempt to explain fundamental concepts, such as the nature of reality, knowledge, and morality. Examples include existentialism, utilitarianism, and metaphysics.

Mathematical Theories

These are theories that use mathematical concepts and models to explain phenomena in various fields, such as physics, economics, and computer science. Examples include set theory, probability theory, and game theory.

Communication Theories

These are theories that attempt to explain the processes and effects of communication, such as the transmission of information, the influence of media, and the development of language. Examples include social penetration theory, media effects theory, and speech act theory.

Biological Theories

These are theories that attempt to explain biological phenomena, such as the functioning of the human body, genetics, and evolution. Examples include the theory of natural selection, the germ theory of disease, and the central dogma of molecular biology.

Environmental Theories

These are theories that attempt to explain the interactions between humans and the natural environment, including the effects of human activities on the environment and the impact of environmental changes on human society. Examples include ecological systems theory, environmental determinism, and sustainability theory.

Educational Theories

These are theories that attempt to explain the processes and effects of learning and education. Examples include behaviorism, constructivism, and social learning theory.

Cultural Theories

These are theories that attempt to explain cultural phenomena, such as the formation and transmission of cultural values, norms, and beliefs. Examples include cultural studies, postcolonial theory, and critical race theory.

Examples of Theories

There are many theories in various fields of study. Here are some examples of theories in different areas:

• Evolutionary Theory: The theory of evolution by natural selection, proposed by Charles Darwin, explains how species change over time in response to their environment.
• Quantum Theory : Quantum theory is the branch of physics that describes the behavior of matter and energy on a very small scale.
• Social Learning Theory: Social learning theory suggests that people learn by observing and imitating the behaviors of others.
• Chaos Theory: Chaos theory is a branch of mathematics that studies complex systems and how they can exhibit unpredictable behavior.
• Cognitive Dissonance Theory : This theory explains how people often experience discomfort or tension when their beliefs, attitudes, and behaviors are inconsistent with each other.
• Attachment Theory: Attachment theory explains how early relationships between infants and their caregivers can shape their emotional and social development later in life.
• General Relativity: General relativity is a theory of gravitation that explains how the force of gravity arises from the curvature of spacetime caused by massive objects.
• Game Theory: Game theory is a mathematical approach used to model and analyze the strategic interactions between individuals or groups.
• Self-Determination Theory: This theory suggests that people are motivated by three fundamental needs: autonomy, competence, and relatedness.
• Systems Theory: Systems theory is a framework for understanding complex systems that emphasizes their interdependence, feedback loops, and dynamic behavior.

Applications of Theories

Applications of Theories are as follows:

• Science : Scientific theories are used to develop new technologies, create new medicines, and explore the natural world. For example, the theory of evolution by natural selection is used to understand the diversity of life on Earth, while the theory of relativity is used to develop new technologies such as GPS.
• Psychology : Theories of psychology are used to understand human behavior and to develop effective therapies. For example, the theory of cognitive dissonance helps us to understand why people resist changing their beliefs, while the theory of operant conditioning is used to help people change their behavior.
• Sociology : Sociological theories are used to understand social structures, institutions, and relationships. For example, the theory of social capital helps us to understand the importance of social networks in promoting economic and social development, while the theory of cultural capital explains how cultural knowledge and practices contribute to social inequality.
• Economics : Economic theories are used to understand markets, trade, and economic growth. For example, the theory of comparative advantage helps to explain why countries specialize in certain goods and services, while the theory of supply and demand helps us to understand the behavior of consumers and producers.
• Education : Theories of learning and teaching are used to develop effective educational practices. For example, the theory of constructivism emphasizes the importance of students constructing their own knowledge, while the theory of multiple intelligences suggests that students have different types of intelligence that should be recognized and nurtured.

Purpose of Theory

The purpose of a theory is to provide a framework or explanation for observed phenomena in a particular field of study. Theories are developed through a process of observation, experimentation, and analysis, and they are used to explain how and why things happen the way they do.

In science, theories are used to describe and predict natural phenomena, while in social sciences, theories are used to explain human behavior and social interactions. Theories can be tested through further observation and experimentation, and they can be modified or discarded if new evidence contradicts them.

Characteristics of Theory

• Explanation : Theories provide an explanation of a phenomenon or event. They identify the causes and underlying mechanisms that contribute to the observed outcomes.
• Predictive power: Theories have the ability to predict future outcomes or behaviors based on the identified causes and mechanisms.
• Testable: Theories are testable through empirical research. They can be subjected to observation, experimentation, and analysis to determine their validity and accuracy.
• Falsifiability : Theories can be falsified if they are found to be inconsistent with empirical evidence. This means that they can be proven to be false if the evidence does not support them.
• Generalizability : Theories are generalizable to other contexts and situations beyond the original research setting. They are not specific to a particular time or place.
• Organizing framework : Theories provide an organizing framework for understanding and interpreting information. They help researchers organize their observations and make sense of complex phenomena.
• Parsimony: Theories are typically simple and concise. They strive to explain phenomena using the fewest number of assumptions or variables possible.

Advantages of Theory

• Framework for research: Theories provide a framework for research by guiding the development of hypotheses and research questions.
• Organizing information: Theories help researchers organize their observations and make sense of complex phenomena. They provide a structure for understanding and interpreting information.
• Prediction: Theories can predict future outcomes or behaviors based on the identified causes and mechanisms.
• Understanding causality: Theories help researchers understand the causal relationships between variables and events.
• Integration of knowledge: Theories integrate existing knowledge and provide a foundation for new discoveries.
• Application : Theories can be applied to real-world problems to develop interventions and policies that address social issues.
• Communication: Theories provide a common language and understanding for researchers, which facilitates communication and collaboration.

Disadvantages of Theory

• Limited scope: Theories are limited by the scope of their research and the context in which they were developed. They may not be applicable to other contexts or situations beyond the original research setting.
• Simplification : Theories often simplify complex phenomena and may oversimplify or exclude important aspects of the phenomenon being studied.
• Bias : Theories can be influenced by researcher bias, which can affect the development and interpretation of the theory.
• Difficulty in testing: Some theories may be difficult to test empirically, making it challenging to determine their validity and accuracy.
• Incomplete understanding : Theories may provide an incomplete understanding of a phenomenon, as they are based on limited research and knowledge.
• Resistance to change : Theories can be resistant to change, making it challenging to update or revise them in light of new evidence.
• Inconsistency: Different theories within the same field may conflict with each other or present different explanations for the same phenomenon, leading to inconsistencies and confusion.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

What is Art – Definition, Types, Examples

What is Anthropology – Definition and Overview

What is Literature – Definition, Types, Examples

Economist – Definition, Types, Work Area

Anthropologist – Definition, Types, Work Area

What is History – Definitions, Periods, Methods

Theory in Social Research

• First Online: 27 October 2022

Cite this chapter

• Mumtaz Ali 4 ,
• Maya Khemlani David 5 &
• Kuang Ching Hei 5  

2304 Accesses

In this chapter, the importance and link between social research and theory are discussed. Social research is taken as the sociological understanding of connections—connections between action, experience, and change—and it is the major vehicle for realizing these connections. The debate on using theory in any scope of social research is being deliberated by various scholars with many emphasizing the merits of using theory in social research. They argue that an appropriate theory clarifies the findings a researcher has uncovered in the study. Without a theory, the researcher could face difficulties in streamlining the study or the researcher may overlook particular phenomena or events from within the study. Thus, the researcher would be unable to relate the variables in the study. A theorist always tries to view things from his/her perspective. Upon reflection, the theorist may develop a refined framework which then becomes the intensive framework, hereby, called a theory. This phenomenon may not necessarily be agreed upon by every social scientist as can be illustrated by cases where, after using a theory, researchers have modified such a theory to suit their outcomes. The theory used in social research supports and facilitates the researcher to raise fundamental questions and facts which could serve as the common core or body of knowledge. From the basis of given facts and explanations, it can be claimed that the theory used in social research has a pivotal role to align the study. In social research, a researcher should make the matter of selecting a theory seriously as a good theory produces a better piece of research work. This chapter elaborates on the various aspects of using theory in social research. Several diagrams are used to make explanations clear.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Bickman, L., & Rog, J. D. (1998). Handbook of applied social research methods . Sage Publications.

Google Scholar  

Brown, K. W. P.C., Cozby, D., & Kee, W. (1999). Research methods in human development . California State University: Mayfield Publishing Company.

Bulmer, M., Gibbs, J., et al. (2010). Social measurement through social surveys: An applied approach. Ashgate Publishing Limited.

Burr, W. R. (1973). Theory construction and the sociology of the family . Wiley Interscience.

Fisher, L. (2008). Rock, paper, scissors: Game theory in everyday life . Basic Books. ISBN. 0786726938.

Gilbert, N. (2005). Researching social life . SAGE Publications Ltd.

Leedy, P. D., & Ormrod, J. E. (2005). Practical research planning and design (5th ed.). Pearson Merrill Prentice Hall.

Mladovsky, P., & Mossialos, E. (2008). A conceptual framework for community-based health insurance in low-income countries: Social capital and economic development. World Development, 36 (4), 590–607. https://doi.org/10.1016/j.worlddev.2007.04.018

Parsons, T. (1975). The present status of ‘structural-functional’ theory in sociology. In Social systems and the evolution of action theory, The free press.

Silver, P. (1983). Educational administration : Theoretical perspectives on practice and research. Harper & Row.

Tavallaei, M., & Abu Talib, M. (2010). A general perspective on role of theory in qualitative research. Uluslararası Sosyal Aratırmalar Dergisi, The Journal of International Social Research, 3 / 11 (Spring 2010), 570–578.

Download references

Author information

Authors and affiliations.

Department of Sociology, University of Sindh, Jamshoro, Sindh, Pakistan

University of Malaya, Kuala Lumpur, Malaysia

Maya Khemlani David & Kuang Ching Hei

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Mumtaz Ali .

Editor information

Editors and affiliations.

Centre for Family and Child Studies, Research Institute of Humanities and Social Sciences, University of Sharjah, Sharjah, United Arab Emirates

M. Rezaul Islam

Department of Development Studies, University of Dhaka, Dhaka, Bangladesh

Niaz Ahmed Khan

Department of Social Work, School of Humanities, University of Johannesburg, Johannesburg, South Africa

Rajendra Baikady

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Ali, M., David, M.K., Hei, K.C. (2022). Theory in Social Research. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_4

Download citation

DOI : https://doi.org/10.1007/978-981-19-5441-2_4

Published : 27 October 2022

Publisher Name : Springer, Singapore

Print ISBN : 978-981-19-5219-7

Online ISBN : 978-981-19-5441-2

eBook Packages : Social Sciences

Share this chapter

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Chapter 4 Theories in Scientific Research

As we know from previous chapters, science is knowledge represented as a collection of “theories” derived using the scientific method. In this chapter, we will examine what is a theory, why do we need theories in research, what are the building blocks of a theory, how to evaluate theories, how can we apply theories in research, and also presents illustrative examples of five theories frequently used in social science research.

Theories are explanations of a natural or social behavior, event, or phenomenon. More formally, a scientific theory is a system of constructs (concepts) and propositions (relationships between those constructs) that collectively presents a logical, systematic, and coherent explanation of a phenomenon of interest within some assumptions and boundary conditions (Bacharach 1989). [1]

Theories should explain why things happen, rather than just describe or predict. Note that it is possible to predict events or behaviors using a set of predictors, without necessarily explaining why such events are taking place. For instance, market analysts predict fluctuations in the stock market based on market announcements, earnings reports of major companies, and new data from the Federal Reserve and other agencies, based on previously observed correlations . Prediction requires only correlations. In contrast, explanations require causations , or understanding of cause-effect relationships. Establishing causation requires three conditions: (1) correlations between two constructs, (2) temporal precedence (the cause must precede the effect in time), and (3) rejection of alternative hypotheses (through testing). Scientific theories are different from theological, philosophical, or other explanations in that scientific theories can be empirically tested using scientific methods.

Explanations can be idiographic or nomothetic. Idiographic explanations are those that explain a single situation or event in idiosyncratic detail. For example, you did poorly on an exam because: (1) you forgot that you had an exam on that day, (2) you arrived late to the exam due to a traffic jam, (3) you panicked midway through the exam, (4) you had to work late the previous evening and could not study for the exam, or even (5) your dog ate your text book. The explanations may be detailed, accurate, and valid, but they may not apply to other similar situations, even involving the same person, and are hence not generalizable. In contrast, nomothetic explanations seek to explain a class of situations or events rather than a specific situation or event. For example, students who do poorly in exams do so because they did not spend adequate time preparing for exams or that they suffer from nervousness, attention-deficit, or some other medical disorder. Because nomothetic explanations are designed to be generalizable across situations, events, or people, they tend to be less precise, less complete, and less detailed. However, they explain economically, using only a few explanatory variables. Because theories are also intended to serve as generalized explanations for patterns of events, behaviors, or phenomena, theoretical explanations are generally nomothetic in nature.

While understanding theories, it is also important to understand what theory is not. Theory is not data, facts, typologies, taxonomies, or empirical findings. A collection of facts is not a theory, just as a pile of stones is not a house. Likewise, a collection of constructs (e.g., a typology of constructs) is not a theory, because theories must go well beyond constructs to include propositions, explanations, and boundary conditions. Data, facts, and findings operate at the empirical or observational level, while theories operate at a conceptual level and are based on logic rather than observations.

There are many benefits to using theories in research. First, theories provide the underlying logic of the occurrence of natural or social phenomenon by explaining what are the key drivers and key outcomes of the target phenomenon and why, and what underlying processes are responsible driving that phenomenon. Second, they aid in sense-making by helping us synthesize prior empirical findings within a theoretical framework and reconcile contradictory findings by discovering contingent factors influencing the relationship between two constructs in different studies. Third, theories provide guidance for future research by helping identify constructs and relationships that are worthy of further research. Fourth, theories can contribute to cumulative knowledge building by bridging gaps between other theories and by causing existing theories to be reevaluated in a new light.

However, theories can also have their own share of limitations. As simplified explanations of reality, theories may not always provide adequate explanations of the phenomenon of interest based on a limited set of constructs and relationships. Theories are designed to be simple and parsimonious explanations, while reality may be significantly more complex. Furthermore, theories may impose blinders or limit researchers’ “range of vision,” causing them to miss out on important concepts that are not defined by the theory.

Building Blocks of a Theory

David Whetten (1989) suggests that there are four building blocks of a theory: constructs, propositions, logic, and boundary conditions/assumptions. Constructs capture the “what” of theories (i.e., what concepts are important for explaining a phenomenon), propositions capture the “how” (i.e., how are these concepts related to each other), logic represents the “why” (i.e., why are these concepts related), and boundary conditions/assumptions examines the “who, when, and where” (i.e., under what circumstances will these concepts and relationships work). Though constructs and propositions were previously discussed in Chapter 2, we describe them again here for the sake of completeness.

Constructs are abstract concepts specified at a high level of abstraction that are chosen specifically to explain the phenomenon of interest. Recall from Chapter 2 that constructs may be unidimensional (i.e., embody a single concept), such as weight or age, or multi-dimensional (i.e., embody multiple underlying concepts), such as personality or culture. While some constructs, such as age, education, and firm size, are easy to understand, others, such as creativity, prejudice, and organizational agility, may be more complex and abstruse, and still others such as trust, attitude, and learning, may represent temporal tendencies rather than steady states. Nevertheless, all constructs must have clear and unambiguous operational definition that should specify exactly how the construct will be measured and at what level of analysis (individual, group, organizational, etc.). Measurable representations of abstract constructs are called variables . For instance, intelligence quotient (IQ score) is a variable that is purported to measure an abstract construct called intelligence. As noted earlier, scientific research proceeds along two planes: a theoretical plane and an empirical plane. Constructs are conceptualized at the theoretical plane, while variables are operationalized and measured at the empirical (observational) plane. Furthermore, variables may be independent, dependent, mediating, or moderating, as discussed in Chapter 2. The distinction between constructs (conceptualized at the theoretical level) and variables (measured at the empirical level) is shown in Figure 4.1.

Figure 4.1. Distinction between theoretical and empirical concepts

Propositions are associations postulated between constructs based on deductive logic. Propositions are stated in declarative form and should ideally indicate a cause-effect relationship (e.g., if X occurs, then Y will follow). Note that propositions may be conjectural but MUST be testable, and should be rejected if they are not supported by empirical observations. However, like constructs, propositions are stated at the theoretical level, and they can only be tested by examining the corresponding relationship between measurable variables of those constructs. The empirical formulation of propositions, stated as relationships between variables, is called hypotheses . The distinction between propositions (formulated at the theoretical level) and hypotheses (tested at the empirical level) is depicted in Figure 4.1.

The third building block of a theory is the logic that provides the basis for justifying the propositions as postulated. Logic acts like a “glue” that connects the theoretical constructs and provides meaning and relevance to the relationships between these constructs. Logic also represents the “explanation” that lies at the core of a theory. Without logic, propositions will be ad hoc, arbitrary, and meaningless, and cannot be tied into a cohesive “system of propositions” that is the heart of any theory.

Finally, all theories are constrained by assumptions about values, time, and space, and boundary conditions that govern where the theory can be applied and where it cannot be applied. For example, many economic theories assume that human beings are rational (or boundedly rational) and employ utility maximization based on cost and benefit expectations as a way of understand human behavior. In contrast, political science theories assume that people are more political than rational, and try to position themselves in their professional or personal environment in a way that maximizes their power and control over others. Given the nature of their underlying assumptions, economic and political theories are not directly comparable, and researchers should not use economic theories if their objective is to understand the power structure or its evolution in a organization. Likewise, theories may have implicit cultural assumptions (e.g., whether they apply to individualistic or collective cultures), temporal assumptions (e.g., whether they apply to early stages or later stages of human behavior), and spatial assumptions (e.g., whether they apply to certain localities but not to others). If a theory is to be properly used or tested, all of its implicit assumptions that form the boundaries of that theory must be properly understood. Unfortunately, theorists rarely state their implicit assumptions clearly, which leads to frequent misapplications of theories to problem situations in research.

Attributes of a Good Theory

Theories are simplified and often partial explanations of complex social reality. As such, there can be good explanations or poor explanations, and consequently, there can be good theories or poor theories. How can we evaluate the “goodness” of a given theory? Different criteria have been proposed by different researchers, the more important of which are listed below:

• Logical consistency : Are the theoretical constructs, propositions, boundary conditions, and assumptions logically consistent with each other? If some of these “building blocks” of a theory are inconsistent with each other (e.g., a theory assumes rationality, but some constructs represent non-rational concepts), then the theory is a poor theory.
• Explanatory power : How much does a given theory explain (or predict) reality? Good theories obviously explain the target phenomenon better than rival theories, as often measured by variance explained (R-square) value in regression equations.
• Falsifiability : British philosopher Karl Popper stated in the 1940’s that for theories to be valid, they must be falsifiable. Falsifiability ensures that the theory is potentially disprovable, if empirical data does not match with theoretical propositions, which allows for their empirical testing by researchers. In other words, theories cannot be theories unless they can be empirically testable. Tautological statements, such as “a day with high temperatures is a hot day” are not empirically testable because a hot day is defined (and measured) as a day with high temperatures, and hence, such statements cannot be viewed as a theoretical proposition. Falsifiability requires presence of rival explanations it ensures that the constructs are adequately measurable, and so forth. However, note that saying that a theory is falsifiable is not the same as saying that a theory should be falsified. If a theory is indeed falsified based on empirical evidence, then it was probably a poor theory to begin with!
• Parsimony : Parsimony examines how much of a phenomenon is explained with how few variables. The concept is attributed to 14 th century English logician Father William of Ockham (and hence called “Ockham’s razor” or “Occam’s razor), which states that among competing explanations that sufficiently explain the observed evidence, the simplest theory (i.e., one that uses the smallest number of variables or makes the fewest assumptions) is the best. Explanation of a complex social phenomenon can always be increased by adding more and more constructs. However, such approach defeats the purpose of having a theory, which are intended to be “simplified” and generalizable explanations of reality. Parsimony relates to the degrees of freedom in a given theory. Parsimonious theories have higher degrees of freedom, which allow them to be more easily generalized to other contexts, settings, and populations.

Approaches to Theorizing

How do researchers build theories? Steinfeld and Fulk (1990) [2] recommend four such approaches. The first approach is to build theories inductively based on observed patterns of events or behaviors. Such approach is often called “grounded theory building”, because the theory is grounded in empirical observations. This technique is heavily dependent on the observational and interpretive abilities of the researcher, and the resulting theory may be subjective and non -confirmable. Furthermore, observing certain patterns of events will not necessarily make a theory, unless the researcher is able to provide consistent explanations for the observed patterns. We will discuss the grounded theory approach in a later chapter on qualitative research.

The second approach to theory building is to conduct a bottom-up conceptual analysis to identify different sets of predictors relevant to the phenomenon of interest using a predefined framework. One such framework may be a simple input-process-output framework, where the researcher may look for different categories of inputs, such as individual, organizational, and/or technological factors potentially related to the phenomenon of interest (the output), and describe the underlying processes that link these factors to the target phenomenon. This is also an inductive approach that relies heavily on the inductive abilities of the researcher, and interpretation may be biased by researcher’s prior knowledge of the phenomenon being studied.

The third approach to theorizing is to extend or modify existing theories to explain a new context, such as by extending theories of individual learning to explain organizational learning. While making such an extension, certain concepts, propositions, and/or boundary conditions of the old theory may be retained and others modified to fit the new context. This deductive approach leverages the rich inventory of social science theories developed by prior theoreticians, and is an efficient way of building new theories by building on existing ones.

The fourth approach is to apply existing theories in entirely new contexts by drawing upon the structural similarities between the two contexts. This approach relies on reasoning by analogy, and is probably the most creative way of theorizing using a deductive approach. For instance, Markus (1987) [3] used analogic similarities between a nuclear explosion and uncontrolled growth of networks or network-based businesses to propose a critical mass theory of network growth. Just as a nuclear explosion requires a critical mass of radioactive material to sustain a nuclear explosion, Markus suggested that a network requires a critical mass of users to sustain its growth, and without such critical mass, users may leave the network, causing an eventual demise of the network.

Examples of Social Science Theories

In this section, we present brief overviews of a few illustrative theories from different social science disciplines. These theories explain different types of social behaviors, using a set of constructs, propositions, boundary conditions, assumptions, and underlying logic. Note that the following represents just a simplistic introduction to these theories; readers are advised to consult the original sources of these theories for more details and insights on each theory.

Agency Theory. Agency theory (also called principal-agent theory), a classic theory in the organizational economics literature, was originally proposed by Ross (1973) [4] to explain two-party relationships (such as those between an employer and its employees, between organizational executives and shareholders, and between buyers and sellers) whose goals are not congruent with each other. The goal of agency theory is to specify optimal contracts and the conditions under which such contracts may help minimize the effect of goal incongruence. The core assumptions of this theory are that human beings are self-interested individuals, boundedly rational, and risk-averse, and the theory can be applied at the individual or organizational level.

The two parties in this theory are the principal and the agent; the principal employs the agent to perform certain tasks on its behalf. While the principal’s goal is quick and effective completion of the assigned task, the agent’s goal may be working at its own pace, avoiding risks, and seeking self-interest (such as personal pay) over corporate interests. Hence, the goal incongruence. Compounding the nature of the problem may be information asymmetry problems caused by the principal’s inability to adequately observe the agent’s behavior or accurately evaluate the agent’s skill sets. Such asymmetry may lead to agency problems where the agent may not put forth the effort needed to get the task done (the moral hazard problem) or may misrepresent its expertise or skills to get the job but not perform as expected (the adverse selection problem). Typical contracts that are behavior-based, such as a monthly salary, cannot overcome these problems. Hence, agency theory recommends using outcome-based contracts, such as a commissions or a fee payable upon task completion, or mixed contracts that combine behavior-based and outcome-based incentives. An employee stock option plans are is an example of an outcome-based contract while employee pay is a behavior-based contract. Agency theory also recommends tools that principals may employ to improve the efficacy of behavior-based contracts, such as investing in monitoring mechanisms (such as hiring supervisors) to counter the information asymmetry caused by moral hazard, designing renewable contracts contingent on agent’s performance (performance assessment makes the contract partially outcome-based), or by improving the structure of the assigned task to make it more programmable and therefore more observable.

Theory of Planned Behavior. Postulated by Azjen (1991) [5] , the theory of planned behavior (TPB) is a generalized theory of human behavior in the social psychology literature that can be used to study a wide range of individual behaviors. It presumes that individual behavior represents conscious reasoned choice, and is shaped by cognitive thinking and social pressures. The theory postulates that behaviors are based on one’s intention regarding that behavior, which in turn is a function of the person’s attitude toward the behavior, subjective norm regarding that behavior, and perception of control over that behavior (see Figure 4.2). Attitude is defined as the individual’s overall positive or negative feelings about performing the behavior in question, which may be assessed as a summation of one’s beliefs regarding the different consequences of that behavior, weighted by the desirability of those consequences.

Subjective norm refers to one’s perception of whether people important to that person expect the person to perform the intended behavior, and represented as a weighted combination of the expected norms of different referent groups such as friends, colleagues, or supervisors at work. Behavioral control is one’s perception of internal or external controls constraining the behavior in question. Internal controls may include the person’s ability to perform the intended behavior (self-efficacy), while external control refers to the availability of external resources needed to perform that behavior (facilitating conditions). TPB also suggests that sometimes people may intend to perform a given behavior but lack the resources needed to do so, and therefore suggests that posits that behavioral control can have a direct effect on behavior, in addition to the indirect effect mediated by intention.

TPB is an extension of an earlier theory called the theory of reasoned action, which included attitude and subjective norm as key drivers of intention, but not behavioral control. The latter construct was added by Ajzen in TPB to account for circumstances when people may have incomplete control over their own behaviors (such as not having high-speed Internet access for web surfing).

Figure 4.2. Theory of planned behavior

Innovation diffusion theory. Innovation diffusion theory (IDT) is a seminal theory in the communications literature that explains how innovations are adopted within a population of potential adopters. The concept was first studied by French sociologist Gabriel Tarde, but the theory was developed by Everett Rogers in 1962 based on observations of 508 diffusion studies. The four key elements in this theory are: innovation, communication channels, time, and social system. Innovations may include new technologies, new practices, or new ideas, and adopters may be individuals or organizations. At the macro (population) level, IDT views innovation diffusion as a process of communication where people in a social system learn about a new innovation and its potential benefits through communication channels (such as mass media or prior adopters) and are persuaded to adopt it. Diffusion is a temporal process; the diffusion process starts off slow among a few early adopters, then picks up speed as the innovation is adopted by the mainstream population, and finally slows down as the adopter population reaches saturation. The cumulative adoption pattern therefore an S-shaped curve, as shown in Figure 4.3, and the adopter distribution represents a normal distribution. All adopters are not identical, and adopters can be classified into innovators, early adopters, early majority, late majority, and laggards based on their time of their adoption. The rate of diffusion a lso depends on characteristics of the social system such as the presence of opinion leaders (experts whose opinions are valued by others) and change agents (people who influence others’ behaviors).

At the micro (adopter) level, Rogers (1995) [6] suggests that innovation adoption is a process consisting of five stages: (1) knowledge: when adopters first learn about an innovation from mass-media or interpersonal channels, (2) persuasion: when they are persuaded by prior adopters to try the innovation, (3) decision: their decision to accept or reject the innovation, (4) implementation: their initial utilization of the innovation, and (5) confirmation: their decision to continue using it to its fullest potential (see Figure 4.4). Five innovation characteristics are presumed to shape adopters’ innovation adoption decisions: (1) relative advantage: the expected benefits of an innovation relative to prior innovations, (2) compatibility: the extent to which the innovation fits with the adopter’s work habits, beliefs, and values, (3) complexity: the extent to which the innovation is difficult to learn and use, (4) trialability: the extent to which the innovation can be tested on a trial basis, and (5) observability: the extent to which the results of using the innovation can be clearly observed. The last two characteristics have since been dropped from many innovation studies. Complexity is negatively correlated to innovation adoption, while the other four factors are positively correlated. Innovation adoption also depends on personal factors such as the adopter’s risk- taking propensity, education level, cosmopolitanism, and communication influence. Early adopters are venturesome, well educated, and rely more on mass media for information about the innovation, while later adopters rely more on interpersonal sources (such as friends and family) as their primary source of information. IDT has been criticized for having a “pro-innovation bias,” that is for presuming that all innovations are beneficial and will be eventually diffused across the entire population, and because it does not allow for inefficient innovations such as fads or fashions to die off quickly without being adopted by the entire population or being replaced by better innovations.

Figure 4.3. S-shaped diffusion curve

Figure 4.4. Innovation adoption process.

Elaboration Likelihood Model . Developed by Petty and Cacioppo (1986) [7] , the elaboration likelihood model (ELM) is a dual-process theory of attitude formation or change in the psychology literature. It explains how individuals can be influenced to change their attitude toward a certain object, events, or behavior and the relative efficacy of such change strategies. The ELM posits that one’s attitude may be shaped by two “routes” of influence, the central route and the peripheral route, which differ in the amount of thoughtful information processing or “elaboration” required of people (see Figure 4.5). The central route requires a person to think about issue-related arguments in an informational message and carefully scrutinize the merits and relevance of those arguments, before forming an informed judgment about the target object. In the peripheral route, subjects rely on external “cues” such as number of prior users, endorsements from experts, or likeability of the endorser, rather than on the quality of arguments, in framing their attitude towards the target object. The latter route is less cognitively demanding, and the routes of attitude change are typically operationalized in the ELM using the argument quality and peripheral cues constructs respectively.

Figure 4.5. Elaboration likelihood model

Whether people will be influenced by the central or peripheral routes depends upon their ability and motivation to elaborate the central merits of an argument. This ability and motivation to elaborate is called elaboration likelihood . People in a state of high elaboration likelihood (high ability and high motivation) are more likely to thoughtfully process the information presented and are therefore more influenced by argument quality, while those in the low elaboration likelihood state are more motivated by peripheral cues. Elaboration likelihood is a situational characteristic and not a personal trait. For instance, a doctor may employ the central route for diagnosing and treating a medical ailment (by virtue of his or her expertise of the subject), but may rely on peripheral cues from auto mechanics to understand the problems with his car. As such, the theory has widespread implications about how to enact attitude change toward new products or ideas and even social change.

General Deterrence Theory. Two utilitarian philosophers of the eighteenth century, Cesare Beccaria and Jeremy Bentham, formulated General Deterrence Theory (GDT) as both an explanation of crime and a method for reducing it. GDT examines why certain individuals engage in deviant, anti-social, or criminal behaviors. This theory holds that people are fundamentally rational (for both conforming and deviant behaviors), and that they freely choose deviant behaviors based on a rational cost-benefit calculation. Because people naturally choose utility-maximizing behaviors, deviant choices that engender personal gain or pleasure can be controlled by increasing the costs of such behaviors in the form of punishments (countermeasures) as well as increasing the probability of apprehension. Swiftness, severity, and certainty of punishments are the key constructs in GDT.

While classical positivist research in criminology seeks generalized causes of criminal behaviors, such as poverty, lack of education, psychological conditions, and recommends strategies to rehabilitate criminals, such as by providing them job training and medical treatment, GDT focuses on the criminal decision making process and situational factors that influence that process. Hence, a criminal’s personal situation (such as his personal values, his affluence, and his need for money) and the environmental context (such as how protected is the target, how efficient is the local police, how likely are criminals to be apprehended) play key roles in this decision making process. The focus of GDT is not how to rehabilitate criminals and avert future criminal behaviors, but how to make criminal activities less attractive and therefore prevent crimes. To that end, “target hardening” such as installing deadbolts and building self-defense skills, legal deterrents such as eliminating parole for certain crimes, “three strikes law” (mandatory incarceration for three offenses, even if the offenses are minor and not worth imprisonment), and the death penalty, increasing the chances of apprehension using means such as neighborhood watch programs, special task forces on drugs or gang -related crimes, and increased police patrols, and educational programs such as highly visible notices such as “Trespassers will be prosecuted” are effective in preventing crimes. This theory has interesting implications not only for traditional crimes, but also for contemporary white-collar crimes such as insider trading, software piracy, and illegal sharing of music.

[1] Bacharach, S. B. (1989). “Organizational Theories: Some Criteria for Evaluation,” Academy of Management Review (14:4), 496-515.

[2] Steinfield, C.W. and Fulk, J. (1990). “The Theory Imperative,” in Organizations and Communications Technology , J. Fulk and C. W. Steinfield (eds.), Newbury Park, CA: Sage Publications.

[3] Markus, M. L. (1987). “Toward a ‘Critical Mass’ Theory of Interactive Media: Universal Access, Interdependence, and Diffusion,” Communication Research (14:5), 491-511.

[4] Ross, S. A. (1973). “The Economic Theory of Agency: The Principal’s Problem,” American Economic Review (63:2), 134-139.

[5] Ajzen, I. (1991). “The Theory of Planned Behavior,” Organizational Behavior and Human Decision Processes (50), 179-211.

[6] Rogers, E. (1962). Diffusion of Innovations . New York: The Free Press. Other editions 1983, 1996, 2005.

[7] Petty, R. E., and Cacioppo, J. T. (1986). Communication and Persuasion: Central and Peripheral Routes to Attitude Change . New York: Springer-Verlag.

• Social Science Research: Principles, Methods, and Practices. Authored by : Anol Bhattacherjee. Provided by : University of South Florida. Located at : http://scholarcommons.usf.edu/oa_textbooks/3/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

MINI REVIEW article

Eye movement desensitisation and reprocessing (emdr) therapy for prolonged grief: theory, research, and practice.

• 1 School of Psychology, Faculty of Health Sciences, Curtin University, Perth, WA, Australia
• 2 School of Psychological Sciences, College of Health and Medicine, University of Tasmania, Launceston, TAS, Australia

Prolonged Grief Disorder occurs within 7-10% of the bereaved population and is a more complicated and persistent form of grief which has been associated with suicidality, mental health disorders, sleep disturbance, poor health behaviors, and work and social impairment. EMDR is a fitting treatment option for those with Prolonged Grief, focusing on processing past memories, blocks, current triggers, future fears, and preparing the person for living life beyond the loss in line with the Adaptive Information Processing Model and grief frameworks. This paper discusses the theory, research regarding the application of EMDR with prolonged grief, and gives insight and guidance to clinicians working in this area including a case example.

Introduction

Despite the universality of death and bereavement, losing a loved one is often a major distressing event in an individual’s life. Although the pain may remain in some form, often over time the loss of someone is accommodated into an individual’s life, and they can live a life of purpose and meaning beyond the loss. Research has demonstrated however that 7-10% of the bereaved population develop a more complicated and prolonged grief response ( 1 ). Although various terms have been used for this grief response, there is general consensus now among experts in the field that Prolonged Grief Disorder (PGD) is the correct classification for this response, now being included in both the International Classification of Diseases, 11 th Edition (ICD-11) and Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR). Prolonged Grief includes various symptoms and challenges such as difficulty accepting the death, a sense of meaninglessness, yearning for the deceased and associated emotional pain and difficulty engaging in new activities, with a diagnosis only being warranted after a minimum 6–12-month time frame. Although there is no direct research on the duration of PGD, it is commonly understood this disorder can last years and decades without significant improvement in symptoms ( 2 ). PGD has been associated with increased rates of suicidality, mental disorder and sleep disturbance, poor health behaviors, and work and social impairment ( 3 – 8 ). Due to the functional impact, increasing rates, and challenges associated with having a prolonged grief response, identifying individuals at risk, and those who need evidenced based treatments is of great importance. This notion is further supported by the findings by Lichtenthal et al. ( 9 ) demonstrating those with prolonged grief are less likely to receive support, despite being the ones in greatest need for intervention. Furthermore, Johnson et al. ( 10 ) demonstrated that more than 90% of individuals experiencing prolonged grief symptoms are relieved to understand their grief is a more complicated form, and report interest in receiving intervention for their prolonged grief symptoms. Having effective and widely available treatments for those with prolonged grief is further supported by Eisma, Boelen, and Lenferink ( 11 ) which have suggested an increase in the rates of prolonged grief since the COVID-19 pandemic due to a lack of social support, limited access to professional grief support, and the individual not being able to engage in traditional grief rituals to assist them in accommodating and adjusting to the loss (saying goodbye, viewing the body, and burial).

Up to this point, the research surrounding interventions for PGD has largely been centered around cognitive behavioral therapy (CBT) interventions. This preference for CBT focused approaches in addressing PGD might stem from previous clinical recommendations, which classified prolonged grief as a type of major depressive disorder ( 12 ). While numerous studies have reported significant outcomes from such interventions, a notable percentage of subjects do not experience meaningful therapeutic benefits ( 13 ). For instance, Bryant et al. ( 2 ) found that post-treatment, 37.9% of participants undergoing CBT continued to meet the criteria for PGD. In more recent years, other more specific interventions have been developed for the treatment of Prolonged Grief Disorder, notably Complicated Grief Treatment (CGT; 14 ). Complicated Grief Treatment is typically provided in a structured weekly format, generally consisting of 16 sessions ( 15 ). CGT is divided into 4 major phases: (1) getting started which involves areas such as getting a grief history, providing psychoeducation, building up social support and various grief related behavioral tasks, (2) core revisiting sequence which involves imaginal exposure based exercises to connect with the grief, (3) midcourse review which involves an evaluation of treatment progress and (4) closing sequence which includes preparation for closure and completing any other necessary supports and interventions ( 16 ). Although CGT has been demonstrated to be more effective than are proven efficacious treatments for depression, including citalopram ( 15 ) and interpersonal therapy ( 14 , 17 ), there is still an absence of large, repeated randomized controlled trials demonstrating consistent results. Furthermore, in some research response rates in CGT were only 51% ( 14 ), and of practical relevance to clinicians, many individuals that may be working with Prolonged Grief are firstly not aware of CGT being an intervention, and secondly would have to become additionally trained and supervised in this approach. It is also important to note that similar with many other clinical presentations such as anxiety and depression, having various different treatment approaches available is of benefit as some clients may respond better to certain treatments provided. Furthermore, often certain components of treatments may be combined and integrated based on a client’s needs and goals. As an example, therapists could integrate elements of the getting started phase of CGT to have a grief focused, expert designed structure to assist with their assessment and preparation work, and then choose to implement EMDR therapy to process distressing elements of the loss, rather than the imaginal exposure-based exercises included in phase 2 of CGT.

Prolonged Grief Disorder has been listed in both the DSM-5-TR and ICD-11 under the trauma and stressor related disorder categories, and many experts within the field including Shear et al. ( 18 ), view PGD as a trauma and stress response. Based on this, treatment approaches should view prolonged grief through a “trauma lens”, allowing the adaptive resolution of information which is impeding on the natural grieving process. This is further supported by the illustration that several elements of PGD show considerable overlap with Post Traumatic Stress Disorder (PTSD) symptoms including intrusive images of the deceased, engaging in avoidance behavior, feeling estranged from others, sleep disturbance, and difficulty concentrating. Differences however do exist such as PTSD more being associated with fear, whereas prolonged grief associated with yearning and sadness. Furthermore, the content of intrusive images in PTSD being related to the traumatic event, whereas in prolonged grief this can be related to the person or factors associated with the loss ( 19 ). It is clear however that based on EMDR being a first line and consistently effective treatment for PTSD ( 20 )., PGD being viewed as a trauma and stress response, and the overlap of PTSD and PGD, that the use of EMDR in this population is warranted. This is also supported by theoretical accounts and building empirical research as discussed below.

Theory and frameworks: integrating our understanding of grief and the adaptive information processing model

In understanding a clients prolonged grief response, the Adaptive Information Processing Model ( 21 ) can act as our underlying framework in understanding as to why a clients natural grieving process has become blocked, complicated, or prolonged in some way. The Adaptive Information Processing Model suggests that mental health challenges and pathology is related to blocked information processing at the time of a traumatic, distressing, or challenging experience, and the subsequent way this information is stored maladaptively within our brain and nervous system. Due to the nature of an event, past history and experiences, and environmental and contextual factors at the time of an experienced event, this may impact on our ability to make sense and process an event or series of events that have occurred. If so, this information frozen and stored in a raw and state specific form can continue to impact on the way we see ourselves, the world, and our interactions with others, and through our engagement with life can be activated on a conscious or unconscious level leading to activation of images, feelings, thoughts, emotions, and subsequently certain behaviors.

In applying the AIP Model in the case of prolonged grief, integrating this theoretical framework with well-known and supported grief frameworks can add greater context to a client’s grief experience, and importantly give insight as to how EMDR therapy should be utilized. One of the most common, and well supported grief frameworks is known as the Dual Process Model by Stroebe and Schut ( 22 ). This model suggests that to be able to adjust and accommodate the loss into our lives, there needs to be a balance and oscillation between loss-oriented tasks and restoration-oriented tasks. In relation to this model, loss-oriented tasks involve aspects of the grief experience such as doing the active grief work, thinking about, and connecting with the loved person, engaging with reflection around what life was like before the loss, and connecting with memories and experiences regarding the person and life you had with them. The ultimate aim and goal of dealing with this area is to emotionally process the loss, involving a whole range of feelings, which does not occur in stages, but happens flexibly at certain time points ( 23 ). Loss oriented aspects of the grief are often more predominant during the initial phases of a loss, and this model considers factors associated with this such as closeness of the relationship, attachment, and other factors that may impede on engagement within this domain such as avoidance. Restoration oriented activities and engagement are focused on what life is like now, and what it will look like beyond the loss, involving components such as figuring out your new life role and identity now that this person is not here, creating new behaviors and experiences, forging new relationships, and recreating a life with meaning and fulfilment, whilst still keeping a connection with the deceased in some way if desired. This important area also includes dealing with secondary loss, such as the loss of an emotional support person due to the bereavement, feelings of isolation, and additional life changes contributing to distress such as increased household responsibilities, or life roles ( 24 ).

If we integrate these models as a unified framework for understanding prolonged grief, we can utilize our knowledge of risk factors in EMDR therapy to gain greater understanding of the possible blocks, information, and contributing factors that may impact or impede on someone’s natural healing process. Regarding loss-oriented tasks, there are several factors that have been consistently demonstrated in the research regarding prolonged grief that may impact on engagement in this domain. These include nature of the relationship with the deceased, childhood trauma, prior experiences of loss and pre-death mental health, attachment style, and schemas, ( 25 – 27 ). As an example, if an individual has prior unprocessed childhood trauma, their capacity to engage in sitting with feelings and processing the loss may be impacted due to challenges with managing distress, therefore there may be a need within our treatment to process this in addition to the grief related memories. Similarly, those with an insecure or avoidant attachment may want to avoid anything to do with loss-oriented tasks, and those with a self-sacrifice schema may put others needs for grieving over their own. Similarly, regarding restoration-oriented activities, various factors identified in the research may impact or impede on someone’s ability to adjust and accommodate the loss. These include areas such as low levels of social support, poorer family functioning, and low levels of optimism ( 28 ), considerations we need to take into account throughout our EMDR assessment, preparation and processing phases. As demonstrated, from combining the AIP model, Dual Process Model and identified risk factors associated with prolonged grief, our case conceptualization and knowledge of targets for processing in EMDR can be strengthened.

EMDR and grief: what the research says

The use of EMDR with bereaved individuals is not uncommon, with Luber ( 29 ) outlining a suggested protocol for grief which involved discussing important considerations for clinicians regarding target assessment, and various goals and components of consideration when using EMDR for grief including connecting with a balanced range of emotions, and to accommodate the loss into an individual’s life. Solomon and Rando ( 30 , 31 ) provided important insights for clinicians through multiple papers highlighting the importance of implementing EMDR within existing grief frameworks, considering all three prongs of EMDR reprocessing (past, present, and future), the need to focus on early life experiences and attachment as necessary, and the illustration of these principles through various case examples. In addition, Solomon and Hensley ( 32 ) discuss the use of EMDR for grief in the context of COVID-19 and more recently Solomon and Meysner ( 33 ) cover the use of EMDR with traumatic grief in the Oxford Handbook of EMDR, providing suggestions and guidance for clinicians working with traumatic bereavement. Regarding clinical research, Hornsveld et al. ( 34 ) investigated the efficacy of eye movements in reducing the emotionality of memories relating to loss. Sixty participants were asked to recall a negative loss-related memory before and after one of three conditions—eye movement, relaxation music, or a control with recall-only. In comparison to the relaxation music and control group, the eye movement group showed significantly greater reductions in both the ability to focus on the loss related memory and emotionality of the memory. In research by Sprang ( 35 ), 50 participants self-selected either EMDR or guided morning (exposure-based grief intervention) for individuals experiencing traumatic grief. Both treatments resulted in significant reductions in outcome measures such as reexperiencing, nightmares, rumination, and intrusive symptoms. This is consistent with Ironson et al. ( 36 ) findings in a PTSD population, however, EMDR participants experienced near complete symptom reduction at a much faster rate (8 sessions) in comparison to the GM condition in 13 sessions, an important finding for time-limited clinical settings. Furthermore, those who engaged in EMDR reported significant increases in positive memories of their loved ones, a finding not demonstrated in the GM condition, demonstrating additional benefits to EMDR as a grief intervention.

In another study by Meysner, Cotter, & Lee ( 37 ) the effectiveness of EMDR was compared with an integrated cognitive behavioral therapy (CBT) intervention for grief. Nineteen participants (12 females and 7 males) who identified themselves as struggling with grief were randomly allocated to treatment conditions for a 7-week treatment intervention. The results demonstrated CBT and EMDR to be equally effective in reducing grief symptoms, trauma symptoms, and distress. In a study using the same participants by Cotter, Meysner, and Lee ( 38 ), qualitative feedback on the benefits of each treatment were provided by participants. Participant reports common to both therapies included increased behavioral engagement, a better and heathier relationship to their deceased loved one, an increase in positive emotions, and an improvement in confidence. Regarding reported differences between each condition, the CBT group described benefits such as learning various emotional regulation skills and feeling like they were now in a new chapter of their life. In the EMDR condition, it was reported a noticeable benefit was that previously disturbing memories were now more distant and clearer.

In 2018 van Denderen et al., ( 39 ) completed a randomized control trial on the use of an integrated EMDR and CBT intervention for homicidally bereaved Dutch adults. The research included 85 participants, and demonstrated an integrated CBT and EMDR intervention was effective in reducing both complicated grief symptoms and Post Traumatic Stress Disorder (PTSD) symptoms. In a further similar study completed by Lennefrink et al. ( 40 ) a combined Cognitive Therapy and EMDR intervention was used with individuals bereaved by a loss from the MH17 plane crash. In their multicenter randomized control trial, self-rated scores for persistent complex bereavement, depression, and PTSD were comparted between an immediate treatment and waitlist control in 39 Dutch adults. The immediate treatment group showed a significant decline in depression, however no significant between group differences were observed regarding PTSD and grief symptoms.

More recently, ( 41 ) presented a case series on the efficacy of short term EMDR with patients with persistent complex bereavement. Three patients who had all lost a first degree relative were included in the case series, with between 1-2 sessions of EMDR being used to process complicated grief targets. In all cases positive improvements were noted on both quantitative clinical instruments, and feedback including changes such as not feeling guilty about the loss, being able to engage in life more, decreased avoidance, and returning to normal routines. In 2020 Solomon and Hensley discussed the application of EMDR in relation to grief and mourning in times of COVID-19. Similar to previous discussed findings, the case presented in this paper demonstrated positive improvement in symptoms and being able to move forward from the loss.

EMDR for grief treatment considerations

EMDR therapy is a transdiagnostic and integrative psychotherapy ( 42 ) and as highlighted has been applied effectively with individuals experiencing various forms of grief. Regarding the application of EMDR with Prolonged Grief, the standard protocol is applied with consideration of all three prongs, and other targets as highlighted that may impact on the individuals natural grieving process. Furthermore, there are important considerations and areas of focus to be aware of as a clinician. Firstly, as previously discussed, in the assessment phase of EMDR when a client has experienced grief, it is of vital importance to understand and assess for the risk factors mentioned to understand a client’s current likelihood of developing a more complicated and prolonged grief response and areas to process within treatment. Regarding our education, not only is it important to frame the clients’ current challenges in line with the AIP mode, incorporating grief frameworks such as the Dual Process Model is essential. In preparation for processing, standard EMDR resource development ( 43 ) and self-regulation techniques ( 44 ) as necessary can be employed. In addition, considering restoration-oriented activities as mentioned and the risk factors that may impact on this area, building up social support, and creating hope and optimism through resource development may be necessary components to enhance clinical efficacy. Previous clinical research has also demonstrated that a positive therapeutic alliance early on in therapy results in a greater reduction of prolonged grief symptoms ( 45 ). Considering this as a core tenet of our approach aligns with recent peer reviewed articles by Piedfort-Marin ( 46 ) and Hase and Brisch ( 47 ) which highlight the core role of the therapeutic relationship in EMDR.

Regarding target assessment, and subsequently desensitization and reprocessing, prior memories that are resulting in the client not being able to grieve or accommodate the loss are often a key focus point of treatment. These targets can often be centered around the themes of responsibility (e.g., I should have done more) or an individual’s ability to cope (I can’t manage this), although each individual may be unique regarding how they process loss related information and previous events underlying their symptoms. As previously discussed, in line with the AIP model, there may be a need to process other past traumatic events, other losses, or other sources of maladaptively stored information impacting or impeding on an individual’s natural grieving process. Furthermore, in working with grief, significant importance is placed on current triggers and the use of the Flash Forward Approach ( 48 ) to process catastrophic or significantly disturbing fears towards the future. Some examples of current triggers may be someone talking about the loved one, being reminded of them from a song or place, or other loss associated material. Processing fears the client may have towards the future (e.g., being alone forever, never enjoying a moment again in life) with the flashforward technique often provides further beneficial effects for clients, allowing them to feel more comfortable about life in the future. Future templates also assist in this way, providing further adaptive information for the person to feel they can cope, keep a connection with their loved one, and still have a sense of meaning in their life. As highlighted, the AIP model and the application of EMDR across all 8 phases can be a fitting and appropriate treatment framework and approach for working with clients whose grief has become complicated and blocked in some form.

Case example

Mary is a 24-year-old who lost her mother in a workplace accident. Since the death Mary has lost interest and engagement with hobbies, connecting with friends, avoids any reminders of the loss including places and people, and feels like life has lost all meaning and purpose. Mary has not engaged in therapy or talked about the grief with anyone for over 3 years. On assessment at the start of therapy, Mary met the PGD criteria based on the Prolonged Grief-13 (PG-13), a diagnostic tool to assess against the criteria for diagnosis. In EMDR therapy utilizing the standard protocol approach, a detailed history was taken with curiosity and assessment of known risk factors for PGD as highlighted earlier, and an assessment of other traumatic experiences which may be impacting on Mary’s current grief experience. Concurrently with gaining information about Mary’s history, and current challenges regarding the grief, Mary focused on building self-regulation skills through EMDR based techniques such as Resource Development and Installation ( 43 ). In addition, boosting up social connection and engagement was a major early focus, and from the preparation phase of EMDR, overall a willingness to start to process components of the grief and past trauma became more evident. This was in addition to Mary starting to have a greater positive connection to her deceased mother, being able to share positive stories and describe things that she loved about her in therapy. EMDR target assessment phase revealed certain EMDR targets for processing including the moment she found out about the death, a previous argument a few days before the death with her mother, previous losses and trauma from childhood, current triggers such as driving past her Mums house or work, and also fears of the future around what life would look like, in addition to positive future template work around being able to cope and manage events such as Christmas, anniversaries, and her Mother’s birthday. Mary responded well to treatment after 12 sessions of EMDR, no longer satisfying the criteria for a diagnosis of PGD. Mary showed improvement around being able to talk about the death or being in certain places related to the loss, has increased her connection with her mother in a way that feels meaningful to her, has been able to connect socially and with hobbies again, and has regained a sense of purpose in her life.

Overall, EMDR is a powerful treatment approach for those whose natural grieving process has become blocked in some way. The AIP model in combination with the Dual Process Model provides a useful framework to understand what has caused these blocks, and how we can support clients in moving forward in life after loss. Although the research in this area is building, there needs to be further research completed, especially considering individuals who have experienced trauma are more at risk of developing a prolonged and complicated grief response. The research so far has been limited in terms of not examining the use of EMDR specifically for those who meet the diagnostic criteria for Prolonged Grief Disorder. Most studies have also mentioned limitations regarding sample size and follow up. Furthermore, no trials completed so far have compared EMDR with Complicated Grief Treatment by Katherine Shear ( 49 ), which currently has the most empirical support for this population group. Further studies examining the comparison of EMDR and CGT independently and also combined would be of benefit to determine whether there is any additional benefit of combining both, in comparison to just using one intervention.

Author contributions

LS: Writing – original draft, Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

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

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. Lundorff M, Holmgren H, Zachariae R, Farver-Vestergaard I, O’Connor M. Prevalence of prolonged grief disorder in adult bereavement: A systematic review and meta-analysis. J Affect Disord . (2017) 212:138–49. doi: 10.1016/j.jad.2017.01.030

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Bryant RA, Kenny L, Joscelyne A, Rawson N, Maccallum F, Cahill C, et al. Treating prolonged grief disorder: A randomized clinical trial. JAMA Psychiatry . (2014) 71:1332–9. doi: 10.1001/jamapsychiatry.2014.1600

3. Szanto K, Prigerson H, Houck P, Ehrenpreis L, Reynolds CF III. Suicidal ideation in elderly bereaved: the role of complicated grief. Suicide Life-Threatening Behav . (1997) 27:194–207. doi: 10.1111/j.1943-278X.1997.tb00291.x

CrossRef Full Text | Google Scholar

4. Germain A, Caroff K, Buysse DJ, Shear MK. Sleep quality in complicated grief. J Traumatic Stress: Off Publ Int Soc Traumatic Stress Stud . (2005) 18:343–6. doi: 10.1002/jts.20035

5. Hardison HG, Neimeyer RA, Lichstein KL. Insomnia and complicated grief symptoms in bereaved college students. Behav Sleep Med . (2005) 3:99–111. doi: 10.1207/s15402010bsm0302_4

6. Mitchell AM, Kim Y, Prigerson HG, Mortimer MK. Complicated grief and suicidal ideation in adult survivors of suicide. Suicide Life-Threatening Behav . (2005) 35:498–506. doi: 10.1521/suli.2005.35.5.498

7. Simon NM, Pollack MH, Fischmann D, Perlman CA, Muriel AC, Moore CW, et al. Complicated grief and its correlates in patients with bipolar disorder. J Clin Psychiatry . (2005) 66:1105–10.

PubMed Abstract | Google Scholar

8. Silverman GK, Jacobs SC, Kasl SV, Shear MK, Maciejewski PK, Noaghiul FS, et al. Quality of life impairments associated with diagnostic criteria for traumatic grief. psychol Med (2000) 30(4):857–62.

9. Lichtenthal WG, Nilsson M, Kissane DW, Breitbart W, Kacel E, Jones EC, et al. Underutilization of mental health services among bereaved caregivers with prolonged grief disorder. Psychiatr Serv . (2011) 62:1225–9. doi: 10.1176/ps.62.10.pss6210_1225

10. Johnson JG, First MB, Block S, Vanderwerker LC, Zivin K, Zhang B, et al. Stigmatization and receptivity to mental health services among recently bereaved adults. Death Stud . (2009) 33:691–711. doi: 10.1080/07481180903070392

11. Eisma MC, Boelen PA, Lenferink LI. Prolonged grief disorder following the Coronavirus (COVID-19) pandemic. Psychiatry Res . (2020) 288:113031. doi: 10.1016/j.psychres.2020.113031

12. American Psychiatric Association. Diagnostic and statistical manual of mental disorders-IV-TR . 4th, Text Revision ed. Washington: DC: American Psychiatric Association (2000).

Google Scholar

13. Breen LJ, Hall CW, Bryant RA. A clinician’s quick guide of evidence-based approaches: Prolonged grief disorder. Clin Psychol . (2017) 21:153–4. doi: 10.1111/cp.12124

14. Shear K, Frank E, Houck PR, Reynolds CF 3rd. Treatment of complicated grief: A randomized controlled trial. J Am Med Assoc . (2005) 293:2601–8.

15. Shear MK, Reynolds CF, Simon NM, Zisook S, Wang Y, Mauro C. Optimizing treatment of complicated grief: A randomized clinical trial. JAMA Psychiatry . (2016) 73:685–94.

16. Shear MK, Gribbin Bloom C. Complicated grief treatment: An evidence-based approach to grief therapy. J Rational-Emotive Cognitive-Behavior Ther . (2017) 35:6–25.

17. Shear MK, Wang Y, Skritskaya N, Duan N, Mauro C, Ghesquiere A. Treatment of complicated grief in elderly persons: a randomized clinical trial. JAMA Psychiatry . (2014) 71:1287–95.

18. Shear K, Monk T, Houck P, Melhem N, Frank E, Reynolds C, et al. An attachment-based model of complicated grief including the role of avoidance. Eur Arch Psychiatry Clin Neurosci (2007) 257:453–61.

19. Szuhany KL, Malgaroli M, Miron CD, Simon NM. Prolonged grief disorder: Course, diagnosis, assessment, and treatment. Focus . (2021) 19:161–72.

20. de Jongh A, de Roos C, El-Leithy S. State of the science: Eye movement desensitization and reprocessing (EMDR) therapy. J Traumatic Stress (2024).

21. Shapiro F. Eye movement desensitization and reprocessing (EMDR): Basic principles, protocols, and procedures . New York City, US: Guilford Press (2001).

22. Stroebe MS, Schut H. Meaning making in the dual process model of coping with bereavement. In Neimeyer RA (Ed.), Meaning reconstruction & the experience of loss (pp. 55–73). American Psychological Association. doi: 10.1037/10397-003

23. Schut MSH. The dual process model of coping with bereavement: Rationale and description. Death Stud . (1999) 23:197–224.

24. Stroebe M, Schut H. The dual process model of coping with bereavement: A decade on. OMEGA-journal Death Dying (2010) 61(4):273–89.

25. He L, Tang S, Yu W, Xu W, Xie Q, Wang J. The prevalence, comorbidity and risks of prolonged grief disorder among bereaved Chinese adults. Psychiatry Res (2014) 219(2):347–52.

26. Schaal S, Jacob N, Dusingizemungu JP, Elbert T. Rates and risks for prolonged grief disorder in a sample of orphaned and widowed genocide survivors. BMC Psychiatry . (2010) 10:55. doi: 10.1186/1471-244X-10-55

27. Thimm JC, Holland JM. Early maladaptive schemas, meaning making, and complicated grief symptoms after bereavement. Int J Stress Manage . (2017) 24:347.

28. Thomas K, Hudson P, Trauer T, Remedios C, Clarke D. Risk factors for developing prolonged grief during bereavement in family carers of cancer patients in palliative care: a longitudinal study. J Pain Symptom Manage . (2014) 47:531–41. doi: 10.1016/j.jpainsymman.2013.05.022

29. Luber M. Protocol for excessive grief. J EMDR Pract Res . (2012) 6:129–35.

30. Solomon RM, Rando TA. Utilization of EMDR in the treatment of grief and mourning. J EMDR Pract Res . (2007) 1:109–17.

31. Solomon RM, Rando TA. Treatment of grief and mourning through EMDR: Conceptual considerations and clinical guidelines. Eur Rev Appl Psychol . (2012) 62:231–9.

32. Solomon RM, Hensley BJ. EMDR therapy treatment of grief and mourning in times of COVID-19. J EMDR Pract Res . (2020) 14(3):162–74. doi: 10.1891/EMDR-D-20-00031

33. Solomon R, Meysner L. EMDR Therapy and Traumatic Grief (Clinical Guidelines) . (2023) In Farrell D., Schubert S, Kiernan M. The Oxford Handbook of EMDR. Oxford University Press.

34. Hornsveld HK, Landwehr F, Stein W, Stomp MP, Smeets MA, van den Hout MA. Emotionality of loss-related memories is reduced after recall plus eye movements but not after recall plus music or recall only. J EMDR Pract Res . (2010) 4:106.

35. Sprang G. The use of eye movement desensitization and reprocessing (EMDR) in the treatment of traumatic stress and complicated mourning: Psychological and behavioral outcomes. Res Soc Work Pract . (2001) 11:300–20.

36. Ironson G, Freund B, Strauss JL, Williams J. Comparison of two treatments for traumatic stress: A community-based study of EMDR and prolonged exposure. J Clin Psychol . (2002) 58:113–28.

37. Meysner L, Cotter P, Lee CW. Evaluating the efficacy of EMDR with grieving individuals: A randomized control trial. J EMDR Pract Res . (2016) 10(1):1–11. doi: 10.1891/1933-3196.10.1.2

38. Cotter P, Lee CW. Evaluating the efficacy of EMDR with grieving individuals: A randomised control trial. J EMDR Pract Res . (2016) 10:3.

39. van Denderen M, de Keijser J, Stewart R, Boelen PA. Treating complicated grief and posttraumatic stress in homicidally bereaved individuals: A randomized controlled trial. Clin Psychol Psychother . (2018) 25:497–508.

40. Lenferink LI, de Keijser J, Smid GE, Boelen PA. Cognitive therapy and EMDR for reducing psychopathology in bereaved people after the MH17 plane crash: Findings from a randomized controlled trial. Traumatology . (2020) 26:427.

41. Usta FD, Yasar AB, Abamor AE, Caliskan M. A case series: Efficacy of short term EMDR on patients with persistent complex bereavement disorder (PCBD). Eur Psychiatry . (2020) 41:S360–1.

42. Dominguez SK. Farrell D, Schubert S, Kiernan, editors. The Oxford Handbook of EMDR. Oxford University Press (2023).

43. Leeds AM, Shapiro F. EMDR and resource installation: Principles and procedures for enhancing current functioning and resolving traumatic experiences. Brief Ther strategies individuals couples . (2000), 469–534.

44. van Genugten L, Dusseldorp E, Massey EK, van Empelen P. Effective self-regulation change techniques to promote mental wellbeing among adolescents: a meta-analysis. Health Psychol Rev . (2017) 11:53–71.

45. Glickman K, Katherine Shear M, Wall MM. Therapeutic alliance and outcome in complicated grief treatment. Int J Cogn Ther . (2018) 11:222–33.

46. Piedfort-Marin O. Transference and countertransference in EMDR therapy. J EMDR Pract Res . (2018) 12:158–72.

47. Hase M, Brisch KH. The therapeutic relationship in EMDR therapy. Front Psychol . (2022) 13:835470.

48. Logie R, De Jongh A. The “Flashforward procedure”: Confronting the catastrophe. J EMDR Pract Res . (2014) 8:25–32.

49. Katherine SM, Mulhare E. Complicated grief. Psychiatr Ann (2008) 38 10.

Keywords: grief, EMDR, prolonged grief, trauma, therapy, mental health

Citation: Spicer L (2024) Eye Movement Desensitisation and Reprocessing (EMDR) therapy for prolonged grief: theory, research, and practice. Front. Psychiatry 15:1357390. doi: 10.3389/fpsyt.2024.1357390

Received: 18 December 2023; Accepted: 26 March 2024; Published: 15 April 2024.

Reviewed by:

Copyright © 2024 Spicer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Liam Spicer, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Share full article

Yellowstone’s Wolves: A Debate Over Their Role in the Park’s Ecosystem

New research questions the long-held theory that reintroduction of such a predator caused a trophic cascade, spawning renewal of vegetation and spurring biodiversity.

Yellowstone’s ecological transformation through the reintroduction of wolves has become a case study for how to correct out-of-balance ecosystems. But new research challenges that notion. Credit... Elizabeth Boehm/Danita Delimont, via Alamy

Supported by

By Jim Robbins

• April 23, 2024

In 1995, 14 wolves were delivered by truck and sled to the heart of Yellowstone National Park in Wyoming, where the animal had long been absent. Others followed.

Since then, a story has grown up, based on early research, that as the wolves increased in number, they hunted the park’s elk herds, significantly reducing them by about half from 17,000.

The wolves’ return and predatory dominance was believed to have had a widespread effect known as a trophic cascade, by decreasing grazing and restoring and expanding forests, grasses and other wildlife. It supposedly even changed the course of rivers as streamside vegetation returned.

Yellowstone’s dramatic transformation through the reintroduction of wolves has become a global parable for how to correct out-of-balance ecosystems.

In recent years, however, new research has walked that story back. Yes, stands of aspen and willows are thriving again — in some places. But decades of damage from elk herds’ grazing and trampling so thoroughly changed the landscape that large areas remain scarred and may not recover for a long time, if ever.

Wolf packs, in other words, are not magic bullets for restoring ecosystems.

“I would say it’s exaggerated, greatly exaggerated,” said Thomas Hobbs, a professor of natural resource ecology at Colorado State University and the lead author of a long-term study that adds new fuel to the debate over whether Yellowstone experienced a trophic cascade.

“You could argue a trophic trickle maybe,” said Daniel Stahler, the park’s lead wolf biologist who has studied the phenomenon. “Not a trophic cascade.”

Not only is the park’s recovery far less robust than first thought, but the story as it has been told is more complex, Dr. Hobbs said.

But the legend of the wolves’ influence on the park persists.

“How in the world does this lovely story — and it is a beautiful story — come to be seen as fact?” Dr. Hobbs wondered. A chapter of a book tried to answer that, concluding that a video called “ How Wolves Change Rivers ,” which has received tens of millions of views, contributed mightily to the tale.

The ecological record is complicated by the fact that, as elk declined, the number of bison increased substantially, continuing some of the same patterns, like heavy grazing in some places. Moreover, Yellowstone is growing warmer and drier with climate change.

Large numbers of elk in the north of the park had caused significant ecological changes — vegetation disappeared, trampled streams led to extensive erosion, and invasive plant species took hold. Riparian vegetation, or the grasses, the trees and the shrubs along riverbanks and streams, provides a critical habitat for birds, insects and other species to flourish and to maintain biodiversity in the park.

Once elk numbers dwindled, willows and aspens returned along rivers and streams and flourished. The beaver, an engineer of ecosystems, reappeared, using the dense new growth of willows for both food and construction materials. Colonies built new dams, creating ponds that enhanced stream habitats for birds, fish, grizzlies and other bears as well as promoting the growth of more willows and spring vegetation.

But wolves were only one piece of a larger picture, argue Dr. Hobbs and other skeptics of a full-blown trophic cascade at Yellowstone. Grizzly bears and humans played a role, too. For eight years after wolves re-entered the park, hunters killed more elk than the wolves did.

“The other members of the predator guild increased, and human harvest outside of the park has been clearly shown to be responsible for the decline in elk numbers the first 10 years after the wolves were introduced,” Dr. Hobbs said.

The changes attributed to the presence of stalking wolves, some research showed, weren’t only the result of fewer elk, but of a change in elk behavior called “the ecology of fear.” Scientists suggested that the big ungulates could no longer safely hang out along river or stream banks and eat everything in sight. They became extremely cautious, hiding in places where they could be vigilant. That allowed a return of vegetation in those places.

Dr. Hobbs and others contend that subsequent research has not borne that theory out.

Another overlooked factor is that around the same time wolves were returning, 129 beavers were reintroduced by the U.S. Forest Service onto streams north of the park. So it wasn’t just wolf predation on elk and the subsequent return of wolves that enabled an increase in beavers, experts say.

Some researchers say the so-called trophic cascade and rebirth of streamside ecosystems would have been far more robust if it weren’t for the park’s growing bison herd. The bison population is at an all-time high — the most recent count last summer found nearly 5,000 animals. Much larger than elk, bison are less likely to be vulnerable to wolves, which numbered 124 this winter.

The park’s bison, some researchers say, are overgrazing and otherwise seriously damaging the ecosystems — allowing the spread of invasive species and trampling and destroying native plants.

The heavily grazed landscape is why, critics say, some 4,000 bison, also a record, left Yellowstone for Montana in the winter of 2023-24, when an unusually heavy snow buried forage. Because some bison harbor a disease, called brucellosis, that state officials say could infect cattle, they are not welcome outside the park’s borders. (There are no documented cases of transmission between bison and cattle.)

Montana officials say killing animals that may carry disease as they leave the park is the only way to stem the flow. During a hunt that began in the winter of 2023, Native Americans from tribes around the region took part. All told, hunters killed about 1,085 bison; 88 more were shipped to slaughter and 282 were transferred to tribes. This year, just a few animals have left the park.

The Park Service is expected to release a bison management plan in the coming months. It is considering three options: to allow for 3,500 to 5,000 animals, 3,500 to 6,000, or a more natural population that could reach 7,000.

Richard Keigley, who was a research ecologist for the federal Geological Survey in the 1990s, has become an outspoken critic of the park’s bison management.

“They have created this juggernaut where we’ve got thousands of bison and the public believes this is the way things always were,” he said. “The bison that are there now have destroyed and degraded their primary ranges. People have to realize there’s something wrong in Yellowstone.”

Dr. Keigley said the bison population in the park fluctuated in the early years of the park, with about 229 animals in 1967. It has grown steadily since and peaked last year at 5,900.

“There is a hyperabundant bison population in our first national park,” said Robert Beschta, a professor emeritus of forest ecosystems at Oregon State University who has studied Yellowstone riparian areas for 20 years. He pointed to deteriorating conditions along the Lamar River from bison overgrazing.

“They are hammering it,” Mr. Beschta said. “The Lamar ranks right up there with the worst cattle allotments I’ve seen in the American West. Willows can’t grow. Cottonwoods can’t grow.”

A warmer and drier climate, he said, is making matters worse.

Such opinions, however, are not settled science. Some park experts believe that the presence of thousands of bison enhances park habitats because of something called the Green Wave Hypothesis.

Chris Geremia, a park biologist, is an author of a paper that makes the case that a large numbers of bison can stimulate plant growth by grazing grasses to the length of a suburban lawn. “By creating these grazing lawns bison and other herbivores — grasshoppers, elk — these lawns are sustaining more nutritious food for these animals,” he said.

Dr. Geremia contends that a tiny portion — perhaps one-tenth of one percent — of the park may be devoid of some plants. “The other 99.9 percent of those habitats exists in all different levels of willow, aspen and cottonwood,” he said.

The Greater Yellowstone Coalition, a conservation organization, favors a bison population of 4,000 to 6,000 animals. Shana Drimal, who heads the group’s bison conservation program, said that park officials needed to monitor closely changing conditions like climate, drought and bison movement to ensure the ecosystems wouldn’t become further degraded.

Several scientists propose allowing the bison to migrate to the buffer zones beyond the park’s borders, where they are naturally inclined to travel. But it remains controversial because of the threat of disease.

“The only solution is to provide suitable winter range outside the park where they should be tolerated,” said Robert Crabtree, a chief scientist for the Yellowstone Ecological Research Center, a nonprofit. “When they migrate outside the park now it’s to habitat they evolved to prefer — and instead we kill them and ship them away.”

Advertisement

Borderline Personality Disorder

The latest theory on borderline personality disorder, new research uses logic to understand people with borderline disorder..

Posted April 27, 2024 | Reviewed by Lybi Ma

• What Is Borderline Personality Disorder?
• Find a therapist to treat BPD
• Theories of borderline personality disorder focus on early emotional experiences as the cause of splitting.
• A new approach uses statistical modeling to see what factors influence the tendency toward splitting.
• People with this disorder can use their experiences to help overcome splitting and achieve integration.

When you think of someone with borderline personality disorder , it’s likely that you imagine their tendency to let their emotions veer out of control, become overly intrusive, and alternate between loving and hating the same individual, or what’s known as “splitting.”

Perhaps you have an in-law who you know has this diagnosis, and as much as you would like to show understanding and empathy, it’s difficult when they decide to make you the target of the hate side of the splitting equation. If only you could figure out what’s behind these stormy changes of heart, perhaps you could get along better, if not become a trusted source of help. New research uses a logical analysis to provide some clues.

The Logic of Splitting in Borderline Personality Disorder

According to the University of College London’s Giles Story and colleagues (2024), traditional theories of this disorder regard splitting as an offshoot of unhealthy early emotional development, in which “an infant structures its experience by discriminating between positive and negative affect… [leading to] disconnected states of extreme satisfaction and frustration.” (p. 750) In ordinary development, individuals learn to bring these two states into balance as the bonds with their caregivers become stable and enduring, regardless of whether their needs are satisfied or frustrated. In BPD, though, this integration fails to occur, and individuals come to acquire split representations not just of other people, but of themselves.

Tracing the development of these “object relations” approaches to BPD, Story and colleagues maintain that these approaches miss the mark by considering only emotional aspects of splitting and not potential cognitive ones. Instead, the UCL authors propose a framework of probabilistic inference. Perhaps people with BPD form the wrong conclusions when they try to establish the causes of other people’s behaviors.

Everyone engages in this type of speculation, whether BPD or not. Even a simple act such as someone smiling at you could have you wonder what’s behind their apparent good intentions. Did you do something they liked, which would be a “situational” cause, or are they just people who tend to smile (“dispositional” cause)? You might then go on to observe their future behavior which would allow you to settle the question.

There are many possible variants of the situational versus dispositional attributional process. If you like someone, you’ll be more inclined to view good behavior as dispositional. If someone generally behaves a certain way and then suddenly shows the opposite behavior, it must be situational. For the most part, these reasoning processes are rational, allowing individuals to test “hypotheses” about why others behave as they do. In BPD, all-or-nothing reasoning sets in, in which people tend to view the causes of other people’s behavior in overly simplistic, all-or-nothing ways.

A Statistical Modeling Approach to Splitting

In ordinary experience, Story and colleagues propose, people go through statistical thinking known as “Bayesian,” in which they change the probabilities of a person’s behavior reflecting dispositional factors based on behaviors as they unfold over time. The first time you see a stranger smile, you have no idea whether they are nice, or not. However, as you see them continuing to smile regardless of what’s happening around them, the odds increasingly shift in favor of a dispositional attribution. If people with BPD can’t make that calculation, they’ll judge someone’s personality based on a snap judgment that won’t change over time. Splitting, in this view, becomes “a distorted causal inference.” They don’t, as the authors propose, update a “person prior” (p. 757). Once nice, always nice, and vice versa. The “split priors,” in which people are all bad or all good become “impervious to learning." (p. 762)

The UCL authors ran statistical models on data from a prior study of participants with BPD and non-BPD controls given the simulated task of judging the moral character of “Person A” toward “Person B.” Ordinarily if you were making such a judgment with no “prior person,” you would have to wait to see what Person A actually did in a given situation and then update your judgments. By running a model against the actual data from participants as they judged whether Person A was a good or a bad “agent,” Story and colleagues could determine whether the groups differed in their ability to draw Bayesian inferences.

Not only did the findings confirm the predictions of differences in attributional processes between BPD and non-BPD groups, but the authors were also able to run a model to show how the tendency to engage in splitting could potentially be modified. Part of the problem in making attributions in general is that sometimes the external context in which people act isn’t all that clear-cut. The lack of clarity only feeds into the tendency of people with BPD to draw conclusions based on their initial impressions of others. However, by creating a model in which precise information is available about Person A’s behavior, attributions based on splitting diminish and a more integrated, and realistic, set of judgments can emerge.

Turning Statistical Modeling into Intervention

The job of intervention is to help “split priors” to be updated by data from experience, using the processes described through the Bayesian modeling. Consider the example provided by the authors. A person with BPD might jump to the conclusion that if someone they’re supposed to meet is late, this proves that “they hate me.” Intervention can help the individual take extra external information into account (such as heavy traffic). Training the individual to be more sensitive to situational factors would reduce the tendency to make judgments based not on what people do, but on how they imagine people to be (all good or all bad).

In general, it is a good idea to let the evidence influence your judgments of people, but for individuals who tend to block out the data from experience, this type of intervention could help the person with BPD become more attuned to what the people around them are actually doing. Additionally, based on the Bayesian model, in which your judgments about probability should shift depending on prior outcomes, training people to monitor their conclusions about people over time would help individuals integrate positive and negative judgments rather than shift toward one or the other.

To sum up, whatever early experiences shape the individual with BPD are less important, in this approach, than the logic of the decisions the individual makes in the here and now. Giving people with this disorder the power and knowledge to observe themselves over time can help build the fulfillment that comes from an integrated view of the people in their lives.

Story, G. W., Smith, R., Moutoussis, M., Berwian, I. M., Nolte, T., Bilek, E., Siegel, J. Z., & Dolan, R. J. (2024). A social inference model of idealization and devaluation. Psychological Review , 131 (3), 749-780. https://doi.org/10.1037/rev0000430

Susan Krauss Whitbourne, Ph.D. , is a Professor Emerita of Psychological and Brain Sciences at the University of Massachusetts Amherst. Her latest book is The Search for Fulfillment.

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

Understanding what emotional intelligence looks like and the steps needed to improve it could light a path to a more emotionally adept world.

• Coronavirus Disease 2019
• Affective Forecasting
• Neuroscience

Help | Advanced Search

Computer Science > Machine Learning

Title: generalizing machine learning evaluation through the integration of shannon entropy and rough set theory.

Abstract: This research paper delves into the innovative integration of Shannon entropy and rough set theory, presenting a novel approach to generalize the evaluation approach in machine learning. The conventional application of entropy, primarily focused on information uncertainty, is extended through its combination with rough set theory to offer a deeper insight into data's intrinsic structure and the interpretability of machine learning models. We introduce a comprehensive framework that synergizes the granularity of rough set theory with the uncertainty quantification of Shannon entropy, applied across a spectrum of machine learning algorithms. Our methodology is rigorously tested on various datasets, showcasing its capability to not only assess predictive performance but also to illuminate the underlying data complexity and model robustness. The results underscore the utility of this integrated approach in enhancing the evaluation landscape of machine learning, offering a multi-faceted perspective that balances accuracy with a profound understanding of data attributes and model dynamics. This paper contributes a groundbreaking perspective to machine learning evaluation, proposing a method that encapsulates a holistic view of model performance, thereby facilitating more informed decision-making in model selection and application.

Submission history

Access paper:.

• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .