Exploring the neural basis of fairness: A model of neuro-organizational justice

Organizational Behavior and Human Decision Processes 110 (2009) 129–139

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Exploring the neural basis of fairness: A model of neuro-organizational justice Constant D. Beugré * Delaware State University, College of Business, 1200 N. Dupont Hwy, Dover, DE 19901, USA

a r t i c l e

i n f o

Article history: Received 29 July 2008 Accepted 28 June 2009 Available online 3 August 2009 Accepted by Dr. Scott Shane Keywords: C-system Fairness Fairness theory of mind Neuro-organizational justice Neuroeconomics Organizational justice X-system

a b s t r a c t Drawing from the literature in neuroeconomics, organizational justice, and social cognitive neuroscience, I propose a model of neuro-organizational justice that explores the role of the brain in how people form fairness judgments and react to situations of fairness and/or unfairness in organizations. The model integrates three levels of analysis: (a) behavioral, (b) mental (cognitive and emotional), and (c) neural. The behavioral level deals with motivated actions displayed by the individual; the mental level deals with information processing mechanisms and emotional arousal; and the neural level concerns the brain systems instantiating mental processes. The paper also describes a fairness theory of mind that could help managers improve their ability to create fair working environments. The model’s implications for further research and management practice are discussed. Ó 2009 Elsevier Inc. All rights reserved.

This paper draws from three streams of research: social cognitive neuroscience (e.g., Lieberman, 2000, 2007; Lieberman, Gaunt, Gilbert, & Trope, 2002; Ochsner & Lieberman, 2001), neuroeconomics (e.g., Camerer, 2007; Camerer, Loewenstein, & Prelec, 2004, 2005; Glimcher, 2003; Glimcher, Camerer, Fehr, & Poldrack, 2009; Loewenstein, Rick, & Cohen, 2008), and organizational justice (e.g., Cropanzano & Greenberg, 1997; Greenberg, 1987, 1990) to develop a conceptual framework that explores the role of the brain in how people form fairness judgments and react to situations of fairness and/or unfairness in organizations. Social cognitive neuroscience encompasses the empirical study of the neural mechanisms underlying social cognitive processes (Blakemore, Winston, & Frith, 2004; Lieberman, 2007), whereas neuroeconomics merges methods from neuroscience and economics to better understand how the human brain generates decisions in economic and social contexts (Camerer et al., 2004; Fehr, Fischbacher, & Kosfeld, 2005; Loewenstein et al., 2008). Organizational justice refers to employee perceptions of fairness (Greenberg, 1987, 1990) and encompasses three dimensions: distributive justice—the fairness of outcomes distribution (e.g., Adams, 1965), procedural justice— the fairness of formal procedures underlying outcomes distribution (e.g., Lind & Tyler, 1988; Thibaut & Walker, 1975), and interactional justice—the fairness of interpersonal treatment (e.g., Bies & Moag, 1986; Cropanzano & Greenberg, 1997).

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Apparently, these three disparate fields have little in common and their scholars barely collaborate with one another. However, an exploration of their respective literatures reveals that these three domains of scientific inquiry do in fact share some commonalities. As neuroeconomists investigate the role of the brain in economic decisions, they often rely on game theory, mainly the Ultimatum Game (UG) and the Prisoner’s Dilemma Game (PDG). These two games include situations of fairness (e.g., Fehr & Schmidt, 1999; Rabin, 1993), a key concept in the study of organizational justice. In fact, organizational justice scholars use the terms fairness and justice interchangeably as I will do in this article. For these scholars, fairness is an important yardstick that employees use to assess outcomes distribution, formal procedures, or interpersonal treatment in organizations. Likewise, judgments of fairness (or unfairness) in organizations occur as a result of social interactions, a domain of interest to social cognitive neuroscientists. Thus, my aim in writing this article is to blend these three research streams together to develop a coherent conceptual framework that explores the neural basis of fairness in an organizational context. The fundamental premise of a neuroscientific approach of organizational justice is that a triggering event leads to an activation of specific brain structures. This activation is followed by a mental process that helps the person construe the event as fair or unfair. Thus, the starting point of fairness evaluations and subsequent reactions to situations of fairness (or unfairness) is the neural circuitry. By using a neuroscientific approach, this article provides new directions for research and practice in the study of organizational

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justice to the extent that there is evidence that fairness is a social organizing principle that may well be ‘built in the brain’ (Van den Bos & Lind, 2002). This is particularly important because most adults spend their working lives as members of organizations— schools, companies, government agencies, and non-profit organizations. As Tabibnia and Lieberman (2007) put it, ‘‘we live in a highly social environment, in which most of the work we do is accomplished through collaboration with others and many of the goods we consume are consumed in the company of others or shared with others” (p. 90). Moreover, perceptions of justice in organizations often stem from exchanges with supervisors, coworkers, or representatives of the organization as a whole. Such exchanges have economic implications. Thus, to the extent that the human mind is the driver of all economic activity (e.g., Braeutigam, 2005; Camerer et al., 2004), organizational justice scholars should incorporate a neural perspective in their analysis of fairness in organizations. The model of neuro-organizational justice builds on the works of Lieberman et al. (2002) and Reynolds (2006) and extends them to the analysis of fairness in organizational contexts. Lieberman et al. (2002) describes two systems in the human brain: the X-system and the C-system that people generally use to make sense of their world. The former represents the parts of the brain that are most closely associated with unconscious environmental stimuli, whereas the latter represents the mechanism in which complicated reasoning is accomplished (Lieberman et al., 2002; Reynolds, 2006). Reynolds (2006) used these two systems to develop a neurocognitive model of ethical decision. However, his model did not correlate ethical behavior with specific brain regions although it was construed as neurocognitive. Despite this limitation, Reynolds’s model may help to shed light on the neurocognitive underpinnings of other organizational phenomena, such as organizational justice. The model of neuro-organizational justice integrates three levels of analysis: neural, mental, and behavioral. The behavioral level deals with motivated actions displayed by the individual; the mental level deals with information processing mechanisms and emotional arousal; and the neural level concerns the brain systems instantiating mental processes (e.g., Ochsner & Lieberman, 2001). Incorporating these three levels of analysis is particularly important because simply showing brain activity is of limited use if it does not help to explain human behaviors when they occur in social contexts (e.g., Lieberman, 2007; Ochsner & Lieberman, 2001). Moreover, such research must be grounded in theory and incorporate multiple levels (Ochsner & Lieberman, 2001). The model of neuro-organizational justice goes further than mere cognitive models that focus on linear progression and thus have a limited ability to fully capture the analysis of fairness issues in organizations. Thus, this model begins by specifying how the individual thinks from the moment the first stimuli are encountered through the transmission of electrochemical signals in the brain, to the engagement of actual behavior (e.g., Reynolds, 2006). Recent research findings reveal that fair offers lead to increased activity in several reward regions of the brain including the ventral striatum, the orbitofrontal cortex, the ventromedial prefrontal cortex, and the left amygdala, compared with unfair proposals of equal monetary value (Sanfey, Rilling, Aronson, Nystrom, & Cohen, 2003). By drawing from several research streams, the model of neuro-organizational justice could help to advance interdisciplinary cross-fertilization. Specifically, the model could add to the extant literature in organizational justice because by studying the underlying neural systems, organizational justice scholars could effectively assess the neural underpinnings of fairness that is so fundamental to the human experience. There is even evidence that non-human primates (monkeys and chimpanzees) have ‘standards of fairness’ and show strong emotional reactions when these stan-

dards are violated (Brosnan, 2009; Brosnan & de Waal, 2003). Indeed, ‘‘a basic sense of fairness and unfairness is essential to many aspects of societal and personal decision making and underlies notions as diverse as ethics, social policy, legal practice, and personal morality” (Sanfey et al., 2003, p. 1757). The model provides a new paradigm in the study of organizational justice and could also contribute to the literature in neuroeconomics and social cognitive neuroscience by applying concepts from both disciplines to organizational contexts. ‘‘Humans are endowed with a natural sense of fairness that permeates social perceptions and interactions” (Moll, de Oliveira-Souza, Eslinger, Gramati, & Mourao-Miranda, 2002, p. 2730). Organizations are entities where such social perceptions and interactions often occur on a daily basis. Although in recent years, the ability of researchers to directly observe brain activity has increased exponentially through the use of functional neuroimaging methods, the organizational sciences have been slower to incorporate such methods (Lee & Chamberlain, 2007). The model could also advance management practice by highlighting the neural mechanisms underlying fairness. It could help managers to acknowledge that fairness is a fundamental human concern and as such, they need to pay special attention to the impact of their own behavior on employees. As Pressman (2006) argues, we do not learn to be fair; fairness is part of our genetic makeup. Thus, managers should be able to develop a fairness theory of mind that could provide guidelines for creating fair working environments. ‘‘Theory of mind refers to our ability to understand other people’s motor intentions and action goals and our ability to understand other people’s beliefs and thoughts” (Singer, 2009, p. 254). It suggests that behaviors are caused by mental states (Premack & Woodruff, 1978). An understanding that employees care about fairness could help managers to develop skills that are necessary in creating fair working environments. The present article is organized as follows. First, I use Lieberman’s X- and C-systems as a conceptual framework to lay the groundwork for discussing the neural correlates of organizational justice. Next, I propose the model of neuro-organizational justice. I then discuss the model’s implications for research and practice.

X- and C-systems Is organizational justice, construed as employee perceptions of fairness, a behavioral reaction, a cognitive process, the result of a neural mechanism, or does it represent a combination of these three processes? Although the organizational justice literature has addressed the first two aspects of the question (e.g., Goldman & Thatcher, 2002; Greenberg, 1990; Lind, 2001; Van den Bos & Lind, 2002), it has largely remained mute with regard to the third one. Fortunately, the growing body of knowledge in social cognitive neuroscience and neuroeconomics may provide valuable insights to organizational justice scholars. Lieberman et al. (2002) and Satpute and Lieberman (2006) described two systems: the X-system and the C-system that may prove useful in exploring the neural correlates of organizational justice (see Table 1). I rely on these two systems to lay the groundwork for the model of neuro-organizational justice. Functions of the X-system Lieberman et al. (2002) and Satpute and Lieberman (2006) describe the X-system as a parallel processing, subsymbolic, pattern matching system that produces the continuous stream of consciousness that each human being experiences. The neuroanatomy of the X-system includes brain areas, such as the amygdala, the basal ganglia, the dorsal anterior cingulate cortex, the lateral temporal cortex, and the ventromedial prefrontal cortex (Lieberman, 2007;

C.D. Beugré / Organizational Behavior and Human Decision Processes 110 (2009) 129–139 Table 1 Features associated with X-system and C-system. X-system

C-system

Parallel processing Fast operating Slow learning Nonreflective consciousness Sensitive to subliminal presentations Spontaneous processes Prepotent responses Typically sensory Output experienced as reality Relation to behavior unaffected by cognitive load Facilitated by high arousal Phylogenetically older Representation of symmetric relations

Serial processing Slow operating Fast learning Reflective consciousness Insensitive to subliminal presentations Intentional processes Regulation of prepotent responses Typically linguistic Output experienced as self-generated Relation to behavior altered by cognitive load Impaired by high arousal Phylogenetically newer Representation of asymmetric relations Representation of special cases Representation of abstract concepts (e.g., negation, time)

Representation of common cases

Adapted from Satpute and Lieberman (2006) and Lieberman (2007).

Lieberman et al., 2002; Satpute & Lieberman, 2006). The neural components of the X-system are proposed to be slow learning, fast operating, bidirectional, and parallel processing structures (Satpute & Lieberman, 2006). Research shows that the amygdala is implicated in many different kinds of phenomena, such as attitudes, stereotyping, person perception, and emotion (Ochsner & Lieberman, 2001). Thus, it can be described as the seat of emotions. Although the amygdala provides quick responses to fearful situations, it can also receive cortical inputs that can moderate its responses or even override them. Camerer et al. (2004) describe the amygdala as an internal ‘hypochondriac’ which provides quick and dirty emotional signals in response to potential fears (p. 561). Suppose for instance, that you leave your office late at night and walk toward your car. You suddenly hear steps behind you. At first, you may wonder who might be following you. The amygdala may be alerted for a potentially threatening situation. However, as you turn to figure out the situation, you realize that it is your colleague who is also leaving his/her office. This previously unfriendly situation may now turn into a friendly one, probably leading to an amicable conversation between you and your colleague. The X-system is construed as a connectionist network comprising units, unit activity, and connection weights and represents ‘‘a pattern matching system whose connectivity weights are determined by experience and whose activation levels are determined by current goals and features of the stimulus input” (Lieberman et al., 2002, p. 211). Cognitive processes are effortless to the extent that the X-system is responsible for automaticity. When behavior is the result of automaticity, the individual may not be able to systematically articulate all its causes and explain it to another individual. As Camerer et al. (2004) put it, ‘‘when good performance becomes automatic (in the form of ‘procedural knowledge’) it is typically hard to articulate, which means human capital of this sort is difficult to reproduce by teaching others” (p. 560). Automaticity may probably explain why sharing tacit knowledge in organizations is so difficult. The X-system uses prototypes that represent stored concepts of objects, concepts, and language. Electrochemical representations of incoming stimuli are matched against prototypes (Lieberman et al., 2002; Reynolds, 2006). Newly encountered events or trends are compared with existing prototypes to determine whether they belong to specific categories or can be seen as being connected in some manner (Baron, 2006). Prototypes are multidimensional in form and contain information necessary for action (Reynolds, 2006). The X-system matches the situation an individ-

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ual faces against stored prototypes. This automatic process helps the brain to avoid using elaborate thought when facing familiar situations. ‘‘When an individual faces a situation, this reflexive pattern matching system is able to conduct a process of nonconscious analysis that accepts stimuli from the environment, transmits and organizes neutral patterns reflecting the stimulus, and compares that pattern against stored prototypes” (Reynolds, 2006, p. 739). Applied to the study of fairness, one may conclude that the X-system holds stored ‘fairness prototypes’. These fairness prototypes represent mental scripts of fair or unfair situations against which current situations are compared. Thus, the X-system can automatically determine whether a given situation is fair or not. Suppose that a manager, John, is perceived as an abusive supervisor, therefore unfair. Evoking the name of John would be associated with the corresponding prototype ‘abusive supervisor’. The good news, however, is that these associations in the X-system are not fixed or rigid but subject to change and alteration (e.g., Lieberman et al., 2002; Satpute & Lieberman, 2006). In the example of John, the abusive supervisor, additional experience or information that reveals that John has changed and is now a considerate supervisor may help to modify the previous association. Instead of being associated with ‘abusive supervisor’, John may now be associated with a new prototype ‘considerate supervisor’. When the X-system cannot automatically handle a new stimulus, it is handed over to the C-system. This may happen under at least two conditions: (1) the situation is a novel one for which there is no existing prototype or (2) the decision is made by the X-system but the individual receives outside information questioning his/her prior judgment. Lieberman et al. (2002, p. 222) note that ‘‘the X-system’s job is to turn information that emanates from the environment into our ongoing experience of that environment, and it does this by matching the incoming patterns of information to the patterns it stores as connection weights. When things match, the system settles into a stable state and the stream of consciousness flows smoothly. When they do not match, the system keeps trying to find a stable state, until finally the cavalry must be called in”. The neural equivalent of the cavalry is the C-system, which comprises neural structures implicated in high order cognitive processes, such as the different areas of the neo-cortex and the hippocampus. Thus, standards of fairness are transmitted to the X-system by the C-system. The C-system is brought in line when the X-system encounters difficulties it cannot solve. Functions of the C-system The C-system is a serial system that uses symbolic logic to produce the conscious thoughts that we experience as reflections on the stream of consciousness (Lieberman et al., 2002, p. 204). It is the mechanism in which conscious thoughts are processed. The neuroanatomy of the C-system includes the lateral prefrontal cortex, the posterior parietal cortex, the medial prefrontal cortex, the rostral anterior cingulate cortex, and the hippocampus, and surrounding medial temporal lobe regions (Lieberman, 2007; Lieberman et al., 2002; Satpute & Lieberman, 2006). The neural components of the C-system are proposed to be fast learning, slow operating, symbolic, or propositional structures (Satpute & Lieberman, 2006). The C-system is capable of rule-based analysis and performs a regulatory role over the X-system. Thus, the X-system is dependent on the C-system for its supply of prototypes. The C-system functions as an adaptive and thought creation mechanism. This adaptation process reflects the human ability to learn from experience (Reynolds, 2006). Learning from experience allows individuals to avoid situations perceived as unfair and the consequences associated with such situations.

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The C-system sets the standards to the extent that an output pattern must adhere or fit a prototype in order to be categorized as of that prototype. The anterior cingulate is linked to both the X- and C-systems (Lieberman et al., 2002). It acts like an alarm that encourages or allows the X-system to express its inability to adequately match particular stimuli to any known prototypes and the need for the C-system to engage in active processing. The C-system also focuses on novel situations. The X-system depends primarily on the associative links formed through extensive learning histories, whereas the C-system can construct arbitrary associations between pieces of information as demanded by the current context (Lieberman et al., 2002). The C-system is a symbolic processing system that produces reflective awareness (Lieberman et al., 2002). It has four phenomenological features: authorship, symbolic logic, capacity limits, and alarm-driven (Lieberman et al., 2002). The C-system provides authorship to our own thoughts and actions to the extent that each individual initiates his/her own thought processes. The symbolic function allows the C-system to give meaning to events. Indeed, ‘‘symbolic logic allows us to escape the limits of empiricism and move beyond the mere representation and association of events in world and into the realms of the possible” (Lieberman et al., 2002, p. 221). Despite its sophistication compared to the X-system, the C-system is limited to the extent that reflective thinking is constrained to one object or category of objects at any given moment in time (Lieberman et al., 2002). Finally, the C-system serves as an alarm because it determines the X-system’s standards. This distinction between X-system and C-system is similar to the one between system 1 and system 2 discussed in the decision making and economics literature (e.g., Camerer et al., 2005; Kahneman, 2003). Like the X-system, system 1 corresponds closely to automatic processing, whereas system 2 like the C-system corresponds closely to controlled processes, monitors the quality of answers provided by system 1 and, in some situations, corrects or overrides these judgments (Cohen, 2005). ‘‘The operations of system 1 are fast, automatic, effortless, associative, and often emotionally charged; they are also governed by habit, and are therefore difficult to control or modify. In contrast, the operations of system 2 are slower, serial, effortful, and deliberately controlled; they are also relatively flexible and potentially rule-governed” (Kahneman, 2003, p. 1451). System 1 involves perceptions, whereas system 2 involves deliberate reasoning (Kahneman, 2003; Lieberman et al., 2002). Attributes that are routinely and automatically produced by the perceptual system or system 1, without intention or effort have been called natural assessments (Tversky & Kahneman, 1983). The evaluation of stimuli as good or bad is a particularly important natural assessment (Kahneman, 2003). The assessment of whether objects are good (and should be approached) or bad (and should be avoided) is carried out quickly and efficiently by specialized neural circuitry (Kahneman, 2003). The concept of natural assessment may also be applied to fairness. Indeed, one may speculate that the evaluation of an event as fair or unfair is a natural assessment. Using the Ultimatum Game, one may conclude that it is easy for players to evaluate offers of $1 or $2 as unfair (and should be rejected) and offers of $5 as fair (and should be accepted). Such an assessment is carried out quickly and efficiently by specialized neural circuitry included in the X-system or system 1. Lieberman, Jarcho, and Satpute (2004) investigated the neural correlates of intuition-based and evidencebased self-knowledge on a sample of participants with high and low experience in soccer and acting who were asked to make self-descriptiveness judgments about words from each domain while being scanned. They found that high-experience domain judgments produced activation in the X-system, whereas lowexperience domain judgments produced activation in the C-system. In other words, people exert less cognitive effort when dealing

with ‘familiar’ knowledge, whereas they spend more cognitive effort when dealing with less familiar knowledge. Likewise, Fiet, Clouse, and Norton (2004) and Baron (2006) note that experienced, repeat entrepreneurs generally search for opportunities in areas or industries where they are already knowledgeable; they do so in a less effortful, more automatic manner. Understanding these two systems is essential to the development of the model of neuroorganizational justice, which is discussed next.

The model of neuro-organizational justice Assumptions of the model The model of neuro-organizational justice depicted in Fig. 1 suggests that justice judgments start with a triggering event. Justice judgments are defined as evaluative statements regarding what is fair or unfair. An example of a triggering event may be an outcome an employee receives, a process that a departmental unit or the company develops and implements, or an interpersonal treatment emanating from a supervisor. All three examples are likely to generate justice judgments. The model further suggests that this triggering event activates specific brain regions. Two types of brain areas, cognition-inducing neural structures and emotion-inducing neural structures may be activated. The former are responsible for cognitive processing of information, whereas the latter are responsible for emotional arousal. The model suggests two paths: a cognitive neuro-organizational justice path and an affective neuro-organizational justice path. The cognitive neuroorganizational justice path implies that the activation of cognition-inducing brain areas would influence the individual’s thought processes that would then influence justice judgments. Justice judgments may yield two outcomes, fair or unfair. Finally, the individual will react depending on the outcome of the justice judgment. The affective neuro-organizational justice path implies that the triggering event activates emotion-inducing neural structures that then influence justice judgments. Using this path, the model contends that justice judgments are strongly influenced by the individual’s emotional reactions toward the triggering event. As Fig. 1 indicates, a triggering event may directly activate brain regions that are involved in emotional arousal. When this occurs, the individual directly acknowledges the emotional connotations of the event. For example, in the Ultimatum Game, respondents may already have a sense of what an unfair offer is. Once they are proposed such offers, brain regions involved in emotional arousal may be directly activated. Thus, respondents may experience anger or resentment toward unfair offers. Such emotions further lead to the rejection of these offers. When emotional intensity is strong, it may lead to a mode of operation where we just react than think (Van Widen, 2007). Wout, Kahn, Sanfey, and Aleman (2006) found that participants experienced more emotional arousal when confronted with an unfair offer as compared to a fair offer. These emotional processes may be automatic and therefore involve the X-system. The double arrow between cognitions and emotions suggests an interactive effect between the two variables. Accumulated evidence suggests that emotion influences cognition and vice versa, and these reciprocal influences are associated with modifications in cerebral activation (Koch et al., 2007). First, cognitions influence emotions to the extent that a cognitive reappraisal of the situation may diminish its emotional impact. The literature on the cognitive control of emotions has demonstrated this effect (e.g., LeDoux, 1995, 2000; Ochsner, Bunge, Gross, & Gabrieli, 2002; Ochsner & Gross, 2005). By interpreting an event differently, the emotional response to that event can be altered (Ochsner & Gross, 2005). As

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Emotions

Triggering event

Neural activation

Justice judgments

Reactions . Attitudinal . Behavioral

Cognitions

Fig. 1. A model of neuro-organizational justice. Note: cognition-related neural activation leads to cognitive processing of information, whereas emotion-related neural activation leads to emotional arousal.

Ochsner et al. (2002, p. 1215) put it, ‘‘We can change the way we feel by changing the way we think, thereby lessening the emotional consequences of an otherwise distressing experience”. LeDoux (2000) and Ochsner et al. (2002) found that the neural correlates of reappraisal were increased activation of the lateral and medial prefrontal regions and decreased activation of the amygdala and medial orbitofrontal cortex. Extended to the study of fairness, one may speculate that the cognitive reappraisal of an unfair situation may activate these same brain regions, thereby allowing the individual to reduce the emotional impact of perceived unfairness. Second, emotions influence cognitions to the extent that feelings play an important role in individual reactions to unfairness (Murphy & Tyler, 2008; Van den Bos, 2003). Van den Bos (2003) found that people’s emotional state at the time of making justice judgments can determine whether or not they perceive an encounter with an authority figure to be procedurally fair or not. Likewise, Murphy and Tyler (2008) found that positive and negative emotional reactions to perceived justice or injustice predict whether people will or will not comply with authority decisions and rules. As Frith and Frith (2006) point out, ‘‘When deciding what to do we are not totally ‘‘rational” in our choice of action; our choice is colored by emotions such as anticipated regret or desire for fairness” (p. 533). The model construes fairness as a three-dimensional phenomenon involving the neural, mental (cognitive and emotional), and behavioral levels. This conceptualization fits well with recent views advocated by several authors in social cognitive neuroscience (e.g., Lee & Chamberlain, 2007; Lieberman, 2007; Ochsner & Lieberman, 2001). Lee and Chamberlain (2007) argue that three layers of theory could be envisioned when exploring the neurocognitive bases of organizational behavior: (a) the neural layer deals with brain activity; (b) the cognitive layer concerns internal mental processes that rely on these neutral substrates; and (c) the behavioral layer concerns how individuals react to situations involving social interactions in conjunction with neural or cognitive mechanisms. The model makes three key assumptions: (a) a triggering event activates specific brain regions, (b) neural activation leads to mental processes including both cognitive processing and emotional arousal depending on the types of brain areas activated, and (c) mental processes influence justice judgments and reactions. It implies that organizational justice involves both cognitive and emotional mechanisms that are dependent on separable neural systems. However, Moll, de Oliveira-Souza, and Zahn (2008) contend that, ‘‘though some brain regions are intrinsically tied to motivational/regulatory mechanisms and others are less so, this does not imply clear boundaries between cognition and emotion”. The

model is used to introduce new insights in answering the two fundamental questions that are the focus of organizational justice theory. How do people form justice judgments? How do people respond when they are fairly or unfairly treated? In the following lines, I use neuroscientific evidence to answer these questions. Because of the lack of studies explicitly focusing on fairness in an organizational context, I rely on previous studies in neuroeconomics that analyze the neural correlates of fairness (see Table 2). The neural basis of justice judgments In the case of an event having fairness implications, the individual processes the available information to decide whether the event is fair or unfair. The model suggests that the occurrence of a particular event activates specific brain areas, which in turn call on the X-system when the situation is a familiar one and the C-system when it is a novel one and requires elaborate cognitive processing. Because people develop fairness prototypes, the features of the current situation are compared to those prototypes. The idea of fairness prototypes has been discussed in the organizational justice literature although it did not include an analysis of their neural correlates. Ambrose and Kulik (2001) argue that an unfamiliar object, person, or situation is categorized through a matching process in which the perceiver compares the features of the new encounter with the features of relevant category prototypes. The fairness prototypes described in this model encompass three areas: (a) distributive justice prototypes, (b) procedural justice prototypes, and (c) interactional justice prototypes. Distributive justice prototypes concern ‘mental scripts’ of fair or unfair outcomes. This is illustrated in the Ultimatum Game, where offers of $1.00 or $2.00 are perceived as unfair, whereas offers of $4.00 or $5.00 are perceived as fair. Procedural justice prototypes refer to ‘mental scripts’ of fair or unfair procedures. People can evaluate the fairness of a particular procedure by comparing its elements to their template of fairness (Ambrose & Kulik, 2001). Interactional justice prototypes are ‘mental scripts’ of fair or unfair interpersonal treatment. Thus, people form prototypes of ‘fair’ and ‘unfair’ outcomes, ‘fair’ and ‘unfair’ procedures, and ‘fair’ and ‘unfair’ interpersonal treatment. These fairness prototypes represent standards, expectations, or thresholds against which current events are assessed. This indicates that justice or fairness exists when there is congruence between what people expect on the basis of salient or appropriate normative rule and what they obtain (Cohen & Greenberg, 1982). Thus, whenever an individual faces a triggering event, he/she matches this event against existing prototypes. In proposing a neurocognitive model of ethics, Reynolds (2006) suggests that such matching process helps people to determine whether a given situ-

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Table 2 Studies assessing the neural correlates of fairnessa. Authors

Techniques

Findings

McCabe et al. (2001) Sanfey et al. (2003) Decety et al. (2004) De Quervain et al. (2004) King-Casas et al. (2005)

fMRI fMRI fMRI PET HyperscanfMRI fMRI

Fairness activates the VMPFC (ventromedial prefrontal cortex) Unfair offers lead to increased activity in the anterior insula Unfairness activates the DLPFC (dorsolateral prefrontal cortex) Sanctions against defectors activate reward areas of the brain The caudate nucleus processes information about the fairness of a social partner’s decision and the intention to repay this partner with trust Gain anticipation activates the ventral striatum. Ventral striatum activation is not evident when people anticipate losses

TMS fMRI fMRI

Disruption of the DLPFC by low-frequency magnetic stimulation reduces willingness to reject unfair offers Fair preferences associated with activation of the orbitofrontal cortex Insula encodes inequity. Individual differences in inequity aversion correlate with activity in inequity and utility regions

Knutson and Peterson (2005) Knoch et al. (2006) Tabibnia et al. (2008) Hsu, Anen, and Quartz (2008) a

Studies including only fairness were considered.

ation is ethical or unethical. Echoing this line of reasoning, Tabibnia, Satpute, and Lieberman (2008) note that fairness processing is relatively automatic and intuitive since the ventral striatum, the amygdala, and the ventromedial prefrontal cortex have all been associated with automatic processes (Lieberman, 2007). Thus, fairness judgment is processed in the X-system. People also attach values to those prototypes to the extent that prototypes of fair outcomes may be more valued than prototypes of unfair outcomes. As a perceptual phenomenon, fairness is influenced by prior recollection and reconstruction. Salancik and Pfeffer (1978) argued that perception is a retrospective process, and therefore, even though an employee may experience an immediate event, the interpretation of that event is derived from prior recall and reconstruction. Thus, employees may compare current events to previous standards of fairness or previous situations of fairness or unfairness. These recollections may help to determine whether the event can be construed as fair or unfair. In a study using fMRI, Tabibnia et al. (2008) found that the ventral striatum, the amygdala, the ventromedial prefrontal cortex (VMPFC), and the orbitofrontal cortex (OFC) were associated with fairness preferences. In discussing how people make procedural justice judgments, Ambrose and Kulik (2001) contend that the question of how individuals evaluate procedures to form fairness judgments is fundamentally a cognitive question to the extent that the meaning given to a particular event will influence the person’s subsequent attitudes and behaviors. However, neuroeconomic evidence suggests that experiencing fairness may be seen as a pleasurable experience and thus may activate the nucleus accumbens, whereas experiencing unfairness may be seen as an unpleasant event that activates the insula (Camerer et al., 2004). The anterior insula is also activated when facing situations leading to decisions of fairness or unfairness (Morse, 2006; Sanfey et al., 2003). Sanfey et al. (2003) found that unfair offers in the Ultimatum Game activated the insula. They also noted that heightened activity in anterior insula for rejected unfair offers suggests an important role for emotions in decision making. Hsu, Anen, and Quartz (2008) found that the anterior insula encodes inequity. Damasio (1994), Bechara, Damasio, Damasio, and Lee (1999), Pillutla and Murnighan (1996), Hegtvedt and Killian (1999), Loewenstein, Weber, Hsee, and Welch (2001), and Glimcher, Kable, and Louie (2007) showed that emotions play an important role in decisions. The somatic marker hypothesis (Damasio, 1994), according to which emotional states are triggered in the brain before and after decision making, could help to explain justice judgments in organizations to the extent that it advocates the view that people learn that fairness is pleasant and unfairness, unpleasant. The key assumption of the somatic marker hypothesis is that decision making is a process that is influenced by marker signals that arise in

bioregulatory processes including those that express themselves in emotions and feelings (Bechara & Damasio, 2005). The somatic marker is engrained in the brain because the amygdala triggers emotional responses to decision outcome experiences, which then become learned associations expressed in the ventromedial prefrontal cortex (VMPFC) (Navqi & Bechara, 2006). The neural evidence suggests that the outcome of behavior is determined by the relative degree of engagement of an emotional response and processes mediated by the prefrontal cortex (Cohen, 2005). The model of neuro-organizational justice may help to elucidate several aspects of fairness heuristic theory (e.g., Lind, 2001; Van den Bos, 2001; Van den Bos, Lind, & Wilke, 2001), and uncertainty management theory (e.g., Lind & Van den Bos, 2002; Van den Bos & Lind, 2002). Fairness heuristics theory posits two basic premises. First, fairness judgments are assumed to serve as a proxy for interpersonal trust in guiding decisions about whether to behave in a cooperative manner in social institutions. Second, people are assumed to use a variety of cognitive shortcuts to ensure that they have a fairness judgment available when they need to make decisions about engaging in a cooperative behavior (Lind, 2001). Uncertainty management theory contends that people rely on fairness information most when they are confronted with uncertainty (e.g., Lind & Van den Bos, 2002; Van den Bos & Lind, 2002), defined as the extent to which an individual does not know what to do in a particular situation or cannot fully assess the implications of the choice he/she makes or does not understand the event well enough to predict it (e.g., Kahneman, Slovic, & Tversky, 1982). The uncertainty management model argues that in the process of forming justice judgments, people tend to look first for justice information that is most relevant in the particular situation in which they find themselves (Van den Bos, 2007). Uncertainty management theory relies on two principles: the substitutability principle and the primacy effect to explain how people form justice judgments. The substitutability effect contends that because information about outcomes is not often available but information about procedures is often available, people rely on the latter to make judgments of fairness concerning their outcomes (Van den Bos & Lind, 2002). The primacy effect suggests that information which is first used to make judgments of fairness tends to influence subsequent fairness judgments. Information that comes first exerts a stronger influence on fairness judgments than information that comes second (Van den Bos & Lind, 2002). Using heuristics to make fairness judgments may involve the X-system to the extent that it frees up cognitive capacity that would otherwise be devoted to trying to assess the requests of the situation (e.g., Lind, 2001). Thus, it implies an automatic process. Consider a relationship between an employee and his/her supervisor in which the former does not have sufficient information about the trustworthiness of the latter. This lack of information about the

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supervisor’s ability to be fair would create an uncertain situation leading the employee to look for any clue that may provide an indication about the supervisor’s trustworthiness. Neural basis of justice reactions As illustrated in Fig. 1, the neural basis of justice reactions includes attitudinal as well as behavioral responses. The attitudinal component represents the feelings generated by fairness evaluations. People may feel happy, satisfied, or committed as a result of fair treatment or they may be angry, disgusted, or frustrated as a result of unfair treatment. Such attitudinal responses may have neural underpinnings as evidenced by the extant research in neuroeconomics. In a study using fMRI, Tabibnia et al. (2008) found that the ventral striatum, the amygdala, the ventromedial prefrontal cortex (VMPFC), and the orbitofrontal cortex (OFC) were associated with fairness preferences. Similarly, Singer et al. (2006) found that the activation of the medial orbitofrontal cortex and the nucleus accumbens were correlated with individuals’ subjective feelings of anger and retribution. These results indicate that being treated fairly activates the reward regions of the brain and are consistent with previous findings (e.g., Dickhaut, McCabe, Ngode, Rustichini, & Pardo, 2003; Sanfey et al., 2003). Dickhaut et al. (2003) found more activity in the orbitofrontal cortex when thinking about gains compared to losses, and more activity in inferior parietal and cerebellar areas when thinking about losses. Perceptions of fairness may be construed as gains or pleasant experiences, whereas perceptions of unfairness may be construed as losses or unpleasant experiences. Thus, they may activate the same brain regions. In a meta-analysis of neuroimaging studies, Kringelbach and Rolls (2004) found that activity in the medial parts of the orbitofrontal cortex was related to the monitoring, learning, and memory of the reward value of reinforcers, whereas activity in the lateral orbitofrontal cortex was related to the evaluation of punishers. Pillutla and Murnighan (1996) found that anger was a better predictor of the rejections of unfair offers than perceptions that the offers were unfair. The authors concluded that emotional reactions provided the critical link that determined when fairness perceptions affected immediately subsequent behavior. Thus, one may speculate that a wide range of employee attitudes, such as job satisfaction, commitment, identification with the organization, and turnover intentions may have neural underpinnings. These attitudinal responses to fairness or unfairness may often translate into actual behaviors. Research in neuroeconomics indicates that human beings may have a natural aversion for inequity (e.g., Cohen, 2005; Fehr & Schmidt, 1999; Henrich et al., 2001; Sanfey et al., 2003). This inequity aversion is so strong that people are willing to sacrifice personal gains to prevent another person from receiving an inequitably better outcome (Fehr & Schmidt, 1999). Most neuroeconomics studies that demonstrate this effect used the Ultimate Game (UG). The Ultimatum Game involves a pair of players who must split an endowment. One player is given a sum of money, usually $10.00 that must be split with a partner who may accept or refuse the offer. If the offer is accepted, each player receives the amount allocated. If the offer is rejected, neither receives the allocated amount and the game is terminated. Standard economic theory would predict that any split that offers an amount greater than zero should be accepted because any amount of money is better than nothing. However, several studies using the game demonstrate that very often, offers of less than 20% are rejected. Commenting on such findings, Cohen (2005) notes that rejections of low offers are driven responses; perhaps, anger at the unfairness of the offer or pleasure at the thought of punishing someone who has tried to take advantage. Increased activation of the anterior

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insula is seen when individuals reject unfair offers, perhaps indicating a higher degree of emotional involvement (Brosnan, 2009). A common conclusion of such studies is that people are likely to forego personal gains to punish someone they think has acted unfairly. Kahneman, Knestch, and Thaler (1986) and Turillo, Folger, Lavelle, Umphress, and Gee (2002) provide evidence for such self-sacrificial behaviors to punish transgressors. Kahneman et al. (1986) conducted an experiment designed to assess the extent to which people self-sacrifice to punish an unfair allocator. Participants indicated a clear preference to divide $10 evenly with a fair allocator rather than divide $12 with an unfair allocator. These results imply that participants were willing to self-sacrifice (losing $1) to punish an unfair allocator. Turillo et al. (2002) replicated Kahneman et al.’s study. Their results supported Kahneman et al.’s (1986) findings that people were likely to self-sacrifice to punish an unfair allocator. In the field of organizational justice, several studies have demonstrated that feelings of unfairness lead to revenge and retaliatory behavior (e.g., Bies, 1987; Skarlicki & Folger, 1997). Research in neuroeconomics indicates that such behaviors may have neural underpinnings. Knoch, Pascual-Leone, Myer, Yreyer, and Fehr (2006) showed that disruption of the right but not the left, dorsolateral prefrontal cortex (DLPFC) by low-frequency repetitive transcranial magnetic stimulation substantially reduces participants’ willingness to reject their partners’ intentionally unfair offers in the Ultimatum Game. However, the participants still judge such offers as unfair. The authors interpret these findings by concluding that the right DLPFC plays a key role in the implementation of fairness-related behaviors. Fairness also activates the ventromedial prefrontal cortex (VMPFC) (McCabe, Houser, Ryan, Smith, & Trouard, 2001), whereas unfairness activates the dorsomedial prefrontal cortex (Decety, Jackson, Sommerville, Chaminade, & Meltzoff, 2004). Sanfey et al. (2003) also found that unfair offers activated the dorsolateral prefrontal cortex (DLPFC) and accepting unfair offers significantly stimulated the DLPFC than the anterior insula thereby demonstrating that acceptance of an unfair offer placed higher cognitive demands on the individual. De Quervain et al. (2004) and Singer et al. (2006) found that punishment of an unfair partner increased activity in the caudate nucleus. Such behavior implies that people exhibit reciprocal fairness (e.g., Bolton & Ockenfels, 2000; Fehr & Gächter, 2000; King-Casas et al., 2005). Reciprocity means that in response to friendly actions, people are frequently much nicer and much more cooperative than predicted by the self-interest model; conversely, in response to hostile actions they are frequently much more nasty and even brutal (Fehr & Gächter, 2000, p. 159). This reciprocity effect perhaps explains the tendency for respondents to refuse unfair offers more frequently from humans rather than from computers that do not have intentions (Brosnan, 2009). By attributing intentions to humans, respondents could be able to include an element of reciprocity involving cooperation in case of fair treatment and punishment in case of unfair treatment. Although recipients may acknowledge unfair offers, they may still decide to accept them. This process involves cognitive efforts. The right DLPFC and VLPFC are implicated in the cognitive control of the impulse to reject unfair offers (Fehr, 2009). Thus, they are crucial to the behavioral implementation of fairness motives. Tabibnia et al. (2008) found that accepted unfair offers were not associated with increased activity in the reward regions of the brain, thereby supporting the interpretation that logical rather than hedonic processes guided these particular decisions. In fact, recognizing or evaluating an offer as unfair and deciding to accept or reject it are two different phenomena. Judging an offer as fair or unfair may involve the X-system as indicated by Tabibnia et al. (2008), whereas deciding to accept it or not would entail a more elaborate process that activates the C-system. In a study using

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the Ultimatum Game, Pillutla and Murnighan (1996) found that anger was a better predictor of the rejections than perceptions that the offers were unfair. The authors concluded that emotional reactions provided the critical link that determined when fairness perceptions affected immediately subsequent behavior. The neural matrix of organizational justice The neural matrix of organizational justice refers to the set of brain structures that are activated by situations of fairness or unfairness. Neuroeconomic evidence shows that fairness implicates several neural areas including the amygdala, the anterior insula, the dorsolateral prefrontal cortex, the dorsomedial prefrontal cortex, the orbitofrontal cortex, the ventrolateral prefrontal cortex, and the ventromedial prefrontal cortex. These neural areas constitute what I labeled the neural matrix of justice or fairness. This matrix includes brain regions that are involved in both the X-system and the C-system. Thus, Table 3 contrasts brain regions involved in justice as well as in the X- and C-systems. As the table illustrates, brain regions implicated in fairness are also parts of the neuroanatomy of the X- and C-systems. For example, the amygdala and the ventromedial prefrontal cortex are involved in both fairness and the X-system. These two brain regions intervene in emotional arousal. Thus, one may speculate that by involving regions that represent the X-system, fairness includes an emotional component. The same is true when one contemplates the role of brain regions involved in the C-system. Neuroeconomic evidence shows that brain regions such as the orbitofrontal cortex, the lateral prefrontal cortex that are involved in the C-system are also implicated in fairness. Thus, the C-system is implicated in fairness when participants decide whether to accept an unfair offer. However, the X-system may intervene when people must decide whether a particular event is fair or not. The implication of both the X- and C-system indicates that fairness involves both cognitions and emotions. ‘‘The neural circuitry of emotion and cognition interact from early perception to decision making and reasoning” (Phelps, 2006, p. 28). As indicated by the model of neuro-organizational justice, the division between cognition and emotion is blurred when attempting to understand the neural circuitry mediating these classes of behavior (Phelps, 2006). Thus, fairness does not depend exclusively on the X-system or the C-system both intervene in fairness judgments and reactions. Van den Bos (2007) describes justice judgments as a ‘hot cognitive’ process—meaning cognition colored by feelings, in which cognitive and affective determinants often work together to produce people’s judgments of what they think is just or unjust.

Discussion/implications The model of neuro-organizational justice is the first of its kind to address the neural correlates of organizational justice and could allow organizational justice scholars to look inside the ‘black box’ in explaining employee reactions to fairness and/or unfairness. It suggests the existence of a neural circuitry involving fairness prototypes and could lay the groundwork for fruitful research avenues in organizational justice. Directions for future research Most research on the neural correlates of fairness in social cognitive neuroscience and neuroeconomics has been conducted in experimental settings. Yet there are important differences between these settings and the social interactions occurring in the workplace. An interesting question for organizational justice scholars is to determine whether employees ‘internalize’ fairness standards

Table 3 Neural structures involved in the X- and C-systems and in fairness/unfairness. X-system

C-system

Fairness/unfairness

Amygdala Basal ganglia Dorsal anterior cingulate cortex Lateral temporal cortex

Lateral prefrontal cortex Posterior parietal cortex Medial prefrontal cortex

Amygdala Anterior insula Dorsolateral prefrontal cortex

Rostral anterior cingulate cortex

Ventromedial prefrontal cortex

Hippocampus

Dorsomedial prefrontal cortex Orbitofrontal cortex

Ventrolateral prefrontal cortex Ventromedial prefrontal cortex

learned from their social environment or are these standards innate? One may speculate that although there may be some ‘builtin’ basis for judgments of fairness, the specifics may be learned from cultural or work environments. McCabe et al. (2001) argue that humans are biologically endowed to engage in personal exchanges and Tabibnia et al. (2008) demonstrate that humans are exquisitely sensitive to fairness. To the extent that such exchanges require cooperation, fairness and reciprocity, the sense of fairness is an integral part of organizational life. The quest for justice is part of a Darwinian algorithm engrained in all of us, to help ensure survival of the group (Goldman & Thatcher, 2002). However, additional studies are needed to determine whether fairness is learned or inherited. Exploring its neural correlates in the workplace may contribute to providing adequate answers to this question. Another fruitful research avenue is whether different brain areas are activated when people experience different types of organizational justice? Is reward circuitry in the brain more activated when people experience distributive justice as compared to procedural and/or interactional justice? Previous studies in neuroeconomics and social cognitive neuroscience have only considered the fairness of outcomes as indicated in the Ultimatum Game. Future studies focusing on the neural correlates of organizational justice could explore the brain areas activated when people contemplate the fairness of formal procedures or interpersonal treatment. Such studies could benefit from forming alliances with the emerging fields of neuroeconomics and social cognitive neuroscience. Neuroeconomics research using the Ultimatum Game often considers the fairness of the offer (distributive justice). No reference is made to the fairness of the process (if any) governing the offer nor to the quality of the interpersonal relationship between the players. Because the organizational justice literature contends that people care not only about the fairness of outcomes but also the fairness of the process governing the allocation of outcomes as well as the fairness of interpersonal treatment, future studies exploring the neural correlates of fairness in organizations should include such dimensions in the game or other methodologies. For example, researchers could use a redesigned version of the Ultimatum Game that includes the fairness of the offer, the fairness of the process underlying the offer and the quality of the interpersonal relationship between the players. Such a redesigned game may help to capture the three dimensions of organizational justice. Organizational justice researchers could also assess whether justice judgments involve the X-system, while decisions following perceived fairness and unfairness involve the C-system. Studies addressing such issues could involve the use of scenarios describing specific workplace events. For example, participants could be

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asked to recall incidents of fair and unfair treatment while their brains are being scanned. They could also be asked to evaluate the degree of fairness of scenarios including specific work events. Finally, participants could be asked to explore the various decisions they would take when analyzing these scenarios. Knowledge gleaned from such studies could help to determine the role of specific brain regions in justice judgments and reactions to situations of fairness and/or unfairness in organizational settings. A caveat of such studies, however, is that organizational life is based on repeated interactions. Thus, perceptions of fairness and subsequent feelings toward perpetrators of unfairness occur over an extended period of time. They are not generally a one-shot deal. How then does the brain adapt to these circumstances? Could the concept of neuroplasticity—the brain’s ability to change its structure and function in response to experience play a role in this process? If mere thoughts alter the physical structure and function of the brain, then one needs not experience injustice or witness it to activate corresponding brain areas. Just thinking about injustice one had suffered in the hands of an abusive supervisor may suffice to ‘light up’ these brain areas. The ‘rumination’ of past injustices may activate corresponding brain regions. Organizational justice scholars do not know yet what a prolonged exposure to abusive supervision might have on the human brain. Likewise, the anticipation of injustice (anticipatory injustice) may also activate specific brain regions. Shapiro and Kirkman (2001) contend that expecting injustice is a negative expectation and thus very similar to fear. As such, anticipating injustice may activate the amygdala to the extent that it is construed as fear or loss. Such issues warrant further empirical and/or experimental studies. A neuroscientific approach could pave the way for assessing the neural basis of gender differences found in the organizational justice literature. Singer et al. (2006) found increased activity in the ventral striatum—a reward region of the brain when men but not women watched unfair proposers receive punishment. These gender-related differences may be explained in two ways. First, women are better coders and decoders of nonverbal communication (Hall, 1978; Henley, 1995). Second, estrogen, a hormone found abundantly in women, initiates a neurophysiological process that allows the brain to develop more complex representations of how different stimuli relate to different outcomes (Lieberman, 2000). McRae, Ochsner, Mauss, Gabrieli, and Gross (2008) conducted an fMRI study designed to assess men and women’s reactions when faced with cognitive regulation. The authors found that compared with women, men showed lesser increases in prefrontal regions that are associated with reappraisal, greater decreases in the amygdala, which is associated with emotional responding and lesser engagement of ventral striatal regions, which are associated with reward processing. The authors explained these findings by suggesting that men may expend less effort when using cognitive regulation, perhaps due to greater use of automatic emotion regulation. They also found that women may use positive emotions in service of reappraising negative emotions to a greater degree. These gender differences may be used to explain cognitive regulation mechanisms related to perceptions of unfairness. Perhaps, men and women may use different mechanisms when trying to cognitively regulate negative emotions that can be elicited by perceptions of unfairness. Knowledge about the role of brain regions in organizational justice cannot advance without sound experimental and/or empirical studies. To this end, organizational justice scholars should explore the different techniques that could help garner knowledge in this area. The following four techniques already used in neuroeconomics and social cognitive neuroscience could help to analyze the role of specific brain regions in perceptions of fairness in organizational settings. They include Electro-encephalogram (EEG), Functional Magnetic Resonance Imaging (fMRI), magnetoencephalography

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(MEG), and Position Emission Tomography (PET). Some of these techniques, such as EEG and MEG measure electromagnetic activity of the brain, whereas others, such as PET and fMRI are sensitive to changes of cerebral blood flow or metabolism (Kenning & Plassmann, 2005). These techniques have both advantages and disadvantages. ‘‘While the ‘where’ of brain activity is more easily assessed by fMRI and PET, the question of ‘when’—meaning the discrimination between parallel and sequencing processing can be more precisely answered by EEG and MEG” (Kenning & Plassmann, 2005, p. 345). Although these techniques could help to garner valuable information about the role of the brain in organizational justice, they are somehow limited to the extent that they use reverse inference in which a particular mental process is inferred from the activation of a specific brain region. Reverse inference reasons backward from the presence of brain activation to the engagement of a particular cognitive function (Poldrack, 2006). Although the strength of reverse inference is the degree to which the region of interest is selectively activated by the cognitive process of interest, the inferences drawn are not deductively valid (Poldrack, 2006). Thus, researchers could be well advised to follow Tabibnia and Lieberman’s (2007) recommendations. ‘‘Because each brain region is involved in more than one process, we cannot confidently infer from the observation of increased signal in a region that activity in that region evoked one mental process rather than another. However, our confidence in the reverse inference could be increased in two ways: (a) convergence of evidence from multiple techniques and (b) activation in two or more regions thought to underlie the same mental processes, particularly if those regions are known to work together in a network” (Tabibnia & Lieberman, 2007, pp. 93–94). Research addressing the questions posed and the techniques described earlier could help to improve management practice. Implications for practice The model of neuro-organizational justice could provide practical implications insofar that it indicates that fairness is ‘engrained’ in the human mind. Thus, one may speculate about the ‘fairness mind’ to refer to the extent to which human beings are endowed to be sensitive to fair treatment and to express a natural disgust for unfair treatment although they may be less sensitive to beneficial inequity—when they get more than they deserve. Although this assumption is not groundbreaking, it finds new meaning when it is substantiated by neural evidence. One implication of this assumption is for managers to acknowledge the importance of fairness as a fundamental human ‘need’. Thus, they could develop ‘a fairness theory of mind’ that could prove useful in the creation of fair working environments. This fairness theory of mind is derived from the theory of mind (e.g., Rilling, Sanfey, Aronson, Nystrom, & Cohen 2004). Such a theory could have practical implications and include three key principles: (a) employees care about fairness, (b) fairness in the workplace is rewarding in itself, and (c) employees have a natural disgust for unfairness so much so that they may take all necessary actions to redress a perceived injustice. Incorporating these principles could help managers to enhance their sensitivity to act fairly and understand the types of thoughts, feelings, and emotions their own behaviors could elicit in the mind of their employees. Understanding others is a key component of the theory of mind (e.g., Lieberman, 2007; Rilling et al., 2004) and could help managers to improve their interpersonal skills. As Rilling et al. (2004) put it, ‘‘one of the distinctive attributes of human cognition is our propensity to build models of other minds: to make inferences about states of others” (p. 1694). Representing other people’s states and goals happens mostly automatically and without awareness (Singer, 2009). This fairness theory of mind could also include

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the two components identified by Lieberman (2007): (a) the recognition that people have minds with thoughts and feelings, and (b) the development of a theory regarding how other people’s minds operate and respond to events in their environment (p. 263). The model of neuro-organizational justice could also help managers to better understand the neural underpinnings of their own decisions. ‘‘We do know that a desire to retaliate, to punish others’ bad behavior, however, mild, even at personal cost, can skew decision making” (Morse, 2006, p. 48). Therefore, when they are emotionally aroused because of specific circumstances, managers should delay critical decisions or seek valuable inputs from others. Understanding oneself is also an important element of the ‘fairness theory of mind’. In fact, it is a prerequisite for controlling oneself. This includes reflecting on one’s own behaviors and their impact on others. Several brain areas may be involved in the theory of mind. Gallagher and Frith (2003) demonstrated that the anterior paracingulate cortex was activated when people mentalized about their thoughts or beliefs and those of others as well. Brain structures involved in theory of mind include the superior temporal sulcus (STS), the temporoparietal junctions (TPJ), the medial prefrontal cortex (mPFC), and the temporal poles (TP) (Singer, 2009). The medial prefrontal cortex may be concerned with anticipating what a person is going to think and feel and thereby predict how he/she will react (Frith & Frith, 2006). Another guideline for management practice that the model could provide is in helping to alter ‘undesirable’ fairness prototypes. Employees arrive in organizations with deeply rooted fairness prototypes. Some of these previously held prototypes may not be in congruence with the organizational fairness prototypes. Because prototypes can be altered, organizations should create conditions that facilitate the modification of such prototypes. By telling employees the new and espoused organizational prototypes, managers could help them align their individual prototypes to that of the organization. To this end, managers should reward the display of desirable behaviors. Such actions could help employees to display the shared prototypes, those that are prevailing in the organization while avoiding the undesirable ones. Conclusions The model of neuro-organizational justice is part of a nascent body of knowledge on the biological basis of management (e.g, Coates, 2003; Heaphy & Dutton, 2008; Lee & Chamberlain, 2007; Morse, 2006; Tabibnia & Lieberman, 2007; Taggart, Robey, & Kroeck, 1985). As such, it should be considered as a work in progress rather than a finished product. Thus, the model could help to spark future studies on the neural correlates of organizational justice. This is particularly important because a science of human life that ignores the brain is akin to a study of the solar system that leaves out the sun (White, 1992). To the extent that perceptions of fairness are endemic to people’s experience in organizations, their study is part of what White calls the science of human life. It is my hope that the present model contribute to the development of a new paradigm in the study of organizational justice. References Adams, S. J. (1965). Inequity in social exchange. In L. Berkowitz (Ed.). Advances in experimental social psychology (Vol. 2, pp. 267–299). New York: Academic Press. Ambrose, M. L., & Kulik, C. T. (2001). How do I know that’s fair? A categorization approach to fairness judgments. In S. Gilliland, D. D. Steiner, & D. Skalicki (Eds.), Theoretical and cultural perspectives on organizational justice (pp. 35–61). Greenwich, CT: Information Age Publishing. Baron, R. A. (2006). Opportunity recognition as pattern recognition: How entrepreneurs ‘‘connect the dots” to identify new business opportunities. Academy of Management Perspectives, 20(1), 104–119. Bechara, A., & Damasio, H. (2005). The somatic marker hypothesis. A neural theory of economic decisions. Games and Economic Behavior, 52(2), 336–372.

Bechara, A., Damasio, H., Damasio, A. R., & Lee, G. P. (1999). Different contributions of the human amygdala and ventromedial prefrontal cortex to decision making. Journal of Neuroscience, 19, 5473–5481. Bies, R. J. (1987). The predicament of injustice: The management of moral outrage. In L. L. Cummings & B. M. Staw (Eds.), Research in organizational behavior (pp. 289–319). Greenwich, CT: JAI Press. Bies, R. J., & Moag, J. S. (1986). Interactional justice: Communication criteria of fairness. In R. J. Lewicki, B. H. Sheppard, & M. H. Bazerman (Eds.). Research on negotiation in organizations (Vol. 1, pp. 43–55). Greenwich, CT: JAI Press. Blakemore, S. J., Winston, J., & Frith, U. (2004). Social cognitive neuroscience: Where are we heading? Trends in Cognitive Sciences, 8(5), 216–222. Bolton, G. E., & Ockenfels, A. (2000). ERC: A theory of equity, reciprocity, and competition. American Economic Review, 90, 166–193. Braeutigam, S. (2005). Neuroeconomics: From neural systems to economic behavior. Brain Research Bulletin, 67, 355–360. Brosnan, S. F. (2009). Responses to inequity in non-humans. In P. W. Glimcher, C. F. Camerer, E. Fehr, & R. A. Poldrack (Eds.), Neuroeconomics: Decision making and the brain (pp. 285–301). New York: Academic Press. Brosnan, S. F., & de Waal, F. B. M. (2003). Monkeys reject unequal pay. Nature, 425, 297–299. Camerer, C. L. (2007). Neuroeconomics: Using neuroscience to make economic predictions. The Economic Journal, 117, C26–C42. Camerer, C. L., Loewenstein, G., & Prelec, D. (2004). Neuroeconomics: Why economics needs brains. Scandinavian Journal of Economics, 106(3), 555–579. Camerer, C. L., Loewenstein, G., & Prelec, D. (2005). Neuroeconomics: How neuroscience can inform economics? Journal of Economic Literature, 43(1), 9–64. Coates, J. F. (2003). Brain science will change human resources. Employee Relations Today, 29(4), 1–9. Cohen, J. D. (2005). The vulcanization of the human brain: A neural perspective on interactions between cognition and emotion. Journal of Economic Perspectives, 19(4), 3–24. Cohen, R. L., & Greenberg, J. (1982). The justice concept in social psychology. In J. Greenberg & R. L. Cohen (Eds.), Equity and justice in social behavior (pp. 1–34). New York: Academic Press. Cropanzano, R., & Greenberg, J. (1997). Progress in organizational justice: Tunneling through the maze. In C. L. Cooper & I. T. Robertson (Eds.), International review of industrial and organizational psychology (pp. 317–372). New York: John Wiley & Sons. Damasio, A. R. (1994). Descartes’ error: Emotion, reason, and the human brain. London, UK: McMillan. De Quervain, D. J. F., Fischbacher, U., Treyer, V., Schellhammer, M., Schnyder, U., Buck, A., et al. (2004). The neural basis of altruistic punishment. Science, 305, 1254–1258. Decety, J., Jackson, P. L., Sommerville, J. A., Chaminade, T., & Meltzoff, A. N. (2004). The neural bases of cooperation and competition: An fMRI investigation. NeuroImage, 23, 744–751. Dickhaut, J., McCabe, K., Ngode, J. C., Rustichini, A., & Pardo, J. V. (2003). The impact of the certainty context on the process of choice. Proceedings of the National Academy of Sciences, 100, 3536–3541. Fehr, E. (2009). Social preferences and the brain. In P. W. Glimcher, C. F. Camerer, E. Fehr, & R. A. Poldrack (Eds.), Neuroeconomics: Decision making and the brain (pp. 215–232). New York: Academic Press. Fehr, E., Fischbacher, U., & Kosfeld, M. (2005). Neuroeconomic foundations of trust and social preferences. In AEA papers and proceedings (Vol. 95(2), pp. 346–351). Fehr, E., & Gächter, S. (2000). Fairness and retaliation: The economics of reciprocity. Journal of Economic Perspectives, 14(3), 159–181. Fehr, E., & Schmidt, K. M. (1999). A theory of fairness, competition, and cooperation. Quarterly Journal of Economics, 114, 817–868. Fiet, J. O., Clouse, V. G. H., & Norton, W. I. Jr., (2004). Systematic search by repeat entrepreneurs. In J. E. Butler (Ed.), Opportunity identification and entrepreneurial behavior (pp. 1–27). Greenwich, CT: Information Age Publishing. Frith, C. D., & Frith, U. (2006). The neural basis of mentalizing. Neuron, 50, 531–534. Gallagher, H. L., & Frith, C. D. (2003). Functional imaging of theory of mind. Trends in Cognitive Science, 7(2), 77–83. Glimcher, P. W. (2003). Decisions, uncertainty, and the brain: The science of neuroeconomics. Cambridge, MA: MIT Press. Glimcher, P. W., Camerer, C. F., Fehr, E., & Poldrack, R. A. (2009). Introduction: A brief history of neuroeconomics. In P. W. Glimcher, C. F. Camerer, E. Fehr, & R. A. Poldrack (Eds.), Neuroeconomics: Decision making and the brain (pp. 1–12). New York: Academic Press. Glimcher, P. W., Kable, J., & Louie, K. (2007). Neuroeconomic studies of impulsivity: Now or just as soon as possible? American Economic Review, 97, 142–147. Goldman, B. M., & Thatcher, S. M. B. (2002). A social information processing view of organizational justice. In S. W. Gilliland, D. D. Steiner, & D. P. Skarlicki (Eds.), Emerging perspectives on managing organizational justice (pp. 103–130). Greenwich, CT: Information Age Publishing. Greenberg, J. (1987). A taxonomy of organizational justice theories. Academy of Management Review, 12, 9–22. Greenberg, J. (1990). Organizational justice: Yesterday, today, and tomorrow. Journal of Management, 16, 399–432. Hall, J. A. (1978). Gender effects in decoding nonverbal cues. Psychological Bulletin, 85, 845–857. Heaphy, E. D., & Dutton, J. E. (2008). Positive social interactions and the human body at work: Linking organizations and physiology. Academy of Management Review, 33, 137–162. Hegtvedt, K. A., & Killian, C. (1999). Fairness and emotions: Reactions to the process and outcomes of negotiations. Social Forces, 78, 269–303.

C.D. Beugré / Organizational Behavior and Human Decision Processes 110 (2009) 129–139 Henley, N. M. (1995). Body politics revisited: What do we know today? In P. J. Kalbfleihn & M. J. Cody (Eds.), Gender, power, and communication in human relationships (pp. 27–61). Hillsdale, NJ: Erlbaum Associates. Henrich, J., Boyd, R., Bowles, S., Camerer, C., Fehr, E., Gintis, H., et al. (2001). Cooperation, reciprocity, and punishment in fifteen small-scale societies. American Economic Review, 91(2), 73–78. Hsu, M., Anen, C., & Quartz, S. R. (2008). The right and the good: Distributive justice and neural encoding of equity and efficiency. Science, 32(5879), 1092–1095. Kahneman, D. (2003). Mapping bounded rationality: Psychology for behavioral economics. American Economic Review, 93, 1449–1475. Kahneman, D., Knestch, J. L., & Thaler, R. H. (1986). Fairness and the assumptions of economics. Journal of Business, 59, 285–300. Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment under uncertainty: Heuristics and biases. New York: Cambridge University Press. Kenning, P., & Plassmann, H. (2005). Neuroeconomics: An overview from an economic perspective. Brain Research Bulletin, 16, 343–354. King-Casas, B., Tomlin, D., Anen, C., Camerer, C. F., Steven, R., Quartz, S. R., et al. (2005). Getting to know you: Reputation and trust in a two-person economic exchange. Science, 308, 78–83. Knoch, D., Pascual-Leone, A., Myer, K., Yreyer, V., & Fehr, E. (2006). Diminishing reciprocal fairness by disrupting the right prefrontal cortex. Science, 314(5800), 829–832. Knutson, B., & Peterson, R. (2005). Neurally reconstructing expected utility. Games and Economic Behavior, 52(2), 305–315. Koch, K., Pauly, K., Kellerman, T., Seiferth, N. Y., Reske, M., Backes, V., et al. (2007). Gender differences in the cognitive control of emotion: An fMRI study. Neuropsychologia, 45, 2744–2754. Kringelbach, M. L., & Rolls, E. T. (2004). The functional neuroanatomy of the human orbitofrontal cortex: Evidence from neuroimaging and neuropsychology. Progress in Neurobiology, 72, 341–372. LeDoux, J. E. (1995). Emotion: Clues from the brain. Annual Review of Psychology, 46, 209–235. LeDoux, J. E. (2000). Emotion circuits in the brain. Annual Review of Neuroscience, 15, 171–182. Lee, N., & Chamberlain, L. (2007). Neuroimaging and psychophysiological measurement in organizational research. In C. Senior & M. J. R. Butler (Eds.). The social cognitive neuroscience of organizations. Annals of the New York Academy of Sciences (Vol. 118, pp. 18–42). Boston: Blackwell Publishing. Lieberman, M. D. (2000). Intuition: A social cognitive neuroscience approach. Psychological Bulletin, 126, 109–237. Lieberman, M. D. (2007). Social cognitive neuroscience: A review of core processes. Annual Review of Psychology, 58, 259–289. Lieberman, M. D., Gaunt, R., Gilbert, D. T., & Trope, Y. (2002). Reflexion and reflection: A social cognitive neuroscience approach to attributional influence. In M. P. Zanna (Ed.). Advances in experimental social psychology (Vol. 34, pp. 199–249). New York: Academic Press. Lieberman, M. D., Jarcho, J. M., & Satpute, A. B. (2004). Evidence-based and intuitionbased self-knowledge: An fMRI study. Journal of Personality and Social Psychology, 87(4), 421–435. Lind, E. A. (2001). Fairness heuristic theory: Justice judgements as pivotal cognitions in organizational relations. In J. Greenberg & R. Cropanzano (Eds.), Advances in organizational justice (pp. 56–88). Stanford: Stanford University Press. Lind, E. A., & Tyler, T. R. (1988). The social psychology of procedural justice. New York: Plenum Press. Lind, E. A., & Van den Bos, K. (2002). When fairness works: Toward a general theory of uncertainty management. In B. M. Staw & R. M. Kramer (Eds.). Research in organizational behavior (Vol. 24, pp. 181–223). Greenwich, CT: JAI Press. Loewenstein, G. F., Rick, S., & Cohen, J. (2008). Neuroeconomics. Annual Review of Psychology, 59, 18–26. Loewenstein, G. F., Weber, E., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological Bulletin, 127, 267–286. McCabe, K., Houser, D., Ryan, L., Smith, V., & Trouard, T. (2001). A functional imaging study of cooperation in two-person reciprocal exchange. Proceedings of the National Academy of Science, 98, 11832–11835. McRae, K., Ochsner, K., Mauss, I. B., Gabrieli, J., & Gross, J. (2008). Gender differences in emotion regulation: An fMRI study of cognitive reappraisal. Group Processes and Intergroup Relations, 11(2), 143–162. Moll, J., de Oliveira-Souza, R., Eslinger, P. J., Gramati, I. E., & Mourao-Miranda, J. (2002). The neural correlates of moral sensitivity: A functional magnetic resonance imaging investigation of basic and moral emotions. The Journal of Neuroscience, 22(7), 2730–2736. Moll, J., de Oliveira-Souza, R., & Zahn, R. (2008). The neural basis of moral cognition: Sentiments, concepts, and values. Annals of the New York Academy of Sciences, 1124, 161–180. Morse, G. (2006). Decisions and desire. Harvard Business Review, 84(1), 42–51. Murphy, K., & Tyler, T. (2008). Procedural justice and compliance behavior: The mediating role of emotions. European Journal of Social Psychology, 38, 652–668. Navqi, N. H., & Bechara, A. (2006). Skin conductance: A psychophysiological approach to the study of decision making. In C. Senior, T. Russell, & M. S. Gazzaniga (Eds.), Methods in mind (pp. 103–122). Cambridge, MA: MIT Press. Ochsner, K. N., Bunge, S. A., Gross, J. J., & Gabrieli, J. D. E. (2002). Rethinking feelings: An fMRI study of the cognitive regulation of emotion. Journal of Cognitive Neuroscience, 14(8), 1215–1229. Ochsner, K. N., & Gross, J. J. (2005). The cognitive control of emotion. Trends in Cognitive Science, 9, 242–249.

139

Ochsner, K. N., & Lieberman, M. D. (2001). The emergence of social cognitive neuroscience. American Psychologist, 56(9), 717–734. Phelps, E. A. (2006). Emotion and cognition: Insights from studies of the human amygdala. Annual Review of Psychology, 57, 27–53. Pillutla, M. M., & Murnighan, J. K. (1996). Unfairness, anger, and spite: Emotional rejections of ultimatum offers. Organizational Behavior and Human Decision Processes, 68, 208–224. Poldrack, R. A. (2006). Can cognitive processes be inferred from neuroimaging? Trends in Cognitive Sciences, 10(2), 59–63. Premack, D., & Woodruff, G. (1978). Does the chimpanzee have a theory of mind? Behavioral Brain Science, 1, 515–526. Pressman, S. (2006). Kahneman, Tversky, and institutional economics. Journal of Economic Issues, XL(2), 501–506. Rabin, M. (1993). Incorporating fairness into game theory and economics. American Economic Review, 83, 1281–1302. Reynolds, J. S. (2006). A neurocognitive model of the ethical decision making process: Implications for study and practice. Journal of Applied Psychology, 91, 737–748. Rilling, J. K., Sanfey, A. G., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2004). The neural correlates of theory of mind within interpersonal interactions. NeuroImage, 22, 1694–1703. Salancik, G. R., & Pfeffer, J. (1978). A social information processing approach to job attitudes and task design. Administrative Science Quarterly, 23, 224–253. Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003). The neural basis of economic decision-making in the ultimate game. Science, 300, 1755–1758. Satpute, A. B., & Lieberman, M. D. (2006). Integrating automatic and controlled processing into neurocognitive models of social cognition. Brain Research, 1079, 86–97. Shapiro, D. L., & Kirkman, B. L. (2001). Anticipatory injustice: The consequences of expecting injustice in the workplace. In J. Greenberg & R. Cropanzano (Eds.), Advances in organizational justice (pp. 152–178). Stanford: Stanford University Press. Singer, T. (2009). Understanding others: Brain mechanisms of theory of mind and empathy. In P. W. Glimcher, C. F. Camerer, E. Fehr, & R. A. Poldrack (Eds.), Neuroeconomics: Decision making and the brain (pp. 251–268). New York: Academic Press. Singer, T., Seymour, B., O’Doherty, J. P., Stephan, K. E., Raymond, J. D., & Frith, C. D. (2006). Empathic neural responses are modulated by the perceived fairness of others. Nature, 439, 466–469. Skarlicki, D. P., & Folger, R. (1997). Retaliation in the workplace: The role of distributive, procedural, and interactional justice. Journal of Applied Psychology, 82, 434–443. Tabibnia, G., & Lieberman, M. D. (2007). Fairness and cooperation are rewarding: Evidence from social cognitive neuroscience. In C. Senior & M. J. R. Butler (Eds.). The social cognitive neuroscience of organizations. Annals of the New York Academy of Sciences (Vol. 118, pp. 90–101). Boston: Blackwell Publishing. Tabibnia, G., Satpute, A. B., & Lieberman, M. D. (2008). The sunny side of fairness: Preference for fairness activates reward circuitry (and disregarding unfairness activates self-control circuitry). Psychological Science, 19(4), 339–347. Taggart, W., Robey, D., & Kroeck, K. G. (1985). Managerial decision styles and cerebral dominance: An empirical study. Journal of Management Studies, 22, 175–192. Thibaut, J. W., & Walker, L. (1975). Procedural justice: A psychological analysis. Hillsdale, NJ: Lawrence Erlbaum Associates. Turillo, C. J., Folger, R., Lavelle, J. J., Umphress, E. E., & Gee, J. O. (2002). Is virtue its own reward? Self-sacrificial decisions for the sake of fairness. Organizational Behavior and Human Decision Processes, 89, 839–865. Tversky, A., & Kahneman, D. (1983). Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment. Psychological Bulletin, 90(4), 293–315. Van den Bos, K. (2001). Fairness heuristic theory: Assessing the information to which people are reacting has a pivotal role in understanding organizational justice. In S. Gilliland, D. Steiner, & D. Skarlicki (Eds.), Theoretical and cultural perspectives on organizational justice (pp. 63–84). Greenwich, CT: Information Age Publishing. Van den Bos, K. (2003). On the subjective quality of social justice: The role of affect as information in the psychology of justice judgments. Journal of Personality and Social Psychology, 85(3), 482–498. Van den Bos, K. (2007). Hot cognition and social justice judgments: The combined influence of cognitive and affective factor on the justice judgment process. In De Cremer (Ed.), Advances in the psychology of justice and affect (pp. 61–84). Charlotte, NC: Information Age Publishing. Van den Bos, K., & Lind, E. A. (2002). Uncertainty management by means of fairness judgments. In M. P. Zanna (Ed.). Advances in experimental social psychology (Vol. 34, pp. 1–60). San Diego, CA: Academic Press. Van den Bos, K., Lind, E. A., & Wilke, H. A. M. (2001). The psychology of procedural and distributive justice viewed from the perspective of fairness heuristic theory. In R. Cropanzano (Ed.). Justice in the workplace: From theory to practice (Vol. 2, pp. 49–66). Mahwah, NJ: Lawrence Erlbaum Associates. Van Widen, F. (2007). Affect and fairness in economics. Social Justice Research, 20(1), 35–52. White, E. (1992). The end of the empty organism: Neurobiology and the sciences of human action. Westport, CT: Praeger. Wout, V. M., Kahn, R. S., Sanfey, A. G., & Aleman, A. (2006). Affective state and decision-making in the ultimatum game. Experimental Brain Research, 169(4), 564–568.