About this sample
About this sample
Words: 2584 |
13 min read
Published: Mar 14, 2019
Words: 2584|Pages: 6|13 min read
Dishonest behaviour is a phenomenon we often encounter in our everyday life. It is used in social situations to achieve goals such as making a good impression, supporting and protecting the people we care about, or to influence other individuals (DePaulo et al., 2003; Ennis, Vrij, & Chance, 2008). However, dishonesty belongs also to some of the greatest personal and societal challenges of our time. We encounter harmful dishonest behaviour among others, in academics, sports and politics. Even the more ordinary forms of dishonest behaviour are causing great societal damage. For example, tax avoidance costs the world's economies billions of dollars every year (Cobham & Jansky, 2017). Because of the high prevalence and costs, investigating what neurocognitive processes determine whether we behave unethically or not, and how this behaviour can be prevented, is highly relevant. Examining these processes has important implications for the study of ethics, psychology, neuroscience and law, but has also more practical implications such as creating interventions to promote more honest behaviour.
Neuroimaging studies have been using many different experimental protocols to investigate dishonest behaviour. Most of them are variations of differentiation of deception or concealed information paradigms (Giorgio Ganis & Keenan, 2009). Concealed information paradigms rely on signs of recognition to differentiate between truthful responses and lies. For example, in the Guilty Knowledge Test (GKT) used by Langleben et al. (2002) participants were given a playing card before they went into the MR scanner. They were instructed to always deny that they possessed the card. While they were in the scanner, participants were shown a series of playing cards and were asked whether or not they possessed each card. The paradigm relies on the fact that when shown the card they earlier received, participants would exhibit signs of recognition, even if they lie and deny possessing it. In contrast, participants that did not receive any of the cards would respond equally to all cards shown, since all cards were equally unfamiliar to them. In this paradigm, however, the dishonest response is confounded with recognition memory. On the other hand, differentiation of deception paradigms, such as instructed lying paradigms, compare conditions that differ in the response that has to be made. In these paradigms, participants are being cued to respond to questions either truthfully or dishonest. By comparing these conditions, the unique neural processes engaged in dishonest responses, compared to truthful response, are indicated (Spence et al., 2008). However, since dishonest behaviour is a social phenomenon, studies started to investigate it in a more natural way, with (hypothetical) interaction partners. For example, in the trust game paradigm used by Baumgartner, Gianotti, & Knoch (2013) participants had to make a promise at the beginning of the experiment indicating how big the possibility was he/she could be trusted and would share the money that could be earned. An interaction partner was then informed about this promise and could decide whether to trust the participant and invest money or to not trust him/her and to keep an initial endowment of money units for themselves. If the interaction partner trusted the participant, the experimenter raised the amount of money the interaction partner invested. The participant could then decide to be honest and keep the promise or to decide to break the promise by not returning any money. The creation of this kind of paradigms allows researchers to investigate dishonest behaviour in a more real-world setting.
Findings fMRI research Despite the different experimental protocols used, previous neuroimaging research has consistently shown that the frontal executive system is associated with dishonest behaviour (Nobuhito Abe, 2009; Christ, Van Essen, Watson, Brubaker, & McDermott, 2009; Giorgi Ganis, Kosslyn, Stose, Thompson, & Yurgelun-Todd, 2003; Gombos, 2006; Hughes et al., 2005; Spence, 2004; Spence & Kaylor-Hughes, 2008). As a matter of fact, sub-regions of the frontal executive system have been found to play an important role in a variety of cognitive domains that are thought to be relevant to dishonest behaviour. For instance, the dorsolateral prefrontal cortex (dlPFC) is important for response selection, cognitive control, and monitoring and manipulation within working memory (MacDonald, Cohen, Stenger, & Carter, 2000; Owen et al., 1999; Rowe, Toni, Josephs, Frackowiak, & Passingham, 2000). Additionally, the ventrolateral prefrontal cortex (vlPFC) has been found to be implicated in task switching and response inhibition (Chikazoe, Konishi, Asari, Jimura, & Miyashita, 2007; Dove, Pollmann, Schubert, Wiggins, & Yves Von Cramon, 2000). Further, the anterior cingulate cortex (ACC) has been implicated in processes such as conflict detection and emotional processing (Kerns et al., 2004; Murphy, Nimmo-Smith, & Lawrence, 2003). Since a dishonest act involves the need to inhibit truthful responses (BlandГіn-Gitlin, Fenn, Masip, & Yoo, 2014), the detection of a conflict between competing response tendencies, and the execution of a controlled dishonest response (Walczyk, Harris, Duck, & Mulay, 2014), it is not surprising that these regions may be associated with dishonest behaviour. And indeed, the dlPFC, vlPFC, medial frontal cortex and posterior parietal cortex are activated during the process of inhibiting truthful responses during a dishonest act (ten Brinke, Lee, & Carney, 2015). Additionally, increased activation of the dlPFC has been associated with controlling the increased working memory load by the tendency for a truthful response and a dishonest response simultaneously (Reuter-Lorenz et al., 2000). The processes of conflict detection and emotional processing have been found to be related to activity in the lPFC, anterior insula and the ACC (Bolin, 2004; Christ et al., 2009; F. Andrew Kozel et al., 2005; MacDonald et al., 2000; NuГ±ez, Casey, Egner, Hare, & Hirsch, 2005; Ridderinkhof, Ullsperger, Crone, & Nieuwenhuis, 2004). Additionally, since dishonest behaviour is often intended to gain a personal benefit or to avoid personal loss, it is presumed that deep brain structures such as the amygdala and ventral striatum are involved as well. During dishonest acts, the cognitive processes of reward-seeking and emotional regulation have been associated with activity in these structures (Nobuhito Abe, Suzuki, Mori, Itoh, & Fujii, 2007; Baumgartner, Fischbacher, Feierabend, Lutz, & Fehr, 2009). Therefore, it seems reasonable to state that during an act of dishonest behaviour, the prefrontal cortex interacts with subcortical areas to achieve the intended goal (Nobuhito Abe, 2011).
Findings EEG research Until now, most EEG research has investigated the spatial-temporal course of neural activity during dishonest responses using event-related potentials (ERPs). The P300 component has been extensively investigated and successfully used for lie detection (Yue, 2014). Multiple studies have shown that dishonesty is correlated with decreased P3 components, which is assigned to the effect of increased task demand in deception (Hu, Wu, & Fu, 2011; Johnson, Barnhardt, & Zhu, 2003; Miller, Rosenfeld, Soskins, & Jhee, 2002; Proverbio, Vanutelli, & Adorni, 2013; Wu, Hu, & Fu, 2009). Next to the P3 component, Hu et al. (2011) have found that deception is associated with increased N1 and N2 components. These findings are suggested to reflect increased attention to the stimuli, the process of detection of a conflict, and response monitoring processes (Hu et al., 2011). Deception has also been found to be associated with a higher N400 component, reflecting the process of conflict resolution (Proverbio et al., 2013). Furthermore, an increased contingent negative variation (CNV) was found for lies as compared to truthful responses (Fang, Liu, & Shen, 2003; S.-Y. Sun, Mai, Liu, Liu, & Luo, 2011). The CNV is observed in the preparation of a response and this increased component as a result of lying was interpreted either as a greater motivation needed to lie or to an additional motor preparation necessary to inhibit the truthful response (Fang et al., 2003). Another component, the medial frontal negativity (MFN) was found to be more negative after deceptive responding compared to after truthful responding. This effect was proposed to reflect the processes of response monitoring and conflict detection (Johnson, Barnhardt, & Zhu, 2004; Johnson, Henkell, Simon, & Zhu, 2008; Yeung & Cohen, 2006). Johnson et al. (2003) found a reduced parietal late positive component (LPC) in deception. They proposed that this effect was due to a dual-task nature of deception. In later studies, they found that the pre-response positive (PRP) potential was also reduced during deception compared to truth-telling and this was thought to reflect the strategic monitoring/conflict resolution before the response (Johnson et al., 2004; Johnson, Barnhardt, & Zhu, 2005). Next to the ERP studies, Kim et al. (2012) examined the differences in cortical activation patterns due to different levels of cognitive demands between deceptive and truthful responses. They assessed cortical activity using event-related desynchronization (ERD) in the alpha frequency band. ERD patterns are influenced by the level of complexity associated with information processing (Fink, Grabner, Neuper, & Neubauer, 2005; Krause et al., 2000). They found that alpha ERD during the deceptive response was in general larger than alpha ERD during the truthful response. It appears that an increased cognitive effort during deception generated larger alpha ERD. In sum, nearly all these studies are in line with the cognitive load hypothesis of deception (Vrij, Fisher, Mann, & Leal, 2006), according to which lying implies the intentional suppression of truthful responses and thereby activates the frontal executive system (Christ et al., 2009), and conflict monitoring brain areas (Nobuhito Abe, 2011).
Limitations of research investigating dishonesty Despite the many studies investigating dishonest behaviour, the ecological validity in moral decision-making research is lacking. Many studies used paradigms of instructed lying and as a result the lying observed in these studies is different from more spontaneous forms of lying as it does not involve the voluntary intention to lie. Additionally, participants are not as motivated to behave dishonestly in instructed lying experiments as compared to real-world situations, in which dishonesty is more of an impulsive act and context-dependent (Giorgio Ganis & Keenan, 2009). In the absence of voluntary intention and motivation the complex executive functions associated with dishonesty might not be fully investigated (Sip, Roepstorff, McGregor, & Frith, 2008). Subsequently, studies using instructed lying paradigms did examine the deception-related cognitive conflict; inhibiting the truth to produce lies, but not the moral one; choosing self-interest and thereby sacrificing honesty (Panasiti et al., 2014). As a result, studies started to compare different types of lies and found that the neural regions and processes involved depend on the type of lie. Regions such as the ACC, the precentral gyrus, and the cuneus seem to be involved in spontaneous lies. By contrast, memorized-scenario lies recruit only the right anterior middle frontal gyrus (Giorgi Ganis et al., 2003). Similarly, Yin, et al. (2016) found that in addition to shared patterns with instructed lying, there are some activation patterns sensitive to spontaneous deception. In this respect, simulated dishonesty in laboratory experiments cannot be considered as being the same as dishonesty in real-world situations. In this respect, more recent studies created new paradigms to study the neural mechanisms of dishonesty in a more natural way. In these new paradigms, participants are tempted to behave dishonestly in return for monetary rewards (N. Abe & Greene, 2014; Baumgartner et al., 2009, 2013; Bhatt, Lohrenz, Camerer, & Montague, 2010; Greene & Paxton, 2009; Sip et al., 2010, 2012; D. Sun, Lee, & Chan, 2015; Volz, Vogeley, Tittgemeyer, von Cramon, & Sutter, 2015). The advantage of these paradigms is that participants themselves decided whether to behave unethically or not, which also captures the moral conflict. However, the findings from these studies are mixed and further research is needed. Simultaneously, when reviewing moral decision-making research an important distinction should be made between deception and cheating behaviour. Deceptive behaviour requires a direct interaction partner and occurs in a social setting (Zuckerman, Depaulo, & Rosenthal, 1981). It also requires a considered decision to deceive the interaction partner. On the contrary, cheating behaviour does not require a direct interaction partner and is, therefore, less interactive and less social. Since there is a difference between the concepts of deception and cheating behaviour, the underlying neural mechanisms involved may also be different. So far, most neuroimaging research focused on deception and almost no research has been done on cheating behaviour. This is surprising, because the most costly forms of dishonest behaviour, such as tax avoidance, are labelled as cheating rather than deception. Since the constructs of deception and cheating share neural processes deception research may be used for insights on cheating, however, the less interactive form of dishonest behaviour should be investigated more extensively.
Present study Therefore, in the present study individual differences in cheating behaviour will be explored using a novel behavioural paradigm. Previous research has shown that individuals differ substantially in the frequency they engage in cheating behaviour (Gino & Ariely, 2012; Gino & Wiltermuth, 2014). These individual differences may be associated with certain personality traits and characteristics. It has been proposed that greedy individuals may require stronger self-control processes when resisting the temptation to cheat, whereas less greedy individuals may not even be tempted to cheat (N. Abe & Greene, 2014). Additionally, Gino & Ariely (2012) have proposed that a creative personality promotes justifying behaviour, which often leads to unethical behaviour. Moreover, it has been proposed that narcissists are more susceptible to unethical behaviour. This is suggested because narcissists are less likely to experience guilt (Brunell, Staats, Barden, & Hupp, 2011), which often determines whether people will behave unethically or not (Tangney, Stuewig, & Mashek, 2007). Lastly, sensation seeking and impulsivity may reflect a lack of self-control which subsequently causes an increased tendency to cheat (DeAndrea, Carpenter, Shulman, & Levine, 2009; Hoyle, Stephenson, Palmgreen, Lorch, & Donohew, 2002). These studies all indicate that there may be several personality characteristics associated with individual differences in the tendency to cheat and therefore we will use these characteristics as a starting point for our study. We hypothesize that individuals that are greedier have an increased tendency to cheat, that creative individuals, impulsive individuals, sensation seekers and individuals exhibiting high levels of narcissism will also engage more often in cheating behaviour. The eventual aim of the present study is to investigate whether the brain mediates these individual differences in the tendency to cheat. We want to explore whether individual differences in the brains resting-state activity underlie the relationship between the personality factors and differences in cheating behaviour.
Previous neuroimaging research has investigated the neural mechanisms of dishonesty during the decision-making process and as a result, these studies do not identify neural traits responsible for predisposing individuals to behave dishonestly. Resting-state EEG provides an ideal neural trait marker to investigate the sources of inter-individual differences in dishonesty due to its intra-individual stability and specificity (Cannon et al., 2012; Gold, Fachner, & Erkkil, 2013; pflin, Wildi, & Sarnthein, 2007; Smit, Posthuma, Boomsma, & De Geus, 2005; Williams et al., 2005). Individual differences in resting-state activity are assumed to be more stable over time and across contexts since they do not depend on a specific task. We hypothesize that individual differences in resting-state activity mediate the relationship between the identified personality traits and cheating behaviour. The present study will contribute to a deeper understanding of individual differences in moral decision-making and will give insights into what neurocognitive mechanisms drive cheating behaviour. In addition to the mechanistic implications, this study has also important practical implications as it can provide insights into which specific processes should be targeted in the promotion of honest behaviour. Identifying personality traits associated with cheating behaviour may be useful in detecting individuals at risk of unethical behaviour in order to apply possible interventions and treatments before unethical actions are committed. The current study is an essential first step in order to develop effective interventions that could reduce cheating behaviour and subsequently the related costs.
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