1. Neuroscience
Download icon

Neural basis of corruption in power-holders

  1. Yang Hu
  2. Chen Hu
  3. Edmund Derrington
  4. Brice Corgnet
  5. Chen Qu  Is a corresponding author
  6. Jean-Claude Dreher
  1. Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, China
  2. Laboratory of Neuroeconomics, Institut des Sciences Cognitives Marc Jeannerod, CNRS, France
  3. Motivation, Brain & Behavior (MBB) Team, Institut du Cerveau et Moelle Epiniere, Hôpital de la Pitié-Salpêtrière, France
  4. Sorbonne Université, France
  5. Université Claude Bernard Lyon 1, France
  6. EmLyon, France
Research Article
  • Cited 0
  • Views 989
  • Annotations
Cite this article as: eLife 2021;10:e63922 doi: 10.7554/eLife.63922

Abstract

Corruption often involves bribery, when a briber suborns a power-holder to gain advantages usually at a cost of moral transgression. Despite its wide presence in human societies, the neurocomputational basis of bribery remains elusive. Here, using model-based fMRI, we investigated the neural substrates of how a power-holder decides to accept or reject a bribe. Power-holders considered two types of moral cost brought by taking bribes: the cost of conniving with a fraudulent briber, encoded in the anterior insula, and the harm brought to a third party, represented in the right temporoparietal junction. These moral costs were integrated into a value signal in the ventromedial prefrontal cortex. The dorsolateral prefrontal cortex was selectively engaged to guide anti-corrupt behaviors when a third party would be harmed. Multivariate and connectivity analyses further explored how these neural processes depend on individual differences. These findings advance our understanding of the neurocomputational mechanisms underlying corrupt behaviors.

Introduction

Corruption, commonly defined as ‘the abuse of entrusted power for private gains’ (Graycar and Smith, 2011), is one of the most pervasive and complex social problems today. It distorts development priorities, aggravates inequality in societies, and challenges organizations and governments globally (Mauro, 1995). Due to its critical societal implications, corruption has been extensively investigated during the past two decades in fields such as political science, sociology, economics, and psychology. Using survey-based measures (Lambsdorff, 2006) and behavioral economic experiments (Serra and Wantchekon, 2012), these studies significantly improved our understanding of corruption at sociological and behavioral levels. However, the neural mechanisms underlying decision-making involving corrupt acts remain unexplored.

One of the most common forms of corruption is bribery, which costs between 1500 and 2000 billion US dollars each year (~2% of the global GDP) (Gaspar and Hagan, 2016). It usually occurs in an interpersonal context in which at least two parties are involved, a briber and a power-holder. The briber often pays the power-holder in some way, and, in return, the power-holder makes decisions that profit the briber (Abbink, 2006; Köbis et al., 2016). Critically, favor exchanges can hardly be considered as bribes unless they involve moral or legal transgression. That is, a bribe takes place when the briber obtains a favorable treatment or an advantageous outcome by paying the power-holder to circumvent a norm of impartiality or a moral rule (Köbis et al., 2016; Serra and Wantchekon, 2012). In addition, bribery often implies harming the interests of a third party, which can be an explicit person or a societal organization (Barr and Serra, 2009; Köbis et al., 2016). For instance, a public official who accepts a bribe regarding a procurement contract will harm competitors whose project might bring greater benefits to society. Thus, one can distinguish two forms of moral costs incurred by the power-holder who takes a bribe: conniving with a fraud committed by the briber and harming an innocent third party. Indeed, previous behavioral studies have identified a crucial role of moral cost of unethical behaviors in explaining why individuals often refrain from behaving immorally. For example, people are reluctant to tell a self-serving lie or inflict physical harm on an anonymous partner for their own benefit even when there is no material cost of doing so (Crockett et al., 2017; Rosenbaum et al., 2014).

To understand the neural bases of the power-holder’s corrupt behavior, it is necessary to start by asking how different moral costs are represented at the neural level while deciding whether to take a bribe or not. Previous fMRI studies in the moral domain provide valuable insights to formulate hypotheses regarding the neural underpinnings of moral costs in the context of bribery. On the one hand, the ventral part of anterior insula (vAI) has been shown to be engaged when moral and social norms are violated. For instance, a stronger vAI signal has been observed when people are deceived by another person (Yin and Weber, 2016) or when deciding whether to lie to obtain additional gains (Yin et al., 2017). A natural hypothesis is that the vAI is more engaged in representing the expected ‘dirty’ personal gains from accepting bribes, which may elicit a negative affective signal due to the moral cost of colluding with the briber who is violating the moral principle of honesty. On the other hand, the temporoparietal junction (TPJ) has been found to contribute to balance personal interests and other’s welfare (Hutcherson et al., 2015; Morishima et al., 2012; Obeso et al., 2018). This region is also more active when one’s decision impacts a person who is in a disadvantageous situation. For instance, a recent fMRI study found that multivoxel patterns of TPJ can discriminate generous from selfish choices during charitable decision-making (i.e., donating to help someone else in desperate need) (Tusche et al., 2016). Another study showed that the posterior part of TPJ encoded the levels of other’s need when individuals decided whether to help another person (Hu et al., 2017). These findings suggested that the TPJ would be sensitive to the moral cost of the bribe-induced financial losses incurred by a third party.

A key question is to understand how these moral costs are integrated with other decision components into a neural value signal during bribery-related decision-making. Here, we developed and tested a number of computational models that assume that a power-holder makes the decision to accept (or reject) a bribe by computing the subjective value (SV) of each option via a utility function. Under the framework of social preference theory (Fehr and Krajbich, 2014), this function takes into account not only the trade-off between one’s own gain and the briber’s gain but also any moral costs specifically brought by taking the bribe. Using a model-based fMRI approach (O'Doherty et al., 2007), we searched for brain regions that were involved in computing such SV signals when deciding whether to accept a bribe. Since the ventromedial prefrontal cortex (vmPFC) is known to be the hub of value computation for both non-social (Bartra et al., 2013) and social decisions (Ruff and Fehr, 2014), we predicted the recruitment of vmPFC in computing an integrative decision value signal preceding the decision of whether to accept a bribe.

We also aimed to identify which brain region(s) guide the specific choices of taking a bribe or not. A recent theory argues that the dorsolateral prefrontal cortex (dlPFC) is key to support the pursuit of moral goals in a context-dependent manner (Carlson and Crockett, 2018). Hence, the dlPFC can be expected to flexibly align with specific bribery scenarios that concern different types of moral costs and regulate specific choices. In particular, the dlPFC plays a critical role in causally modulating self-serving dishonest behaviors (Maréchal et al., 2017; Zhu et al., 2014) and norm-enforcing decisions (Buckholtz et al., 2015; Knoch et al., 2006). A recent fMRI study also identified selective recruitment of the dlPFC in evaluating ill-gotten money obtained by harming others (vs. self) (Crockett et al., 2017). More intriguingly, recent work has highlighted the modulatory role of dlPFC on the value signal in vmPFC especially when the decision-making process requires individuals to exert self-control to inhibit the impulse to choose immediate rewards (vs. long-term rewards; Hare et al., 2009; Hare et al., 2014) or personal profits (vs. moral values; Baumgartner et al., 2011; Dogan et al., 2016). Here, we tested whether the functional connectivity between vmPFC and dlPFC would be enhanced during bribery-related decision-making. Based on the evidence above, we hypothesized that we would observe a stronger vmPFC–dlPFC functional coupling during the decisions of whether to accept or reject a bribe (vs. offers in the Control condition) in which participants as power-holders might need more self-control to overcome the lure of accepting bribes that result in moral costs.

Our last question was to examine how individual differences in neural activities during bribery-related decision-making explain variations across power-holders, in preferences for bribery, characterized by moral costs due to bribe-taking. A growing body of literature links the dlPFC signal to individual variations in the levels of self-serving dishonesty (Dogan et al., 2016; Yin and Weber, 2018) or harm aversion (Crockett et al., 2017), which are closely related with two types of moral costs specific to corrupt acts measured in the current task. Hence, we performed an exploratory analysis, again with a focus on the dlPFC given the evidence above, to probe whether such a relationship exists, in a bribery setting, by applying a multivariate approach. We performed an inter-subject representational similarity analysis (IS-RSA) to uncover the neural–behavioral relationship across individuals (van Baar et al., 2019). Compared with the mass-univariate approach, IS-RSA allows us to associate multidimensional behavioral measures with a geometric representation of information based on multivoxel neural patterns across individuals, rather than simply linking a single behavioral measure with averaged activities across voxels in a certain region (Kriegeskorte et al., 2008; Popal et al., 2019). Here, with the help of IS-RSA, we were able to map differences in neural signals during bribery-related decision-making (i.e., Bribe vs. Control) directly onto key model-based parameters that characterize different types of moral costs specifically involved during bribery-related decision-making.

To address these questions, we designed a novel paradigm that captured the essence of a bribery situation while keeping it as simple as possible for the fMRI setting. Participants in the MRI scanner played the role of a power-holder and decided whether a fictitious proposer would earn a given amount of money or not (see Figure 1). The proposer was either alone or paired with a fictitious third party (scenario: Solo vs. Dyad). Critically, the proposer could obtain more profit by either lying or telling the truth (experimental condition: Bribe vs. Control; see below). To achieve this, the proposer offered an amount of money from this larger profit to bribe the power-holder. The task for the participants was to decide whether to accept or reject offers. Accepting the offer would profit both the power-holder and the proposer, whereas rejection would earn nothing for either of them. In the Dyad scenario, accepting the offer in the Bribe condition additionally harmed the interest of an innocent third party. Here, bribery is operationally defined as accepting an offer from a proposer who is fraudulently making a proposition that is more beneficial than that which should be honestly reported. Thus, the proposer who commits fraud for a higher personal profit can be regarded as a briber. Notably, this design allowed us to distinguish two types of moral costs that could be incurred by participants, as power-holders, when accepting bribes, namely conniving with a fraud committed by the briber and harming a third party. The former occurs in both scenarios, whereas the latter occurs only in the Dyad scenario.

Task and design.

(A) Schematic illustration of the behavioral paradigm. Here, we show the bribe case in the Dyad scenario (i.e., the Dyad Bribe condition [DB]). This condition consists of three roles: a proposer, a third party, and a power-holder (the real participant). Participants were endowed with the power to decide whether the proposer would earn a higher profit by lying, which also caused financial losses to a third party in a previous online study. The proposer hence bribes the power-holder, whose task was to decide whether to take the bribe. Notably, all proposers and third parties were framed as participants in a previous online study (‘Game of Chance’), which was actually fictitious. (B) Illustration of all experimental conditions. We manipulated two factors, that is, scenario (Solo or Dyad) and proposer’s conduct (Bribe or Control), which yielded four experimental conditions. (C) Trial procedure in the DB condition. In this example, a proposer (E.L.) lied by reporting the non-selected payoff option, which additionally harmed the interest of an innocent third party (A.K.), and bribed the power-holder with a certain amount of money from his/her potential gain (i.e., 38 out of 96 CNY). The participant needed to decide whether to accept or reject the bribe within 8 s (s). Once the decision was made (i.e., accepting the bribe here), a yellow bar appeared on the corresponding option to highlight the choice for 0.5 s, which was followed by a jittered fixation (i.e., 3–7 s with a mean of 5 s).

Results

Behavioral and modeling results

Power-holders accepted less offers during bribery-related decision-making especially when it harmed a third party

We first investigated how participants’ choice behaviors varied depending on the scenario (Solo/Dyad), and the proposer’s conduct (Bribe/Control), after controlling for the effect of the offer proportion and that of the larger gain the proposer would earn in the reported option. Mixed-effect logistic analyses showed that participants were less likely to accept an offer in the Dyad scenario (main effect of the scenario: χ2(1)=8.53, p=0.003) and in the Bribe condition (main effect of the proposer’s conduct: χ2(1)=25.3, p<0.001). More importantly, we observed a significant two-way interaction with respect to whether the offer was accepted (χ2(1)=4.73, p=0.030; see Figure 2A, Figure 2—figure supplement 1). Simple-effect analyses further revealed that participants were much less likely to accept bribes (vs. offers in the Control condition), in the Dyad scenario, where a third party was involved (acceptance rate: 61.6 ± 22.6% vs. 82.8 ± 13.2%; odds ratio = 0.15, b = −1.91 [–2.83, –1.25], SE = 0.41, p<0.001), in comparison to the Solo scenario (acceptance rate: 67.0 ± 20.7% vs. 83.3 ± 14.8%; odds ratio = 0.13, b = −2.03 [–2.77, –1.08], SE = 0.39, p<0.001; see Supplementary file 1a for details of the regression output).

Figure 2 with 5 supplements see all
Results of behaviors and computational modeling.

(A) Acceptance rate (%) as a function of context (Solo/Dyad) and the proposer’s conduct (Control/Bribe). The significance was not marked because we did not perform statistical tests directly on these two dependent variables. Individual acceptance rates in each experimental condition are denoted by the black dots; the size of the dots represents the relevant sample size. (B) Posterior mean of individual-level key parameters characterizing moral costs (θ and ω) based on the winning model. Each black filled dot represents the group-level mean; each red dot represents the data of a single participant; the density curve represents the distribution of the data across all participants. Error bars represent the SEM; Significance: ***p<0.001.

Paralleling these findings, we found similar results with respect to the post-task subjective rating on the degree of the moral inappropriateness and the fraud of the proposer’s behaviors (in the online game), and on the degree of the moral conflict during decision-making and the moral inappropriateness of their own behaviors. A within-subject multivariate analysis of variance (MANOVA) test showed a main effect of scenario (F(1, 38)=5.59, p=0.001) and the proposer’s conduct (F(1, 38)=85.25, p=0.001) on these ratings. We also observed a significant two-way interaction effect (F(1, 38)=4.87, p=0.003; see Figure 2—figure supplement 1), which was mainly driven by higher ratings in the Bribe (vs. Control) condition in the Dyad (F(1, 38)=124.43, p<0.001) in comparison to the Solo scenario (F(1, 38)=50.05, p<0.001).

Power-holders took into account the moral costs during bribery-related decision-making

Next, we performed a model-based analysis on choice to understand the computational mechanisms underlying decisions of whether to take or refuse a bribe. To this end, we tested and compared a total of seven models with different utility functions (see Materials and methods for details). Parameters were estimated using the hierarchical Bayesian approach (HBA) via the ‘hBayesDM’ package. R-hat values of all estimated parameters of all models were smaller than 1.1 (the maximum R-hat value: 1.05), indicating adequate convergence of the Markov Chain Monte Carlo (MCMC) chains (Gelman and Rubin, 1992).

Based on hierarchical Bayesian model comparison (see Supplementary file 1b), we identified Model 5 (Equation 1) as the winning model, which was defined as below:

(1) SV(pP,pT,pPH)={βPpP+(βPHθq)pPH+γ|pPpPH|ifSoloscenarioβPpP+ωqpT+(βPHθq)pPH+γ|pPpPH|ifDyadscenario

where, in a given trial, SV denotes the subjective value, pP, pPH, pT represents the payoff (i.e., monetary gain) for the proposer (P), the power-holder (PH; participants), and the third party (T) given different choices (i.e., accept or reject the offer), q is an indicator reflecting whether the trial tool place in the Bribe (q = 1) or the Control (q = 0) condition. βP and βPH are two independent free parameters capturing the weights on the payoff of the proposer and the power-holder, respectively. θ describes the moral cost brought by conniving with a fraud committed by a proposer. ω captures the moral cost brought by harming the interest of a third party. We also incorporated γ to account for the absolute payoff inequity between the proposer and the power-holder denoted in the offer (parameters range: −20 ≤ βP, βPH, θ, ω, γ <= 20). Parameter recovery analysis showed that these true estimates (i.e., values we obtained from the actual choice data that we collected) positively correlated with the recovered estimates (i.e., what we obtained from the simulated data generated with these true parameters) for all parameters across individuals involved in the winning model (Pearson correlation: rs > 0.71, ps <0.001; see Figure 2—figure supplement 2; see Materials and methods for details), which suggests that our model is robustly identifiable. The posterior predictive check (PPC) further revealed that the proportion of acceptance predicted by this model successfully recovered the actual proportion of acceptance (all conditions: the within-sample PPC: rs > 0.90, ps <0.001; the out-of-sample PPC: rs > 0.68, ps <0.001; see Figure 2—figure supplement 3, Figure 2—figure supplement 4, and Materials and methods for details), which strengthened the results of the model comparisons.

Further analyses of the individual-level posterior mean of these parameters revealed that participants weighed the payoffs for themselves positively (βPH: 16.02 ± 2.47, t(38) = 40.51, p<0.001, Cohen’s d = 6.49) but did not do so for the proposer (βP: −0.88 ± 2.48, t(38) = −2.21, p=0.033, Cohen’s d = −0.35). Moreover, participants decreased the weights of their own payoffs when they were being bribed (θ: 5.94 ± 4.28, t(38) = 8.67, p<0.001, Cohen’s d = 1.39) and apparently displayed concern for the financial loss of the innocent third party (ω: 0.97 ± 0.82, t(38) = 7.32, p<0.001, Cohen’s d = 1.17; see Figure 2B; also see Supplementary file 1c for the bivariate correlation between all parameters in the winning model). These findings suggest that participants considered both forms of moral costs when deciding on whether to accept bribes. In addition, participants also disliked the inequality in the absolute payoff between themselves and the proposer (γ: −2.35 ± 2.23, t(38) = −6.59, p<0.001, Cohen’s d = −1.06).

Power-holders responded more slowly when accepting a bribe

We also investigated how participants’ reaction time (RT) was modulated by the scenario and the proposer’s conduct depending on the specific decisions (accept/reject), after controlling for the effect of the offer proportion and that of the larger gain the proposer would earn in the reported option. Before the analyses, we did a log-transformation on the RT due to its non-normal distribution (i.e., Anderson–Darling normality test: A = 232.54, p<0.001). Mixed-effect linear regression on log-transformed RT) showed that participants responded slower in the Dyad scenario (main effect of the scenario: F(1, 5567)=15.52, p<0.001), in the Bribe condition (main effect of the proposer’s conduct: F(1, 5570)=64.28, p<0.001), and when they rejected the offer (main effect of decision: F(1, 5582)=50.08, p<0.001). Moreover, we detected a two-way interaction between the decision (accept/reject) and the proposer’s conduct (bribe/control; F(1, 5575)=20.79, p<0.001). Post-hoc analyses further showed that participants responded more slowly when they accepted offers in the Dyad scenario (vs. Solo; b = 0.07 (0.03, 0.11), SE = 0.02, t(4093) = 3.724, p<0.001, Cohen’s d = 0.12) or when the proposer was bribing them (vs. Control; b = 0.20 (0.16, 0.24), SE = 0.02, t(4095) = 9.990, p<0.001, Cohen’s d = 0.31). Neither of these main effects was identified for rejection decisions (Dyad vs. Solo: b = 0.02 (−0.06, 0.10), SE = 0.04, t(1435) = 0.561, p=0.575, Cohen’s d = 0.03; Bribe vs. Control: b = −0.01 (-0.08, 0.06), SE = 0.04, t(1439) = −0.269, p=0.788, Cohen’s d = −0.01; see Figure 2—figure supplement 5 and Supplementary file 1d for the descriptive summary of the original RT; see Supplementary file 1e for details of the regression output.

Neuroimaging results

vAI represents the effect of the moral cost of conniving with fraud on expected personal profits

We implemented the general linear model (GLM) analyses to test specific hypotheses concerning different research questions (see Materials and methods for details of GLM analyses). Notably, all a-priori hypotheses and planned analyses were not pre-registered. We first examined brain regions encoding the moral cost of conniving with a briber on the expected gains for both the proposer and the participant due to bribe-taking (as parametric modulators, PM; GLM1a), with a focus on vAI. Consistent with our hypothesis, we observed stronger engagement of the left vAI in the Bribe (vs. Control) condition regardless of scenarios (peak MNI coordinates: −40/10/−6, t(114) = 3.81, p(SVC-FWE)=0.025) when investigating brain regions encoding the expected personal profits (i.e., the profits earned by participants had they accepted the offer). To examine the lateralization of this effect, we lowered the statistical threshold and found that the right vAI delineated similar neural responses (peak MNI coordinates: 36/12/–14, t(114) = 3.27, p(SVC-FWE)=0.090; see Figure 3A). We also found that the right vAI displayed an increased sensitivity to the expected personal gains in the Dyad (vs. Solo) scenario (peak MNI: 36/14/–14; t(114) = 3.86, p(SVC-FWE)=0.022), whereas the left vAI showed a similar trend that did not reach statistical significance (peak MNI: −36/10/−16; t(114) = 3.09, p(SVC-FWE)=0.146; see Supplementary file 1f for results of other contrasts under a lenient threshold). In addition, we also observed a stronger activity in the dorsal part of anterior cingulate cortex (dACC) during bribery-related decision-making (i.e., event contrast: Bribe vs. Control in GLM1c; same below) across scenarios, under a whole-brain threshold (see Figure 3—figure supplement 1). No region was observed in other contrasts (see Supplementary file 1f for other regions activated under a lenient threshold).

Figure 3 with 3 supplements see all
Parametric modulation of expected personal gains and potential loss for the third-party during bribery-related decision-making.

(A) Enhanced ventral part of anterior insula (vAI) signal by the modulation of the expected personal gain during bribery-related decision-making (Bribe vs. Control; GLM1a). The contrast values of the activated cluster were extracted for visualization. (B) The parametric modulation of the expected potential loss for the third party in the temporoparietal junction/posterior superior temporal sulcus (TPJ/pSTS) (GLM1b). Notably, this parametric modulation only exists in the Dyad Bribe condition due to the experimental design. To visualize the effect of parametric modulation, we split these continuous parameters into three bins (low: 0–33%; medium: 33–67%; and high: 67–100%), re-estimated the general linear model and extracted the percent signal change (PSC) in relevant activated clusters via rfxplot (http://rfxplot.sourceforge.net/; Gläscher, 2009). Each line refers to the linear fit between the bin and the PSC. (C) Multivariate patterns of TPJ/pSTS distinguished bribe (vs. Control) only in the Dyad scenario. The regions of interest (ROI) of TPJ/pSTS was defined based on a whole-brain parcellation map from the Neurosynth database (left). The receiver operating characteristic curve showed that the multivoxel TPJ/pSTS activity pattern during decision-making can distinguish the Bribe condition from the Control condition only in the Dyad scenario but not in the Solo scenario (middle). Permutation test (N = 5000) further illustrated that the two-choice forced alternatives decoding accuracy in TPJ/pSTS in the Dyad scenario were unlikely achieved by chance (p=0.020; right). The violin plot (right) describes the null distribution of decoding accuracy of the multivoxel TPJ patterns during decision-making that distinguishes the bribe from the Control condition in the Solo and Dyad scenarios, respectively, obtained via 5000 times of permutation. Significance: *p<0.05. Error bars refer to SEM. Display threshold: voxel-level p (uncorrected) <0.005 (A, B). L: left; R: right.

TPJ encodes the expected losses for the third party and distinguishes bribery-related decision-making in the two scenarios

We then examined whether TPJ was involved in encoding the expected losses incurred by the innocent third party due to bribe-taking (as the only PM in the Dyad Bribe [DB] condition; GLM1b). As predicted, we observed a strong involvement of the right TPJ, extending to the posterior part of the superior temporal sulcus, in encoding this PM (pSTS; peak MNI coordinates: 54/–-48/–4, t(38) = 4.70, p(SVC-FWE)=0.010; see Figure 3B; also see Supplementary file 1f for other activated regions). Results still held after controlling the effect of expected gains for both the proposer and the participant (peak MNI: 54/–48/–4; t(38) = 4.63, p(SVC-FWE)=0.012; see Figure 3—figure supplement 2).

This finding suggested another possibility that TPJ/pSTS might be selectively engaged during bribery-related decision-making in the Dyad (vs. Solo) scenario, where the interest of a third party is explicitly compromised. To test this hypothesis, we first performed post-hoc univariate analyses but did not find any supportive evidence (i.e., no significant TPJ/pSTS signal was observed in the following contrasts of GLM1c; contrast 1: DB vs. Dyad Control [DC]; contrast 2: (DB – DC) vs. (Solo Bribe [SB] – Solo Control [SC])). However, using a post-hoc multivariate-based decoding analysis with a leave-one-subject-out (LOSO) cross-validated approach, we found that the decision-relevant multivoxel activation patterns of TPJ/pSTS only dissociated the bribery-related decision-making in the Dyad (DB vs. DC; two-choice forced-alternative accuracy ± SE: 69.2 ± 11.1%, p=0.024; ppermutation = 0.020) but not the Solo scenario (SB vs. SC; 53.8 ± 8.6%, p=0.749; ppermutation = 0.300; see Figure 3C; also see Figure 3—figure supplement 3 for decoding results of the left and right TPJ/pSTS). This finding corroborated what we found in the parametric analysis and further indicated that TPJ/pSTS works differently between the Solo and the Dyad scenarios by showing distinct activation patterns rather than a mean activation intensity across voxels, during bribery-related decision-making.

vmPFC encodes relative SV during bribery-related decision-making

Next, we investigated the brain regions encoding the relative SV during bribery-related decision-making. To this end, we established GLM2a to distinguish the Bribe condition from the Control condition (by pooling the Solo and Dyad scenarios). The relative SV was defined by subtracting the SV of the non-chosen option from that of the chosen option (i.e., relative SV = SVchosen– SVunchosen) based on the winning model. As expected, the vmPFC was more engaged in integrating the value signals in the Bribe condition (peak MNI coordinates: 0/34/–10, t(38) = 4.73, p(SVC-FWE)=0.007). A smaller effect was also observed in the Control condition (peak MNI coordinates: 0/52/–10, t(38) = 3.76, p(SVC-FWE)=0.064; see Figure 4A). These findings suggest that the vmPFC played a common role in computing an integrated value signal during the decision period regardless of whether the decision-making concerned bribery. To further test this hypothesis, we performed additional analyses that provided supportive evidence.

Figure 4 with 1 supplement see all
Value computation in the ventromedial prefrontal cortex (vmPFC) during bribery-related decision-making.

(A) Ventromedial prefrontal cortex (vmPFC) encodes relative subjective value (SV) in the Bribe and the Control conditions, respectively. Relative SV was defined as the SV difference between the chosen and the non-chosen option. To visualize the effect of parametric modulation, we split the relative SV into three bins, that is, low (0–33%), medium (33–67%), and high (67–100%), re-estimated the general linear model and extracted the percent signal change (PSC) in the activated cluster via rfxplot (http://rfxplot.sourceforge.net/). The line refers to the linear fit between the bin and the PSC. (B) Procedure of pattern similarity analyses. For each participant, we extracted the multivoxel neural patterns (i.e., those heat maps; only for illustration) within the vmPFC mask from the parametric contrast of relative SV in Bribe and Control conditions, respectively (pooling the Solo and Dyad scenarios). Next, we computed the similarity (correlation) between these neural patterns in the two conditions. For statistical analysis, all correlation coefficients were transformed to Fisher’s z value. (C) Histogram of the distribution of pattern similarity across all participants. The vertical dashed line refers to the mean of the pattern similarity.

First, a direct comparison between the value signal of vmPFC in the Bribe and the Control conditions did not reveal any significant difference. A supplementary pattern similarity analysis revealed a significantly positive correlation between multivoxel vmPFC patterns of value computation in the Bribe and the Control conditions (r = 0.14 ± 0.36, p(permutation)=0.023; see Figure 4B, C). In addition, we also ran a separate GLM (GLM 2b) that pooled all conditions as a single regressor to investigate the neural network generally involved in value computation. We found that vmPFC, together with bilateral middle/inferior temporal gyri and posterior cingulate cortex extending to cuneus, correlated positively with the relative SV signal (see Figure 4—figure supplement 1; also see Supplementary file 1g for other activated regions).

dlPFC is selectively engaged in rejecting bribes in the Dyad scenario

We also examined how neural signals during bribery-related decision-making were associated with different choices and how they varied depending on scenarios. To this end, we established GLM3 that contained the onset of each condition with respect to specific choices (i.e., eight onset regressors in total, namely accept or reject in conditions of SC, SB, DC, and DB). To simplify the analysis, we computed the neural activity specific to rejecting as opposed to accepting offers (i.e., reject vs. accept) in all four conditions and then defined the anti-corruption neural signals with such rejection-specific neural activity in the Bribe condition (i.e., contrast: Bribe(reject - accept) – Control(reject - accept)). Given our hypothesis, we focused on the dlPFC in this analysis. We found an increased anti-corruption signal in the dlPFC in the Dyad scenario compared with the Solo scenario (left dlPFC: peak MNI coordinates: −38/48/18, t(96) = 3.70, p(SVC-FWE)=0.024; right dlPFC: peak MNI coordinates: 42/50/16, t(96) = 3.52, p(SVC-FWE)=0.050; see Supplementary file 1h for other activated regions). Importantly, post-hoc analyses revealed that the anti-corruption signal in the dlPFC was significantly higher than 0 only in the Dyad scenario (ps <0.002), but not the Solo scenario (ps >0.056; see Figure 5A, Figure 5—figure supplement 1).

Figure 5 with 1 supplement see all
Context-dependent anti-corruption signal and the functional connectivity results.

(A) Anti-corruption signal in dorsolateral prefrontal cortex (dlPFC) was specifically enhanced in the Dyad scenario. We defined the anti-corruption signal as the activities (i.e., contrast value) of rejection (vs. acceptance) choice that were specific to the Bribe (vs. Control) condition. Post-hoc analyses on anti-corruption signals within the activated clusters of bilateral dlPFC showed that the interaction was mainly driven by increased anti-corruption signals in the Dyad scenario. Display threshold: voxel-level p (uncorrected) <0.001. Error bars represent the SEM; significance: ***p<0.001. (B) Individual differences in θ negatively modulate the functional connectivity between ventromedial prefrontal cortex (vmPFC) and right dlPFC during bribery-related decision-making (i.e., Bribe vs. Control). The seed region of vmPFC was defined based on a whole-brain parcellation given a meta-analytic functional coactivation map of the Neurosynth database (http://neurovault.org/images/39711/). To visualize the relationship, we extracted the contrast value (cv) of the psycho-physiological interaction regressors of the Bribe and Control conditions within the activated cluster and plotted the correlation between the cv and the individual parameter. Each gray dot represents the data of a single participant; the blue line represents the linear fit using robust correlation (r = −0.54, 95% CI: −0.88,–0.19, p=0.003); the gray density curve indicates the distribution of each variable, respectively.

Individual differences in susceptibility to the moral cost of conniving with fraud modulates vmPFC–dlPFC functional connectivity during bribery-related decision-making

Using a general psycho-physiological interaction (gPPI) analysis, we further tested whether there was functional connectivity between the vmPFC and dlPFC (and other brain regions) during bribery-related decision-making. No region was detected that significantly enhanced the functional connectivity with vmPFC during bribery-related decision-making in general. However, we observed a negative correlation between the functional coupling of the vmPFC and the right dlPFC during bribery-related decision-making (i.e., PPI contrast: Bribe vs. Control) and the parameter θ(peak MNI coordinates: 46/36/30, t(37) = 4.78, p(SVC-FWE)=0.005; see Figure 5B). This parameter characterizes the aversion to conniving with the fraud committed by the briber. The control analysis confirmed the above results in the right dlPFC after ruling out the effect of parameter ω (peak MNI coordinates: 46/36/30, t(36) = 4.58, p(SVC-FWE)=0.014; see Supplementary file 1i for other activated regions). The left dlPFC also showed a similar connectivity pattern with vmPFC when we adopted a lenient threshold (peak MNI coordinates: 42/44/18, t(37) = 3.40, p(SVC-FWE)=0.093; after controlling for the effect of parameter ω: peak MNI coordinates: 42/44/18, t(37) = 3.58, p(SVC-FWE)=0.065).

Individual differences in bribery-related dlPFC activity represent variations in bribery-specific preference

Finally, we investigated how inter-individual differences in bribery-specific preferences were implemented in the brain. In particular, we focused on the dlPFC because decision-related neural signals in this region have been shown to be modulated by inter-individual variations of moral behaviors (e.g., self-serving dishonesty; Yin and Weber, 2018) and other-regarding preferences in moral decision-making (e.g., harm aversion; Crockett et al., 2017).

To this end, we used an IS-RSA, which allowed us to directly investigate whether the neural patterns in dlPFC during bribery-related decision-making are similar for participants who displayed similar bribery-specific preferences for bribery, measured by the moral costs involved during bribe-related decision-making (i.e., θ and ω). In particular, we established a parameter representational dissimilarity matrix (RDM) by calculating the Euclidean distance between the two parameters specifically characterizing the moral costs (i.e., θ and ω; the correlation between these two parameters: r(37) = 0.258, p=0.113). We next constructed the neural RDMs by calculating the correlation dissimilarity of the parameter estimates of subjective-level contrasts during bribery-related decision-making extracted from the bilateral dlPFC (i.e., the contrast of Bribe vs. Control). We then measured the similarity between the neural RDMs and the preference RDM (see Figure 6). The results revealed a significant inter-subject similarity effect in the decision activity pattern, specific to corruption in the dlPFC (Spearman’s rho = 0.108, ppermutation = 0.019).

Illustration of the inter-subject representational similarity analysis.

We created a parameter representational dissimilarity matrix (RDM), which measures the dissimilarity across participants in the bribery-specific preference that is calculated by the Euclidean distance between each pair of participants in θ and ω driven from the winning model. Both parameters together characterize the individual differences in bribery-specific preference (see the scatter plot: each dot represents the data of a single participant; the density curve indicates the distribution of each parameter, respectively). We also created a neural RDM, which measures the dissimilarity across participants in the neural activities within bilateral dorsolateral prefrontal cortex (dlPFC) during bribery-related decision-making (i.e., Bribe vs. Control) that is measured by the correlation distance between the multivoxel patterns of each pair of participants (i.e., those heat maps; only for illustration). The region of dlPFC was defined based on a whole-brain parcellation given a meta-analytic functional coactivation map of the Neurosynth database (http://neurovault.org/images/39711/). We then calculated the Spearman rank-order correlation between these two RDMs and implemented a permutation test to confirm the statistical significance.

Discussion

We have used a novel paradigm that captures the core components of real-life bribery to offer a behavioral and neural characterization of how a power-holder determines whether or not to accept bribes. Both the model-free behavioral results and the computational modeling findings indicate that when facing the temptation of a bribe power-holders take into account the moral costs of conniving with the briber (measured by the parameter θ) and the harm inflicted on a third party (measured by the parameter ω). Incorporating these moral costs into the SV computation explains why the probability of accepting a bribe is lower than that of accepting an otherwise-comparable honest offer. This is especially the case when accepting the bribe also harms a third party. Post-task subjective ratings also confirm that participants did feel more moral conflict while making decisions in the Bribe (vs. Control) condition. They also felt it was more morally inappropriate to accept bribes than honest offers, especially when doing so harmed a third party. Our study extends to the context of corruption previous behavioral results on dishonesty that reveal people are generally aversive to dishonesty (López-Pérez and Spiegelman, 2013) and that deception is less morally acceptable when it harms another’s interests (Lindskold and Walters, 1983).

At the neural level, we explored the neurocomputational mechanisms underlying bribery-related decision-making using model-based fMRI. First, we identified the neural bases of two types of moral costs engaged when deciding whether to take a bribe (i.e., Bribe vs. Control). The vAI was more sensitive to the expected ill-gotten personal gains (PM contrast: Bribe vs. Control). Interestingly, we also observed that the vAI (especially the right part) is more engaged in encoding expected personal gains in the Dyad (vs. Solo) scenario. The vAI plays a critical role in guiding dishonest decisions under various circumstances (Yin et al., 2017) and in perceiving other’s dishonest intentions (Yin and Weber, 2016). These findings can be broadly linked to the modulation of aversive feelings by vAI that generate motivation to social norm enforcement (Bellucci et al., 2018). Consistent with previous studies, our results show that a key computation performed by the vAI signal is to encode bribery-related profits, especially when a potential victim is involved in the social context. This signal might reflect an aversive feeling towards moral transgression due to bribe-taking behavior. This might contribute, but not necessarily lead, to preventing power-holders from being corrupted. Moreover, the right TPJ/pSTS shows the expected role in tracking the expected loss to a third party due to potential bribe-taking decisions in the Dyad scenario. It is well-established that TPJ/pSTS (especially the right part) functions as a core component of the mentalizing brain network (Schurz et al., 2014). Mentalizing ability is a prerequisite for making judgments and decisions that take into account the welfare of others (de Waal, 2008), mainly via recruitment of the right TPJ (Young et al., 2010). Indeed, recent neuroimaging studies have shown that TPJ/pSTS is critically involved in pitting personal interests against other-regarding (or moral) concerns during prosocial decision-making (Morishima et al., 2012; Tusche et al., 2016). Supporting this claim, we also found that the multivoxel neural patterns of TPJ extending to pSTS selectively discriminated bribery-related decision-making (i.e., Bribe vs. Control) only when a third party victim was explicitly involved. Both our findings indicate that TPJ/pSTS is likely to reflect the moral cost incurred by participants when harming a third party.

We also observed a strong involvement of dACC during bribery-related decision-making (event contrast: Bribe vs. Control). Together with the aforementioned results of brain regions encoding moral cost, these findings also build an interesting link to the literature of guilt. Several neuroimaging studies adopting different paradigms (Bastin et al., 2016; Takahashi et al., 2004; Yu et al., 2014) consistently report the engagement of dACC (adjacent to the mid-anterior cingulate or dorsomedial prefrontal areas), AI, and TPJ when guilt-specific feelings are elicited. Indeed, theoretical and behavioral evidence shows that either guilt proneness (as a personality trait) or (anticipated or elicited) guilt feelings might curb immoral behaviors including cheating or bribe-taking (Balafoutas, 2011; Cohen et al., 2012; Köbis et al., 2016; Motro et al., 2016). Although investigating the role of guilt in corrupt actions is not the goal of the current study, our results suggest a promising future direction of exploring the effect of social/moral emotions in bribery-related decision-making.

We further showed how value computation engaged in the trade-off between personal profits and bribe-related moral costs is implemented in the brain. As predicted, these moral costs along with monetary payoffs are integrated into a value signal computed in the vmPFC, together with other regions (e.g., posterior cingulate cortex). The vmPFC is known to be a central hub of the brain valuation network (Bartra et al., 2013). This region has been reported to be involved in guiding choices in various domains via the computation of the SV of the available options, and it is thought to encode the common neural currency for value (Levy and Glimcher, 2012). Our findings extend recent fMRI studies indicating that vmPFC is involved in the trade-off between moral values and monetary profits to the domain of corruption (Crockett et al., 2017; Qu et al., 2020).

Another critical contribution of this study is to address how specific choices, such as accepting or rejecting a bribe, shape the neural substrates of decision-making in various bribe-related scenarios. We found that a higher anti-corruption signal in the dlPFC, defined as the neural activity of rejecting (vs. accepting) an offer specifically in the Bribe (vs. Control) condition, was identified in the Dyad scenario but not the Solo scenario. This finding parallels our behavioral results showing that participants were less likely to accept a bribe and rated this behavior as the most morally inappropriate when the interests of a third party were compromised. Thus, these findings suggest that the dlPFC plays a dedicated role in ethical behavior to guide the choice of rejecting a bribe in a situation combining different forms of moral costs and is specifically sensitive to harm caused to third parties. Consistent with this claim, recent studies reported that the lateral prefrontal cortex is more engaged in encoding profits that were obtained by inflicting physical pain on others (Crockett et al., 2017) and when individuals flexibly align moral goals with value representations (Carlson and Crockett, 2018).

We also provide novel empirical evidence regarding how inter-individual differences in bribery-specific preferences modulate neural activities during bribery-related decision-making. On the one hand, the functional coupling between vmPFC and dlPFC during bribery-related decision-making (i.e., Bribe vs. Control), decreased with the inter-individual level of the moral cost of conniving with a briber (i.e., a fraudulent proposer; as reflected by the θ parameter). Previous literature has consistently shown an increased vmPFC–dlPFC functional connectivity when self-control is required during various types of value-based decision-making (Hare et al., 2009; Hare et al., 2014). This leads to our initial prediction that a generally stronger vmPFC–dlPFC functional connectivity would be observed during bribery-related decision-making (Bribe vs. Control). However, recent evidence also suggests that the strength of the vmPFC–dlPFC coupling, enhanced by self-control, might vary from person to person. For instance, a recent study has shown inter-individual differences in vmPFC–dlPFC connectivity when resisting to trade honesty values against economic benefits (Dogan et al., 2016). This indicates that the modulatory role of dlPFC on value representation is flexible, according to individuals’ other-regarding preferences. In agreement, our result of functional connectivity suggests that more self-control might be needed to devalue the temptation to earn morally tainted profits for those power-holders who are less aversive to the briber’s fraudulence (i.e., participants with smaller θ). On the other hand, using IS-RSA, we revealed that neural patterns during bribery-related decision-making in the dlPFC were similar for participants who displayed similar preferences for bribery (i.e., indexed by a dissimilarity matrix of θ and ω across participants). Standard univariate regression analyses have established that this region is linked to individual variations in concerns for dishonesty and related behaviors (Dogan et al., 2016; Yin and Weber, 2018), as well as in harm aversion (Crockett et al., 2017). Compared with the traditional univariate approaches, IS-RSA can directly map multidimensional psychological states (computations) between individuals to specific neural patterns (Kriegeskorte et al., 2008; Popal et al., 2019). Thus, the multivariate approach allows us to leverage both individual differences in task-based preferences of corruption and decision-relevant multivoxel neural patterns. This reveals that the dlPFC represents a complex geometry characterized by a multidimensional model space of moral preferences across individuals.

Notably, all these results provide evidence for a critical role of the dlPFC in different aspects of bribery-related decision-making. The univariate result (based on GLM3) suggests that, from a within-subject perspective, the dlPFC plays a dedicated role in guiding the choice to reject bribes in a context-dependent manner. Both the functional connectivity results and the IS-RSA results concern the between-subject perspective. In general, these findings indicate that the dlPFC signature (or pattern) might reflect the preference of whether to accept or refuse a bribe across individuals as power-holders. These results are consistent with a causal role of dlPFC in ethical behaviors (Maréchal et al., 2017; Zhu et al., 2014) and may inspire future studies to investigate whether such a causal role extends to the corrupt behaviors and how it varies between individuals.

Several issues concerning the present study need further discussion. First, although the present task captures the essence of corruption from the perspective of a person in power, it only simulates a specific type of corruption in a lab setting. The real phenomenon is far more complex and diverse. This obviously constrains the generalization of the current findings to corruption in field settings. Second, the present task did not contain a condition in which the proposer honestly reported the option with the lower payoff having been randomly selected by the computer. We did this purposely to preserve the symmetry of the experimental design and maintain the motivation of proposers’ behaviors to being entirely rational and explainable by the optimization of personal profits. Thus the offer proposition, in either the Bribe or the Control condition, was realistic for the proposer, that is, it would not make sense if the proposer proposed an offer to earn a lower payoff. In addition, adding such trials to the current task would inevitably prolong the duration of the experiment, which might make participants more tired and thus influence the quality of the data we collected. Third, the present task adopted a multiround single-shot economic game that did not involve any real partner physically present during the experiment. We decided to use such an experimental setting not only because it is commonly used in neuroeconomics studies (e.g., Aimone et al., 2014; Hu et al., 2018; Spitzer et al., 2007) but also because, here, each choice can be considered independent, thus alleviating the potentially confounding effects of learning and concerns of reputation in repeated interactions. Although the lack of social interactions in the task might diminish the involvement of participants and influence their beliefs about the authenticity of the experiment setting, our behavioral results were consistent with the predictions that were built on the assumption that participants believed the existence of proposers and third parties. Nevertheless, the concern raised above cannot be completely ruled out because participants were never asked about whether or not they believed the cover story. All these limitations, we believe, should be addressed in future studies.

To conclude, the present study provides a neurocomputational account characterizing how a power-holder reaches corrupt decisions by weighing bribe-related moral costs and material gains. It shows that corruption is controlled by an interconnected brain network with nodes processing specific computational signals. Moreover, our study offers new clues on how individuals who vary in bribery-specific preferences differ in neural signals during bribery-related decision-making. These findings open a new gate for improving our understanding of the neurobiological mechanisms underlying corruption from a micro-level using a multidisciplinary research approach. At the societal level, our results may have mechanistic implications for the design of institutions that aim to promote honest conduct and prevent corruption of officials with entrusted powers. Our study also provides insights for future research on corruption, such as investigating the neurocomputations required from a briber’s perspective when proposing a bribe, and how the brain-synchrony between a briber and a power-holder predicts the subsequent success of bribery attempts.

Materials and methods

Participants

Forty Chinese-speaking undergraduates and graduate students (25 females; mean age: 20.0 ± 2.0 years, ranging from 18 to 27 years; two left-handed) were recruited via online fliers from the South China Normal University (SCNU). The sample size was determined based on previous human fMRI studies in similar topics including recent human fMRI studies on dishonesty (Yin et al., 2017; N = 47) and harm-aversion (Crockett et al., 2017; N = 28), which also adopted single-shot multiround economic decision-making paradigms. All participants had normal or corrected-to-normal vision and reported no history of psychiatric or neurological disorders. The study was performed at the Imaging Center of SCNU and was approved by the local ethics committees. All experimental protocols and procedures were conducted in accordance with the IRB guidelines for experimental testing and were in compliance with the latest revision of the Declaration of Helsinki (BMJ 1991; 302: 1194).

Cover story

Request a detailed protocol

Participants were assigned the role of the power-holder who decides to accept or reject financial offers (see Figure 1A). They were informed that they would be presented with a series of choices from an independent group, whose data were previously collected by the experimenter. This independent group was actually fake, and the choices made by this group were controlled by the experimenters (see Stimuli for details). Specifically, participants were led to believe that this independent group of online attendants participated in a ‘Game of Chance’ and had been randomly assigned to one of two roles, that is, a proposer and the third party. The proposer played either alone (i.e., the Solo scenario) or with a randomly matched third party (i.e., the Dyad scenario). In the Solo scenario, each proposer was presented with two options that would earn them different payoffs with the total of the two payoffs fixed at CNY ¥100. Similarly, in the Dyad scenario, each proposer was presented with two offers involving different combinations of payoffs divided between themself and the third party (i.e., the total amount of each payoff was also fixed to ¥100). In either scenario, one of the payoffs was randomly indicated by the computer. According to the rules of the game, the proposer should report the option indicated by the computer, which determined his/her payoff (and that of the third party if he/she was involved). However, the response of the proposer was never checked by the experimenters. This allowed the proposer to lie by reporting the alternative option that had not been indicated by the computer when this brought him/her more profits. The third party in the Dyad scenario would only be presented with the option reported by the proposer without knowing the alternative one, thus being left with no choice but to accept the monetary distribution involved in the reported option.

Importantly, the real participants were also told that each proposer had been informed that whether he or she would obtain the payoff of the reported options crucially depended on the decisions of the real participants in their role as power-holders. Therefore, to obtain the profits in the reported options, the proposer could ‘share’ a portion of the money from their payoff (i.e., the payoff in the reported option) to influence the power-holder. The task for the real participants was to decide whether to accept or reject the offer given the information above. If accepted, both the proposer and the participant would benefit from the payoff. However, in the Dyad scenario this could harm the interests of the innocent third party. If the participant rejected a proposition, neither the proposer nor the participant earned anything. The third party, if involved, would still be paid according to the option indicated by the computer.

Design

Request a detailed protocol

We implemented a 2-by-2 within-subject design (i.e., scenario: Solo/Dyad; proposer’s conduct: control/bribe) forming four experimental conditions (i.e., SC, SB, DC, and DB; see Figure 1B). Here, we operationally defined corrupt behaviors as the acceptance of offers proposed by the proposer in the Bribe conditions (SB and DB). In these conditions, moral costs would be elicited by conniving with the proposer to increase the payoff when the proposer lies and reports the wrong proposition, and, in the Dyad scenario, additionally harming the interests of an innocent third party. In other words, we deliberately made the potential victim (i.e., the experimenter) implicit in the Solo scenario to disentangle this kind of bribe from the one defined in the Dyad scenario where the potential victim was the third party. The other conditions (SC and DC) control the corrupt-nonspecific preference (e.g., inequity aversion) as in the typical Ultimatum game.

Each trial began with a 1.5 s information screen displaying the two payoff options together with the computer’s choice (indicated by a computer icon) and the proposer’s report (indicated by a blue arrow). Next, the participant was presented with an offer (proposed by the proposer) and asked to decide whether to accept or reject the offer by pressing relevant buttons with either left or right index finger within 8 s. A yellow bar appeared below the corresponding option for 0.5 s once the decision was made. If an invalid response was made (i.e., no response in 8 s or response less than 0.2 s), a warning screen showed up and this trial was repeated at the end of the scanning session. Each trial ended up with an inter-trial interval showing a jittered fixation (i.e., 3–7 s; see Figure 1C).

To further clarify our design, several aspects need to be noted. First, due to the potential framing effect elicited by the wording in the instruction (Abbink and Hennig-Schmidt, 2006), we adopted the word ‘persuade’ in place of words such as ‘bribe’ and ‘corrupt’ to avoid demand characteristics. Second, identities of all proposers and innocent third parties were indicated only by the initials of their names to control other confounding effects when using photos (e.g., gender, attractiveness, facial expression, dominance, and trustworthiness levels). Third, to avoid possible learning effects or strategic responses, participants were led to believe that each trial was independent and was matched with different proposers and innocent third parties. Fourth, the positions of payoff options were randomized within participants and that of the decision options (i.e., accept or reject) were counterbalanced across participants. Fifth, participants were told that they would be paid based on the decision of only one trial (among all trials) randomly selected after the experiment. Furthermore, participants were told that the proposer (as well as the innocent third party if in the Dyad scenario) in the selected trial would also be paid accordingly.

Stimuli

Request a detailed protocol

The payoffs of the proposer met the following criteria: (1) total payoffs indicated in both options (i.e., the computer-indicated option and the alternative option) were always fixed at ¥100 and (2) the payoff in the reported option by the briber was always the higher of the two for the briber (i.e., from ¥ 56 to ¥ 96 with an increment of ¥ 8). These criteria mimicked the motivation for a rational selfish proposer to lie, which increased the contextual validity of the present study. In addition, the payoffs for the innocent third party in both options (i.e., only in the Dyad scenario) were always equal to ¥100 minus the proposer’s payoff in the corresponding option.

Regarding the offers (i.e., the monetary distribution between the proposer and the participant), we set the payoffs based on a fundamental principle that the proposer is selfish and always earns more for themselves in the reported options than in the alternative ones, even after bribing the power-holder (i.e., the real participant) at their own cost. The offer proportion was defined as the proportion of the amount the proposer decided to share with the power-holder from the payoff the proposer would have earned in the reported option, ranging from 10% to 90%, which in turn increases the variance of participants’ responses and further benefited the computational modeling analyses. As a result, 36 different offers were adopted for the present study (see Supplementary file 1j for details). Each unique offer appeared once in each of the four experimental conditions (i.e., SC, SB, DC, and DB).

An event-related design was adopted and consisted of two functional runs with each containing 72 trials and lasting about 12–15 min. For each participant, we randomly divided the 36 offers into two equal datasets, then randomly associated two conditions with the first subset and two other conditions with the second subset, and assigned these stimuli to the first run (e.g., subset 1: SC, DB; subset 2: SB, DC) and the rest to the second run (e.g., subset 1: SB, DC; subset 2: SC, DB). These steps ensured that each specific bribe appears exactly twice in each run (i.e., once for two of the four conditions, respectively).

All stimuli were presented using Presentation v14 (Neurobehavioral Systems Inc, Albany, CA, USA) and were back-projected on a screen outside the scanner using a mirror system attached to the head coil.

Procedure

Request a detailed protocol

Upon arrival, participants signed the written informed consent according to the Declaration of Helsinki. They were provided with the instructions for the task and completed a series of comprehension questions to ensure that they fully understood the task. Before the incentivized task, participants completed a practice session to get familiar with the paradigm and the response button. Participants were additionally informed at the beginning that the whole study included two independent tasks and the current task was always set to be the first task. To rule out the possibility of hedging the income risk across two tasks, they were informed that only one task would be randomly chosen by the computer to determine their final payoff at the end of the experiment. A 6 min structural scanning was performed at the end of the MRI session.

After that, participants filled out a battery of questionnaires. In particular, they were asked to report the subjective feeling towards (1) the moral inappropriateness of the proposer’s behavior in the previous online test, (2) the seriousness of the fraud committed by the proposer, (3) the degree of their own moral conflict during decision-making, and (4) the moral inappropriateness when they accepted offers in the four experimental conditions, respectively, on a 0–100 Likert scale. In the end, participants were debriefed on all task-relevant information, informed about their final payoffs (i.e., ¥80–¥166, where ¥1 approximates to €0.13) via mobile payment, and thanked.

Data acquisition

Request a detailed protocol

The imaging data were acquired on a 3-Tesla Siemens Trio MRI system (Siemens, Erlangen, Germany) with a 32-channel head coil at the Imaging Center of SCNU. Functional data were acquired using T2*-weighted echo-planar imaging sequences employing a BOLD contrast (TR = 2000 ms, TE = 30 ms; flip angle = 90°; slice thickness = 3.5 mm, slice gap = 25%, matrix = 64 × 64, FoV = 224 × 224 mm2) in 32 axial slices. Slices were axially oriented along the AC-PC plane and acquired in ascending order. A high-resolution structural T1-weighted image was also collected for every participant using a 3D MRI sequence (TR = 1900 ms, TE = 2.52 ms; flip angle = 9°; slice thickness = 1 mm, matrix = 256 × 256, FoV = 256 × 256 mm²).

Data analyses

Request a detailed protocol

One participant was excluded due to excessive head movements (>3 mm), thus leaving a total of 39 participants whose data were further analyzed (24 females; mean age ± SD = 19.9 ± 1.9 years, ranging from 18 to 27 years; two left-handed).

Behavioral analyses

Request a detailed protocol

All behavioral analyses and visualization were conducted using R (http://www.r-project.org/) and relevant packages. All reported p values are two-tailed, and p<0.05 was considered statistically significant.

For subjective rating, we implemented the within-subject MANOVA test due to the high correlation between rating measures (i.e., Pearson rs >0.67, ps <0.001) by the manova function in ‘stat’ package. For choice data, we performed the repeated mixed-effect logistic regression on the decision of choosing the ‘accept’ option by the glmer function in ‘lme4’ package, with scenario (dummy variable; reference level: Solo), proposer’s conduct (dummy variable; reference level: Control), and their interaction as the fixed-effect predictors controlling for the effect of offer proportion (continuous variable; grand mean-centered) and the larger payoff the proposer would earn in the reported option (continuous variable; grand mean-centered). For the random-effect structure, we followed the ‘maximal’ principle (Barr et al., 2013) and incorporated scenario, proposer’s conduct, and their interaction as the by-subject random-effect slopes. Once the regression model failed to converge, we dropped the highest interaction by-subject random-effect slope and refitted the model. In addition, we included random-effect intercepts that vary across participants and (fMRI) runs. For the statistical inference on each predictor, we performed the type II Wald chi-square test on the model fits by using the Anova function in ‘car’ package.

For the RT, we first did the log-transformation due to its non-normal distribution (i.e., Anderson–Darling normality test: A = 232.54, p<0.001) and then performed a mixed-effect linear regression on the log-transformed RT by the lmer function in ‘lme4’ package, with decision (dummy variable; reference level: accept), scenario, proposer’s conduct, and their interactions (i.e., decision × scenario, decision × proposer’s conduct, scenario × proposer’s conduct, decision × scenario × proposer’s conduct) as fixed-effect predictors controlling for the effect of offer proportion and the gain the proposer would earn in the reported option. Random-effect predictors were specified in the same way as above. We followed the procedure recommended by Luke, 2017 to obtain the statistics of each predictor by applying the Satterthwaite approximations on the restricted maximum likelihood model fit via the ‘lmerTest’ package.

Computational modeling

Request a detailed protocol

To provide a refined characterization of how power-holders (real participants) integrated information to determine their final decision of accepting or rejecting the offer, we tested and compared a total of seven models with different utility functions. We started from a simple model assuming that power-holders care about the offer (i.e., the monetary allocation between the proposer and oneself) differentially in terms of whether the proposer commits a bribe or not. The utility function (Model 1; Equation 2) is defined as follows:

(2) SV(pP,pPH)=βPpP+(βPHθq)pPH

where, in a given trial, SV denotes the subjective value, pP and pPH represent the payoff (i.e., monetary gain) for the proposer (P) and power-holder (PH) given different choices (i.e., accept or reject the offer), q is an indicator reflecting whether the proposer bribes (q = 1) or not (q = 0; same for models below). βP and βPH are two independent free parameters capturing the weights on the payoff of the proposer and the power-holder, respectively. θ describes the moral cost brought by conniving with a fraud committed by a briber (the prior range of these parameters: −20 <= βB, βP, θ <= 20; same for models below).

Model 2 and 3 are variations established on the basis of Model 1. Specifically, Model 2 (Equation 3) additionally assumes that power-holders also take into consideration the unsigned inequality in the payoffs between the proposer and themselves, scaled by a free parameter γ (−20 <= γ <= 20).

(3) SV(pP,pPH)=βPpP+(βPHθq)pPH+γ|pPpPH|

Model 3 (Equation 4) hypothesizes that power-holders bear an extra moral cost to their own payoff as a result of accepting a bribe that harms the interests of an innocent third party. This moral cost is captured by an extra parameter δ (−20 <= δ <= 20). These utility functions are defined as follows:

(4) SV(pP,pPH)={βPpP+(βPHθq)pPHifSoloscenarioβPpP+(βPH(θ+δ)q)pPHifDyadscenario

Model 4 (Equation 5) integrates both (additional) computational components in Models 2 and 3 into the model, which is defined as

(5) SV(pP,pPH)={βPpP+(βPHθq)pPH+γ|pPpPH|ifSoloscenarioβPpP+(βPH(θ+δ)q)pPH+γ|pPpPH|ifDyadscenario

Model 5 (Equation 1) differs from Model 4 in the way of representing the moral cost associated with the harm to the innocent third party’s interest due to the acceptance of a bribe. Thus, Model 5 assumes that power-holders take into account the exact payoff loss to the innocent third party due to the acceptance of the bribe (i.e., pT represents the payoff for the third party), which is captured by parameter ω (−20 <= ω <= 20; see Equation 1 in the Results).

We also tested the Fehr–Schmidt model (Models 6 and 7), which is adapted to the current setting. Model 6 (Equation 6) assumes that power-holders only consider the inequality in payoffs between themselves and the proposer, which is defined as follows:

(6) SV(pP,pPH)=pPHαmax(pPpPH,0)βmax(pPHpP,0)

where α and β measure the degree of aversion to payoff inequality in disadvantageous and advantageous situations, respectively (i.e., how much power-holders dislike it when they earn less/more than the proposer). Here, we vary α and β according to different conditions (i.e., αSC, αSB, αDC, αDB, βSC, βSB, βDC, βDB; −20 <= α, β <= 20).

Model 7 (Equation 7) is established based on the three-person version of the Fehr–Schmidt model, namely assuming that participants are concerned about payoff inequality between themselves and either of the other agents in the context. The utility function is defined as follows:

(7) SV(pP,pPH)=pPHαmax(pPpPH,0)βmax(pPHpP,0)ifSoloscenarioSV(pP,pr,pPH)=pPH0.5(αpmax(pppPH,0)+αrmax(prpPH,0))0.5(βpmax(pPHpP,0)+βrmax(pPHpPr,0))ifdyadscenario

Here, we varied α and β according to different agents depending on conditions (i.e., Solo scenario: αSC, αSB, βSC, βSB; Dyad scenario: αP:DC, βP:DC for the inequality aversion to the proposer, αT:DB, βT:DB for that to the third party).

The probability of accepting the offer was determined by the softmax function (Equation 8):

(8) p(SVaccept)=eτSVaccepteτSVaccept+eτSVreject

where τ is the inverse softmax temperature parameter (0 <= τ <= 10) denoting the sensitivity of an individual’s choice to the difference in SV between the choice of accepting the offer and that of rejecting the offer.

We fitted all the above-mentioned candidate models using the HBA approach via the ‘hBayesDM’ package (Ahn et al., 2017). The ‘hBayesDM’ package is developed based on the Stan language (https://mc-stan.org/), which adopts a MCMC sampling scheme to perform full Bayesian inference and obtain the actual posterior distribution. We used HBA rather than maximum likelihood estimation (MLE) method because HBA provides much more stable and accurate estimates than MLE does (Ahn et al., 2011). Following the approach in ‘hBayesDM’ package, we assumed the individual-level parameters were drawn from a group-level normal distribution: individual-level parameters ~ Normal (μ, σ). In HBA, all group-level parameters and individual-level parameters were simultaneously estimated by the Bayes rule given the behavioral data. We fitted each candidate model with four independent MCMC chains using 1000 iterations after 2000 warm-up iterations for both studies for initial algorithm warmup per chain, resulting in 4000 valid posterior samples. Convergence of the MCMC chains was assessed through Gelman–Rubin R-hat Statistics (Gelman and Rubin, 1992).

For model comparison, we computed the score of leave-one-out information criterion (LOOIC) for each model (Vehtari et al., 2017). Compared to the point-estimate information criterion (e.g., Akaike Information Criterion), the LOOIC score offers the estimate of out-of-sample predictive accuracy in a fully Bayesian way. Conventionally, the lower LOOIC score indicates better out-of-sample prediction accuracy of the candidate model. A difference score of 10 on the information criterion scale is regarded as decisive (Burnham and Anderson, 2004). We selected the model with the lowest LOOIC as the winning model for subsequent analysis.

We also perform a parameter recovery analysis to ensure that our model was robustly identifiable. We first generated a simulated dataset (choices) for each participant using the individual-level posterior mean of these parameters (i.e., the true value) corresponding to that specific participant based on the winning model. Next, we fitted our winning model to the simulated dataset with the same methods (see Materials and methods for details) and obtained the individual-level posterior mean of these parameters (i.e., the recovered value). We quantified the performance of the parameter recovery by calculating the bivariate correlation, with Pearson correlation test, between the true value and the recovered value for each parameter, respectively.

To further examine the absolute performance of the winning model (i.e., whether the prediction of the winning model could characterize the features of real choices), we also performed a PPC (Zhang et al., 2020). Specifically, we generated new choice datasets, given each individual’s joint posterior MCMC samples (i.e., 4000 times), in accordance with the actual trial-wise stimuli sequences presented to each participant, resulting in 4000 choices per trial per participant. Thus, for each participant, we obtained the model prediction by calculating the average offers given these new datasets in the four experimental conditions, respectively. We also performed an out-of-sample PPC to avoid the overfitting concern. Given that the stimuli (e.g., proposer’s offers) in each condition were randomly but evenly distributed into two fMRI runs for each participant (see Materials and methods for details), we used the choice data from Run 1 for model estimation based on the winning model. Then, we used the mean of the posterior distribution of the individual-level parameters to simulate an independent dataset based on the stimuli used in Run 2 and calculated the acceptance rate for each participant in the different conditions (i.e., predicted acceptance rate), as we did for the real choice data in Run 2 (i.e., actual acceptance rate). For both PPC, we examined to what degree the individual difference in model prediction was correlated with that of the actual acceptance proportion using the Pearson correlation test.

fMRI data analyses

Functional imaging data were analyzed using SPM12 (Wellcome Trust Centre for Neuroimaging, University College London, London, UK). The preprocessing procedure followed the pipeline recommended by SPM12. Functional images were first realigned to the first volume to correct motion artifacts, unwarped, and corrected for slice timing. Next, the structural T1 image was segmented into white matter, gray matter, and cerebrospinal fluid with the skull removed, and co-registered to the mean functional images. Then all functional images were normalized to the MNI space, resampled with a 2 × 2 × 2 mm3 resolution, based on parameters generated in the previous step. The normalized images were smoothed using an 8 mm isotropic full width half maximum Gaussian kernel. High-pass temporal filtering was performed with a default cut-off value of 128 s to remove low-frequency drifts.

Univariate analyses

Request a detailed protocol

For each participant, we constructed the following GLMs to address specific aims.

Specifically, GLM1 was built to address how different types of moral cost were represented in the brain during bribery-related decision-making, which included two variants. GLM1a identified brain regions encoding the moral cost of conniving with a briber (i.e., a fraudulent proposer) on the expected gains (i.e., the profits for both the proposer and the participant had the participant accepted the offer) during bribery-related decision-making. This GLM included the onset of decision period in each condition separately as regressors of interest (i.e., SC, SB, DC, and DB). These events were modeled with the duration of the actual decision time in each trial. Furthermore, GLM1a incorporated trial-wise expected gains for both the participant and the proposer associated with each condition event as two PMs. Notably, the default orthogonalization process on the PMs was switched off, allowing that these two PMs competed for variance during the estimation. We also performed a supplementary GLM analysis (GLM1a-s) to explore regions that encode the above PMs across all trials. GLM1b aimed at investigating regions tracking the losses to the innocent third party because of corruption. It was constructed similarly to GLM1a, except that only the onset of the decision period in DB condition was associated with the trial-wise expected loss to the third party (i.e., the absolute payoff difference of the innocent third party had the participant accepted the offer) as the sole PM. We did not add the same PM to the DC condition as the payoff to the innocent third party would not change as a result of the participant’s decision (i.e., the third party would receive the payoff indicated in the computer-chosen option anyway) and the expected loss would always be 0. Notably, we chose not to incorporate the PM of expected loss to the third party into the GLM1a because this GLM aims to identify brain regions specifically encoding the expected gains due to bribe-taking. For this reason, it is better to keep the design matrix balanced so that each onset regressor of the decision event is attached with the exact same PMs (i.e., PM of expected gains for the proposer and the participant). To control for the possible effect of these PMs on the result, we implemented a supplementary GLM analysis (i.e., GLM1b-s) in which we not only added the PM of the expected loss (the first PM) but also incorporated the PMs of expected gains for the proposer and the participant to the DB condition (the second and third PM). The orthogonalization was performed to control for the co-linearity between these PMs (see Supplementary file 1k). We also established GLM1c, which did not contain any PM for subsequent functional connectivity and multivariate analyses.

GLM2 identified regions computing relative SV (i.e., relative SV) during bribery-related decision-making. The relative SV was defined by subtracting the SV of the non-chosen option from that of the chosen option (i.e., relative SV = SVchosen - SVunchosen) based on the winning model. GLM2 consisted of two variants. GLM2a distinguished the Bribe condition from the Control condition (pooling the Solo and Dyad scenarios), which contained the onset of relevant trials during the decision period with the trial-wise relative SV as the associated PMs as the regressor of interest. GLM2b was similar to GLM2a except that it pooled all trials as the single regressor of interest, which examined a general value-related neural network regardless of experimental conditions.

GLM3 examined the neural activities during bribery-related decision-making with regard to specific choices. Thus, GLM3 contained the onset of decision period of each condition with respect to specific choices (i.e., accept or reject in conditions of SC, SB, DC, DB), resulting in eight regressors of interest. To control the differential effect of SV on trials with regard to specific choice, we also attached each onset regressor with corresponding trial-wise relative SV as PMs. Notably, six participants had to be further excluded from this specific GLM analyses due to the missing type of choices in one or more of the four conditions. To simplify further analyses, we computed the neural activity specific to rejecting as opposed to accepting offers (i.e., reject vs. accept) in each of the four conditions.

For all GLMs above, we also separately modeled the onsets of information period for each condition, together with the onset of the button press. Furthermore, once the participant showed invalid responses, a regressor modeling events of no interest was included, which contained decision onsets of invalid trials (i.e., for trials for which RT of making choices was less than 200 ms, duration equals the actual RT; for trials of no response, duration equals 8 s) as well as the warning feedback (duration equals 1 s). Onsets of these events were regarded as regressors of no interest. The six movement parameters were added to all models as covariates to account for the artifacts of head motion. The canonical hemodynamic response function (HRF) was applied to model the fMRI signal.

Individual-level contrasts were fed to group-level random-effect analyses. One-sample t-tests were mainly adopted to test the parametric effect or to compare simple contrasts between two conditions. A 2 × 2 within-subject flexible factorial ANOVA model was also adopted to examine the main effect of scenario (i.e., Dyad vs. Solo), the main effect of the proposer’s conduct (i.e., Bribe vs. Control), and their two-way interaction (i.e., (DB – DC) – (SB - SC)) on neural activity while applicable (see Results for details of the tests we used for specific analyses).

For all univariate analyses, we adopted a whole-brain corrected threshold of p<0.05 at the cluster-level controlling for family-wise error (FWE) rate with an uncorrected voxel-level threshold of p<0.001 as the cluster-defining threshold (Eklund et al., 2016). Moreover, a small volume correction (SVC) was conducted within these hypothetical regions of interest (ROI). All masks were defined based on a whole-brain parcellation given a meta-analytic functional coactivation map of the Neurosynth database (https://neurovault.org/collections/2099/). Compared with an ROI definition based on specific peak coordinates, this approach generates masks that are much less biased by a specific scientific research (e.g., an empirical study or a meta-analysis) and are larger in volume (vs. a sphere with a radius of 6–8 mm). This ensures the validity and the reliability of the SVC. Regions were labeled according to the AAL template via the xjView toolbox (http://www.alivelearn.net/xjview8/). To illustrate the parametric effect of potential loss for the third party (GLM1b, GLM1b-s), and relative SV (GLM2a), we adopted the rfxplot toolbox (Gläscher, 2009) (http://rfxplot.sourceforge.net/).

Multivariate decoding analyses

Request a detailed protocol

To investigate whether TPJ/pSTS was selectively involved in bribery-related decision-making that harmed a third party, we performed an ROI-based multivariate decoding analysis via python-based nltools package (v 0.3.6 in Python 3.7.1; http://github.com/ljchang/nltools). Specifically, we trained a linear support vector machine (slack parameter C = 1 chosen as default) classifier, a widely used algorithm to deal with the binary classification, to discriminate decision-relevant activity pattern in TPJ/pSTS in Bribe vs. Control for the Solo (i.e., individual contrast maps in GLM1: SB vs. SC; the first condition was coded as 1 and the second as −1, same below) and the Dyad scenario (DB vs. DC) separately.

A LOSO cross-validation procedure was adopted such that it trained the classifier on N-1 participants and generated a weight map that best classified the sample, and then tested the classification on the left-out participant. This procedure looped for all participants once to obtain their respective cross-validated signature response values. As a result, the classifier obtained a hyperplane that best discriminates the individual contrast maps in two conditions (e.g., SB vs. SC). Receiver operating characteristic curve was created based on the performance of classification (i.e., the two-choice forced-alternative accuracy) via Canlab Matlab toolbox (https://github.com/canlab). Statistical significance was obtained via the permutation test (i.e., 5000 times of permutation).

Pattern similarity analyses

Request a detailed protocol

To further examine whether vmPFC was engaged in value computation during decision-making in both the Bribe and the Control conditions, we implemented a multivariate pattern similarity analysis with the nltools package.

For each participant, we extracted the multivoxel patterns within the vmPFC mask (defined based on the Neurosynth coactivation map; see above for details) from parametric contrasts of relative SV in the Bribe and the Control conditions (GLM2a). Then we calculated the similarity between these two patterns via the Pearson correlation and performed Fisher's z transformation on these correlation coefficients for statistical analyses. Group-level statistical significance was obtained via the permutation-based one-sample t-test (i.e., 5000 times of permutation).

Functional connectivity analyses

Request a detailed protocol

To explore how the value signal in the vmPFC (i.e., the relative SV effect in GLM2a) interacts with other parts of the brain during the bribery-related decision-making process, we implemented a PPI analysis (Friston et al., 1997) using the gPPI toolbox (https://www.nitrc.org/projects/gppi) (McLaren et al., 2012). To this end, for each participant, we constructed a PPI-GLM containing the following regressors: (1) the de-convolved time series in the vmPFC within the parcellation-based vmPFC mask as the physiological regressor, (2) all onsets of the decision period in GLM1c as the psychological regressors, and (3) the generated PPI regressors by multiplying the physiological regressor with each psychological regressor. These regressors were all convolved with the canonical HRF to model the BOLD signal. In addition, we also incorporated six movement parameters as covariates to account for artifacts of head motion. Given the purpose of this analysis, we focused on the PPI contrast of Bribe vs. Control with dlPFC as our target region and examined whether the dlPFC enhanced functional connectivity with vmPFC during the bribery-related decision-making.

Inter-subject representational similarity analysis (IS-RSA)

Request a detailed protocol

The IS-RSA was treated as an exploratory analysis with a focus on dlPFC and conducted using the nltools package. We first computed the corruption-relevant contrast (Bribe vs. Control) during the decision period derived from GLM1c. Next, we extracted the parameter estimates (i.e., contrast values) of all voxels from the bilateral dlPFC (i.e., neural activity patterns; see above for details of mask selection) and constructed neural representational dissimilarity matrices (neural RDM) using pairwise correlation dissimilarity of these neural patterns between each pair of participants. To characterize bribery-specific preferences across participants, we also created a parameter RDM that measures the Euclidean distance between each pair of participants in a parameter space reflecting two forms of bribery-specific moral costs (i.e., θ and ω). Then, we computed the correlation between the parameter RDM and the neural RDMs using Spearman’s rank-order correlation, which did not assume a linear behavior–brain relationship. Statistical significance was obtained via the permutation t-test (i.e., 5000 times of permutation).

Data availability

All relevant data and codes have already been uploaded to an open data depository (https://github.com/huyangSISU/2021_Corruption, copy archived at https://archive.softwareheritage.org/swh:1:rev:97a721939333b7da7123baa6258057bdddfec5ef/).

References

  1. Book
    1. Abbink K
    (2006)
    Laboratory experiments on corruption
    In: Rose-Ackerman S, editors. International Handbook on The Economics Of Corruption. Edward Elgar Pub. pp. 418–437.
    1. Burnham KP
    2. Anderson DR
    (2004)
    Multimodel inference: understanding AIC and BIC in model selection
    Sociological Methods & Research 33:261–304.
  2. Book
    1. Graycar A
    2. Smith RG
    (2011)
    Handbook of Global Research and Practice in Corruption
    Edward Elgar Publishing.
    1. Lambsdorff JG
    (2006)
    Measuring corruption–the validity and precision of subjective indicators (CPI)
    Measuring Corruption 81:81.
    1. Mauro P
    (1995) Corruption and growth
    The Quarterly Journal of Economics 110:681–712.
    https://doi.org/10.2307/2946696
  3. Book
    1. Serra D
    2. Wantchekon L
    (2012)
    New advances in experimental research on corruption
    Emerald Group Publishing.
    1. Yin L
    2. Weber B
    (2018)
    I lie, why don't you: neural mechanisms of individual differences in self-serving lying
    Human Brain Mapping 40:1–13.

Decision letter

  1. Thorsten Kahnt
    Reviewing Editor; Northwestern University, United States
  2. Michael J Frank
    Senior Editor; Brown University, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This manuscript will be of interest for social psychologists and neuroscientists as it presents a first step toward understanding the neural correlates of decisions that involve corruption and harm third parties. The behavioral and neuroimaging data support the conclusion that different moral costs associated with such choices are correlated with activity in different brain areas (anterior insula and temporo-parietal junction), but integrated into a value signal in the ventromedial prefrontal cortex.

Decision letter after peer review:

Thank you for submitting your article "Neural basis of corruption in power-holders" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Michael Frank as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.

Summary:

This human fMRI study focuses on the behavioral and neural correlates of decisions involving fraud and corruption. In a novel behavioral task, subjects could either accept or reject offers from a proposer who divided a monetary payoff in a recommended or fraudulent way. The latter involves a bribe to the participant as well as harm to a third party. The key finding is that the two types of moral costs at stake here (norm violation and harming a third-party) correlate with activation in different brain regions (insula and TPJ).

All reviewers agreed that this study addresses an important question and that it is well executed, with an impressive set of analytical tools. They also agreed that the manuscript will be of interest to a broad readership. However, there was consensus on several important issues that need to be addressed in a revised version of the manuscript.

Revisions:

Reviewers identified several essential issues that need to be addressed before the manuscript can be accepted for publication in eLife:

1) There is a concern about selective reporting and cherry-picking. Findings should be reported more comprehensively in the main text and SI. And clarification is needed regarding how correction for multiple comparisons was applied (ROIs, models, etc.).

2) There were concerns about the lack of random effects in the mixed linear models for the analysis of behavioral data.

3) It would be important to present data to confirm that subjects believed the cover story as a basic manipulation check of the experiment.

4) Please include a more detailed plan for data and code sharing.

5) Please revise the framing of the manuscript.

6) The individual differences analysis is likely underpowered and the results should clearly be described as exploratory.

Reviewer #1:

This manuscript assesses the behavioral and neural correlates of decisions to accept an offer from a proposer who either follows a monetary payoff recommended by the computer (control condition) or lies by choosing the option not recommended by the computer (bribe condition). In addition, in a solo scenario, the proposer's offer to the participant does not harm anyone, while in a dyad condition it harms a third party whose payoff will be decreased as a consequence of the proposer behaving dishonestly by not following the computer choice. The authors report that these two moral costs (the proposer "lying" and the third party being harmed) both reduce the participants' propensity to accept the proposer's offer, and can be captured by two parameters in a computational model. Finally, a combination of univariate and multivariate analyses of the neuroimaging data is reported to identify how some of the model-predicted signals are encoded in the brain, and particularly in the anterior insula, TPJ, vlPFC and dlPFC.

This study is impressively executed, the manuscript is clearly written, and the topic of moral transgression and integration between dishonest behavior and third-party harm is novel and very relevant. However, I still have concerns that I would like to see addressed before I can recommend this manuscript for publication.

1) Study framing and ecological validity

a) Given the current climate in the world of misinformation spreading and the media tendency to misinterpret scientific results and jump to conclusions, I would recommend the authors to use a title and a framing that reflects more precisely the findings of their study. As they acknowledge, corruption is a very complex process, and what their task assesses is a small part of what can lead to corruption, namely the role of two forms of moral transgressions (fraud and third-party harm) in the decision to accept a bribe, and their representation and integration in the brain. Generally, I would refrain from using such a strong term as corruption, except maybe in the Discussion where the implications of the findings in light of understanding corruption can be brought up.

b) This concern mostly stems from the overall lack of ecological validity of the task used. Specifically, the proposer's behavior was fully controlled by the experimenter (and a cover story was used to pretend otherwise) and it is unclear whether participants fully believed the cover story or not. Was participants' belief that proposers and third parties were attendants of a previous study actually tested? If participants don't believe this cover story and instead suspect that everything is fictitious, their behavior would not constitute a moral transgression. In the dyad situation, it also seems like the third party will never know that they actually would have gotten a better outcome if the proposer hadn't lied. If that's indeed the case, that would make the current task less likely to mimic real-life situations where third parties are aware they were harmed (e.g. competitors for a project who do not get selected).

c) If I understand the design correctly, there was never a situation where the computer picked the low payoff option and the proposer also honestly reported this offer. This appears to me as a major drawback of the task as the participant could interpret the proposer's behavior as being simply value maximizing rather than fraudulent or dishonest. Also, while helpful for the analysis, the fact that essentially every single trial contained a bribe is concerning. If proposers' choices had been obtained in a previous online study, they would have likely looked very different than what is being displayed to the participants in the current task. Presumably, many proposers would have followed the computer choice even for the low payoff option, and/or would not have offered to split their offer by the indicated proportions. Additionally, it is likely that the share offered by the proposer would be higher in the bribe than in the control condition. I believe all these concerns should be acknowledged in the Discussion.

2) fMRI analysis and interpretation of results

Generally, I find that the fMRI results lack cohesion and a clear interpretation that tie them together, mostly due to the combination of many methods/GLMs used and not always justified, to the unclear process of selecting and using multiple regions of interest, and to potential confounds in the contrasts and regressors examined.

a) How many regions of interests were included? The authors state in the Materials and methods that they used a whole-brain cluster-level FWE correction at P<0.05, but most the key results are in fact small-volume corrected. It seems very unlikely that the authors had only one a priori region of interest in mind for each analysis they report. In the Introduction for example, they seem to focus on four key regions (vAI, TPJ, vmPFC, dlPFC), but other studies on dishonesty have reported potential roles of the amygdala (Garrett et al., 2016, Nature Neuroscience), or nucleus accumbens (Speer et al., 2020, PNAS) in dishonest behavior. Were other ROIs considered initially? If so, correction for the number of ROIs may be needed.

b) Given the design of the task, any activation contrasting the bribe vs control condition cannot distinguish between representing the proposer lying vs being honest and the computer recommending the low vs high payoff offer, as the proposer always lies when the computer chooses the low payoff. Because of this, I find the results in Figure 3A difficult to interpret. Why would we expect vAI to track expected personal profits positively in the DB condition but negatively in the SC condition, and not at all in the other two conditions? There also seems to be a main effect of scenario (dyad vs solo) on vAI tracking on expected profits, which is not discussed.

c) Why wasn't the expected loss to the third party added as a parametric modulator of the DB condition in GLM1a, thus allowing to control for the expected gains of the participant and the proposer? Is this because the loss to the third party is highly correlated with the gain of the proposer? If so, then those two signals can't be separated, and this should be addressed.

d) The rationale for examining vmPFC-dlPFC functional connectivity analyses is unclear to me. Was connectivity with other regions of interest, like TPJ or vAI, tested but not significant? If so, the authors should be clear about this and correct for the number of regions tested. Similarly, the Materials and methods section about the inter-subjects RSA suggests that several "hypothesized regions including bilateral dlPFC" were tested, but then the results focus exclusively on the dlPFC. If other regions were indeed tested, this should be clarified and accounted for.

e) The interpretation of the IS-RSA results in the Discussion is unclear, especially in what those results mean in and of themselves, and how they can be reconciled with the univariate dlPFC result and the vmPFC-dlPFC functional connectivity analysis.

Reviewer #2:

The reported study investigates the neural basis of corruption in social interactions. The main finding is that two types of moral costs in bribery (norm violation versus harming a third-party) correlate with activation in dissociable brain regions (insula versus TPJ). There is much to like about this well-written manuscript: it addresses an important question, and the experimental manipulations appear sound. The manuscript will certainly be of interest to a broad readership working on social interactions or on the neural basis of decision making. Nevertheless, I have a couple of concerns, particularly regarding data analysis, which the authors need to address in a revision.

1) The GLMs used for the fMRI analyses should be specified in more detail already in the Results section, not only in the Materials and methods. This would make it easier to understand which contrasts show significant activation in insula or TPJ. In general, I find the presentation of the imaging results rather confusing, mainly because a large number of analyses was computed (nine in total: GLM1a-c, GLM2a-b, GLM3, PPI, multivariate analysis, IS-RSA), and the authors often just selectively report one contrast of each GLM, while the results of other contrasts are not even reported in supplementary tables. This analysis approach raises questions regarding the robustness of the imaging results. For example, when testing the hypothesis that the TPJ shows enhanced activation in dyad versus solo scenarios, no significant results are observed in two contrasts for GLM1c, but only in an additional multivariate analysis. However, these non-significant results are ignored in the Discussion section, and the significant TPJ finding in the multivariate analysis is taken as evidence for the authors' hypothesis. It also remains unclear why the authors specified separate models with parametric modulators for personal benefits (GLM1a) and third-party loss (GLM1b), as in principle these variables could be modelled within one model. All this leaves the impression of cherry-picking and makes the imaging results appear less robust and convincing than as they are presented.

2) Regarding the mixed linear models for the analysis of behavioral data, the authors state that "factors allowing varying intercept across participants" were entered as random-effect predictors. Please be more precise regarding which random effects were specified in the model. It is generally recommended to maximize the random effects structure in order to minimize the risk of type I errors (Barr et al., 2013). As the current study seems to follow a strict within-subject design, I think that all fixed-effect predictors should be modelled as random slopes in addition to random intercepts

3) The task did not involve real social interactions, but the offers were computer-generated. I might have missed it, but it seems nowhere stated whether participants believed the cover story or not (was this assessed at all). In any case, the authors should add a caveat in the Discussion section clarifying that the social interactions in the study were only hypothetical.

Reviewer #3:

Dr. Hu et al. report a neuroimaging study of corruption in which (computer agent) proposers provide participants the opportunity to personally benefit from turning a blind eye to deception that in some cases has monetary costs for a third party. Through a series of computational models, the authors confirm that participants incur a moral cost, beyond inequity models, for engaging in the corrupt act, that is at its worst when a third party is injured. Each component of the model is then tied to a specific aspect of brain function that aligns with prior findings. The authors conclude that an inhibition mechanism is associated with reduced participation in corrupt acts.

I enjoyed the paper. It covers an interesting topic, is methodical, and shows a great deal of expertise in a wide range of methods.

1) Tests for the involvement of a particular brain region in a given step of the corruption decision are mixed without justification. GLM and MVPA analyses seem to be deployed as tests with increasing sensitivity rather than to test for computational differences. Not making a distinction is understandable since there is not a consensus on how each should be interpreted. However, this sort of variation on testing until a finding is reached can lead to overestimation of effect sizes and confirmation bias of previously proposed roles. Is there a reason beyond sensitivity for using multivariate models in some cases rather than others?

2) A number of GLMs are used to reach different conclusions presumably because of covariance issues between regressors. This, in and of itself, is not a problem if the covariance tables are shown and the repartitioning of common variance is acknowledged/interpreted.

3) Individual difference model – individual difference studies of forty participants assume a large effect size to be considered reliable. Most psychological (and biological indices) do not fall in this range. The result is fine as an exploratory analysis but that means it should be described as such.

4) Deception success measures. Was any data collected to confirm the believability of the proposer and third-party deception? Were participants debriefed afterwards?

5) The brain data is largely used to lend construct validity to the corruption task and confirm psychological interpretations of the processes involved in the decision. Ideally, the neural data would be used to adjudicate between two competing behavioral models (and/or behavioral data used to adjudicate between competing neural models). This somewhat lessens the utility of a promising neuroimaging dataset.

6) Complete analysis scripts (Main and SI, behavioral and neuroimaging) and minimally processed imaging data should be posted and referenced for the article. It is not clear that these fit in the 'source data' option for eLife without an accession number noted in the article.

https://doi.org/10.7554/eLife.63922.sa1

Author response

Revisions:

Reviewers identified several essential issues that need to be addressed before the manuscript can be accepted for publication in eLife:

1) There is a concern about selective reporting and cherry-picking. Findings should be reported more comprehensively in the main text and SI. And clarification is needed regarding how correction for multiple comparisons was applied (ROIs, models, etc.).

2) There were concerns about the lack of random effects in the mixed linear models for the analysis of behavioral data.

3) It would be important to present data to confirm that subjects believed the cover story as a basic manipulation check of the experiment.

4) Please include a more detailed plan for data and code sharing.

5) Please revise the framing of the manuscript.

6) The individual differences analysis is likely underpowered and the results should clearly be described as exploratory.

We thank the editor for summarizing these points which are key to our revisions. First, regarding the comprehensive report of our results, we have included new tables to describe them (see Supplementary File 1F-1I) and we have added more details to clarify how we implemented the ROI-based analyses (point #1). We have also performed additional analyses to address the random-slope issue (point #2). We agree with the reviewer’s concern about analyses of individual differences, and we have therefore re-framed this part as exploratory analyses (point #6). Regarding issues relevant to the cover-story and manuscript framing, we do admit the importance of these issues and we have incorporated them into the revised manuscript for an extensive discussion. We have also made a detailed response to justify the validity of the experimental setup (point #3) and the reason why we think the current framing worked (point #5). Last but not least, we fully embrace the open-and-transparency policy advocated by eLife and have already uploaded the most relevant data and codes to an open data depository copy archived at https://github.com/huyangSISU/2021_Corruption which will be public for readers who are interested in this study once the paper is accepted (point #4).

Reviewer #1:

This manuscript assesses the behavioral and neural correlates of decisions to accept an offer from a proposer who either follows a monetary payoff recommended by the computer (control condition) or lies by choosing the option not recommended by the computer (bribe condition). In addition, in a solo scenario, the proposer's offer to the participant does not harm anyone, while in a dyad condition it harms a third party whose payoff will be decreased as a consequence of the proposer behaving dishonestly by not following the computer choice. The authors report that these two moral costs (the proposer "lying" and the third party being harmed) both reduce the participants' propensity to accept the proposer's offer, and can be captured by two parameters in a computational model. Finally, a combination of univariate and multivariate analyses of the neuroimaging data is reported to identify how some of the model-predicted signals are encoded in the brain, and particularly in the anterior insula, TPJ, vlPFC and dlPFC.

This study is impressively executed, the manuscript is clearly written, and the topic of moral transgression and integration between dishonest behavior and third-party harm is novel and very relevant. However, I still have concerns that I would like to see addressed before I can recommend this manuscript for publication.

1) Study framing and ecological validity

a) Given the current climate in the world of misinformation spreading and the media tendency to misinterpret scientific results and jump to conclusions, I would recommend the authors to use a title and a framing that reflects more precisely the findings of their study. As they acknowledge, corruption is a very complex process, and what their task assesses is a small part of what can lead to corruption, namely the role of two forms of moral transgressions (fraud and third-party harm) in the decision to accept a bribe, and their representation and integration in the brain. Generally, I would refrain from using such a strong term as corruption, except maybe in the Discussion where the implications of the findings in light of understanding corruption can be brought up.

This is a critical point. On the one hand, we fully agree with the reviewer that the present study only adopted a simplified setting in the lab to simulate a certain type of corruption, which is actually a complicated issue that involves various forms and complex cognitive processes. We are also aware of the reviewer’s concern about possible misinterpretation by the media and its potential consequences. On the other hand, we believe that our design has successfully captured the essence of corruption, especially from the perspective of a person in power, and transferred it to an experimentally testable situation. As we stated in the Introduction, our design introduced an interpersonal context where a power-holder and a briber form a reciprocal relationship so that they can earn morally-tainted benefits together, sometimes at the expense of a third party. All these points are key components in defining bribe-taking behavior and distinguishing it from other types of immoral behaviors such as dishonesty, betrayal or aggression. Moreover, the inclusion of the Control condition reinforces that the observed behavioral and neural effects were specific to bribery-related decision-making rather than social decision-making in general.

Furthermore, given that almost all neuroimaging studies investigating the neural basis of a given cognitive process in social contexts are performed in a lab environment, changing the current title to something like ‘Neural basis of corruption in a lab setting’ is a statement of the obvious and would actually restrict our topics. In addition, if we focused on only the moral costs brought by bribe-taking and modify the title to something like “Neural basis of fraud and harm incurred by a third party during bribery-related decision-making”, it becomes needlessly too specific.

Taken together, we still believe that the framing of the current manuscript fits the theme and the scope of corruption. However, we do respect the reviewer’s concern, and we have now incorporated these points into the Discussion as a limitation, which reads as follows:

“Several issues concerning the present study need further discussion. First, although the present task captures the essence of corruption from the perspective of a person in power, it only simulates a specific type of corruption in a lab setting. The real phenomenon is far more complex and diverse. This obviously constrains the generalization of the current findings to corruption in field settings …”

Additionally, we will be very careful when communicating to the media not to misinterpret our results and to restrict its application to a lab situation.

b) This concern mostly stems from the overall lack of ecological validity of the task used. Specifically, the proposer's behavior was fully controlled by the experimenter (and a cover story was used to pretend otherwise) and it is unclear whether participants fully believed the cover story or not. Was participants' belief that proposers and third parties were attendants of a previous study actually tested? If participants don't believe this cover story and instead suspect that everything is fictitious, their behavior would not constitute a moral transgression. In the dyad situation, it also seems like the third party will never know that they actually would have gotten a better outcome if the proposer hadn't lied. If that's indeed the case, that would make the current task less likely to mimic real-life situations where third parties are aware they were harmed (e.g. competitors for a project who do not get selected).

The present task adopts a context without involving real interpersonal interactions (regardless of real partners or counterparts). Instead, we took a multi-round single-shot economic game, which is commonly used in neuroeconomics studies (e.g., Spitzer et al., 2007; Aimone et al., 2014; Hu et al., 2018). Compared with the interactive games that involve the same partner (or a few partners), repeatedly occurring during the experiment, the multi-round single shot games usually introduce a scenario in which participants are presented with choices collected from another independent group in a previous experiment. Although this kind of task, to some extent, reduced the ecological validity as the reviewer pointed out, we chose to use it here because each choice can be considered independent and this reduces the confounding effects of learning or concerns of reputation via repeated interactions with the same partner that might lead to different computations and neural mechanisms (e.g., strategic decision-making).

Given the experiment setting (or cover story) of the multi-round one-shot task, it would be impossible to invite all “partners” to the lab (i.e., proposers and third parties in the current task) to prove their existence, no matter whether they are real partners, confederates, or fictitious agents. In our view, these problems make such a belief test unfeasible. Moreover, asking such a question at the end of the experiment might also cause other unexpected issues. For example, participants who actually believe the cover story during the experiment might be prompted to doubt the cover story, simply because they were asked about it. For these reasons, studies in the literature of neuroeconomics that use this kind of task do not usually elicit participants’ belief after the experiment (see references mentioned above).

Following this tradition of previous studies, we did not explicitly ask whether participants believed our cover story. However, several lines of evidence converge to indicate that they actually believed it: (1) Before the task, all participants successfully passed the comprehension quiz; (2) The observed behavioral pattern (i.e., choices in the fMRI task and subjective rating afterward) conformed to our prediction that was built based on the assumption that participants took into account the morality of the proposer’s (briber’s) offer and the harm to the third party; (3) None of the participants raised any doubts with respect to the reality of the cover story when we debriefed them at the end of the experiment. In spite of that, we do think the lack of real social interactions in the present study is an important point to address and thus we have added it to the Discussion as follows:

“…Third, the present task adopted a multi-round single-shot economic game that did not involve any real partner physically present during the experiment. We decided to use such an experimental setting not only because it is commonly used in neuroeconomics studies (e.g., Spitzer et al., 2007; Aimone et al., 2014; Hu et al., 2018) but also because, here, each choice can be considered independent, thus alleviating the potentially confounding effects of learning and concerns of reputation in repeated interactions. Although the lack of social interactions in the task might diminish the involvement of participants and influence their beliefs about the authenticity of the experimental setting, no participants explicitly raised doubts about the reality of the cover story and our behavioral results were consistent with the predictions which were built on the assumption that participants believed the existence of proposers and third parties. Nevertheless, the concerns raised above cannot be completely ruled out …”

The Dyad scenario was designed in terms of the sender-receiver game, a classical economic game used for investigating deception (for details of the game, see Gneezy 2005). In this game, only the sender (i.e., the proposer in the current task) was informed about the full information of the payoffs, which was unknown to the receiver (i.e., the third party in the current task). While in some cases, as the reviewer pointed out, third parties are aware they have been harmed because of corruption, it is also very frequent that third parties remain ignorant of bribery-related harm. This is especially the case when third parties have lower social status, which isolates them from the truth behind the scenes. For instance, a real estate developer could bribe a government official who may turn a blind eye when the developer uses substandard materials on public housing development. The families who subsequently live in these apartments would remain ignorant that they had been cheated and those substandard materials might harm their quality of life and even health.

c) If I understand the design correctly, there was never a situation where the computer picked the low payoff option and the proposer also honestly reported this offer. This appears to me as a major drawback of the task as the participant could interpret the proposer's behavior as being simply value maximizing rather than fraudulent or dishonest. Also, while helpful for the analysis, the fact that essentially every single trial contained a bribe is concerning. If proposers' choices had been obtained in a previous online study, they would have likely looked very different than what is being displayed to the participants in the current task. Presumably, many proposers would have followed the computer choice even for the low payoff option, and/or would not have offered to split their offer by the indicated proportions. Additionally, it is likely that the share offered by the proposer would be higher in the bribe than in the control condition. I believe all these concerns should be acknowledged in the Discussion.

As the reviewer pointed out, the current task did not contain a condition where the proposer honestly reported the option with a lower payoff randomly selected by the computer. While it would definitely be possible to add this kind of trial, we did not do this purposely to maintain the symmetry of the experimental setting. For the sake of a balanced design, such trials should be incorporated into both the control and bribe condition. However, this would result in completely irrational behavior by the proposer if we were to do so. Take the Solo scenario for example. While the proposer’s motivation is clear in the Control condition when honestly reporting a computer-chosen disadvantageous option (i.e., the proposer honestly chose less profits for himself), this would lead to a counter-intuitive situation in the Bribe condition when the proposer chose a disadvantageous option when an advantageous option was selected by the computer (i.e., the proposer cheated to obtain less profits for himself). Such situations would make the cover story bizarre because it would be very difficult for participants, as power-holders, to understand the motivation underlying such “white lies” in this situation (i.e., cheating to benefit others). Furthermore, adding such trials in either condition to the current task would make it meaningless to propose offers (bribes) for the power-holder because doing so would make the proposer earn even less (compared with reporting the option with a larger payoff for himself). Thus, the inclusion of such trials would have conflicted with the logic of a (fictive) proposer who always wants to optimize his gains. In addition, adding these trials would inevitably prolong the duration of the experiment, fatiguing participants more, and thus perhaps reducing the quality of the data we collected.

Despite these reasons for not including such trials, we agree with the reviewer’s concern about the potential drawback of the present design, and we therefore added these points to the Discussion, which now reads as follows:

“… Second, the present task did not contain a condition in which the proposer honestly reported the option with the lower payoff having been randomly selected by the computer. We did this purposely to preserve the symmetry of the experimental design and to maintain the motivation of proposers’ behavior to being entirely rational and explainable by the optimization of personal profits. Thus the offer proposition, in either the Bribe or the Control condition, was realistic for the proposer, i.e., it would not make sense if the proposer proposes an offer to earn a lower payoff. In addition, adding such trials to the current task would inevitably prolong the duration of the experiment, which might make participants more tired and thus influence the quality of the data we collected.”

2) fMRI analysis and interpretation of results

Generally, I find that the fMRI results lack cohesion and a clear interpretation that tie them together, mostly due to the combination of many methods/GLMs used and not always justified, to the unclear process of selecting and using multiple regions of interest, and to potential confounds in the contrasts and regressors examined.

a) How many regions of interests were included? The authors state in the Materials and methods that they used a whole-brain cluster-level FWE correction at P<0.05, but most the key results are in fact small-volume corrected. It seems very unlikely that the authors had only one a priori region of interest in mind for each analysis they report. In the Introduction for example, they seem to focus on four key regions (vAI, TPJ, vmPFC, dlPFC), but other studies on dishonesty have reported potential roles of the amygdala (Garrett et al., 2016, Nature Neuroscience), or nucleus accumbens (Speer et al., 2020, PNAS) in dishonest behavior. Were other ROIs considered initially? If so, correction for the number of ROIs may be needed.

As reported in the Introduction, we adopted a total of 4 ROIs (vAI, TPJ, vmPFC and dlPFC) based on clear hypotheses with regard to the specific analyses throughout the whole study. Please allow us to re-explain our key hypotheses. First, we investigated how different forms of moral costs brought by bribe-taking were encoded in the brain of a power-holder. Specifically, we came up with two analyses. On the one hand, we tested whether the moral cost of conniving with a fraudulent proposer altered the valuation of the expected personal gains due to the acceptance of the bribe (GLM1a). In the moral domain, vAI is known to be engaged when social norms or moral principles are violated. For instance, a stronger vAI signal has been observed when people are treated unfairly (Sanfey et al., 2003) or deceived by another person (Yin and Weber, 2015). Such negative affect produced by vAI is considered to drive the enforcement of moral norms (Bellucci et al., 2018) such as fairness (Gao et al., 2018) and honesty (Yin et al., 2017; Yin and Weber, 2018). Thus, we hypothesized that the vAI is more engaged in representing the “dirty” personal profits (i.e., expected personal gains from accepting offers in the Bribe vs. Control condition).

On the other hand, we tested how the moral cost of harming a third party, reflected by the expected loss to the third party due to the acceptance of the bribe, was represented in the brain (GLM1b). Substantial evidence has shown that the temporoparietal junction (TPJ) is crucially engaged in representing the mind of others (Schurz et al., 2014), and thus contributes to the trade-off between self- interest and the welfare of others (Hutcherson et al., 2015; Morishima et al., 2012; Obeso et al., 2018). The TPJ is also more active when one’s decision impact a person who is in a disadvantageous condition, such as charity donations (Tusche et al., 2016) and costly helping behavior (Hu et al., 2018). These findings suggested that the TPJ would be sensitive to the moral cost of the bribe-induced financial losses incurred by a third party. We next investigated how these moral costs are integrated with other decision components into a neural value signal during bribery-related decision-making (GLM2). Given the well-established neural account of the ventromedial prefrontal cortex (vmPFC) in value computation (Levy and Glimcher, 2012; Bartra et al., 2013; Ruff and Fehr, 2014), it was natural to predict that vmPFC would be recruited in computing the decision value by integrating various components associated with corruption-related actions (e.g., personal gains and moral costs of taking the bribe).

We also aimed to link the neural signature during bribery-related decision-making to specific choices (i.e., Bribe(reject – accept) – Control(reject – accept), hereafter referred as “the anti-corruption signal”) and to identify brain regions sensitive to the contextual modulation (i.e., Dyad vs. Solo) on the anti-corruption signal (GLM3). The dorsolateral prefrontal cortex (dlPFC) is well known to play a pivotal role in guiding various ethical behaviors concerning fairness (Knoch et al., 2006), justice (Buckholtz et al., 2015), honesty (Zhu et al., 2014; Marechal et al., 2017), and harm (Crockett et al., 2017). Based on these findings, a recent theory posits that the dlPFC is key to flexibly support the pursuit of moral goals in a context-dependent manner (Carlson and Crockett, 2018). Thus, the dlPFC was clearly expected to be engaged in specific choices during bribery-related decision-making and might further be modulated by the specific bribery scenarios.

For all analyses above, a small volume correction (SVC) was conducted within the hypothetical ROIs, as reported in the fMRI data analyses section. Since we did not consider other regions of interest for these analyses, it is unnecessary to correct the number of ROIs in any of our analyses.

As pointed out by the reviewer, we agree that there are other regions that also play some roles in moral behaviors. One of the other “candidate” regions proposed by the reviewer is amygdala, a region typically known as a key hub for processing (negative) basic emotion information (Phelps and LeDoux, 2005). In a recent paper by Garret and others (2016), activity in this region has been found to decrease as dishonest behaviors are repeated (i.e., an adaptation effect of dishonesty). This novel finding was interpreted in terms of an adaptation account, which proposed that the amygdala’s response to an emotion-evoking stimulus weakens with repeated exposure. However, this finding is far from any of the research questions that we aimed to test in our analyses. Moreover, it should be noted that this is the only study so far, to our knowledge, identifying a central role of amygdala in (im)moral behaviors. The same effect was rarely reported in follow-up studies. For these reasons, we did not hypothesize the involvement of amygdala in the current task and we found it was difficult to associate this region with any of our analyses.

Regarding another “candidate” region, the nucleus accumbens (NAcc), this region was typically known as a hub for processing different types of reward (Haber and Knutson, 2009). Consistent with this account, NAcc is found to encode the reward magnitude earned by deception in a recent study (Speer et al., 2020), which was subject to the individual level of cheating behaviors. Although the NAcc was not on the list of our initial interest, we thought that, inspired by this finding, it might also be interesting to examine whether the decision-related neural signal of the NAcc during bribery-related decision-making could predict the corrupt behaviors across individuals (based on GLM1c), in post-hoc analyses. To this end, we built up contrasts of bribery-related decision-making for each individual in two separate scenarios (i.e., contrast 1: SB – SC; contrast 2: DB – DC) and the contrast that averages across scenarios (i.e., contrast 3: Bribe – Control), based on GLM1c. Correspondingly, we calculated the acceptance rate in the Bribe condition in each scenario (i.e., SB or DB) or average acceptance rates across scenarios for each individual. Then, we performed a group-level regression analysis with the brain contrast as the dependent measure and the acceptance rate as the predictor, focusing on the NAcc as ROI (defined in the same way as we used for other ROI analyses). However, we did not find that the decision-related NAcc signal specific to corruption was correlated to the acceptance rate of offers in the Bribe condition across individuals in any case (see Author response table 1).

Author response table 1
Relationship between decision-related NAcc activity specific to corrupt decision-making with acceptance rate of bribes across individuals.
Brain Contrast (Y)Predictor (X)Results
SB – SCAcceptance rate of offers in the SB conditionPositive: No activated voxels
Negative: No activated voxels
DB – DCAcceptance rate of offers in the DB conditionPositive: Peak MNI: 12/2/2 t(37) = 3.28, p(SVC-FWE) = 0.157
Negative: No activated voxels
bribe – controlMean acceptance rate of offers across SB and DB conditionsPositive: Peak MNI: 6/10/0, t(37) = 2.72, p(SVC-FWE) = 0.243
Negative: No activated voxels

Note: These contrasts were built based on GLM1c. We adopted the voxel-level family-wise error correction (FWE) within the search volume of NAcc (small volume correction, SVC).

Abbreviations: SC: Solo Control, SB: Solo Bribe, DC: Dyad Control, DB: Dyad Bribe.

To sum up, we believe that all predictions concerning specific analyses connecting to specific ROI can be justified based on the evidence mentioned above. With regard to the two “candidate” regions proposed by the reviewer, we argue that their roles in moral decision-making were less typical and actually had little to do with the target behavior and contextual modulators in the current study. Hence we hypothesized neither of them in our analyses.

b) Given the design of the task, any activation contrasting the bribe vs control condition cannot distinguish between representing the proposer lying vs being honest and the computer recommending the low vs high payoff offer, as the proposer always lies when the computer chooses the low payoff. Because of this, I find the results in Figure 3A difficult to interpret. Why would we expect vAI to track expected personal profits positively in the DB condition but negatively in the SC condition, and not at all in the other two conditions? There also seems to be a main effect of scenario (dyad vs solo) on vAI tracking on expected profits, which is not discussed.

There are three key points we would like to clarify. First, we did not perform the contrast between the Bribe and the Control condition using the onset regressor of the decision event. Instead, we compared the parametric modulator (PM) of the expected personal gains between the Bribe and the Control condition. The main focus of GLM1a is to identify, in a power-holder, which brain regions specifically encode the expected personal gains resulting from the acceptance of bribes.

Second, the PM of expected personal gains used in GLM1a did not change depending on the option selected by the computer. Instead, it referred to the amount of money involved in the offer proposed by the proposer, which is always calculated based on a certain proportion of the large payoff reported by the proposer (i.e., the computer chosen option in the Control condition or the computer non-chosen option in the Bribe condition). This ensures values of PM exactly the same in the Bribe and the Control condition. Take the following case in the Solo scenario as example: according to the current study, the fictive proposer (E.L.) could propose an offer based on the reported payoff for the participant (as a power-holder), which was always 96 CNY in either condition as indicated by the blue arrow. (see Figure 1B).

Third, we did not form such a specific prediction about the ventral anterior insula (vAI) as the reviewer mentioned in the comment. What we actually hypothesized is that vAI is more engaged in representing the expected personal gains from accepting offers in the Bribe condition (i.e., PM contrast: Bribe vs. Control), as we delineated in the Introduction based on previous literature. Given this hypothesis, we compared the PM of expected personal gains between the Bribe and the Control condition with a focus on vAI, and found that this region (especially the left side) was indeed more sensitive to the expected personal gains from a fraudulent proposer (i.e., Bribe vs. Control). This result thus conforms to our hypothesis. We extracted the parameter estimates (i.e., contrast values of the activated vAI cluster) only for visualization and we are not trying to interpret why the vAI tracks expected personal profits negatively in the Solo Control (SC) condition.

According to the reviewer’s suggestion, we investigated whether there is a main effect of scenario (Dyad vs. Solo) in vAI. To do this, we compared the PM of expected personal gains between the Dyad and the Solo scenario, within the search volume of vAI. We found that the right vAI displayed an increased sensitivity to the expected personal gains in the Dyad (vs. Solo) scenario (peak MNI: 36/14/-14; t(114) = 3.86, p(SVC-FWE) = 0.022), while the left vAI showed a similar trend that did not reach statistical significance (peak MNI: -36/10/-16; t(114) = 3.09, p(SVC-FWE) = 0.146;). We have now brought up this point in the Discussion, which reads as follows:

“… Interestingly, we also observed that the vAI (especially the right part) is more engaged in encoding expected personal gains in the Dyad (vs. Solo) scenario. The vAI plays a critical role in guiding dishonest decisions under various circumstances (Yin et al., 2017) and in perceiving other’s dishonest intentions (Yin and Weber, 2015). These findings can be broadly linked to the modulation of aversive feelings by vAI, that generate motivation to social norm enforcement (Bellucci et al., 2018). Our results show that a key computation performed by the vAI signal is to encode bribery-related profits, especially when a potential victim is involved in the social context. This signal might reflect an aversive feeling towards moral transgression brought by bribe-taking behavior, which could contribute to (but not necessarily lead to) preventing powerholders from being corrupted.…”

c) Why wasn't the expected loss to the third party added as a parametric modulator of the DB condition in GLM1a, thus allowing to control for the expected gains of the participant and the proposer? Is this because the loss to the third party is highly correlated with the gain of the proposer? If so, then those two signals can't be separated, and this should be addressed.

We chose not to incorporate the PM of expected loss to the third party into the GLM1a because this GLM aims to identify brain regions specifically encoding the expected gains due to bribe-taking (regardless of whether or not a third party is present). For this reason, it is preferable to keep the design matrix balanced so that each onset regressor of the decision event is attached with the exact same PMs (i.e., PM of expected gains for the proposer and the participant). Since the expected loss to the third party only takes place in the Dyad Bribe (DB) condition, adding this PM to GLM1a would leave the design matrix unbalanced between conditions which might bias the results with regard to the main focus of GLM1a.This information has now been added to the Materials and methods.

To comply with the reviewer’s suggestion, we have now investigated the brain regions tracking the expected loss to the third party while controlling for the effect of expected gains (either for the proposer or the participant). To this end, we ran an additional GLM analysis in which we not only added the PM of the expected loss (as we did in GLM1b; the first PM) but also attached the PMs of expected gains for the proposer and the participant to the onset regressor of the decision event in the DB condition (the second and third PM). Here we adopted the default orthogonalization by SPM12 to control for the co-linearity between these PMs. The results showed a significant parametric modulation of the expected loss to the third party in the right TPJ even after controlling for the other two PMs (peak MNI: 54/-48/-4; t(38) = 4.63, p(SVCFWE) = 0.012). We have added this information in the Materials and methods and Results (see Figure 3—figure supplement 2).

d) The rationale for examining vmPFC-dlPFC functional connectivity analyses is unclear to me. Was connectivity with other regions of interest, like TPJ or vAI, tested but not significant? If so, the authors should be clear about this and correct for the number of regions tested. Similarly, the Materials and methods section about the inter-subjects RSA suggests that several "hypothesized regions including bilateral dlPFC" were tested, but then the results focus exclusively on the dlPFC. If other regions were indeed tested, this should be clarified and accounted for.

One of the questions we wanted to address was how is value computation linked to the final decision in a social context involving corrupt acts (i.e., the power-holder’s bribe-taking behavior) at the brain system level. The previous literature strongly suggested a crucial role of the vmPFC in value computation (Levy and Glimcher, 2012; Bartra et al., 2013; Ruff and Fehr, 2014) and of the dlPFC in guiding various types of decisions (Tanji and Hoshi, 2008; Figner et al., 2010; Ruff et al., 2013; Marechal et al., 2017). Importantly, recent work has highlighted the modulatory role of dlPFC on the value signal in vmPFC, especially when the decision-making process requires individuals to employ self-control to inhibit the impulse to choose immediate rewards (vs. long-term rewards; Hare et al., 2009; 2014) or personal profits (vs. moral values; Baumgartner et al., 2011; Dogan et al., 2016). Based on this evidence, we hypothesized that we might observe a stronger vmPFC-dlPFC functional coupling during the decision period in the Bribe (vs. control) condition, in which participants as power-holders need more self-control to overcome the lure of accepting bribes that result in moral costs. Hence, we tested this hypothesis using psycho-physiological interaction (PPI) with vmPFC as the seed region and dlPFC as the only region of interest (ROI). Since we did not hypothesize that the functional coupling between vmPFC and other regions (e.g., TPJ or vAI) changed during bribery-related decision-making, we did not implement the analyses using these regions as ROIs.

Similarly, in the inter-subject representational similarity analysis (IS-RSA), we explicitly treated it as an exploratory analysis because IS-RSA is a fairly novel methodological approach in the neuroscience literature. Nevertheless, we still focused on the dlPFC in this analysis because recent evidence on social and moral decisionmaking indicated that the decision-related signal in dlPFC could be modulated by individual variations of self-serving dishonesty (Dogan et al., 2016; Yin and Weber, 2018) and harm aversion (Crockett et al., 2017), which are related with the two types of moral costs measured in the current task.

We have incorporated more detailed statements about the rationale underlying our hypotheses and analyses in the Introduction, which reads as follows:

“… More intriguingly, recent work has highlighted the modulatory role of dlPFC on the value signal in vmPFC when the decision-making process requires individuals to exert self-control to inhibit the impulse to choose immediate rewards (vs. long-term rewards; Hare et al., 2009; Hare et al., 2014) or personal profits (vs. moral values; Baumgartner et al., 2011; Dogan et al., 2016). […] Hence, we performed an exploratory analysis, again with a focus on the dlPFC given the evidence above, to probe whether such a relationship exists, in a bribery setting, by applying a multivariate approach.”

e) The interpretation of the IS-RSA results in the Discussion is unclear, especially in what those results mean in and of themselves, and how they can be reconciled with the univariate dlPFC result and the vmPFC-dlPFC functional connectivity analysis.

We thank the reviewer for pointing this out, and we agree that it is necessary to add more details with regard to the methodological rationale behind IS-RSA which helps to improve the understanding of this result. As a novel analytical approach, IS-RSA applies RSA to uncover the neural-behavioral relationship across individuals. Compared with the mass-univariate approach, IS-RSA allows us to associate multidimensional behavioral measures with a geometric representation of information based on multi-voxel neural patterns across individuals, rather than simply linking a single behavioral measure with averaged activities across voxels in a certain region (Kriegeskorte et al., 2008; Popal et al., 2020).

Here, with the help of IS-RSA, we were able to map differences in neural signals during bribery-related decision-making (i.e., Bribe vs. Control) directly onto our behavioral model characterizing corrupt behaviors of a power-holder, which contains two key parameters capturing different types of moral costs (i.e., θ: the moral cost brought by conniving with a fraud committed by a proposer; ω: the moral cost brought by harming the interest of a third party). Given the previous literature (see our reply to comment 2d), we focused on dlPFC and tested whether the neural patterns during bribery-related decision-making in dlPFC were similar for participants who showed similar context-dependent corrupt behaviors, reflected by bribery-specific preferences (i.e., an RDM built upon θ and ω across individuals). Our results showed that this is indeed the case, which provides novel evidence for the role of dlPFC in representing geometric information concerning a multidimensional model of moral preferences across participants.

In our view, all these dlPFC-related results provide evidence for a critical role of dlPFC in multiple aspects of bribery-related decision-making. The univariate result (based on GLM3), from a within-subject perspective, showed an increased anticorruption dlPFC signal in the Dyad (vs. Solo) scenario. This result suggests that dlPFC plays a specific role in guiding the choice of rejecting bribes that specifically involved harm to a third party. Both the functional connectivity (PPI) results and the IS-RSA results concerned the between-subject perspective. Compared with the IS-RSA result (see our interpretations above), the PPI result provides evidence from a brain network angle and suggests that the modulatory role of dlPFC on the value signal in vmPFC during bribery-related decision-making was sensitive to individual variations when considering the moral cost brought by conniving with a fraud committed by a proposer. In general, these findings indicate that the dlPFC signature, or pattern, might reflect the preference to take or refuse a bribe across individuals as power-holders.

Following the reviewer’s suggestions, we have incorporated these points in the revised manuscript accordingly (Discussion), which hopefully clarify the interpretation of the IS-RSA results and its relationship with the PPI results. Now it reads as follows:

“… Notably, all these results provide evidence for a critical role of the dlPFC in different aspects of bribery-related decision-making. The univariate result (based on GLM3) suggests that, from a within-subject perspective, the dlPFC plays a dedicated role in guiding the choice to reject bribes in a context-dependent manner. Both the functional connectivity results and the IS-RSA results concern the between-subject perspective. These findings indicate that the dlPFC signature (or pattern) might reflect the preference of whether to accept or refuse a bribe across individuals as powerholders. These results are consistent with a causal role of the dlPFC in ethical behaviors (Maréchal et al., 2017; Zhu et al., 2014), and may inspire future studies to investigate whether such a causal role extends to corrupt behaviors and how it varies between individuals.”

Reviewer #2:

The reported study investigates the neural basis of corruption in social interactions. The main finding is that two types of moral costs in bribery (norm violation versus harming a third-party) correlate with activation in dissociable brain regions (insula versus TPJ). There is much to like about this well-written manuscript: it addresses an important question, and the experimental manipulations appear sound. The manuscript will certainly be of interest to a broad readership working on social interactions or on the neural basis of decision making. Nevertheless, I have a couple of concerns, particularly regarding data analysis, which the authors need to address in a revision.

1) The GLMs used for the fMRI analyses should be specified in more detail already in the Results section, not only in the Materials and methods. This would make it easier to understand which contrasts show significant activation in insula or TPJ. In general, I find the presentation of the imaging results rather confusing, mainly because a large number of analyses was computed (nine in total: GLM1a-c, GLM2a-b, GLM3, PPI, multivariate analysis, IS-RSA), and the authors often just selectively report one contrast of each GLM, while the results of other contrasts are not even reported in supplementary tables. This analysis approach raises questions regarding the robustness of the imaging results. For example, when testing the hypothesis that the TPJ shows enhanced activation in dyad versus solo scenarios, no significant results are observed in two contrasts for GLM1c, but only in an additional multivariate analysis. However, these non-significant results are ignored in the Discussion section, and the significant TPJ finding in the multivariate analysis is taken as evidence for the authors' hypothesis. It also remains unclear why the authors specified separate models with parametric modulators for personal benefits (GLM1a) and third-party loss (GLM1b), as in principle these variables could be modelled within one model. All this leaves the impression of cherry-picking and makes the imaging results appear less robust and convincing than as they are presented.

We thank the reviewer for this suggestion. Following this suggestion, we have updated the relevant part before we introduced each of our fMRI results to specify the details of GLMs used for the fMRI analyses, which reads as follows:

“We implemented the general linear model (GLM) analyses to test specific hypotheses concerning different research questions (see Materials and methods for details of GLM analyses). […] To simplify the analysis, we computed the neural activity specific to rejecting as opposed to accepting offers (i.e., reject vs. accept) in all four conditions and then defined the anti-corruption neural signals with such rejection-specific neural activity in the Bribe condition (i.e., contrast: Bribe(reject – accept) – Control(reject – accept))…”.

With regard to the rationale underlying our analyses mentioned in the current manuscript, we argued that each analysis has its own goal with a clear hypothesis, based on previous literature. Briefly, both GLM1a and GLM1b investigated how different forms of moral costs were encoded in the brain of a power-holder. More specifically, GLM1a tested whether the moral cost of conniving with a fraudulent proposer altered the valuation of the expected personal gains, due to the acceptance of the bribe. This was focused on the vAI as the region of interest (ROI). GLM 1b examined how the moral cost of harming a third party, reflected by the expected loss to the third party due to the acceptance of the bribe, was represented in the brain, with a focus on the TPJ as ROI. The decoding analysis was only used to corroborate the TPJ finding from a multivariate perspective. GLM2 aimed to investigate how these moral costs were integrated with other decision components into a neural value signal during bribery related decision-making, with a focus on vmPFC. GLM3 also aimed to link the neural signature during bribery-related decision-making to specific choices (i.e., accept or reject) and examine how this neural signal was modulated by the scenario (i.e., Dyad vs. Solo), with a focus on the dlPFC. While the GLM analyses focused more on the within-subject experimental effect on local activations, the PPI analysis was planned to examine the functional coupling between brain regions (i.e., vmPFC and dlPFC) during bribery-related decision-making (Bribe vs. Control) from a network perspective. Lastly, the IS-RSA concerned the between-subject effect, which allowed us to map differences in neural signals of bribery-related decision-making (Bribe vs. Control) directly onto a multi-dimensional space of model parameters that characterized the context-dependent corrupt behaviors. Although this latter analysis is exploratory, we still focused on the dlPFC, given its critical role in representing moral preferences and behaviors across individuals, as proposed in the literature, taking a univariate approach.

While it is theoretically possible to model all these parametric modulators (PM) within one GLM, we chose not to do so because GLM1a aims to identify brain regions specifically encoding the expected gains due to bribe-taking. For this reason, it would be better to keep the design matrix balanced so that each onset regressor of the decision event is attached with the exact same parameter modulators (i.e., PM of expected gains for the proposer and the participant). Since the expected loss to the third party only took place in the Dyad Bribe (DB) condition, adding this PM to GLM1a would leave the design matrix unbalanced between conditions which might bias the results. However, as the reviewer suggested, we performed an additional GLM analysis and found that the right TPJ tracked the expected loss to the third party, after controlling for the effect of expected gains for either the proposer or the participant (see Figure 3—figure supplement 2 and Supplementary File 1F).

Regarding the missing GLM contrasts, it is highly likely that no region survived even under a lenient whole-brain threshold (p < 0.001 uncorrected at the voxel-level) so that we had no significant results to report. However, we agree that this might cause the impression of cherry-picking, and thus we have reported all possible GLM contrasts in Supplementary File 1F-1I.

2) Regarding the mixed linear models for the analysis of behavioral data, the authors state that "factors allowing varying intercept across participants" were entered as random-effect predictors. Please be more precise regarding which random effects were specified in the model. It is generally recommended to maximize the random effects structure in order to minimize the risk of type I errors (Barr et al., 2013, Journal of Memory and Language). As the current study seems to follow a strict within-subject design, I think that all fixed-effect predictors should be modelled as random slopes in addition to random intercepts

We agree with the reviewer’s suggestion and have re-done all the mixed-effect analyses as suggested (see the Materials and methods and the Results).

3) The task did not involve real social interactions, but the offers were computer-generated. I might have missed it, but it seems nowhere stated whether participants believed the cover story or not (was this assessed at all). In any case, the authors should add a caveat in the Discussion section clarifying that the social interactions in the study were only hypothetical.

As the reviewer pointed out, the present study did not adopt a task involving real interactions but a multi-round, single-shot economic game instead. This allowed us to treat each decision independently and thus to rule out the confounding effect of learning and reputation concerns via repeated interactions with the same partner. Since this concern was also raised by reviewer #1, please find our responses above that explains why we did not formally test whether participants believed our cover story and what evidence that converges to indicate that the participants were likely to have believed the experimental setting.

Despite that, we agree with the reviewer that the lack of real social interaction in the present study is an important point and we have added it as a caveat to the Discussion.

Reviewer #3:

Dr. Hu et al. report a neuroimaging study of corruption in which (computer agent) proposers provide participants the opportunity to personally benefit from turning a blind eye to deception that in some cases has monetary costs for a third party. Through a series of computational models, the authors confirm that participants incur a moral cost, beyond inequity models, for engaging in the corrupt act, that is at its worst when a third party is injured. Each component of the model is then tied to a specific aspect of brain function that aligns with prior findings. The authors conclude that an inhibition mechanism is associated with reduced participation in corrupt acts.

I enjoyed the paper. It covers an interesting topic, is methodical, and shows a great deal of expertise in a wide range of methods.

1) Tests for the involvement of a particular brain region in a given step of the corruption decision are mixed without justification. GLM and MVPA analyses seem to be deployed as tests with increasing sensitivity rather than to test for computational differences. Not making a distinction is understandable since there is not a consensus on how each should be interpreted. However, this sort of variation on testing until a finding is reached can lead to overestimation of effect sizes and confirmation bias of previously proposed roles. Is there a reason beyond sensitivity for using multivariate models in some cases rather than others?

We apologize for the lack of justification of the multivariate analyses used in the present study. In fact, we adopted this approach to address two specific questions. The first, (i.e., the decoding analysis), concerns whether the temporo-parietal junction (TPJ) extending to the posterior temporal sulcus (pSTS) was selectively engaged in briberyrelated decision-making that involved harm (i.e., financial losses) incurred by a third party (i.e., the Dyad scenario). Notably, this analysis was originally implemented as a supplementary analysis to corroborate the univariate finding (see Figure 3B). The second, (i.e., the inter-subject representational similarity analysis, IS-RSA), concerns how the preferences for bribery, characterized by two parameters, reflecting different forms of moral costs brought by bribe-taking behaviors derived from a computational model, were represented in the brain across individuals.

It is indeed correct that multivariate analyses, compared with the univariate analyses, generally use more information and thus increase the sensitivity to detect meaningful differences (Hebart and Baker, 2018; Haynes, 2015). However, this is not the main reason that led us to adopt multivariate analyses. In fact, these different approaches were used because they provide rich results that complement each other. In particular, the decoding analysis revealed that TPJ/pSTS work differently during bribery-related decision-making between the Solo and the Dyad scenario by showing distinct activation patterns (but possibly not the mean activation intensity across voxels).

Moreover, the IS-RSA allowed us to map differences in neural signals of bribery-related decision-making (i.e. with a focus on the dlPFC) directly onto a multi-dimensional space of model parameters that characterize the context-dependent corrupt behaviors. Taken together, we believe the use of multivariate approaches, together with other analyses, provides novel evidence regarding the role of TPJ/pSTS and dlPFC in complex social behaviors.

2) A number of GLMs are used to reach different conclusions presumably because of covariance issues between regressors. This, in and of itself, is not a problem if the covariance tables are shown and the repartitioning of common variance is acknowledged/interpreted.

It is perfectly true that due to the present design, some of the payoff-related parametric modulators (PM) are correlated (see Supplementary File 1K). Although multi-colinearity between predictors (PMs) biases the estimation of the GLM to some extent and was indeed one reason driving us to implement separate GLMs, the other reason for not incorporating all payoff-related PMs into one GLM1 was that GLM1a aims to identify brain regions that specifically encode the expected gains due to bribetaking behavior. Therefore, we decided to keep the design matrix balanced so that each onset regressor of the decision event was attached with the exact same PMs (i.e., the PM of expected gains for the proposer and the participant). Since the expected loss to the third party only took place in the Dyad Bribe (DB) condition, adding this PM to GLM1a would leave the design matrix unbalanced between conditions which might bias the results (see the Materials and methods and the Results).

As suggested by the reviewer, we have incorporated the correlation table between three key payoff-related PMs in Supplementary File 1K and acknowledged this point in the Materials and methods accordingly.

3) Individual difference model – individual difference studies of forty participants assume a large effect size to be considered reliable. Most psychological (and biological indices) do not fall in this range. The result is fine as an exploratory analysis but that means it should be described as such.

We reported two analyses with regard to individual differences. Although we agree that the results of inter-individual difference analyses may not be reliable given the sample size, we implemented these analyses with specific goals. As mentioned earlier, we explicitly framed the IS-RSA as an exploratory analysis, because it is a novel analytical approach that emerges in recent fMRI literature. However, we still focused on the dlPFC in this analysis because recent evidence reveals the link between decisionrelated signals in dlPFC and individual variations of self-serving dishonesty (Dogan et al., 2016; Yin and Weber, 2018) and harm aversion (Crockett et al., 2017), which are related with the two types of moral costs measured in the current task (see the Introduction).

Unlike the IS-RSA, we actually had a clear prediction in the functional connectivity analysis using PPI, that the vmPFC-dlPFC connectivity would be enhanced during bribery-related decision-making (Bribe vs. Control). This prediction was based on previous literature concerning economic and social decision-making that requires selfcontrol (Hare et al., 2009; 2014; Baumgartner et al., 2011; Dogan et al., 2016). Although our expected result was not observed, we showed that such vmPFC-dlPFC connectivity during bribery-related decision-making was modulated by individual differences in the moral cost of conniving with the fraudulent briber. Taken together, we have clarified this point and revised statements in the Introduction and Materials and methods accordingly.

4) Deception success measures. Was any data collected to confirm the believability of the proposer and third-party deception? Were participants debriefed afterwards?

We did not formally test whether participants believed our cover story in the present study. Since this concern was also raised by the other two reviewers, please find our detailed responses above, explaining why we did not do so and what evidence converges to indicate that they were very likely to have believed the cover story, to avoid unnecessary repetition. Despite that, we agree that this is an important point to address and we have thus added it to the Discussion accordingly.

As mentioned in the Materials and methods, we did debrief the participants at the very end of the experiment.

5) The brain data is largely used to lend construct validity to the corruption task and confirm psychological interpretations of the processes involved in the decision. Ideally, the neural data would be used to adjudicate between two competing behavioral models (and/or behavioral data used to adjudicate between competing neural models). This somewhat lessens the utility of a promising neuroimaging dataset.

As the reviewer pointed out, the neural imaging data were mainly used to identify the brain regions (signals or patterns) involved in different aspects during briberyrelated decision-making. Critically, these data were linked in two ways to the behavioral model of context-dependent corrupt acts. First, we observed a strong vmPFC value signal, given the winning model, during decision-making in both the Bribe and the Control condition. This result can be considered as neural evidence that validates our behavioral model. Second, we identified a potential role of dlPFC in representing bribery-related preferences, characterized by two model-based parameters reflecting different forms of moral costs brought by bribe-taking behaviors, across individuals. Although it would be ideal to adjudicate the behavioral model with neural data or vice versa, we found it difficult to apply this set of analyses to the present case because our behavioral models were mainly based on economic utility models which all assumed the construction of a (subjective) decision value in a certain way, and were less informed by the neural data.

6) Complete analysis scripts (Main and SI, behavioral and neuroimaging) and minimally processed imaging data should be posted and referenced for the article. It is not clear that these fit in the 'source data' option for eLife without an accession number noted in the article.

We fully embrace the open-and-transparency policy advocated by eLife and have already uploaded the most relevant codes and data to an open data depository (https://github.com/huyangSISU/2021_Corruption) which will be available to the public once the paper is accepted.

https://doi.org/10.7554/eLife.63922.sa2

Article and author information

Author details

  1. Yang Hu

    1. Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
    2. Laboratory of Neuroeconomics, Institut des Sciences Cognitives Marc Jeannerod, CNRS, Lyon, France
    Present address
    School of Psychological and Cognitive Sciences, Peking University, Beijing, China
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7659-5782
  2. Chen Hu

    1. Motivation, Brain & Behavior (MBB) Team, Institut du Cerveau et Moelle Epiniere, Hôpital de la Pitié-Salpêtrière, Paris, France
    2. Sorbonne Université, Paris, France
    Contribution
    Formal analysis, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2289-743X
  3. Edmund Derrington

    1. Laboratory of Neuroeconomics, Institut des Sciences Cognitives Marc Jeannerod, CNRS, Lyon, France
    2. Université Claude Bernard Lyon 1, Lyon, France
    Contribution
    Writing - review and editing
    Competing interests
    No competing interests declared
  4. Brice Corgnet

    EmLyon, Ecully, France
    Contribution
    Writing - review and editing
    Competing interests
    No competing interests declared
  5. Chen Qu

    Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
    Contribution
    Conceptualization, Data curation, Supervision, Funding acquisition, Validation, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    fondest@163.com
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8465-8007
  6. Jean-Claude Dreher

    1. Laboratory of Neuroeconomics, Institut des Sciences Cognitives Marc Jeannerod, CNRS, Lyon, France
    2. Université Claude Bernard Lyon 1, Lyon, France
    Contribution
    Conceptualization, Data curation, Supervision, Funding acquisition, Validation, Methodology, Writing - original draft, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2157-1529

Funding

Agence Nationale de la Recherche (ANR-16-IDEX-0005)

  • Jean-Claude Dreher

China Postdoctoral Science Foundation (2019M660007)

  • Yang Hu

National Natural Science Foundation of China (31970982)

  • Chen Qu

Agence Nationale de la Recherche (ANR-11-LABX-0042)

  • Jean-Claude Dreher

Agence Nationale de la Recherche (ANR-11-IDEX-007)

  • Jean-Claude Dreher

Agence Nationale de la Recherche (ANR n°16-NEUC-0003-01)

  • Jean-Claude Dreher

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This research has benefited from the financial support of IDEXLYON from Université de Lyon (project INDEPTH) within the Programme Investissements d’Avenir (ANR-16-IDEX-0005) and of the LABEX CORTEX (ANR-11-LABX-0042) of Université de Lyon, within the program Investissements d’Avenir (ANR-11-IDEX-007) operated by the French National Research Agency. This work was also supported by grants from the Agence Nationale pour la Recherche and NSF in the CRCNS program to JCD (ANR n°16-NEUC-0003-01), National Science Foundation of China to QC (31470995), and China Postdoctoral Science Foundation to YH (2019M660007). We thank Sima Hakimi, Zixuan Tang, Siying Li, and Yaner Su for helpful assistance with data collection. We also thank Dr. Xiaoxue Gao for the assistance of implementing the multivariate fMRI analyses and Zhewen (Vane) He for proofreading the early draft of the manuscript.

Ethics

Human subjects: The study was performed at the Imaging Center of SCNU and was approved by the local ethics committees. All experimental protocols and procedures were conducted in accordance with the IRB guidelines for experimental testing and were in compliance with the latest revision of the Declaration of Helsinki (BMJ 1991; 302: 1194). Upon arrival, participants signed the written informed consent.

Senior Editor

  1. Michael J Frank, Brown University, United States

Reviewing Editor

  1. Thorsten Kahnt, Northwestern University, United States

Publication history

  1. Received: October 10, 2020
  2. Accepted: March 9, 2021
  3. Version of Record published: March 24, 2021 (version 1)

Copyright

© 2021, Hu et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 989
    Page views
  • 91
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Neuroscience
    Sahana Sitaraman et al.
    Research Article

    Gap junctions between neurons serve as electrical synapses, in addition to conducting metabolites and signaling molecules. During development, early-appearing gap junctions are thought to prefigure chemical synapses, which appear much later. We present evidence for this idea at a central, glutamatergic synapse and provide some mechanistic insights. Loss or reduction in the levels of the gap junction protein Gjd2b decreased the frequency of glutamatergic miniature excitatory postsynaptic currents (mEPSCs) in cerebellar Purkinje neurons (PNs) in larval zebrafish. Ultrastructural analysis in the molecular layer showed decreased synapse density. Further, mEPSCs had faster kinetics and larger amplitudes in mutant PNs, consistent with their stunted dendritic arbors. Time-lapse microscopy in wild type and mutant PNs reveals that Gjd2b puncta promote the elongation of branches and that CaMKII may be a critical mediator of this process. These results demonstrate that Gjd2b-mediated gap junctions regulate glutamatergic synapse formation and dendritic elaboration in PNs.

    1. Neuroscience
    Amicia D Elliott et al.
    Research Article Updated

    Identifying neural substrates of behavior requires defining actions in terms that map onto brain activity. Brain and muscle activity naturally correlate via the output of motor neurons, but apart from simple movements it has been difficult to define behavior in terms of muscle contractions. By mapping the musculature of the pupal fruit fly and comprehensively imaging muscle activation at single-cell resolution, we here describe a multiphasic behavioral sequence in Drosophila. Our characterization identifies a previously undescribed behavioral phase and permits extraction of major movements by a convolutional neural network. We deconstruct movements into a syllabary of co-active muscles and identify specific syllables that are sensitive to neuromodulatory manipulations. We find that muscle activity shows considerable variability, with sequential increases in stereotypy dependent upon neuromodulation. Our work provides a platform for studying whole-animal behavior, quantifying its variability across multiple spatiotemporal scales, and analyzing its neuromodulatory regulation at cellular resolution.