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. 2013 Aug 14;9(10):1498–1505. doi: 10.1093/scan/nst131

Neural responses to unfairness and fairness depend on self-contribution to the income

Xiuyan Guo 1, Li Zheng 2, Xuemei Cheng 3, Menghe Chen 2, Lei Zhu 4,✉, Jianqi Li 3, Luguang Chen 3, Zhiliang Yang 2
PMCID: PMC4187258  PMID: 23946001

Abstract

Self-contribution to the income (individual achievement) was an important factor which needs to be taken into individual’s fairness considerations. This study aimed at elucidating the modulation of self-contribution to the income, on recipient’s responses to unfairness in the Ultimatum Game. Eighteen participants were scanned while they were playing an adapted version of the Ultimatum Game as responders. Before splitting money, the proposer and the participant (responder) played the ball-guessing game. The responder’s contribution to the income was manipulated by both the participant’s and the proposer’s accuracy in the ball-guessing game. It turned out that the participants more often rejected unfair offers and gave lower fairness ratings when they played a more important part in the earnings. At the neural level, anterior insula, anterior cingulate cortex, dorsolateral prefrontal cortex and temporoparietal junction showed greater activities to unfairness when self-contribution increased, whereas ventral striatum and medial orbitofrontal gyrus showed higher activations to fair (vs unfair) offers in the other-contributed condition relative to the other two. Besides, the activations of right dorsolateral prefrontal cortex during unfair offers showed positive correlation with rejection rates in the self-contributed condition. These findings shed light on the significance of self-contribution in fairness-related social decision-making processes.

Keywords: unfairness, self-contribution, AI, ACC, DLPFC, TPJ

INTRODUCTION

Human behaviors in social decision-making are driven greatly by fairness considerations. Individuals may accept an unequal distribution when they view it as fair. On the contrary, in order to eliminate inequality beyond their fairness views, they might also reject an unequal distribution even at the price of their own benefits. Empirical evidences demonstrated that individuals’ fairness considerations were not only determined by comparisons between self and others’ gains (Fehr and Fischbacher, 2003; Camerer, 2005) but also affected by some contextual factors, such as the intentions of the allocator (Falk et al., 2003; Sutter, 2007; Güroğlu et al., 2010, 2011), the social distance between the allocator and the recipient (Bohnet and Frey, 1999; Wu et al., 2011) and the loss or gain context (Buchan et al., 2005; Zhou and Wu, 2011; Guo et al., 2013).

Among these contextual factors, individual achievement has a great impact on fairness considerations. It is indicated that individual achievements made in the phase of production had an effect on participants’ distribution of income and their fairness considerations (Konow, 2000; Cappelen et al., 2007). It was also evident that with the increase of age, children began to justify some inequalities they viewed as fair rather than were strict egalitarians, i.e. they would accept unequal distributions in consideration of individual achievements (Almas et al., 2010). It seems to be fair that people who make most contribution get the largest share, consistent with the Parecon principle, a basic social norm requiring distributions according to one’s effort or contribution (Fotopoulos, 2005; Tungodden, 2005). In other words, the more individuals contributed to the earnings, the more unfair they experienced and the more difficult they found it to justify unfair distributions. In the present study, we aimed to elucidate how self-contribution to the income modulated fairness considerations and its underlying neural mechanisms by using the Ultimatum Game (UG).

In a typical UG, two players split a sum of money (e.g. Guth et al., 1982; Thaler, 1988; Camerer and Thaler, 1995). One player proposes how to split it and the other one responds (i.e. the allocator and the responder). The responder can accept or reject the offer. Her/his acceptance leads to the suggested division of money, whereas the rejection results in both players receiving nothing. In order to test the modulation of self-contribution to fairness considerations, we designed an adapted version of the UG. In this version, before splitting money, the proposer and the participant (responder) played a game, i.e. guess the ball located in which box (Figure 1). If at least one of them was correct, they could get a sum of ￥50 (≈8.04 US$, WIN). If neither of them was correct, they received nothing (LOSE). Thus, the proposer and the responder did not split the money and this led to the end of this round. The responder’s contribution (self-contribution) to the income was manipulated by both the participant’s and the proposer’s accuracy. In one condition, the proposer was correct, whereas the participant was wrong; in the second condition, both of them were correct; in the third condition, only the participant was correct (self-contribution increased from condition 1 to condition 3). In the fourth condition, both of them were wrong and received nothing. This adapted version of the UG provided the possibility to compare participant’s reactions to the same unfair offer (i.e. ￥5:￥45 ≈ 7.24US$, ￥10:￥40 ≈ 6.44US$, or ￥15:￥35 ≈ 5.63US$) and fairness perception under different levels of self-contribution.

Fig. 1.

Fig. 1

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Experimental procedure. Participants were scanned while playing the game for 48 trials (12 LOSE trials and 36 WIN trials). Participants and their partners played a ball-guessing game first. If at least one of them was correct, they win. Otherwise, they lose. For LOSE trials, they acquired nothing (￥0:￥0). In contrast, all the WIN trials involved splitting ￥50. Fair offers (￥25:￥25) were given in six trials of each contribution condition. In the other six trials of each condition, offers were unfair (1 trials of ￥35:￥15, 2 trials of ￥40:￥10 and 3 trials of ￥45:￥5).

Several fairness-related brain regions involved in UG have been identified in previous neuroimaging studies, including anterior insula (AI), anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (DLPFC) (Sanfey et al., 2003; Dulebohn et al., 2009; Güroğlu et al., 2010, 2011; Corradi-Dell’Acqua et al., 2013). Empirical evidences recently suggested that AI and/or ACC were engaged in detecting and responding to norm violations (Montague and Lohrenz, 2007; Spitzer et al., 2007; King-Casas et al., 2008; Güroğlu et al., 2010, 2011; Strobel et al., 2011). Activities of AI and ACC during unfair offers in UG might be associated with behaviors violating social norms (de Quervain et al., 2004; Spitzer et al., 2007; King-Casas et al., 2008; Guroglu et al., 2010, 2011). DLPFC activity has been also observed in decision-making during the UG paradigm. The engagement of DLPFC in UG was related to decisions to unfairness (acceptance or rejection) (Sanfey et al., 2003; van’t Wout et al., 2005; Knoch et al., 2006; Güroğlu et al., 2010, 2011; Baumgartner et al., 2011).

In this study, we scanned 18 participants using functional magnetic resonance imaging (fMRI) while they were playing the adapted version of UG as responders. We were interested in behavioral and neural responses to unfair offers in the three conditions (other-contributed, both-contributed and self-contributed) with self-contribution level increasing from the other-contributed to the self-contributed condition. At the behavioral level, we predicted that acceptance rates and fairness ratings of unfair offers would decrease when the self-contribution increased. At the neural level, activations to unfair offers were expected to be modulated by the levels of self-contribution. In the self-contributed condition, there should be stronger activations in unfairness-related regions involving AI, ACC and DLPFC to unfair offers. Besides, based on Tabibnia et al. (2008), we expected that equal offers might be experienced more rewarding with more ventral striatum and medial orbitofrontal gyrus engagements in the other-contributed condition compared with the other two conditions.

In addition, the self-contribution manipulation would also modulate the brain regions which have previously been associated with understanding others’ intentions, beliefs and actions, specifically the temporoparietal junction (TPJ; Saxe and Kanwisher, 2003; German et al., 2004; Vollm et al., 2006; Williams et al., 2006; Decety and Lamm, 2007; Overwalle, 2009). Previous studies have demonstrated the involvement of TPJ during decision-making in UG (Rilling et al., 2004; Baumgartner et al., 2011; Güroğlu et al., 2010, 2011; Guo et al., 2013). In this study, although unfair offers might be readily justified in the other-contributed condition, the proposer who gave unfair offers was hard to be understood in the self-contributed condition. Thus, it was predicted that more efforts would be exerted on understanding others and increased activation in TPJ would be observed in the self-contributed condition.

METHODS

Participants

Eighteen right-handed volunteers from the university community with normal or corrected-to-normal vision [5 men, mean age = 21.06 ± 2.10 (s.d.) years] participated in this experiment. None of the participants reported a significant abnormal neurological history. All the participants gave informed consent before scanning and were paid according to outcomes from a random selection of 10% trials plus a 50RMB bonus.

Materials

Fourty-eight face pictures were selected from Chinese Facial Affective Picture System (Gong et al., 2011) as materials, which were randomly allocated to 3 contribution (other-contributed, both-contributed and self-contributed) × 2 fairness (fair vs unfair) conditions and 1 lose (both wrong) condition. There were 12 pictures in lose (L) condition and 36 pictures for win trials, respectively six pictures in other-contributed-fair (OF), other-contributed-unfair (OU), both-contributed-fair (BF), both-contributed-unfair (BU), self-contributed-fair (SF) and self-contributed-unfair (SU) conditions, half of which were women faces. The emotional valence, arousal and attractiveness of pictures were counterbalanced across different conditions.

Procedure

Before scanning, participants were told the rules of the game and that they would play with 48 different partners (students in another university in Shanghai) whose choices at ball-guessing game and offers about splitting the money were collected before the experiment. They were also informed that their decision in each trial would not affect other partners’ offers and their decisions with one partner would not be informed to the other partners. In addition, they were told that both she/he and the proposer in each trial would be paid according to her/his decision (after some kind of transformation to reduce the amount of money involved). They would be paid with a basic payment for their participation (50RMB, ≈8.04 US$) plus or minus the amount of money obtained or lost from a random selection of 10% trials in the game.

Then, they completed 48 trials (Figure 1) in the scanner. All the trials were presented randomly and functional images were acquired simultaneously. Each trial began with a 2 s presentation of the partner’s face, followed by a 3 s presentation of two boxes. Participants were required to guess which box the ball located in. Once the participants responded, a green frame outside the selected box would appear. Participants were told that their partner also made a choice. If at least one of them was correct, they won. Otherwise, they lost. Then, the outcome (LOSE or WIN) of the ball-guessing game, along with the accuracy of participants’ and their partners’ choice which indicated the level of self-contribution in the income, was presented for 4 s. For LOSE trials, the final outcome (no gains, ￥0:￥0) was viewed for 6 s in the end. In contrast, all the WIN trials involved splitting ￥50 (8.04US$). Fair offers (￥25 ≈ 4.02US$:￥25) were given in six trials of each contribution condition. In the other six trials of each condition, offers were unfair (1 trial of ￥35 ≈ 5.63US$:￥15, 2 trials of ￥40 ≈ 6.44US$:￥10 and 3 trials of ￥45 ≈ 7.24US$:￥5). Partners’ offers lasted for 6 s. After that, a 4 s decision cue appeared and participants were required to decide (accept or reject) within 4 s. Each trial was jittered with inter-stimulus intervals from 1 to 3 s, during which a black fixation cross was presented against a gray background.

After scanning, the participants were presented with the same stimuli in the same sequence as inside the scanner and asked to rate how fair they felt for each offer using a 9-point Likert-type scale where 1 indicated extremely unfair and 9 indicated extremely fair.

fMRI image acquisition and analysis

Scanning was carried out on a 3 T Siemens scanner at the Functional MRI Lab (East China Normal University, Shanghai). Functional images were acquired using a gradient echo-planar imaging (EPI) sequence (repetition time (TR) = 2200 ms, echo time (TE) = 30 ms, field of view (FOV) = 220 mm, matrix size = 64 × 64). Thirty-five slices paralleled to the AC–PC line (slice thickness = 3 mm, gap = 0.3 mm) were acquired and covered the whole brain. The first five TRs acquired were discarded to allow for T1 equilibration. Before the functional run, a high-resolution structural image was acquired using a T1-weighted, multiplanar reconstruction sequence (TR = 1900 ms, TE = 3.42 ms, 192 slices, slice thickness = 1 mm, FOV = 256 mm, matrix size = 256 × 256).

Data preprocessing and statistical analyses were performed with Statistical Parametric Mapping (SPM5, Wellcome Department of Cognitive Neurology, London). During data preprocessing, all volumes were realigned spatially to the first volume of the first time series. Then, the resulting images which were re-sampled to 2 × 2 × 2 mm voxel size were spatially normalized to a standard EPI template based on the Montreal Neurological Institute (MNI) reference brain by using the ‘unified segmentation’ function in SPM5 and smoothed with an 8 mm full-width, half-maximum isotropic Gaussian kernel.

Statistical analyses were performed using the general linear model implemented in SPM5. An event-related design was used at the first level analysis with seven types of events (L, OF, OU, BF, BU, SF and SU). Events were convolved with a canonical hemodynamic response function and its time derivatives. All the encoding trials were modeled from the onset time of the offers (with zero duration). Additional regressors of no interest were created for partner presentation, ball-guessing, outcome of ball-guessing game and decision. Six regressors modeling movement-related variance and one modeling the overall mean were also employed in the design matrix. Simple main effects for six types of events (OF, OU, BF, BU, SF and SU) at the first-level analysis for each subject were computed by applying the ‘1 0’ contrasts. The six first-level individual contrast images were then analyzed at the second group level employing a random-effects model (flexible factorial design in SPM5). Brain activation related to perceiving unfair offers or fair offers was defined using (Unfair − Fair) contrast or the reversed contrast. Then, the F-test of unfairness*contribution interaction was carried out to extract specific regions showing modulation of unfairness perception by the level of self-contribution. The 2(Fair − Unfair)Other − [(Fair − Unfair)Both + (Fair − Unfair)Self] contrast was also tested to extract specific regions showing modulation of perception of fair offers by the level of self-contribution.

Importantly, additional parametric analysis, an efficient statistical procedure to reveal voxels that show a particular pattern of activation throughout several conditions (Buchel et al., 1998), was conducted at the first-level to assess how brain activity modulated by the level of self-contribution in the income. Specifically, a parametric regressor was created to represent the different weighting for unfair trials in three different conditions (OU = 1, BU = 2, SU = 3). The resulting subject-specific estimates of the parametric regressor at each voxel were then entered into a second-level one sample t-tests treating participants as a random variable. Regions showing increased activations to unfair offers with the increment of self-contribution were identified by parametric analyses.

Further, parametric analyses with fairness ratings as the parametric regressor separately for three different contribution conditions were conducted. The resulting subject-specific estimates of the parametric regressor at each voxel were then entered into a second-level one sample t-tests treating participants as a random variable. Regions showing decreased activations with fairness ratings in three conditions were identified by parametric analyses.

Last, several correlation analyses were performed separately for all the trials, other-contributed trials, both-contributed trials and self-contributed trials. Regions showing significant positive or negative correlations between brain BOLD signal change when unfair vs fair offers and corresponding rejection rates of unfair offers were defined across participants.

The resulting statistical maps were thresholded at a voxel-wise P value of P < 0.0001 (uncorrected) and a spatial-extent threshold of 40 contiguous re-sampled voxels, unless otherwise indicated. Then, the statistical maps were corrected for multiple comparisons within areas involved in decision-making in the UG [insula, ACC, DLPFC, striatum (putamen and caudate nucleus), medial orbitofrontal gyrus] by applying small volume corrections with anatomical maps of these areas created by WFU-Pickatlas for SPM (FWE, P < 0.05). Peak voxels were reported in MNI coordinates. To further test how the self-contribution affects brain activations to unfair offers, specific activations identified by unfairness*contribution interaction were used to compute regions of interest (ROIs). All the significant voxels in the activated clusters within 6 mm spherical regions centered on the peak coordinate were included in each ROI. Parameter estimates across ROIs for six events (OF, OU, BF, BU, SF and SU) were extracted for further statistics using the MarsBaR toolbox in SPM5.

RESULTS

Behavioral results

Behavioral results revealed that participants accepted all the fair offers (Table 1), but they rejected some of the unfair offers. Consistent with our prediction, acceptance rates and fairness ratings to unfair offers decreased when the self-contribution increased (Fs > 33.24, Ps < 0.01). The differences of acceptance rates and fairness ratings between every two unfair conditions were significant (ts > 2.68, Ps < 0.05). This finding suggests that participants reacted more strongly when they knew themselves contributed more to the income.

Table 1.

Acceptance rates and fairness ratings for each type of offers in three conditions

Other-contributed
 Both-contributed
 Self-contributed
Unfair Fair Unfair Fair Unfair Fair
Acceptance rate 0.87 (0.07) 1.00 (0.00) 0.39 (0.10) 1.00 (0.00) 0.20 (0.08) 1.00 (0.00)
Fairness rating 4.20 (0.38) 8.79 (0.17) 2.53 (0.17) 7.78 (0.38) 1.92 (0.18) 7.08 (0.51)
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Standard errors are given in parentheses.

fMRI data

Main effects of unfairness

The (Unfair − Fair) contrast not only revealed DLPFC (L: −40 34 28; R: 38 38 28) but also AI (L: −28 24 −2; R: 34 30 −4), ACC (8 30 22) and TPJ (−50 −44 32) (Table 2). The reverse contrast revealed no significant activations.

Table 2.

Regions showing main effects of unfairness

Peak activation
Region X Y Z t-Value Voxels
Unfair − Fair
L Supplementary motor area −8 12 52 12.1 58 177
L     Insula −28 24 −2 8.84a
R 34 30 −4 6.57a
L     ACC −4 34 28 8.04
R 8 30 22 7.11a
L     TPJ −50 −44 32 6.02
L     DLPFC −40 34 28 5.60a
R 38 38 28 4.92a
R Superior frontal gyrus 28 66 0 5.38 67
R Superior medial gyrus 12 66 20 4.94 55
Fair − Unfair
No regions
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Coordinates (mm) are in MNI space. L, left hemisphere; R, right hemisphere. P < 0.0001(uncorrected), k > 40.

aAfter small volume corrections (FWE, P < 0.05).

Unfairness*contribution interaction effects

The F-test of unfairness*contribution interaction revealed significant activations in left DLPFC (lDLPFC, −30 46 28), left AI (lAI, −28 18 6) and left TPJ (lTPJ, −46 44 28; Figure 2A and Table 3). ROIs in lDLPFC, lAI and lTPJ according to the interaction were defined and parameter estimates across ROIs were extracted (Figure 2B). 2 Unfairness*3 contribution ANOVAs on ROI parameter estimates revealed that both the main effects and the interaction were significant (Fs > 4.03; Ps < 0.05). Further simple analyses revealed that the effects of unfairness (unfair vs fair) were significant in the both-contributed and self-contributed conditions (Fs > 7.06; Ps < 0.05), but insignificant in the other-contributed condition (Fs < 2.99; Ps > 0.05).

Fig. 2.

Fig. 2

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Unfairness*contribution interaction. (A) lDLPFC, lAI, lTPJ showed the modulation of unfairness perception by the level of self-contribution. (B) Parameter estimates across lDLPFC, lAI and lTPJ ROIs for six events. l, left hemisphere. Error bars indicate standard error of the mean. P < 0.0001(uncorrected), k > 40.

Table 3.

Regions showing unfairness*contribution interaction effect

Peak activation
Region X Y Z F-value Voxels
L Cuneus −14 −60 26 28.84 507
R Rolandic operculum 48 −18 12 26.54 843
R Middle cingulate cortex 12 8 40 21.07 1210
L −12 −30 44 12.04 50
L Insula −26 18 6 18.85 450
L −28 18 6 16.73a
L Superior temporal gyrus −40 −30 4 18.14 247
R Precentral gyrus 64 4 16 17.34 61
R Postcentral gyrus 20 −34 64 17.3 187
L DLPFC −32 44 30 16.06 70
L −30 46 28 14.27a
R Inferior temporal gyrus 40 −56 −6 15.34 77
L Inferior frontal gyrus −58 6 8 15.32 66
L Middle temporal gyrus −48 −60 18 15.21 56
L TPJ −46 −44 28 13.6 102
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Coordinates (mm) are in MNI space. L, left hemisphere; R, right hemisphere. P < 0.0001(uncorrected), k > 40.

aAfter small volume corrections (FWE, P < 0.05).

In order to examine whether fair offers were experienced more rewarding in the other-contributed condition compared with other two conditions, we then tested the contrast 2(Fair − Unfair)Other − [(Fair − Unfair)Both + (Fair − Unfair)Self] to search for regions showing modulation of perception of fair offers by the level of self-contribution. Ventral striatum (L: −32 −2 −6; R: 34 6 −2) and left medial orbitofrontal gyrus (−8 50 −12; Table 4 and Figure 3) resulted from the analysis. The reverse contrast revealed no significant activations.

Table 4.

Regions identified by the (Fair − Unfair) in other-contributed condition relative to the other two

Peak activation
Region X Y Z t-Value Voxels
2(Fair − Unfair)Other − [(Fair − Unfair)Both + (Fair − Unfair)Self]
L Cuneus −14 −60 26 7.59 794
R Rolandic operculum 48 −18 12 7.24 1344
R Middle cingulate cortex 10 8 38 6.10 2042
L −12 −30 44 4.87 364
L Superior temporal gyrus −40 −30 4 6.03 1129
L     TPJ −46 −44 28 5.19
R Postcentral gyrus 20 −34 64 5.89 476
R Precentral gyrus 64 4 16 5.86 241
L −46 −4 48 4.36 65
L DLPFC −32 44 30 5.62 393
L −32 42 34 4.82a
R Inferior temporal gyrus 40 −54 −6 5.54 117
L Inferior frontal gyrus −58 6 8 5.35 152
L Middle temporal gyrus −48 −60 18 5.28 153
L Dorsal striatum (putamen) −26 14 4 5.40a 1002
L     Ventral striatum (putamen) −32 −2 −6 5.20a
R 34 6 −2 4.53a 201
L Medial orbitofrontal gyrus −8 50 −10 4.96 166
L −8 50 −12 4.92a
L Middle frontal gyrus −36 60 4 4.52 48
L Thalamus −6 −14 8 4.30 53
[(Fair − Unfair)Both + (Fair − Unfair)Self] − 2(Fair − Unfair)Other
No regions
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Coordinates (mm) are in MNI space. L, left hemisphere; R, right hemisphere. P < 0.0001(uncorrected), k > 40.

aAfter small volume corrections (FWE, P < 0.05).

Fig. 3.

Fig. 3

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Regions identified by the (Fair − Unfair) in other-contributed condition relative to the other two. Left ventral striatum (lVS), right ventral striatum (rVS) and left medial orbitofrontal gyrus (lMOG) showed the modulation of perception of fair offers by the level of self-contribution. P < 0.0001(uncorrected), k > 40.

Parametric analyses on self-contribution

Parametric analyses were carried out to search for regions showing increased activations to unfair offers with the increment of self-contribution. ACC (0 34 24), lAI (−30 18 −16) and lTPJ (−58 42 28; Table 5) resulted from the analysis. No regions showed decreased activations to unfair offers with the increment of self-contribution.

Table 5.

Regions showing increased activations to unfair offers with the increment of self-contribution

Peak activation
Region X Y Z t-Value Voxels
Increased with the increment of self-contribution
R Olfactory cortex 16 10 −16 8.17 573
L Hippocampus −14 −36 4 8.15 312
L Superior frontal gyrus −18 4 58 7.40 2141
L     ACC −4 34 26 5.00
L/R 0 34 24 4.99a
R Superior frontal gyrus 18 −6 62 5.51 57
L Thalamus −10 −12 12 7.17 1836
L     Insula −28 10 −14 6.45
L −30 18 −16 5.67a
L −30 20 4 5.45a
R Precuneus 8 −38 54 6.84 89
L −8 −56 62 5.91 95
L Middle occipital gyrus −28 −78 2 6.69 1470
R 28 −94 14 6.35 73
L Cuneus −16 −60 22 6.66 114
L Superior temporal gyrus −50 −8 −12 6.55 68
R Linual gyrus 30 −44 −8 6.44 155
L Postcentral gyrus −42 −10 46 6.28 269
R Calcarine gyrus 26 −64 10 6.14 159
R Inferior temporal gyrus 48 −56 −8 5.60 41
R Precentral gyrus 50 0 50 5.52 117
L TPJ −58 −42 28 5.46 69
Decreased with the Increment of Self-contribution
no regions
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Coordinates (mm) are in MNI space. L, left hemisphere; R, right hemisphere. P < 0.0001(uncorrected), k > 40.

aAfter small volume corrections (FWE, P < 0.05).

Parametric analyses on fairness ratings

Parametric analyses on fairness ratings for three different contribution conditions were conducted. It is revealed that lAI (−28 22 10) showed decreased activations with fairness ratings in both-contributed condition and lAI (−32 22 8) showed decreased activations with fairness ratings in self-contributed condition (Table 6). Further analyses found that the beta estimates of the parametrical regressors of lAI in self-contributed condition were significantly lower than that in both-contributed condition (t = 1.85; P < 0.05, one-tailed). No regions showed increased activations with fairness ratings.

Table 6.

Regions showing decreased activations with fairness ratings in three contribution conditions

Peak activation
Region X Y Z t-Value Voxels
Other-contributed condition
No regions
Both-contributed condition
L Insula −26 20 12 5.90 42
L −28 22 10 5.26a
L Thalamus −4 −16 16 5.36 109
L/R Supplementary motor area 0 18 48 5.25 73
Self-contributed condition
R Supplementary motor area 2 10 62 7.35 1745
R Medial temporal pole 52 12 −24 7.22 66
R Linual gyrus 24 −86 −2 6.95 312
L Inferior frontal gyrus −34 22 12 6.68 167
L     Insula −32 22 8 4.52a
L Superior temporal gyrus −48 −6 −12 6.32 101
L Parahippocampal gyrus −12 −2 −22 6.27 58
L Fusiform −40 −50 −20 6.15 265
R 26 −46 −14 5.02 47
R Temporal pole 24 8 −20 5.85 90
L Superior parietal lobule −28 −62 52 5.62 184
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Coordinates (mm) are in MNI space. L, left hemisphere; R, right hemisphere. P < 0.0001(uncorrected), k > 40.

aAfter small volume corrections (FWE, P < 0.05).

Overlaps

In order to clarify whether the main effects of unfairness were driven by the effects of self-contribution, we overlapped the main effect of unfairness (Unfair − Fair) and F-test of unfairness*contribution interaction (Figure 4A). It turned out that clusters in lAI and lTPJ overlapped. We also overlapped activations from parametric analyses on fairness ratings with main effect of unfairness and F-test of unfairness*contribution interaction (Figure 4B and C). A cluster in lAI overlapped.

Fig. 4.

Fig. 4

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Overlaps. (A) Clusters in lAI and lTPJ overlapped between the main effects of unfairness and unfairness*contribution interaction. (B) A cluster in lAI overlapped between the main effects of unfairness and activations decreased with self-contribution in both-contributed and self-contributed conditions. (C) A cluster in lAI overlapped between unfairness*contribution interaction and activations decreased with self-contribution. l, left hemisphere. P < 0.0001(uncorrected), k > 40.

Correlation analyses

First, correlation analyses were performed to determine the regions whose BOLD signal change detected from the (Unfair − Fair) contrast positively or negatively varied with the corresponding rejection rates of unfair offers in different conditions, respectively. We observed a cluster located in right ventral striatum (rVS, 6 12 −8) showed strong negative correlations (r = −0.74, P < 0.001) with rejection rates in both-contributed condition, whereas a cluster located in right DLPFC (rDLPFC, 52 36 24) showed strong positive correlations (r = 0.68, P < 0.005) with rejection rates in self-contributed condition at the threshold of P < 0.001 (uncorrected). No other significant correlations were observed in the related regions. We further examined the correlations between activations in the above-mentioned rVS and rDLPFC in the other conditions and the corresponding rejection rates. No statistically significant correlations were found (rs < 0.23, Ps > 0.05).

DISCUSSIONS

This study used an adapted version of the UG to investigate how self-contribution to the income affected fairness considerations and the underlying neural mechanisms. Consistent with our predictions, behavioral results revealed that both acceptance rates and fairness ratings of unfair offers decreased with the increment of self-contribution. Lower acceptance rates and fairness ratings were observed in the self-contributed condition compared with the other two conditions. At the neural level, greater AI, ACC, DLPFC and TPJ activities to unfair offers were found when self-contribution increased. Together, these results indicated that individuals’ behavioral and neural responses to unfairness were modulated by different levels of self-contribution.

In our data set, stronger AI activation was found during unfair vs fair offers in the self-contributed and both-contributed conditions, but not in the other-contributed condition. Furthermore, AI showed increased activations to unfairness with the increment of self-contribution. Since all the income was acquired due to the participants’ performance, an unfair distribution in the self-contributed condition would be treated as more norm-violated compared with that in the both-contributed or the other-contributed condition. Thus, more AI activity during self-contributed condition in this study might be related to greater norm violations, which was consistent with prior arguments that AI was associated with detecting norm violations in social context (Güroğlu et al., 2010, 2011). Considering parametric analysis of fairness ratings, it was revealed that such AI activations (overlapped between different analyses) decreased more sharply with fairness ratings in the self-contributed condition than the other two, providing further evidence for the role of AI in neural responses to norm violation behaviors. Accompanied by AI activity, ACC also showed increased activations during unfair offers with the increment of self-contribution, indicating the involvement of AI/ACC network in norm violations (Singer et al., 2006; Güroğlu et al., 2010, 2011).

In this study, the activations of rDLPFC during unfair offers showed positive correlation with rejection rates in the self-contributed condition, suggesting individuals with higher rejection rates had higher DLPFC activity during unfair offers in the self-contributed condition. The engagement of DLPFC in decision-making is related to top-down executive control of impulses to reject unfair offers (Sanfey et al., 2003) or accept unfair offers (van’t Wout et al., 2005; Knoch et al., 2006; Güroğlu et al., 2010, 2011; Baumgartner et al., 2011). Our result suggested that DLPFC might be engaged in executive control of acceptance impulses. It is a pity that the lack of enough accepted or rejected unfair trials in different contribution conditions prevented us from probing the neural mechanism underlying decision-making processes in UG.

Consistent with previous studies (Rilling et al., 2004; Baumgartner et al., 2011), TPJ activities were found when identifying activations associated with unfairness. Furthermore, TPJ activity to unfair offers increased with the increment of self-contribution. Previous studies on theory of mind repeatedly revealed that TPJ engaged in mentalization, i.e. the capacity to interpret and predict others’ behaviors based on an understanding their internal mental states (Saxe and Kanwisher, 2003; German et al., 2004; Vollm et al., 2006; Williams et al., 2006; Decety and Lamm, 2007; Overwalle, 2009). Thus, it is plausible that participants engaged in higher levels of mentalizing during unfair offers in the self-contributed condition in which all the income was owed to the participant’s performance.

In addition, self-contribution also modulates responses to fairness. More ventral striatum and medial orbitofrontal gyrus engagements were observed during fair offers in the other-contributed condition compared to other two conditions. The engagement of ventral striatum and medial orbitofrontal gyrus in UG may relate to reward processing (Tabibnia et al., 2008). More engagement of ventral striatum and medial orbitofrontal gyrus during fair offers in the other-contributed condition may suggest that fair offers are experienced more rewarding in the other-contributed condition compared with other two conditions.

CONCLUSIONS

Self-contribution to the income was an important factor which needs to be taken into individual’s fairness considerations (Konow, 2000; Cappelen et al., 2007; Almas et al., 2010). This study further revealed that self-contribution could modulate responders’ behavioral and neural responses to unfair offers in our adapted UG paradigm. At the behavioral level, participants reported lower fairness ratings and rejected more often in the self-contributed condition. At the neural level, AI, ACC, DLPFC and TPJ which were involved in the unfairness-related neural network showed greater activities to unfairness when self-contribution increased, whereas ventral striatum and medial orbitofrontal gyrus showed higher activations to fair offers with the decrement of self-contribution. Besides, the activations of rDLPFC during unfair offers showed positive correlation with rejection rates in the self-contributed condition. Taken together, our data indicated that participants experienced more unfairness in UG with the growing self-contribution to the income, inducing more unfairness-related neutral activities with the increment of self-contribution. These findings shed light on the significance of self-contribution in fairness-related social decision-making processes.

Acknowledgments

This research was supported by National Natural Science Foundation of China (31271090, 31100728 & 90924013), Projects Planning in Shanghai Philosophy and Social Sciences Research (2012JJY001), Innovation Program of Shanghai Municipal Education Commission (12ZS046), 985 Project of Fudan University (2011SHKXZD008).

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