Available via license: CC BY 4.0
Content may be subject to copyright.
ORIGINAL RESEARCH
published: 09 May 2022
doi: 10.3389/fnsys.2022.765720
Frontiers in Systems Neuroscience | www.frontiersin.org 1May 2022 | Volume 16 | Article 765720
Edited by:
Conrado Arturo Bosman,
University of Amsterdam, Netherlands
Reviewed by:
Rodrigo Montefusco-Siegmund,
Austral University of Chile, Chile
Keith J. Yoder,
The University of Chicago,
United States
*Correspondence:
Ali M. Miraghaie
sam.miraghaie@gmail.com
Hamidreza Pouretemad
pouretemad.h@gmail.com
Alessandra Lintas
alessandra.lintas@unil.ch
Received: 27 August 2021
Accepted: 05 April 2022
Published: 09 May 2022
Citation:
Miraghaie AM, Pouretemad H,
Villa AEP, Mazaheri MA,
Khosrowabadi R and Lintas A (2022)
Electrophysiological Markers of
Fairness and Selfishness Revealed by
a Combination of Dictator and
Ultimatum Games.
Front. Syst. Neurosci. 16:765720.
doi: 10.3389/fnsys.2022.765720
Electrophysiological Markers of
Fairness and Selfishness Revealed
by a Combination of Dictator and
Ultimatum Games
Ali M. Miraghaie 1,2
*, Hamidreza Pouretemad 3
*, Alessandro E. P. Villa2,
Mohammad A. Mazaheri 3, Reza Khosrowabadi 4and Alessandra Lintas 2,5
*
1Faculty of Psychology, Shahid Beheshti University, Tehran, Iran, 2NeuroHeuristic Research Group, HEC-Lausanne,
University of Lausanne, Lausanne, Switzerland, 3Department of Clinical and Health Psychology, Shahid Beheshti University,
Tehran, Iran, 4Institute for Cognitive and Brain Science, Shahid Beheshti University, Tehran, Iran, 5LABEX, HEC-Lausanne,
University of Lausanne, Lausanne, Switzerland
Individual behavior during financial decision making is motivated by fairness, but an
unanswered question from previous studies is whether particular patterns of brain activity
correspond to different profiles of fairness. Event Related Potentials (ERPs) were recorded
from 39 participants who played the role of allocators in a Dictator Game (DG) and
responders in an Ultimatum Game (UG). Two very homogeneous groups were formed
by fair and selfish individuals. At fronto-central cortical sites, the latency of ERP early
negativity (N1) was 10 ms shorter in selfish participants than in fair participants. In fair DG
players, the subsequent positive wave P2 suggested that more cognitive resources were
required when they allocated the least gains to the other party. P2 latency and amplitude
in the selfish group supported the hypothesis that these participants tended to maximize
their profit. During UG, we observed that medial frontal negativity (MFN) occurred earlier
and with greater amplitude when selfish participants rejected less favorable endowment
shares. In this case, all players received zero payoffs, which showed that MFN in selfish
participants was associated with a spiteful punishment. At posterior-parietal sites, we
found that the greater the selfishness, the greater the amplitude of the late positive
component (LPC). Our results bring new evidence to the existence of specific somatic
markers associated with the activation of distinct cerebral circuits by the evaluation of fair
and unfair proposals in participants characterized by different expressions of perceived
fairness, thus suggesting that a particular brain dynamics could be associated with moral
decisions.
Keywords: cooperation, EEG, N1, P2, MFN, late positive potential, spiteful punishment, costly punishment
1. INTRODUCTION
The maximization of an individual’s own gain is considered to represent the main behavioral drive
during an economic transaction. However, the balance of individuals’ self-interest and acceptance
of others’ benefit is a common observation based on culturally shaped moral phenomena such as
fairness and altruism (Rachlin, 2002; Fehr and Rockenbach, 2004; Altman, 2005; Henrich et al.,
2010). Unrelated individuals try to ensure fairness to others in daily life and hope to get fair
Miraghaie et al. ERP Markers of Fairness and Selfishness
treatment and cooperation in return (Kocher et al., 2012; Rand
and Nowak, 2013). The strong reciprocity model, as one of
the strategies for maintaining and urging people to commit to
the norms, has many practical and effective use in the light of
cooperation (Gintis et al., 2003; Stallen and Sanfey, 2013) and
people act differently in diverse societies to apply it (Herrmann
et al., 2008). Cooperation induced by the sacrifice of one’s own
self-interest in punishing violations of the social norm defines
the so-called altruistic punishment (Fehr and Gächter, 2002;
Fowler, 2005; Du and Chang, 2015; Balafoutas et al., 2016).
A different kind of punishment, so-called spiteful punishment,
appears when an individual spends personal resources to punish
one or several other individuals who behave against his presumed
interest within the context of collective norms and rules (Jensen,
2010; Marlowe et al., 2011; Brañas-Garza et al., 2014; Yamagishi
et al., 2017).
The Ultimatum Game (UG) has been one of the most prolific
experimental tasks aimed at investigating the nature of human
fairness over the last decades (Güth et al., 1982). A typical
UG involves two players, the proposer, who offers how to
share an endowment in two parts, and the responder, who can
either accept the offer to share it accordingly or reject it with
both players receiving a zero payoff. Empirical studies have
demonstrated that proposers typically offered approximately 40%
(fair offers) of the total amount at stake, with responders being
more likely to reject 20% or less of positive, albeit low (unfair)
offers (Camerer, 2003; Sanfey et al., 2003). Rejection of an unfair
offer at UG might be triggered by cooperative and popular
motivations or it can be considered to be an expression of a costly
punishment aimed at enforcing the social norm of fairness (Fehr
and Schmidt, 1999; Fehr and Fischbacher, 2004; Henrich et al.,
2006; Hewig et al., 2011). This argument has been used to support
the strong reciprocity model of the evolution of cooperation
(Bear and Rand, 2016; Righi and Takács, 2018; Chen et al.,
2019), but competitive and spiteful motivation associated with
less prosocial attitudes have also been suggested (Kirchsteiger,
1994; Brañas-Garza et al., 2014). The Dictator Game (DG) task
(Kahneman et al., 1986; Artinger et al., 2014) is basically a single-
player game because the proposer (called the allocator in DG)
determines at his own will whether to send a fraction (ranging
from nothing to all) of the initial endowment to the responder
(called the Recipient in DG). The recipient plays a passive role
and has no influence over the outcome of the game. It has been
observed that the vast majority of allocators give something to the
recipients, usually about 20% of the amount at stake, despite the
fact that allocators have no reason to give up some of the money
(Camerer, 2003; Artinger et al., 2014).
Contactless monetary transactions are rapidly increasing
in all socio-cultural environments and in the absence of
effective face-to-face negotiations, it seems crucial to gain a
better understanding of decision-making processes. The Somatic
Markers Hypothesis (Bechara et al., 1994; Damasio, 1996) was
proposed following the observation that some individuals, who
make decisions that are unfavorable to their personal life and
social status, are characterized by biophysical measurements
associated with brain activity. Marker signals refer to body-
state structure and regulation even when they arise in the
representation of the body by the brain. As stated by Damasio
(1996),The key idea in the hypothesis is that “marker” signals
influence the processes of response to stimuli, at multiple levels of
operation, some of which occur overtly (consciously, “in mind”)
and some of which occur covertly (non-consciously, in a non-
minded manner). In the investigation of the neural basis of
decision making processes, the key question is searching of where
and how a “decision” is taken in a recursive way (Bechara
et al., 1994; Bechara and Damasio, 2005; Rilling and Sanfey,
2011; Villa et al., 2012). Functional magnetic resonance imaging
(fMRI) has been mainly used to investigate the localization of
brain activity associated with the violations of social norms and
expectations in decision-making tasks (Sanfey et al., 2003; Spicer
et al., 2007). The search of “how” requires a fine grain temporal
resolution (in the order of 1 ms time scale), which is a feature
of electroencephalography (EEG) by means of recording brain
activity in a non invasive way with external electrodes placed over
many standard locations of the scalp (Boksem and De Cremer,
2010; Wu et al., 2011a; Luo et al., 2014; Mesrobian et al., 2015; Yu
et al., 2015; Peterburs et al., 2017; Cui et al., 2019).
Transient electric potentials associated in time with a sensory
or mental occurrence are termed event-related potentials (ERPs)
(Picton et al., 2000). A fundamental prediction of this model
is that multiple occurrences of the same event evoke a similar
pattern of activity in the same cerebral circuits. Hence, the
usual analysis of ERPs requires to average the activity triggered
by multiple repetitions of the same event. Among the ERP
wave components observed in cognitive studies, we introduce
those that have been associated with experimental protocols
of interest with respect to our study. The most common
triggering events are the stimulus onset corresponding, in UG,
to the time of the decision (acceptance/rejection) taken by the
responder following the presentation of the offer by the proposer
and, in DG, to the time of the decision (which endowment
share offered to the Recipient) taken by the allocator. In the
chronological order, we introduce N1 (peaking near 130 ms
after stimulus onset), P2 (or P200, peaking at 220 ms), medial-
frontal negativity (MFN, peaking at 310 ms), and a late positive
component (LPC, extending between 400 and 650 ms). In several
experimental paradigms, ERPs can be stimulus-locked at the
time of presentation of the feedback information on the decision
made. This is not the case in the current study and ERPs triggered
by the onset of the feedback phase are not further discussed.
In a UG protocol, the amplitude of frontal N1 was modulated
by the facial attractiveness of the player (Ma and Hu, 2015;
Weiss et al., 2020) and the following observed nonverbal
social interactions (friendly vs. nonfriendly) with proposers
(Moore et al., 2021). Parietal N1 amplitude was reported in
association with the process of emotional stimuli regardless of
the trustworthiness of the player (Mei et al., 2020). In UG,
it was observed that an increase in P2 is larger in response
to high-value over low-value offers (Weiss et al., 2020), in
participants informed by the opponent’s low social status (Hu
et al., 2014). A similar observation was reported in participants
who feel gratification in response to rewarding results but also
by seeing the opponent player’s bad luck (Falco et al., 2019).
The MFN amplitude was larger following unfair as compared to
Frontiers in Systems Neuroscience | www.frontiersin.org 2May 2022 | Volume 16 | Article 765720
Miraghaie et al. ERP Markers of Fairness and Selfishness
fair offers (Polezzi et al., 2008; Boksem and De Cremer, 2010;
Wu et al., 2011a; Alexopoulos et al., 2012; Mesrobian et al.,
2015). In DG, MFN is observed in recipient’s ERP after receiving
disadvantageous inequitable allocations (Li et al., 2020), but it
is also dependent on the recipients’ social status and altruistic
tendency (Sun et al., 2015; Cui et al., 2019; Mayer et al., 2019).
A late positive component (LPC) observed at latencies of 450–
650 ms over central and parietal areas was sometimes termed
late positive potential and often regarded as a sustained P300
response to be associated with emotion-driven decision making
(Ito et al., 1998; Wu et al., 2011b; Cui et al., 2019). In UG, the
P300/LPC amplitude was larger in conditions with higher risk-
tendency, such as following responses to a fair offer made by an
unknown proposer in comparison with the same payoff offered
by a socially known and close individual (Polezzi et al., 2010;
San Martín, 2012; Yu et al., 2015; Lintas et al., 2017; Harris et al.,
2020). This amplitude also tended to be larger and localized more
frontally after the rejection of unfair offers (Mesrobian et al.,
2015) and after receiving fair over unfair offers (Xu et al., 2020).
An unanswered question from previous studies, which usually
pooled participants in decision-making tasks in one group
is whether somatic markers are different in individuals with
different profiles of fairness toward accepting/rejecting wretched
offers in UG. This question has been investigated in this
study with an original design combining DG and UG. The
cooperation is tested with the same participant playing the
roles of allocator in DG and responder in UG, thus allowing
to separate individuals’ profiles following an index of altruism
determined during UG and an index of selfishness determined
during DG. The experimental prediction is that the combination
of these games will reveal the existence of different groups
of individuals characterized by differences in selfishness and
altruism. To our knowledge, this is the first study to carry out
a systematic comparison of ERPs following the behavior of the
same participants performing both DG and UG neuroeconomic
games. The fronto-central N1 wave component is expected
to be associated with top-down prefrontal control of decision
making, meaning that N1 should be different in groups, if any,
clearly separated by selfish and altruistic behaviors. Rewarding
choices are expected to evoke larger P2 in participants who
feel gratification, meaning that P2 is expected to be larger in
altruistic and fair participants rather than in selfish and conceit
participants. Being associated with disadvantageous inequitable
offers and with altruistic tendencies, MFN is expected to be larger
in altruistic participants. The amplitude of posterior-parietal
LPC is associated with motivation and subsequent allocation
of attention, meaning that it is expected to be large in selfish
allocators playing DG and in altruistic responders playing UG.
An additional careful analysis of N1, P2, MFN, and LPC latencies
for distinct behavioral groups is expected to point out interacting
factors, if any, associated with the outcome of decision making.
2. METHOD
2.1. Participants
An a priori power analysis was conducted to compute the
required sample size on the basis of repeated measures and
within-between interaction (Faul et al., 2007). We planned 6
measurements per value (taken by three experienced users at
two adjacent electrode sites) and two factors, each one with two
levels (i.e., the accept/reject response and the UG/DG game),
using the desired effect size of 0.33 (to represent a medium
effect) with a power of 0.95 (1-βerror probability). The power
analysis indicated that we need a sample size of 44 to achieve this
result. A total of 44 healthy volunteer young male adults were
recruited via posted announcement at the university campus
of the Shahid Beheshti University of Tehran (Iran), but only
39/44 completed the protocol. Then, our participant count is
restricted to N=39 volunteers aged between 18 and 32 years
old (M =22.5, SEM =0.5). This sample size corresponds to a
power of 0.92. All participants followed an academic education
(8 undergraduate, 22 postgraduate, and 9 PhD students) and all
were right-handed with normal or corrected-to-normal vision.
The research protocol was approved by the mandatory Ethics
Committees requested by Shahid Beheshti University and with
written informed consent from all participants, in accordance
with the latest version of the Declaration of Helsinki (World
Medical Association., 2000). Prior to the experimental session,
all participants were interviewed to asses that they were not
reporting any neurological or neuropsychiatric diseases and
not taking any psychoactive medication. The whole experiment
lasted between 2 and 2 and half hours. The participants were
compensated for their time with a fixed amount of cash
(200,000 IRR, Iranian Rials, approximately corresponding to 4
USD). In addition, the participants received performance-based
cash rewards (comprised between 200,000 and 800,000 IRR)
corresponding to the cumulative outcome of the UG and DG
earned at the end of the experimental session.
2.2. Experimental Procedure
The participants were seated comfortably in a sound-attenuated,
electrically shielded, and dimly lit cabin with the instruction to
maintain their gaze on a white fixation cross at the center of a
32-inch computer screen at a viewing distance of 100 cm. At the
beginning of the recording session, the EEG was recorded during
2 min while the participants kept their eyes closed and for 2 min
while they fixated a cross on the center of the computer screen.
The participants were randomly assigned to two groups, those
who played UG at first and those who played DG at first. Each
game started with 10 trials of practice. After completing the first
game, a short break (5–10 min) was set to allow the participant to
get acquainted with the rules of the second game.
Each game was composed of 5 blocks with 48 trials each (240
trials for each game) and the participants took a break after each
block. Each trial started with a fixation cross appearing for 800–
1,200 ms. Then, a circular pie chart representing the endowment
at stake, divided into two colored pieces, was presented with a
diameter of 8.0 visual degree centered on a 32-inch computer
screen at a 100 cm viewing distance. The pie chart appeared on
the screen for 1,200 ms. The red piece of the pie corresponded
always to the participant’s payoff of the endowment share. The
participant had up to 3,000 ms to make the decision either to
accept the endowment subdivision represented by the pie chart—
by pressing the “K” letter of the computer keyboard with the
Frontiers in Systems Neuroscience | www.frontiersin.org 3May 2022 | Volume 16 | Article 765720
Miraghaie et al. ERP Markers of Fairness and Selfishness
medium finger of the right hand—or to reject it—by pressing
the “H” letter with the index finger of the same hand. During
this interval, a schematic drawing appeared on the computer
screen with a “thumb up” emoji above the letter “K” on the
right and a “thumb down” emoji above the letter “H” on the
left. After making the decision, the players fixated the cross for
800–1,200 ms.
At the end of each trial, the outcome of the game showing the
payoff earned by the participant and his opponent was presented
on the computer screen for 2,000 ms. The stimulus presentation
and data acquisition were performed using PsychLab and EEG8
amplifiers (Precision Instruments, Glastonbury, Somerset, BA6
8AQ, UK). The statistical analyses were performed using the R
language and environment for statistical computing (Venables
and Ripley, 2002; R Core Team, 2021).
2.3. Dictator Game
In the DG of this study, the allocator/proposer could not
impose a priori the amount to be divided with the other
player (the recipient/responder), but he had to refuse or
agree with a randomly selected allocation appearing on the
computer screen. As in a usual DG, the recipient/responder
was completely passive and had to accept whatever amount was
imposed by the allocator/proposer. Both players received zero
payoffs if the allocator/proposer refused the randomly selected
allocation. All participants played the role of allocator/proposer
and they were informed that the recipient/responder was
an anonymous player located elsewhere. The participants
played 240 trials subdivided into five blocks of 48 trials.
The allocations were randomly selected from among five
([allocator/proposer] : [recipient/responder]) possibilities
{(50:50), (60:40), (70:30), (80:20), (90:10)}—a fraction (80:20)
meaning 80% of the amount allocated to the participant and
20% allocated to the other player. A single block of 48 trials
was not necessarily balanced for the five possible fractions, but
the complete sequence of 240 (5 ×48) trials were uniformly
distributed, such that the sequence contained the same number
of trials for each pre-determined allocation. In this version
of DG, the performance-based cash reward of the participant
(always playing the role of allocator/proposer) was determined
by the average payoff of all accepted proposals. Figure 1 presents
a schematic illustration of the time course of two DG trials.
2.4. Ultimatum Game
All participants played the role of responder, who could either
accept or reject the offers appearing on the computer screen. The
participants were informed that the proposer was an anonymous
player located elsewhere. In case of acceptance, each player
received a payoff according to the proposed split, otherwise,
both players did not receive anything. The overall sequence of
240 trials was subdivided into five blocks of 48 trials with five
possibilities of (proposer:responder) endowment shares {(50:50),
(60:40), (70:30), (80:20), (90:10)}—a fraction (80:20) meaning
80% of the endowment paid to the proposer (i.e., the opponent
player) and 20% paid to the participant, who always played
the role of responder. The trials were uniformly randomly
distributed such that the complete sequence contained the same
number of trials for each pre-determined endowment share.
The performance-based cash reward of the participant was
determined, as usually in UG, by the total payoff of all accepted
endowment shares. Figure 2 presents a schematic illustration of
the time course of two UG trials.
2.5. EEG Recording and ERP Processing
The electroencephalogram (EEG) of each Participant was
recorded using 32 scalp dry titanium/titanium nitride (Ti/TiN)
electrodes, mounted on a headcap (international 10/20 layout)
referenced to the left mastoid bone. Electrophysiological signals
were sampled at 1,024 Hz and filtered with a band-pass of
0.1–200 Hz, with electrode impedances kept near 5 kfor all
recordings. The analysis was performed using the EEGLAB_v12
(Delorme and Makeig, 2004) and ERPLAB_v7 toolboxes (Lopez-
Calderon and Luck, 2014) of MATLAB 2017b (The Mathworks,
Inc., Natick, MA, USA). Raw data were preprocessed with a band-
pass IIR Butterworth filter from 0.1 to 32 Hz (−36dB/octave
roll off). Blink, saccade, and eyelid artifact components were
corrected or set to zero, based on their respective shape
and topography after using Independent Component Analysis
(ICA) (Jung et al., 2000; Plöchl et al., 2012). Epochs time-
locked to markers were obtained after off-line segmentation
of the continuous EEG. The epochs were defined for the
interval between –200 and 800 ms around the trigger and
corrected to baseline 200 ms prior to the marker. Noisy data
were rejected after further visual inspection of the epochs for
contamination by muscular or electrode artifacts. We analyzed
four ERP components defined by the early negative peak near
110–160 ms after the trigger (N1), followed by a positive
wave peaking in the 180–240 ms interval post trigger (P2),
a negative component observed in the 260–400 ms interval
(MFN), and the most positive peak observed in the 430–630 ms
interval (LPC). For each participant and at each electrode site,
measurements of each peak latency and amplitude, if peaks
of wave components were clearly identified, were taken by
three independent trained experimenters. In this way, we could
perform repeated measurements (three at maximum) for each
wave component at each electrode site.
2.6. Statistical Analyses
The statistical software R v4.0.5 (R Core Team, 2021) was used for
all the analyses with packages effectsize (Makowski et al.,
2019), rstatix (Kassambara, 2020), and sjstats (Lüdecke,
2021). In general, the grouped values were reported as median,
Mean ±SEM. The null hypothesis of homoscedasticity (i.e.,
equal variances) in the data samples was tested with Levene’s
test. Unpaired comparisons between groups were performed
by Student t-test if variances were equal and by Welch t-
test, otherwise. For multiple comparisons, the p-value of the t-
test was adjusted following Bonferroni correction. The factorial
analysis was performed using linear mixed effects models (with
package lmerTest;Kuznetsova et al., 2017), and goodness of
fit was assessed by F-statistics based on Satterthwaite’s method for
denominator degrees-of-freedom and F-statistic. The generalized
eta squared (η2
G) was used to estimate the effect size of main
and interaction factors (negligible if η2
G<0.01; small if 0.01 ≤
η2
G<0.06; medium if 0.06 ≤η2
G<0.14; large if 0.14 ≤η2
G).
Frontiers in Systems Neuroscience | www.frontiersin.org 4May 2022 | Volume 16 | Article 765720
Miraghaie et al. ERP Markers of Fairness and Selfishness
FIGURE 1 | Schematic illustration of the time course of the Dictator Game (DG) task. The participant always played the role of allocator/proposer with the possibility to
accept or refuse an allocation randomly presented on the computer screen. The red piece of the pie corresponds to the participant’s payoff. (A) Example of a trial
when the allocator/proposer agreed with the randomly proposed allocation—in this example, 70% for the participant and 30% for the other player. (B) Example of a
trial when both players ended with a zero payoff because the allocator/proposer refused the randomly proposed allocation (50:50).
For independent two-samples comparisons, the effect size of t-
test was assessed by Cohen’s d(negligible if d<0.2; small if
0.2 ≤d<0.5; medium if 0.5 ≤d<0.8; large if 0.8 ≤d).
3. RESULTS
3.1. Behavioral Analysis
On average (M ±SEM), the participants to the DG completed
237.3 ±0.7 out of 240 trials. This difference is due to the fact
that, in a few trials, some participants did not take any decision
(to agree or refuse the proposed allocation) within the maximum
interval of 3,000 ms. The valid DG trials were subdivided into
three sets: (i) DG selfish behavior including the trials when the
participant playing the role of allocator/proposer agreed with
the most favorable proposals of allocations for himself {(90:10),
(80:20)}(i.e., 90 or 80% of the amount in favor of the participant)
and refused the least favorable amounts {(60:40), (50:50)};(ii) DG
neutral behavior including all trials (both agreed and refused)
with an allocation (70:30); (iii) DG fair behavior including the
trials when the participant playing the role of allocator/proposer
agreed with the least favorable allocations for himself {(60:40),
(50:50)}(i.e., 60 or 50% of the amount in favor of the participant)
and refused the most favorable allocations {(90:10), (80:20)}.
Hence, on the basis of the number (Nb.) of trials belonging to
these three sets, we computed the index DGselfishness (in the range
[–1,+1]), such that the higher the index the more selfish the
behavior, defined as follows:
DGselfishness =(Nb(DG selfish behavior)−Nb(DG fair behavior))
(Nb(DG selfish behavior)+Nb(DG fair behavior)).
(1)
In the UG, all participants played the role of responder and
completed 238.1 ±0.4 trials. The behavioral analysis during UG
was based on the subdivision of valid trials in the next three
sets: (i) UG altruistic behavior including the trials when the
responder accepted the least favorable offers for himself {(90:10),
(80:20)}(i.e., 10 or 20% of the share for himself) and rejected
Frontiers in Systems Neuroscience | www.frontiersin.org 5May 2022 | Volume 16 | Article 765720
Miraghaie et al. ERP Markers of Fairness and Selfishness
FIGURE 2 | Schematic illustration of the time course of the Ultimatum Game (UG) task. The participant always played the role of responder and could either accept or
reject the endowment share randomly presented on the computer screen. The red piece of the pie corresponds to participant’s payoff. (A) Example of a trial when a
participant accepted the randomly proposed share—in this example, 60% for the other player and 40% for the participant playing the role of responder. (B) Example
of a trial when a participant rejected the randomly proposed share—in this example, 90% for the the other player and 10% for the participant—thus ending with a zero
payoff for both players.
the equitable splits {(60:40), (50:50)};(ii) UG neutral behavior
including all trials (both accepted and rejected) offering an
endowment share (70:30); (iii) UG conceit behavior including the
trials when the responder accepted the equitable offers {(60:40),
(50:50)}and rejected the least favorable offers {(90:10), (80:20)}.
Notice that in this version of UG, the equitable splits {(60:40),
(50:50)}corresponded also to the most favorable payoffs that
the participant could expect to receive. In a way similar to DG,
we computed an index termed UGaltruism (in the range [–1,+1])
based on the number of trials in each set, such that the higher
the index the more altruistic (i.e., the less conceit) the behavior,
defined as follows:
UGaltruism =(Nb(UG altruistic behavior)−Nb(UG conceit behavior))
(Nb(UG altruistic behavior)+Nb(UG conceit behavior)).
(2)
On the basis of both indices DGselfishness and UGaltruism, we
performed an agglomerative hierarchical clustering using
the function hclust set with a metric Euclidean distance
and the complete-linkage option of the standard stats
package of R (R Core Team, 2021). In the beginning,
the procedure considered the possibility of 39 clusters,
i.e., one cluster for each participant. Then, the algorithm
analyzed iteratively the outcome of the clustering such
that the number of clusters decreased at each step and
the procedure finally stabilized and stopped with four
clusters, labeled “GrpS,” “GrpB,” “GrpC,” and “GrpF.” The
procedure was repeated with different random seeds at the
initialization. The outcome of repeating the procedure was
that the attribution of three individuals to groups “GrpS”
or “GrpB” depended on the initial random seed. Then, we
defined an additional cluster labeled “GrpA” formed by those
three individuals.
Frontiers in Systems Neuroscience | www.frontiersin.org 6May 2022 | Volume 16 | Article 765720
Miraghaie et al. ERP Markers of Fairness and Selfishness
Table 1 reports the behavioral features of the clusters
of participants and cluster “GrpA” was composed of three
participants whose behavior was intermediate between “GrpS”
and “GrpB.” Participants who expressed a conceit behavior
during UG but a low level of selfishness during DG formed
a stable cluster of fair participants (GrpF). Participants with
a very high value of DGselfishness and the lowest level of
UGaltruism formed the stable cluster of selfish participants (GrpS).
Cluster GrpB corresponded to participants with a medium–
low UGaltruism index and medium–high DGselfishness index. GrpB
participants could be considered a group of more altruistic/less
conceit participants within the context of this study. The most
heterogeneous group of participants (GrpC) was characterized
by variable UGaltruism and medium–low DGselfishness.Figure 3
shows the scatterplot of the 39 participants distributed on
a 2D feature space defined by their corresponding values of
UGaltruism and DGselfishness.
3.2. Reaction Times
Reaction times were measured between the time of the
presentation of the allocation proposal in DG (presentation of
the endowment share in UG) and the time of pressing the letter
key on the computer keyboard. In either game, separately, the
RTs for all 39 participants were faster when the participants
agreed vs. refused the allocation during DG [median RT 381.0,
492.0 ±4.9 ms vs. 408.0, 514.2 ±5.1 ms; Student t(9,093) =
−3.146, p=0.002, d= −0.07] or accepted vs. rejected
the proposed endowment share during UG [median RT 403.0,
500.3±4.3 ms vs. 426.5, 534.1 ±6.4 ms; Student t(8,961) = −4.516,
p<0.001, d= −0.10]. Despite these differences, RTs were
distributed as usual, i.e., a positively skewed distribution with a
long tail which reflected occasional slow responses. These long-
tailed distributions of RTs introduced a bias that was amplified
by the differences between single participants. For this reason, to
account within and between participants, variance of raw RTs,
a mixed model ANOVA with repeated measurements did not
reveal the main effect of response choice [DG: F(1, 76) =2.519,
p=0.117, η2
G=0.03; UG: F(1, 74) =1.694, p=0.197, η2
G=
0.02]. Instead of raw RTs, z-scores of RTs computed for each
participant and each game separately were considered further for
factorial analysis using mixed models with repeated (i.e., single
trials) measurements. With z-scores of RTs, the effect of factor
response was significant in each game [DG: F(1, 76) =14.47,
p<0.001, η2
G=0.16; UG: F(1, 74) =24.62, p<0.001,
η2
G=0.25].
We focused on less conceit/more altruistic (GrpB), fair
(GrpF), and selfish (GrpS) participants, which corresponded to
the three most homogenous groups revealed by the behavioral
analysis. For each game separately, we run an analysis with
factors response (2 levels: Agreed/Accepted and Refused/Rejected
in DG/UG) and group (3 levels: GrpB, GrpF and GrpS)
with repeated measurements. A linear mixed model fit by
maximum likelihood was performed due to the presence
of unbalanced samples (Table 2). A strong main effect of
response was observed in both games, with reaction times
faster when the participants agreed with the allocation or
accepted the proposed endowment share irrespective of the
participants’ group.
During DG, we categorized four sets of trials according to
the behavior: (i) the Selfish trials with the allocator/proposer
in “agreement” with the randomly selected allocations {(90:10),
(80:20)}(i.e., 90 or 80% of the share for the participant); (ii)
the Selfish trials with the allocator/proposer “refusing” the least
favorable (in the current task design) allocations for himself, i.e.,
{(60:40), (50:50)}; (iii) the Fair trials with the allocator/proposer
in “agreement” with the allocations {(60:40), (50:50)}; (iv) the Fair
trials with the allocator/proposer “refusing” the most favorable
allocations for himself, i.e., {(90:10), (80:20)}. Along a similar
scheme, four sets of trials were categorized during UG: (i) the
Altruistic trials when the responder “accepted” the least favorable
outcomes that granted only 10% or 20% of the share to the
participant [i.e., shares {(90:10), (80:20)}]; (ii) the Altruistic
trials when the responder “rejected” equitable offers (i.e., shares
{(60:40), (50:50)}); (iii) the Conceit trials when the responder
“accepted” the equitable offers (i.e., the participant received 40%
or 50% of the share that are also the most advantageous payoff
in the current task design); (iv) the Conceit trials when the
responder “rejected” the least favorable payoff. Note that during
UG, any trial ended without a payoff for the participant if he
rejected the offer (either altruistic or conceit).
For both games, the factorial analysis (Table 2) was run with
factors response (2 levels) and behavior (2 levels). During UG,
we observed a very strong effect of behavior (RTs during conceit
trials shorter than during altruistic trials) [F(1, 89) =4.111, p=
0.045, η2
G=0.21] and confirmed the main effect (although
weaker) of the response choice [F(1, 89) =23.53, p<0.001, η2
G=
0.04]. On the contrary, during DG, no main effects of response
and behavior were observed in groups (GrpB, GrpF, GrpS).
Multiple regression was conducted to see if the indices
DGselfishness and UGaltruism predicted the RTs in a specific set of
trials. During DG, it was found that the level of DGselfishness alone
always explained a significant amount of the variance in RTs in
all sets of trials. We considered both linear and quadratic fits for
raw RTs and z-scaled RTs as explained above, but quadratic fits
always provided better results. During selfish trials (Figure 4A),
irrespective of the response choice, RTs tended to be faster for
participants whose DGselfishness index was high [F(2, 53) =17.00,
p<0.001, R2=0.391, R2
Adjusted =0.368]. During fair trials, it
is interesting to note a specular curve (Figure 4B), characterized
by faster RTs for participants whose DGselfishness index was low
[F(2, 53) =26.66, p<0.001, R2=0.447, R2
Adjusted =0.430].
During UG, UGaltruism alone explained the most of the variance
only in a specific set of trial, i.e., when participants rejected
the proposed endowment share during conceit trials [F(1, 35) =
16.53, p<0.001, R2=0.321, R2
Adjusted =0.301].
3.3. Electrophysiological Results
Figure 5 shows the grand average ERPs for all trials recorded at
fronto-central (FCz and Fz) and posterior-parietal sites (Pz and
CPz) triggered by the stimulus onset in both games and followed
by a response (accept or reject) for all 39 participants grouped
together. The visual inspection of the grand-averaged waveforms
Frontiers in Systems Neuroscience | www.frontiersin.org 7May 2022 | Volume 16 | Article 765720
Miraghaie et al. ERP Markers of Fairness and Selfishness
TABLE 1 | Relative frequencies (median, Mean ±SEM) of the behavioral responses to the Dictator Game (DG) and Ultimatum Game (UG) and corresponding behavioral
indices DGselfishness and UGaltruism of the five groups of participants sorted after an agglomerative hierarchical clustering procedure described in the text.
“GrpS” “GrpA” “GrpB” “GrpC” “GrpF”
N=39 (n=8) (n=3) (n=10) (n=8) (n=10)
Dictator Game
Selfish behavior (%) 79.2 65.0 69.4 45.0 3.1
77.4 ±1.3 67.9 ±5.7 68.5 ±2.6 49.5 ±4.1 8.6 ±2.9
Fair behavior (%) 0.8 16.4 10.6 34.9 77.0
2.7 ±1.3 12.6 ±5.8 11.5 ±2.7 30.4 ±4.1 71.5 ±2.9
DGselfishness 0.98 0.60 0.73 0.13 –0.92
0.93 ±0.03 0.69 ±0.14 0.71 ±0.07 0.24 ±0.10 –0.79 ±0.07
Ultimatum Game
Altruistic behavior (%) 1.3 12.2 23.9 28.4 1.3
4.5 ±2.0 12.7 ±3.8 245 ±1.9 29.4 ±7.2 2.2 ±0.6
Conceit behavior (%) 78.7 67.5 56.1 51.5 78.6
75.4 ±2.0 67.1 ±3.9 55.4 ±1.9 50.5 ±7.2 77.8 ±0.51
UGaltruism -0.97 –0.69 –0.40 –0.29 –0.97
–0.89 ±0.05 –0.68 ±0.10 –0.39 ±0.05 –0.26 ±0.18 –0.95 ±0.01
FIGURE 3 | Scattergram of the behavioral analysis of 39 participants, identified by their identification tag, distributed in a 2D feature space defined by the
corresponding values of UGaltruism and DGselfishness. Five clusters of colored points were identified on the bases of an agglomerative hierarchical clustering procedure,
i.e. “GrpS” (green), “GrpA” (brown), “GrpB” (blue), “GrpC” (turquoise), and “GrpF” (red). The dashed lines correspond to ideal separatrix lines in the feature space.
Frontiers in Systems Neuroscience | www.frontiersin.org 8May 2022 | Volume 16 | Article 765720
Miraghaie et al. ERP Markers of Fairness and Selfishness
TABLE 2 | Reaction times.
Factor Dictator game: Allocator Ultimatum game: Responder
Response choice Agreed Refused Accepted Rejected
Participants’ group
n=855 n=1, 032 n=14, 81 n=395
GrpB (N=10) 355.0 373.5 390.0 409.0
423.5 ±9.1 464.5 ±9.5 502.1 ±8.7 556.2 ±20.3
(z-score) (−0.031 ±0.029) (0.024 ±0.029) (−0.027 ±0.022) (0.110 ±0.052)
n=763 n=1, 077 n=890 n=922
GrpF (N=10) 398.0 432.0 400.0 430.0
515.1 ±12.5 543.6 ±11.1 470.9 ±9.5 520.9 ±11.4
(z-score) (–0.066 ±0.033) (0.047 ±0.027) (–0.064 ±0.028) (0.065 ±0.031)
n=769 n=738 n=724 n=692
GrpS (N=8) 391.0 415.0 440.0 426.5
488.3 ±11.0 521.8 ±12.1 521.7 ±11.8 512.6 ±13.0
(z-score) (–0.064 ±0.030) (0.066 ±0.035) (–0.032 ±0.031) (0.036 ±0.036)
Homogeneity of variances
Levene’s test: F(5, 50) =0.921, p=0.475 F(5, 50) =9.008, p<0.001
Anova-like table
Effect: Response F(1, 50) =12.41, p<0.001 F(1, 50) =23.76, p<0.001
Group F(1, 50) =0.225, p=0.800 F(2, 50) =2.688, p=0.078
Response ×Group F(1, 50) =0.537, p=0.588 F(2, 50) =3.108, p=0.053
Participants’ behavior Fair trials Altruistic trials
n=862 n=1,099 n=622 n=77
395.5 429.0 463.0 298.0
518.6 ±12.3 533.2 ±10.2 597.8 ±16.5 625.3 ±78.5
(z-score) (–0.047 ±0.035) (0.020 ±0.029) (0.242 ±0.048) (0.573 ±0.217)
Selfish trials Conceit trials
n=1, 525 n=1, 748 n=2, 473 n=1, 932
364.0 390.0 393.0 428.5
448.3 ±6.9 494.2 ±8.0 472.6 ±5.7 521.0 ±7.7
(z-score) (-0.070 ±0.023) (0.063 ±0.026) (−0.145 ±0.016) (0.047 ±0.023)
Homogeneity of variances
Levene’s test: F(3, 88) =1.150, p=0.334 F(3, 89) =18.36, p<0.001
Anova-like table
Effect: Response F(1, 88) =0.196, p=0.659 F(1, 89) =4.111, p=0.045
Behavior F(1, 88) =1.537, p=0.218 F(1, 89) =23.53, p<0.001
Response ×Behavior F(1, 88) =0.312, p=0.578 F(1, 89) =1.018, p=0.316
The table reports measurements of the reaction times (RTsin ms) (median, mean ±SEM values and number of trials n) when participants agree (or disagree) with the proposed allocation
during the DG, and when they accept (or reject) the proposal of endowment share during the UG. Statistics of Levene’s test for heteroscedascity (homogeneity of variances test) and
ANOVA-like tables for a linear mixed model fit by maximum likelihood with corresponding main factors and interaction are reported for z-scores of RTs compute for each participant
separately.
showed several components, which were generally observed in
both games and at various scalp locations, but their amplitudes
varied greatly as a function of the participant and of the recording
site. The earliest component was a negative wave (N1) ranging in
latency between 110 and 160 ms post trigger, particularly visible
at fronto-central sites, and at FCz in particular. A posterior-
parietal N1 was often observed, but it was tiny and noisy in the
ERP of several participants, which led us to discard this wave
from further quantitative analysis. This wave was followed by
P2, a positive component much sharper at fronto-central sites
peaking at approximately 220 ms post trigger, and by a negative
wave peaking within the interval 260– 400 ms after trigger onset.
Note that the amplitude of this negative component was deeper
at fronto-central sites for both games, which led us to identify it
as the medial frontal negativity (MFN). At posterior-parietal sites
(Figure 5), the form and latency of this negativity were different
and the component was called N2. A positive wave extending
from 400 to 650 ms after stimulus onset followed MFN/N2 at all
sites. This extended positivity was characterized by two successive
peaks, which might be identified as the putative P3 and the late
positive potential. At posterior-parietal sites, the amplitude of
this positivity was larger than at other sites and the second peak
Frontiers in Systems Neuroscience | www.frontiersin.org 9May 2022 | Volume 16 | Article 765720
Miraghaie et al. ERP Markers of Fairness and Selfishness
FIGURE 4 | Reaction times (in ms, top panels; corresponding z-score in bottom panels) for all 39 participants during the DG as a function of the DGselfishness
behavioral index. All participants played the role of allocator. RTs during Selfish trials (A) and Fair trials (B). Green dots correspond to participants’ RTs when they
agreed with the proposed allocation and red triangles when they refused the allocation. Green (long dashed) and red curves correspond to quadratic fitted
regressions. The black curves correspond to quadratic fits irrespective of the response choice.
was larger than the first peak. Then, the second peak at posterior-
parietal sites appeared as being the most representative and was
identified as the peak of the late positive component (LPC).
A comparative analysis of the grand averaged ERPs during
DG and UG, with all trials pooled irrespective of payoff and
participants’ decision, was carried out on peak latencies and
amplitudes. The analysis was focused on less conceit/more
altruistic (GrpB), fair (GrpF), and selfish (GrpS) participants,
which corresponded to the three most homogenous groups
revealed by the behavioral analysis. We run an analysis with
factors Game (2 levels: UG and DG) and Group (3 levels: GrpB,
GrpF, and GrpS) with repeated measures (i.e., two electrodes for
each area and three measurements at each site, as described in
the Methods section). Due to the presence of unbalanced groups,
we performed the factor analysis with a linear mixed model fit
by maximum likelihood and present the results in an ANOVA-
like (Table 3). Note that wave peaks were not always visible in
all participants and in both games, and therefore the number of
measurements (nin Table 3) for each variable was not always a
regular multiple of the number of participants. For example, in
group GrB with N=10 participants, the maximum number of
measurements for a variable was n=60, but it could actually be
as low as n=48 (e.g., N1 peak of GrpB during DG) if a peak
could be uniquely identified at all electrodes.
In selfish participants (dashed green curves in Figure 6),
the amplitude of N1 peak negativity was reduced in both
games, which accounted for the main effect of the factor Group
(Figure 6). In fair participants (solid red curves in Figure 6),
P2 peak latency was much shorter during DG (lower panel)
than UG (upper panel) [t(110) = −6.198, p<0.001, d=
−1.17]. During DG (lower panel), the negativity of MFN in fair
participant was reduced [GrpF vs. GrpB: t(118) =3.819, p=
0.001, d=0.70; GrpF vs. GrpS: t(106) =5.828, p<0.001,
d=1.13]. At posterior-parietal sites, the main effect of the factor
Group for LPC amplitude (Table 3) was determined by a larger
LPC wave of GrpS during UG [GrpS vs. GrpB: t(106) =6.733,
p=0.001, d=1.30; GrpS vs. GrpF: t(106) =9.562, p<
0.001, d=1.85]. Note that at the fronto-central sites of selfish
participants, a sustained late positive wave also appeared during
UG (dashed green curves in Figure 6, upper panel). Overall, these
observations highlight different brain dynamics between fair and
selfish participants, but these curves were obtained with a super
Frontiers in Systems Neuroscience | www.frontiersin.org 10 May 2022 | Volume 16 | Article 765720
Miraghaie et al. ERP Markers of Fairness and Selfishness
TABLE 3 | Event related potential (ERP) wave components.
Fronto-central N1 Fronto-central P2
Latency [ms] Amplitude [µV] Latency [ms] Amplitude [µV]
Factor Game DG: Allocator UG: Responder DG: Allocator UG: Responder DG: Allocator UG: Responder DG: Allocator UG: Responder
Participants’ group
n=48 n=54 n=48 n=54 n=60 n=58 n=60 n=58
GrpB (N=10) 139.1 130.8 –4.04 –3.94 222.1 222.7 5.01 4.72
139.3 ±1.7 130.0 ±1.2 –4.67 ±0.36 –3.92 ±0.20 217.2 ±2.4 223.4 ±2.0 4.81 ±0.26 4.77 ±0.14
n=60 n=60 n=60 n=60 n=56 n=56 n=56 n=56
GrpF (N=10) 138.4 140.7 –2.83 –3.53 201.3 226.0 4.40 4.83
139.2 ±1.2 141.0 ±2.0 –3.14 ±0.28 –3.65 ±0.17 201.8 ±2.5 227.1 ±3.2 4.40 ±0.26 4.98 ±0.22
n=39 n=48 n=39 n=48 n=48 n=48 n=48 n=48
GrpS (N=8) 128.2 127.8 –1.66 –2.33 205.9 211.3 3.31 3.74
128.9 ±2.3 131.9 ±2.0 –1.40 ±0.19 –2.66 ±0.30 207.0 ±3.5 212.5 ±2.4 4.15 ±0.34 4.12 ±0.32
unpaired t-test (GrpF, GrpS) t(97) =4.357 t(106) =3.159 t(97) = −4.623 t(106) = −2.993 t(102) = −1.239 t(102) =3.515 t(102) =0.603 t(102) =2.254
p-value p<0.001 p=0.012 p<0.001 p=0.018 p=0.218 p=0.004 p=0.548 p=0.156
Cohen’s d d =0.90 d=0.61 d= −0.95 d= −0.58 d= −0.24 d=0.69 d=0.12 d=0.44
Homogeneity of variances
Levene’s test: F(5, 46) =1.080, p=0.384 F(5, 46) =0.896, p=0.492 F(5, 50) =0.595, p=0.704 F(5, 50) =1.188, p=0.329
Anova-like table
Effect: Game F(1, 46) =0.334, p=0.566 F(1, 46) =0.566, p=0.456 F(1, 50) =4.109, p=0.048 F(1, 50) =0.019, p=0.892
Group F(2, 46) =2.087, p=0.136 F(2, 46) =6.055, p=0.005 F(2, 50) =1.130, p=0.331 F(2, 50) =0.427, p=0.655
Game ×Group F(2, 46) =1.016, p=0.370 F(2, 46) =1.334, p=0.273 F(2, 50) =1.068, p=0.351 F(2, 50) =0.083, p=0.920
Fronto-central MFN Posterior-parietal LPC
Latency [ms] Amplitude [µV] Latency [ms] Amplitude [µV]
Factor Game DG: Allocator UG: Responder DG: Allocator UG: Responder DG: Allocator UG: Responder DG: Allocator UG: Responder
Participants’ group
n=60 n=60 n=60 n=60 n=60 n=60 n=60 n=60
GrpB (N=10) 326.5 328.1 -2.23 1.07 524.3 531.1 5.12 3.66
323.1 ±4.3 319.7 ±3.8 -0.91 ±0.58 0.10 ±0.42 525.6 ±3.8 537.5 ±6.2 4.45 ±0.32 3.88 ±0.20
n=60 n=56 n=60 n=56 n=60 n=60 n=60 n=60
GrpF (N=10) 314.6 338.1 1.26 0.62 520.6 581.0 4.19 2.14
307.8 ±4.2 336.5 ±2.4 1.56 ±0.29 0.84 ±0.17 520.1 ±4.3 574.3 ±5.9 3.97 ±0.32 2.20 ±0.30
n=48 n=42 n=48 n=42 n=48 n=48 n=48 n=48
GrpS (N=8) 292.4 318.3 -2.13 0.68 551.4 530.5 4.96 5.75
307.2 ±5.0 315.9 ±4.5 –1.38 ±0.43 –0.30 ±0.41 558.2 ±5.9 535.7 ±3.5 4.67 ±0.21 5.82 ±0.21
unpaired t-test (GrpF, GrpS) t(106) =0.096 t(96) =4.309 t(106) =5.828 t(96) =2.818 t(106) = −5.360 t(106) =5.293 t(106) = −1.726 t(106) = −9.562
p-value p=0.924 p<0.001 p<0.001 p=0.036 p<0.001 p<0.001 p=0.261 p<0.001
Cohen’s d d =0.02 d=0.88 d=1.13 d=0.58 d= −1.04 d=1.02 d= −0.33 d= −1.85
Homogeneity of variances
Levene’s test: F(5, 49) =0.861, p=0.514 F(5, 49) =2.310, p=0.058 F(5, 50) =1.503, p=0.206 F(5, 50) =0.890, p=0.495
Anova-like table
Effect: Game F(1, 49) =1.682, p=0.201 F(1, 49) =0.416, p=0.522 F(1, 50) =1.843, p=0.181 F(1, 50) =0.510, p=0.479
Group F(2, 49) =0.561, p=0.574 F(2, 49) =2.490, p=0.093 F(2, 50) =0.969, p=0.386 F(2, 50) =4.876, p=0.012
Game ×Group F(2, 49) =1.275, p=0.289 F(2, 49) =0.350, p=0.706 F(2, 50) =4.173, p=0.021 F(2, 50) =2.228, p=0.118
The table reports measurements of the peak latency (in ms) and amplitude (in µV) (median, mean ±SEM values and number of measurements n) of N1, P2, and medial frontal negativity
(MFN) recorded at the fronto-central sites (Fz and FCz) and late positive component (LPC) recorded at the posterior-parietal sites (Pz and CPz) irrespective of the endowment share and
of participants’ decision. The table reports Levene’s test for heteroscedascity (homogeneity of variances test) and the type III Anova-like table for a linear mixed model fit by maximum
likelihood with factors Grp and Game, and unpaired t-test comparisons for values of only fair and selfish groups of participants.
Frontiers in Systems Neuroscience | www.frontiersin.org 11 May 2022 | Volume 16 | Article 765720
Miraghaie et al. ERP Markers of Fairness and Selfishness
FIGURE 5 | Grand average (all 39 participants and all trials pooled together) of the ERPs triggered by stimulus presentation during the UG (left panels) and the DG
(right panels). The dotted lines show the confidence intervals determined by the standard error of the mean (SEM). All peaks indicated with arrows correspond to the
wave components whose latency and peak were analyzed in detail: P2 and medial frontal negativity (MFN) at the fronto-central sites (Fz and FCz, upper panels) and
the late positive component (LPC) at the posterior-parietal sites (Pz and CPz, lower panels). All peaks identified within parentheses indicate the wave components that
were not considered in the quantitative analysis.
pool of trials, which means all trials regardless of the amount of
the endowment share, and of the decision-making process with
a related payoff, if any. In the next two subsections, we present
the most salient results on the ERP wave components for each
game, separately.
3.3.1. Dictator Game
We considered separately four sets of ERP recorded trials
following the same categories used for the analysis of reaction
times: Selfish trials when the participant (i) “agreed” with the
highest allocations for himself and (ii) “disagreed” with the
least advantageous allocations; Fair trials when the participant
(iii) “agreed” with the least advantageous allocations and (iv)
“disagreed” with the most favorable allocations. For each
participant, all single trials of one set were averaged together at
each electrode site. The four sets of trials were analyzed by three
observers at two electrode sites (Fz and FCz for fronto-central
ERP waves and Pz and CPz for LPC). We focused our analysis
on the three main groups of participants, which represented a
total of 28 participants. Then, the theoretical maximum number
of measurements of a kind for the whole set of four trials was n=
28 participants×4 sets of trials×2 electrodes×3 observers =672
for each ERP wave.
We analyzed the effect of factors response (2 levels: Agreed
and Refused) and behavior (2 levels: Selfish trials and Fair trials)
on latencies and amplitudes of major ERP peaks by the linear
mixed model with repeated measures (Table 4). In different trials
and in different participants, ERP wave peaks could not always
be distinguished by all observers at all electrode sites, which
ultimately represented an overall number (n) of measurements
between 476 and 513. In order to analyze the effect of these
factors independently, the table reports the values measured for
the response type irrespective of the behavior and the values
measured for the behavior irrespective of the response. Note that
a significant main effect of participant’s behavior was observed
on both latency and amplitude for all ERP waves (Table 4). The
main effect of response choice was significant for MFNlatency and
LPCamplitude. This finding extends the differences between the
groups of fair and selfish participants observed for the latency of
MFN and amplitude of LPC when all trials were pooled together.
It is interesting also to note that the interaction effects
between behavior and response were statistically significant for
P2 and MFN. We investigated this interaction following response
choices (i.e., agreed vs. refused proposed allocations) recorded
in the most representative trials for each group of participants,
which were Selfish trials in selfish participants (GrpS) and Fair
trials in fair participants (GrpF). Selfish participants who refused
fair allocations during DG were characterized by longer P2latency
[t(61) =9.134, p<0.001, d=2.70] and greater P2amplitude
[t(61) =16.727, p<0.001, d=4.95] over refusal of selfish
payoffs. This suggests that refusal of fair payoffs was a rewarding
choice for selfish participants. In fair participants (GrpF), longer
P2latency [t(88) =3.673, p<0.01, d=0.82] was observed
when they refused selfish allocations over fair ones. This suggests
that refusal of selfish gains was a rewarding choice for fair
participants. Accordingly, when fair participants refused selfish
allocations (Figure 7A, left panel, dashed line), fronto-central
P2amplitude was greater [t(118) =3.43, p<0.01, d=0.63] than
after acceptance of least favorable gain for themselves (Figure 7A,
left panel, solid line).
In fair participants, the depth of MFN negativity was reduced
compared to the other groups (as shown in Table 3). In GrpF,
Frontiers in Systems Neuroscience | www.frontiersin.org 12 May 2022 | Volume 16 | Article 765720
Miraghaie et al. ERP Markers of Fairness and Selfishness
FIGURE 6 | Grand average ERPs (all trials pooled together) recorded at fronto-central sites (Fz and FCz pooled together) during the UG (top panel) and the DG
(bottom panel). Colored curves correspond to fair (GrpF, solid red curves), selfish (GrpS, dashed green curves) and less conceit (GrpB, dotted blue curves) participants
with confidence intervals determined by the standard error of the mean (SEM). The arrows point out at several significant differences emphasized in the text. See also
Table 3.
MFNlatency was shorter by approximately 60 ms in fair trials
[t(118) = −11.333, p<0.001, d= −2.07] when they accepted
fair (bringing the least favorable gains) over the refusal of selfish
(with the most advantageous payoffs) allocations (Figure 7A,
left panel). In the group formed by less conceit/more altruistic
participants (GrpB), MFNlatency was also shorter by 60 ms [t(76) =
−8.089, p<0.001, d= −1.88] and with greater negativity
[t(76) =3.175, p<0.05, d=0.74] after acceptance
of the least advantageous allocations during fair trials. When
these participants accepted the least advantageous allocations,
MFNlatency was also shorter by approximately 70 ms [t(88) =
−7.567, p<0.001, d= −1.69] and with greater negativity
[t(88) =5.029, p<0.001, d=1.12] over acceptance
of selfish (with the most advantageous payoffs) allocations.
Then, it appears that it is rather the proposal of the fair
and least advantageous allocations that evoked larger MFN in
GrpB participants.
In these participants (GrpB, the less conceit/more altruistic),
the amplitude of posterior-parietal LPC (Figure 7A, middle
panel) was greater in agreed (solid line) than refused (dashed
line) most advantageous (selfish) trials [t(118) =5.72, p<
0.001, d=1.04]. When these participants refused selfish
allocations, the LPClatency was shorter by approximately 60 ms
over refusal of fair allocations [t(81.7) = −12.878, p<0.001,
d= −2.539]. Among fair allocators, the most relevant finding
was a LPClatency longer by approximately 40 ms after fair
proposals, irrespective of the response. Overall, these findings
emphasize that different dynamics distinguished the groups of
participants based on behavioral responses. Fair participants
were mainly characterized by ERP markers at fronto-central
sites during the early stages of decision making in our DG
task, while less conceit and more altruistic participants showed
the most salient markers in the mid-late components of
the ERPs.
3.3.2. Ultimatum Game
We considered separately four sets of ERP recorded trials
following the same categories used for the analysis of reaction
times during UG: Altruistic trials when the responder (i)
“accepted” the least favorable offers and (ii) “rejected” equitable
offers and received zero payoff; Conceit trials when the
responder (iii) “accepted” the equitable offers (that are the
most advantageous payoff in the current task design) and
(iv) “rejected” with the least favorable endowment shares
and received zero payoffs. All single trials of each set were
averaged, pooled at fronto-central and posterior-parietal sites,
and analyzed following the same procedure used for the DG.
We analyzed P2, MFN, and LPC peaks, and the overall number
of measurements (n) was 448, 518, and 514, respectively.
Table 5 reports the latencies and amplitudes of major ERP peaks
measured for the response type irrespective of the behavior
and the values measured for the behavior irrespective of
the response.
In the UG, the factors for the analysis with the linear
mixed model with repeated measures were response (2 levels:
Accepted, Rejected) and behavior (2 levels: Altruistic trials,
Frontiers in Systems Neuroscience | www.frontiersin.org 13 May 2022 | Volume 16 | Article 765720
Miraghaie et al. ERP Markers of Fairness and Selfishness
TABLE 4 | Dictator game.
Dictator game Allocator
Fronto-central P2 (n=476) Fronto-central MFN (n=513) Posterior-parietal LPC (n=507)
Factor Latency [ms] Amplitude [µV] Latency [ms] Amplitude [µV] Latency [ms] Amplitude [µV]
Response choice
n=227 n=227 n=252 n=252 n=243 n=243
Agreed 210.6 4.99 310.6 –1.13 540.3 5.64
216.4 ±1.6 4.82 ±0.22 315.6 ±2.9 –1.24 ±0.35 544.7 ±3.6 4.81 ±0.24
n=249 n=249 n=261 n=261 n=264 n=264
Refused 219.7 4.84 343.0 –0.52 535.4 3.92
219.9 ±1.5 5.13 ±0.24 342.9 ±2.4 –1.05 ±0.28 544.3 ±2.6 3.53 ±0.19
Participants’ behavior
n=221 n=221 n=237 n=237 n=228 n=228
Fair trials 213.3 5.37 330.4 –0.47 582.7 3.87
221.2 ±1.8 5.54 ±0.25 327.2 ±2.8 –1.54 ±0.35 566.6 ±4.0 3.66 ±0.25
n=255 n=255 n=276 n=276 n=279 n=279
Selfish trials 215.6 4.61 327.8 –1.05 530.9 4.84
215.7 ±1.4 4.50 ±0.21 331.5 ±2.7 –0.80 ±0.28 526.4 ±1.6 4.54 ±0.18
Homogeneity of variances test
Levene’s test: F(3, 77) =0.203 F(3,77) =0.205 F(3, 87) =1.164 F(3, 87) =0.656 F(3, 84) =8.064 F(3, 84) =1.093
p=0.894 p=0.893 p=0.328 p=0.581 p<0.001 p=0.357
Anova-like table
Effect: Response F(1, 451) =3.366 F(1, 455) =1.424 F(1, 485) =125.5 F(1, 488) =1.585 F(1, 482) =0.081 F(1, 481) =23.72
p=0.067 p=0.233 p<0.001 p=0.209 p=0.776 p<0.001
Behavior F(1, 463) =15.79 F(1, 471) =10.40 F(1, 492) =5.586 F(1, 499) =16.19 F(1, 502) =76.49 F(1, 497) =6.018
p<0.001 p=0.001 p=0.018 p<0.001 p<0.001 p=0.015
Response ×Behavior F(1, 447) =6.605 F(1, 450) =35.24 F(1, 485) =120.6 F(1, 488) =6.189 F(1, 482) =2.973 F(1, 480) =3.131
p=0.010 p<0.001 p<0.001 p=0.013 p=0.085 p=0.077
All participants played the role of allocators. The table reports measurements of each peak’s latency and amplitude (median, mean ±SEM values and number of measurements n) for
trials independently sorted according to factors response and behavior. Statistics and significance are reported for Levene’s test for heteroscedascity (homogeneity of variances test)
and the type III Anova-like table for a linear mixed model fit by maximum likelihood.
Conceit trials). In the absence of main effects, the strong
interaction effect observed for P2 suggested a more detailed
analysis. In selfish participants, it is interesting that P2latency was
longer by approximately 35 ms [t(64) =5.841, p<0.001,
d=1.44] after acceptance of the least favorable outcomes
(paying only 10 or 20% of the share to the participant) over the
acceptance of equitable shares (that granted 40 or 50% of the
share). This comparison was weaker but also held for P2amplitude,
which was greater after accepting the least favorable outcomes
[t(53.7) =3.204, p<0.05, d=0.80]. This suggests that even
acceptance of the least favorable payoffs represents a rewarding
choice for selfish participants, in agreement with the hypothesis
that selfish participants tend to maximize their gain under all
circumstances. GrpB participants (i.e., the least conceit/the most
altruistic) also showed an interesting effect on P2. In this group,
the latency of P2 was longer by approximately 40 ms [t(70) =
4.610, p<0.001, d=1.46] and P2 amplitude was greater
[t(12.5) =3.422, p<0.05, d=1.25] after acceptance of
least favorable over equitable shares. This supports the rationale
that accepting less favorable payoffs was a rewarding choice for
altruistic participants.
For MFN, significant main effects of participant’s behavior
and response choice were observed on both peak latency and
amplitude (Table 5). When selfish participants rejected the least
favorable payoff over the acceptance of the equitable (most
advantageous in this task design) offers (Figure 7B, left panel),
MFNlatency was shorter by 30 ms [t(94) = −5.968, p<0.001,
d= −1.22] and MFN negativity was greater [Welch’s t(93.2) =
4.003, p<0.001, d=0.82]. In fair participants, after
acceptance of least favorable over the acceptance of equitable
(most advantageous) payoffs, the latency of MFN was also shorter
by approximately 30 ms [t(94) = −6.684, p<0.001, d=
−1.41] and MFN negativity was greater [Welch’s t(39.7) =6.903,
p<0.001, d=1.59].
A similar pattern observed for MFN at fronto-central sites
between fair and selfish participants did not hold anymore
at posterior-parietal sites. The main effect of the participant’s
behavior was very strong for latencies and amplitudes of LPC
peak (Table 5), and the pattern for LPC was opposite between
fair and selfish participants (Figure 7B). LPClatency during conceit
trials was shorter in selfish participants after the rejection of
least advantageous over the acceptance of most advantageous
Frontiers in Systems Neuroscience | www.frontiersin.org 14 May 2022 | Volume 16 | Article 765720
Miraghaie et al. ERP Markers of Fairness and Selfishness
FIGURE 7 | Averaged ERPs (and ±SEM bands) recorded at fronto-central sites (Fz, FCz) and posterior-parietal sites (Pz, CPz). (A) DG: all participants played the role
of allocator/proposer. The most representative trials for GrpS (left panel) and GrpB (middle panel) participants were Selfish trials and Fair trials for GrpF (right panel).
Time zero corresponds to the presentation of the endowment share to the allocator. Solid lines correspond to trials when the participant agreed with the proposal and
dashed lines when the participant refused the proposal. (B) UG: all participants played the role of responder. The most representative trials for all groups of
participants were Conceit trials. Time zero corresponds to the presentation of the endowment share to the responder. Solid lines correspond to trials when the
participant accepted the proposal and dashed lines when the participant rejected the proposal. Asterisks and labeled wave peaks indicate the most noticeable
differences also commented in the text.
(equitable) offers [Welch’s t(81.8) = −3.945, p<0.001,
d=0.82], while it was shorter in fair participants after
acceptance of most advantageous payoffs over rejection of
least advantageous offers [Welch’s t(104.7) =4.266, p<
0.001, d=0.71].
During UG, fewer Altruistic trials were recorded than
Conceit trials because participants in all groups expressed little
altruism and more conceitedness. However, it is worth reporting
some noticeable differences in LPC wave between the groups
of participants during altruistic trials. In selfish participants,
acceptance of most favorable payoffs corresponded to a shorter
LPClatency by about 90 ms [Welch’s t(57.8) =11.220, p<
0.001, d=2.82]. On the opposite, in fair participants, it
is the rejection of most favorable payoffs that corresponded
to a shorter LPClatency by about 130 ms [Welch’s t(51.84) =
18.891, p<0.001, d=5.12]. Participants showing less
conceitedness and more altruism (GrpB) showed an intermediate
pattern in comparison with selfish and fair participants. In
this group, we observed shorter latency [t(110.7) = −5.427,
p<0.001, d= −1.01]) and greater amplitude of LPC
[t(108.2) =3.584, p<0.001, d=0.67] when they accepted
equitable (but also most advantageous) offers over acceptance
of inequitable splits of the endowment. Overall, the analysis of
ERPs during the UG showed selfish participants being strongly
characterized at the early stages of the decision making and
with a pattern of brain activity opposite to fair participants at
later stages.
3.3.3. Dimensional Analysis of ERP Peaks
The differences between the participants’ groups observed in
Tables 4,5suggest that the ERP peaks might also be correlated
with the behavioral indices, which defined the participants’
groups. This analysis requires samples of data with behavioral
ratings distributed throughout the values range, but the range
of UGaltruism was limited to values between –1 and 0 due to
the bias toward conceitedness. The most significant correlations
were observed with peak latencies, which are characterized by less
variance than amplitudes. During fair trials of DG, fronto-central
P2 peak latency correlated linearly with DGselfishness [Pearson
correlation coefficient r=0.63, 95% CI [0.38, 0.79], t(36) =
4.778, p<0.001] when the participant agreed or refused the
allocation (Figure 8A). Irrespective of the response choice, the
Frontiers in Systems Neuroscience | www.frontiersin.org 15 May 2022 | Volume 16 | Article 765720
Miraghaie et al. ERP Markers of Fairness and Selfishness
TABLE 5 | Ultimation game.
Ultimatum game Responder
Fronto-central P2 (n=448) Fronto-central MFN (n=518) Posterior-parietal LPC (n=514)
Factor Latency [ms] Amplitude [µV] Latency [ms] Amplitude [µV] Latency [ms] Amplitude [µV]
Response choice
n=264 n=264 n=300 n=300 n=288 n=288
Accepted 223.4 5.28 322.3 –0.05 517.3 5.59
226.3 ±1.5 5.26 ±0.24 318.6 ±1.9 –0.65 ±0.32 521.8 ±2.2 5.56 ±0.20
n=184 n=184 n=218 n=218 n=226 n=226
Rejected 218.1 4.67 302.0 –0.72 513.6 5.41
218.3 ±1.7 4.61 ±0.35 303.4 ±2.0 –1.31 ±0.42 516.0 ±3.5 5.55 ±0.35
Participants’ behavior
n=158 n=158 n=188 n=188 n=184 n=184
Altruistic trials 223.0 5.48 289.8 –3.43 527.9 6.34
222.9 ±2.4 5.67 ±0.49 296.7 ±2.3 -2.90 ±0.56 530.0 ±4.2 6.57 ±0.41
n=290 n=290 n=330 n=330 n=330 n=330
Conceit trials 219.9 4.82 319.8 0.67 514.4 4.86
223.1 ±1.2 4.63 ±0.16 321.0 ±1.6 0.19 ±0.22 513.3 ±1.9 4.99 ±0.18
Homogeneity of variances test
Levene’s test: F(3, 72) =0.409 F(3, 72) =6.421 F(3, 83) =1.360 F(3, 83) =7.078 F(3, 83) =5.632 F(3 ,83) =4.075
p=0.747 p<0.001 p=0.261 p<0.001 p<0.01 p<0.01
Anova-like table
Effect: Response F(1, 430) =3.689 F(1, 441) =1.738 F(1, 498) =63.19 F(1, 499) =5.624 F(1, 499) =0.032 F(1, 499) =5.896
p=0.055 p=0.188 p<0.001 p=0.018 p=0.859 p=0.016
Behavior F(1, 429) =1.057 F(1, 439) =5.864 F(1, 491) =143.2 F(1, 492) =59.10 F(1, 489) =16.51 F(1, 489) =36.77
p=0.304 p=0.016 p<0.001 p<0.001 p<0.001 p<0.001
Response ×Behavior F(1, 430) =13.80 F(1, 441) =4.713 F(1, 497) =2.605 F(1, 498) =0.271 F(1, 501) =0.282 F(1, 501) =11.16
p<0.001 p=0.030 p=0.107 p=0.603 p=0.595 p=0.001
All participant played the role of responders. The table reports measurements of each peak’s latency and amplitude (median, mean ±SEM values and number of measurements n) for
trials independently sorted according to factors response and behavior. The same legend of Table 4.
slopes of both regression curves were similar and the smaller the
selfishness, the shorter the latency of the P2 peak [F(1, 40) =17.45,
p<0.001, R2=0.304, R2
Adjusted =0.286]. These results should
be viewed taking into account the correlation between RTs and
selfishness during DG (i.e., the smaller the selfishness, the shorter
the RTs, Figure 8B).
A positive correlation was also observed during altruistic
trials of UG (Figure 8B) between the values of selfishness and
the posterior-parietal LPC peak latency [r=0.92, 95% CI
[0.69, 0.98], t(8) =6.657, p<0.001] when the participants
rejected the proposed payout [F(1, 8) =44.31, p<0.001,
R2=0.847, R2
Adjusted =0.828]. The only ERP wave
that correlated well with altruism is the fronto-central MFN
(Figures 8C,D) when the participants agreed with the proposed
allocation. Interestingly, the smaller the altruism, the shorter
the latency of MFN peak [r=0.67, 95% CI [0.36, 0.84],
t(22) =4.186, p<0.001] during selfish trials [F(1, 22) =
17.52, p<0.001, R2=0.443, R2
Adjusted =0.418] and
the larger the altruism, the deeper the amplitude of MFN
peak [r= −0.58, 95% CI [−0.82, −0.16], t(16) = −2.860,
p<0.001 during fair trials F(1, 16) =8.180, p=0.011,
R2=0.338, R2
Adjusted =0.297].
4. DISCUSSION
Human life is characterized by situations where the maximization
of the individual payoff in face-to-face negotiations are
challenged by the socio-cultural environment that contributed to
shaping moral phenomena such as altruism and fairness (Altman,
2005) and the social distance to the other party, which affects the
subjective perception of justice (Yu et al., 2015). In the context
of an increasing number of contactless monetary transactions,
our study aimed to test the hypothesis whether the characteristic
patterns of brain activity—recorded by ERPs—were somatic
markers associated with the behavioral profiles of individuals
determined by a combination of DG and UG performances
in the absence of effective human interactions between the
parties. The analysis of the major ERP wave components
Frontiers in Systems Neuroscience | www.frontiersin.org 16 May 2022 | Volume 16 | Article 765720
Miraghaie et al. ERP Markers of Fairness and Selfishness
FIGURE 8 | Scattergrams of significant correlations between ERP peaks and behavioral indices. (A) Latency of P2 peak vs. DGselfishness during fair trials of DG. The
black curve corresponds to the linear fit irrespective of the response choice. (B) Latency of LPC peak vs. DGselfishness during altruistic trials of UG. (C) Latency of MFN
peak vs. UGaltruism during selfish trials of DG. (D) Amplitude of MFN peak vs. UGaltruism during fair trials of DG. Green dots correspond to participants’ peak latencies
when they agreed with the proposed allocation and red triangles when they refused the allocation. Green (long dashed) and red curves correspond to linear fitted
regressions.
showed that our observations were in agreement with several
findings reported in the literature, which were mainly focused
on only one of those classical neuroeconomic games (Nagel,
2001; De Martino et al., 2006; Fellner et al., 2009; Güth, 2010;
De Neys et al., 2011; Fiori et al., 2013). The identification
of two homogeneous groups of participants, corresponding to
selfish and fair participants, allowed to report more precisely
the activity patterns associated with clearly modeled behaviors
characterized by spiteful and costly punishments, which are,
respectively, positively and negatively correlated with impulsive
choices (Sanfey et al., 2003; Rilling and Sanfey, 2011; Rodrigues
et al., 2018).
4.1. Behavioral Results
Distinct groups of individuals were identified following a
hierarchical cluster analysis based on indices associated with
conceitedness/altruism and selfishness/fairness determined by
performance during Ultimatum and DG. The definitions of
conceitedness/altruism and selfishness/fairness are operational
and we do not pretend that they necessarily overlap with
semantic definitions that are culturally biased. The most
representative and homogeneous groups were formed by fair
participants (GrpF, individuals expressing a conceit behavior
during UG and with a low level of selfishness during DG), by
individuals characterized by less conceitedness and medium-high
Frontiers in Systems Neuroscience | www.frontiersin.org 17 May 2022 | Volume 16 | Article 765720
Miraghaie et al. ERP Markers of Fairness and Selfishness
selfishness (GrpB), and selfish participants (GrpS, individuals
with highest values of selfishness and lowest levels of altruism).
The validity of the categorization of these groups of participants
is further confirmed by the observation that RTs and, even more
importantly, several characteristics of the ERP peaks —i.e., the
somatic markers—were correlated with the behavioral indices.
Hence, these indices are likely to reflect what they claim to
reflect but they should not be extended beyond our experimental
protocol without careful consideration.
In the UG, it is rationale to expect that the proposer offers the
smallest possible amount and the responder accepts any offer. In
this study, we could not fully evaluate a true altruistic attitude
in behavioral performance. All participants played the role of
responder and expressed conceitedness to some extent, because
the most favorable payoffs for the responder were equitable offers
(i.e., with 40 or 50% of the share for the participant himself). The
rejection of an unfair offer by the responder in the UG is often
explained by a bias toward profit maximization in association
with positive social factors, such as common ethical principles
and friendship, by negative factors, such as fear of the perceived
consequences of having one’s offer rejected, and from the sense of
guilt linked to worries about the opponent’s outcome (Carver and
Miller, 2006; Marchetti et al., 2011; Gaertig et al., 2012). To test
a responder’s attitude from selfless/altruistic to truly greedy, even
in the absence of face-to-face interaction, UG’s task design would
have to include endowment shares paying 60–70% of the amount
in favor of the responder and the most inequitable endowment
shares (i.e., splits that provide 80–90% of the amount to the
responder). We are aware of the limitations of the task design
chosen in this study, which was driven by a compromise between
the total duration of the experiment and the number of test
repetitions that are necessary to get meaningful ERPs for both
neuroeconomic games.
During DG, all participants played the role of allocator, who is
the player imposing an endowment share on the other party, and
the behavioral performance distinguished two opposite groups,
selfish (GrpS) and fair (GrpF) participants. However, during UG,
all participants played the role of responder and both groups
GrpS and GrpF tended to reject unequal offers (Table 1). The
rejection of the least favorable offers by the participants (i.e.,
payoffs of 10–20% of the amount at stake) is considered to
be the expression of a punishment toward a selfish proposer
(Forber and Smead, 2014; Ma and Hu, 2015) and was interpreted
either as a spiteful punishment (Marlowe et al., 2011) or as
costly—rather than altruistic—punishment (Brethel-Haurwitz
et al., 2016; Yamagishi et al., 2017).
During UG, when responders are in passive position expecting
to receive fair offers (Polezzi et al., 2008; Boksem and De Cremer,
2010), selfish participants (GrpS) were driven by spiteful
punishment and fair participants (GrpF) by costly punishment.
GrpA and GrpB participants also expressed a tendency
toward selfishness, thus suggesting that spiteful behavior was
predominant. This is not surprising because a single altruistic
decision is likely to lack any reward for an individual unless its
repetition over time proves it is truly beneficial. The time frame
of our experiment is very limited and the feature of learned
behavior is in contrast to the spiteful punishment exercised by
selfish participants who feel emotionally aroused when exposed
to an unfair proposal even in a short lasting experimental context.
4.2. Socio-Cultural Dimension
Spiteful punishment is a kind of anti-social punishment applied
in various ways that reflect the psychological and social
dimensions of regional-cultural values (Chuah et al., 2007;
Sylwester et al., 2013). It is recognized that the evolution of
fairness is promoted by spitefulness and inhibited by altruism
(Fehr and Gächter, 2002; Fehr and Rockenbach, 2004; Zhang
and Fu, 2018). Behavioral differences have been observed in
association with cross-country differences in socio-economic
status and respect for authority (Inglehart, 2000; Oosterbeek
et al., 2004; Chuah et al., 2007). High levels of cooperation and
altruistic behavior can be observed in societies where people face
economic disparities and poor social standing and where they are
exposed to punitive behavior if they violate certain norms and are
treated as inappropriate (Fehr and Gächter, 2002; Nowak, 2006;
Egas and Riedl, 2008).
In the present study, the socio-cultural dimension of
our sample of participants is homogenous and made up of
young Iranian males with academic education. The choice
to test participants without including both genders avoided
the introduction of a gender effect discussed elsewhere in
the literature (Eckel and Grossman, 2001; Servátka, 2009;
García-Gallego et al., 2012; Li et al., 2018). Despite the
evidence of altruistic tendencies in the Iranian people, such as
willingness to pay for health services (Javan-Noughabi et al.,
2017), organ donation (Abbasi et al., 2018), blood donation
(Javadzadeh Shahshahani et al., 2006), moral sensitivity of nurses
(Borhani et al., 2017; Amiri et al., 2020), the identification of
the mechanisms associated with the perception of fairness has
never been carefully investigated. Our results show that despite
spitefulness appeared to prevail over fairness, we have observed
a sizable sample of participants expressing costly/altruistic
punishment and a tendency toward less conceit, somehow
altruistic, behavior. It has also been established that in many
experimental neuroeconomic contexts the game appears to
reflect common interactional patterns of daily life, with greater
behavioral variability between social groups than has been found
among societies (Henrich et al., 2005), and the multiple play of
UG does not affect rates of rejection (Oosterbeek et al., 2004).
Therefore, we are confident that the participants included in
this study did not introduce any strong behavioral bias toward
an extreme behavioral strategy due to the homogeneous socio-
demographic composition of our group of participants.
4.3. Fronto-Central N1
In ERP studies, the latency and amplitude of a cortical wave
component vary with the processing speed and the amount of
cognitive resources required to reach the necessary degree of
information processing for the completion of a certain mental
task (Fabiani et al., 2007). The earliest ERP wave component that
we have analyzed is the N1 negative component, much larger at
fronto-central sites, in the 110–160 ms time window after trigger
onset. It is important to distinguish the frontal N1 discussed here
from the well known and described N1 component at occipital
Frontiers in Systems Neuroscience | www.frontiersin.org 18 May 2022 | Volume 16 | Article 765720
Miraghaie et al. ERP Markers of Fairness and Selfishness
and temporal scalp sites associated with visual-spatial selective
attention (Kutas et al., 1994; Mangun, 1995). The fronto-central
N1 wave component is expected to be associated with visual
processing of a functional representation of the stimulus (Antal
et al., 2000; Proverbio et al., 2007) and when participants expected
a stimulus for decision making on whether to act or not to
act (Di Russo et al., 2019). Such top-down prefrontal control
of decision making (Fuster, 2015) suggests that N1 reflects a
proactive cognitive control to access the procedural knowledge
necessary to enable an active motor response (Petit et al., 2006;
Proverbio et al., 2007; Di Russo et al., 2019).
We observed that fronto-central N1 did not vary much
between the games, but it was strongly associated with the group
of participants. The greater the selfishness, the shorter the latency
of N1, and in the group of selfish participants, this component
peaked about 10 ms earlier than fair participants and with
smaller amplitude. In our tasks, the visual cues carry a cognitive
value associated with the costs and benefits determined by the
behavioral/personality traits of each individual. Hence, we can
interpret the N1 difference between fair and selfish participants
as the first evidence in ERPs between participants who have
otherwise expressed costly and spiteful forms of punishment.
Longer N1 latencies and larger amplitudes in fair participants
are compatible with top-down control of a rather rich and less
stereotyped set of initial activity patterns. Our results might also
be interpreted in agreement with an anterior N1 enhancement
assumed to reflect the cost of a modality change and readjustment
of attentional weight-setting in order to optimize target detection
(Töllner et al., 2009; Hsieh and Wu, 2011).
4.4. Fronto-Central P2
The P2 wave peaked in the range of 180 to 260 ms and
was most salient in the fronto-central area, where a top-down
process using previous experience and expectations is indicative
of stimulus evaluation rather than response production (Potts,
2004; Rauss and Pourtois, 2013; Fuster, 2015). Increased fronto-
central P2 is expected in response to rewarding contextual
outcomes, in participants who feel gratification following the
amount of cognitive resources activated by a reward signal in
the brain, which may include satisfaction when observing the
opponent’s misfortune (Falco et al., 2019; Weiss et al., 2020).
In previous decision-making studies, it was also suggested that
P2 is modulated by the degree of predictability of the outcomes
(Polezzi et al., 2008) and by the social context (Hu et al., 2014;
Liu and Huang, 2015). Our results are in agreement with the
rationale that refusal of fair payoffs is a rewarding choice for
selfish participants and refusal of selfish payoffs is a rewarding
choice for fair participants. These findings support the hypothesis
that in DG, a selfish player required more cognitive resources
when he refused to allocate a fair share and a fair player required
more cognitive resources when he agreed to assign a selfish
profit. In UG, the analysis of the P2 wave suggested that selfish
participants tend to maximize their gain with greater and longer
P2 even after acceptance of less favorable payoffs. Moreover,
we found evidence that accepting less favorable payoffs was a
rewarding choice for the group of more altruistic participants (or
less conceit, GrpB).
4.5. Medial Frontal Negativity
The medial frontal negativity is a wave component that
has been observed in association with some assessments of
fairness (Botvinick et al., 1999; Gehring and Willoughby, 2002;
Alexopoulos et al., 2012; Li et al., 2020), social status, and
altruistic bias (Van der Veen and Sahibdin, 2011; Sun et al.,
2015; Cui et al., 2019; Mayer et al., 2019), but only one study
has reported quantitative ERP results in allocators during DG (Li
et al., 2020). However, these studies did not clearly distinguish
between behavioral groups. In our study, we observed that in
both games the negativity of MFN tended to be greater when
the latency was shorter. In general, the greater the selfishness,
the deeper the negativity of MFN. Being associated with
disadvantageous inequitable offers and with altruistic tendency,
MFN is expected to be larger in altruistic participants. This
was indeed our observation for GrpB participants playing the
DG, who expressed less conceit and more altruistic behavior.
This observation was further confirmed by the correlation of
MFN amplitude and the index UGaltruism in the fair trials of DG
when the participants playing the role of allocator accepted the
proposed amount.
In fair participants, the latency of MFN was shorter during DG
after accepting to allocate a fair amount to the other player than
after rejection of a selfish allocation, and during UG the latency of
MFN was longer after acceptance of the most advantageous over
the acceptance of least favorable splits of the endowment share. In
selfish participants, spiteful punishment was revealed by shorter
latency and reduced MFN negativity after the rejection of the
least favorable payoffs over the acceptance of most advantageous
offers. These results support the hypothesis that in UG the
MFN amplitude of responders is likely to be associated with
an emotional reaction to the expectation of fairness by the
proposers. Thus, the perception of the offers by the responders—
all participants of this study played this role—depended on
the behavioral group, and not on the absolute value of the
endowment share (Van der Veen and Sahibdin, 2011; Mayer
et al., 2019; Spapé et al., 2019; Li et al., 2020). In general, our
results may be interpreted in accordance with the hypothesis of a
short latency of MFN as a sign of an emotional response, which
required little cognitive evaluation and expressed reckless mental
processing (Brookhuis et al., 1981; Kok, 1997; Codispoti et al.,
2007; Fukushima and Hiraki, 2009; Boksem et al., 2011). This is
also in agreement with the presumed generation of MFN in the
anterior cingular cortex in association with emotional regulation
(Etkin et al., 2011; Kanske and Kotz, 2011; Stevens et al., 2011;
García-Cabezas and Barbas, 2017).
4.6. Posterior-Parietal Late Positive
Component
Evidence reported in the literature indicates that LPC-associated
activity is likely to be generated by a widespread network of
cerebral sources including the frontal lobe, the cingulate cortex,
and the parietal regions involved in attention control (Sabatinelli
et al., 2007; McDonald and Green, 2008; Scharmüller et al., 2011;
Sun et al., 2017). LPC is closely related to relevant motivational
stimuli, to the subsequent sustained allocation of attention (Ito
Frontiers in Systems Neuroscience | www.frontiersin.org 19 May 2022 | Volume 16 | Article 765720
Miraghaie et al. ERP Markers of Fairness and Selfishness
et al., 1998; Wu et al., 2011b; Bamford et al., 2015), and to
the saliency of an outcome also in social comparisons (Sun
et al., 2020; Sharif et al., 2021; Zhang et al., 2021). It could be
hypothesized that a large LPC wave should characterize selfish
allocators playing the Dictator Game. In both games, we have
indeed observed greater LPC (and at shorter latency in UG) in
selfish over fair participants. Selfish and fair participants were
characterized by quiet opposite patterns of activity recorded
at the posterior-parietal area. Recent evidence provided by
the analysis of a modified Dictator Game exploring the role
of social statuses of players (Cui et al., 2019) revealed that
LPC amplitude was more sensitive to the interaction of the
recipient’s status and fairness— i.e., with a tendency of greater
amplitude triggered by unfair endowment shares for a high-
status recipient and greater amplitude triggered by fair splits
for a medium-status recipient. It is interesting to note that
selfish participants were also characterized during UG by a
sustained late positive wave at the unusual fronto-central sites.
This observation suggests that in these participants a potential
source probably not located at the cortical level, but deeper in
the brain, might contribute to a late positive wave visible at
fronto-central and at posterior-parietal areas.
It is rationale that altruistic players expect equitable shares
of the endowment during the UG. Being associated with
motivation and subsequent allocation of attention, the amplitude
of posterior-parietal LPC is expected to be greater in altruistic
responders playing the UG. In agreement with this hypothesis,
we observed greater amplitude of LPC in GrpB participants when
they accepted equitable over inequitable splits of the sum at stake.
Moreover, during DG, the latency of LPC was shorter in GrpB
allocators when they refused more advantageous allocations over
the refusal of fair allocations. Overall, the differences between
selfish and fair participants support the hypothesis that LPC at
posterior-parietal sites reflects a processing stage that evaluates
norms of fairness (Wu et al., 2011b) in association with saliency
interacting with context (Minnix et al., 2013; Stolz et al., 2019;
Sun et al., 2020; Bauer et al., 2021).
4.7. Limitations
A critical limitation of our study is common to most
neuroeconomic studies, that is whether studies based on
experimental protocols in laboratory settings can really give
insights into the human brain activity outside real-world
situations (Clithero et al., 2008; Huettel and Kranton, 2012;
Konovalov and Krajbich, 2019). There is no doubt that
interaction between real people plays a key role in business
relationships, which are based on a much more complex set
of contextual signals with an individual realm of emotions and
feelings that help shape individual personalities. In our protocol,
the participants were informed that they were playing against
a human party located elsewhere, without specifying whether
the other party or other participants was the experimenter. This
is a workaround, but it is important that the participants were
not simply playing for nothing because they ultimately received
a real amount of money that depended on the performance
accumulated at the games. Future studies should also require
the participants to fill out questionnaires defining personality
traits (e.g., HEXACO; Ashton et al.,2014) but a validated Persian
version of this inventory scale (Basharpoor et al., 2019) was not
yet available at the time our study was run and completed.
The semantic definition of benefit in the economic context
set by neuroeconomic games refers to a rational choice aimed at
achieving the optimal maximization of gain (Bernheim, 2009).
The definitions of altruism, selfishness, and fairness are very
subjective, but we have defined the sets of conceit/altruistic and
selfish/fair trials within a precise operational context defined
by our experimental protocol, based on the same participant
playing both games. The observation of irrational choices, i.e.,
low levels of altruism and opposing levels of selfishness (GrpF
and GrpS), indicates that one’s own assessment of the coherence
of actions can lead to beneficial outcomes that are biased
compared to a rational evaluation of monetary payment. In
this respect, another limitation of the current study is the lack
of neurophenomenological interview (Bockelman et al., 2013;
Høffding et al., 2021; Klinke and Fernandez, 2022) in order to
understand the subject’s own perception of his/her spiteful or
costly punishment.
The somatic marker hypothesis posits the existence of
decision-making processes associated with emotional markers
generated by cortical and subcortical circuits (Bechara et al.,
1994; Damasio, 1996). We recognize that our study was not
designed to separate what would be a “neural” activity due to
“somatic” origins or a “neural” activity due to other processes
and computations. The amygdala plays a key role in triggering
somatic states from primary emotions and the ventromedial part
of the prefrontal cortex for the generation of secondary emotions
from primary ones (Bechara et al., 2003; Martins, 2011; Kanbara
and Fukunaga, 2016; Šimi´
c et al., 2021). Despite the fact that
these areas of the brain are hardly accessible by EEG, N1 and late
positive ERP components have been associated with emotional
salience (Yang et al., 2012; Yoder and Decety, 2014; Brudner et al.,
2018; Cui et al., 2021; Schupp and Kirmse, 2021). Our results
showed that N1 and LPC were actually associated with behaviors
of participants determined by fairness and selfishness indices,
thus suggesting that N1 and LPC are likely to be considered
markers in the context of the somatic marker hypothesis and P2
and MFN are likely to be associated with other processes.
Last but not least, we recognize methodological limitations
associated with the sample size when we consider that the
number of trial repetitions needed for averaging ERPs was limited
by the duration of one session and the unbalanced number
per set determined by individual strategies that could not be
foreseen before the experiment. We are aware that peak latencies
and amplitudes reported in our study were sensitive to high-
frequency noise. The signal-to-noise level is low in our study
due to averaging over a small number of trials and this brings
some methodological considerations. The limits of the time
windows of an ERP wave are extremely difficult to set firmly
for all participants because of the individual differences in the
adopted strategy and may vary between participants across the
various sets of trials (selfish trials: accepted vs. rejected, fair trials:
accepted/rejected). These time limits could be considered only
after a careful post hoc analysis, which is contradictory to the
measurement of the mean amplitude (instead of peak amplitude)
Frontiers in Systems Neuroscience | www.frontiersin.org 20 May 2022 | Volume 16 | Article 765720
Miraghaie et al. ERP Markers of Fairness and Selfishness
and fractional area latency (instead of peak latency) that are
usually considered more meaningful values in itself (Fabiani
et al., 2007; Woodman, 2010; Luck, 2014). Our aim was not the
establishment of standardized ERP values for the experimental
protocols of neuroeconomic games, but to determine if somatic
markers could be reliably observed in participants who clearly
adopted different strategies. In the absence of a large number
of trials at hand to overcome the difficulties in measuring peak
values with a jackknife-based approach (Kiesel et al., 2008), we
have adopted the method of repeated measurements taken by
several observers. Another method to compute peak values with
high-frequency noise is an adaptive-mean amplitude approach,
which consists to average the timepoints located within a window
of 25 ms around each peak (Clayson and Larson, 2019; Clayson
et al., 2021). In future studies, we also believe that the newly
presented standardized measurement error for amplitude and
latency values carries the potential for greater replicability of
ERP studies in neuroeconomic experimental protocols, making it
possible to identify participants with low-quality data and noisy
channels (Luck et al., 2021).
5. CONCLUSION
This study has presented a novel combination of Dictator and
UG with the aim of investigating whether ERP markers support
the hypothesis that spiteful and costly punishments can be
differentiated. We have presented evidence that different groups
of participants can be identified on the basis of their performance
at the combination of DG and UG. Two groups of participants,
both with little tendencies toward altruism, were characterized
by a selfish or fair attitude during DG. An early ERP negative
component (N1), peaking in the interval of 110 to 160 ms at
fronto-central sites, distinguished fair participants from selfish
ones. Analysis of middle frontal negativity (MFN) suggested that
selfish participants were likely to exhibit spiteful punishment
positively related to impulsive choice. The differences in late
positive component (LPC) observed between the groups could
be interpreted as that costly punishment behavior, enabled by
fair participants, must be stable to make sense and requires less
attention than selfish participants, whose cognitive-behavioral
contradiction requires more attention and activations of a larger
brain network to complete the information processing necessary
to make a decision. In conclusion, this study brings an original
contribution and new evidence to the existence of different
circuits activated by the evaluation of fair and unfair proposals in
participants characterized by different expressions of perception
of fairness.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by Ethics Committees of Shahid Beheshti University.
The patients/participants provided their written informed
consent to participate in this study.
AUTHOR CONTRIBUTIONS
AM, HP, MM, and RK: conceptualization. HP, MM, and
AL: formal analysis and project administration. HP and AL:
funding acquisition. MM: clinical investigation. AM, HP, and AL:
methodology. HP, AL, and AV: supervision and validation. AV
and AL: visualization and writing—review and editing. AM, AV,
and AL: writing original draft. All authors contributed to the
article and approved the submitted version.
FUNDING
This research was partially supported by the Swiss National
Science Foundation grant no. IZSEZ0_183401 to AV.
ACKNOWLEDGMENTS
The authors extend their gratitude and acknowledgments to all
study participants and study team members for their time and
energy spent on this project.
REFERENCES
Abbasi, M., Kiani, M., Ahmadi, M., and Salehi, B. (2018). Knowledge
and Ethical Issues in Organ Transplantation and Organ Donation:
Perspectives from Iranian Health Personnel. Ann. Transplant 23, 292–299.
doi: 10.12659/AOT.908615
Alexopoulos, J., Pfabigan, D. M., Lamm, C., Bauer, H., and Fischmeister,
F. P. (2012). Do we care about the powerless third? An ERP study
of the three-person ultimatum game. Front. Hum. Neurosci. 6, e00059.
doi: 10.3389/fnhum.2012.00059
Altman, M. (2005). The ethical economy and competitive markets:
Reconciling altruistic, moralistic, and ethical behavior with the rational
economic agent and competitive markets. J. Econ. Psychol. 26, 732–757.
doi: 10.1016/j.joep.2005.06.004
Amiri, E., Ebrahimi, H., Namdar Areshtanab, H., Vahidi, M., and
Asghari Jafarabadi, M. (2020). The Relationship between Nurses’ Moral
Sensitivity and Patients’ Satisfaction with the Care Received in the Medical
Wards. J. Caring Sci. 9, 98–103. doi: 10.34172/JCS.2020.015
Antal, A., Kéri, S., Kovács, G., Janka, Z., and Benedek, G. (2000). Early
and late components of visual categorization: an event-related potential
study. Brain Res. Cogn. Brain Res. 9, 117–119. doi: 10.1016/s0926-6410(99)0
0053-1
Artinger, F., Exadaktylos, F., Koppel, H., and Sääksvuori, L. (2014). In Others’
Shoes: Do Individual Differences in Empathy and Theory of Mind Shape Social
Preferences? PLoS ONE 9, e92844. doi: 10.1371/journal.pone.0092844
Ashton, M. C., Lee, K., and de Vries, R. E. (2014). The HEXACO Honesty-
Humility, Agreeableness, and Emotionality factors: a review of research
and theory. Pers. Soc. Psychol. Rev.