Facial attractiveness influenced cooperative behavior in the Stag Hunt game: Evidence from neural electrophysiology

Abstract

Facial attractiveness plays a significant role in interpersonal interactions, influencing various aspects of life. This study is the first to explore, from a neurological perspective, the impact of facial attractiveness on individual cooperative behavior in the context of the Stag Hunt game. Twenty-six participants took part in a two-person Stag Hunt experimental task, while their electroencephalogram (EEG) data were recorded. Participants had to decide whether to cooperate with or to defect from a virtual partner in the game, with photos of these partners (high or low attractiveness) shown before the decision. Analysis of the behavioral data indicates that faces with high attractiveness can promote individual cooperative behavior. EEG data analysis revealed that during the facial stimulus presentation phase, low attractiveness faces elicited more negative N2 amplitudes, smaller late positive potential amplitudes, and larger alpha oscillations compared to high attractiveness faces. During the outcome feedback phase, high attractiveness faces elicited smaller feedback-related negativity (FRN) amplitudes, larger P300 amplitudes, and stronger theta oscillations than low attractiveness faces, while loss feedback elicited more negative FRN amplitudes, smaller P300 amplitudes, and larger theta oscillations than gain feedback. These findings indicate that the processing of facial attractiveness occurs early and automatically, and it also influences individuals’ evaluation of behavioral outcomes.

Keywords

ERP ERO decision-making face attractiveness Stag Hunt game

Introduction

Classical game theory focuses on the competition and/or cooperation between rational decision-makers (Myerson, 1997). However, individuals in human societies are not wholly rational, and their cooperative behaviors are affected by a range of factors. Among these, facial attractiveness plays a significant role in shaping interpersonal interactions, contributing to the stereotype that “beauty is good” (Dion et al., 1972). Extensive research indicates that facial attractiveness influences human social behavior. Individuals with high facial attractiveness possess certain advantages with regard to social activities such as job hunting, mate selection, and political elections (Bzdok et al., 2011; Langlois et al., 2000; Maurer-Fazio & Lei, 2015; Milazzo & Mattes, 2016; Poutvaara, 2014; Stockemer & Praino, 2017).

Cooperative behavior, as a form of social behavior, is similarly influenced by facial attractiveness. This phenomenon can be understood through three primary perspectives: economic, social psychology, and evolutionary psychology. From an economic perspective, the taste-based discrimination model suggests that people prefer individuals with high attractiveness (HA) faces, regardless of their behavior or personality traits (Becker, 2010); the statistical discrimination model posits that attractive individuals tend to perform better in the workplace (Guryan & Charles, 2013). From a social psychology perspective, stereotypes are considered the main reason facial attractiveness influences human social behavior, with individuals possessing HA faces perceived as having better personality traits (Langlois et al., 2000). From the perspective of evolutionary psychology, it is argued that over the long course of evolution, humans developed mate selection preferences, with attractive faces representing superior genes, leading to behavioral preferences for individuals with high facial attractiveness (Rhodes, 2006).

Empirical research supports the idea that facial attractiveness affects cooperative behavior. For example, Mulford et al. (1998) demonstrated this relationship using the Prisoner’s Dilemma game, showing that individuals are more inclined to cooperate with partners who possess high facial attractiveness. These findings highlight the pervasive influence of facial attractiveness on human social interactions, particularly in cooperative contexts.

As neuroscience technology advances swiftly, techniques such as functional magnetic resonance imaging and electroencephalography have been extensively applied to research on human behavior (Jaffe et al., 2023; Jiang et al., 2021, 2024; Woolnough et al., 2023). Functional magnetic resonance imaging experiments have demonstrated that, compared to faces of low attractiveness (LA), HA faces significantly activate brain regions such as the nucleus accumbens (Aharon et al., 2001), ventral striatum (Chuan-Peng et al., 2020; Kampe et al., 2001), right amygdala (Winston et al., 2007), and medial orbitofrontal cortex (Ishai, 2007; O’Doherty et al., 2003; Tsukiura & Cabeza, 2011; Winston et al., 2007)—areas associated with reward and emotion—indicating that HA faces possess higher reward and emotional value. Functional magnetic resonance imaging accurately captures the activation of brain regions in spatial terms, yet it falls short in terms of precisely depicting the temporal dynamics of neural activity. Event-related potential (ERP) technology has a high temporal resolution, and many scholars have used this technique to study the temporal dynamics of facial attractiveness processing. Here, the brain electrical data of participants is collected while they complete a task related to facial attractiveness. One frequently studied ERP component is the late positive potential (LPP), which peaks in amplitude between 300 and 700 ms after stimulus onset and is most prominent in the central-parietal scalp region (Hajcak & Foti, 2020; Hajcak et al., 2010; Qu et al., 2013; Rugg & Curran, 2007). The LPP has been shown to be sensitive to facial attractiveness. The effect of facial attractiveness on LPP amplitude has been frequently reported. Some studies have found that, compared to faces of moderate attractiveness, both attractive and unattractive faces elicited greater LPP amplitudes (Marzi & Viggiano, 2010; Munoz & Martin-Loeches, 2015; Schacht et al., 2008). In other studies, faces in the moderately attractive category were excluded, and it was found that HA faces elicited greater LPP amplitudes compared to faces with LA (Johnston & Oliver-Rodriguez, 2007; Ma et al., 2017; Van Hooff et al., 2011; Werheid et al., 2007). In some decision-making tasks related to facial attractiveness, the focus has been on the N2 component, a negative component that appears about 200 ms after participants receive facial stimuli. In an ultimatum game experiment by Ma et al. (2015), a dictator game experiment by Pei and Meng (2018), and an economic decision-making task by Jin et al. (2017), it was found that unattractive faces elicited a larger N2 amplitude compared to attractive faces. In our experiment, we utilized the high temporal resolution of ERP technology to examine the temporal characteristics of brain activity in response to HA and LA faces during a two-person Stag Hunt game task. Based on previous findings, we anticipated observing similar patterns, with facial attractiveness modulating ERP components such as the LPP and N2, offering further insight into the neural mechanisms underlying the processing of facial attractiveness.

The Stag Hunt is a classic model in game theory, widely used in the study of cooperation issues (Skyrms, 2004; Yoshida et al., 2010). This paper uses the two-person Stag Hunt model as an example for studying cooperative behavior. In this model, two players make strategic choices simultaneously, with each player having two options, “cooperate” or “defect,” this is shown in the payoff matrix in Figure 1A. Here, the numbers in the table represent the Player A’s payoffs, with 0 < x < 1. For Player A, choosing “cooperate” represents the risky behavior of “hunting the stag.” Cooperative behavior is influenced by the actions of the partner, and only if both players choose to cooperate will Player A receive a payoff of 1, which is the maximum payoff. However, if Player A chooses to cooperate while Player B chooses to defect, Player A’s payoff for cooperation will be 0. Choosing “defect” equates to the risk-free behavior of “hunting the hare,” here defection is not influenced by the partner’s actions, and the payoff is always x (0 < x < 1). The Stag Hunt is a unique coordination game that has two Nash equilibria. When both people choose to cooperate, it results in a payoff-dominant equilibrium, where each person’s payoff is maximized and is also Pareto optimal (Ideally for resource allocation, there is no other configuration outcome that makes some people better off without making others worse off). Both partners choosing to defect leads to a risk-dominant equilibrium, which avoids the risk of the other not cooperating.

Studies in the labor market reveal that attractiveness has a significant motivational influence (characterized as the “beauty premium” and “plainness penalty” (Beller et al., 1994), allowing individuals with HA to secure more job opportunities and higher wages. This study anticipates that the “beauty premium” will also affect cooperative behavior in the Stag Hung game, with participants likely to engage in more cooperative actions when faced with a partner with an HA face.

Some researchers have incorporated classic game theory models, such as the Prisoner’s Dilemma, Ultimatum Game, Trust Game, and Chicken Game, into electroencephalogram (EEG) experiments to investigate the cognitive processes underlying decision-making and social interactions. Among these, the Prisoner’s Dilemma is frequently employed in studies focusing on cooperation (Constantino et al., 2019; Mulford et al., 1998; Zhang et al., 2022). However, a limitation of the Prisoner’s Dilemma is that its Nash equilibrium corresponds to mutual defection, while mutual cooperation, despite being desirable, is neither stable nor sustainable within the game’s structure. This fails to accurately reflect the mutual benefit and win–win dynamics commonly observed in real-world human interactions and social systems. In contrast, the Stag Hunt game is a classic cooperative game in game theory, where mutual cooperation among participants yields greater rewards, while defection by any party leads to reduced benefits. Cooperation represents a sustainable and stable state, highlighting the importance of mutual benefit and win–win scenarios in human interactions. To the best of our knowledge, no ERP studies have yet utilized the two-person Stag Hunt game, and the neural mechanisms underlying cooperative behavior in this game remain unclear. We employed the Stag Hunt game, a classic game theory model, to investigate the neural mechanisms by which facial attractiveness influences cooperative behavior.

Many ERP experimental studies indicate that after participating in games, the brain’s response to different outcomes can be represented by two ERP components: the feedback-related negativity (FRN) and P300 (Boksem & De Cremer, 2010; Chen et al., 2012; Jin et al., 2017; Polezzi et al., 2008; Qu et al., 2013; Wang et al., 2014; Wu et al., 2011). The FRN component is a type of negative deflection ERP component that appears 200 to 350 ms after feedback stimuli (Miltner et al., 1997), and according to relevant ERP source analysis and functional magnetic resonance studies, FRN is likely generated in the anterior cingulate cortex (Cohen & Ranganath, 2007; Gehring & Willoughby, 2002; Holroyd & Coles, 2002; Nieuwenhuis et al., 2005b, 2007; Rushworth & Behrens, 2008). FRN is a response to reinforcement learning signals, whose transmission in the brain helps in cognitive learning and adjusting behavioral decisions (Schönberg et al., 2007). Research shows that negative feedback induces a larger FRN amplitude compared to positive feedback (Gehring & Willoughby, 2002; Jin et al., 2015; Kobza et al., 2011; Yeung & Sanfey, 2004). In the outcome feedback phase of the Stag Hunt game in this experiment, loss feedback is a form of negative feedback, so we expect loss feedback to elicit more negative FRN amplitudes compared to gain feedback. Tao et al. (2023) demonstrated using the balloon analog risk task that decision-makers elicited more negative FRN amplitudes in negative emotional contexts compared to neutral emotional contexts. Participants may experience negative emotions when paired with LA game partners, so we expect that during the outcome feedback phase, the results of games with LA partners will elicit more negative FRN amplitudes compared to those with HA partners. The P300 component is a positive component that peaks within 300 to 600 ms after feedback stimulus presentation, typically recorded in the midline parietal lobe (Sato et al., 2005; Yeung & Sanfey, 2004). Numerous studies reveal that the P300 component is sensitive to the valence and magnitude of outcome rewards, with larger rewards or positive feedback inducing a larger P300 amplitude (Hajcak et al., 2005; Sato et al., 2005; Wu & Zhou, 2009). In the outcome feedback process, gain feedback is positive feedback; thus, we expect gain feedback to elicit larger P300 amplitudes compared to loss feedback. The P300 component also reflects late-stage attentional resource allocation during the evaluation process (Pfabigan et al., 2011; Yu & Sun, 2013). In this experiment, game partners with HA faces may capture more of the participants’ attentional resources. We expect HA faces to elicit larger P300 amplitudes compared to LA faces.

Scalp responses recorded by EEG can be analyzed not only from the temporal domain to study ERPs but also from the time–frequency perspective to explore oscillatory activity (event-related oscillations [ERO]). Rhythmic oscillations in the 8 to 12 Hz range in EEG are called alpha waves, which are considered effective markers of cortical excitability (Klimesch et al., 2007; Haegens et al., 2011; Jäncke et al., 2006). Klimesch et al. (2007) proposed the inhibition timing hypothesis, suggesting that reduced alpha activity reflects a higher excitability state, while increased alpha activity indicates an inhibitory state (lower excitability). Participants may exhibit a higher excitability state when playing games with HA partners, resulting in reduced alpha activity. Thus, we expect participants to generate lower alpha power when interacting with HA partners compared to LA partners. Theta waves are rhythmic oscillations in the 4 to 7 Hz range of the brain, associated with attention processing and cognitive control (Nigbur et al., 2011; Senoussi et al., 2022). Gevins et al. (1997) found in their study that in working memory tasks with varying memory loads, theta power in the frontal regions of the brain was most pronounced under conditions requiring the highest levels of sustained attention. During the outcome feedback phase of the game, results under HA face conditions may maintain participants’ higher levels of sustained attention. Thus, we expect larger theta oscillations to be elicited during the outcome feedback phase when interacting with HA partners. Moreover, studies on brain oscillations in conflict and reward contexts suggest that theta-band oscillations are also associated with error and loss processing (Cavanagh et al., 2009). The brain’s processing of negative feedback induces greater theta activity, with negative feedback eliciting larger theta power compared to positive feedback (Cohen et al., 2007). Therefore, we expect that during the outcome feedback phase, loss feedback will produce greater theta power than gain feedback.

In summary, this paper uses the two-person Stag Hunt model to study the neural mechanisms of how facial attractiveness affects cooperative behavior. During each trial, participants were first shown a photograph of their game partner, then presented with the payoff matrix of the game (as illustrated in Figure 1B) in order to make their decision, culminating in the display of the game’s outcome. The study utilizes ERP technology and ERO techniques to investigate the neural response processes in two phases: the facial stimulus presentation phase and the game outcome feedback phase.

Materials and methods

Participants

This study used two statistical analysis methods: paired-sample t-test and 2 × 2 repeated measures analysis of variance (ANOVA). For the paired-sample t-test, G*Power software (version 3.1.9.7) was used to estimate the sample size required for the experiment (Faul et al., 2007). The effect size dz was set at 0.5, statistical power at 0.8, and significance level alpha at .05. The results showed that a minimum of 34 participants were required to achieve a moderate effect size. For the 2 × 2 repeated measures ANOVA, MorePower software (version 6.0.4) was used to estimate the required sample size for the experiment (Campbell & Thompson, 2012). The effect size η² was set to .06, statistical power to 0.8, and significance level alpha to .05. Considering all main effects and interactions, the results indicated that 126 participants would be sufficient to achieve 80% statistical power at a moderate effect size. Due to difficulties in recruiting participants and the significant resources (including time and facilities) required to test each individual, we recruited 26 undergraduate or graduate students (13 females and 13 males) from Wuhan University of Science and Technology to participate in this study. The participants were aged 18 to 29 years (mean = 21.65 years, SD = 2.51 years). All participants were native Mandarin speakers, right-handed, with normal or corrected-to-normal vision, and self-reported as having no psychiatric disorders or relevant family history, and no head injuries. EEG data were collected in the Cognitive Function Examination Room of the Central Theater Command General Hospital of the Chinese People’s Liberation Army. According to all the provisions of the Declaration of Helsinki, the experiment was approved by the Ethics Committee of Wuhan University. Prior to the experiment, all participants signed an informed consent form for the EEG study.

Stimulus materials

In each trial of the experiment, the initial stimulus presented to the participants was a photo of a Chinese face. All of these facial photos were sourced from an online facial database and the internet, and were uniformly processed into grayscale using Photoshop software and them cropped to a standard size (7.9 × 7.1 cm, 300 × 270 pixels). The photos did not include movie stars, singers, or other celebrities, and were of people unfamiliar to the participants.

Prior to the start of the ERP experiment, 30 current students from the Wuhan University of Science and Technology (who were not involved in the ERP experiment) rated the attractiveness of the 500 Chinese facial photos that were collected. The rating scale had seven levels, where 1 represented the lowest attractiveness and 7 the highest, with attractiveness increasing progressively from 1 to 7. Ultimately, 280 face photos were selected for use as facial stimuli in the ERP experiment; 140 HA face photos (70 of each gender) and 140 LA face photos (70 of each gender). The attractiveness ratings of the two groups of facial images were compared using paired sample t-tests, revealing significant differences in attractiveness (mean [HA] = 4.509, mean [LA] = 2.517; t [29] = 11.277, p < .001, Cohen’s d = 0.968).

Experimental procedure

Each participant conducted the ERP experiment in a closed, soundproofed, temperature-controlled room with electromagnetic isolation. The participants comfortably sat in front of a 17-inch monitor, approximately 100 cm away, and a keyboard was provided for them to make their choices of “cooperate” or “defect.” The participants were instructed to read the experiment instructions, which detailed the rules of the two-person Stag Hunt game, followed by undertaking preparations like wearing an EEG cap and applying conductive paste. The experimental procedure was programmed using E-Prime 2.0 software. To familiarize the participants with the procedure, there was a practice session of 10 trials before the official experiment (the 10 facial photos used for the practice trials were randomly selected from the 500 collected facial photos not chosen for the formal experiment). The formal experiment consisted of 280 trials (280 facial photos), divided into 4 blocks, each containing 70 trials, including 35 HA face photos and 35 LA face photos.

The flowchart for a single trial is shown in Figure 1C. Initially, a black cross appears in the center of the screen for 1,000 ms to remind the participant to concentrate. Then, a facial photo of a game partner (HA or LA) is displayed for 2,000 ms, followed by a blank screen for 800 to 1,000 ms, and then, a payoff table appears. The numbers in the table represent the amount of money (in Yuan) that the participant earns based on the choices made. The participant must then make a “cooperate” or “defect” decision using the keyboard, pressing the F key for cooperate and the J key for defect, with their choice being highlighted in red. After another 800 to 1,000 ms of blank screen, the joint choice of the participant and the game partner is displayed, showing only the participant’s earnings based on the choices of both parties, with the other numbers hidden. Participants were informed that the photos and choice data of their game partners were obtained from previous experiments and that they would be playing with real game partners. In reality, after the participant made a choice, the computer randomly made a “cooperate” or “defect” choice with a 50% probability. At the end of the experiment, the participant’s compensation included a base fee of 80 Yuan plus the average earnings from each trial.

EEG recordings

The EEG data recording equipment used was the NeuroHub EEG multimodal data terminal produced by Neuracle Medical Technology (Shanghai, China) Co., Ltd., and an international standard 10 to 20 system 32-lead Ag/AgCl gel electrode cap compatible with the NeuroHub EEG multimodal data terminal. The sampling rate was 1,000 Hz, with a bandpass filter of 0 to 100 Hz. The CPz electrode was selected as the online reference, and offline analysis was re-referenced to the average of bilateral mastoid electrodes A1 and A2. During data collection, the impedance between the electrodes and the scalp was maintained below 5 kΩ. Behavioral data were recorded simultaneously with the EEG data collection.

Behavioral data analysis

For the behavioral data, we calculated the number of times each participant chose “cooperate” and “defect” under the stimulus of HA and LA face photos. Paired sample t-tests were used to compare the rate of cooperation under the two types of facial photo stimuli, to see if there were significant differences. The cooperation rate was calculated as the number of cooperative choices made by participants under a specific attractiveness condition (HA or LA) divided by the total number of games played under that condition. A 2 (attractiveness: high and low) × 2 (behavioral choice: cooperate and defect) repeated measures ANOVA was conducted on the reaction times of the participants’ choices. The Greenhouse–Geisser method (Greenhouse & Geisser, 1959) was employed for corrections when sphericity assumptions were violated. The Bonferroni method (Dickhaus, 2014) was used for multiple comparison corrections.

ERP analysis

For the EEG data, offline analysis was conducted using the EEGLAB toolbox in MATLAB. A notch filter was used to remove 50-Hz power line interference, and this was followed by digital filtering in the 1 to 30 Hz range using a bandpass filter. The ERP epochs ranged from 200 ms before the stimulus (facial photo stimulus and outcome feedback stimulus) to 1,000 ms after the stimulus, with the 200 ms prior to the stimulus serving as the baseline. Independent component analysis was used to correct artifacts caused by blinking and head movements, and trials with uncorrectable artifacts were manually removed. During the presentation of facial photo stimuli, EEG epochs for both HA and LA facial photos were averaged. In the presentation of game outcomes, according to the designed payoff matrix, the outcome stimulus was categorized as “gain” for both “cooperate–cooperate” and “defect–defect” choices between the participant and the computer partner, and as “loss” for the other two combinations. The EEG epochs for attractiveness (high/low) and outcome (gain/loss) were averaged separately, resulting in four conditions: HA-gain, LA-gain, HA-loss, and LA-loss.

To explore the neural processing of facial attractiveness, we analyzed two EEG components, N2 and LPP, during the facial stimulus presentation phase. Based on visual inspection of the averaged EEG waveforms and scalp distribution (see Figure 2B), eight electrodes (F3, F4, Fz, FC1, FC2, C3, C4, Cz) in the frontal-central area were selected to analyze the average amplitude of the N2 component (210–270 ms time window post facial photo stimulus), and five electrodes (CP1, CP2, P3, P4, Pz) in the central-parietal area were chosen for the LPP component (350–550 ms time window). According to Luck and Gaspelin (2007), averaging across electrode sites during analysis can enhance statistical effects. Therefore, we averaged the electrode sites selected for the N2 and LPP components for analysis. Paired-sample t-tests were conducted on the mean amplitudes of the N2 and LPP components elicited under HA and LA face conditions.

During the outcome feedback phase, we analyzed two ERP components: FRN and P300. Based on relevant research and visual inspection of the grand average waveforms, we analyzed the FRN component using the average amplitude within 260 to 330 ms after the outcome stimulus presentation. The P300 component was analyzed using the average amplitude within 360 to 460 ms after the outcome stimulus presentation. Based on scalp potential distribution, the electrodes for FRN analysis were eight (Fz, F3, F4, FC1, FC2, Cz, C3, C4) in the frontal and central regions, and for P300, five electrodes (Cz, C3, C4, CP1, CP2) in the parietal region. The FRN and P300 components were analyzed by averaging across selected electrode sites and conducting a 2 (attractiveness: high and low) × 2 (outcome feedback: gain and loss) two-factor repeated-measures ANOVA. The Greenhouse–Geisser method (Greenhouse & Geisser, 1959) was employed for corrections when sphericity assumptions were violated. The Bonferroni method (Dickhaus, 2014) was used for multiple comparison corrections.

ERO analyses

EEG epochs from 1,000 ms before to 2,000 ms after both facial photo and outcome feedback stimuli were resampled and stored as EEG data. Short-time Fourier transform was employed for ERO analysis of EEG data, obtaining instantaneous power estimates at each time point within the 1 to 30 Hz range. This technique first involved single-trial analysis of the EEG data, followed by averaging over multiple trials, so as to eventually obtain oscillatory power values under each condition. These power values were baseline-corrected using the pre-stimulus period from 800 to 200 ms. Based on visual inspection of the event-related spectral perturbation (ERSP) spectrogram and scalp topography, the Cz electrode was selected for analysis during the facial stimulus presentation phase. Paired-sample t-tests were conducted on the average oscillatory power values in the alpha band (8–13 Hz) within the 190 to 230 ms time range following facial image stimuli under HA and LA face conditions. During the outcome feedback phase, the Fz electrode was used for analysis, averaging the oscillatory power values in the theta band (4–7 Hz) from 250 to 450 ms post-outcome stimulus for a 2 (attractiveness: high and low) × 2 (valence: gain and loss) repeated measures ANOVA. The Greenhouse–Geisser method (Greenhouse & Geisser, 1959) was used for corrections when sphericity assumptions were violated, and the Bonferroni method (Dickhaus, 2014) was employed for multiple comparison corrections.

Results

Results of the participants’ behavioral decisions

The results of the paired-samples t-test indicated that the cooperation rate of participants was significantly higher under the HA face condition (mean = 0.575, SD = 0.189) than under that the LA face condition (mean = 0.333, SD = 0.195), t [25] = 5.822, p < .001, Cohen’s d = 0.212. The results of the repeated measures ANOVA on reaction times indicated that the main effect of attractiveness was not significant (F [1,25] = 1.847, p = .186, η_p² = .069), and nor was the main effect of the behavioral choice (F [1,25] = 0.193, p = .664, η_p² = .008). The interaction between attractiveness and behavioral choice, however, was significant (F [1,25] = 4.606, p = .042, η_p² = .156). Simple effects analysis revealed that the reaction time of participants when choosing to cooperate was shorter under the condition of HA faces (mean = 522.523 ms, SD = 36.564 ms) compared to LA faces (mean = 573.725 ms, SD = 41.897 ms), p = .014. η_p² = .220.

ERP analysis results

Facial stimulus presentation phase

As observed in Figure 2, both HA and LA facial photos elicited pronounced N2 and LPP components, with more negative N2 amplitudes induced under the LA facial photo condition and larger LPP amplitudes induced under the HA facial condition. The results of the paired-samples t-test for the N2 and LPP amplitudes indicated that the N2 amplitudes induced by LA faces (mean = −1.580 μV, SD = 0.643 μV) was significantly higher than that induced by HA faces (mean = −0.110 μV, SD = 0.637 μV), t (25) = 6.277, p < .001, Cohen’s d = 1.194. The LPP amplitudes induced by HA faces (mean = 4.809 μV, SD = 0.395 μV) being larger than that induced by LA faces (mean = 4.060 μV, SD = 0.367 μV), t (25) =5.006, p < .001, Cohen’s d = 0.763.

Game outcome feedback phase

The results of the two-way repeated measures ANOVA on the FRN amplitudes (see Figure 3A) showed that the main effect of attractiveness was significant (F [1,25] = 8.588, p = .007, η_p² = .256), with the FRN amplitudes elicited under LA face conditions (mean = 1.352 μV, SD = 0.780 μV) being significantly more negative than that under HA face conditions (mean = 2.190 μV, SD = 0.837 μV). The main effect of outcome feedback was also significant (F [1,25] = 13.351, p = .001, η_p² = .039), with the amplitude of the FRN induced by loss feedback (mean = 1.205 μV, SD = 0.824 μV) being significantly more negative than that induced by gain feedback (mean = 2.337 μV, SD = 0.797 μV). The interaction between attractiveness and outcome feedback was not significant (F [1,25] = 8.927, p = .087, η_p² = .112).

The results of the two-way repeated measures ANOVA for the P300 component (as shown in Figure 3C) indicate the significant effect of attractiveness (F [1,25] = 19.958, p = .001, η_p² = .444), with the P300 component elicited under the HA face condition (mean = 7.280 μV, SD = 0.771 μV) being significantly larger than that under the LA face condition (mean = 5.885 μV, SD = 0.769 μV). The main effect of valence was also significant (F [1,25] = 11.212, p = .003, η_p² = .310), with the P300 component induced by gain feedback (mean = 7.045 μV, SD = 0.759 μV) being significantly larger than that induced by loss feedback (mean = 6.119 μV, SD = 0.775 μV). The interaction between attractiveness and outcome feedback was not significant (F [1,25] = 0.825, p = .373, η_p² = .032).

ERO analysis results

Facial stimulus presentation phase

As shown in Figure 4A, during the facial stimulus presentation phase, there was significant alpha frequency activity in the central frontal region. The results of the paired-samples t-test on alpha frequency power indicate the alpha frequency power values produced by LA faces (mean = 0.769 μV²/Hz, SD = 0.118 μV²/Hz) being significantly higher than those produced by HA faces (mean = 0.637 μV²/Hz, SD = 0.114 μV²/Hz), t (25) = 2.576, p = .016, Cohen’s d = 0.262.

Game outcome feedback phase

The results of the repeated measures ANOVA for the theta frequency band (as shown in Figure 4B) indicate the significant main effect of attractiveness (F [1,25] = 4.612, p = .042, η_p² = .156), with the theta frequency power values produced by HA faces (mean = 0.564 μV²/Hz, SD = 0.106 μV²/Hz) being significantly higher than those produced by LA faces (mean = 0.450 μV²/Hz, SD = 0.094 μV²/Hz). The main effect of valence was also significant (F [1,25] = 6.975, p = .014, η_p² = .218), with the theta frequency power values resulting from loss feedback (mean = 0.593 μV²/Hz, SD = 0.117 μV²/Hz) being significantly higher than those from gain feedback (mean = 0.421 μV²/Hz, SD = 0.085 μV²/Hz). The interaction between attractiveness and valence was not significant (F [1,25] = 0.103, p = .751, η_p² = .004).

Discussion

This study utilized high-resolution ERP technology to study the impact of facial attractiveness on individual cooperative behavior in a gaming environment. Participants engaged in an improved two-person Stag Hunt game under the stimulus of facial images with varying levels of attractiveness, while their behavioral data and EEG data were collected simultaneously.

Behavioral data indicated that, compared to partners with LA faces, participants had a higher cooperation rate when facing partners with HA faces. This further supports the “beauty premium” theory (Solnick & Schweitzer, 1999), suggesting that individuals with HA faces receive more opportunities for cooperation. When interacting with HA individuals, people tend to develop more positive attitudes and have a greater propensity for positive behavioral choices, leading to increased cooperative behavior. In the Stag Hunt game, choosing to cooperate is a high-risk behavior. However, under the influence of HA faces, participants develop positive expectations (Wilson & Eckel, 2006), leading them to overlook the risks in group interactive behavior (Stirrat & Perrett, 2010). As a result, participants exhibited a higher cooperation rate with partners having HA faces. Moreover, reaction time data showed that participants took significantly less time to choose cooperative actions when facing partners with HA faces as compared to those with LA faces, indicating that HA faces can steer the decision-making process toward a choice of cooperative behavior.

During the facial presentation phase, consistent with previous studies (Jin et al., 2017; Ma et al., 2015; Pei & Meng, 2018), LA faces elicited a larger early N2 amplitude compared to HA faces. This indicates that the brain’s judgment of facial attractiveness is rapid, and that facial attractiveness is processed early. The N2 component is related to cognitive control and conflict detection, with high-conflict environments eliciting larger N2 amplitudes compared to low-conflict environments (Folstein & Van Petten, 2008; Veen & Carter, 2002). In a task assessing facial attractiveness conducted by Zhang et al. (2011), it was found that low-attractiveness faces elicited a larger early (250–350 ms) negative wave. The authors asserted that the attributes of the judgment task triggered conflict detection. In this study, participants always anticipated viewing HA faces. The appearance of LA faces caused intense psychological conflict, leading to more negative N2 amplitudes for LA faces.

Furthermore, during the 350 to 550 ms of facial photo stimulation, HA faces elicited a larger LPP component than LA faces, which is consistent with the findings of previous research (Johnston & Oliver-Rodriguez, 1997; Schacht et al., 2008). The LPP component is related to emotional processing, and HA faces may evoke more significant emotional arousal (Presti et al., 2023; Werheid et al., 2007). Facial attractiveness can also be considered a form of reward value, capable of stimulating the reward circuitry in people (Aharon et al., 2001). HA faces may provide greater emotional rewards, thereby eliciting a larger LPP component.

Previous studies have shown that worse outcomes can elicit a larger FRN component than better outcomes (Hajcak et al., 2005; Yeung & Sanfey, 2004), and similar results were obtained in this study. During the game outcome feedback phase, we found that loss feedback elicited larger FRN amplitudes than gain feedback. FRN is believed to be related to reinforcement learning, with neural activity in the midbrain dopamine system encoding reward prediction error signals (Holroyd & Coles, 2002). When individuals engage in decision-making behaviors, outcomes may be better or worse than expected; worse outcomes lead to an increase in the FRN component (Hewig et al., 2007; Holroyd & Krigolson, 2007). In this study, when participants chose to cooperate, it indicated that they subjectively expected their partners to cooperate as well, aiming for a mutually beneficial outcome (gain feedback). However, if the partner chose to defect (loss feedback), this violated the participants’ psychological expectations. Similarly, when participants chose defection, they expected their partners to also choose defection (gain feedback). If the partner chose to cooperate (loss feedback), this also violated participants’ expectations, as cooperation would have yielded greater benefits for them. Therefore, loss feedback elicited more negative FRN amplitudes compared to gain feedback. In the Stag Hunt game, the outcomes of “cooperate–cooperate” and “defect–defect” represent two Nash equilibrium points (with both participants’ outcome feedback being “gain”). Achieving Nash equilibrium aligns with participants’ expectations, as no player can increase their payoff by unilaterally changing their strategy. Failing to achieve Nash equilibrium violates participants’ expectations, eliciting larger FRN amplitudes. This phenomenon is not observed in other game paradigms, providing new insights into Nash equilibrium in game theory. In this study, participants were more eager to see the outcomes of games with HA partners and less so with LA partners, resulting in more negative FRN amplitudes under LA conditions. Moreover, studies have shown that FRN amplitudes increase under negative emotional contexts compared to positive ones (Tao et al., 2023). The negative emotions elicited by LA faces may be another reason for the increased FRN amplitudes. This study also found that the interaction between attractiveness and outcome feedback was not significant, but the p-value was small, which may be due to insufficient power caused by the small sample size.

The repeated measures ANOVA of the P300 component showed that gain feedback elicited larger P300 amplitudes than loss feedback, indicating that the P300 component is related to the feedback of outcomes. This aligns with previous research (Hajcak et al., 2005; Leng & Zhou, 2010; Wu & Zhou, 2009), where positive outcomes produced larger P300 amplitudes than negative outcomes. The P300 component is associated with attention allocation, motivational level, and emotional significance (Leng & Zhou, 2010; Nieuwenhuis et al., 2005a; Yeung & Sanfey, 2004). When the outcome feedback is gain, the result of “cooperate–cooperate” achieves a mutually beneficial outcome, and the participant experiences significant emotional meaning. In the “defect–defect” outcome, if the partner switches to cooperation, their payoff becomes 0. However, the partner’s choice to defect allows them to gain, and the participant experiences empathy. In summary, when the game reaches a Nash equilibrium, participants perceive greater emotional/motivational significance, leading to larger P300 amplitudes elicited by gain feedback compared to loss feedback. This further supports the view of Fan and Han (2008) that differences in P300 amplitude may be related to the empathy process. From the perspective of attention allocation, participants may focus more on the outcomes of games with HA partners. Thus, participants observing the outcomes of games with HA partners show larger P300 amplitudes compared to those with LA partners. Additionally, analysis of the P300 component revealed that the interaction between facial attractiveness and outcome feedback was not significant, suggesting that their effects on P300 amplitude are independent.

Time–frequency analysis results indicate that during the facial stimulus presentation phase, LA faces produced larger alpha rhythm oscillations than HA faces. This suggests that alpha activity is regulated by the attractiveness of faces. A classic explanation suggests that alpha activity is a neural representation of the brain’s idling state (Pfurtscheller et al., 1996), for instance, evident during rest. Alpha activity also represents the top-down control mechanisms of the brain during visual processing (Helfrich et al., 2017) and is related to attentional inhibition (Klimesch, 2012). HA faces can capture more cognitive (attentional) resources (Morgan & Kisley, 2014). Some studies suggest that, compared to the bottom-up processes triggered by sexual attractiveness, top-down processes associated with facial beauty and attractiveness allocate more cognitive resources (Schacht et al., 2008; Zeng et al., 2012). In this study, compared to LA faces, participants allocated more cognitive resources when viewing HA faces, moving further from the idling state and thus generating smaller alpha oscillations.

During the outcome feedback phase, we observed that theta activity was modulated by facial attractiveness and outcome feedback. The theta band reflects attention processing and cognitive control (Nigbur et al., 2011; Senoussi et al., 2022). Evidence suggests that sustained attention leads to increased theta power (Gevins et al., 1997). In this experiment, participants were likely more concerned about the outcomes of games with HA partners. Consequently, during the outcome feedback phase, they allocated more attention to results under HA conditions, leading to larger theta oscillations. Additionally, theta oscillations reflect the brain’s processing of negative outcomes (Cohen et al., 2007; Gehring & Willoughby, 2002), with negative feedback eliciting greater theta activity. Therefore, during the outcome feedback phase of the Stag Hunt game, loss feedback elicited larger theta oscillations compared to gain feedback.

Conclusions

In summary, this study has been the first to use ERP and ERO technology to investigate the impact of facial attractiveness on cooperative behavior in the Stag Hunt game. In the Stag Hunt game, the facial attractiveness of a partner influences an individual’s cooperative behavior. When facing a partner with an HA face, individuals engaged in more cooperative behaviors and display shorter reaction times in choosing to cooperate. The assessment of facial attractiveness occurs automatically and at an early stage, with HA faces eliciting smaller N2 amplitudes and larger LPP amplitudes, as well as generating lower alpha power, compared to LA faces. During the outcome feedback phase, HA face conditions elicited smaller FRN amplitudes and larger P300 amplitudes and generated greater theta power than LA conditions. Loss feedback elicited more negative FRN components, smaller P300 components, and greater theta power compared to gain feedback. This indicates that facial attractiveness influences individuals’ evaluation of game outcomes. We also proposed a new insight: when participants observe game outcomes at Nash equilibrium, it elicits smaller FRN amplitudes and larger P300 amplitudes. Our findings help unravel the “black box” of brain decision-making mechanisms and offer valuable insights for management science. Furthermore, this study has some potential limitations. The gender of the faces used may influence neural and behavioral responses, and the gender factor of the participants was not taken into account. The insufficient sample size might have led to some effects not being significant. Future investigations are necessary to validate the kinds of conclusions that can be drawn from this study.

Footnotes

Author contributions

Xianjia Wang: Conceptualization; funding acquisition; supervision; writing—review & editing. Wei Cui: Data curation; formal analysis; methodology; project administration; investigation; writing—original draft; writing—review & editing. Shuochen Wang: Investigation; Yang Liu: Investigation; Hao Yu: Investigation; Jian Song: Resources; writing—review & editing.

Data availability

Data supporting the findings of this study are available upon reasonable request to the corresponding author.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded as a Key Project of the National Natural Science Foundation (No. 72031009) and a Major Project of Chinese National Funding of Social Sciences (No. 20&ZD058).

ORCID iD

Wei Cui

References

Aharon

Etcoff

Ariely

Chabris

C. F.

O’connor

Breiter

H. C.

(2001). Beautiful faces have variable reward value: fMRI and behavioral evidence. Neuron, 32(3), 537–551.

Becker

G. S.

(2010). The economics of discrimination. University of Chicago press.

Beller

Borjas

Tienda

Bloom

Grenier

(1994). Beauty and the labor market. The American Economic Review, 84(5), 1174–1194.

Boksem

M. A.

De Cremer

(2010). Fairness concerns predict medial frontal negativity amplitude in ultimatum bargaining. Social Neuroscience, 5(1), 118–128.

Bzdok

Langner

Caspers

Kurth

Habel

Zilles

Laird

Eickhoff

S. B.

(2011). ALE meta-analysis on facial judgments of trustworthiness and attractiveness. Brain Structure and Function, 215, 209–223.

Campbell

J. I.

Thompson

V. A.

(2012). MorePower 6.0 for ANOVA with relational confidence intervals and Bayesian analysis. Behavior Research Methods, 44, 1255–1265.

Cavanagh

J. F.

Cohen

M. X.

Allen

J. J.

(2009). Prelude to and resolution of an error: EEG phase synchrony reveals cognitive control dynamics during action monitoring. Journal of Neuroscience, 29(1), 98–105.

Chen

Zhong

Zhang

Tan

(2012). Electrophysiological correlates of processing facial attractiveness and its influence on cooperative behavior. Neuroscience Letters, 517(2), 65–70.

Chuan-Peng

Huang

Eickhoff

S. B.

Peng

Sui

(2020). Seeking the “beauty center” in the brain: A meta-analysis of fMRI studies of beautiful human faces and visual art. Cognitive, Affective, & Behavioral Neuroscience, 20, 1200–1215.

10.

Cohen

M. X.

Elger

C. E.

Ranganath

(2007). Reward expectation modulates feedback-related negativity and EEG spectra. Neuroimage, 35(2), 968–978.

11.

Cohen

M. X.

Ranganath

(2007). Reinforcement learning signals predict future decisions. Journal of Neuroscience, 27(2), 371–378.

12.

Constantino

F. C.

Garat

Nicolaisen-Sobesky

Paz

Martínez-Montes

Kessel

Cabana

Gradin

V. B.

(2019). Neural signals relating future cooperation from prisoner’s dilemma feedback are outcome-contingent. bioRxiv, 2019, 784033.

13.

Dickhaus

(2014). Simultaneous statistical inference: With applications in the life sciences. Springer.

14.

Dion

Berscheid

Walster

(1972). What is beautiful is good. Journal of Personality and Social Psychology, 24(3), 285.

15.

Fan

Han

(2008). Temporal dynamic of neural mechanisms involved in empathy for pain: an event-related brain potential study. Neuropsychologia, 46(1), 160–173.

16.

Faul

Erdfelder

Lang

A. G.

Buchner

(2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191.

17.

Folstein

J. R.

Van Petten

(2008). Influence of cognitive control and mismatch on the N2 component of the ERP: A review. Psychophysiology, 45(1), 152–170.

18.

Gehring

W. J.

Willoughby

A. R.

(2002). The medial frontal cortex and the rapid processing of monetary gains and losses. Science, 295(5563), 2279–2282.

19.

Gevins

Smith

M. E.

McEvoy

(1997). High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. Cerebral Cortex, 7(4), 374–385.

20.

Greenhouse

S. W.

Geisser

(1959). On methods in the analysis of profile data. Psychometrika, 24(2), 95–112.

21.

Guryan

Charles

K. K.

(2013). Taste-based or statistical discrimination: The economics of discrimination returns to its roots. The Economic Journal, 123(572), F417–F432.

22.

Haegens

Nácher

Luna

Romo

Jensen

(2011). α-Oscillations in the monkey sensorimotor network influence discrimination performance by rhythmical inhibition of neuronal spiking. Proceedings of the National Academy of Sciences, 108(48), 19377–19382.

23.

Hajcak

Foti

(2020). Significance?. . . Significance! Empirical, methodological, and theoretical connections between the late positive potential and P300 as neural responses to stimulus significance: An integrative review. Psychophysiology, 57(7), e13570.

24.

Hajcak

Holroyd

C. B.

Moser

J. S.

Simons

R. F.

(2005). Brain potentials associated with expected and unexpected good and bad outcomes. Psychophysiology, 42(2), 161–170.

25.

Hajcak

MacNamara

Olvet

D. M.

(2010). Event-related potentials, emotion, and emotion regulation: an integrative review. Developmental neuropsychology, 35(2), 129–155.

26.

Helfrich

R. F.

Huang

Wilson

Knight

R. T.

(2017). Prefrontal cortex modulates posterior alpha oscillations during top-down guided visual perception. Proceedings of the National Academy of Sciences of the United States of America, 114(35), 9457–9462.

27.

Hewig

Trippe

Hecht

Coles

M. G.

Holroyd

C. B.

Miltner

W. H.

(2007). Decision-making in blackjack: An electrophysiological analysis. Cerebral Cortex, 17(4), 865–877.

28.

Holroyd

C. B.

Coles

M. G.

(2002). The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109(4), 679.

29.

Holroyd

C. B.

Krigolson

O. E.

(2007). Reward prediction error signals associated with a modified time estimation task. Psychophysiology, 44(6), 913–917.

30.

Ishai

(2007). Sex, beauty and the orbitofrontal cortex. International Journal of Psychophysiology, 63(2), 181–185.

31.

Jäncke

Lutz

Koeneke

(2006). Converging evidence of ERD/ERS and BOLD responses in motor control research. Progress in Brain Research, 159, 261–271.

32.

Jaffe

P. I.

Poldrack

R. A.

Schafer

R. J.

Bissett

P. G.

(2023). Modelling human behaviour in cognitive tasks with latent dynamical systems. Nature Human Behaviour, 7(6), 986–1000.

33.

Jiang

Fan

Liu

Liang

Wang

(2024). An ERP study on the certainty of epistemic modality in predictive inference processing. Quarterly Journal of Experimental Psychology, 77(3), 577–592.

34.

Jiang

Wang

(2021). The neural and cognitive time course of intention reasoning: Electrophysiological evidence from ERPs. Quarterly Journal of Experimental Psychology, 74(4), 733–745.

35.

Jin

Fan

Dai

(2017). Beauty premium: Event-related potentials evidence of how physical attractiveness matters in online peer-to-peer lending. Neuroscience Letters, 640, 130–135.

36.

Jin

(2015). Neural basis of intrinsic motivation: Evidence from event-related potentials. Computational Intelligence and Neuroscience, 2015, 7.

37.

Johnston

V. S.

Oliver-Rodriguez

J. C.

(1997). Facial beauty and the late positive component of event-related potentials. Journal of Sex Research, 34(2), 188–198.

38.

Kampe

K. K.

Frith

C. D.

Dolan

R. J.

Frith

(2001). Reward value of attractiveness and gaze. Nature, 413(6856), 589–589.

39.

Klimesch

Sauseng

Hanslmayr

(2007). EEG alpha oscillations: the inhibition–timing hypothesis. Brain Research Reviews, 53(1), 63–88.

40.

Klimesch

(2012). Alpha-band oscillations, attention, and controlled access to stored information. Trends in Cognitive Sciences, 16(12), 606–617.

41.

Kobza

Thoma

Daum

Bellebaum

(2011). The feedback-related negativity is modulated by feedback probability in observational learning. Behavioural Brain Research, 225(2), 396–404.

42.

Langlois

J. H.

Kalakanis

Rubenstein

A. J.

Larson

Hallam

Smoot

(2000). Maxims or myths of beauty? A meta-analytic and theoretical review. Psychological Bulletin, 126(3), 390.

43.

Leng

Zhou

(2010). Modulation of the brain activity in outcome evaluation by interpersonal relationship: an ERP study. Neuropsychologia, 48(2), 448–455.

44.

Luck

S. J.

Gaspelin

(2017). How to get statistically significant effects in any ERP experiment (and why you shouldn’t). Psychophysiology, 54(1), 146–157.

45.

Jiang

Meng

(2015). The undermining effect of facial attractiveness on brain responses to fairness in the Ultimatum Game: an ERP study. Frontiers in Neuroscience, 9, 115867.

46.

Zhang

Pei

Abdeljelil

H. M.

(2017). Neural process of the preference cross-category transfer effect: Evidence from an event-related potential study. Scientific Reports, 7(1), 3177.

47.

Marzi

Viggiano

M. P.

(2010). When memory meets beauty: Insights from event-related potentials. Biological Psychology, 84(2), 192–205.

48.

Maurer-Fazio

Lei

(2015). “As rare as a panda”: How facial attractiveness, gender, and occupation affect interview callbacks at Chinese firms. International Journal of Manpower, 36(1), 68–85.

49.

Milazzo

Mattes

(2016). Looking good for election day: Does attractiveness predict electoral success in Britain?. The British Journal of Politics and International Relations, 18(1), 161–178.

50.

Miltner

W. H.

Braun

C. H.

Coles

M. G.

(1997). Event-related brain potentials following incorrect feedback in a time-estimation task: evidence for a “generic” neural system for error detection. Journal of Cognitive Neuroscience, 9(6), 788–798.

51.

Morgan

L. K.

Kisley

M. A.

(2014). The effects of facial attractiveness and perceiver’s mate value on adaptive allocation of central processing resources. Evolution and Human Behavior, 35(2), 96–102.

52.

Mulford

Orbell

Shatto

Stockard

(1998). Physical attractiveness, opportunity, and success in everyday exchange. American Journal of Sociology, 103(6), 1565–1592.

53.

Munoz

Martin-Loeches

(2015). Electrophysiological brain dynamics during the esthetic judgment of human bodies and faces. Brain Research, 1594, 154–164.

54.

Myerson

R. B.

(1997). Game theory: Analysis of conflict. Harvard University Press.

55.

Nieuwenhuis

Aston-Jones

Cohen

J. D.

(2005a). Decision making, the P3, and the locus coeruleus—Norepinephrine system. Psychological Bulletin, 131(4), 510.

56.

Nieuwenhuis

Heslenfeld

D. J.

von Geusau

N. J. A.

Mars

R. B.

Holroyd

C. B.

Yeung

(2005b). Activity in human reward-sensitive brain areas is strongly context dependent. Neuroimage, 25(4), 1302–1309.

57.

Nieuwenhuis

Schweizer

T. S.

Mars

R. B.

Botvinick

M. M.

Hajcak

(2007). Error-likelihood prediction in the medial frontal cortex: A critical evaluation. Cerebral Cortex, 17(7), 1570–1581.

58.

Nigbur

Ivanova

Stürmer

(2011). Theta power as a marker for cognitive interference. Clinical Neurophysiology, 122(11), 2185–2194.

59.

O’Doherty

Winston

Critchley

Perrett

Burt

D. M.

Dolan

R. J.

(2003). Beauty in a smile: The role of medial orbitofrontal cortex in facial attractiveness. Neuropsychologia, 41(2), 147–155.

60.

Pei

Meng

(2018). What do we expect from a beauty? Facial attractiveness of the opposite sex gives rise to discrepancies in males’ anticipation and demand. International Journal of Psychology, 53(6), 411–416.

61.

Pfabigan

D. M.

Alexopoulos

Bauer

Sailer

U. T. A.

(2011). Manipulation of feedback expectancy and valence induces negative and positive reward prediction error signals manifest in event-related brain potentials. Psychophysiology, 48(5), 656–664.

62.

Pfurtscheller

Stancak

Jr Neuper

(1996). Event-related synchronization (ERS) in the alpha band—An electrophysiological correlate of cortical idling: A review. International Journal of Psychophysiology, 24(1–2), 39–46.

63.

Polezzi

Daum

Rubaltelli

Lotto

Civai

Sartori

Rumiati

(2008). Mentalizing in economic decision-making. Behavioural Brain Research, 190(2), 218–223.

64.

Poutvaara

(2014). Facial appearance and leadership: An overview and challenges for new research. The Leadership Quarterly, 25(5), 801–804.

65.

Presti

Galasso

G. M.

Ruzzon

Avanzini

Caruana

Rizzolatti

Vecchiato

(2023). Architectural experience influences the processing of others’ body expressions. Proceedings of the National Academy of Sciences of the United States of America, 120(41), e2302215120.

66.

Wang

Huang

(2013). Social exclusion modulates fairness consideration in the ultimatum game: An ERP study. Frontiers in Human Neuroscience, 7, 505.

67.

Rhodes

(2006). The evolutionary psychology of facial beauty. Annual Review of Psychology, 57(1), 199–226.

68.

Rugg

M. D.

Curran

(2007). Event-related potentials and recognition memory. Trends in Cognitive Sciences, 11(6), 251–257.

69.

Rushworth

M. F.

Behrens

T. E.

(2008). Choice, uncertainty and value in prefrontal and cingulate cortex. Nature Neuroscience, 11(4), 389–397.

70.

Sato

Yasuda

Ohira

Miyawaki

Nishikawa

Kumano

Kuboki

(2005). Effects of value and reward magnitude on feedback negativity and P300. Neuroreport, 16(4), 407–411.

71.

Schacht

Werheid

Sommer

(2008). The appraisal of facial beauty is rapid but not mandatory. Cognitive, Affective, & Behavioral Neuroscience, 8(2), 132–142.

72.

Schönberg

Daw

N. D.

Joel

O’Doherty

J. P.

(2007). Reinforcement learning signals in the human striatum distinguish learners from nonlearners during reward-based decision making. Journal of Neuroscience, 27(47), 12860–12867.

73.

Senoussi

Verbeke

Desender

De Loof

Talsma

Verguts

(2022). Theta oscillations shift towards optimal frequency for cognitive control. Nature Human Behaviour, 6(7), 1000–1013.

74.

Skyrms

(2004). The stag hunt and the evolution of social structure. Cambridge University Press.

75.

Solnick

S. J.

Schweitzer

M. E.

(1999). The influence of physical attractiveness and gender on ultimatum game decisions. Organizational Behavior and Human Decision Processes, 79(3), 199–215.

76.

Stirrat

Perrett

D. I.

(2010). Valid facial cues to cooperation and trust: Male facial width and trustworthiness. Psychological Science, 21(3), 349–354.

77.

Stockemer

Praino

(2017). Physical attractiveness, voter heuristics and electoral systems: The role of candidate attractiveness under different institutional designs. The British Journal of Politics and International Relations, 19(2), 336–352.

78.

Tao

Zhang

Zhao

Han

Dai

Zheng

Zhang

(2023). A negative emotional context disrupts the framing effect on outcome evaluation in decision making under uncertainty: An ERP study. Psychophysiology, 60(4), e14207.

79.

Tsukiura

Cabeza

(2011). Remembering beauty: Roles of orbitofrontal and hippocampal regions in successful memory encoding of attractive faces. Neuroimage, 54(1), 653–660.

80.

Van Hooff

J. C.

Crawford

Van Vugt

. (2011). The wandering mind of men: ERP evidence for gender differences in attention bias towards attractive opposite sex faces. Social Cognitive and Affective Neuroscience, 6(4), 477–485.

81.

Veen

V. V.

Carter

C. S.

(2002). The timing of action-monitoring processes in the anterior cingulate cortex. Journal of Cognitive Neuroscience, 14(4), 593–602.

82.

Wang

Yuan

Roberts

Wang

Lin

Simons

R. F.

(2014). How friendly is a little friendly competition? Evidence of self-interest and empathy during outcome evaluation. International Journal of Psychophysiology, 91(3), 155–162.

83.

Werheid

Schacht

Sommer

(2007). Facial attractiveness modulates early and late event-related brain potentials. Biological Psychology, 76(1–2), 100–108.

84.

Wilson

R. K.

Eckel

C. C.

(2006). Judging a book by its cover: Beauty and expectations in the trust game. Political Research Quarterly, 59(2), 189–202.

85.

Winston

J. S.

O’Doherty

Kilner

J. M.

Perrett

D. I.

Dolan

R. J.

(2007). Brain systems for assessing facial attractiveness. Neuropsychologia, 45(1), 195–206.

86.

Woolnough

Donos

Murphy

Rollo

P. S.

Roccaforte

Z. J.

Dehaene

Tandon

(2023). Spatiotemporally distributed frontotemporal networks for sentence reading. Proceedings of the National Academy of Sciences of the United States of America, 120(17), e2300252120.

87.

Zhou

(2009). The P300 and reward valence, magnitude, and expectancy in outcome evaluation. Brain Research, 1286, 114–122.

88.

Leliveld

M. C.

Zhou

(2011). Social distance modulates recipient’s fairness consideration in the dictator game: An ERP study. Biological Psychology, 88(2–3), 253–262.

89.

Yeung

Sanfey

A. G.

(2004). Independent coding of reward magnitude and valence in the human brain. Journal of Neuroscience, 24(28), 6258–6264.

90.

Yoshida

Seymour

Friston

K. J.

Dolan

R. J.

(2010). Neural mechanisms of belief inference during cooperative games. Journal of Neuroscience, 30(32), 10744–10751.

91.

Sun

(2013). To conform or not to conform: spontaneous conformity diminishes the sensitivity to monetary outcomes. PLoS One, 8(5), e64530.

92.

Zeng

Wang

Zhang

(2012). An ERP study on decisions between attractive females and money. PLoS One, 7(10), e45945.

93.

Zhang

Jin

Xia

Syed Abdullah

S. M.

(2022). The effects of social distance and asymmetric reward and punishment on individual cooperative behavior in dilemma situations. Frontiers in Psychology, 13, 816168.

94.

Zhang

Kong

Chen

Jackson

Han

Meng

Yang

Gao

Najam

Hasan

(2011). Identifying cognitive preferences for attractive female faces: An event-related potential experiment using a study-test paradigm. Journal of Neuroscience Research, 89(11), 1887–1893.