Edited by: Peter Bossaerts, École Polytechnique Fédérale de Lausanne, Switzerland
Reviewed by: Giorgio Coricelli, University of Southern California, USA; NaYoung So, Columbia University, USA
*Correspondence: Lei Wang
This article was submitted to Decision Neuroscience, a section of the journal Frontiers in Psychology
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The present research examined the influence of perceived ownership (self/other) and perceived chooser (self/other) of stocks on brain activity, and investigated whether differential brain responses to stock outcomes as a result of perceived differences in ownership of stock would be modulated by perceived chooser of stock. We used a 2 (stock chooser: self, other) × 2 (stock owner: self, other) within-subject design to represent four types of chooser-owner relationships. Brain potentials were recorded while participants observed increasing and decreasing stock prices. Results showed that observations of stock outcomes among four types of chooser-owner relationships elicited differentiated feedback-related negativity (d-FRN: differences in FRN waves between losses and gains, reflecting violations of expectancy to stock outcomes): (1) Self-chosen-other-owned stocks evoked significantly larger d-FRN discrepancies than self-chosen-self-owned stocks, indicating a greater expectancy violation to others' losses than to one's own, demonstrating a
People may take on diverse financial roles in the stock market: observers, buyers, fund managers, or clients. These four roles can be classified according to two categories: ownership (the individual who owns the stock) and choosership (the individual who selected the stock). The roles concerning ownership (self-owned stocks vs. other-owned stocks) and the roles concerning choosership (self-chosen stocks vs. other-chosen stocks) are combined to create four chooser-owner relationships. Intriguingly, people may take on all four financial roles simultaneously. How ownership and choosership of stocks individually and interactively influence brain activities while observing stock outcomes (gains or losses) across the four roles remains unclear. The present study aimed to investigate the individual and interactive influence of perceived ownership and perceived choosership of stocks on stock outcome evaluations.
It has been found that ownership and choosership are two factors that influence preference of objects (Huang et al.,
Ownership effect is the tendency to overvalue self-owned possessions to maintain a positive self-image due to a self-enhancement motivation (Belk,
The effect of choosership on outcome evaluation has been studied extensively over several decades and has been deemed as both desirable and powerful (Iyengar and Lepper,
The present research aimed to study the influence of perceived ownership and perceived choosership on stock outcome evaluations by recording brain activities, and also investigate whether differences in brain activity as a response to differential ownership would be modulated by choosership. To this end, we investigated the individual and interactive influence of perceived ownership and perceived choosership on stock outcome evaluations by developing a 2 (stock chooser: self, other) × 2 (stock owner: self, other) × 2 (stock outcome: gain, loss) within-subject design. By doing so, we developed a special situation that combines both
When applying electrophysiological methods, converging evidence implies that feedback related negativity responses (FRNs) reflects the neural mechanisms underlying the evaluation of one's own losses and gains (Yeung and Sanfey,
The current research aimed to investigate how individuals respond to financial outcomes in different person-stock relationships. We proposed a new scenario-simulation paradigm to assign four financial roles by priming both ownership and choosership, which can compare the processing of outcome evaluation reflected by FRN when assigned to each of the four different roles. We focused on a financial context in which the “other” and the “self” are not simply friends or strangers; instead, they are bound together by profit-based relationships. This helps further understand the functional significance of FRN involved across multiple financial roles, and by doing so, contributes to the new research area of
A total of 22 healthy students (9 males, 13 females; mean age ± SD = 21.40 ± 1.73 years) from different universities voluntarily participated in the study. All participants were right-handed with normal or corrected-to-normal vision. No participants with chronic diseases, mental disorders, medication, or those smoked or abused alcohol were recruited for the experiment.
The experiment was conducted in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki). Participants were given written instructions before the experiment began. Each participant knew that at the end of the experiment, they would be reimbursed for their time with US $15. We used a fixed payment since subjects didn't make any operations that changed the results in stock exchange.
The experiment adopted a 2 × 2 × 2 within-subjects design, with the choosership of the stocks (self-chosen, other-chosen), the ownership of the stocks (self-owned, other-owned), and the stock outcome (gain, loss) as the three within-subject factors. We used the combination of choosership and ownership to set up four person-stock relationships in which participants played four financial roles. In the four person-stock relationships, the corresponding four types of stocks were named A/B/X/Y (shown in Figure
Participants were told that the experiment consisted of three parts: a practice task, a main experiment task, and a questionnaire task. In the practice part, participants completed a 5-min training session prior to the main experiment. After the main experiment, participants were asked to report their feelings.
In the main experiment task, participants read the following two-page scenarios on paper:
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To make sure that the participants had memorized which stocks belonged to them and which to the “other,” they were asked to write down on a blank paper the stocks' names in each scenario before the EEG recording. Altogether, there were 12 stocks (A1, A2, A3, B1, B2, B3, X1, X2, X3, Y1, Y2, Y3) divided into four person-stock relationships. For control purposes, participants were further instructed that all stocks started at the same price.
Each trial began with the presentation of a fixation cross at the center of the screen for 500 ms against a black background. Then the name of one of the twelve memorized stocks (e.g., A1) was presented for 1000 ms. During the experiment, we asked participants to view ratios of stock price changes. After a jittered blank interval of 200, 300, or 400 ms, a ratio of stock rose or declined by 3, 6, or 9% (white and Song font, size 32) could be used to describe the change of previous stocks. The ratio was presented for 800 ms. Participants' EEG signals from −200 to 800 ms of this screen were extracted for analyses. The ratio was followed by another jittered blank interval of 200, 300, or 400 ms. We then asked them to state whether the stocks belonged to themselves or to the other and whether they had chosen the stocks themselves or if they were chosen by the other. At the end of each trial, one of the following two questions, “Who chose the stock (A1),” or “Who owned the stock (A1)” appeared as the “question” screen. The two answer options: the “self” and the “other” (in words), were randomly presented on the left or right side of the “question” screen. The participants were asked to judge the owner or the chooser of the stock by pressing a corresponding key (n, m) as quickly as possible within 2 s. The “question” screen did not disappear until participants responded. The last screen in the trial is a blank and lasted for 500 ms (see Figure
The participant was seated comfortably about 1 m in front of a computer screen in a dimly lit and electromagnetically shielded room. The experiment was administered on an Intel(R) Core(TM) i7-3770 CPU computer with a Del 24-in. CRT display, using Presentation software (Neurobehavioral System Inc.) to control the presentation and timing of stimuli. Participants completed a 5-min training session prior to the commencement of the main experiment. After that, each participant received 4 blocks of 696 trials, with each of the 2 (stock chooser: self, other) × 2(stock owner: self, other) × 2(stock outcome: rise, fall) 8 experimental conditions concludes 87 trials (39 trials for 3 and 9% ratio and 9 trials for 6%). We introduced this design with various stock price change ratios to simulate realistic stock market fluctuations as well as avoiding subjects' desensitization to experimental stimuli. The order of the trials was counterbalanced across 4 blocks using M-sequences (Buracas and Boynton,
After the main experiment task, participants completed a 40-item mini-marker scale of Big-Five Personality trait (Saucier,
EEGs were recorded from 32 scalp sites using tin electrodes mounted in an elastic cap (Brain Products, Munich, Germany) according to the international 10–20 systems. The vertical electrooculogram (VEOG) was recorded supra-orbitally from the right eye. The horizontal EOG (HEOG) was recorded from electrodes placed at the outer canthus of left eye. All EEGs and EOGs were referenced online to an external electrode that was placed on the tip of nose and were re-referenced offline to the mean of the left and right mastoids. All electrode impedance was kept below 5 kΩ. The bio-signals were amplified with a band pass from 0.016 to 100 Hz and digitized on-line with a sampling frequency of 1000 Hz. EEG epochs of 1200 ms (with a 200-ms pre-stimulus baseline) were extracted offline for ERPs time-locked to the onset of the ratio of the stocks. Ocular artifacts were corrected with an eye-movement correction algorithm, which employs a regression analysis in combination with artifact averaging (Semlitsch et al.,
The EEG and EOG data were analyzed off-line using the Brain Vision Analyzer Software Package (Brain Products, Munich, Germany). All data were re-referenced off-line to the mean of the left and right mastoids. Ocular artifacts were corrected with an eye-movement correction algorithm (Semlitsch et al.,
The ERP component analyzed in the current experiment is the FRN. Based on visual inspection of the ERP waveforms (Figure
Additionally, to minimize the overlap of FRN with negative ERP components, we calculated the difference of FRN waveforms by subtracting the ERPs elicited by the gain trials from the ERPs elicited by loss trials in the 340–420 ms time window, following the methods of Holroyd and Krigolson (
This FRN difference effects (d-FRN) was defined as the mean value of the most negative component distributed on the anterior scalp over 320–420 ms in the experiments. We selected 10 electrodes of F3, F4, FC1, FC2, Fz, Cz, C3, C4, CP1, and CP2 in frontal area for FRN and d-FRN in statistical analysis (see Figure
A three way repeated measures of analysis of variance (ANOVA) on the amplitude of d-FRN component was conducted with stock chooser (self, other), stock owner (self, other), and electrode position (10 electrodes: F3, F4, FC1, FC2, Fz, Cz, C3, C4, CP1, and CP2).
In addition, we averaged ERPs on the above-mentioned 10 electrodes in conjuction, taking the 10 electrodes as the anterior scalp area. A 2 × 2 × 2 mixed ANOVA on the selected 10 electrodes for FRN was performed, the three factors were: stock chooser (self, other), stock owner (self, other), and valence (gain, loss).
The computer program SPSS (version 20.0) was used. The Greenhouse–Geisser correction for violation of the assumption of sphericity was applied where appropriate.
Trials in which the participants did not respond within 2 s or responded incorrectly and trials in which the reaction times (RTs) exceeded three standard deviations from the mean in each experimental condition were excluded from data analysis. Approximately 1.00% of the total data points were lost due to these exclusions.
We conducted a 2 (stock chooser: self, other) × 2 (stock owner: self, other) × 2 (stock outcome: gain, loss) mixed ANOVA on the RTs. The means of RTs showed in Figure
Choosership | 3.195 | 0.088 | 0.132 |
Ownership | 15.908 | 0.001 | 0.431 |
Outcome (gain, loss) | 10.482 | 0.004 | 0.333 |
Choosership * ownership | 14.079 | 0.001 | 0.401 |
Choosership * outcome | 0.096 | 0.76 | 0.005 |
Ownership * outcome | 4.526 | 0.045 | 0.177 |
Choosership * ownership* outcome | 0.427 | 0.521 | 0.020 |
Results revealed a significant main effect of ownership,
Based on visual inspection of the ERP waveforms (Figure
It is clear from Figures
ANOVA with stock chooser (self, other), stock owner (self, other), valence of the stock outcomes (rise, fall), and the electrode (F3, F4, FC1, FC2, Fz, Cz, C3, C4, CP1, and CP2) yielded a significant three-way interaction between the stock chooser, ownership of the stocks, and the valence of the outcome,
Then we performed analysis for the self-chosen stocks (SC) and other-chosen stocks (OC) separately. For the SC stocks, a 2 (stock owner: self, other) × 2 (stock outcome: gain, loss) × 10 (electrode: F3, F4, FC1, FC2, Fz, Cz, C3, C4, CP1, and CP2) repeated ANOVA revealed a significant main effect of valence (stock outcome), which indicated that the mean amplitude of the FRN across loss trials (0.61 ± 2.34 μV) had a more negative-going than across gain trials (2.25 ± 2.35 μV),
Simple tests were conducted for each of the two owners. For the SCSO stocks, the loss outcome (1.04 ± 2.45 μV) evoked more negative-going responses than the gain outcome (2.09 ± 2.32 μV),
Moreover, we focused on FRN difference (d-FRN) waves calculated by the brain responses evoked by the falling stock minus the brain responses evoked by the rising stock. For the difference of the waves of the FRN amplitude in SC stocks, a 2 (stock owner: self, other) × 10 (electrode: F3, F4, FC1, FC2, Fz, Cz, C3, C4, CP1, and CP2) ANOVA revealed that the main effect of stock owner was significant,
For the OC stocks, a 2 (stock owner: self, other) × 2 (stock outcome: gain, loss) × 10 (electrode: F3, F4, FC1, FC2, Fz, Cz, C3, C4, CP1, and CP2) repeated ANOVA was conducted, The main effect of valence (stock outcome) was significant,
A
In terms of the d-FRN's statistical analysis from the other-chosen stocks, a 2 (stock owner: self, other) × 10 (electrode: F3, F4, FC1, FC2, Fz, Cz, C3, C4, CP1, and CP2) ANOVA produced a marginally significant main effect of ownership,
The second part of analysis was conducted for the brain responses to the self-owned (SO) and other-owned stocks (OO) separately.
For the SO stocks, a 2 (stock chooser: self, other) × 2 (stock outcome: gain, loss) × 10 (electrode: F3, F4, FC1, FC2, Fz, Cz, C3, C4, CP1, and CP2) repeated ANOVA revealed a significant main effect of valence (stock outcome), which indicated that the mean amplitude of the FRN across loss trials (0.96 ± 2.32 μV) was more negative-going than that across gain trials (1.88 ± 2.39 μV),
For the d-FRN amplitude of self-owned stocks (SO), an ANOVA of 2 (stock chooser: self, other) × 10 (electrode: F3, F4, FC1, FC2, Fz, Cz, C3, C4, CP1, and CP2) revealed that the main effect of the stock chooser was not significant,
For the other-owned stocks (OO), 2 (stock chooser: self, other) × 2 (stock outcome: gain, loss) × 10 (electrode: F3, F4, FC1, FC2, Fz, Cz, C3, C4, CP1, and CP2) repeated ANOVA revealed a significant main effect of valence, which indicated that the mean amplitude of the FRN across the loss trials (0.98 ± 2.15 μV) was more negative-going than across the gain trials (2.16 ± 2.69 μV),
In terms of the d-FRN's statistical analysis with OO stocks, an ANOVA of 2 (stock chooser: self, other) × 10 (electrode: F3, F4, FC1, FC2, Fz, Cz, C3, C4, CP1, and CP2) showed a significant main effect of the stock chooser,
The third part of the analysis analyzed FRN for gains and also for losses separately. We averaged the above 10 sites and conducted a two way ANOVA of 2 (stock chooser: self, other) × 2 (stock owner: self, other) on the FRN evoked by stock increase. No significant effect was found,
Choosership | 3.342 | 0.082 | 0.137 |
Ownership | 1.534 | 0.229 | 0.068 |
Choosership * ownership | 0.025 | 0.875 | 0.001 |
As the simple tests shown in Table
SCSO vs. SCOO | 1.29 | 0.26 | 0.058 |
OCSO vs. OCOO | 0.30 | 0.585 | 0.014 |
SCSO vs. OCSO | 1.78 | 0.197 | 0.078 |
SCOO vs. OCOO | 1.33 | 0.262 | 0.06 |
A two way ANOVA of 2 (stock chooser: self, other) × 2 (stock owner: self, other) on the FRN evoked by stock decrease showed that a main effect of choosership was significant (shown in Table
Choosership | 18.57 | 0.000 | 0.469 |
Ownership | 0.024 | 0.879 | 0.001 |
Choosership * ownership | 13.20 | 0.002 | 0.386 |
SCSO vs. SCOO | 12.24 | 0.002 | 0.368 |
OCSO vs. OCOO | 9.97 | 0.005 | 0.322 |
SCSO vs. OCSO | 0.234 | 0.634 | 0.011 |
SCOO vs. OCOO | 43.58 | 0.000 | 0.675 |
The FRN evoked by SCSO stocks decrease (1.045 ± 1.75 μV) was not significantly different from that by OCSO stocks decrease (0.882 ± 2.19 μV) (showed as SCSO vs. OCSO in Table
We collected post-recording questionnaires and performed a correlation analysis between the d-FRN amplitude and the big-five personality factors (Extraversion, Agreeableness, Conscientiousness, Emotional stability, and Openness). The coefficient alpha was 0.79 for Extraversion of the Big Five Personality, 0.88 for Agreeableness, 0.86 for Conscientiousness, 0.90 for Emotional Stability, and 0.83 for Openness.
A reliable negative correlation was observed between the difference waves of the FRN elicited by SCOO stocks and the participants' subjective rating of conscientiousness from the Five-Factor Model,
Using a new experimental paradigm, we manipulated four person-stock financial relationships to investigate the individual and interactive influences of perceived ownership and perceived choosership on stock outcome evaluation, which were reflected by neural responses. The results revealed that the ERP component FRN varied when individuals assumed different financial roles. This provides a more comprehensive picture of how our evaluations function when facing gains and losses belonging to ourselves and to others in large-scale financial markets. By doing so, we can explore the feature of human social emotions in monetary activities.
Results of response time analyses showed that the responses to gains were significantly faster than responses to losses. Results of brain potentials showed that FRN elicited by stock losses was larger than that elicited by stock gains. These results indicated that compared with stock gains, stocks losses seem to be of expectation violation which evoked more salient FRN at the brain level and increased peoples' reaction times at the behavioral level. Despite this relationship between the overall patterns in the behavioral and the brain responses to losses and gains, the brain responses were not significantly different between self-owned stocks (SO) and other-owned stocks (OO) while the reaction times were faster to SO than to OO stocks. However, in self-chosen stocks, people responded slower to SCOO stocks than SCSO stocks, and d-FRN was larger to SCOO stocks than SCSO stocks. Hence, FRN revealed sensitivity to losses and gains, while reaction time showed sensitivity to the ownership (SO vs. OO).
Electro-physiologically, participants displayed a statistically significant trend toward more negative FRN when observing losses than when observing gains of SCSO stocks. The trend indicated the presence of the FRN effect, that is, the d-FRN discrepancy (the difference wave between gains and losses). The reinforcement theory (Holroyd and Coles,
A surprising finding was that participants had more significantly negative FRN patterns when observing the losses than the gains of SCOO stocks, showing an expectancy violation for others' losses. This mimics the agent role. When assuming the role of an agent, the stock chooser (the agent) may show a guilty response to the stock owner's (the client's) losses. This result is in line with the hypothesis that FRN is a reflection of the motivational/affective impact of outcome events (Gehring and Willoughby,
The scenario of SCOO stocks was used to mimic the agent-client contract relationship. The economic actors (the agent and his/her client) develop contractual arrangements in the agency relationship, where each of the parties executes the legal obligations assigned to them. The agent takes charge of the transaction of the financial products for his/her client. Analogously, we argue that the participants' sense of responsibility to their clients was elicited when they were playing the agent role. In this scenario, guilt responses may be induced when seeing the client's loss. The correlation between questionnaire data and ERP data showed that trait
A similar study (Li et al.,
Additionally, aside from the conscientiousness point of view, the reputation of one's decision-making may also affect the evaluation. The decision reputation of transactions for clients is very important for the agent, because clients may judge transaction results. According to the self-enhancement theory (McCrea and Hirt,
Moreover, existing studies showed that the participant (the observer)'s FRN patterns reflected an empathic response to the other (the performer) in a social context (Fukushima and Hiraki,
Taken together, in the comparison Group One, people feel bad about both their own losses and others' losses. Although, humans are self-interested to some extent (Ma et al.,
As for evaluation comparison, ownership modulated the d-FRN effect. When participants assumed the buyer and agent roles, SCOO stock outcomes evoked a significantly larger d-FRN discrepancy than SCSO stocks outcomes, indicating that participants have more positive expectations toward other-owned outcomes than self-owned outcomes. In terms of unfavorable outcomes, a much higher expectancy violation to SCOO stocks losses than SCSO stocks losses was reflected by a larger FRN, while the FRN patterns elicited by gains were not significantly different. It seems that concern for others was stronger than self-regarding concerns when the chooser was the self. We infer that the other-regarding approach, which is induced by responsibility for others and self-related reputation, may be more dominant than the self-interested approach in a financial context. This intriguing finding was surprisingly contradictory to the ownership effect (Beggan,
We deduced that when participants confronted losses of their own and their clients, instead of adopting a selfish approach, the participants strengthened their responsibility toward the other and thus exhibited selfless behaviors, making them even more sensitive to the others' losses than to their own. If this is the case, interpersonal responsibility might be a more dominant feature of human beings than selfishness in the agency relationship, which in turn might ultimately be self-benefitting through the maintenance of a good reputation.
As for the comparison in the comparison Group Two, the results showed that ownership modulated the d-FRN effect: the d-FRN discrepancy was significant for the OCSO stock outcome evaluations but not for the OCOO outcome evaluations. When the chooser was the other, participants tended to have more positive expectations for self-owned stock outcomes than for other-owned stock outcomes, demonstrating the ownership effect (Beggan,
Participants exhibited no statistically significant FRN discrepancy between losses and gains when observing OCOO stock outcomes, indicating no sympathetic reactions toward strangers. This finding replicates results from previous neuro-economic laboratory studies that have revealed an indifferent attitude (i.e., no empathy) or even derogation of others when observing strangers' losses (Fukushima and Hiraki,
The d-FRN elicited by SCSO stock outcomes was not significantly different from that elicited by OCSO stock outcomes, suggesting that whether the chooser was the self or the other had little influence when the participant was the owner. This ERP data indicated that the choosership effect was overshadowed by the ownership effect. However, this finding is inconsistent with a previous object evaluation study (Huang et al.,
When the owner was the other, the self-chosen stock outcomes elicited a significantly larger d-FRN than the other-chosen stock outcomes, demonstrating a stronger positive expectation (see Figure
Our finding is the first to show that in a financial context, the agent's FRN is more negative toward a client's losses than toward the agent's own losses (see Figure
The social distance between the “self” and the “other” was manipulated by different financial relationships in our paradigm. We decomposed the financial relationship into the owner role and the chooser role in the current study. Compared with previously used empirical economic situations (e.g., gamble games), our paradigm seems to be more relevant to the real world stock market that is the aggregation of buyers and sellers (a loose network of economic transactions, not a physical facility, or discrete entity) of stocks and imagined stock trade actions. The outcome of stocks, even a small percentage of gain or loss, usually involves a large amount of monetary value change, which is different from the relatively small amount of cash reward (e.g., several dollars or even cents) in traditional gambling games. The financial relationships between the “self” and the “other” in our study was assigned to anonymous unacquainted strangers and then primed by our experimental scenario. Participants in the simulated stock market were assigned different financial roles that included buyers of stocks for oneself (similar to those buying stocks for themselves and individual retail investors), buyers of stocks for others (similar to brokers), those who delegate others to buy stocks on their behalf (similar to those who buy funds), and those who simply observe others buying and selling stocks. We set up four roles according to perceived ownership and choosership, which helped to examine the neural foundation of complex human cognitive and emotional reactions to gains and losses in financial activities. This design is particularly useful for investigating responsibility, selfishness, and altruism in a more comparable setting.
The current study has a practical implication as well. This study mimics a financial agent in a real stock market. Results suggested the “agent” might feel more discomfort toward the client's loss than to the agent's own loss. This pattern of EEG/FRN results implies that people who work as stock brokers and fund managers, which, as high-paying careers, might be especially susceptible to tremendous pressure induced by the responsibility for others' outcomes. Our findings shed new light into the recruitment and training of these professionals.
The current study may have certain limitations that can be addressed in future research. First, although we measured the conscientious trait to represent responsibility, we only established the relationship between personality trait and d-FRN data. This is not a robust evidence to support that responsibility may result in a large d-FRN in the self-chosen-other-owned condition, and such causality needs to be further tested. Second, we did not directly measure individuals' self-reputation concerns. Third, we did not measure online emotional reactions related to the outcomes, such as guilt. Future research is needed to confirm the emotions that we speculated in this study. Fourth, we used an imaginary scenario and did not include actual stock investment actions. The ecological validity was limited and we did not have evidence to support our paradigm's validity. However, although we did not provide evidence that our paradigm was able to represent the large monetary scale of financial markets, it represents an important step to try to mimic the complicated financial market. Future research could examine the current findings among actual stockholders or fund managers in the real world to increase the external validity.
Additionally, although we used M-sequence to balance the sequence effect between all eight conditions, the sequence of ratios (3, 6, 9%) was presented randomly, which may induce a negligible history effect. We could not fully exclude the possible sequence effect of different ratios for sure. Finally, we did not separate different ratios of price change (3 vs. 9%) because the number of trials for each ratio was not enough for ERP analysis in our design. We only recorded the increase or decrease of price change. There may be a value size effect between 3 and 9%. Future studies could study the size effect on outcome evaluation in the four financial scenarios.
In conclusion, by recording participants' neural and behavioral reactions to stock outcomes, the present study investigated the neural mechanism of how financial contextual factors affect outcome evaluations, reflected by amplitudes of tFRN, in a stock observation task. We found a “reversed ownership effect” for the self-chosen stocks (comparison group One) and the ownership effect in other chosen stocks (comparison Group Two). The choosership effect disappeared under the influence of the perceived ownership (self-owned stocks, comparison Group Three), whereas the choosership effect was robust for other-owned stocks (comparison Group Four). Our findings refute the hypothesis from traditional economics that people only act to maximize their self-interest and provides concrete neural evidence showing that people are social creatures and can be other-regarding in financial situations (Fehr and Camerer,
Committee for Protecting Human and Animal Subjects, Department of Psychology, Peking University. The human participants were told to record their brain responses by EEG equipment which will not hurt to them. They were told there would be no any dangers while they were doing the experiment in which they would see some texts in a computer screen and react by push some buttons on the keyboard. They were told their rights and they can decide to or not to participate in this experiment, and they had the right to quit the experiment at any time of the experiment. They were reward for about 15 US dollars for their participation.
LW proposed the main research idea; ZS and LW made the research design; ZS designed the experimental materials and ran the statistics; ZS and HW conducted the experiment; ZS and LW made the discussion and wrote the manuscript.
NSFC Grant #71021001, #91224008, and #91324201; Beijing Positive Psychology Foundation Grant #0020344. It was also supported by Taetea Group.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We would like to thank the Editor and the two reviewers for their constructive comments on the previous manuscripts. We also would like to thank Taiyong Bi, Yin Wu, Xilin Zhang, Nihong Chen, Hongbo Yu, Lu Yin, Yi Liu, Jie Hu, Philip Blue, Qinglin Yu, Fangxuan Xue, Jenny C. Li, Weipeng Lin, Jingjing Ma for their assistance.