1. Introduction
Several neurobehavioral mechanisms, including trust, social influence, observational learning, conformity, and behavioral contagion, influence human social decision-making. These mechanisms have been extensively studied in social and cognitive neuroscience (Charpentier et al., 2020; Mahmoodi et al., 2022; Rilling & Sanfey, 2011; Zhang & Gläscher, 2020), often through a neuroeconomic approach that employs experimental designs derived from game theory (Lee, 2008; Ridderinkhof et al., 2004; Sanfey, 2007). One particularly important phenomenon in social decision-making is behavioral contagion, which refers to how the choices of others influence an individual’s decision (Suzuki et al., 2016; Thomas et al., 2022). This contagion can manifest in both positive (e.g. altruistic or prosocial) and negative (e.g. risky or antisocial) behaviors, each governed by complex neurobehavioral processes (Dimant, 2019; Martínez et al., 2023; Suzuki et al., 2016; Tsvetkova & Macy, 2014). Such a phenomenon has been investigated using neuroimaging techniques like electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). For instance, Suzuki et al. (2016) showed that the contagion of risky behaviors due to observing the dangerous behavior of others can be explained by a neurocomputational approach. In this regard, EEG-based analysis has raised interest because of its high temporal resolution.
Electrophysiological studies, particularly those utilizing event-related potentials (ERPs), have traditionally been used to examine decision-making processes. These studies typically analyze ERP components based on two factors: Amplitude and latency (Congedo, 2018; Donoghue & Voytek, 2022). One of the shortcomings and challenges in past ERP time series studies is the number of components and the time windows of the components. These deficiencies could be covered by visibility graph (VG) analysis, as a promising method for understanding the nonlinear dynamics of neural time series data. Unlike conventional ERP analysis, VG maps time series data into a graph structure, which enables us to identify hidden patterns within high-volume sequential data (Sannino et al., 2017; Sengupta et al., 2013; Sulaimany & Safahi, 2023; Zheng et al., 2021). Prior research suggests that behavioral contagion influences ERP time series (Deldoost et al., 2024), making it important to explore new analytical frameworks that can capture these dynamics more effectively. Because ERP time series contain both positive and negative fluctuations, examining them in polar space through graph-based methods may provide deeper insights into contagion effects. Given its ability to model complex nonlinear phenomena, VG analysis presents a compelling alternative to traditional ERP assessment methods (Congedo, 2018).
In this study, we apply VG analysis to ERPs associated with behavioral contagion in social decision-making. The VG approach (Zheng et al., 2021) is a strong abstraction of time series ERP data based on points of high-volume sequential data (Lacasa et al., 2008). Our primary objective is to examine the characteristics of the graph structures derived from ERP data in individuals who exhibit behavioral contagion. Specifically, we aim to determine whether neural indicators of behavior contagion can be identified through these graph-based features, including clustering coefficient, local and global efficiency, path lengths, modularity, and radius. To investigate this, we designed an experiment based on the dictator game, a well-established task in neuroeconomics and game theory, while simultaneously recording brain electrical activity.
By leveraging this novel methodological approach, our research seeks to advance the understanding of neural mechanisms underlying behavioral contagion and contribute to the development of new analytical techniques in cognitive neuroscience.

2. Materials and Methods
Study participants 
The study included 30 healthy participants aged between 20 and 30 years (mean age: 27±2.1 years), comprising 15 men and 15 women, who voluntarily entered the test based on public calls, and all were right-handed with no history of neurological or psychological disorders and were not on any medication. Among them, 2 participants were removed. Additionally, 6 USD was allocated for one hour of testing. 

Stimuli and procedure
The experiment consisted of the following stages: A general description of the experiment and addressing participants’ questions, followed by the main phase of the test after a trial run to ensure their learning. During the behavioral task, a modified dictator game (it should be noted that various models of the dictator game have been used in research) (Engel, 2011), which may be interactive or non-interactive, was used, meaning the dictator and recipient can switch roles (Grech & Nax, 2020). In this study, a non-interactive model was used, where the dictator does not receive feedback in the form of reward or punishment. Both anonymous and identified peer types are utilized in research (Rilling & Sanfey, 2011). Our version is similar to the paper by Fehr’s group (Krajbich et al., 2015), and the paradigm is identical to that of Mobasseri et al (2025). 
As Heinrich and Weinmann (2013) explained, in the classic dictator game introduced by Forsythe et al. (1994), the budget constraint has a slope of −1, reflecting the fact that each cent transferred to the recipient decreases the dictator’s earnings by the same amount. However, in altered versions of the game, this trade-off rate—represented by the slope of the budget line—can differ (Heinrich & Weimann, 2013).
An EEG recording was also conducted simultaneously. These experimental stages are conceptually illustrated in Figure 1.

Overall, the behavioral experiment consisted of three phases: Phase 1, which involved the individual’s own decisions; phase 2, which involved observing others’ decisions (first predicting someone else’s choice and then observing); and phase 3, which involved the individual’s own decisions.
General description of the experiment 

Preparation and familiarization with the experiment 
First, explanations about the generalities of the experiment were provided, stating that the dictator game was explained to them, and it was emphasized that there is no concept of right/wrong or good/bad choices, nor any winning/losing in this experiment, to minimize potential effects of these factors. Then, the preparation of the EEG cap and device was done, and a trial was conducted to ensure the participant had learned the tasks.

Leaving the laboratory and starting the recording 
To prevent social priming of the participant by the experimenter (Gilder & Heerey, 2018), after ensuring that the participant fully understood the experiment and was briefed, the experimenter visibly left the laboratory and discreetly monitored the participant to minimize the effect of the experimenter’s presence on the participant’s decision-making. To reduce stress, they were advised to call out loudly whenever needed for the experimenter to come (although no one felt the need to do so during this time).

End of the experiment and answering potential questions from the participant 
At this stage, potential questions from the participant were answered, and in addition to monetary compensation, a certificate of appreciation was also awarded to them.

Behavioural analysis
To observe the behavior contagion, based on similar studies (Suzuki et al., 2016), the differences between the decisions in the first and third phases of the experiment, that is, the differences between one’s decision-making before and after observing the decisions of others, were examined in such a way that the differences in trials in decision-making were calculated using MATLAB software, version R2018b. Then, the participants were divided into two groups: Contagion and no contagion. Based on recent studies to determine the threshold, behavioural contagion rate was chosen (Mobasseri et al., 2025) such that participants with a difference in the number of decisions in phases 1 and 3 greater than or equal to 6 were classified as the contagion group, while the remaining participants with a smaller difference were placed in the no contagion group.

EEG recording and preprocessing
The data obtained from EEG was recorded using a 64-channel gel-based device with a sampling rate of 1000 Hz. Data acquisition was performed with a monopolar setup, and the EEG cap was placed on the subjects’ heads using the standard 10-20 method. 
In the preprocessing stage of the data, motion artifacts, eye blink noise, eye movement noise, EMG noise, and ECG noise were removed using the ICA method. A bandpass filter from 0.1 to 30 Hz was applied to the data, which was processed using MATLAB-based software EEGLAB version 2023.0. The obtained data were then analyzed using ERPLAB version 9.20, considering a 200-ms window before and an 800-ms window after the stimulus.

Data analysis
VG 
After obtaining the ERP time series, these time series were analyzed using the VG analysis approach, such that the ERP time series for each EEG channel was considered input. After calculating the VG network analysis using Python software, version 3.8 with the NetworkX library, the values related to the VG were separately calculated for each EEG channel for the first phase and the third phase of the experiment. The graph features included clustering coefficient, path lengths, global efficiency, local efficiency, modularity, and radius. 
The VG is defined as below if and only if: 
 

Therefore, to implement the VG on ERP time series:




Statistical analysis
Behavioral data consisted of continuous integer types, and VG data included clustering coefficient, path length, global efficiency, local efficiency, modularity, and radius of discrete type, all of which were continuous decimal types. After calculating the quantitative values of the graph features, the Kolmogorov-Smirnov test was used to check for normality. Then, considering the non-normal distribution of the data, the Wilcoxon signed-rank statistical test was used to examine significance. Finally, the family-wise error (FWE) correction method with the Bonferroni-Hochberg approach was employed to determine the most significant components in each EEG channel for each graph feature to create the related maps and diagrams.
Subsequently, the relationship between behavioral changes and significant changes in the brain graph features in the first and third phases of the experiment will be calculated using Pearson correlation.

3. Results
Behavioral results 
The results of the behavioral data show that 13 individuals were placed in the contagion group and 15 individuals in the no-contagion group, such that the individuals in the contagion group changed more than 6 decisions before and after the second phase (observing others’ behavior). The results of the behavioral analysis in the two groups, contagion and no contagion, are presented in Table 1.


After conducting the Wilcoxon statistical test in the first and third phases for the visibility features of the contagion group, significant channels with a P<0.05 for each feature are presented in Table 2.


The topoplots obtained from the averages and P of each feature of the VG are shown in Figure 5.

These average topoplots are for phase 1 and phase 3 of the experiment, as well as for comparing phase 1 and phase 3 with P.
The first column indicates the average of features in phase 1, the second column indicates the average of features in phase 3, and the third column indicates the P value of differences between phase 3 and phase 1. 
Note that A refers to clustering coefficient, B to global efficiency, C to local efficiency, D to path lengths, E to radius, and F to modularity.
For the relationship between behavioral changes and changes in each of the graph features, the most significant EEG channel was calculated. According to Figure 6, regression lines with coefficients r and P were drawn, showing that the highest significant relationship was for radius (r=0.689, P=0.009) and modularity (r=0.586, P=0.035).

4. Discussion
In this research, we sought a model for the brain mechanism of behavioral contagion based on the VG, as this type of modeling represents a novel approach for processing brain signals according to previous studies (Sulaimany & Safahi, 2023). Since the VG transforms time series data into a graph, we used this nonlinear model to analyze ERP data, as the nature of these brain signals is inherently complex and nonlinear (Poikonen et al., 2023; Zhang & Hu, 2024). After conducting behavioral experiments and simultaneously recording EEG, we calculated the features of the VG for the ERPs obtained from it. For the contagion group, the results are observable in a topoplot for the mean and also the significance levels of the comparison between phases 1 and 3 of the experiment. On the other hand, considering the most significant EEG channels in this contagion group, the results indicate that the obtained clustering coefficient feature in channel P7 is the most significant area of the brain for this feature. Additionally, for the obtained pathlengths feature, channel CP3 is the most significant, while for the obtained global efficiency feature, the most significant channel is also CP3. For the obtained local efficiency feature, the most significant channel is P7, and for the obtained modularity feature, the most significant channel is PO3. Furthermore, for the obtained radius feature, the most significant channel is also PO3. Given that these areas were exclusively observed in the contagion group, in behavior contagion, based on the results of the clustering coefficient and local efficiency, individuals are more influenced by visual and spatial cues. Therefore, improving visual-spatial information processing should enhance sensitivity to the behavior of others, which is associated with increased social information processing through observing others. 
Regarding the results of global efficiency and path lengths in the left centroparietal region, it is associated with better integration of sensory-motor and social information. Therefore, concerning the behavior contagion, it can be concluded that better coordination between observing the behaviors of others and decision-making for contagion is likely related. Additionally, the results of modularity in the PO3 region signify more independent and modular processing of spatial social information. Thus, it can be concluded that regarding behavior contagion, there is more specialized processing of visual and social information in this area, with a greater focus on social cues, which may indicate a higher sensitivity to the behaviors of others that play an important role in behavior contagion. In addition to the results from radius, in the PO3 region, it can be concluded that this area of the brain may play an important role in the brains of individuals in the contagion group and sends processed social information faster. Previous studies have also shown that the parieto-occipital region is related to the decision-making selection phase, which is corroborated by our findings in this study, showing alignment with previous results (Weinstein, 2023). Previous studies have shown that social influence is more related to the DLPFC and TPJ regions. Our data in this study also aligns with previous results, suggesting that these areas play an effective role in the transmission of others’ behavior. Previous studies have shown that social decision-making is more closely associated with vmPFC regions. This area is critical for prosocial decision-making in social decisions. Our findings in this study are consistent with previous results, and we may refer to the role of prosocial decision-making versus antisocial when making decisions regarding the spread of behavior, which is associated with the involvement of this area of the brain (Lockwood et al., 2024). Additionally, previous studies have indicated that a specific neural network is engaged in decision-making under social influence, involving the rTPJ, DLPFC, and vmPFC regions. Our data in this study are consistent with these findings, further supporting the role of these brain regions in behavioral contagion and the networked nature of brain function (Hu et al., 2022). Moreover, prior research has demonstrated that the occipitoparietal region is associated with social conformity and behavioral contagion. Our findings also align with these studies, suggesting the crucial role of this region in the contagion of behavior (Berns et al., 2005). Considering that a significant correlation was observed only in the contagion group between modularity (r=0.7, P<0.05) and radius (r=0.6, P<0.05) with behavioral changes—while this correlation was absent in the no-contagion group—it can be concluded that this relationship may serve as a specific neural marker for behavioral contagion, indicating a neural mechanism underlying social imitation. Therefore, PO3 appears to be a brain region where increased processing differentiation (as reflected by the modularity index) and faster information integration (as indicated by the radius index) could lead to behavior contagion. In addition, given that the predictive indices of VG-based behavior contagion were observed more in the left side of the brain, it can be inferred that behavior contagion exhibits laterality in the brain.
One of the limitations of this study is the task design, as we generalized human-computer interaction to human-human interaction. This study lacks face-to-face interaction data and differs from real-world human communication conditions (Ibanez, 2022; Sonkusare et al., 2019). Therefore, future studies are recommended to use hyper-scanning EEG recording, which allows for simultaneous brain activity recording of two individuals (Hakim et al., 2023). Considering the different results obtained from various VG parameters across different channels, future research should utilize the VG analysis approach with community detection to distinguish ERP components better (Zheng et al., 2021), especially in studies related to behavioral contagion. Additionally, to account for cultural effects (Henrich et al., 2023), since culture has a significant impact on the formation of our social norms and our judgment of others, perhaps the influence of culture could be considered important in the behavior contagion. This is because if an altruistic or selfish behavior is to spread, individualistic or collectivistic cultural factors may be involved in our evaluation of the behavior and its contagion. In an individualistic culture, people are more focused on their personal interests and individual goals, whereas in a collectivistic culture, people prioritize the group’s benefit. Studies show that people from collectivist cultures (e.g. East Asia, Latin America) tend to give more in the dictator game than those from individualist cultures (e.g. U.S., U.K.) (Henrich et al., 2005). However, given the phenomenon of cultural evolution and the complexity of modern societies (Mesoudi, 2016), further research should be conducted regarding the generalization of results based on community culture, and future studies with larger datasets are recommended. Moreover, given the role of hormones such as oxytocin in social decision-making (Flechsenhar et al., 2024), it is suggested that future research designs incorporate this factor.

5. Conclusion
In this study, the neural mechanism of behavioral contagion was examined. Using nonlinear time series analysis of ERP with the VG method, we identified a neural marker for behavioral contagion. Specifically, the features modularity and radius showed a direct relationship with behavioral contagion in the group exhibiting this behavior, whereas this relationship was absent in the group without contagion. These findings suggest that modularity and radius can serve as neural predictors of behavioral contagion.

Ethical Considerations
Compliance with ethical guidelines
This study was approved by Ethics Committee of University of Tehran, Tehran, Iran (Code: IR.UT.IRICSS.REC.1400.034). All participants signed an informed consent form to participate in the experiment.

Funding
This paper was extracted from PhD dissertation of Mohammad Hossein Sorayani, approved by the Department of Cognitive Neuroscience _Computation and Artificial Intelligent, Institute for Cognitive Science Studies, Tehran, Iran.

Authors' contributions
Conceptualization: Abdol-Hossein Vahabie, Reza Khosrowabadi, and Javad Hatami; Methodology and investigation: All authors; Software development, validation and formal analysis: Reza Khosrowabadi, Abdol-Hossein Vahabie, and Mohammad Hossein Sorayani; Visualization: Mohammad Hossein Sorayani, Reza Khosrowabadi, and Abdol-Hossein Vahabie; Resources, data curation and writing the original draft: Reza Khosrowabadi, Abdol-Hossein Vahabie, and Mohammad Hossein Sorayani; Review and editing: Reza Khosrowabadi, Abdol-Hossein Vahabie and Javad Hatami; Supervision and project administration: Reza Khosrowabadi and Abdol-Hossein Vahabie.

Conflict of interest
The authors declared no conflict of interest.

Acknowledgments
The authors are grateful to all those who helped them in this research, especially all the participants. More specifically, The authors would like to thank Mohammad Rabiei Ghahfarokhi and Masoume Sadeghi Damavandi for their generous support in the experimental procedure. 


References
Berns, G. S., Chappelow, J., Zink, C. F., Pagnoni, G., Martin-Skurski, M. E., & Richards, J. (2005). Neurobiological correlates of social conformity and independence during mental rotation. Biological Psychiatry, 58(3), 245-253. [DOI:10.1016/j.biopsych.2005.04.012] [PMID]
Charpentier, C. J., Iigaya, K., & O’Doherty, J. P. (2020). A Neuro-computational Account of arbitration between choice imitation and goal emulation during human observational learning. Neuron, 106(4), 687-699.e7. [DOI:10.1016/j.neuron.2020.02.028] [PMID] 
Congedo, M. (2018). The analysis of event-related potentials. In: C. H. Im (Eds), Computational EEG Analysis. Biological and Medical Physics, Biomedical Engineering. Singapore: Springer. [DOI:10.1007/978-981-13-0908-3_4]
Deldoost, M., Khosrowabadi, R., & Kamiński, M. (2024). Exploring the neuropsychological basis of behavioral contagion during learning about another agent’s social preferences: Evidence from an ERP study. Acta Neurobiologiae Experimentalis, 84(2), 165-179. [DOI:10.55782/ane-2024-2500] [PMID]
Dimant, E. (2019). Contagion of pro- and antisocial behavior among peers and the role of social proximity. Journal of Economic Psychology, 73, 66-88. [DOI:10.1016/j.joep.2019.04.009]
Donoghue, T., & Voytek, B. (2022). Automated meta-analysis of the event-related potential (ERP) literature. Scientific Reports, 12(1), 1867. [DOI:10.1038/s41598-022-05939-9] [PMID] 
Engel, C. (2011). Dictator games: A meta study. Experimental Economics, 14(4), 583-610. [DOI:10.1007/s10683-011-9283-7]
Flechsenhar, A., Levine, S. M., Müller, L. E., Herpertz, S. C., & Bertsch, K. (2024). Oxytocin and social learning in socially anxious men and women. Neuropharmacology, 251, 109930. [DOI:10.1016/j.neuropharm.2024.109930] [PMID]
Forsythe, R., Horowitz, J. L., Savin, N. E., & Sefton, M. (1994). Fairness in simple bargaining experiments. Games and Economic Behavior, 6(3), 347-369. [DOI: 10.1006/game.1994.1021]
Gilder, T. S. E., & Heerey, E. A. (2018). The role of experimenter belief in social priming. Psychological Science, 29(3), 403-417. [DOI:10.1177/0956797617737128] [PMID]
Grech, P. D., & Nax, H. H. (2020). Rational altruism? On preference estimation and dictator game experiments. Games and Economic Behavior, 119, 309-338. [DOI:10.1016/j.geb.2019.10.004]
Hakim, U., De Felice, S., Pinti, P., Zhang, X., Noah, J. A., & Ono, Y., et al. (2023). Quantification of inter-brain coupling: A review of current methods used in haemodynamic and electrophysiological hyperscanning studies. NeuroImage, 280, 120354. [DOI:10.1016/j.neuroimage.2023.120354] [PMID]
Heinrich, T., & Weimann, J. (2013). A note on reciprocity and modified dictator games. Economics Letters, 121(2), 202-205. [DOI:10.1016/j.econlet.2013.08.004]
Henrich, J., Blasi, D. E., Curtin, C. M., Davis, H. E., Hong, Z., & Kelly, D., et al. (2023). A cultural species and its cognitive phenotypes: Implications for philosophy. Review of Philosophy and Psychology, 14(2), 349-386. [DOI:10.1007/s13164-021-00612-y]
Henrich, J., Boyd, R., Bowles, S., Camerer, C., Fehr, E., & Gintis, H., et al. (2005). Economic man” in cross-cultural perspective: Behavioral experiments in 15 small-scale societies. Behavioral and Brain Sciences, 28(6), 795-815. [DOI:10.1017/S0140525X05000142] [PMID]
Hu, X., Zhang, Y., Liu, X., Guo, Y., Liu, C., & Mai, X. (2022). Effects of transcranial direct current stimulation over the right dorsolateral prefrontal cortex on fairness-related decision-making. Social Cognitive and Affective Neuroscience, 17(8), 695-702. [DOI:10.1093/scan/nsac004] [PMID] 
Ibanez, A. (2022). The mind’s golden cage and cognition in the wild. Trends in Cognitive Sciences, 26(12), 1031-1034. [DOI:10.1016/j.tics.2022.07.008] [PMID] 
Krajbich, I., Hare, T., Bartling, B., Morishima,Y., & Fehr, E. (2015). A common mechanism underling food choice and social decisions., PLoS Computational Biology,11(10), e1004371. [DOI:10.1371/journal.pcbi.1004371] [PMID] 
Lacasa, L., Luque, B., Ballesteros, F., Luque, J., & Nuño, J. C. (2008). From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences of the United States of America, 105(13), 4972–4975. [DOI:10.1073/pnas.0709247105] [PMID] 
Lee, D. (2008). Game theory and neural basis of social decision making. Nature Neuroscience, 11(4), 404–409. [DOI:10.1038/nn2065] [PMID] 
Lockwood, P. L., Cutler, J., Drew, D., Abdurahman, A., Jeyaretna, D. S., & Apps, M. A. J., et al. (2024). Human ventromedial prefrontal cortex is necessary for prosocial motivation. Nature Human Behaviour, 8(7), 1403-1416. [DOI:10.1038/s41562-024-01899-4] [PMID] 
Mahmoodi, A., Nili, H., Bang, D., Mehring, C., & Bahrami, B. (2022). Distinct neurocomputational mechanisms support informational and socially normative conformity. PLoS Biology, 20(3), e3001565. [DOI:10.1371/journal.pbio.3001565] [PMID] 
Martínez, V., Jiménez-Molina, Á., & Gerber, M. M. (2023). Social contagion, violence, and suicide among adolescents. Current Opinion in Psychiatry, 36(3), 237-242. [DOI:10.1097/YCO.0000000000000858] [PMID] 
Mesoudi, A. (2016). Cultural evolution: A review of theory, findings and controversies. Evolutionary Biology, 43(4), 481-497. [DOI:10.1007/s11692-015-9320-0]
Mobasseri, M., Vahabie, A. H., Jafari, G., Hatami, J., & Khosrowabadi, R. (2025). Tracing brain functional connectivity during observation of others’ Preferences could point to Occurrence of behavioral contagion. Journal of Neurodevelopmental Cognition, 6(1), 52-62. [Link]
Poikonen, H., Zaluska, T., Wang, X., Magno, M., & Kapur, M. (2023). Nonlinear and machine learning analyses on high-density EEG data of math experts and novices. Scientific Reports, 13(1), 8012. [DOI:10.1038/s41598-023-35032-8] [PMID] 
Ridderinkhof, K. R., Ullsperger, M., Crone, E. A., & Nieuwenhuis, S. (2004). The role of the medial frontal cortex in cognitive control. Science, 306(5695), 443-447. [DOI:10.1126/science.1100301] [PMID]
Rilling, J. K., & Sanfey, A. G. (2011). The neuroscience of social decision-making. Annual Review of Psychology, 62, 23-48. [DOI:10.1146/annurev.psych.121208.131647] [PMID]
Sanfey, A. G. (2007). Social decision-making: Insights from game theory and neuroscience. Science (New York, N.Y.), 318(5850), 598–602. [DOI:10.1126/science.1142996] [PMID]
Sannino, S., Stramaglia, S., Lacasa, L., & Marinazzo, D. (2017). Visibility graphs for fMRI data: Multiplex temporal graphs and their modulations across resting-state networks. Network Neuroscience, 1(3), 208-221. [DOI:10.1162/NETN_a_00012] [PMID] 
Sengupta, A., Routray, A., & Kar, S. (2013). Complex brain networks using visibility graph synchronization. Paper presented at: 2013 Annual IEEE India Conference (INDICON), Mumbai, India, 13-15 December 2013. [DOI:10.1109/INDCON.2013.6726126] 
Sonkusare, S., Breakspear, M., & Guo, C. (2019). Naturalistic stimuli in neuroscience: Critically acclaimed. Trends in Cognitive Sciences, 23(8), 699-714. [DOI:10.1016/j.tics.2019.05.004] [PMID]
Sulaimany, S., & Safahi, Z. (2023). Visibility graph analysis for brain: Scoping review. Frontiers in Neuroscience, 17, 1268485.[DOI:10.3389/fnins.2023.1268485] [PMID] 
Suzuki, S., Jensen, E. L., Bossaerts, P., & O'Doherty, J. P. (2016). Behavioral contagion during learning about another agent's risk-preferences acts on the neural representation of decision-risk. Proceedings of the National Academy of Sciences of the United States of America, 113(14), 3755–3760. [DOI:10.1073/pnas.1600092113] [PMID] 
Thomas, L., Lockwood, P. L., Garvert, M. M., & Balsters, J. H. (2022). Contagion of temporal discounting value preferences in neurotypical and autistic adults. Journal of Autism and Developmental Disorders, 52(2), 700-713. [DOI:10.1007/s10803-021-04962-5] [PMID] 
Tsvetkova, M., & Macy, M. W. (2014). The social contagion of generosity. Plos One, 9(2), e87275. [DOI:10.1371/journal.pone.0087275] [PMID] 
Weinstein, A. M. (2023). Reward, motivation and brain imaging in human healthy participants - a narrative review. Frontiers in Behavioral Neuroscience, 17, 1123733. [DOI:10.3389/fnbeh.2023.1123733] [PMID] 
Zhang, K., & Hu, X. (2024). Unsupervised separation of nonlinearly mixed event-related potentials using manifold clustering and non-negative matrix factorization. Computers in Biology and Medicine, 178, 108700. [DOI:10.1016/j.compbiomed.2024.108700] [PMID]
Zhang, L., & Gläscher, J. (2020). A brain network supporting social influences in human decision-making. Science Advances, 6(34), eabb4159. [DOI:10.1126/sciadv.abb4159] 
Zheng, M., Domanskyi, S., Piermarocchi, C., & Mias, G. I. (2021). Visibility graph based temporal community detection with applications in biological time series. Scientific Reports, 11(1), 5623. [DOI:10.1038/s41598-021-84838-x] [PMID]