Interpersonal Synchrony in Mentor-Mentee Dyads: An Analysis of Nonverbal Synchrony and Trait-Empathy



Interpersonal synchrony is a notable aspect of communication; it is evident at various levels, including nonverbal. However, research on interpersonal synchrony in the workplace is limited in general and for mentoring in particular. Empathy is essential for both interpersonal synchrony and mentoring. This study aims to investigate how trait-empathy contributes to nonverbal synchrony in mentor-mentee dyads. Thirty-seven pairs were recruited from the Mentorship Program and engaged in conversations on work and leisure topics. Empathy was assessed using The Empathy Quotient, and nonverbal synchrony was measured via Motion Energy Analysis. A significant contribution of the mentee’s cognitive empathy to the averaged head movement synchrony was found. Cognitive empathy enhances the mentee’s understanding of a mentor’s perspective and expectations through nonverbal cues, particularly facial expressions and head movements. The relationship between cognitive empathy and nonverbal synchrony could be considered in mentoring programs for pairing, as previous research has shown that synchronized dyads are more successful in achieving joint outcomes. However, further research using other methods and a larger sample size is needed.

General Information

Keywords: interpersonal synchrony, nonverbal synchrony, empathy, mentorship, interpersonal communication, nonverbal communication, nonverbal behaviors, Motion Energy Analysis, Empathy Quotient

Journal rubric: General Psychology

Article type: scientific article


Funding. This work is supported by the Ministry of Science and Higher Education of the Russian Federation (Agreement No. 075-10-2021-093; Project COG-RND-2104).

Acknowledgements. The authors would like to thank the Sirius Educational Center for assistance in finding participants for the study, and D.V. Tkachenko, L.V. Korostelev, and S.D. Sukhorukov for their help in collecting and pre-processing video data.

Received: 31.01.2024


For citation: Vodneva A.R., Oreshina G.V., Kustova T.A., Tkachenko I.O., Tcepelevich M.M., Grigorenko E.L. Interpersonal Synchrony in Mentor-Mentee Dyads: An Analysis of Nonverbal Synchrony and Trait-Empathy [Elektronnyi resurs]. Sovremennaia zarubezhnaia psikhologiia = Journal of Modern Foreign Psychology, 2024. Vol. 13, no. 1, pp. 47–57. DOI: 10.17759/jmfp.2024130104.

Full text

In English


Interpersonal synchrony (IS) is an important topic of inquiry in the field of interpersonal communication. IS, a temporal attunement, occurs between participants during interactions and can manifest itself at different levels [10; 1]. IS includes nonverbal synchrony, i.e., mirroring and moving in unison [7; 26]. It has been explored across diverse forms of movements during ecologically valid conditions, such as problem-solving and conversing, and studies have shown that IS strengthens the relation between persons, enhances empathy and prosocial behavior, and can increase cooperation [22]. However, research on IS in the workplace in general and on mentoring in particular is limited [12]. The only study using a sample of mentor-mentee dyads established no significant association between IS and positive aspects of alliance or outcomes [9]. We are interested in workplace mentoring, which typically is structured hierarchically and focused on mutual respect for efficient skills and knowledge sharing between more and less experienced co-workers [8; 22]. Highly attuned mentorship is characterized by mutual sharing and commitment, as well as attention to verbal and nonverbal cues [26]. It builds on affective components of trust, affiliative bonding, and empathy [14]. Both situational [23] and trait-empathy [6], essential for interpreting nonverbal cues, have been shown to promote and be enhanced by IS [25]. Empathy consists of cognitive and affective components as well as their interconnectedness as a contemporary view [6]. Cognitive component, which involves understanding another person’s thoughts, contributes to the successful IS in dyads of musicians [18]. Emotional component allows empathizers to consider and actively help their empathic targets. Thus, music intervention that incorporated synchronization enhanced children’s emotional empathy [25]. Together, these components of empathy enable individuals to coordinate their behavior with others in real-time, leading to successful IS.
In mentoring, empathy could serve as a “function of how well mentors and mentees were able to internalize each other’s experiences, to understand emotionally where the other was coming from” [4, p. 11]. It fosters a healthy psychological environment by providing an emotional connection between the mentor and mentee, which, in turn, helps to normalize the mentee’s concerns and strengthen the bond [22]. It can elicit feelings of being “on the same boat” or “by [their] side” [4, p. 13], which is also a common phrase to describe a feeling of being in synchrony. Thereby, empathy serves as a base for regulation in mentoring dynamics, cultivated through consistent interactions accompanied by IS.
Both the IS and mentoring literature emphasize the importance of empathy, but at this point, it remains unclear what contribution the empathy of the mentor and mentee carry to their attunement. This study aims to examine the interplay between nonverbal synchrony and both emotional and cognitive aspects of empathy within the mentor-mentee dyads. We hypothesize that a high level of empathy of at least one of the participants impacts nonverbal synchrony. Studying a sample of workplace mentor-mentee dyads, we anticipate that understanding rather than empathizing serves the purpose of attunement due to the work-related nature of interactions. So, we expect that the cognitive, rather than emotional, component could influence attunement more effectively.



Thirty-seven pairs of mentors and mentees were recruited during five cycles of the month-long Mentorship Program (MP) of the Sirius Educational Center (Sirius federal territory, Russia). The goal of the MP is to assist novice curators (mentees) in adapting to their duties by obtaining the expertise required for their positions as curators. Throughout the MP, mentees collaborate with experienced curators (mentors) in pairs. This system entails formal one-on-one mentorship in the workplace, which includes constant daily paired work, problem-solving, and collaboration to achieve common goals.
All participants were screened for any history of neurological and psychiatric diagnoses. They were instructed by the researchers about the study procedures and provided written informed consent along with consent to personal data processing. As a reward, each participant was given a gift card from a local bookstore valued at 1500 Russian rubles.
The data from two dyads were excluded from the analysis: one due to a technical problem with the equipment and the second due to a participant’s health condition. The descriptive characteristics of the final sample are presented in Table 1. According to self-reported sex, the sample contains 21 same-sex female-female dyads and 14 opposite-sex dyads. Four mentors participated twice with different mentees, and each such case was treated as a unique case for synchrony assessment. Participants in each dyad self-reported that they did not know each other prior to the joint work.
Table 1
Descriptive characteristics of the sample










Sample size (n)







Age (M(SD)

23,9 (1,76)

23,5 (1,71)

24,8 (1,62)

23,14 (2,11)

22,96 (1,94)

25 (3,46)

Incomplete higher Education (n)







Higher Education (n)







Specialized secondary or vocational Education (n)








Design and Procedure

This study was performed using a quasi-experimental design to establish the characteristics of mentoring in a natural environment that reflects mentor-mentee routine social interaction within their workplace. During the first half of the MP, the participants filled out questionnaires regarding demographic information and empathy level. In the final week of the MP, an experimental procedure was conducted to collect video records of the interaction.
For the experiment, participants were invited to a room at their workplace to sit face-to-face in two chairs set at an angle of 90 degrees with a light-colored monochrome wall background. Experimental interaction consisted of two conditions. The first one involved a 10—15-minute problem-solving discussion (DIS) of complex work cases. The second one was a 5-minute free conversation (FC) on any topic of interest except work issues.


Since being on the autistic spectrum is considered a confounding factor for the ability to synchronize [22], we selected a questionnaire designed to be sensitive to detecting autistic features. The Empathy Quotient (EQ; [2]) is a self-report empathy scale consisting of 60 items. Respondents are asked to rank their level of agreement with each item on a 4-point Likert scale (from “Strongly agree” to “Strongly disagree”). The EQ consists of 20 filler questions to prime participants to consider empathy (e.g., “I try to keep up with the current trends and fashions”) and 40 questions that directly test empathy (e.g., “I really enjoy caring for other people”). The Russian adaptation of the EQ was developed by [16], who, based on previous research, identified three scales corresponding to the elements of empathy: Cognitive empathy, Emotional reactivity (Emotional empathy), and Social skills. Cognitive empathy involves the ability to understand and infer others’ mental states, Emotional empathy refers to the tendency to experience emotions similar to those of others, and Social skills involve the ability to respond appropriately to others in social situations. A short 21-question variant of the Russian EQ has been shown to be reliable, with a Cronbach’s alpha of 0,78, based on the results of the study with 221 native Russian-speaking volunteers (121 females) of various occupations with a mean age of 24,9 years (SD=7,7). For our study, there were no missing data. The EQ results were converted into raw scores using Excel tables, following the endorsed three-factor structure, transformed into raw scores, and aggregated into scores by the scales.
The coordination of participants’ movements was evaluated via a Motion Energy Analysis (MEA) software for computerized video coding of motion dynamics [20]. The MEA algorithm is based on the calculation of pixel changes between neighboring frames in regions of interest (ROIs). The video data recording at 25 frames per second was compressed and divided by experimental conditions (DIS, FC). Next, using MEA software, two ROIs were highlighted manually for each participant (head, body) and automatically analyzed (Fig. 1). These regions are most often highlighted to assess and compare nonverbal synchrony during face-to-face communication [21].
Preprocessing and analysis of the MEA data was performed in RStudio (ver. 2022.02.1) using rMEA package (ver. 1.2.2, [15]). The data were read separately for each paradigm divided by participant’s roles (Mentor, Mentee) and ROIs (head, body). Preprocessing steps were performed with default settings recommended by [15]: deleting outliers, data filtering, and data rescaling and centering. The indexes of nonverbal synchrony were then calculated via window cross-correlation (WCC) analysis for simultaneous (without time lag, zero lag), as well as for averaged (across the chosen frame of time lags, all lags) values.
Fig. 1: Example of an experiment setup with four predefined ROIs: the mentor’s head (magenta) and body (cyan) and the mentee’s head (red) and body (purple).

Analysis Strategy

For EQ, average scores and standard deviation were calculated. Regarding nonverbal synchrony evaluation, there was no homogeneity in the overlapping setting for the WCC analysis [9]. Thus, we compared the results between different shifts of the overlap values using statistical tests for dependent samples (Wilcoxon or t-tests based on Shapiro-Wilk normality test results). The following settings for the WCC analysis were applied: 5 sec. of the time lag between time series data (lagSec); 30 sec. for cross-correlation window size (winSec); 10 sec. for overlapping steps between windows (incSec). During WCC analysis, the data were z-normalized and converted to absolute values. To verify the obtained synchrony values, we created pseudo data (n = 35) by shuffling the time series of participants while maintaining their roles. This generated dyads of partners who did not interact with each other. Then, the same analysis was completed. Real and pseudo data comparison was performed separately for each condition, ROIs, and nonverbal synchrony values using paired Wilcoxon or paired t-tests based on data normal distribution test results.
Further statistical analysis was performed using RStudio (ver. 4.3.0). Afterwards, ridge regression was employed to model the relationship between nonverbal synchrony and empathy. Only WCC scores within ROIs, conditions, and nonverbal synchrony values that significantly differed from those of pseudo dyads were utilized as dependent variables. The explanatory variables included Cognitive and Emotional empathy scores.
Prior to analysis, we scrutinized the dataset for univariate outliers, defined as values that surpass three standard deviations from the mean. This led to the exclusion from the analysis of one dyad due to extremely low mentee’s empathy scores. Furthermore, in the data for the body ROI in DIS, we identified outlier values for zero lag synchrony in one dyad and all lag synchrony in another. Consequently, models using these dependent variables were constructed with data from 33 dyads, while the remaining models considered 34 dyads. To improve pairwise linearity, dependent variables were logarithmically transformed prior to modeling. Ridge regression models were constructed for each dependent variable using the semi-automatic calculation of the regularization parameter proposed by the authors of the Ridge package [5]. This approach was employed to identify the most significant predictors, taking into account the correlation among questionnaire scales and the limited sample size relative to the number of predictors under consideration. After identifying significant relationships, we further fitted simple linear regression models to examine the relationships between nonverbal synchrony and each of the predictors specifically. Interaction terms were added to the equations to capture the potential reciprocal influence between a mentees’ and the mentors’ empathy levels.


Among the participants, we observed a high level of general empathy (24±8,54 scores), with slightly higher scores for mentees (25±6,65) than for mentors (22±9,92). Such a tendency occurred for scales, too, so that mentees had slightly higher performance (Cognitive empathy: 8±3,24; Emotional empathy: 8±2,81) as compared to mentors (Cognitive empathy: 7±3,97; Emotional empathy: 7±3,04).
For nonverbal synchrony analysis, according to a literature search, 0,1 sec. and 10 sec time stamps were chosen for overlap settings comparison. 10 sec. overlap was accepted for all variables due to the nature of the interaction and the necessity of data analysis unification. The results showed no significant differences in the WCC values for zero lag nonverbal synchrony in DIS for both ROIs (head: V=353, p=0,54; body: V=256, p=0,48) and in FC for head ROI (V=325, p=0,88) and significant difference in FC for body ROI (t(34)=-2,33, p=0,026).
Relative to zero lag value, nonverbal synchrony in the real dyads was significantly higher in body ROI in DIS compared to the pseudo dyads (V=541, p<0,001). In head ROI in DIS, as well as in body and head ROIs in FC, no significant differences were found between real and pseudo dyads nonverbal synchrony values (V=343,5, p=0,65; t(34)=1,1, p=0,28; V=374, p=0,19, respectively). For all lags values, nonverbal synchrony in the real data was significantly higher in both ROIs in DIS (body: V=565, p<0,001; head: V=461, p=0,016) and only in head ROI in FC (t(34)=2,3, p=0,03) compared to the pseudo dyads. Comparison of real and pseudo data in body ROI in FC showed no significant differences (t(34)=0,15, p=0,9). The four variables that showed significant differences from pseudo dyads (zero lag synchrony for the body in DIS, all lags head movement synchrony in FC, and all lags head and body movement synchrony in DIS) were included in further analyses as dependent variables. The study utilized ridge regression to estimate coefficients for multiple-regression models, which included highly correlated empathy scales as predictors. Out of the 16 simple regression models examined, only one exhibited a statistically significant association between synchrony and empathy. Specifically, the mentees’ Сognitive empathy emerged as a borderline significant predictor of all lags head movement synchrony in FC (β=0,03, p=0,06). The identified relationship was also confirmed through simple linear regression (β1=0,06, p=0,035, R2adj=0,104). The coefficients for the remaining predictors in all other models were shrunk toward zero, indicating that they did not have a statistically significant effect on any of the dependent variables (all p>0,10).
To investigate the potential relationship between mentor and mentee empathy as predictors of all lags head movement synchrony, we examined the interaction terms in two additional models. Mentees’ Cognitive empathy was shown to be a significant predictor after controlling for mentors’ Emotional empathy (β1=0,07, p=0,036, R2adj=0,088), but not mentors’ Cognitive empathy (β1=0,06, p=0,068, R2adj=0,056). In both cases, no significant interaction was detected (ps>0,05), indicating that the effect of one predictor variable on all lags head movement synchrony does not vary across different values of the other predictor variable.


This study investigated how trait-empathy contributes to nonverbal synchrony in mentor-mentee dyads in a work context, as captured by a discussion of work-related issues and a free conversation. Only mentees’ Cognitive empathy scores predicted the averaged head movement synchrony in the in FC, with higher mentees’ Cognitive empathy scores corresponding to increased attunement in informal communication. Mentees’ Emotional empathy and the mentor’s Cognitive and Emotional empathy did not play a significant role in predicting any kind of nonverbal synchrony in both conditions and other ROIs.
Such results align with previous studies, which yielded varying outcomes. For example, no significant relationship was found between empathy and inter-brain IS in duets of pianists, but at the same time, a significant negative correlation with nonverbal synchrony was found in one of the conditions [17]. However, in this study, participants were in more controlled conditions, which may have affected the extent to which empathic skills were displayed. Female empathy was associated with an increase in psychophysiological linkage during conversation [19], and an empathic perspective was shown to enhance interpersonal coordination in duets [22]. Considering that physiological synchrony serves as a proxy for experience sharing, stimulated by visual and linguistic cues [3, 14], сognitive empathy likely plays a critical role in attunement and interaction [24]. Given that, we hypothesize that mentees, eager to assimilate their mentors’ experience, may express empathetic nonverbal cues like head-nodding [11].
The IS assessment method used does not assess facial expressions but does record these changes along with head and neck movements. A meta-analysis of 28 studies showed a relationship between empathy and facial mimicry, but the contribution of cognitive and emotional components appeared to be equal [13]. However, the question of the relationship between IS and mimicry remains open.
It should be noted that this study has a number of limitations. First and foremost, our results should be considered carefully due to the modest sample size. Secondly, using EQ, we ensured our ability to spot any participants on the autism spectrum but probably narrowed the spread of individual differences. The significant relationship with the averaged nonverbal value (all lags) may suggest limitations in our analysis method, potentially affecting our ability to fully assess dynamic attunement during reciprocal interaction. Furthermore, we focused on simultaneous synchrony, but it seems relevant to estimate the role of the leader and the follower within the interaction. Considering future studies, it is important for mentoring research to assess empathy in mentees before and after mentorship to test the hypothesis of whether it develops within such interaction and to compare pairs with low or high empathy scores in both participants to close the gap in our knowledge about IS.


As a result of the analysis of the nonverbal synchrony of mentor-mentee dyads during formal and informal communication conditions, a significant contribution of the mentee’s cognitive empathy to the averaged head movement synchrony was found. No other significant relationships were discovered. During communication, people tend to mirror each other’s nonverbal signals. Cognitive empathy enhances the mentee’s understanding of a mentor’s perspective and expectations through nonverbal cues, particularly facial expressions and head movements being more prominent and imitable. Micro-movements of the head become significant, and synchronizing these movements aids in maintaining mutual understanding.
Our findings suggest that mentorship programs could benefit from training participants in cognitive empathy and nonverbal cue recognition, enhancing mentor-mentee interactions. Thus, a longitudinal experiment could be conducted to examine this idea in intervention form. Moreover, longitudinal studies can help fill a gap in knowledge about the dynamics of IS within relationship development. The findings also reflect that there is a relationship between empathy and nonverbal synchrony that may be considered when pairing members in mentoring programs, as previous research indicates that synchronized dyads are more successful in achieving joint outcomes [1; 22]. To test this idea experimentally, participants could be paired based on their level of empathy (high-high, low-low, high-low). Furthermore, future research using other methods and larger samples is needed. All in all, the measurement of IS at the physiological and neuronal level, facial expression analysis, and behavioral coding represent a great potential for research. But the finishing touch for any of the suggested study designs can be an added parameter of mentorship quality or mentoring program effectiveness.


  1. Vakhrushev D.S., Zhukova M.A. Aktual'nyi vzglyad na mekhanizm diadnoi sinkhronizatsii [Current view on the dyadic synchrony mechanism]. Sovremennaya zarubezhnaya psikhologiya = Journal of Modern Foreign Psychology, 2021. Vol. 10, no. 2, pp. 86—95. DOI:10.17759/jmfp.2021100209 (In Russ.).
  2. Baron-Cohen S., Wheelwright S. The Empathy Quotient: An Investigation of Adults with Asperger Syndrome or High Functioning Autism, and Normal Sex Differences. Journal of Autism and Developmental Disorders, 2004. Vol. 2, no. 34, pp. 163—175. DOI:10.1023/b:jadd.0000022607.19833.00
  3. Carmichael C.L., Mizrahi M. Connecting cues: The role of nonverbal cues in perceived responsiveness. Current Opinion in Psychology, 2023. Vol. 53, article ID 101663. DOI:
  4. Cruz J., Goff M.H., Marsh J.P. Building the mentoring relationship: Humanism and the importance of storytelling between mentor and mentee. Mentoring & Tutoring: Partnership in Learning, 2020. Vol. 2, no. 28, pp. 104—125. DOI:10.1080/13611267.2020.1749344
  5. Cule E., Moritz S., Frankowski D. ridge: Ridge Regression with Automatic Selection of the Penalty Parameter. R package version 3.3, 2022. DOI:
  6. Davis M.H. Empathy: A social psychological approach. London: Taylor and Francis Group, 2019. 272 p. DOI:10.4324/9780429493898
  7. Dunbar N.E., Burgoon J.K., Fujiwara, K. Automated Methods to Examine Nonverbal Synchrony in Dyads. Understanding Social Behavior in Dyadic and Small Group Interactions, 2022. Pp. 204—217.
  8. Eby L.T., Robertson M.M. The psychology of workplace mentoring relationships. Annual Review of Organizational Psychology and Organizational Behavior, 2020. Vol. 7, no. 1, pp. 75—100. DOI:
  9. Erdös T., Ramseyer F.T. Change process in coaching: Interplay of nonverbal synchrony, working alliance, self-regulation, and goal attainment. Frontiers in Psychology, 2021. Vol. 12, article ID 580351. 23 p. DOI:10.3389/fpsyg.2021.580351
  10. Atzil-Slonim D., Soma C.C., Zhang X., Paz A., Imel Z.E. Facilitating dyadic synchrony in psychotherapy sessions: Systematic review and meta-analysis. Psychotherapy Research, 2023. Vol. 33, no. 7, pp. 898—917. DOI:
  11. Freiermuth M.R., Hamzah N.H. “I agree!” empathetic head-nodding and its role in cultural competences development. Lingua, 2023. Vol. 296, article ID 103629. 25 p. DOI:10.1016/j.lingua.2023.103629
  12. Göritz A.S., Rennung M. Interpersonal synchrony increases social cohesion, reduces work-related stress and prevents sickdays: A longitudinal field experiment. Gruppe. Interaktion. Organisation. Zeitschrift Für Angewandte Organisationspsychologie (GIO), 2019. Vol. 50, no. 1, pp. 83—94. DOI:
  13. Holland A.C., O’Connell G., Dziobek I. Facial mimicry, empathy, and emotion recognition: a meta-analysis of correlations. Cognition and Emotion, 2020. Vol. 1, no. 35, pp. 150—168. DOI:10.1080/02699931.2020.1815655
  14. Ivey G.W., Dupré K.E. Workplace Mentorship: A Critical Review. Journal of Career Development, 2020. Vol. 3, no. 49, pp. 714—729. DOI:10.1177/0894845320957737
  15. Kleinbub J.R., Ramseyer F.T. rMEA: An R package to assess nonverbal synchronization in motion energy analysis time-series. Psychotherapy Research, 2020. Vol. 6, no. 31, pp. 817—830. DOI:10.1080/10503307.2020.1844334
  16. Kosonogov V. The psychometric properties of the Russian version of the Empathy Quotient. Psychology in Russia: State of the Art, 2014. Vol. 1, no. 7, pp. 96—104. DOI:10.11621/PIR.2014.0110
  17. Washburn A., Román I., Huberth M., Gang N., Dauer T., Reid W., Nanou C., Wrigh M., Fujioka T. Musical Role Asymmetries in Piano Duet Performance Influence Alpha-Band Neural Oscillation and Behavioral Synchronization. Frontiers in Neuroscience, 2019. Vol. 13, article ID 1088. 18 p. DOI:10.3389/fnins.2019.01088
  18. Novembre G., Mitsopoulos Z., Keller P.E. Empathic perspective taking promotes interpersonal coordination through music. Scientific Reports, 2019. Vol. 1, no. 9,  article ID 12255. 12 p. DOI:10.1038/s41598-019-48556-9
  19. Coutinho J., Oliveira-Silva P., Fernandes E., Gonçalves O.F., Correia D., Mc-Govern K.P., Tschacher W. Psychophysiological synchrony during verbal interaction in romantic relationships. Family Process, 2019. Vol. 58, no. 3, pp. 716—733. DOI:10.1111/famp.12371
  20. Ramseyer F.T. Motion energy analysis (MEA): A primer on the assessment of motion from video. Journal of Counseling Psychology, 2020. Vol. 4, no. 67, pp. 536—549. DOI:10.1037/cou0000407
  21. Jospe K., Genzer S., Klein Selle N., Ong D., Zaki J., Perry A. The contribution of linguistic and visual cues to physiological synchrony and empathic accuracy. Cortex, 2020. Vol. 132, pp. 296—308. DOI: 10.1016/j.cortex.2020.09.001
  22. Hu Y., Cheng X., Pan Y., Hu Y. The intrapersonal and interpersonal consequences of interpersonal synchrony. Acta Psychologica, 2022. Vol. 224, article ID 103513. 7 p. DOI:10.1016/j.actpsy.2022.103513
  23. Zhou K., Aiello L.M., Scepanovic S., Quercia D., Konrath, S. The language of situational empathy. Proceedings of the ACM on Human-Computer Interaction, 2021. Vol. 5 (CSCW1). pp. 1–19. DOI:
  24. van Dijke J., van Nistelrooij I., Bos P., Duyndam J. Towards a relational conceptualization of empathy. Nursing Philosophy, 2020. Vol. 21, no. 3, article ID e12297. DOI:
  25. Tzanaki P. The Positive Feedback Loop of Empathy and Interpersonal Synchronisation: Discussing a Theoretical Model and its Implications for Musical and Social Development. Music & Science, 2022. Vol. 5, pp. 1—16. DOI:10.1177/20592043221142715
  26. Pryce J., Deane K.L., Barry J.E., Keller T.E. Understanding Youth Mentoring Relationships: Advancing the Field with Direct Observational Methods. Adolescent Research Review, 2020. Vol. 6, pp. 45—56. DOI:10.1007/s40894-019-00131-z

Information About the Authors

Alena R. Vodneva, PhD Student, Junior Researcher, Sirius University of Science and Technology, Federal territory "Sirius", Russia, ORCID:, e-mail:

Galina V. Oreshina, PhD Student, Junior Researcher, Sirius University of Science and Technology, Sochi, Russia, ORCID:, e-mail:

Tatiana A. Kustova, PhD Student, Junior Researcher, Sirius University of Science and Technology, Federal territory "Sirius", Russia, ORCID:, e-mail:

Irina O. Tkachenko, Junior Researcher, Sirius University of Science and Technology, Federal territory "Sirius", Russia, ORCID:, e-mail:

Margarita M. Tcepelevich, PhD Student, Junior Researcher, Sirius University of Science and Technology, Federal territory "Sirius", Russia, ORCID:, e-mail:

Elena L. Grigorenko, PhD, Hugh Roy and Lillie Cranz Cullen Distinguished Professor of Psychology, University of Houston, Houston, TX, USA; Adjunct Senior Research Scientist, Moscow State University of Psychology and Education, Moscow, Russia; Professor and Acting Director, Center for Cognitive Sciences, Sirius University of Science and Technology, Federal territory "Sirius", Russia; Adjunct Professor, Child Study Center and Adjunct Senior Research Scientist, Haskins Laboratories, Yale University, New Haven, CT, USA; Research Certified Professor, Baylor College of Medicine, Member of the editorial boards of the journals “Clinical and Special Educatiom”, “Experimental Psychology” and “Psychological Science and Education”, Houston, USA, ORCID:, e-mail:



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