Communicative Interactions: Analytic Review

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Abstract

For many years, communicative interactions between people have been the subject of conceptual and heuristic consideration in the frameworks of the psychological and social sciences. About ten years ago, the study of communicative interactions began using the methods of experimental neurosciences. Until now research in this area has focused on the accumulation of various phenomenа and the development of methodology. Basic directions and perspectives of communicative interactions research by means of experimental neuroscience methods and mathematical modeling have been considered.

General Information

Keywords: communicative interactions, analytic review, research perspectives, experimental neuroscience

Journal rubric: Psycholinguistics

Article type: review article

DOI: https://doi.org/10.17759/exppsy.2022150111

Funding. This work is supported by the Russian Ministry of Science and Higher Education, project no. 2019-218-11-8185 under the Decree no. 218 “Creation of high-tech production neurotechnology-based software complex for human capital management for enterprises of the Russian Federation high-tech sector”.

Acknowledgements. The research was supported by the Strategic Academic Leadership Program of the Southern Federal University (“Priority 2030”).

Received: 05.11.2020

Accepted:

For citation: Podladchikova L.N., Shaposhnikov D.G. Communicative Interactions: Analytic Review. Eksperimental'naâ psihologiâ = Experimental Psychology (Russia), 2022. Vol. 15, no. 1, pp. 177–186. DOI: 10.17759/exppsy.2022150111. (In Russ., аbstr. in Engl.)

References

  1. Anderson N., Bischof W., Laidlaw W., Risko E. Kingstone A. Recurrence quantification analysis of eye movements. Behavior research methods, 2013. Vol. 45, no. 3, pp. 842—856. DOI 10.3758/s13428-012-0299-5
  2. Botvinick M., Ritter S., Wang J., Kurth-Nelson Z., Blundell C., Hassabis D. Reinforcement learning, fast and slow. Trends in Cognitive Sciences, 2019. Vol. 23, no. 5, pp. 408—422. DOI /10.1016/j.tics.2019.02.006
  3. Cichy R., Kaiser D. Deep neural networks as scientific models. Trends in Cognitive Sciences, 2019. Vol. 23, no. 4, pp. 305—317. DOI 10.1016/j.tics.2019.01.009
  4. García A., Ibáñez A. Two-person neuroscience and naturalistic social communication: the role of language and linguistic variables in brain-coupling research. Frontiers in psychiatry, 2014. Vol. 5, pp. 124. DOI 10.3389/fpsyt.2014.00124
  5. Golland Y., Arzouan Y., Levit-Binnun N. The mere co-presence: synchronization of autonomic signals and emotional responses across co-present individuals not engaged in direct interaction. PLoS ONE, 2015. Vol. 10, no. 5, pp. e0125804. DOI 10.1371/journal.pone.0125804.g001
  6. Gunkel D.J. Computational interpersonal communication: communication studies and spoken dialogue systems. Communication+ 1, 2016. Vol. 5, no. 1, pp. 1—20. DOI 10.7275/R5VH5KSQ
  7. Hari R., Himberg T., Nummenmaa L., Hämäläinen M., Parkkonen L. Synchrony of brains and bodies during implicit interpersonal interaction. Trends in cognitive sciences, 2013. Vol. 17, no. 3, pp. 105—106. DOI 10.1016/j.tics.2013.01.003
  8. Kharitonov A., Zhegallo A., Ananyeva K., Kurakova O. Registering eye movements in collaborative tasks: methodological problems and solutions. Perception ECVP abstract, 2012. Vol. 41, pp. 104—105.
  9. Liu D., Liu Sh., Liu X., Zhang Ch., Li A., Jin Ch., Chen Y., Wang H., Zhang X. Interactive brain activity: review and progress on EEG-based hyperscanning in social interactions. Frontiers in psychology, 2018. Vol. 9, pp. 1862. DOI 10.3389/fpsyg.2018.01862
  10. Lyyra P., Myllyneva A., Hietanen J.K. Mentalizing eye contact with a face on a video: gaze direction does not influence autonomic arousal. Scandinavian journal of psychology, 2018. Vol. 59, no. 4, pp. 360—367. DOI 10.1111/sjop.12452
  11. Macdonald R.G., Tatler B.W. Gaze in a real-world social interaction: a dual eye-tracking study. Quarterly Journal of Experimental Psychology, 2018. Vol. 71, no. 10, pp. 2162-2173. DOI 10.1177/1747021817739221
  12. Nummenmaa L., Glerean E., Viinikainen M., Jääskeläinen I.P., Hari R., Sams M. Emotions promote social interaction by synchronizing brain activity across individuals. Proceedings of the National Academy of Sciences, 2012. Vol. 109, no. 24, pp. 9599—9604. DOI 10.1073/pnas.1206095109
  13. Petukhov A., Polevaya S. Modeling of communicative individual interactions through the theory of information images. Current psychology, 2017. Vol. 36, no. 3, pp. 428—433. DOI 10.1007/s12144-016- 9431-5
  14. Pfeiffer U.J., Vogeley K., Schilbach L. From gaze cueing to dual eye-tracking: novel approaches to investigate the neural correlates of gaze in social interaction. Neuroscience and Biobehavioral Reviews, 2013. Vol. 37, no. 10, pp. 2516—2528. DOI 10.1016/j.neubiorev.2013.07.017
  15. Podladchikova L.N., Koltunova T.I., Shaposhnikov D.G., Lomakina O.V. Individual features of viewing emotionally significant images. Neuroscience and Behavioral Physiology, 2017. Vol. 47, no. 8, pp. 941—947. DOI 10.1007/s11055-017-0495-y
  16. Podladchikova L.N., Shaposhnikov D.G., Koltunova T.I. Spatial and temporal properties of gaze return fixations while viewing affective images. Russian Journal of Physiology, 2018. Vol. 104, no. 2, pp. 245—254 (In Russian).
  17. Podladchikova L.N., Shaposhnikov D.G., Kozubenko E.A. Towards neuroinformatic approach for second-person neuroscience. Advances in Neural Computation, Machine Learning, and Cognitive Research IV, 2020. pp. 143—148. DOI 10.1007/978-3-030-60577-3_16.
  18. Privitera C.M., Stark L.W. Scanpath Theory, attention, and image processing algorithms for predicting human eye fixations. Neurobiology of Attention, 2005. pp. 296—299. DOI 10.1016/B978-012375731- 9/50052-5.
  19. Redcay E., Schilbach L. Using second-person neuroscience to elucidate the mechanisms of social interaction. Nature Reviews Neuroscience, 2019. Vol. 20, no. 8, pp. 495—505. DOI 10.1038/s41583-019- 0179-4
  20. Rogers S.L., Speelman C.P., Guidetti O., Longmuir M. Using dual eye tracking to uncover personal gaze patterns during social interaction. Scientific reports, 2018. Vol. 8, no. 1, pp. 1—9. DOI 10.1038/s41598-018- 22726-7
  21. Rubo M., Gamer M. Social content and emotional valence modulate gaze fixations in dynamic scenes. Scientific reports, 2018. Vol. 8, no. 1, pp. 1—11. DOI 10.1038/s41598-018-22127-w
  22. Samarin A., Koltunova T., Osinov V., Shaposhnikov D., Podladchikova L. Scanpaths of complex image viewing: insights from experimental and modeling studies. Perception, 2015. Vol. 44, no. 8—9, pp. 1064— 1076. DOI 10.1177/0301006615596872
  23. Samarin A.I., Podladchikova L.N., Petrushan M.V., Shaposhnikov D.G. Active vision: from theory to application. Optical Memory and Neural Networks, 2019. Vol. 28, no. 3, pp. 185—191. DOI 10.3103/ S1060992X19030068
  24. Scheller E., Büchel C., Gamer M. Diagnostic features of emotional expressions are processed preferentially. PLoS ONE, 2012. Vol. 7, no. 7, pp. e41792. DOI 10.1371/journal.pone.0041792
  25. Schilbach L., Timmermans B., Reddy V., Costall A., Bente G., Schlicht T., Vogeley K. Toward a second-person neuroscience. Behavioral and brain sciences, 2013. Vol. 36, no. 4, pp. 393—414. DOI 10.1017/ S0140525X12000660
  26. Smith T.J., Mital P.K. Attentional synchrony and the influence of viewing task on gaze behavior in static and dynamic scenes. Journal of vision, 2013. Vol. 13, no. 8, pp. 16—16. DOI 10.1167/13.8.16
  27. Yarbus A.L. Eye movements and vision. Springer, 2013. DOI 10.1007/978-1-4899-5379-7
  28. Yun K., Watanabe K., Shimojo Sh. Interpersonal body and neural synchronization as a marker of implicit social interaction. Scientific reports, 2012. Vol. 2, pp. 959. DOI 10.1038/srep00959
  29. Zhang D., Yao L., Zhang X, Wang S., Chen W., Boots R. Cascade and parallel convolutional recurrent neural networks on eeg-based intention recognition for brain computer interface. AAAI, 2018. pp. 1703— 1710. arXiv:1708.06578 [cs.HC]

Information About the Authors

Lubov N. Podladchikova, PhD in Biology, Leading Research Associate, Research Center for Neurotechnology, Southern Federal University, Rostov-na-Donu, Russia, ORCID: https://orcid.org/0000-0002-5557-6045, e-mail: lnpodladchikova@sfedu.ru

Dmitry G. Shaposhnikov, PhD in Engineering, Leading Research Associate, Research Center for Neurotechnology, Southern Federal University, Rostov-na-Donu, Russia, ORCID: https://orcid.org/0000-0002-1797-6232, e-mail: dgshaposhnikov@sfedu.ru

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