Skip to main content
Springer Nature Link
Log in

Neural Correlates of Robot Personality Perception: An fNIRS Study

  • Conference paper
  • First Online:
Cross-Cultural Design (HCII 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14702))

Included in the following conference series:

  • 1044 Accesses

Abstract

Robot personality design has garnered research interest for its crucial role in enhancing the robot’s social capabilities and promoting user experience. This study aims to use machine learning classification techniques to predict the personality of a robot by analyzing the neural activities in the prefrontal cortex (PFC) when individuals interact with the robot that features a specific personality design (i.e., extroverted or introverted). We recruited 64 participants and divided them into two groups to interact with an extroverted or introverted robot. We collected data using a functional near-infrared spectroscopy (fNIRS) device and, after data preprocessing, selected signal means as features for analysis. After applying six machine learning methods for data classification, we found significant differences in the performance of different classifiers. In addition, we observed that personality classification based on left and right brain data showed different performance. According to the results, we can determine the type of robot personality with which users are interacting based on the medial PFC activities during user‒robot interactions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Zhou, H., Wang, X., Au, W., Kang, H., Chen, C.: Intelligent robots for fruit harvesting: recent developments and future challenges. Precision Agric. 23, 1856–1907 (2022). https://doi.org/10.1007/s11119-022-09913-3

    Article  Google Scholar 

  2. Paetzel-Prüsmann, M., Perugia, G., Castellano, G.: The Influence of robot personality on the development of uncanny feelings. Comput. Hum. Behav. 120, 106756 (2021). https://doi.org/10.1016/j.chb.2021.106756

    Article  Google Scholar 

  3. Esteban, P.G., et al.: Should i be introvert or extrovert? a pairwise robot comparison assessing the perception of personality-based social robot behaviors. Int J of Soc Robotics. 14, 115–125 (2022). https://doi.org/10.1007/s12369-020-00715-z

    Article  Google Scholar 

  4. Nagamachi, M.: Kansei engineering: a new ergonomic consumer-oriented technology for product development. Int. J. Ind. Ergon. 15, 3–11 (1995). https://doi.org/10.1016/0169-8141(94)00052-5

    Article  Google Scholar 

  5. Hu, X., Zhuang, C., Wang, F., Liu, Y.-J., Im, C.-H., Zhang, D.: fNIRS evidence for recognizably different positive emotions. Front. Hum. Neurosci. 13, 120 (2019)

    Google Scholar 

  6. Dixon, M., Thiruchselvam, R., Todd, R., Christoff, K.: Emotion and the prefrontal cortex: an integrative review. Psychol. Bull. 143(10), 1033–1081 (2017). https://doi.org/10.1037/bul0000096

  7. Yorgancigil, E., Yildirim, F., Urgen, B.A., Erdogan, S.B.: An exploratory analysis of the neural correlates of human-robot interactions with functional near infrared spectroscopy. Front. Hum. Neurosci. 16, 883905 (2022)

    Google Scholar 

  8. Camerer, C., Loewenstein, G., Prelec, D.: Neuroeconomics: how neuroscience can inform economics. J. Econ. Lit. 43, 9–64 (2005). https://doi.org/10.1257/0022051053737843

    Article  Google Scholar 

  9. Zhu, G., Jiang, B., Tong, L., Xie, Y., Zaharchuk, G., Wintermark, M.: Applications of deep learning to neuro-imaging techniques. Front. Neurol. 10, 869 (2019). https://doi.org/10.3389/fneur.2019.00869

    Article  Google Scholar 

  10. Kumar, V., Shivakumar, V., Chhabra, H., Bose, A., Venkatasubramanian, G., Gangadhar, B.N.: Functional near infra-red spectroscopy (fNIRS) in schizophrenia: a review. Asian J. Psychiatr. 27, 18–31 (2017). https://doi.org/10.1016/j.ajp.2017.02.009

    Article  Google Scholar 

  11. Zhou, L., Wu, B., Deng, Y., Liu, M.: Brain activation and individual differences of emotional perception and imagery in healthy adults: a functional near-infrared spectroscopy (fNIRS) study. Neurosci. Lett. 797, 137072 (2023). https://doi.org/10.1016/j.neulet.2023.137072

    Article  Google Scholar 

  12. Bandara, D., Hirshfield, L., Velipasalar, S.: Classification of affect using deep learning on brain blood flow data. J. Near Infrared Spectrosc. 27, 206–219 (2019). https://doi.org/10.1177/0967033519837986

    Article  Google Scholar 

  13. Wood, J.N., Grafman, J.: Human prefrontal cortex: processing and representational perspectives. Nat. Rev. Neurosci. 4, 139–147 (2003). https://doi.org/10.1038/nrn1033

    Article  Google Scholar 

  14. Gao, W., et al.: Evidence on the emergence of the brain’s default network from 2-week-old to 2-year-old healthy pediatric subjects. Proc. Natl. Acad. Sci. 106, 6790–6795 (2009). https://doi.org/10.1073/pnas.0811221106

    Article  Google Scholar 

  15. Yuan, P., Raz, N.: Prefrontal cortex and executive functions in healthy adults: a meta-analysis of structural neuroimaging studies. Neurosci. Biobehav. Rev. 42, 180–192 (2014). https://doi.org/10.1016/j.neubiorev.2014.02.005

    Article  Google Scholar 

  16. Grossmann, T.: The role of medial prefrontal cortex in early social cognition. Front. Hum. Neurosci. 7, 340 (2013)

    Google Scholar 

  17. Lim, G., Kim, H.: Distinctive roles of mPFC subregions in forming impressions and guiding social interaction based on others’ social behaviour. Soc. Cogn. Affect. Neurosci. 17, 1118–1130 (2022). https://doi.org/10.1093/scan/nsac037

    Article  Google Scholar 

  18. Xu, H., et al.: A disinhibitory microcircuit mediates conditioned social fear in the prefrontal cortex. Neuron 102, 668-682.e5 (2019). https://doi.org/10.1016/j.neuron.2019.02.026

    Article  Google Scholar 

  19. Déziel, R.A., Tasker, R.A.: Bilateral Ischaemic lesions of the medial prefrontal cortex are Anxiogenic in the rat. Acta Neuropsychiatrica 30, 181–186 (2018). https://doi.org/10.1017/neu.2017.32

    Article  Google Scholar 

  20. Lee, E., Hong, J., Park, Y.-G., Chae, S., Kim, Y., Kim, D.: Left brain cortical activity modulates stress effects on social behavior. Sci. Rep. 5, 13342 (2015). https://doi.org/10.1038/srep13342

    Article  Google Scholar 

  21. Cerqueira, J.J., Almeida, O.F.X., Sousa, N.: The stressed prefrontal cortex. Left? Right!. Brain Behav. Immun. 22, 630–638 (2008). https://doi.org/10.1016/j.bbi.2008.01.005

  22. Tranel, D., Bechara, A., Denburg, N.L.: Asymmetric functional roles of right and left ventromedial prefrontal cortices in social conduct, decision-making, and emotional processing. Cortex 38, 589–612 (2002). https://doi.org/10.1016/S0010-9452(08)70024-8

    Article  Google Scholar 

  23. Gainotti, G., Caltagirone, C., Zoccolotti, P.: Left/right and cortical/subcortical dichotomies in the neuropsychological study of human emotions. Cogn. Emot. 7, 71–93 (1993). https://doi.org/10.1080/02699939308409178

    Article  Google Scholar 

  24. Kato, T.: Kansei Robotics: bridging human beings and electronic gadgets through Kansei engineering. In: 2013 International Conference on Biometrics and Kansei Engineering, pp. 327–331. IEEE, Tokyo, Japan (2013). https://doi.org/10.1109/ICBAKE.2013.88

  25. Reeves, B., Nass, C.: The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Pla. Bibliovault OAI Repository, the University of Chicago Press (1996)

    Google Scholar 

  26. Dautenhahn, K.: Socially intelligent robots: dimensions of human–robot interaction. Philos. Trans. R. Soc. B: Biol. Sci. 362, 679–704 (2007). https://doi.org/10.1098/rstb.2006.2004

    Article  Google Scholar 

  27. Song, Y., Tao, D., Luximon, Y.: In robot we trust? The effect of emotional expressions and contextual cues on anthropomorphic trustworthiness. Appl. Ergon. 109, 103967 (2023). https://doi.org/10.1016/j.apergo.2023.103967

    Article  Google Scholar 

  28. Aly, A., Tapus, A.: Towards an intelligent system for generating an adapted verbal and nonverbal combined behavior in human–robot interaction. Auton. Robot. 40, 193–209 (2016). https://doi.org/10.1007/s10514-015-9444-1

    Article  Google Scholar 

  29. Lee, K.M., Peng, W., Jin, S.-A., Yan, C.: Can robots manifest personality?: An empirical test of personality recognition, social responses, and social presence in human-robot interaction. J. Commun. 56, 754–772 (2006). https://doi.org/10.1111/j.1460-2466.2006.00318.x

    Article  Google Scholar 

  30. Esterwood, C., Robert, L.P.: A systematic review of human and robot personality in health care human-robot interaction. Front. Robot. AI 8, 748246 (2021)

    Google Scholar 

  31. Kaplan, A.D., Sanders, T., Hancock, P.A.: The relationship between extroversion and the tendency to anthropomorphize robots: a bayesian analysis. Front. Robot. AI. 5, 135 (2019)

    Google Scholar 

  32. Mulders, P., Llera, A., Tendolkar, I., van Eijndhoven, P., Beckmann, C.: Personality profiles are associated with functional brain networks related to cognition and emotion. Sci. Rep. 8, 13874 (2018). https://doi.org/10.1038/s41598-018-32248-x

    Article  Google Scholar 

  33. Szabóová, M., Sarnovský, M., Maslej Krešňáková, V., Machová, K.: Emotion analysis in human-robot interaction. Electronics 9, 1761 (2020). https://doi.org/10.3390/electronics9111761

    Article  Google Scholar 

  34. Javed, H., Park, C.H.: Behavior-based risk detection of autism spectrum disorder through child-robot interaction. In: Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 275–277. ACM, Cambridge United Kingdom (2020). https://doi.org/10.1145/3371382.3378382

  35. Friedman, N.P., Robbins, T.W.: The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacol. 47, 72–89 (2022). https://doi.org/10.1038/s41386-021-01132-0

    Article  Google Scholar 

  36. Faul, F., Erdfelder, E., Lang, A.-G., Buchner, A.: G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39, 175–191 (2007). https://doi.org/10.3758/BF03193146

    Article  Google Scholar 

  37. Khan, H., Noori, F.M., Yazidi, A., Uddin, M.Z., Khan, M.N.A., Mirtaheri, P.: Classification of individual finger movements from right hand using fNIRS signals. Sensors 21, 7943 (2021). https://doi.org/10.3390/s21237943

    Article  Google Scholar 

  38. Blonder, L., Bowers, D., Heilman, K.: The role of the right hemisphere in emotional communication. Brain 114(Pt 3), 1115–1127 (1991). https://doi.org/10.1093/brain/114.3.1115

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by Natural Science Foundation of Zhejiang Province [LQ24G010005].

Author information

Authors and Affiliations

  1. School of Management, Zhejiang University of Technology, Hangzhou, China

    Yikai Wang, Fengyuan Liu & Xin Lei

Authors
  1. Yikai Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fengyuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xin Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Lei .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Pei-Luen Patrick Rau

Ethics declarations

The authors have no competing interests to declare that are relevant to the content of this article.

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, Y., Liu, F., Lei, X. (2024). Neural Correlates of Robot Personality Perception: An fNIRS Study. In: Rau, PL.P. (eds) Cross-Cultural Design. HCII 2024. Lecture Notes in Computer Science, vol 14702. Springer, Cham. https://doi.org/10.1007/978-3-031-60913-8_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-60913-8_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-60912-1

  • Online ISBN: 978-3-031-60913-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics