Skip to main content
SpringerLink
Log in

How do Robot Touch Characteristics Impact Users’ Emotional Responses: Evidence from ECG and fNIRS

  • Published:
International Journal of Social Robotics Aims and scope Submit manuscript

Abstract

Robot touch is a vital interaction mode for emotional communication and human mental support in HRI. However, little is known about how robot touch characteristics influence the users’ subjective perception of emotion and physiological reactions. Therefore, a within-subject experiment of robot touches was conducted with the touch type (contact versus grip), length (long versus short), and location (hand versus forearm) as independent variables. The subjective perception was measured with PANAS scales. Electrocardiography (ECG) and functional near-infrared spectroscopy (fNIRS) were utilized to measure the participant’s cardiac autonomic nervous system responses and cerebral central nervous system responses. Results showed that touch type and length jointly affected users’ subjective perception of emotion and cerebral activity, and location affected users’ heart rate variability and cerebral activity. The results suggest that robot short-grip and long-contact behaviors might bring users more positive emotions. Although robot touch on forearms might not induce more positive emotional perception, it might be helpful for emotional regulation and might mobilize more cognitive resources.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Leo-Liu J (2023) Loving a defiant AI companion? The gender performance and ethics of social exchange robots in simulated intimate interactions. Comput Hum Behav 141:107620. https://doi.org/10.1016/j.chb.2022.107620

    Article  Google Scholar 

  2. Chen TL, King C-HA, Thomaz AL, Kemp CC (2014) An investigation of responses to robot-initiated touch in a nursing context. Int J Social Robot 6(1):141–161. https://doi.org/10.1007/s12369-013-0215-x

    Article  Google Scholar 

  3. Richert A, Schiffmann M, Yuan C (2020) A nursing robot for social interactions and health assessment. Advances in Human Factors in Robots and Unmanned Systems: Proceedings of the AHFE 2019 International Conference on Human Factors in Robots and Unmanned Systems, July 24–28, 2019, Washington DC, USA 10:83–91. https://doi.org/10.1007/978-3-030-20467-9_8

  4. Betlej A (2022) Designing robots for elderly from the perspective of potential end-users: a sociological approach. Int J Environ Res Public Health 19(6):3630. https://doi.org/10.3390/ijerph19063630

    Article  Google Scholar 

  5. Belpaeme T, Kennedy J, Ramachandran A, Scassellati B, Tanaka F (2018) Social robots for education: a review. Sci Rob 3(21):eaat5954. https://doi.org/10.1126/scirobotics.aat5954

    Article  Google Scholar 

  6. Zheng X, Shiomi M, Minato T, Ishiguro H (2021) Modeling the timing and duration of grip behavior to Express emotions for a Social Robot. IEEE Rob Autom Lett 6(1):159–166. https://doi.org/10.1109/lra.2020.3036372

    Article  Google Scholar 

  7. Shiomi M, Zheng X, Minato T, Ishiguro H (2021) Implementation and evaluation of a grip Behavior Model to Express emotions for an Android Robot. Front Robot AI 8:755150. https://doi.org/10.3389/frobt.2021.755150

    Article  Google Scholar 

  8. Zheng X, Shiomi M, Minato T, Ishiguro H (2020) What kinds of Robot’s Touch Will Match expressed emotions? IEEE Rob Autom Lett 5(1):127–134. https://doi.org/10.1109/lra.2019.2947010

    Article  Google Scholar 

  9. Andreasson R, Alenljung B, Billing E, Lowe R (2018) Affective Touch in Human–Robot Interaction: conveying emotion to the Nao Robot. Int J Social Robot 10(4):473–491. https://doi.org/10.1007/s12369-017-0446-3

    Article  Google Scholar 

  10. Willemse CJAM, Toet A, van Erp JBF (2017) Affective and behavioral responses to Robot-initiated Social Touch: toward understanding the opportunities and limitations of Physical Contact in Human–Robot Interaction. Frontiers in ICT 4 https://doi.org/10.3389/fict.2017.00012

  11. Block AE, Kuchenbecker KJ (2018) Emotionally supporting humans through robot hugs. Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction:293-4

  12. Akiyoshi T, Nakanishi J, Ishiguro H, Sumioka H, Shiomi M (2021) A robot that encourages self-disclosure to reduce anger mood. IEEE Rob Autom Lett 6(4):7925–7932. https://doi.org/10.1109/LRA.2021.3102326

    Article  Google Scholar 

  13. Sumioka H, Kumazaki H, Muramatsu T, Yoshikawa Y, Ishiguro H, Higashida H et al (2021) A huggable device can reduce the stress of calling an unfamiliar person on the phone for individuals with ASD. PLoS ONE 16(7):e0254675. https://doi.org/10.1371/journal.pone.0254675

    Article  Google Scholar 

  14. Shiomi M, Hagita N (2021) Audio-Visual Stimuli Change not only Robot’s hug impressions but also its stress-buffering effects. Int J Social Robot 13(3):469–476. https://doi.org/10.1007/s12369-019-00530-1

    Article  Google Scholar 

  15. Zheng X, Shiomi M, Minato T, Ishiguro H (2020) How can Robots make people feel intimacy through Touch? J Robot Mechatron 32(1):51–58. https://doi.org/10.20965/jrm.2020.p0051

    Article  Google Scholar 

  16. DiSalvo C, Gemperle F, Forlizzi J, Montgomery E (2003) The hug: an exploration of robotic form for intimate communication. The 12th IEEE International Workshop on Robot and Human Interactive Communication 2003 Proceedings ROMAN 2003 403–8. https://doi.org/10.1109/ROMAN.2003.1251879

  17. Fitter NT, Kuchenbecker KJ (2020) How does it feel to clap hands with a robot? Int J Social Robot 12(1):113–127. https://doi.org/10.1007/s12369-019-00542-x

    Article  Google Scholar 

  18. Fukuda H, Shiomi M, Nakagawa K, Ueda K (2012) Midas Touch’ in Human-Robot Interaction: evidence from event-related potentials during the Ultimatum game. Int Conf Human-robot Interact. https://doi.org/10.1145/2157689.2157720

    Article  Google Scholar 

  19. Shiomi M, Nakata A, Kanbara M, Hagita N (2021) Robot reciprocation of hugs increases both Interacting Times and Self-disclosures. Int J Social Robot 13(2):353–361. https://doi.org/10.1007/s12369-020-00644-x

    Article  Google Scholar 

  20. Vasara D, Surakka V (2021) Haptic responses to angry and happy faces. Int J Human–Computer Interact 1–11. https://doi.org/10.1080/10447318.2021.1898849

  21. Teyssier M, Bailly G, Pelachaud C, Lecolinet E (2022) Conveying emotions through device-initiated Touch. IEEE Trans Affect Comput 13(3):1477–1488. https://doi.org/10.1109/taffc.2020.3008693

    Article  Google Scholar 

  22. Block AE, Kuchenbecker KJ (2019) Softness, Warmth, and responsiveness improve Robot hugs. Int J Social Robot 11(1):49–64. https://doi.org/10.1007/s12369-018-0495-2

    Article  Google Scholar 

  23. Hertenstein MJ, Holmes R, McCullough M, Keltner D (2009) The communication of emotion via touch. Emotion 9(4):566–573. https://doi.org/10.1037/a0016108

    Article  Google Scholar 

  24. Hertenstein MJ, Keltner D, App B, Bulleit BA, Jaskolka AR (2006) Touch communicates distinct emotions. Emotion 6(3):528. https://doi.org/10.1037/1528-3542.6.3.528

    Article  Google Scholar 

  25. Suvilehtoa JT, Glereana E, Dunbarb RIM, Haria R, Nummenmaaa L (2015) Topography of social touching depends on emotional bonds between humans. Proc Natl Acad Sci U S A 112(48):E6718. https://doi.org/10.1073/pnas.1521810112

    Article  Google Scholar 

  26. Moszkowski RJ, Stack DM, Girouard N, Field TM, Hernandez-Reif M, Diego M (2009) Touching behaviors of infants of depressed mothers during normal and perturbed interactions. Infant Behav Dev 32(2):183–194. https://doi.org/10.1016/j.infbeh.2008.12.009

    Article  Google Scholar 

  27. Ree A, Mayo LM, Leknes S, Sailer U (2019) Touch targeting C-tactile afferent fibers has a unique physiological pattern: a combined electrodermal and facial electromyography study. Biol Psychol 140:55–63. https://doi.org/10.1016/j.biopsycho.2018.11.006

    Article  Google Scholar 

  28. Salminen K, Surakka V, Raisamo J, Lylykangas J, Raisamo R, Mäkelä K et al (2013) Cold or hot? how thermal stimuli are related to human emotional system? Haptic and Audio Interaction Design: 8th International Workshop, HAID 2013, Daejeon, Korea, April 18–19, 2013, Revised Selected Papers 8:20–9

  29. Ogawa K, Nishio S, Koda K, Balistreri G, Watanabe T, Ishiguro H (2011) Exploring the natural reaction of Young and aged person with Telenoid in a Real World. J Adv Comput Intell Intell Inf 15(5):592–597

    Article  Google Scholar 

  30. Turkle S, Breazeal C, Dasté O (2006) B S Encounters with Kismet and Cog: Children Respond to Relational Artifacts. Digital media: Transformations in human communication 120

  31. Shiomi M, Nakata A, Kanbara M, Hagita N (2017) A hug from a robot encourages prosocial behavior. 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) 418–423. https://doi.org/10.1109/ROMAN.2017.8172336

  32. Cooney MD, Nishio S, Ishiguro H (2012) Recognizing affection for a touch-based interaction with a humanoid robot. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems 1420–1427. https://doi.org/10.1109/iros.2012.6385956

  33. Blythe E, Garrido L, Longo MR (2023) Emotion is perceived accurately from isolated body parts, especially hands. Cognition 230:105260. https://doi.org/10.1016/j.cognition.2022.105260

    Article  Google Scholar 

  34. Law T, Malle BF, Scheutz M (2021) A touching connection: how observing robotic Touch can affect Human Trust in a Robot. Int J Social Robot. https://doi.org/10.1007/s12369-020-00729-7

    Article  Google Scholar 

  35. Willis FN, Briggs LF (1992) Relationship and touch in public settings. J Nonverbal Behav 16(1):55–63

    Article  Google Scholar 

  36. McGlone F, Wessberg J, Olausson H (2014) Discriminative and affective touch: sensing and feeling. Neuron 82(4):737–755. https://doi.org/10.1016/j.neuron.2014.05.001

    Article  Google Scholar 

  37. Morrison I, Löken LS, Minde J, Wessberg J, Perini I, Nennesmo I et al (2011) Reduced C-afferent fibre density affects perceived pleasantness and empathy for touch. Brain 134(4):1116–1126. https://doi.org/10.1093/brain/awr011

    Article  Google Scholar 

  38. Rolls ET (2010) The affective and cognitive processing of touch, oral texture, and temperature in the brain. Neurosci Biobehavioral Reviews 34(2):237–245. https://doi.org/10.1016/j.neubiorev.2008.03.010

    Article  Google Scholar 

  39. Bickmore TW, Fernando R, Ring L, Schulman D (2010) Empathic touch by Relational agents. IEEE Trans Affect Comput 1(1):60–71. https://doi.org/10.1109/t-affc.2010.4

    Article  Google Scholar 

  40. Lange CG (1885) The mechanism of the emotions. The classical psychologists: 672–84

  41. Izard CE (1991) The psychology of emotions. Springer Science & Business Media

  42. Lazarus RS, Folkman S (1984) Stress, appraisal, and coping. Springer publishing company

  43. Ekman P (1992) An argument for basic emotions. Cognition & Emotion 6(3–4):169–200. https://doi.org/10.1080/02699939208411068

    Article  Google Scholar 

  44. Ekman P, Friesen WV, O’sullivan M, Chan A, Diacoyanni-Tarlatzis I, Heider K et al (1987) Universals and cultural differences in the judgments of facial expressions of emotion. J Personal Soc Psychol 53(4):712

    Article  Google Scholar 

  45. Jamil MH, Park W, Eid M (2020) Emotional responses to watching and touching 3d emotional face in a virtual environment. Virtual Reality 25(2):553–564. https://doi.org/10.1007/s10055-020-00473-3

    Article  Google Scholar 

  46. Watson D, Tellegen A (1985) Toward a consensual structure of mood. Psychol Bull 98(2):219. https://doi.org/10.1037/0033-2909.98.2.219

    Article  Google Scholar 

  47. Block AE (2021) HuggieBot: an interactive hugging Robot with Visual and Haptic Perception. ETH Zurich, pp 125–229. https://doi.org/10.3929/ethz-b-000508212

  48. Kreibig SD (2010) Autonomic nervous system activity in emotion: a review. Biol Psychol 84(3):394–421. https://doi.org/10.1016/j.biopsycho.2010.03.010

    Article  Google Scholar 

  49. Shiota MN, Neufeld SL, Yeung WH, Moser SE, Perea EF (2011) Feeling good: autonomic nervous system responding in five positive emotions. Emotion 11(6):1368–1378. https://doi.org/10.1037/a0024278

    Article  Google Scholar 

  50. Golland Y, Keissar K, Levit-Binnun N (2014) Studying the dynamics of autonomic activity during emotional experience. Psychophysiology 51(11):1101–1111. https://doi.org/10.1111/psyp.12261

    Article  Google Scholar 

  51. Christian C, Evelyne V-M, Georges D, Andre D (1997) Autonomic nervous system response patterns specificity to basic emotions. J Auton Nerv Syst 62(1–2):45–57. https://doi.org/10.1016/S0165-1838(96)00108-7

    Article  Google Scholar 

  52. Hidehiro N, Shinichi F, Satoshi O, Tsutomu M, Hiroshi K (2009) Emotion-related changes in heart rate and its variability during performance and perception of music. Ann N Y Acad Sci 1169(1):359–362. https://doi.org/10.1111/j.1749-6632.2009.04788.x

    Article  Google Scholar 

  53. Wascher CA (2021) Heart rate as a measure of emotional arousal in evolutionary biology. Philosophical Trans Royal Soc B 376(1831):20200479

    Article  Google Scholar 

  54. Brosschot JF, Thayer JF (2003) Heart rate response is longer after negative emotions than after positive emotions. Int J Psychophysiol 50(3):181–187. https://doi.org/10.1016/S0167-8760(03)00146-6

    Article  Google Scholar 

  55. Spodick DH, Raju P, Bishop RL, Rifkin RD (1992) Operational definition of normal sinus heart rate. Am J Cardiol 69(14):1245–1246. https://doi.org/10.1016/0002-9149(92)90947-W

    Article  Google Scholar 

  56. Levenson RW, Ekman P, Friesen WV (1990) Voluntary facial action generates emotion-specific autonomic nervous system activity. Psychophysiology 27(4):363–384

    Article  Google Scholar 

  57. Hubert W, de Jong-Meyer R (1990) Psychophysiological response patterns to positive and negative film stimuli. Biol Psychol 31(1):73–93. https://doi.org/10.1016/0301-0511(90)90079-C

    Article  Google Scholar 

  58. Kleiger RE, Stein PK, Bigger JT Jr (2005) Heart rate variability: measurement and clinical utility. Ann Noninvasive Electrocardiol 10(1):88–101. https://doi.org/10.1111/j.1542-474X.2005.10101.x

    Article  Google Scholar 

  59. Montano N, Porta A, Cogliati C, Costantino G, Tobaldini E, Casali KR et al (2009) Heart rate variability explored in the frequency domain: a tool to investigate the link between heart and behavior. Neurosci Biobehavioral Reviews 33(2):71–80. https://doi.org/10.1016/j.neubiorev.2008.07.006

    Article  Google Scholar 

  60. Electrophysiology TFESCNASP (1996) Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation 93(5):1043–1065. https://doi.org/10.1161/01.CIR.93.5.1043

    Article  Google Scholar 

  61. Zupan M, Buskas J, Altimiras J, Keeling LJJP (2016) behavior Assessing positive emotional states in dogs using heart rate and heart rate variability. 155:102–11. https://doi.org/10.1016/j.physbeh.2015.11.027

  62. Triscoli C, Croy I, Steudte-Schmiedgen S, Olausson H, Sailer U (2017) Heart rate variability is enhanced by long-lasting pleasant touch at CT-optimized velocity. Biol Psychol 128:71–81. https://doi.org/10.1016/j.biopsycho.2017.07.007

    Article  Google Scholar 

  63. Christou-Champi S, Farrow TF, Webb TL (2015) Automatic control of negative emotions: evidence that structured practice increases the efficiency of emotion regulation. Cogn Emot 29(2):319–331. https://doi.org/10.1080/02699931.2014.901213

    Article  Google Scholar 

  64. Iadecola C (2017) The neurovascular unit coming of age: a journey through neurovascular coupling in health and disease. Neuron 96(1):17–42. https://doi.org/10.1016/j.neuron.2017.07.030

    Article  Google Scholar 

  65. Friston KJ, Fletcher P, Josephs O, Holmes A, Rugg M, Turner R (1998) Event-related fMRI: characterizing differential responses. NeuroImage 7(1):30–40

    Article  Google Scholar 

  66. Damasio AR, Grabowski TJ, Bechara A, Damasio H, Ponto LLB, Parvizi J et al (2000) Subcortical and cortical brain activity during the feeling of self-generated emotions. Nat Neurosci 3(10):1049–1056

    Article  Google Scholar 

  67. Hu X, Zhuang C, Wang F, Liu YJ, Im CH, Zhang D (2019) fNIRS evidence for recognizably different positive emotions. Front Hum Neurosci 13:120. https://doi.org/10.3389/fnhum.2019.00120

    Article  Google Scholar 

  68. Himichi T, Fujita H, Nomura M (2015) Negative emotions impact lateral prefrontal cortex activation during theory of mind: an fNIRS study. Soc Neurosci 10(6):605–615. https://doi.org/10.1080/17470919.2015.1017112

    Article  Google Scholar 

  69. Chick CF, Rolle C, Trivedi HM, Monuszko K, Etkin A (2020) Transcranial magnetic stimulation demonstrates a role for the ventrolateral prefrontal cortex in emotion perception. Psychiatry Res 284:112515. https://doi.org/10.1016/j.psychres.2019.112515

    Article  Google Scholar 

  70. Ahern GL, Schwartz GE (1985) Differential lateralization for positive and negative emotion in the human brain: EEG spectral analysis. Neuropsychologia 23(6):745–755. https://doi.org/10.1016/0028-3932(85)90081-8

    Article  Google Scholar 

  71. Wheeler RE, Davidson RJ, Tomarken AJ (1993) Frontal brain asymmetry and emotional reactivity: a biological substrate of affective style. Psychophysiology 30(1):82–89. https://doi.org/10.1111/j.1469-8986.1993.tb03207.x

    Article  Google Scholar 

  72. Herrington JD, Mohanty A, Koven NS, Fisher JE, Stewart JL, Banich MT et al (2005) Emotion-modulated performance and activity in left dorsolateral prefrontal cortex. Emotion 5(2):200. https://doi.org/10.1037/1528-3542.5.2.200

    Article  Google Scholar 

  73. Etkin A, Egner T, Kalisch R (2011) Emotional processing in anterior cingulate and medial prefrontal cortex. Trends Cogn Sci 15(2):85–93. https://doi.org/10.1016/j.tics.2010.11.004

    Article  Google Scholar 

  74. Grafton ST, Arbib MA, Fadiga L, Rizzolatti G (1996) Localization of grasp representations in humans by positron emission tomography: 2. Observation compared with imagination. Exp Brain Res 112:103–111

    Article  Google Scholar 

  75. Binkofski F, Amunts K, Stephan KM, Posse S, Schormann T, Freund HJ et al (2000) Broca’s region subserves imagery of motion: a combined cytoarchitectonic and fMRI study. Hum Brain Mapp 11(4):273–285. https://doi.org/10.1002/1097-0193(200012)11:4>273::AID-HBM40<3.0.CO;2-0

  76. Binkofski F, Buccino G (2004) Motor functions of the Broca’s region. Brain Lang 89(2):362–369. https://doi.org/10.1016/S0093-934X(03)00358-4

    Article  Google Scholar 

  77. Gardecki A, Podpora M (2017) Experience from the operation of the Pepper humanoid robots. 2017 Progress in Applied Electrical Engineering (PAEE):1–6. https://doi.org/10.1109/PAEE.2017.8008994

  78. Faul F, Erdfelder E, Lang A-G, Buchner A (2007) G* power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods 39(2):175–191

    Article  Google Scholar 

  79. Dahlbäck N, Jönsson A, Ahrenberg L (1993) Wizard of Oz studies—why and how. Knowl Based Syst 6(4):258–266

    Article  Google Scholar 

  80. Hoffmann L, Kramer NC (2021) The persuasive power of robot touch. Behavioral and evaluative consequences of non-functional touch from a robot. PLoS ONE 16(5):e0249554. https://doi.org/10.1371/journal.pone.0249554

    Article  Google Scholar 

  81. Watson D, Clark LA, Tellegen A (1988) Development and validation of brief measures of positive and negative affect: the PANAS scales. J Personal Soc Psychol 54(6):1063–1070. https://doi.org/10.1037/0022-3514.54.6.1063

    Article  Google Scholar 

  82. Makowski D, Pham T, Lau ZJ, Brammer JC, Lespinasse F, Pham H et al (2021) NeuroKit2: a Python toolbox for neurophysiological signal processing. Behav Res Methods 53(4):1689–1696. https://doi.org/10.3758/s13428-020-01516-y

    Article  Google Scholar 

  83. Singh AK, Okamoto M, Dan H, Jurcak V, Dan I (2005) Spatial registration of multichannel multi-subject fNIRS data to MNI space without MRI. NeuroImage 27(4):842–851. https://doi.org/10.1016/j.neuroimage.2005.05.019

    Article  Google Scholar 

  84. Gallese V, Fadiga L, Fogassi L, Rizzolatti G (1996) Action recognition in the premotor cortex. Brain 119(2):593–609. https://doi.org/10.1093/brain/119.2.593

    Article  Google Scholar 

  85. Hou X, Zhang Z, Zhao C, Duan L, Gong Y, Li Z et al (2021) NIRS-KIT: a MATLAB toolbox for both resting-state and task fNIRS data analysis. Neurophotonics 8(1):010802. https://doi.org/10.1117/1.NPh.8.1.010802

    Article  Google Scholar 

  86. Strangman G, Culver JP, Thompson JH, Boas DA (2002) A quantitative comparison of simultaneous BOLD fMRI and NIRS recordings during functional brain activation. NeuroImage 17(2):719–731. https://doi.org/10.1006/nimg.2002.1227

    Article  Google Scholar 

  87. Cope M, Delpy DT (1988) System for long-term measurement of cerebral blood and tissue oxygenation on newborn infants by near infra-red transillumination. Med Biol Eng Comput 26(3):289–294

    Article  Google Scholar 

  88. Fishburn FA, Ludlum RS, Vaidya CJ, Medvedev AV (2019) Temporal derivative distribution repair (TDDR): a motion correction method for fNIRS. NeuroImage 184:171–179. https://doi.org/10.1016/j.neuroimage.2018.09.025

    Article  Google Scholar 

  89. Björnsdotter M, Olausson H (2011) Vicarious responses to social touch in posterior insular cortex are tuned to pleasant caressing speeds. J Neurosci 31(26):9554–9562. https://doi.org/10.1523/JNEUROSCI.0397-11.2011

    Article  Google Scholar 

  90. Huisman G (2017) Social touch technology: a survey of haptic technology for social touch. IEEE Trans Haptics 10(3):391–408. https://doi.org/10.1109/TOH.2017.2650221

    Article  Google Scholar 

  91. Balconi M, Grippa E, Vanutelli ME (2015) What hemodynamic (fNIRS), electrophysiological (EEG) and autonomic integrated measures can tell us about emotional processing. Brain Cogn 95:67–76. https://doi.org/10.1016/j.bandc.2015.02.001

    Article  Google Scholar 

  92. Turnbull A, Wang H, Murphy C, Ho N, Wang X, Sormaz M et al (2019) Left dorsolateral prefrontal cortex supports context-dependent prioritisation of off-task thought. Nat Commun 10(1):3816

    Article  Google Scholar 

  93. Murray LJ, Ranganath C (2007) The dorsolateral prefrontal cortex contributes to successful relational memory encoding. J Neurosci 27(20):5515–5522. https://doi.org/10.1523/JNEUROSCI.0406-07.2007

    Article  Google Scholar 

  94. Lu X, Li T, Xia Z, Zhu R, Wang L, Luo YJ et al (2019) Connectome-based model predicts individual differences in propensity to trust. Hum Brain Mapp 40(6):1942–1954. https://doi.org/10.1002/hbm.24503

    Article  Google Scholar 

  95. Duncan J, Owen AM (2000) Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends Neurosci 23(10):475–483. https://doi.org/10.1016/S0166-2236(00)01633-7

    Article  Google Scholar 

  96. Saygin ZM, Osher DE, Koldewyn K, Reynolds G, Gabrieli JD, Saxe RR (2012) Anatomical connectivity patterns predict face selectivity in the fusiform gyrus. Nat Neurosci 15(2):321–327. https://doi.org/10.1038/nn.3001

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 72071035). No conflict of interest exists in submitting this paper, and all authors approve it for publication. We are grateful to all the experimental participants for this study. Furthermore, we are genuinely pleased to extend our gratitude to editors and reviewers for their valuable work.

Author information

Authors and Affiliations

  1. Northeastern University School of Business Administration, Shenyang, China

    Fu Guo, Chen Fang, Zenggen Ren & Zeyu Zhang

  2. Department of Industrial Engineering, College of Management Science and Engineering, Anhui University of Technology, Ma’anshan, China

    Mingming Li

Authors
  1. Fu Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chen Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mingming Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zenggen Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zeyu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chen Fang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, F., Fang, C., Li, M. et al. How do Robot Touch Characteristics Impact Users’ Emotional Responses: Evidence from ECG and fNIRS. Int J of Soc Robotics 16, 619–634 (2024). https://doi.org/10.1007/s12369-024-01110-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12369-024-01110-8

Keywords

Search

Navigation