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Cognitive Processing of Humanoid Robot Faces: Empirical Evidence and Factors Influencing Anthropomorphism

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Abstract

In the present work, we explored whether (and when) people cognitively process humanoid robot faces similarly to human faces, i.e., whether they cognitively anthropomorphize them. To investigate this, we conducted three experiments (N = 346) that utilized adapted versions of the face-scrambled task. This cognitive paradigm is commonly used to determine whether specific stimuli elicit configural processing, which is typical for human faces, or analytical processing, which is typical for nonhuman stimuli such as objects. Overall, our experiments revealed that robot faces may elicit configural processing (i.e., cognitive anthropomorphism), but this process is influenced by specific factors. Robot faces with high human-likeness are more strongly anthropomorphized than those with low human-likeness (Experiment 1). Additionally, this form of anthropomorphism emerges only when considering a first-order configural processing, but not when considering a second-order configural processing (Experiment 2). However, and most importantly, when the social categorization between humans and robots is lowered, the anthropomorphism of robot faces also emerges when considering a second-order configural processing (Experiment 3). Theoretical and practical implications of these findings are discussed, with a particular focus on the interaction between higher and lower cognitive processes involved in the social perception of humanoid robots.

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Data Availability

Experimental material and datasets generated and analysed during the current research are available through the Open Science Framework (https://osf.io/buakq/?view_only=cdb00b99b62943e7bcea295546b7c09b).

Notes

  1. Throughout the paper, the term “humanness” refers to the perception or attribution to a given target of characteristics (e.g., physical appearance, mental states, emotions) considered typical of humans and distinct from other living beings.

References

  1. Hansen CH, Hansen RD (1988) Finding the face in the crowd: an anger superiority effect. J Pers Soc Psychol 54(6):917–924. https://doi.org/10.1037/0022-3514.54.6.917

    Article  MATH  Google Scholar 

  2. Hewig J, Trippe RH, Hecht H, Straube T, Miltner WHR (2008) Gender differences for specific body regions when looking at men and women. J Nonverbal Behav 32:67–78. https://doi.org/10.1007/s10919-007-0043-5

    Article  Google Scholar 

  3. Stangor C, Lynch L, Duan C, Glas B (1992) Categorization of individuals on the basis of multiple social features. J Pers Soc Psychol 62(2):207–218. https://doi.org/10.1037/0022-3514.62.2.207

    Article  Google Scholar 

  4. Wilkinson N, Paikan A, Gredebäck G, Rea F, Metta G (2014) Staring us in the face? An embodied theory of innate face preference. Dev Sci 17(6):809–825. https://doi.org/10.1111/desc.12159

    Article  Google Scholar 

  5. Ekman P (1993) Facial expression and emotion. Am Psychol 48(4):384–392. https://doi.org/10.1037/0003-066X.48.4.384

    Article  MATH  Google Scholar 

  6. Ekman P, Oster H (1979) Facial expressions of emotion. Ann Rev Psychol 30(1):527–554. https://doi.org/10.1146/annurev.ps.30.020179.002523

    Article  MATH  Google Scholar 

  7. Todorov A (2017) Face value: the irresistible influence of first impressions. Princeton University Press, Princeton. https://doi.org/10.1515/9781400885725

    Article  MATH  Google Scholar 

  8. Waytz A, Heafner J, Epley N (2014) The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle. J Exp Soc Psychol 52:113–117. https://doi.org/10.1016/j.jesp.2014.01.005

    Article  Google Scholar 

  9. Ruijten PAM, Haans A, Ham J et al (2019) Perceived human-likeness of social robots: testing the Rasch model as a method for measuring anthropomorphism. Int J of Soc Robotics 11:477–494. https://doi.org/10.1007/s12369-019-00516-z

    Article  Google Scholar 

  10. Duffy BR (2003) Anthropomorphism and the social robot. Robot Auton Syst 42(3–4):177–190. https://doi.org/10.1016/S0921-8890(02)00374-3

    Article  MATH  Google Scholar 

  11. Sciutti A, Mara M, Tagliasco V, Sandini G (2011) Humanizing human-robot Interaction: on the importance of mutual understanding. IEEE Technol Soc Mag 37:22–29. https://doi.org/10.1109/MTS.2018.2795095

    Article  MATH  Google Scholar 

  12. Spatola N, Chaminade T (2022) Cognitive load increases anthropomorphism of humanoid robot. The automatic path of anthropomorphism. Int J Hum Comput 167:102–884. https://doi.org/10.1016/j.ijhcs.2022.102884

    Article  Google Scholar 

  13. Fink J (2012) Anthropomorphism and human-likeness in the design of robots and human-robot interaction. In: Proceedings of 4th international conference on social robotics, pp 199–208. https://doi.org/10.1007/978-3-642-34103-8_20

  14. Roselli C, Ciardo F, De Tommaso D, Wykowska A (2022) Human-likeness and attribution of intentionality predict vicarious sense of agency over humanoid robot actions. Sci Rep 12(1):138–145. https://doi.org/10.1038/s41598-022-18151-6

    Article  Google Scholar 

  15. Natarajan M, Gombolay M (2020) Effects of anthropomorphism and accountability on trust in human robot interaction. In: Proceedings of the 15th ACM/IEEE international conference on Human-Robot interaction, pp 33–42. https://doi.org/10.1145/3319502.3374839

  16. Kok BC, Soh H (2020) Trust in robots: challenges and opportunities. Curr Robot Rep 1:297–309. https://doi.org/10.1007/s43154-020-00029-y

    Article  MATH  Google Scholar 

  17. Riek LD, Rabinowitch TC, Chakrabarti B, Robinson P (2009) How anthropomorphism affects empathy toward robots. In: Proceedings of the 4th ACM/IEEE international conference on Human-Robot interaction, pp 245–246. https://doi.org/10.1145/1514095.1514158

  18. Złotowski J, Sumioka H, Nishio S, Glas D, Bartneck C, Ishiguro H (2016) Appearance of a robot affects the impact of its behaviour on perceived trustworthiness and empathy. J Behav Robot 7(1):000010151520160005. https://doi.org/10.1515/pjbr-2016-0005

    Article  Google Scholar 

  19. Epley N, Waytz A, Cacioppo JT (2007) On seeing human: a three-factor theory of anthropomorphism. Psychol Rev 114(4):864–886. https://doi.org/10.1037/0033-295X.114.4.864

    Article  MATH  Google Scholar 

  20. Hirai M, Hiraki K (2007) Differential neural responses to humans vs. robots: an event-related potential study. Brain Res 1165:105–115. https://doi.org/10.1016/j.brainres.2007.05.078

    Article  MATH  Google Scholar 

  21. Sacino A, Cocchella F, De Vita G, Bracco F, Rea F, Sciutti A, Andrighetto L (2022) Human-or object-like? Cognitive anthropomorphism of humanoid robots. PLoS ONE 17(7):e0270787. https://doi.org/10.1371/journal.pone.0270787

    Article  Google Scholar 

  22. Zlotowski J, Bartneck C (2013) The inversion effect in HRI: are robots perceived more like humans or objects? In: Proceedings of the 8th ACM/IEEE international conference on human-robot interaction, pp 365–372. https://doi.org/10.1109/HRI.2013.6483611.

  23. DiSalvo CF, Gemperle F, Forlizzi J, Kiesler S (2002) All robots are not created equal: the design and perception of humanoid robot heads. In: Proceedings of the 4th conference on designing interactive systems: processes, practices, methods, and techniques, pp 321–326. https://doi.org/10.1145/778712.778756

  24. Maurer D, Le Grand R, Mondloch CJ (2002) The many faces of configural processing. Trends Cogn Sci 6(6):255–260. https://doi.org/10.1016/S1364-6613(02)01903-4

    Article  MATH  Google Scholar 

  25. Piepers DW, Robbins RA (2012) A review and clarification of the terms “holistic”,“configural”, and “relational” in the face perception literature. Front Psychol 3:559. https://doi.org/10.3389/fpsyg.2012.00559

    Article  MATH  Google Scholar 

  26. Yin RK (1969) Looking at upside-down faces. J Exp Psychol 81(1):141–145. https://doi.org/10.1037/h0027474

    Article  MATH  Google Scholar 

  27. Rossion B, Gauthier I (2002) How does the brain process upright and inverted faces? Cogn Neurosci Rev 1(1):63–75. https://doi.org/10.1177/1534582302001001004

    Article  MATH  Google Scholar 

  28. Stekelenburg JJ, de Gelder B (2004) The neural correlates of perceiving human bodies: an ERP study on the body-inversion effect. NeuroReport 15(5):777–780

    Article  MATH  Google Scholar 

  29. Valentine T (1988) Upside-down faces: a review of the effect of inversion upon face recognition. Br J Psychol 79(4):471–491. https://doi.org/10.1111/j.2044-8295.1988.tb02747.x

    Article  MATH  Google Scholar 

  30. McKone E, Yovel G (2009) Why does picture-plane inversion sometimes dissociate perception of features and spacing in faces, and sometimes not? Toward a new theory of holistic processing. Psychon Bull Rev 16:778–797. https://doi.org/10.3758/PBR.16.5.778

    Article  MATH  Google Scholar 

  31. Civile C, Obhi SS (2016) Power, objectification, and recognition of sexualized women and men. Psychol Women Q 40(2):199–212. https://doi.org/10.1177/0361684315604820

    Article  Google Scholar 

  32. Diamond R, Carey S (1986) Why faces are and are not special: an effect of expertise. J Exp Psychol 115(2):107–117. https://doi.org/10.1037/0096-3445.115.2.107

    Article  MATH  Google Scholar 

  33. Rhodes G, Brake S, Atkinson AP (1993) What’s lost in inverted faces? Cognition 47(1):25–57. https://doi.org/10.1016/0010-0277(93)90061-Y

    Article  MATH  Google Scholar 

  34. Reed CL, Stone VE, Grubb JD, McGoldrick JE (2006) Turning configural processing upside down: part and whole body postures. J Exp Psychol Hum Percept Perform 32(1):73–87. https://doi.org/10.1037/0096-1523.32.1.73

    Article  Google Scholar 

  35. Hackel LM, Looser CE, Van Bavel JJ (2014) Group membership alters the threshold for mind perception: the role of social identity, collective identification, and intergroup threat. J Exp Soc Psychol 52:15–23. https://doi.org/10.1016/j.jesp.2013.12.001

    Article  Google Scholar 

  36. Hugenberg K, Young S, Rydell RJ, Almaraz S, Stanko KA (2016) The face of humanity: configural face processing influences ascriptions of humanness. Soc Psychol Pers 7(2):167–175. https://doi.org/10.1177/194855061560973

    Article  Google Scholar 

  37. Van Bavel JJ, Packer DJ, Cunningham WA (2008) The neural substrates of in-group bias: a functional magnetic resonance imaging investigation. Psychol Sci 19(11):1131–1139. https://doi.org/10.1111/j.1467-9280.2008.02214.x

    Article  MATH  Google Scholar 

  38. Van Bavel JJ, Packer DJ, Cunningham WA (2011) Modulation of the fusiform face area following minimal exposure to motivationally relevant faces: evidence of in-group enhancement (not out-group disregard). J Cogn Neurosci 23(11):3343–3354. https://doi.org/10.1162/jocn_a_00016

    Article  Google Scholar 

  39. Tajfel H, Turner JC (1986) The social identity theory of intergroup behavior. Nelson-Hall, Chicago

    MATH  Google Scholar 

  40. Michel C, Caldara R, Rossion B (2006) Same-race faces are perceived more holistically than other-race faces. Vis cogn 14(1):55–73. https://doi.org/10.1080/13506280500158761

    Article  Google Scholar 

  41. Baenninger M (1994) The development of face recognition: featural or configurational processing? J Exp Child Psychol 57(3):377–396. https://doi.org/10.1006/jecp.1994.1018

    Article  MATH  Google Scholar 

  42. Valentine T, Bruce V (1986) The effects of distinctiveness in recognizing and classifying faces. Perception 15(5):525–535. https://doi.org/10.1068/p150525

    Article  MATH  Google Scholar 

  43. Brysbaert M (2019) How many participants do we have to include in properly powered experiments? A tutorial of power analysis with reference tables. J Cogn 2(1):16. https://doi.org/10.5334/joc.72

    Article  MATH  Google Scholar 

  44. Tanaka JW, Farah MJ (1993) Parts and wholes in face recognition. Q J Exp Psychol 46(2):225–245. https://doi.org/10.1080/14640749308401045

    Article  MATH  Google Scholar 

  45. Phillips E, Zhao X, Ullman D, Malle BF (2018) What is human-like? Decomposing robots' human-like appearance using the anthropomorphic robot (abot) database. In: Proceedings of the 13th ACM/IEEE international conference on human-robot interaction, pp 105–113. https://doi.org/10.1145/3171221.3171268

  46. Jaeger TF (2008) Categorical data analysis: away from ANOVAs (transformation or not) and towards logit mixed models. J Mem Lang 59(4):434–446. https://doi.org/10.1016/j.jml.2007.11.007

    Article  MATH  Google Scholar 

  47. Gallucci M (2020) GAMLj suite for jamovi.

  48. The jamovi project (2022). jamovi. (Version 2.3) [Computer Software]. Retrieved from https://www.jamovi.org.

  49. Bruce V, Doyle T, Dench N, Burton M (1991) Remembering facial configurations. Cognition 38(2):109–144. https://doi.org/10.1016/0010-0277(91)90049-A

    Article  Google Scholar 

  50. Dahl CD, Logothetis NK, Bülthoff HH, Wallraven C (2011) Second-order relational manipulations affect both humans and monkeys. PLoS ONE 6(10):e25793. https://doi.org/10.1371/journal.pone.0025793

    Article  MATH  Google Scholar 

  51. Freire A, Lee K, Symons LA (2000) The face-inversion effect as a deficit in the encoding of configural information: direct evidence. Perception 29(2):159–170. https://doi.org/10.1068/p3012

    Article  MATH  Google Scholar 

  52. Haxby JV, Hoffman EA, Gobbini MI (2000) The distributed human neural system for face perception. Trends Cogn Sci 4(6):223–233. https://doi.org/10.1016/S1364-6613(00)01482-0

    Article  MATH  Google Scholar 

  53. Gaertner SL, Dovidio JF, Banker BS, Houlette M, Johnson KM, McGlynn EA (2000) Reducing intergroup conflict: from superordinate goals to decategorization, recategorization, and mutual differentiation. Group Dyn: Theory, Res Pract 4(1):98. https://doi.org/10.1037/1089-2699.4.1.98

    Article  Google Scholar 

  54. Paluck EL, Green DP (2009) Prejudice reduction: what works? A review and assessment of research and practice. Ann Rev Psychol 60:339–367. https://doi.org/10.1146/annurev.psych.60.110707.163607

    Article  MATH  Google Scholar 

  55. Gaertner SL, Rust MC, Dovidio JF, Bachman BA (1996) The contact hypothesis: the role of a common ingroup identity on reducing intergroup bias among majority and minority group members. In: What's social about social cognition? Research on socially shared cognition in small groups pp 230-260 https://doi.org/10.4135/9781483327648.n10

  56. Stevenage SV, Lee EA, Donnelly N (2005) The role of familiarity in a face classification task using thatcherized faces. Q J Exp Psychol 58(6):1103–1118. https://doi.org/10.1080/02724980443000494

    Article  MATH  Google Scholar 

  57. Civile C, Elchlepp H, McLaren R, Galang CM, Lavric A, McLaren IPL (2018) The effect of scrambling upright and inverted faces on the N170. Q J Exp Psychol 71(11):2464–2476. https://doi.org/10.1177/1747021817744455

    Article  MATH  Google Scholar 

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Funding

This work has been supported by Curiosity Driven (2018)- D36C18001720005 grant to the first author and funded by the University of Genova. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Educational Science, University of Genova, Genova, Italy

    Luca Andrighetto

  2. Department of Neuroscience, Imaging, and Clinical Sciences, Chieti-Pescara University, Chieti, Italy

    Alessandra Sacino

  3. Cognitive Architecture for Collaborative Technologies Unit, Istituto Italiano di Tecnologia, Genova, Italy

    Francesca Cocchella, Francesco Rea & Alessandra Sciutti

  4. Department of Informatics, Bioengineering, Robotics and Engineering (DIBRIS), University of Genova, Genova, Italy

    Francesca Cocchella

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  1. Luca Andrighetto
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  2. Alessandra Sacino
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Correspondence to Luca Andrighetto.

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Procedures performed in studies were in accordance with the ethical standards of the local Ethical Research Committee, with the APA ethical guidelines and with the 1964 Helsinki Declaration and its later amendments. Full informed consent was obtained before participants started the experiments, and they were fully debriefed after each experimental session.

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Andrighetto, L., Sacino, A., Cocchella, F. et al. Cognitive Processing of Humanoid Robot Faces: Empirical Evidence and Factors Influencing Anthropomorphism. Int J of Soc Robotics 17, 117–131 (2025). https://doi.org/10.1007/s12369-024-01199-x

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