Ir para o conteúdo principal Ir para o menu de navegação principal Ir para o rodapé
Scientific Electronic Archives
Educação e Ensino
DOI: 10.36560/17520241986
Enviado: 2024-07-25
Publicado: 2024-08-28

Human-Social Robot Interaction in the Light of ToM and Metacognitive Functions

Net Media Lab Mind - Brain R&D ΙΙΤ - N.C.S.R. "Demokritos" , Athens, Greece
N.C.S.R. Demokritos
human-robot interaction, social robotics, theory of mind, metacognition, metareasoning

Resumo

Theory of Mind (ToM) and Metacognition constitute two superior mental mechanisms that promote the smooth integration and adaptation of the individual in society. In particular, the ability to read minds introduces the individual into the social world, contributing to understanding oneself and others. Metacognition focuses on individual knowledge, control, regulation, and readjustment regarding the cognitive mechanism and its influence on cognitive performance and the mental and social development of the individual. At the basis of the development of the two mechanisms is the activation of social interaction, which determines their levels of development. The innovative approaches and great expectations of technology and Artificial Intelligence for improving the artificial mind brought social robots to the fore. Robots with social action are gradually entering human life. Their interaction with the human factor is anticipated to become more and more frequent, expanded, and specialized. Hence, the investigation of equipping artificial systems with integrated social-cognitive and metacognitive capabilities was necessary, constituting the subject of study of the current narrative review. Research findings show that intelligent systems with introspection, self-evaluation, and perception-understanding of emotions, intentions, and beliefs can develop safe and satisfactory communication with humans as long as their design and operation conform to the code of ethics.

Referências

  1. Abubshait, A., & Wiese, E. (2017). You look human, but act like a machine: agent appearance and behavior modulate different aspects of human–robot interaction. Frontiers in psychology, 8, 277299. https://doi.org/10.3389/fpsyg.2017.01393
  2. Astington, J. W. & Jenkins, J. M. (1995). Theory of mind development and social understanding. Cognition & Emotion, 9 (2-3), 151-165. https://doi.org/10.1080/02699939508409006
  3. Bakola, L. N., Drigas, A., & Skianis, C. (2022). Emotional Intelligence vs. Artificial Intelligence: The interaction of human intelligence in evolutionary robotics. Research, Society and Development, 11(16). http://dx.doi.org/10.33448/rsd-v11i16.38057
  4. Bamicha, V., & Drigas, A. (2022a). The Evolutionary Course of Theory of Mind-Factors That Facilitate or Inhibit Its Operation & the Role of ICTs. Technium Soc. Sci. J., 30, 138-158. https://doi.org/10.47577/tssj.v30i1.6220
  5. Bamicha, V., & Drigas, A. (2022b). ToM & ASD: The interconnection of Theory of Mind with the social-emotional, cognitive development of children with Autism Spectrum Disorder. The use of ICTs as an alternative form of intervention in ASD. Technium Social Sciences Journal, 33, 42-72. https://orcid.org/0000-0001-5637-9601
  6. Bamicha, V., & Drigas, A. (2023a). Consciousness influences in ToM and Metacognition functioning-An artificial intelligence perspective. Res earch, Society and Development, 12(3). https://doi.org/10.33448/rsd-v12i3.40420
  7. Bamicha, V., & Drigas, A. (2023b). Theory of Mind in relation to Metacognition and ICTs. A metacognitive approach to ToM. Scientific Electronic Archives, 16(4). https://doi.org/10.36560/16420231711
  8. Bamicha, V., & Salapata, Y. (2024). LLLT applications may enhance ASD aspects related to disturbances in the gut microbiome, mitochondrial activity, and neural network function. Brazilian Journal of Science, 3(1), 140-158. https://doi.org/10.14295/bjs.v3i1.457
  9. Bamicha, V., & Drigas, A. (2024). Strengthening AI via ToM and MC dimensions. Scientific Electronic Archives, 17(3). http://dx.doi.org/10.36560/17320241939
  10. Banks, J. (2020). Theory of mind in social robots: Replication of five established human tests. International Journal of Social Robotics, 12(2), 403-414. https://doi.org/10.1007/s12369-019-00588-x
  11. Baraka, K., Alves-Oliveira, P., & Ribeiro, T. (2020). An extended framework for characterizing social robots. In: Jost, C., et al. Human-Robot Interaction. Springer Series on Bio- and Neurosystems, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-42307-0_2
  12. Basich, C., Biswas, J., & Zilberstein, S. (2023). Competence-Aware Autonomy: An Essential Skill for Robots in the Real World. Retrieved from: https://www.researchgate.net/search.Search.html?query=Competence-Aware+Autonomy%3A+An+Essential+Skill+for+Robots+in+the+Real+World&type=publication
  13. Bianco, F., & Ognibene, D. (2019). Transferring adaptive theory of mind to social robots: Insights from developmental psychology to robotics. In Social Robotics: 11th International Conference, ICSR 2019, Madrid, Spain, November 26–29, 2019, Proceedings 11 (pp. 77-87). Springer International Publishing. https://doi.org/10.1007/978-3-030-35888-4_8
  14. Breazeal, C., Gray, J., & Berlin, M. (2009). An embodied cognition approach to mindreading skills for socially intelligent robots. The International Journal of Robotics Research, 28(5), 656-680. https://doi.org/10.1177/0278364909102796
  15. Breazeal, C., Dautenhahn, K., & Kanda, T. (2016). Social robotics. Springer handbook of robotics, 1935-1972. https://doi.org/10.1007/978-3-319-32552-1_72
  16. Brock, L. L., Kim, H., Gutshall, C. C., & Grissmer, D. W. (2018). The development of theory of mind: Predictors and moderators of improvement in kindergarten. Early Child Development and Care. https://doi.org/10.1080/03004430.2017.1423481
  17. Brooks, C., & Szafir, D. (2019). Building second-order mental models for human-robot interaction. arXiv preprint arXiv:1909.06508. https://doi.org/10.48550/arXiv.1909.06508
  18. Caro, M. F., Cox, M. T., & Toscano-Miranda, R. E. (2022). A Validated Ontology for Metareasoning in Intelligent Systems. Journal of Intelligence, 10(4), 113. https://doi.org/10.3390/jintelligence10040113
  19. Castelfranchi, C., & Falcone, R. (2019). Self-Awareness Implied in Human and Robot Intentional Action. In AAAI Spring Symposium: Towards Conscious AI Systems. Retrieved from: https://ceur-ws.org/Vol-2287/paper7.pdf
  20. Cerrato, L., & Campbell, N. (2017). Engagement in dialogue with social robots. Dialogues with Social Robots: Enablements, Analyses, and Evaluation, 313-319. https://doi.org/10.1007/978-981-10-2585-3_25
  21. Chaidi, E., Kefalis, C., Papagerasimou, Y., & Drigas, A. (2021). Educational robotics in Primary Education. A case in Greece. Research, Society and Development, 10(9), e17110916371-e17110916371. http://dx.doi.org/10.33448/rsd-v10i9.16371
  22. Chella, A., Pipitone, A., Morin, A., & Racy, F. (2020). Developing self-awareness in robots via inner speech. Frontiers in Robotics and AI, 7, 16. https://doi.org/10.3389/frobt.2020.00016
  23. Chella, A. (2022). Robots and machine consciousness. MIT Press, In Special Collection: CogNet. ISBN electronic: 9780262369329. https://doi.org/10.7551/mitpress/13780.003.0029
  24. Chen, X., Sui, Z., & Ji, J. (2013). Towards metareasoning for human-robot interaction. In Intelligent Autonomous Systems 12: Volume 2 Proceedings of the 12th International Conference IAS-12, held June 26-29, 2012, Jeju Island, Korea (pp. 355-367). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-33932-5_34
  25. Chen, B., Vondrick, C., & Lipson, H. (2021). Visual behavior modelling for robotic theory of mind. Scientific Reports, 11(1), 424. https://doi.org/10.1038/s41598-020-77918-x
  26. Choi, J. J., Kim, Y. K., & Kwak, S. S. (2014). The autonomy levels and the human intervention levels of robots: The impact of robot types in human-robot interaction. In 23rd IEEE International Symposium on Robot and Human Interactive Communication, IEEE RO-MAN 2014 (pp. 1069-1074). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ROMAN.2014.6926394
  27. Clodic, A., & Alami, R. (2021). What Is It to Implement a Human-Robot Joint Action?. Robotics, AI, and Humanity: Science, Ethics, and Policy, 229-238. https://doi.org/10.1007/978-3-030-54173-6_19
  28. Cox, M., & Raja, A. (2008). Metareasoning: A manifesto. BBN Technical.
  29. Cross, E. S., Hortensius, R., & Wykowska, A. (2019). From social brains to social robots: applying neurocognitive insights to human–robot interaction. Philosophical Transactions of the Royal Society B, 374(1771), 20180024. https://doi.org/10.1098/rstb.2018.0024
  30. Daglarli, E. (2020). Computational modeling of prefrontal cortex for meta-cognition of a humanoid robot. IEEE Access, 8, 98491-98507. https://doi.org/10.1109/ACCESS.2020.2998396
  31. Danso, S., Annan, M. A. O., Ntem, M. T. K., Baah-Acheamfour, K., & Awudi, B. (2023). Artificial intelligence and human communication: A systematic literature review. World Journal of Advanced Research and Reviews, 19(01), 1391–1403. https://doi.org/10.30574/wjarr.2023.19.1.1495
  32. Dautenhahn, K. (2007). Socially intelligent robots: dimensions of human–robot interaction. Philosophical transactions of the royal society B: Biological sciences, 362(1480), 679-704. https://doi.org/10.1098/rstb.2006.2004
  33. De Graaf, M. M., & Malle, B. F. (2017). How people explain action (and autonomous intelligent systems should too). In 2017 AAAI Fall Symposium Series. Retrieved from: https://cdn.aaai.org/ocs/16009/16009-69853-1-PB.pdf
  34. De Santis, A., Siciliano, B., De Luca, A., & Bicchi, A. (2008). An atlas of physical human–robot interaction. Mechanism and Machine Theory, 43(3), 253-270. https://doi.org/10.1016/j.mechmachtheory.2007.03.003
  35. Di Dio, C., Manzi, F., Peretti, G., Cangelosi, A., Harris, P. L., Massaro, D., & Marchetti, A. (2020). Shall I Trust You? From Child–Robot Interaction to Trusting Relationships. Frontiers in Psychology, 11, 469. https://doi.org/10.3389/fpsyg.2020.00469
  36. Drigas, A., & Karyotaki, M. (2018). Mindfulness Training & Assessment and Intelligence. International Journal of Recent Contributions from Engineering, Science & IT (iJES), 6(3), 70-85. https://doi.org/10.3991/ijes.v6i3.9248
  37. Drakatos, N., & Christou, A. (2023). The impact of robotics on development, of hot and cold executive functions, and their role in improving them in special education. World Journal of Advanced Engineering Technology and Sciences, 9(2), 070-080. https://doi.org/10.30574/wjaets.2023.9.2.0198
  38. Drigas, A., & Karyotaki, M. (2018). Mindfulness Training & Assessment and Intelligence. International Journal of Recent Contributions from Engineering, Science & IT (iJES), 6(3), 70-85. https://doi.org/10.3991/ijes.v6i3.9248
  39. Drigas, A., & Sideraki, A. (2021). Emotional intelligence in autism. Technium Soc. Sci. J., 26, 80. https://doi.org/10.47577/tssj.v26i1.5178
  40. Drigas, A., Papanastasiou, G., & Skianis, C. (2023). The school of the future: The role of digital technologies, metacognition and emotional intelligence. International Journal of Emerging Technologies in Learning (Online), 18(9), 65. https://doi.org/10.3991/ijet.v18i09.38133
  41. Drigas, A., & Bamicha, V. (2023). PoM & ToM-Harnessing the Power of Mind in Theory of Mind by shaping beneficial mental states in Preschoolers and the ICT’s role. Research, Society and Development, 12(5). http://dx.doi.org/10.33448/rsd-v12i5.41590
  42. Drigas, A., & Papoutsi, C. (2023). A New Pyramid Model of Empathy: The Role of ICTs and Robotics on Empathy. Int. J. Online Biomed. Eng., 19(2), 67-91. https://doi.org/10.3991/ijoe.v19i02.33591
  43. Iasechko, M., Kharlamov, M., Gontarenko, L., Skrypchuk, H., Fadyeyeva, K., & Sviatnaia, O. (2021). Artificial intelligence as a technology of the future at the present stage of development of society. Laplage em Revista (International), n. Extra D, 7, 391-397. http://dx.doi.org/10.24115/S2446-622020217Extra-D1119p.391-397
  44. Incao, S., Rea, F., & Sciutti, A. (2021). A Self for robots: core elements and ascription by humans. Interactions, 5(14), 15. https://doi.org/10.5281/zenodo.5645583
  45. Fabbro, F., Cantone, D., Feruglio, S., & Crescentini, C. (2019). Origin and evolution of human consciousness. Progress in Brain Research, 250, 317-343. https://doi.org/10.1016/bs.pbr.2019.03.031
  46. Frasca, T., Krause, E., Thielstrom, R., & Scheutz, M. (2020). " Can you do this?" Self-Assessment Dialogues with Autonomous Robots Before, During, and After a Mission. arXiv preprint arXiv:2005.01544. https://doi.org/10.48550/arXiv.2005.01544
  47. Frith, U. & Happé, F.G.E (1999). Theory of Mind and Self-Consciousness: What Is It Like to Be Autistic? Mind & Language, 14 (1), 1–22. https://doi.org/10.1111/1468-0017.00100
  48. Frith, C. D. & Frith, U. (2012). Mechanisms of Social Cognition. Annual Review of Psychology, 63:287-313. https://doi.org/10.1146/annurev-psych-120710-100449
  49. Haddadin, S., & Croft, E. (2016). Physical human–robot interaction. Springer handbook of robotics, 1835-1874. https://doi.org/10.1007/978-3-319-32552-1_69
  50. Hampton, R. R. (2009). Multiple demonstrations of metacognition in nonhumans: Converging evidence or multiple mechanisms? Comparative cognition & behavior reviews, 4, 17. https://doi.org/10.3819%2Fccbr.2009.40002
  51. He, H., Gray, J., Cangelosi, A., Meng, Q., McGinnity, T. M., & Mehnen, J. (2021). The challenges and opportunities of human-centered AI for trustworthy robots and autonomous systems. IEEE Transactions on Cognitive and Developmental Systems, 14(4), 1398-1412. https://doi.org/10.1109/TCDS.2021.3132282
  52. Hegel, F., Krach, S., Kircher, T., Wrede, B., & Sagerer, G. (2008, March). Theory of mind (ToM) on robots: A functional neuroimaging study. In Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction, 335-342. https://doi.org/10.1145/1349822.1349866
  53. Geraci, A., D'Amico, A., Pipitone, A., Seidita, V., & Chella, A. (2021). Automation inner speech as an anthropomorphic feature affecting human trust: Current issues and future directions. Frontiers in Robotics and AI, 8, 620026. https://doi.org/10.3389/frobt.2021.620026
  54. Gloor, J. L., Howe, L., De Cremer, D., & Yam, K. C. S. (2020). The funny thing about robot leadership. European Business Law Review, online. https://www.alexandria.unisg.ch/handle/20.500.14171/111535
  55. Goel, A. K., Fitzgerald, T., & Parashar, P. (2020). Analogy and metareasoning: Cognitive strategies for robot learning. In Human-Machine shared contexts (pp. 23-44). Academic Press. https://doi.org/10.1016/B978-0-12-820543-3.00002-X
  56. González-Docasal, A., Aceta, C., Arzelus, H., Álvarez, A., Fernández, I., & Kildal, J. (2021). Towards a natural human-robot interaction in an industrial environment. Conversational Dialogue Systems for the Next Decade, 243-255. https://doi.org/10.1007/978-981-15-8395-7_18
  57. Görür, O. C., & Albayrak, Ş. (2016). A cognitive architecture incorporating theory of mind in social robots towards their personal assistance at home. In Proceedings of the Workshop on Bio-inspired Social Robot Learning in Home Scenarios at IEEE/RSJ International Conference on Intelligent Robots and Systems 2016 (IROS’16).
  58. Görür, O., Rosman, B. S., Hoffman, G., & Albayrak, S. (2017). Toward integrating Theory of Mind into adaptive decision-making of social robots to understand human intention. http://hdl.handle.net/10204/9653
  59. Greenhalgh, T., Thorne, S., & Malterud, K. (2018). Time to challenge the spurious hierarchy of systematic over narrative reviews?. European journal of clinical investigation, 48(6). https://doi.org/10.1111%2Feci.12931
  60. Gutiérrez, S. M., & Steinbauer-Wagner, G. (2022). The Need for a Meta-Architecture for Robot Autonomy. arXiv preprint arXiv:2207.09712. https://doi.org/10.4204/EPTCS.362.9
  61. Javaid, M., Estivill-Castro, V., & Hexel, R. (2020). Enhancing humans trust and perception of robots through explanations. Proceedings of the ACHI, 10(1912), 4071. https://doi.org/10.25904/1912/4071
  62. Jokinen, K. (2021). Exploring Boundaries Among Interactive Robots and Humans. In: D'Haro, L.F., Callejas, Z., Nakamura, S. (eds) Conversational Dialogue Systems for the Next Decade. Lecture Notes in Electrical Engineering, 704. Springer, Singapore. https://doi.org/10.1007/978-981-15-8395-7_20
  63. Joksimovic, S., Ifenthaler, D., Marrone, R., De Laat, M., & Siemens, G. (2023). Opportunities of artificial intelligence for supporting complex problem-solving: Findings from a scoping review. Computers and Education: Artificial Intelligence, 100138. https://doi.org/10.1016/j.caeai.2023.100138
  64. Jung, M., & Hinds, P. (2018). Robots in the wild: A time for more robust theories of human-robot interaction. ACM Transactions on Human-Robot Interaction (THRI), 7(1), 1-5. https://doi.org/10.1145/3208975
  65. Kaivo-Oja, J., Roth, S., & Westerlund, L. (2017). Futures of robotics. Human work in digital transformation. International Journal of Technology Management, 73(4), 176-205. https://doi.org/10.1504/IJTM.2017.083074
  66. Karyotaki, M., Drigas, A., & Skianis, C. (2024). Mobile/VR/Robotics/IoT-Based Chatbots and Intelligent Personal Assistants for Social Inclusion. International Journal of Interactive Mobile Technologies, 18(8). http://dx.doi.org/10.3991/ijim.v18i08.46473
  67. Kneer, M. (2021). Can a robot lie? Exploring the folk concept of lying as applied to artificial agents. Cognitive Science, 45(10), e13032. https://doi.org/10.1111/cogs.13032
  68. Kralik, J. D., Lee, J. H., Rosenbloom, P. S., Jackson Jr, P. C., Epstein, S. L., Romero, O. J., ... & McGreggor, K. (2018). Metacognition for a common model of cognition. Procedia computer science, 145, 730-739. https://doi.org/10.1016/j.procs.2018.11.046
  69. Lasota, P. A.; Fong, T.; & Shah, J. A. (2017). A survey of methods for safe human-robot interaction. Foundations and Trends in Robotics, 5(4):261–349. http://dx.doi.org/10.1561/2300000052
  70. Losey, D. P., McDonald, C. G., Battaglia, E., & O'Malley, M. K. (2018). A review of intent detection, arbitration, and communication aspects of shared control for physical human–robot interaction. Applied Mechanics Reviews, 70(1), 010804. https://doi.org/10.1115/1.4039145
  71. Lu, M. (2023). The ability and importance of human communication in the age of AI. Geographical Research Bulletin, 2, 187-189. https://doi.org/10.50908/grb.2.0_187
  72. Malle, B. F., & Scheutz, M. (2019). Learning how to behave: Moral competence for social robots. Handbuch maschinenethik, 255-278. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-17483-5_17
  73. Martini, M. C., Buzzell, G. A., & Wiese, E. (2015). Agent appearance modulates mind attribution and social attention in human-robot interaction. In Social Robotics: 7th International Conference, ICSR 2015, Paris, France, October 26-30, 2015, Proceedings 7 ,431-439. Springer International Publishing. https://doi.org/10.1007/978-3-319-25554-5_43
  74. Martini, M. C., Gonzalez, C. A., & Wiese, E. (2016). Seeing minds in others–Can agents with robotic appearance have human-like preferences? PloS one, 11(1), e0146310. https://doi.org/10.1371/journal.pone.0146310
  75. Miranda, A., Berenguer. C., Roselló, B., Baixauli, I. & Colomer, C. (2017). Social Cognition in Children with High-Functioning Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder. Associations with Executive Functions. Frontiers in Psychology, 8:1035. https://doi.org/10.3389/fpsyg.2017.01035
  76. Mitsea, E., Lytra, N., Akrivopoulou, A., & Drigas, A. (2020). Metacognition, Mindfulness and Robots for Autism Inclusion. Int. J. Recent Contributions Eng. Sci. IT, 8(2), 4-20. https://doi.org/10.3991/ijes.v8i2.14213
  77. Mishra, D., Lugo, R.G., Parish, K., Tilden, S. (2023). Metacognitive Processes Involved in Human Robot Interaction in the School Learning Environment. In: Kurosu, M., Hashizume, A. (eds) Human-Computer Interaction. HCII 2023. Lecture Notes in Computer Science, vol 14012. Springer, Cham. https://doi.org/10.1007/978-3-031-35599-8_6
  78. Moraiti, I., & Drigas, A. (2023). AI Tools Like ChatGPT for People with Neurodevelopmental Disorders. International Journal of Online & Biomedical Engineering, 19(16). DOI:10.3991/ijoe.v19i16.43399
  79. Natarajan, M., Seraj, E., Altundas, B., Paleja, R., Ye, S., Chen, L., ... & Gombolay, M. (2023). Human-robot teaming: grand challenges. Current Robotics Reports, 4(3), 81-100. https://doi.org/10.1007/s43154-023-00103-1
  80. Ntaountaki, P., Lorentzou, G., Lykothanasi, A., Anagnostopoulou, P., Alexandropoulou, V., & Drigas, A. (2019). Robotics in Autism Intervention. Int. J. Recent Contributions Eng. Sci. IT, 7(4), 4-17. https://doi.org/10.3991/ijes.v7i4.11448
  81. Onnasch, L., & Roesler, E. (2021). A taxonomy to structure and analyze human–robot interaction. International Journal of Social Robotics, 13(4), 833-849. https://doi.org/10.1007/s12369-020-00666-5
  82. Pergantis, P., & Drigas, A. (2023). Sensory integration therapy as enabler for developing emotional intelligence in children with autism spectrum disorder and the ICT’s role. Brazilian Journal of Science, 2(12), 53-65. https://doi.org/10.14295/bjs.v2i12.422
  83. Pergantis, P. (2024). A new era of ICTs for combating symptoms of neurodevelopmental disorders. World Journal of Biology Pharmacy and Health Sciences, 18(1), 036-047. https://doi.org/10.30574/wjbphs.2024.18.1.0145
  84. Pergantis, P., & Drigas, A. (2024). The Effect of Drones in the Educational Process: A Systematic Review. Education Sciences, 14(6), 665. https://doi.org/10.3390/educsci14060665
  85. Pipitone, A., Lanza, F., Seidita, V., & Chella, A. (2019, March). Inner Speech for a Self-Conscious Robot. In AAAI Spring Symposium: Towards Conscious AI Systems. https://ceur-ws.org/Vol-2287/paper14.pdf
  86. Pipitone, A., Geraci, A., D’Amico, A., Seidita, V., & Chella, A. (2023). Robot’s inner speech effects on human trust and anthropomorphism. International Journal of Social Robotics, 1-13. https://doi.org/10.1007/s12369-023-01002-3
  87. Rakoczy, H. (2022). Foundations of theory of mind and its development in early childhood. Nature Reviews Psychology, 1(4), 223-235. https://doi.org/10.1038/s44159-022-00037-z
  88. Rosenthal, D. M. (2005). Consciousness and Mind. Oxford University Press.Rother, E. T. (2007). Systematic literature review X narrative review. Acta paulista de enfermagem, 20, v-vi. https://doi.org/10.1590/S0103-21002007000200001
  89. Rossi, A., Rossi, S., Andriella, A., & van Maris, A. (2022, March). The Road to a Successful HRI: AI, Trust and ethicS (TRAITS) Workshop. In 2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (pp. 1284-1286). IEEE.
  90. Sandini, G., Sciutti, A., & Vernon, D. (2021). Cognitive robotics. In Encyclopedia of Robotics (pp. 1-7). Springer-Verlag.
  91. SCASSELLATI, B. (2002). Theory of Mind for a Humanoid Robot. Autonomous Robots, 12, 13-24. https://doi.org/10.1023/A:1013298507114
  92. Schleidgen, S., & Friedrich, O. (2022). Joint Interaction and Mutual Understanding in Social Robotics. Science and Engineering Ethics, 28(6), 48. https://doi.org/10.1007/s11948-022-00407-z
  93. Sheridan, T. B. (2016). Human–robot interaction: status and challenges. Human factors, 58(4), 525-532. https://doi.org/10.1177/0018720816644364
  94. Shmatko, N., & Volkova, G. (2020). Bridging the skill gap in robotics: Global and national environment. Sage Open, 10(3). https://doi.org/10.1177/2158244020958736
  95. Sideraki, A., & Drigas, A. (2021). Artificial Intelligence (AI) in Autism. Technium Soc. Sci. J., 26, 262. http://dx.doi.org/10.33448/rsd-v11i16.38057
  96. Syriopoulou-Delli, C., Deres, I., & Drigas, A. (2021). Intervention program using a robot for children with Autism Spectrum Disorder. Research, Society and Development, 10(8), e35010817512-e35010817512. https://doi.org/10.33448/rsd-v10i8.17512
  97. Thellman, S., Silvervarg, A., & Ziemke, T. (2017). Folk-Psychological Interpretation of Human vs. Humanoid Robot Behavior: Exploring the Intentional Stance toward Robots. Frontiers in Psychology, 8, 280953. https://doi.org/10.3389/fpsyg.2017.01962
  98. Thellman, S., De Graaf, M., & Ziemke, T. (2022). Mental state attribution to robots: A systematic review of conceptions, methods, and findings. ACM Transactions on Human-Robot Interaction (THRI), 11(4), 1-51. https://doi.org/10.1145/3526112
  99. Vernon, D., Thill, S., & Ziemke, T. (2016). The Role of Intention in Cognitive Robotics. In: Esposito, A., Jain, L. (eds) Toward Robotic Socially Believable Behaving Systems - Volume I. Intelligent Systems Reference Library, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-319-31056-5_3
  100. Vinanzi, S., Patacchiola, M., Chella, A., & Cangelosi, A. (2019). Would a robot trust you? Developmental robotics model of trust and theory of mind. Philosophical Transactions of the Royal Society B, 374(1771), 20180032. https://doi.org/10.1098/rstb.2018.0032
  101. Vinanzi, S., Cangelosi, A., & Goerick, C. (2021). The collaborative mind: intention reading and trust in human-robot interaction. Iscience, 24(2). doi: 10.1109/DEVLRN.2019.8850698.
  102. Vouglanis, T. (2023). The use of robotics in the education of students with special educational needs. World Journal of Advanced Research and Reviews, 19(1), 464-471. https://doi.org/10.30574/wjarr.2023.19.1.1331
  103. Vygotsky, L. (1962). Thought and language. (E. Hanfmann & G. Vakar, Eds.). MIT Press. https://doi.org/10.1037/11193-000
  104. Wallach, W., Franklin, S., & Allen, C. (2010). A conceptual and computational model of moral decision making in human and artificial agents. Topics in cognitive science, 2(3), 454-485. https://doi.org/10.1111/j.1756-8765.2010.01095.x
  105. Wang, Z., & Frye, D. A. (2021). When a Circle Becomes the Letter O: Young Children’s Conceptualization of Learning and Its Relation With Theory of Mind Development. Frontiers in psychology, 11, 596419. https://doi.org/10.3389/fpsyg.2020.596419
  106. Whitebread, D., Almeqdad, Q., Bryce, D., Demetriou, D., Grau, V., & Sangster, C. (2010). Metacognition in young children: Current methodological and theoretical developments. Trends and prospects in metacognition research, 233-258.
  107. Wiese, E., Metta, G., & Wykowska, A. (2017). Robots as intentional agents: using neuroscientific methods to make robots appear more social. Frontiers in psychology, 8, 281017. https://doi.org/10.3389/fpsyg.2017.01663
  108. Williams, J., Fiore, S. M., & Jentsch, F. (2022). Supporting Artificial Social Intelligence With Theory of Mind. Frontiers in artificial intelligence, 5, 750763. https://doi.org/10.3389/frai.2022.750763n
  109. Worley, P. (2018). Plato, metacognition and philosophy in schools. Journal of Philosophy in Schools, 5(1). http://doi.org/10.21913/jps.v5i1.1486
  110. Wortham, R. H., Theodorou, A., & Bryson, J. J. (2016, June). What does the robot think? Transparency as a fundamental design requirement for intelligent systems. In IJCAI 2016 Ethics for AI Workshop. http://opus.bath.ac.uk/50294/1/WorthamTheodorouBryson{_}EFAI16.pdf
  111. Xu, T., Zhang, H., & Yu, C. (2016). See you see me: The role of eye contact in multimodal human-robot interaction. ACM Transactions on Interactive Intelligent Systems (TiiS), 6(1), 1-22. https://doi.org/10.1145/2882970
  112. Yang, G. Z., Dario, P., & Kragic, D. (2018). Social robotics—Trust, learning, and social interaction. Science Robotics, 3(21), eaau8839. https://doi.org/10.1126/scirobotics.aau8839
  113. Zouganeli, E., & Lentzas, A. (2022). Cognitive robotics-towards the development of next-generation robotics and intelligent systems. In Symposium of the Norwegian AI Society (pp. 16-25). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-17030-0_2

Como Citar

Bamicha, V., & Drigas, A. (2024). Human-Social Robot Interaction in the Light of ToM and Metacognitive Functions. Scientific Electronic Archives, 17(5). https://doi.org/10.36560/17520241986