Skip to main content

Advertisement

SpringerLink
Log in

Models for Computational Emotions from Psychological Theories Using Type I Fuzzy Logic

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Emotions have been subject of research and deliberation in philosophy and psychology mainstream for a long time. In contrast, emotions have only emerged in artificial intelligence research as a serious topic in the last two decades. Year 2000, in particular, experienced a shift in attitude towards emotions and their relationship to human reasoning and human–computer interaction. This shift continued slowly but surely over the years and computational emotions can be seen as a mainstream research topic within artificial intelligence and cognitive systems. In this paper, we attempt to contribute to the development of this area by interpreting psychological theories of emotions computationally and translating them into machine implementable models. These models are generic and application independent which most of the current computational emotions models lack. We have selected two psychological theories, namely Millenson (The psychology of emotion: theories of emotion perspective, vol 4. Wiley, New Jersey, pp 35–36, 1967) and Scherer (Soc Sci Inf 44(4):695–729, 2005), that lend themselves, with varying degrees of difficulty, to the computational interpretation. Fuzzy logic was utilised as a tool to keep the fidelity of psychological interpretation of emotion. The paper discusses in details the computational interpretation of these psychological models and presents a full theoretical formalism in fuzzy logic type 1, implementation and detailed analysis of this psychologically grounded generic computational models.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Notes

  1. Please contact the authors for a copy.

References

  1. Boehner K, DePaula R, Dourish P, Sengers P. How emotion is made and measured. Int J Hum Comput Stud. 2007;65(4):275–91.

    Article  Google Scholar 

  2. Mandryk RL, Atkins MS. A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies. Int J Hum Comput Stud. 2007;65(4):329–47.

    Article  Google Scholar 

  3. Ayesh A. Perception and emotion based reasoning: a connectionist approach. Informatica. 2003;27(2):119–26.

    Google Scholar 

  4. Breazeal C. Emotion and sociable humanoid robots. Int J Hum Comput Stud. 2003;59(1–2):119–55.

    Article  Google Scholar 

  5. El-Nasr MS, Yen J, Ioerger TR. Flame—fuzzy logic adaptive model of emotions. Auton Agent Multi Agent Syst. 2000;3(3):219–57.

    Article  Google Scholar 

  6. Ayesh A, Stokes J, Edwards R. Fuzzy individual model (fim) for realistic crowd simulation: preliminary results. In: IEEE International conference on Fuzzy systems, London, 2007. FUZZ-IEEE 2007. 2007, p. 1–5.

  7. Davis DN. Cognitive architectures for affect and motivation. Cogn Comput. 2010;2:199–216. doi:10.1007/s12559-010-9053-4.

    Article  Google Scholar 

  8. Davis D. Agents, emergence, emotion and representation. In: Industrial electronics society, 2000. IECON 2000. 26th Annual conference of the IEEE, Vol. 4, 2000, p. 2577–82.

  9. Dillon C, Freeman J, Keogh E. Pressing the right buttons: taking the viewer there. Interact Comput. 2004;16(4):739–49. doi:10.1016/j.intcom.2004.06.008.

    Article  Google Scholar 

  10. Thomaz A, Berlin M, Breazeal C. An embodied computational model of social referencing. In: IEEE international workshop on robot and human interactive communication, 2005. ROMAN 2005, p. 591–8. doi:10.1109/ROMAN.2005.1513844.

  11. Perlovsky LI. Toward physics of the mind: concepts, emotions, consciousness, and symbols. Phys Life Rev. 2006;3(1):23–55. doi:10.1016/j.plrev.2005.11.003.

    Article  Google Scholar 

  12. Muramatsu R, Hanoch Y. Emotions as a mechanism for boundedly rational agents: the fast and frugal way. J Econ Psychol. 2005;26(2):201–21.

    Article  Google Scholar 

  13. Gros C. Cognition and emotion: perspectives of a closing gap. Cogn Comput. 2010;2:78–85. doi:10.1007/s12559-010-9034-7.

    Article  Google Scholar 

  14. Hu J, Guan C. An architecture for emotional agent. In: International conference on computational intelligence and security, vol. 1, 2006, p. 481–5. doi:10.1109/ICCIAS.2006.294181.

  15. Sanz R, Sánchez-Escribano MG, Herrera C. A model of emotion as patterned metacontrol. Biol Inspir Cogn Archit. 2013;4:79–97.

    Google Scholar 

  16. Su WP, Pham B, Wardhani A. Personality and emotion-based high-level control of affective story characters. IEEE Trans Visual Comput Graph. 2007;13(2):281–93. doi:10.1109/TVCG.2007.44.

    Article  Google Scholar 

  17. Ayesh A. Emotionally motivated reinforcement learning based controller. In: IEEE SMC 2004, vol. 1, The Hague, The Netherlands, 2004, p. 874–8.

  18. Gershenson C. Modelling emotions with multidimensional logic. In: Fuzzy information processing society, 1999. NAFIPS. 18th International conference of the North American, 1999, p. 42–6. doi:10.1109/NAFIPS.1999.781649.

  19. Blewitt W, Ayesh A, John RI, Coupland S. A millenson-based approach to emotion modelling. In: 2008 Conference on human system interactions, 2008, p. 491–6.

  20. Blewitt WF, Ayesh A. Modeling the emotional state of an agent through fuzzy logic with reference to the geneva emotion wheel. In: European simulation and modelling (ESM’2008) conference, Le Havre, France, 2008, p. 279–83.

  21. Blewitt W, Ayesh A. Implementation of millenson’s model of emotions in a game environment. In: AISB convention: AI and games symposium, AISB, Edinburgh, UK, 2009.

  22. Ekman P. Are there basic emotions? Psychol Rev. 1992;99:550–3.

    Article  CAS  PubMed  Google Scholar 

  23. Parrott WG. Emotions in social psychology. New York: Psychology Press; 2001.

  24. Darwin C. The expression of the emotions in man and animals. Oxford: Harper Collins/Oxford University Press; 1872/1998.

  25. Plutchik R. Emotion: theory, research, and experience: vol. 1. Theories of emotion, New York: Academic, 1980, Ch. A general psychoevolutionary theory of emotion, p. 3–33.

  26. Scherer KR. What are emotions? And how can they be measured? Soc Sci Inf. 2005;44(4):695–729.

    Article  Google Scholar 

  27. Ekman P. An argument for basic emotions. Cogn Emot. 1992;6:169–200.

    Article  Google Scholar 

  28. Ekman P. Handbook of cognition and emotion. New Jersey: Wiley; 1999. Ch. 3, Basic Emotions, p. 45–60.

  29. Wundt W. Principles of physiological psychology. New York, NY: Macmillan; 1904.

  30. Russell JA. A circumplex model of affect. J Pers Soc Psychol. 1980;39:1161–78.

    Article  Google Scholar 

  31. Millenson JR. The psychology of emotion: theories of emotion perspective. New Jersey: Wiley; 1967, vol. 4, p. 35–6.

  32. Watson JB. Psychology. From the standpoint of a behaviourist. Philadelphia: Lippincott; 1929.

    Google Scholar 

  33. Watson JB. Behaviorism. Chicago: University of Chicago Press; 1930.

    Google Scholar 

  34. Watson JB, MacDougall W. The battle of behaviorism: an exposition and an exposure. New York: W. W. Norton & Co; 1929.

  35. Sartre J-P. Sketch for a theory of the emotions. Routledge; 2 edn (12 Oct 2001), 1939.

  36. Bamba E, Nakazato K. Fuzzy theoretical interactions between consciousness and emotions. In: Proceedings of the 9th IEEE international workshop on robot and human interactive communication, 2000. RO-MAN 2000. p. 218–23.

  37. Nakatsu R, Nicholson J, Tosa N. Emotion recognition and its application to computer agents with spontaneous interactive capabilities. Knowl Based Syst. 2000;13(7–8):497–504.

    Article  Google Scholar 

  38. Picard R. Synthetic emotion. Comput Graph Appl IEEE. 2000;20(1):52–3.

    Article  Google Scholar 

  39. Picard RW, Klein J. Computers that recognise and respond to user emotion: theoretical and practical implications. Interact Comput. 2002; 14(2):141–69. doi:10.1016/S0953-5438(01)00055-8.

  40. Picard R, Vyzas E, Healey J. Toward machine emotional intelligence: analysis of affective physiological state. IEEE Trans Pattern Anal Mach Intell. 2001;23(10):1175–91.

    Article  Google Scholar 

  41. Turner JC, Meyer DK, Schweinle A. The importance of emotion in theories of motivation: empirical, methodological, and theoretical considerations from a goal theory perspective. Int J Educat Res. 2003;39(4–5):375–93. doi:10.1016/j.ijer.2004.06.005.

  42. Naqvi N, Shiv B, Bechara A. The role of emotion in decision making: a cognitive neuroscience perspective. Curr Dir Psychol Sci. 2006;15(5):260–4.

    Article  Google Scholar 

  43. Gratch J, Marsella S. A domain-independent framework for modeling emotion. Cogn Syst Res. 2004;5(4):269–306.

    Article  Google Scholar 

  44. Shi XF, Wang ZL, Ping A, Zhang LK. Artificial emotion model based on reinforcement learning mechanism of neural network. J China Univ Posts Telecommun. 2011;18(3):105–9.

  45. Bosse T, Pontier M, Treur J. A computational model based on gross? Emotion regulation theory. Cogn Syst Res. 2010;11(3):211–30.

    Article  Google Scholar 

  46. Ayesh A. Swarms-based emotions modelling. Int J Bio Inspired Comput. 2009;1(1/2):118–24.

    Article  Google Scholar 

  47. Ghazi D, Inkpen D, Szpakowicz S. Prior and contextual emotion of words in sentential context. Comput Speech Lang. 2014;28(1):76–92.

    Article  Google Scholar 

  48. Larue O, Poirier P, Nkambou R. The emergence of (artificial) emotions from cognitive and neurological processes. Biol Inspir Cogn Archit. 2013;4:54–68.

    Google Scholar 

  49. Ren D, Wang P, Qiao H, Zheng S. A biologically inspired model of emotion eliciting from visual stimuli. Neurocomputing. 2013;121:328–36.

    Article  Google Scholar 

  50. Rolls ET. Emotion and decision-making explained, no. 978-0-19-965989-0, Ox, 2013.

  51. Ekman P, Friesen WV, Hager JC. Facial action coding system (facs), A technique for the measurement of facial action. Consulting, Palo Alto.

  52. Bartlett MS, Hager JC, Ekman P, Sejnowski TJ. Measuring facial expressions by computer image analysis. Psychophysiology. 1999;36(2):253–63.

    Article  CAS  PubMed  Google Scholar 

  53. Donato G, Bartlett MS, Hager JC, Ekman P, Sejnowski TJ. Classifying facial actions. IEEE Trans Pattern Anal Mach Intell. 1999;21(10):974–89.

    Article  PubMed Central  PubMed  Google Scholar 

  54. Ekman P, Rosenberg EL. What the face reveals: basic and applied studies of spontaneous expression using the Facial Action Coding System (FACS). Oxford: Oxford University Press; 1997.

  55. Frank MG, Ekman P. Not all smiles are created equal: the differences between enjoyment and nonenjoyment smiles. Humor. 1993;6:9.

    Article  Google Scholar 

  56. Frank MG, Ekman P, Friesen WV. Behavioral markers and recognizability of the smile of enjoyment. J Pers Soc Psychol. 1993;64(1):83.

    Article  CAS  PubMed  Google Scholar 

  57. Levenson RW, Ekman P, Friesen WV. Voluntary facial action generates emotion-specific autonomic nervous system activity. Psychophysiology. 1990;27(4):363–84.

    Article  CAS  PubMed  Google Scholar 

  58. Ekman P, Friesen WV, O’Sullivan M. Smiles when lying. J Pers Soc Psychol. 1988;54(3):414.

    Article  CAS  PubMed  Google Scholar 

  59. Ekman P, Friesen WV. Felt, false, and miserable smiles. J Nonverbal Behav. 1982;6(4):238–52.

    Article  Google Scholar 

  60. Ekman P, Hager JC, Friesen WV. The symmetry of emotional and deliberate facial actions. Psychophysiology. 1981;18(2):101–6.

    Article  CAS  PubMed  Google Scholar 

  61. Ekman P, Freisen WV, Ancoli S. Facial signs of emotional experience. J Pers Soc Psychol. 1980;39(6):1125.

    Article  Google Scholar 

  62. Cambria E, Livingstone A, Hussain A. The hourglass of emotions. In: Esposito A, Esposito A, Vinciarelli A, Hoffmann R, Müller V, editors. Cognitive behavioural systems, vol. 7403., Lecture notes in computer science Berlin: Springer; 2012. p. 144–57.

    Chapter  Google Scholar 

  63. Ayesh A, Arevalillo-Herráez M, Ferri FJ. Cognitive reasoning and inferences through psychologically based personalised modelling of emotions using associative classifiers. In: The 13th IEEE international conference on cognitive informatics and cognitive computing (ICCICC 2014); 2014.

  64. Cambria E, Hussain A. Sentic computing: techniques, tools, and applications. Dordrecht: SpringerBriefs in Cognitive Computation; 2012.

    Book  Google Scholar 

  65. Vidrascu L, Devillers L. Real-life emotion representation and detection in call centers data. In: Affective computing and intelligent interaction, New York: Springer; 2005, p. 739–46.

  66. Kim S, Georgiou PG, Lee S, Narayanan S. Real-time emotion detection system using speech: multi-modal fusion of different timescale features. In: IEEE 9th Workshop on multimedia signal processing, 2007. MMSP 2007. IEEE, 2007, p. 48–51.

  67. Bourbakis N, Esposito A, Kavraki D. Extracting and associating meta-features for understanding people’s emotional behaviour: face and speech. Cogn Comput. 2011;3(3):436–48.

    Article  Google Scholar 

  68. Li T, Ogihara M. Content-based music similarity search and emotion detection. In: Proceedings of the IEEE International Conference on acoustics, speech, and signal processing, 2004. (ICASSP’04). vol. 5, IEEE, 2004, p. V-705.

  69. Sun Y, Sebe N, Lew MS, Gevers T. Authentic emotion detection in real-time video. In: Computer vision in human-computer interaction. New York: Springer; 2004, p. 94–104.

  70. Pal P, Iyer AN, Yantorno RE. Emotion detection from infant facial expressions and cries. In: Proceedings of the 2006 IEEE international conference on acoustics, speech and signal processing, 2006. ICASSP 2006, Vol. 2, IEEE, 2006, p. II.

  71. Busso C, Lee S, Narayanan S. Analysis of emotionally salient aspects of fundamental frequency for emotion detection. IEEE Trans Audio Speech Lang Process. 2009;17(4):582–96.

    Article  Google Scholar 

  72. Cambria E, Hussain A. Sentic album: content-, concept-, and context-based online personal photo management system. Cogn Comput. 2012;4(4):477–96.

    Article  Google Scholar 

  73. Popovici Vlad O, Vachkov G, Fukuda T. Fuzzy emotion interpolation system for emotional autonomous agents. In: SICE 2002. Proceedings of the 41st SICE annual conference, vol. 5, 2002, p. 3157–62.

  74. Abdelhak H, Ayesh A, Olivier D. Cognitive emotional based architecture for crowd simulation. J Intell Comput. 2012;3(2):55–66.

    Google Scholar 

  75. Park G-Y, Lee S-I, Kwon W-Y, Kim J-B. Neurocognitive affective system for an emotive robot. In: 2006 IEEE/RSJ international conference on intelligent robots and systems, 2006, p. 2595–600.

  76. Mamdani EH. Applications of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans Comput. 1977;26(12):1182–91.

    Article  Google Scholar 

  77. Zadeh LA. Fuzzy logic and its application to approximate reasoning. Inf Process. 1974;74:591–4.

    Google Scholar 

  78. Qiao R, Zhong X, Yang S, He H. Surprise simulation using fuzzy logic. In: Proceedings of the 9th international conference on intelligent computing theories and technology, ICIC13. Berlin: Springer; 2013, p. 110–9.

  79. Zadeh LA. Fuzzy sets, fuzzy logic and fuzzy systems. New Jersey: World Scientific Press; 1996.

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Technology, De Montfort University, Leicester , LE1 9BH, UK

    Aladdin Ayesh

  2. School of Computing Science, Newcastle University, Newcastle upon Tyne , NE1 7RU, UK

    William Blewitt

Authors
  1. Aladdin Ayesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. William Blewitt
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aladdin Ayesh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ayesh, A., Blewitt, W. Models for Computational Emotions from Psychological Theories Using Type I Fuzzy Logic. Cogn Comput 7, 285–308 (2015). https://doi.org/10.1007/s12559-014-9287-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-014-9287-7

Keywords

Search

Navigation