Cognitive Computation

, Volume 7, Issue 3, pp 285–308 | Cite as

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

  • Aladdin Ayesh
  • William Blewitt


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.


Emotion Fuzzy logic Artificial intelligence Emotion representation Geneva Emotion Wheel 


  1. 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.CrossRefGoogle Scholar
  2. 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.CrossRefGoogle Scholar
  3. 3.
    Ayesh A. Perception and emotion based reasoning: a connectionist approach. Informatica. 2003;27(2):119–26.Google Scholar
  4. 4.
    Breazeal C. Emotion and sociable humanoid robots. Int J Hum Comput Stud. 2003;59(1–2):119–55.CrossRefGoogle Scholar
  5. 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.CrossRefGoogle Scholar
  6. 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.Google Scholar
  7. 7.
    Davis DN. Cognitive architectures for affect and motivation. Cogn Comput. 2010;2:199–216. doi: 10.1007/s12559-010-9053-4.CrossRefGoogle Scholar
  8. 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.Google Scholar
  9. 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.CrossRefGoogle Scholar
  10. 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. 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.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. 13.
    Gros C. Cognition and emotion: perspectives of a closing gap. Cogn Comput. 2010;2:78–85. doi: 10.1007/s12559-010-9034-7.CrossRefGoogle Scholar
  14. 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. 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. 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.CrossRefGoogle Scholar
  17. 17.
    Ayesh A. Emotionally motivated reinforcement learning based controller. In: IEEE SMC 2004, vol. 1, The Hague, The Netherlands, 2004, p. 874–8.Google Scholar
  18. 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. 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.Google Scholar
  20. 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.Google Scholar
  21. 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.Google Scholar
  22. 22.
    Ekman P. Are there basic emotions? Psychol Rev. 1992;99:550–3.CrossRefPubMedGoogle Scholar
  23. 23.
    Parrott WG. Emotions in social psychology. New York: Psychology Press; 2001.Google Scholar
  24. 24.
    Darwin C. The expression of the emotions in man and animals. Oxford: Harper Collins/Oxford University Press; 1872/1998.Google Scholar
  25. 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.Google Scholar
  26. 26.
    Scherer KR. What are emotions? And how can they be measured? Soc Sci Inf. 2005;44(4):695–729.CrossRefGoogle Scholar
  27. 27.
    Ekman P. An argument for basic emotions. Cogn Emot. 1992;6:169–200.CrossRefGoogle Scholar
  28. 28.
    Ekman P. Handbook of cognition and emotion. New Jersey: Wiley; 1999. Ch. 3, Basic Emotions, p. 45–60.Google Scholar
  29. 29.
    Wundt W. Principles of physiological psychology. New York, NY: Macmillan; 1904.Google Scholar
  30. 30.
    Russell JA. A circumplex model of affect. J Pers Soc Psychol. 1980;39:1161–78.CrossRefGoogle Scholar
  31. 31.
    Millenson JR. The psychology of emotion: theories of emotion perspective. New Jersey: Wiley; 1967, vol. 4, p. 35–6.Google Scholar
  32. 32.
    Watson JB. Psychology. From the standpoint of a behaviourist. Philadelphia: Lippincott; 1929.Google Scholar
  33. 33.
    Watson JB. Behaviorism. Chicago: University of Chicago Press; 1930.Google Scholar
  34. 34.
    Watson JB, MacDougall W. The battle of behaviorism: an exposition and an exposure. New York: W. W. Norton & Co; 1929.Google Scholar
  35. 35.
    Sartre J-P. Sketch for a theory of the emotions. Routledge; 2 edn (12 Oct 2001), 1939.Google Scholar
  36. 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.Google Scholar
  37. 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.CrossRefGoogle Scholar
  38. 38.
    Picard R. Synthetic emotion. Comput Graph Appl IEEE. 2000;20(1):52–3.CrossRefGoogle Scholar
  39. 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. 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.CrossRefGoogle Scholar
  41. 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. 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.CrossRefGoogle Scholar
  43. 43.
    Gratch J, Marsella S. A domain-independent framework for modeling emotion. Cogn Syst Res. 2004;5(4):269–306.CrossRefGoogle Scholar
  44. 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.Google Scholar
  45. 45.
    Bosse T, Pontier M, Treur J. A computational model based on gross? Emotion regulation theory. Cogn Syst Res. 2010;11(3):211–30.CrossRefGoogle Scholar
  46. 46.
    Ayesh A. Swarms-based emotions modelling. Int J Bio Inspired Comput. 2009;1(1/2):118–24.CrossRefGoogle Scholar
  47. 47.
    Ghazi D, Inkpen D, Szpakowicz S. Prior and contextual emotion of words in sentential context. Comput Speech Lang. 2014;28(1):76–92.CrossRefGoogle Scholar
  48. 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. 49.
    Ren D, Wang P, Qiao H, Zheng S. A biologically inspired model of emotion eliciting from visual stimuli. Neurocomputing. 2013;121:328–36.CrossRefGoogle Scholar
  50. 50.
    Rolls ET. Emotion and decision-making explained, no. 978-0-19-965989-0, Ox, 2013.Google Scholar
  51. 51.
    Ekman P, Friesen WV, Hager JC. Facial action coding system (facs), A technique for the measurement of facial action. Consulting, Palo Alto.Google Scholar
  52. 52.
    Bartlett MS, Hager JC, Ekman P, Sejnowski TJ. Measuring facial expressions by computer image analysis. Psychophysiology. 1999;36(2):253–63.CrossRefPubMedGoogle Scholar
  53. 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.CrossRefPubMedCentralPubMedGoogle Scholar
  54. 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.Google Scholar
  55. 55.
    Frank MG, Ekman P. Not all smiles are created equal: the differences between enjoyment and nonenjoyment smiles. Humor. 1993;6:9.CrossRefGoogle Scholar
  56. 56.
    Frank MG, Ekman P, Friesen WV. Behavioral markers and recognizability of the smile of enjoyment. J Pers Soc Psychol. 1993;64(1):83.CrossRefPubMedGoogle Scholar
  57. 57.
    Levenson RW, Ekman P, Friesen WV. Voluntary facial action generates emotion-specific autonomic nervous system activity. Psychophysiology. 1990;27(4):363–84.CrossRefPubMedGoogle Scholar
  58. 58.
    Ekman P, Friesen WV, O’Sullivan M. Smiles when lying. J Pers Soc Psychol. 1988;54(3):414.CrossRefPubMedGoogle Scholar
  59. 59.
    Ekman P, Friesen WV. Felt, false, and miserable smiles. J Nonverbal Behav. 1982;6(4):238–52.CrossRefGoogle Scholar
  60. 60.
    Ekman P, Hager JC, Friesen WV. The symmetry of emotional and deliberate facial actions. Psychophysiology. 1981;18(2):101–6.CrossRefPubMedGoogle Scholar
  61. 61.
    Ekman P, Freisen WV, Ancoli S. Facial signs of emotional experience. J Pers Soc Psychol. 1980;39(6):1125.CrossRefGoogle Scholar
  62. 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.CrossRefGoogle Scholar
  63. 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.Google Scholar
  64. 64.
    Cambria E, Hussain A. Sentic computing: techniques, tools, and applications. Dordrecht: SpringerBriefs in Cognitive Computation; 2012.CrossRefGoogle Scholar
  65. 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.Google Scholar
  66. 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.Google Scholar
  67. 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.CrossRefGoogle Scholar
  68. 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.Google Scholar
  69. 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.Google Scholar
  70. 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.Google Scholar
  71. 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.CrossRefGoogle Scholar
  72. 72.
    Cambria E, Hussain A. Sentic album: content-, concept-, and context-based online personal photo management system. Cogn Comput. 2012;4(4):477–96.CrossRefGoogle Scholar
  73. 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.Google Scholar
  74. 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. 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.Google Scholar
  76. 76.
    Mamdani EH. Applications of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans Comput. 1977;26(12):1182–91.CrossRefGoogle Scholar
  77. 77.
    Zadeh LA. Fuzzy logic and its application to approximate reasoning. Inf Process. 1974;74:591–4.Google Scholar
  78. 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.Google Scholar
  79. 79.
    Zadeh LA. Fuzzy sets, fuzzy logic and fuzzy systems. New Jersey: World Scientific Press; 1996.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Faculty of TechnologyDe Montfort UniversityLeicester UK
  2. 2.School of Computing ScienceNewcastle UniversityNewcastle upon Tyne UK

Personalised recommendations