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Models for Computational Emotions from Psychological Theories Using Type-II Fuzzy Logic

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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 paper presents the second part of our research in this area, which aimed at interpreting psychological theories of emotions computationally and translating them into machine-implementable models. We continue with the previously selected psychological theories, namely Millenson (the psychology of emotion: theories of emotion perspective, Wiley, Hoboken, 1967) and Scherer (Soc Sci Inf 44(4):695–729, 2005), with exploration of the nature of emotional state. Fuzzy logic (FL) type-II is utilised here as a tool to capture the finer details of the psychological and computational interpretations of these theories. The paper presents a full theoretical formalism in FL type-II accompanied by implementation, using the De Montfort University Type-II Fuzzy Logic Toolbox, and detailed discussion of the complexity of the models’ computational interpretation and implementation.

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References

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

    Article  Google Scholar 

  2. 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 

  3. 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 

  4. Ayesh A, Stokes J, Edwards R. Fuzzy individual model (fim) for realistic crowd simulation: Preliminary results, in: Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International, IEEE, London, 2007, pp. 1–5.

  5. 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, pp. 279–283.

  6. Sellers M. Toward a comprehensive theory of emotion for biological and artificial agents. Biol Inspir Cognit Archit. 2013;4(0):3–26.

    Google Scholar 

  7. Blewitt W, Ayesh A, John RI, Coupland S. A millenson-based approach to emotion modelling. In: Human System Interactions, 2008 Conference on, 2008, p. 491–496.

  8. Ayesh A. Perception and emotion-based reasoning: a connectionist approach. In: Informatica. 2003;27:119–26.

  9. 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 

  10. Ayesh A, Blewitt W. Models for computational emotions from psychological theories part 1: using type i fuzzy logic. Cognit Comput.

  11. Millenson JR. The psychology of emotion: theories of emotion perspective. 4th ed. Hoboken: Wiley; 1967. p. 35–6.

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

  18. Bamba E, Nakazato K. Fuzzy theoretical interactions between consciousness and emotions. In: The 9th IEEE International Workshop on Robot and Human Interactive Communication (RO-MAN 2000) Proceedings, 2000, p. 218–223.

  19. Davis D. Agents, emergence, emotion and representation. In: The 26th Annual Confjerence of the IEEE Industrial Electronics Society (IECON 2000), Vol. 4, 2000, p. 2577–2582vol.4.

  20. 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 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  24. 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 

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

    Article  Google Scholar 

  26. 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.

    Article  Google Scholar 

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

    Article  Google Scholar 

  28. 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 

  29. Larue O, Poirier P, Nkambou R. The emergence of (artificial) emotions from cognitive and neurological processes. Biol Inspir Cognit Archit. 2013;4(0):54–68.

    Google Scholar 

  30. Ren D, Wang P, Qiao H, Zheng S. A biologically inspired model of emotion eliciting from visual stimuli. Neurocomputing. 2013;121(0):328–36.

    Article  Google Scholar 

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

  32. Sanz R, Snchez-Escribano MG, Herrera C. A model of emotion as patterned metacontrol. Biol Inspir Cognit Archit. 2013;4(0):79–97.

    Google Scholar 

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

  34. 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 

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

    Article  Google Scholar 

  36. 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 

  37. 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 

  38. 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 

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

    Article  Google Scholar 

  40. 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 

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

  42. Pal P, Iyer AN, Yantorno RE. Emotion detection from infant facial expressions and cries. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006) Proceedings., IEEE, 2006.

  43. Li T, Ogihara M. Content-based music similarity search and emotion detection. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’04) Proceedings, IEEE, 2004.

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

  45. Popovici Vlad O, Vachkov G, Fukuda T. Fuzzy emotion interpolation system for emotional autonomous agents. In: Proceedings of the 41st SICE Annual Conference (SICE 2002), 2002, p. 3157–3162.

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

    Google Scholar 

  47. Park G-Y, Lee S-I, Kwon W-Y, Kim J-B. Neurocognitive affective system for an emotive robot. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006, p. 2595–2600.

  48. Zadeh LA. Fuzzy sets. Inf Control. 1965;8:338–53.

    Article  Google Scholar 

  49. Mamdani E, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud. 1975;7(1):1–13.

    Article  Google Scholar 

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

    Article  Google Scholar 

  51. Ekman P. Handbook of cognition and emotion, Wiley 1999, Ch. 3 - Basic Emotions, p. 45–60.

  52. Zadeh LA. Calculus of fuzzy restriction. In: Zadeh L, Fu K-S, Tanaka K, Shimura M, editors. Fuzzy sets and their applications to coginitive and decision processes. New York: Academic Press; 1975. p. 1–39.

    Chapter  Google Scholar 

  53. Zadeh LA. The concept of a linguistic variable and its application to approximate reasoning. Inf Sci. 1975;8:199–249.

    Article  Google Scholar 

  54. Zadeh LA. The concept of a linguistic variable and its application to approximate reasoning - II. Inf Sci. 1975;8:301–57.

    Article  Google Scholar 

  55. Zadeh LA. The concept of a linguistic variable and its application to approximate reasoning - III. Inf Sci. 1975;9:43–80.

    Article  Google Scholar 

  56. Mendel JM, John RI. Type-2 fuzzy sets made simple. IEEE Trans Fuzzy Syst. 2002;10(2):117–27.

  57. Zadeh LA. Fuzzy sets, fuzzy logic and fuzzy systems. Singapore: World Scientific Press; 1996.

    Book  Google Scholar 

Download references

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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

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  1. Aladdin Ayesh
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  2. William Blewitt
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Correspondence to Aladdin Ayesh.

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Ayesh, A., Blewitt, W. Models for Computational Emotions from Psychological Theories Using Type-II Fuzzy Logic. Cogn Comput 7, 309–332 (2015). https://doi.org/10.1007/s12559-014-9286-8

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