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Computation in Emotional Processing: Quantitative Confirmation of Proportionality Hypothesis for Angry Unhappy Emotional Intensity to Perceived Loss

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Abstract

A computational model of emotion is derived (using minimalistic assumptions) to quantify how emotions are evolved to estimate the accuracy of an internally generated brain model that predicts the external world. In this model, emotion is an emergent property serving as a self-derived feedback that monitors the accuracy of the internal model via the discrepancy (error measure) between the (internal) subjective reality and (external) objective reality—reality-check subconsciously. Minimization of error (computed by the “gain” toward the desired outcome) will optimize congruency between internal and external worlds—resulting in happy emotion. Unhappy emotion is resulted from the discrepancy between internal and external worlds, which can serve as feedback for self-correction to minimize the “loss” (error) between desired and actual outcomes. Unhappiness provides the internal guide to self-identify whether the cause of error is due to input (sensory perception) error, output (motor execution) error, or modeling (internal model) error. Experimental validation of the hypothesis using the ultimatum game paradigm confirmed the inverse proportional relationship of anger to perceived gain (or direct proportionality to loss) that estimates the discrepancy between what we want and what we get. It also characterizes specific emotional biases by shifting the emotional intensity curve quantitatively.

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References

  1. Abler B, Walter H, Erk S, Kammerer H, Spitzer M. Prediction error as a linear function of reward probability is coded in human nucleus accumbens. Neuroimage. 2006;31:790–5.

    Article  PubMed  Google Scholar 

  2. Adams CD. Variations in the sensitivity of instrumental responding to reinforcer devaluation. Q J Exp Psychol. 1982;34B:77–98.

    Google Scholar 

  3. Adams CD, Dickinson A. Instrumental responding following reinforcer devaluation. Q J Exp Psychol. 1981;33B:109–21.

    Google Scholar 

  4. Barto AG, Sutton RS. Landmark learning: an illustration of associative search. Biol Cybern. 1981;42:1–8.

    Article  PubMed  CAS  Google Scholar 

  5. Barto AG, Anderson CW, Sutton RS. Synthesis of non-linear control surfaces by a layered associative search network. Biol Cybern. 1982;43:175–85.

    Article  PubMed  CAS  Google Scholar 

  6. Bechara A. The role of emotion in decision-making: evidence from neurological patients with orbitofrontal damage. Brain Cogn. 2004;55:30–40.

    Article  PubMed  Google Scholar 

  7. Bender VA, Feldman DE. A dynamic spatial gradient of Hebbian learning in dendrites. Neuron. 2006;51:153–5.

    Article  PubMed  CAS  Google Scholar 

  8. Berridge KC. The debate over dopamine’s role in reward: the case for incentive salience. Psychopharmacology (Berl). 2007;191:391–431.

    Article  CAS  Google Scholar 

  9. Braun DA, Ortega PA, Wolpert DM. Nash equilibria in multi-agent motor interactions. PLoS Comput Biol. 2009;5:e1000468.

    Article  PubMed  Google Scholar 

  10. Bray S, O’Doherty J. Neural coding of reward-prediction error signals during classical conditioning with attractive faces. J Neurophysiol. 2007;97:3036–45.

    Article  PubMed  Google Scholar 

  11. Brosnan SF, De Waal FB. Monkeys reject unequal pay. Nature. 2003;425:297–9.

    Google Scholar 

  12. Burnham TC. High-testosterone men reject low ultimatum game offers. Proc Biol Sci. 2007;274:2327–30.

    Article  PubMed  Google Scholar 

  13. Bush D, Philippides A, Husbands P, O’Shea M. Spike-timing dependent plasticity and the cognitive map. Front Comput Neurosci. 2010;15:142.

    Google Scholar 

  14. Butz M, Wörgötter F, van Ooyen A. Activity-dependent structural plasticity. Brain Res Rev. 2009;60:287–305.

    Article  PubMed  Google Scholar 

  15. Caporale N, Dan Y. Spike timing-dependent plasticity: a Hebbian learning rule. Annu Rev Neurosci. 2008;31:25–46.

    Article  PubMed  CAS  Google Scholar 

  16. Chauvin Y. Principal component analysis by gradient descent on a constrained linear Hebbian cell. In: Proceedings of IJCNN, Washington, vol. I. 1989. p. 373–80.

  17. Civai C, Corradi-Dell’Acqua C, Gamer M, Rumiati RI. Are irrational reactions to unfairness truly emotionally-driven? Dissociated behavioural and emotional responses in the Ultimatum Game task. Cognition. 2010;114:89–95.

    Article  PubMed  Google Scholar 

  18. Crockett MJ. The neurochemistry of fairness: clarifying the link between serotonin and prosocial behavior. Ann NY Acad Sci. 2009;1167:76–86.

    Article  PubMed  CAS  Google Scholar 

  19. Crockett MJ, Clark L, Tabibnia G, Lieberman MD, Robbins TW. Serotonin modulates behavioral reactions to unfairness. Science. 2008;320:1739.

    Article  PubMed  CAS  Google Scholar 

  20. Dar-Nimrod I, Rawn CD, Lehman DR, Schwartz B. The maximization paradox: the costs of seeking alternatives. Pers Individ Differ. 2009;46:631–5.

    Article  Google Scholar 

  21. Dickinson A, Nicholas DJ, Adams CD. The effect of instrumental training contingency on susceptibility to reinforcer devaluation. Q J Exp Psychol. 1983;35B:35–51.

    Google Scholar 

  22. Duan WQ, Stanley HE. Fairness emergence from zero-intelligence agents. Phys Rev E Stat Nonlinear Soft Matter Phys. 2010;81:026104.

    Article  Google Scholar 

  23. Eisenegger C, Naef M, Snozzi R, Heinrichs M, Fehr E. Prejudice and truth about the effect of testosterone on human bargaining behaviour. Nature. 2010;463:356–9.

    Google Scholar 

  24. Emanuele E, Brondino N, Bertona M, Re S, Geroldi D. Relationship between platelet serotonin content and rejections of unfair offers in the ultimatum game. Neurosci Lett. 2008;437:158–61.

    Article  PubMed  CAS  Google Scholar 

  25. Emanuele E, Brondino N, Re S, Bertona M, Geroldi D. Serum omega-3 fatty acids are associated with ultimatum bargaining behavior. Physiol Behav. 2009;96:180–3.

    Article  PubMed  CAS  Google Scholar 

  26. Falk A, Fehr E, Fuschbacher U. On the nature of fair behavior. Econ Inquiry. 2003;41:20–6.

    Google Scholar 

  27. Greene JD, Nystrom LE, Engell AD, Darley JM, Cohen JD. The neural bases of cognitive conflict and control in moral judgment. Neuron. 2004;44:389–400.

    Article  PubMed  CAS  Google Scholar 

  28. Güroğlu B, van den Bos W, Rombouts SA, Crone EA. Unfair? It depends: neural correlates of fairness in social context. Soc Cogn Affect Neurosci. 2010 (Advance Access published March 28, 2010).

  29. Güroğlu B, van den Bos W, Crone EA. Fairness considerations: increasing understanding of intentionality during adolescence. J Exp Child Psychol. 2009;104:398–409.

    Article  PubMed  Google Scholar 

  30. Halko ML, Hlushchuk Y, Hari R, Schürmann M. Competing with peers: mentalizing-related brain activity reflects what is at stake. Neuroimage. 2009;46:542–8.

    Article  PubMed  Google Scholar 

  31. Harlé KM, Sanfey AG. Incidental sadness biases social economic decisions in the Ultimatum Game. Emotion. 2007;7:876–81.

    Article  PubMed  Google Scholar 

  32. Hebb DO. The organization of behavior. New York: Wiley; 1949.

    Google Scholar 

  33. Herwig U, Baumgartner T, Kaffenberger T, Brühl A, Kottlow M, Schreiter-Gasser U, Abler B, Jäncke L, Rufer M. Modulation of anticipatory emotion and perception processing by cognitive control. Neuroimage. 2007;37:652–62.

    Article  PubMed  Google Scholar 

  34. Jensen K, Call J, Tomasselo M. Chimpanzees are rational maximizers in an ultimatum game. Nature. 2007;318:107–9.

    CAS  Google Scholar 

  35. Johnson AW, Gallagher M, Holland PC. The basolateral amygdala is critical to the expression of Pavlovian and instrumental outcome-specific reinforcer devaluation effects. J Neurosci. 2009;29:696–704.

    Article  PubMed  CAS  Google Scholar 

  36. Kagel JH, Roth AE. The handbook of experimental economics. Princeton: Princeton Univ Press; 1995.

    Google Scholar 

  37. Khamassi M, Mulder AB, Tabuchi E, Douchamps V, Wiener SI. Anticipatory reward signals in ventral striatal neurons of behaving rats. Eur J Neurosci. 2008;28:1849–66.

    Article  PubMed  Google Scholar 

  38. Kienhorst IC, De Wilde EJ, Diekstra RF, Wolters WH. Adolescents’ image of their suicide attempt. J Am Acad Child Adolesc Psychiatry. 1995;34:623–8.

    Article  PubMed  CAS  Google Scholar 

  39. Koenigs M, Tranel D. Irrational economic decision-making after ventromedial prefrontal damage: evidence from the Ultimatum Game. J Neurosci. 2007;27:951–6.

    Article  PubMed  CAS  Google Scholar 

  40. Kraft TL, Jobes DA, Lineberry TW, Conrad A, Kung S. Brief report: why suicide? Perceptions of suicidal inpatients and reflections of clinical researchers. Arch Suicide Res. 2010;14:375–82.

    Article  PubMed  Google Scholar 

  41. Krogh A, Hertz J. Hebbian learning of principal components. In: Eckmiller R, Hartmann G, Hauske G, editors. Parallel processing in neural systems and computers. Amsterdam: Elsevier; 1990. p. 183–6.

    Google Scholar 

  42. Ma W, Yu C, Zhang W. Monte Carlo simulation of early molecular evolution in the RNA World. Biosystems. 2007;90:28–39.

    Article  PubMed  CAS  Google Scholar 

  43. Magno E, Simões-Franklin C, Robertson IH, Garavan H. The role of the dorsal anterior cingulate in evaluating behavior for achieving gains and avoiding losses. J Cogn Neurosci. 2009;21:2328–42.

    Article  PubMed  Google Scholar 

  44. McClure SM, Laibson DI, Loewenstein G, Cohen JD. Separate neural systems value immediate and delayed monetary rewards. Science. 2004;306:503–7.

    Article  PubMed  CAS  Google Scholar 

  45. Miller E, Cohen J. An integrative theory of prefrontal cortex function. Annu Rev Neurosci. 2001;24:167–202.

    Article  PubMed  CAS  Google Scholar 

  46. Morewedge CK. Negativity bias in attribution of external agency. J Exp Psychol Gen. 2009;138:535–545.

    Article  PubMed  Google Scholar 

  47. Morrison SE, Salzman CD. Re-valuing the amygdala. Curr Opin Neurobiol. 2010;20:221–30.

    Article  PubMed  CAS  Google Scholar 

  48. Murray EA, Izquierdo A. Orbitofrontal cortex and amygdala contributions to affect and action in primates. Ann NY Acad Sci. 2007;1121:273–96.

    Article  PubMed  Google Scholar 

  49. Murray EA, Wise SP. Interactions between orbital prefrontal cortex and amygdala: advanced cognition, learned responses and instinctive behaviors. Curr Opin Neurobiol. 2010;20:212–20.

    Article  PubMed  CAS  Google Scholar 

  50. Nash J. Essays on game theory. Cheltenham: Elgar; 1996.

    Google Scholar 

  51. Niv Y. Reinforcement learning in the brain. J Math Psychol. 2009;53:139–54.

    Article  Google Scholar 

  52. Nowak MA, Page KM, Sigmund K. Fairness versus reason in the ultimatum game. Science. 2000;289:1773–5.

    Article  PubMed  CAS  Google Scholar 

  53. O’Doherty JP. Lights, camembert, action! The role of human orbitofrontal cortex in encoding stimuli, rewards, and choices. Ann NY Acad Sci. 2007;1121:254–72.

    Article  PubMed  Google Scholar 

  54. Oja E. A simplified neuron model as a principal components analyzer. J Math Biol. 1982;15:267–73.

    Article  PubMed  CAS  Google Scholar 

  55. Oja E. Principal components, minor components, and linear neural networks. Neural Netw. 1992;5:927–36.

    Article  Google Scholar 

  56. Oja E, Ogawa H, Wangviwattana J. Learning in non-linear constrained Hebbian networks. In: Kohonen T, Mikisara K, Simula O, Kangas J, editors. Artificial neural networks. Amsterdam: North-Holland; 1991. p. 385–90.

    Google Scholar 

  57. Page KM, Nowak MA. A generalized adaptive dynamics framework can describe the evolutionary Ultimatum Game. J Theor Biol. 2001;209:173–9.

    Article  PubMed  CAS  Google Scholar 

  58. Pillutla MM, Murnighan JK. Unfairness, anger, and spite: emotional rejections of ultimatum offers. Organ Behav Hum Decis Process. 1996;68:208–24.

    Article  Google Scholar 

  59. Plato. The republic (trans: Jowett B). 360 B.C.E. http://www.gutenberg.org/ebooks/1497.

  60. Quirk GJ, Beer JS. Prefrontal involvement in the regulation of emotion: convergence of rat and human studies. Curr Opin Neurobiol. 2006;16:723–7.

    Article  PubMed  CAS  Google Scholar 

  61. Rilling JK, Sanfey AG, Aronson JA, Nystrom LE, Cohen JD. The neural correlates of theory of mind within interpersonal interactions. Neuroimage. 2004;22(4):1694–703.

    Article  PubMed  Google Scholar 

  62. Rodriguez PF, Aron AR, Poldrack RA. Ventral-striatal/nucleus-accumbens sensitivity to prediction errors during classification learning. Hum Brain Mapp. 2006;27:306–13.

    Article  PubMed  CAS  Google Scholar 

  63. Rolls ET. Brain mechanisms of emotion and decision-making. Int Congr Ser. 2006;1291:3–13.

    Article  Google Scholar 

  64. Rumelhart DE, McClelland JL, The PDP Research Group. Parallel distributed processing—vol 1, Foundations. Cambridge: MIT Press; 1986.

    Google Scholar 

  65. Sánchez A, Cuesta JA. Altruism may arise from individual selection. J Theor Biol. 2005;235:233–40.

    Article  PubMed  Google Scholar 

  66. Sanfey AG, Loewenstein G, McClure SM, Cohen JD. Neuroeconomics: cross-currents in research on decision- making. Trends Cogn Sci. 2006;10:108–16.

    Article  PubMed  Google Scholar 

  67. Sanfey AG, Rilling JK, Aronson JA, Nystrom LE, Cohen JD. The neural basis of economic decision-making in the Ultimatum Game. Science. 2003;300:1755–8.

    Article  PubMed  CAS  Google Scholar 

  68. Schultz W, Tremblay L, Hollerman JR. Reward processing in primate orbitofrontal cortex and basal ganglia. Cereb Cortex. 2000;10:272–84.

    Article  PubMed  CAS  Google Scholar 

  69. Seip EC, van Dijk WW, Rotteveel M. On hotheads and Dirty Harries: the primacy of anger in altruistic punishment. Ann N Y Acad Sci. 2009;1167:190–6.

    Article  PubMed  Google Scholar 

  70. Sigmund K, Hauert C, Nowak MA. Reward and punishment. PNAS. 2001;98:10757–62.

    Article  PubMed  CAS  Google Scholar 

  71. Smith P, Silberberg A. Rational maximizing by humans (Homo sapiens) in an ultimatum game. Anim Cogn. 2010;13:671–7.

    Article  PubMed  Google Scholar 

  72. Staudinger MR, Erk S, Abler B, Walter H. Cognitive reappraisal modulates expected value and prediction error encoding in the ventral striatum. Neuroimage. 2009;47:713–21.

    Article  PubMed  Google Scholar 

  73. Stefani MR, Moghaddam B. Rule learning and reward contingency are associated with dissociable patterns of dopamine activation in the rat prefrontal cortex, nucleus accumbens, and dorsal striatum. J Neurosci. 2006;26:8810–9918.

    Article  PubMed  CAS  Google Scholar 

  74. Sutton RS, Barto AG. Toward a modern theory of adaptive networks: expectation and prediction. Psychol Rev. 1981;88:135–70.

    Article  PubMed  CAS  Google Scholar 

  75. Szanto K, Gildengers A, Mulsant BH, Brown G, Alexopoulos GS, Reynolds CF III. Identification of suicidal ideation and prevention of suicidal behaviour in the elderly. Drugs Aging. 2002;19:11–24.

    Article  PubMed  Google Scholar 

  76. Takagishi H, Kameshima S, Schug J, Koizumi M, Yamagishi T. Theory of mind enhances preference for fairness. J Exp Child Psychol. 2009;105:130–7.

    Article  PubMed  Google Scholar 

  77. Tam DC. A positive/negative reinforcement learning model for associative search network. In: Shirazi B, editor. Proceedings of the 1st annual IEEE symposium on parallel and distributed processing. 1989. p. 300–7.

  78. Tam DC. Computation of cross-correlation function by a time-delayed neural network. In: Dagli CH, Burke LI, Fernández BR, Ghosh J, editors. Intelligent engineering systems through artificial neural networks, vol. 3. New York: American Society of Mechanical Engineers Press; 1993. p. 51–5.

    Google Scholar 

  79. Tam D. Theoretical analysis of cross-correlation of time-series signals computed by a time-delayed Hebbian associative learning neural network. Open Cybern Syst J. 2007;1:1–4.

    Google Scholar 

  80. Tam D. EMOTION-I model: a biologically-based theoretical framework for deriving emotional context of sensation in autonomous control systems. Open Cybern Syst J. 2007;1:28–46.

    Article  Google Scholar 

  81. Tam D. EMOTION-II model: a theoretical framework for happy emotion as a self-assessment measure indicating the degree-of-fit (congruency) between the expectancy in subjective and objective realities in autonomous control systems. Open Cybern Syst J. 2007;1:47–60.

    Article  Google Scholar 

  82. Tam D. A theoretical model of emotion processing for optimizing the cost function of discrepancy errors between wants and gets. BMC Neuroscience. 2009;10(Suppl 1):P11.

    Google Scholar 

  83. Tam D. Variables governing emotion and decision-making: human objectivity underlying its subjective perception. BMC Neurosci. 2010;11(Suppl 1):P96.

    Article  Google Scholar 

  84. Tam D. Temporal associative memory (TAM) by spike-timing dependent plasticity. BMC Neurosci. 2010;11(Suppl 1):P105.

    Article  Google Scholar 

  85. Tam D. Gender difference in emotional perception of love in a decision-making task. Program No. 307.19. Neuroscience Meeting Planner. San Diego: Society for Neuroscience; 2010c (online).

  86. Tam D. Cognitive perception of happy emotion: proportionality relationships with gains and losses when getting what one wants; 2011 (submitted).

  87. Tam D. Cognitive computation of jealousy emotion: inverse proportionality relationships with gains/losses when one wants something that one cannot get; 2011 (submitted).

  88. Tam D. Objectivity in subjective perception of fairness: relativity in proportionality relationship with equity by switching frame of reference –- a fairness-equity model; 2011 (submitted).

  89. Von Neumann J, Morgenstern O. Theory of games and economic behavior. Princeton: Princeton University Press; 1953.

    Google Scholar 

  90. Wu S, Chow TW. Self-organizing and self-evolving neurons: a new neural network for optimization. IEEE Trans Neural Netw. 2007;18:385–96.

    Article  PubMed  Google Scholar 

  91. Yamagishi T, Horita Y, Takagishi H, Shinada M, Tanida S, Cook KS. The private rejection of unfair offers and emotional commitment. Proc Natl Acad Sci. 2009;106:11520–3.

    Article  PubMed  CAS  Google Scholar 

  92. Zhang SQ, Ching WK, Ng MK, Akutsu T. Simulation study in Probabilistic Boolean Network models for genetic regulatory networks. Int J Data Min Bioinform. 2007;1:217–40.

    Article  PubMed  Google Scholar 

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Acknowledgments

I appreciate the comments and suggestions by the anonymous reviewers. I also thank Richelle Trube and Krista Smith for proofreading the manuscript.

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Tam, D.N. Computation in Emotional Processing: Quantitative Confirmation of Proportionality Hypothesis for Angry Unhappy Emotional Intensity to Perceived Loss. Cogn Comput 3, 394–415 (2011). https://doi.org/10.1007/s12559-011-9095-2

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