Based on the disparity theory of emotion, the role of sad emotion is an internal assessment of the error-correction process to reduce the disparity between the expected and actual outcomes (loss reduction) in the reality check process. This computational theory of emotion is consistent with the psychological characteristics that sadness is an emotional response to the sense of loss (such as loss of loved ones, valuables, possessions, or achieved goals). This emotional theory of sadness also includes the emotional resolution process by accepting that nothing can be done to change the actual outcomes, and resolving the emotion by reducing the perceived loss. This self-corrective mechanism is used as an internal feedback to assess the incongruence between the expectation and the actuality, such that the perceived loss can be reduced, resolving the sad emotion in the process. Thus, sadness can serve as a motivating feedback to an individual to make a decision to reduce the loss in the emotional resolution process. The classical ultimatum game (UG) paradigm is used to elicit self-generated emotion in human subjects experimentally in response to the disparity between the proportions of money being offered to share with. Results showed that the sadness level is quantified by the sadness stimulus-response function. The level of sadness intensity is proportional to perceived loss (or inversely proportional to the perceived gain). The results also showed that there was a shifting of the baseline sadness level from a less sad level for the acceptance decision to a more sad level for the rejection decision. This shows that the sad emotion can be resolved by accepting the monetary offer in the UG paradigm, which reduces the loss compared to the decision to reject the money. These results confirmed the emotional disparity hypothesis that the level of sadness is proportional to the perceived loss, and sadness can be resolved by reducing the loss in the self-regulated internal processing of emotion. Implications on emotional intelligence are also addressed so that one of the effective skills to resolve sadness is the reduction of the perceived losses.
Nicoladie D. Tam,
Quantitative Assessment of Sad Emotion, Psychology and Behavioral Sciences.
Vol. 4, No. 2,
2015, pp. 36-43.
D. Tam, “EMOTION-I model: A biologically-based theoretical framework for deriving emotional context of sensation in autonomous control systems,” Open Cybern Sys J, vol. 1, pp. 28-46, 2007.
D. Tam, “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 Sys J, vol. 1, pp. 47-60, 2007.
D. N. Tam, “Computation in emotional processing: quantitative confirmation of proportionality hypothesis for angry unhappy emotional intensity to perceived loss,” Cogn Comput, vol. 3, pp. 394-415, 2011/06/01 2011.
N. D. Tam, “Quantification of happy emotion: Proportionality relationship to gain/loss,” Psychol Behav Sci, vol. 3, pp. 60-67, April 6, 2014 2014.
N. D. Tam, “Quantification of happy emotion: Dependence on decisions,” Psychol Behav Sci, vol. 3, pp. 68-74, April 6, 2014 2014.
N. D. Tam and K. M. Smith, "Cognitive computation of jealous emotion," Psychology and Behavioral Sciences, vol. 3, pp. 1-7, Dec. 31, 2014 2014.
J. Jaeger, J. C. Borod, and E. Peselow, “Facial expression of positive and negative emotions in patients with unipolar depression,” J Affect Disord, vol. 11, pp. 43-50, Jul-Aug 1986.
F. Schneider, R. C. Gur, R. E. Gur, and L. R. Muenz, “Standardized mood induction with happy and sad facial expressions,” Psychiatry Res, vol. 51, pp. 19-31, Jan 1994.
S. Srivastava, H. O. Sharma, and M. K. Mandal, “Mood induction with facial expressions of emotion in patients with generalized anxiety disorder,” Depress Anxiety, vol. 18, pp. 144-8, 2003.
I. Laeger, C. Dobel, U. Dannlowski, H. Kugel, D. Grotegerd, J. Kissler, et al., “Amygdala responsiveness to emotional words is modulated by subclinical anxiety and depression,” Behav Brain Res, vol. 233, pp. 508-16, Aug 1 2012.
W. H. Liu, L. Z. Wang, S. H. Zhao, Y. P. Ning, and R. C. Chan, “Anhedonia and emotional word memory in patients with depression,” Psychiatry Res, vol. 200, pp. 361-7, Dec 30 2012.
M. J. van Tol, L. R. Demenescu, N. J. van der Wee, R. Kortekaas, M. A. N. Marjan, J. A. Boer, et al., “Functional magnetic resonance imaging correlates of emotional word encoding and recognition in depression and anxiety disorders,” Biol Psychiatry, vol. 71, pp. 593-602, Apr 1 2012.
K. M. Harle and A. G. Sanfey, “Incidental sadness biases social economic decisions in the Ultimatum Game,” Emotion, vol. 7, pp. 876-881, Nov 2007.
J. von Neumann, O. Morgenstern, and A. Rubinstein, Theory of games and economic behavior. Princeton, NJ: Princeton University Press, 1953.
J. H. Kagel and A. E. Roth, The handbook of experimental economics: Princeton University Press, 1995.
D. A. Braun, P. A. Ortega, and D. M. Wolpert, “Nash equilibria in multi-agent motor interactions,” PLoS Comput Biol, vol. 5, p. e1000468, Aug 2009.
K. Sigmund, C. Hauert, and M. A. Nowak, “Reward and punishment,” Proc Natl Acad Sci U S A, vol. 98, pp. 10757-10762, Sep 11 2001.
C. Civai, C. Corradi-Dell'Acqua, M. Gamer, and R. I. Rumiati, “Are irrational reactions to unfairness truly emotionally-driven? Dissociated behavioural and emotional responses in the Ultimatum Game task,” Cognition, vol. 114, pp. 89-95, Jan 2010.
J. K. Rilling, A. G. Sanfey, J. A. Aronson, L. E. Nystrom, and J. D. Cohen, “The neural correlates of theory of mind within interpersonal interactions,” Neuroimage, vol. 22, pp. 1694-1703, Aug 2004.
A. G. Sanfey, J. K. Rilling, J. A. Aronson, L. E. Nystrom, and J. D. Cohen, “The neural basis of economic decision-making in the Ultimatum Game,” Science, vol. 300, pp. 1755-1758, Jun 13 2003.
A. G. Sanfey, G. Loewenstein, S. M. McClure, and J. D. Cohen, “Neuroeconomics: cross-currents in research on decision-making,” Trends Cogn Sci, vol. 10, pp. 108-16, Mar 2006.
P. Smith and A. Silberberg, “Rational maximizing by humans (Homo sapiens) in an ultimatum game,” Anim Cogn, vol. 13, pp. 671-7, Jul 2010.
T. Yamagishi, Y. Horita, H. Takagishi, M. Shinada, S. Tanida, and K. S. Cook, “The private rejection of unfair offers and emotional commitment,” Proc Natl Acad Sci U S A, vol. 106, pp. 11520-11523, Jul 14 2009.
A. Bechara, “The role of emotion in decision-making: evidence from neurological patients with orbitofrontal damage,” Brain Cogn, vol. 55, pp. 30-40, Jun 2004.
J. D. Greene, L. E. Nystrom, A. D. Engell, J. M. Darley, and J. D. Cohen, “The neural bases of cognitive conflict and control in moral judgment,” Neuron, vol. 44, pp. 389-400, Oct 14 2004.
M. M. Pillutla and J. K. Murnighan, “Unfairness, Anger, and Spite: Emotional Rejections of Ultimatum Offers,” Org Behav Human Decis Proc, vol. 68, pp. 208-224, 1996.
S. M. McClure, D. I. Laibson, G. Loewenstein, and J. D. Cohen, “Separate neural systems value immediate and delayed monetary rewards,” Science, vol. 306, pp. 503-7, Oct 15 2004.
E. K. Miller and J. D. Cohen, “An integrative theory of prefrontal cortex function,” Annu Rev Neurosci, vol. 24, pp. 167-202, 2001.
G. J. Quirk and J. S. Beer, “Prefrontal involvement in the regulation of emotion: convergence of rat and human studies,” Curr Opin Neurobiol, vol. 16, pp. 723-7, Dec 2006.
E. T. Rolls, “Brain mechanisms of emotion and decision-making,” Int Congress Series, vol. 1291, pp. 3-13, 2006.
M. van’t Wout , R. S. Kahn, A. G. Sanfey, and A. Aleman, “Affective state and decision-making in the Ultimatum Game,” Exp Brain Res, vol. 169, pp. 564-8, Mar 2006.
D. Tam, “Variables governing emotion and decision-making: human objectivity underlying its subjective perception,” BMC Neuroscience, vol. 11, p. P96, Jul 20 2010.
N. D. Tam, “Quantification of fairness perception by including other-regarding concerns using a relativistic fairness-equity model,” Adv in Soc Sci Research J, vol. 1, pp. 159-169, 2014.
N. D. Tam, “Quantification of fairness bias in relation to decisions using a relativistic fairness-equity model,” Adv in Soc Sci Research J, vol. 1, pp. 169-178, 2014.
N. D. Tam, “Rational decision-making process choosing fairness over monetary gain as decision criteria,” Psychol Behav Sci, in press.
N. D. Tam, “A decision-making phase-space model for fairness assessment,” Psychol Behav Sci, in press.