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

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 selfderived 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 selfidentify 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.

Quantification of Happy Emotion: Dependence on Decisions

“…2). This is consistent with similar shifts in anger threshold crossover point [9], and decision threshold crossover point [39] were reported. This analysis suggests that the level of happiness is related to the decision, independent of the sense of fairness.…”

“…The graphs also reveal the bias in happiness threshold, which is not centered on $5 : $5 fair-share offer (at the axes-origin for neutral emotion), but shifted to either left or right of the graph depending on the subject's subjective bias to fairness [9,[36][37][38]. The crossover emotional threshold point is shifted to the left when the subject accepted the offer (see Fig.…”

“…Using this UG paradigm, a stimulus-response function for emotion has been quantified for happy emotion [4] and angry emotion [9]. The emotional stimulus-response function provides a quantifiable measure for revealing how emotions are biased by other factors, such as fairness and decisions.…”

“…These other self-reported ratings served as distracters to reduce the likelihood of the subjects to skew their responses, if they expected which variables the experimenter is addressing. The subjects were not asked explicitly why they opted to accept/reject the offer, similar to other UG studies [9,13,19,24,25,[27][28][29][30][31][32][33][34][35], so as not to skew the subject's perception. The experimental protocol was approved by the Institutional Review Board.…”

“…It has been shown that fairness bias occurs in other studies. Human subjects do tolerate a small amount of unfairness by accepting some inequity as fair rather than insisting absolute equity as fair [9,38]. The fairness threshold can be shifted, such that an inequitable offer-ratio of $2 : $8 is still considered as fair instead of $5 : $5 [9,38].…”