Quantification of Happy Emotion: Dependence on Decisions

Tam, David C.

doi:10.11648/j.pbs.20140302.16

PBS

2014

DOI: 10.11648/j.pbs.20140302.16

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Quantification of Happy Emotion: Dependence on Decisions

David C. Tam

Abstract: Abstract:In the previously proposed theoretical model of emotion, emotion serves as an internal feedback to assess the disparity between the internal prediction and the actual outcomes in the external world, so that congruency between the desirable wants and needs can be met by resolving the emotions. The Emotional-Gain Model predicts that the happy emotional intensity is proportional to the magnitude of the desirable gain signals, while unhappy emotional intensity is proportional to the magnitude loss signals… Show more

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Cited by 5 publications

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“…The study describes emotional intelligence as the ability to identify one's emotions and evaluate role of emotions for a wise decision. The emotional-gain model [19] is introduced, and the findings show that decisionmaking is correlated with a positive mood. In other words, accepting an offer is associated with a happier emotional change, whereas rejecting an offer is associated with a less happy emotional change.…”

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Quantifying and leveraging emotions to fight a pandemic

2023

IJATEE

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Coronavirus infectious disease 2019 also known as COVID-19 was declared a pandemic by World Health Organization (WHO) in March 2020 [1]. The outbreak significantly impacted the nations worldwide and brought the social and economic lives to a standstill. A world that hummed with the buzz of everyday activities suddenly fell quiet. Around sixty percent of the world populace was put under a total or a partial lockdown. Resultantly, economic activity across countries significantly decelerated, taking away millions of livelihoods. This, coupled with the inadequate medical facilities, and no known cure for the novel coronavirus fuelled anxiety, leading to despair and mental depression [24]. *Author for correspondenceVaccination was naturally the most sought of public health initiative during these challenging times. History stands witness that the development of vaccines in times of pandemics, such as the flu in 1918, not just saved the humanity from extinction but also eventually helped eradication of the underlying cause [5]. Naturally therefore, as the number of COVID-19 cases worldwide continued to skyrocket, the countries world over started preparing for the largest vaccination program in history. However, the immunization drives received a mixed response from the people. Scepticism and vaccine hesitancy negatively affected the inoculation rate and also offset the immunization drives launched by countries to curb the pandemic [68]. It was reported that a roughly thirty percent drop in vaccination coverage may be attributed to a one percent rise in vaccine scepticism [9]. And a major contributing factor for the public scepticism was the lack of assurance about the safety Re search Article

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Quantifying and leveraging emotions to fight a pandemic

2023

IJATEE

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Quantification of Happy Emotion: Proportionality Relationship to Gain/Loss

Tam¹

2014

PBS

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Abstract:Toward understanding the role of emotion played in cognitive processing, an emotional model has been proposed to quantify the computation involved in assessing the disparity between the expected and the actual outcomes. This study provides the experimental evidence to validate the above emotional model. In this model, emotion serves as an internal feedback to assess the disparity between the internal predicted outcomes and the actual (external) outcomes in reality. It predicts that emotion provides a feedback to reduce the discrepancy between the expected (subjective) reality and actual (objective) reality. The hypothesis for this model is that the intensity of emotional response is proportional to the disparity between the expected outcome and the actual outcome (i.e., gain/loss magnitude). Happiness is an emotional feedback that indicates the congruency between the predicted and actual outcomes. In order to validate this theoretical model of emotion, the classical Ultimatum Game (UG) is used as an experimental paradigm to elicit self-generated (endogenous) emotions in response to a monetary offer, so that the emotional responses with respect to the perceived monetary gain/loss can be assessed by the stimulus-response function. The results showed that the self-reported happiness intensity is directly proportional to the magnitude of the desirable monetary gain. An empirically derived emotion stimulus-response function is shown to quantify the specific emotional biases graphically by the emotional-disparity graph. The results validated the hypothesis that the intensity of self-reported happy emotion is directly proportional to the monetary gain. The analysis also showed that the happy emotional sensitivity is also changed by the perception of fairness (whether the offer is fair or unfair), which can be represented graphically by the emotional-disparity graph.

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Quantitative Assessment of Sad Emotion

Tam

2015

PBS

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Abstract: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.

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Quantification of Happy Emotion: Dependence on Decisions

Cited by 5 publications

References 47 publications

Quantifying and leveraging emotions to fight a pandemic

Quantifying and leveraging emotions to fight a pandemic

Quantification of Happy Emotion: Proportionality Relationship to Gain/Loss

Quantitative Assessment of Sad Emotion

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