Cognitive Computation of Jealous Emotion

Abstract

The computational role of jealous emotion has been proposed in a model of emotion, in which the desirable gain (or loss) is used as a measure for computing the emotional feedback that assesses the discrepancy between what an individual wants and gets. The jealous emotion is elicited when the perception that the other individuals have more than one has, or that the desire of wanting what others have, but cannot get. Such self-identified error measure is used as an internal measure to monitor the incongruence between model prediction and actual outcome, such that the accuracy of predictions by the brain can be assessed. Jealousy can serve as a motivating signal to an individual to self-correct errors that may exist. This error signal signifies the incongruence between the desirable and the actual outcomes. This (unhappy) jealous emotion provides the necessary feedback to self-correct any potential source of errors, which may originate from the errors in (input) perception, (output) execution or (internal) model. An ultimatum game (UG) paradigm is used to elicit self-generated emotion. Results showed that the emotional intensity of jealousy is inversely proportional to perceived gains (and proportional to the perceived losses). Subjective jealousy biases are represented by shifting of the emotional stimulus-response function. This suggested that jealousy can be resolved by correcting (1) the perception of unfairness (perceptual error), (2) wrong decision (execution error) and (3) faulty assumption of entitlement (model prediction error) in this experimental UG paradigm. The results confirmed the hypothesis that self-regulated jealousy is processed cognitively in proportional to the perceived loss, when one wants to gain something that one cannot get. Implications on emotional intelligence are also addressed.