Dataset Information

Revenge is sweet: Investigation of the effects of Approach-Motivated anger on the RewP in the motivated anger delay (MAD) paradigm.

ABSTRACT: Past research has found that neural activity associated with feedback processing is enhanced by positive approach-motivated states. However, no past work has examined how reward processing changes in the context of revenge. Using a novel aggression paradigm, we sought to explore the influence of approach-motivated anger on neural responses to feedback indicating the opportunity to seek revenge against an offending opponent by examining the reward positivity (RewP), an event-related potential indexing performance feedback. In Experiment 1, after receiving insulting feedback from an opponent, participants played a reaction time game with three trial types: revenge trials, aggravation trials, and no-consequence trials. Results revealed that RewP amplitudes were larger to revenge trial win feedback than no-consequence trial win feedback or revenge trial loss feedback. RewP amplitudes were larger to both aggravation trial win and loss feedback than on no-consequence trials. Experiment 2 examined the influence of approach-motivated anger during the acquisition of rewards on the RewP without the possibility of retribution from the offending individual. Participants played a reaction time game similar to Experiment 1, except instead of giving or receiving noise blasts, participants could win money from the insulter (revenge trials) or a neutral-party (e.g., bank). Results indicated that revenge wins elicited larger RewP amplitudes than bank wins. These results suggest that anger enhances revenge-related RewP amplitudes to obtaining revenge opportunities and further aggravation wins or losses. Anger appears to enhance the pleasurable feelings of revenge.

SUBMITTER: Threadgill AH

PROVIDER: S-EPMC7643382 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Revenge is sweet: Investigation of the effects of Approach-Motivated anger on the RewP in the motivated anger delay (MAD) paradigm.

Threadgill A Hunter AH Gable Philip A PA

Human brain mapping 20200828 17

Past research has found that neural activity associated with feedback processing is enhanced by positive approach-motivated states. However, no past work has examined how reward processing changes in the context of revenge. Using a novel aggression paradigm, we sought to explore the influence of approach-motivated anger on neural responses to feedback indicating the opportunity to seek revenge against an offending opponent by examining the reward positivity (RewP), an event-related potential ind ...[more]

PMID: 32856760

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Project description:Over the last 20 years, a significant part of the research in exploratory robotics partially switches from looking for the most efficient way of exploring an unknown environment to finding what could motivate a robot to autonomously explore it. Moreover, a growing literature focuses not only on the topological description of a space (dimensions, obstacles, usable paths, etc.) but rather on more semantic components, such as multimodal objects present in it. In the search of designing robots that behave autonomously by embedding life-long learning abilities, the inclusion of mechanisms of attention is of importance. Indeed, be it endogenous or exogenous, attention constitutes a form of intrinsic motivation for it can trigger motor command toward specific stimuli, thus leading to an exploration of the space. The Head Turning Modulation model presented in this paper is composed of two modules providing a robot with two different forms of intrinsic motivations leading to triggering head movements toward audiovisual sources appearing in unknown environments. First, the Dynamic Weighting module implements a motivation by the concept of Congruence, a concept defined as an adaptive form of semantic saliency specific for each explored environment. Then, the Multimodal Fusion and Inference module implements a motivation by the reduction of Uncertainty through a self-supervised online learning algorithm that can autonomously determine local consistencies. One of the novelty of the proposed model is to solely rely on semantic inputs (namely audio and visual labels the sources belong to), in opposition to the traditional analysis of the low-level characteristics of the perceived data. Another contribution is found in the way the exploration is exploited to actively learn the relationship between the visual and auditory modalities. Importantly, the robot-endowed with binocular vision, binaural audition and a rotating head-does not have access to prior information about the different environments it will explore. Consequently, it will have to learn in real-time what audiovisual objects are of "importance" in order to rotate its head toward them. Results presented in this paper have been obtained in simulated environments as well as with a real robot in realistic experimental conditions.

Dataset Information

Revenge is sweet: Investigation of the effects of Approach-Motivated anger on the RewP in the motivated anger delay (MAD) paradigm.

Publications

Revenge is sweet: Investigation of the effects of Approach-Motivated anger on the RewP in the motivated anger delay (MAD) paradigm.

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