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The competition dynamics of approach and avoidance motivations following interpersonal transgression.

Bo ShenYang ChenZhewen HeWeijian LiHongbo YuXiaolin Zhou
Published in: Proceedings of the National Academy of Sciences of the United States of America (2023)
Two behavioral motivations coexist in transgressors following an interpersonal transgression-approaching and compensating the victim and avoiding the victim. Little is known about how these motivations arise, compete, and drive transgressors' decisions. The present study adopted a social interaction task to manipulate participants' (i.e., the transgressor) responsibility for another's (i.e., the victim) monetary loss and measure the participants' tradeoff between compensating the victim and avoiding face-to-face interactions with the victim. Following each transgression, participants used a computer mouse to choose between two options differing in the amount of compensation to the victim and the probability of face-to-face contact with the victim. Results showed that as participants' responsibility increased, 1) the decision weights on contact avoidance relative to compensation increased, and 2) the onset of the contact-avoidance attribute was expedited and that of the compensation attribute was delayed. These results demonstrate how competing social motivations following transgression evolve and determine social decision-making and shed light on how social-affective state modulates the dynamics of decision-making in general.
Keyphrases
  • healthcare
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  • deep learning
  • machine learning