yunkim828 / loadedEmpathy

MA Thesis: Multinomial Processing Tree Modeling of the Effects of Working Memory Load on Pain Empathy

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

Multinomial Processing Tree Modeling of the Effects of Cognitive Load on Pain Empathy

Authors

  • Seongyun Kim (Department of Psychology, Yonsei University)
  • Do-Joon Yi (Department of Psychology, Yonsei University, dojoon.yi@yonsei.ac.kr)

Abstract

Empathy for others’ pain may arise spontaneously, but it needs to be modulated by top-down factors to produce desirable outcomes. To understand such controlled processes of pain empathy, we analyzed the effects of cognitive load on identifying others’ pain. In a pain identification task, participants viewed successive prime and target images depicting another person’s hand or foot in painful or nonpainful situations and judged the target experience as painful or nonpainful while ignoring the prime images. Participants performed pain identification with or without a concurrent color memory task. To dissociate the processes involved in pain empathy, we fitted a three-parameters multinomial processing tree model to the pain identification responses, as suggested by Cameron and colleagues (2017). The results showed that the estimate of Intentional Empathy, a parameter for controlled processes, decreased with working memory loads. In contrast, the estimates of Unintentional Empathy, a parameter for automatic processes, and Response Bias did not change. Furthermore, Intentional Empathy was positively correlated with the working memory capacity of individual participants. This study demonstrated capacity-limited aspects of pain empathy and suggests that working memory capacity could be a critical factor in better understanding individual differences in pain empathy.

Korean Journal of Cognitive and Biological Psychology, 2022, 34(4), 249-266.

About

MA Thesis: Multinomial Processing Tree Modeling of the Effects of Working Memory Load on Pain Empathy


Languages

Language:HTML 100.0%