Empathic pain: Exploring the multidimensional impacts of biological and social aspects in pain.

Empathic pain refers to an individual's perception, judgment, and emotional response to others' pain. This complex social cognitive ability is crucial for healthy interactions in human society. In recent years, with the development of multidisciplinary research in neuroscience, psychology and sociology, empathic pain has become a focal point of widespread attention in these fields. However, the neural mechanism underlying empathic pain remain a controversial and unresolved area. This review aims to comprehensively summarize the history, influencing factors, neural mechanisms and pharmacological interventions of empathic pain. We hope to provide a comprehensive scientific perspective on how humans perceive and respond to others' pain experiences and to provide guidance for future research directions and clinical applications. This article is part of the Special Issue on "Empathic Pain".

Mining human preference via self-correction causal structure learning.

Spurred by causal structure learning (CSL) ability to reveal the cause-effect connection, significant research efforts have been made to enhance the scalability of CSL algorithms in various artificial intelligence applications. However, less effort has been made regarding the stability and the interpretability of CSL algorithms. Thus, this work proposes a self-correction mechanism that embeds domain knowledge for CSL, improving the stability and accuracy even in low-dimensional but high-noise environments by guaranteeing a meaningful output. The suggested algorithm is challenged against multiple classic and influential CSL algorithms in synthesized and field datasets. Our algorithm achieves a superior accuracy on the synthesized dataset, while on the field dataset, our method interprets the learned causal structure as a human preference for investment, coinciding with domain expert analysis.