神经生物学与计算机科学融合视角下的共情研究进展

doi:10.3969/j.issn.1674-8115.2026.04.015

摘要/Abstract

摘要：

共情使个体能够共享并理解他人的感受，对人类生存、社会交往有重要意义，也是心理治疗中的关键因素之一。来自动物模型、临床和非临床人群的研究证据表明，共情的核心脑网络包括前扣带回皮层、前脑岛、杏仁核、前额叶皮层等脑区；共情还涉及自主神经系统和内分泌系统的活动。基于共情的神经生物学基础，研究者将行为学实验与神经影像学和生理信号测量相结合，进一步理解和量化共情；人工智能技术的发展推动了共情计算的突破，实现了从多模态数据中提取特征，以进行共情的自动化评估和共情模拟。未来研究需开发更具生态效度的行为学范式，整合视觉、语言、生理信号等多模态信息，优化算法伦理与跨文化适用性，以促进共情研究在心理治疗、教育与人机交互等领域的应用。该文从神经生物学与计算机科学跨学科融合视角，回顾了共情研究的进展，涵盖共情的神经生物学机制、测量与评估方法，以及人工智能技术在共情研究中的应用。

关键词: 共情, 神经生物学机制, 共情计算, 心理治疗, 人机交互

Abstract:

Empathy, which enables individuals to share and understand others' feelings, is of great significance for human survival and social interaction and serves as one of the key elements in psychotherapy. Evidence from animal models, as well as clinical and non-clinical populations, indicates that the core brain network of empathy involves the anterior cingulate cortex, anterior insula, amygdala, anterior cingulate cortex,etc. Empathy also involves activities in the autonomic nervous and endocrine systems. Based on the neurobiological foundation of empathy, researchers have combined behavioral experiments with neuroimaging and physiological signal measurements to further understand and quantify empathy. Breakthroughs in computational empathy, driven by advances in artificial intelligence, have enabled the automated assessment of empathy through features extracted from multimodal data and the simulation of empathy. Future research needs to develop behavioral paradigms with higher ecological validity, integrate multimodal data from visual, linguistic, and physiological signals, and optimize algorithmic ethics and cross-cultural applicability to facilitate the application of empathy research in domains such as psychotherapy, education, and human-computer interaction.The paper reviews recent advances in empathy research from an interdisciplinary perspective, integrating neurobiology and computer science. It covers the neurobiological mechanisms of empathy, methods for measuring and assessing empathy, and the application of artificial intelligence techniques in empathy research.

Key words: empathy, neurobiological mechanisms, computational empathy, psychotherapy, human-computer interaction

中图分类号:

R395.1

周欣雨, 仇剑崟. 神经生物学与计算机科学融合视角下的共情研究进展[J]. 上海交通大学学报（医学版）, 2026, 46(4): 545-554.

Zhou Xinyu, Qiu Jianyin. Advances in empathy research from the perspective of the integration of neurobiology and computer science[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2026, 46(4): 545-554.

图/表 2

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Measurement method	Key metric	Advantage	Limitation	Scenario/target population
Psychometric scale	Subjective reports/manual coding scores	Extensive theoretical foundation and ease of administration	Susceptibility to reporting bias/manual coding is labor-intensive	Psychotherapy research; clinical and non-clinical assessment
Behavioral paradigm	Scores from experimental tasks	Convenient and standardized	Lacking ecological validity	Clinical and non-clinical populations
Neuroscience technique	EEG components, frequency band power, MEG patterns, fMRI BOLD signals, and functional connectivity	High temporal or spatial resolution, enabling analysis of temporal dynamics or spatial localization	Highly susceptible to environmental noise and requires strict experimental conditions	Research on neural mechanism; clinical and non-clinical assessment
Physiological measure	EMG, EDA, HRV, and physiological synchrony	Objective and non-invasive; reflects dyadic interaction	Some sensors may interfere with natural interaction	Psychotherapy research; clinical and non-clinical assessment
Empathy computing	Model prediction accuracy benchmarked against human coding	Automated processing of large-scale data with objective metrics	Issues with model interpretability; high reliance on large-scale and high-quality labeled data	Automated objective evaluation of large-scale datasets

Measurement method	Key metric	Advantage	Limitation	Scenario/target population
Psychometric scale	Subjective reports/manual coding scores	Extensive theoretical foundation and ease of administration	Susceptibility to reporting bias/manual coding is labor-intensive	Psychotherapy research; clinical and non-clinical assessment
Behavioral paradigm	Scores from experimental tasks	Convenient and standardized	Lacking ecological validity	Clinical and non-clinical populations
Neuroscience technique	EEG components, frequency band power, MEG patterns, fMRI BOLD signals, and functional connectivity	High temporal or spatial resolution, enabling analysis of temporal dynamics or spatial localization	Highly susceptible to environmental noise and requires strict experimental conditions	Research on neural mechanism; clinical and non-clinical assessment
Physiological measure	EMG, EDA, HRV, and physiological synchrony	Objective and non-invasive; reflects dyadic interaction	Some sensors may interfere with natural interaction	Psychotherapy research; clinical and non-clinical assessment
Empathy computing	Model prediction accuracy benchmarked against human coding	Automated processing of large-scale data with objective metrics	Issues with model interpretability; high reliance on large-scale and high-quality labeled data	Automated objective evaluation of large-scale datasets