通过统计学习和因果推理的整合,推断群体的内部结构
Isaac Davis1, Julian Jara-Ettinger2,3, Yarrow Dunham2,3
1Department of Psychology, Yale University, New Haven, CT, USA. isaac.davis@yale.edu.
Nature communications
|January 23, 2026
概括
人类通过将统计学学习与社会模型相结合,迅速推断出复杂的社会结构. 这允许预测和规划,即使从有限的交互数据.
科学领域:
- 认知科学 认知科学
- 社会心理学 社会心理学
- 计算神经科学是一种神经科学.
背景情况:
- 人类的社会互动形成复杂的网络 (友,等级).
- 可观察到的相互作用往往是稀疏和杂的,阻碍了结构推理.
- 了解社交网络对于社会认知和行为至关重要.
研究的目的:
- 研究人类如何从有限的交互数据推断潜在的社会结构.
- 测试一个集统计学习和社会模型的计算模型.
- 确定社会网络推断和预测背后的机制.
主要方法:
- 三个行为实验使用社会互动的抽象视频.
- 参与者推断了社会结构,预测了行为,并推理了影响力.
- 基于统计学学习和因果推理的计算模型被开发和测试.
主要成果:
- 参与者成功地推断出了潜在的社会结构.
- 判断与计算模型的预测保持一致.
- 性能不能用更简单的基于提示的模型来解释.
结论:
- 人类将域一般统计学学习与特定领域的社会模型相结合.
- 这种整合形成了社会理解的因果表征.
- 统计学学习和因果推理共同工作,实现灵活的社会认知.
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