网络模型揭示了超越潜伏构造模型的自然环境中的高维社会推理
1Department of Psychology, University of California San Diego, La Jolla, CA, USA. jul140@ucsd.edu.
Communications psychology
|July 7, 2025
概括
社会推断比以前认为的要复杂得多,一个高维网络模型更好地解释人们如何看待他人. 这种网络方法揭示了超越简单的温暖和能力维度的动态和多样化的社会感知.
科学领域:
- 社会心理学 社会心理学
- 认知科学 认知科学
- 计算社会科学 计算社会科学
背景情况:
- 传统模型提出,社会推断是基于一些潜在的维度,如温暖和能力.
- 以前的研究缺乏对更复杂,更高维度的社会推理结构的经验支持.
研究的目的:
- 用自然主义刺激来研究社会推断的高维性质.
- 测试网络模型是否能比传统的潜伏构建方法更好地代表社会推理.
主要方法:
- 两项预先注册的研究采用了大量多样化的样本 (研究1中的N=1598;研究2中的亚洲N=651;欧洲N=792).
- 参与者提供了各种自然主义视频中个体的自由形态描述.
- 分析涉及到隐性维度的交叉验证和推断相关性的稀疏网络建模.
主要成果:
- 确定了25个潜在维度,仅解释了15%的方差,表明了低维模型的局限性.
- 一个稀疏的网络模型为数据提供了更好的匹配,捕获了推理之间的独特相关性.
- 网络模型揭示了社会推论的动态,包括共发生和时间展开 (具体到抽象).
- 第二项研究表明,亚洲和欧洲样本中社会推理之间的关系存在文化差异.
结论:
- 高维网络方法提供了一个更全面的模型,用于理解社会推断在自然主义的背景下.
- 这种方法为社会知觉的动态性和文化多样性提供了新的见解.
- 这些发现挑战了静态的,普遍的模型,并突出了社会信息的心理表征的复杂性.
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