Network models reveal high-dimensional social inferences in naturalistic settings beyond latent construct models.

Junsong Lu, Chujun Lin
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Abstract

Long-standing research suggests that social inferences are captured by a few latent dimensions (e.g., warmth and competence). Others argue that social inferences are more complex but lack sufficient empirical support. Here, we conducted two pre-registered studies to test the high-dimensional properties of social inferences. To maximize generalizability, we computationally sampled diverse naturalistic videos and recruited U.S. representative participants (Study 1, N = 1598). Participants freely described people in videos using their own words. Cross-validation identified 25 latent dimensions which explained only 15% of the variance in the data. Alternatively, a sparse network model representing the unique correlations between inferences better represented the data. The network models informed the dynamics of naturalistic inferences, revealing how different inferences co-occurred and how they unfolded over time from concrete to abstract (Study 1). The network models also indicated cultural differences in how one inference was related to another between samples (Study 2, Asian N = 651, European N = 792). Together, these findings show that the high-dimensional network approach provides an alternative model for understanding the mental representation of social inferences in naturalistic contexts, which provides new insights into the dynamics and diversities of social inferences beyond the static, universal structure found with traditional low-dimensional latent-construct approaches.

网络模型揭示了超越潜在构式模型的高维社会推理。
长期以来的研究表明,社会推断是由几个潜在的维度(例如,热情和能力)捕获的。另一些人则认为,社会推断更为复杂,但缺乏足够的经验支持。在这里,我们进行了两个预先注册的研究来测试社会推理的高维特性。为了最大限度地提高普遍性,我们计算采样了不同的自然主义视频,并招募了美国代表性参与者(研究1,N = 1598)。参与者可以自由地用自己的话描述视频中的人。交叉验证确定了25个潜在维度,仅解释了数据中15%的方差。另外,稀疏网络模型表示推理之间的唯一相关性,可以更好地表示数据。网络模型揭示了自然主义推理的动态,揭示了不同的推理是如何共同发生的,以及它们是如何随着时间从具体到抽象展开的(研究1)。网络模型还表明,样本之间的一个推断与另一个推断之间存在文化差异(研究2,亚洲N = 651,欧洲N = 792)。总之,这些发现表明,高维网络方法为理解自然情境下社会推理的心理表征提供了另一种模型,超越了传统低维潜在结构方法所发现的静态、普遍结构,为社会推理的动态和多样性提供了新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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