Abstract：
First impressions formed from facial appearance predict important social outcomes. Existing models of these impressions indicate they are underpinned by dimensions of Valence and Dominance, and are typically derived by applying data reduction methods to explicit ratings of faces for a range of traits. However, this approach is potentially problematic because the trait ratings may not fully capture the dimensions on which people spontaneously assess faces. Here, we used natural language processing to extract \'topics\' directly from participants\' free-text descriptions (i.e., their first impressions) of 2222 face images. Two topics emerged, reflecting first impressions related to positive emotional valence and warmth (Topic 1) and negative emotional valence and potential threat (Topic 2). Next, we investigated how these topics were related to Valence and Dominance components derived from explicit trait ratings. Collectively, these components explained only ~44% of the variance in the topics extracted from free-text descriptions and suggested that first impressions are underpinned by correlated valence dimensions that subsume the content of existing trait-rating-based models. Natural language offers a promising new avenue for understanding social cognition, and future work can examine the predictive utility of natural language and traditional data-driven models for impressions in varying social contexts.
摘要：
由面部外观形成的第一印象预测重要的社会结果。这些印象的现有模型表明，它们是由效价和优势的维度支撑的，通常是通过将数据缩减方法应用于一系列特征的面部明确评级而得出的。然而,这种方法存在潜在的问题,因为特质评级可能无法完全捕获人们自发评估面孔的维度.这里,我们使用自然语言处理直接从参与者的自由文本描述中提取“主题”(即，他们的第一印象）2222张人脸图像。出现了两个话题，反映与积极情绪效价和温暖（主题1）以及消极情绪效价和潜在威胁（主题2）相关的第一印象。接下来,我们调查了这些主题如何与来自显性性状评级的效价和优势成分相关.总的来说，这些成分仅解释了从自由文本描述中提取的主题差异的约44％，并建议第一印象由包含现有基于特质评级的模型的内容的相关效价维度支撑。自然语言为理解社会认知提供了一个有希望的新途径，未来的工作可以检查自然语言和传统数据驱动模型在不同社会背景下的印象的预测效用。
