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Facial Attractiveness Ratings from Video-Clips and Static Images Tell the Same Story

机译:视频剪辑和静态图像对面部吸引力的评价讲述了同样的故事

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摘要

Most of what we know about what makes a face attractive and why we have the preferences we do is based on attractiveness ratings of static images of faces, usually photographs. However, several reports that such ratings fail to correlate significantly with ratings made to dynamic video clips, which provide richer samples of appearance, challenge the validity of this literature. Here, we tested the validity of attractiveness ratings made to static images, using a substantial sample of male faces. We found that these ratings agreed very strongly with ratings made to videos of these men, despite the presence of much more information in the videos (multiple views, neutral and smiling expressions and speech-related movements). Not surprisingly, given this high agreement, the components of video-attractiveness were also very similar to those reported previously for static-attractiveness. Specifically, averageness, symmetry and masculinity were all significant components of attractiveness rated from videos. Finally, regression analyses yielded very similar effects of attractiveness on success in obtaining sexual partners, whether attractiveness was rated from videos or static images. These results validate the widespread use of attractiveness ratings made to static images in evolutionary and social psychological research. We speculate that this validity may stem from our tendency to make rapid and robust judgements of attractiveness.
机译:我们对使人脸变得有吸引力以及为什么拥有偏好的大多数了解是基于人脸静态图像(通常是照片)的吸引力等级。但是,有几篇报道称此类评级与动态视频剪辑的评级没有显着相关性,动态视频剪辑提供了丰富的外观样本,这对该文献的有效性提出了挑战。在这里,我们使用大量男性面孔样本测试了对静态图像进行的吸引力评级的有效性。我们发现,尽管视频中存在更多信息(多视角,中性和微笑的表情以及与语音相关的动作),但这些评分与这些男性视频的评分非常吻合。毫不奇怪,鉴于这种高度共识,视频吸引力的组成也与先前报道的静态吸引力非常相似。具体来说,平均性,对称性和男性气质都是视频评价吸引力的重要组成部分。最后,无论是从视频还是静态图像对吸引力进行评估,回归分析都对成功获取性伴侣产生了非常相似的吸引力影响。这些结果验证了在进化和社会心理学研究中对静态图像的吸引力等级的广泛使用。我们推测,这种有效性可能源于我们对吸引力进行快速,可靠判断的趋势。

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