ABSTRACT

This study examines the limits of image variability, commonly referred to as Ambient Images, in face learning. To measure face learning, the authors used the face sorting paradigm from Jenkins et al. [(2011). Variability in photos of the same face. Cognition, 121(3), 313–323]. Before completing the face sorting task, participants viewed either 5, 15, or 45 ambient images of an unfamiliar person’s face. The authors aimed to observe whether there is an incremental benefit of ambient images and whether studying many ambient images could predict perfect performance. The results revealed that performance greatly improved from a low to medium exposure group; however, performance plateaued after viewing 15 ambient images. In addition, participants who viewed 45 images did not always achieve perfect performance. Results of this study also found that time data can serve as a quantitative measure of familiarity. The authors concluded that future research must extend past ambient images to fully understand the process of familiarity.

摘要

这项研究探讨了在面部学习中图像可变性（通常称为“环境图像”）的局限性。为了衡量人脸学习，作者使用了詹金斯等人的人脸分类范例。[（2011）。同一张脸的照片中的变异性。认知，121（3），313–323]。在完成面部分类任务之前，参与者查看了5张，15张或45张陌生人面部​​的周围图像。作者旨在观察环境图像是否有增加的好处，研究许多环境图像是否可以预测完美的性能。结果表明，从低暴露水平到中等暴露水平，性能得到了很大改善。但是，在查看15张环境图像后，性能稳定下来。此外，观看45张图像的参与者并非总是能获得完美的表现。这项研究的结果还发现，时间数据可以用来衡量熟悉程度。作者得出结论，未来的研究必须扩展过去的环境图像，以充分了解熟悉的过程。