Previous research has shown that exposure to within-person variability facilitates face learning. A different body of work has examined potential benefits of providing multiple images in face matching tasks. Viewers are asked to judge whether a target face matches a single face image (as when checking photo-ID) or multiple face images of the same person. The evidence here is less clear, with some studies finding a small multiple-image benefit, and others finding no advantage. In four experiments, we address this discrepancy in the benefits of multiple images from learning and matching studies. We show that multiple-image arrays only facilitate face matching when arrays precede targets. Unlike simultaneous face matching tasks, sequential matching and learning tasks involve memory and require abstraction of a stable representation of the face from the array, for subsequent comparison with a target. Our results show that benefits from multiple-image arrays occur only when this abstraction is required, and not when array and target images are available at once. These studies reconcile apparent differences between face learning and face matching and provide a theoretical framework for the study of within-person variability in face perception.

先前的研究表明，暴露于人际变异会促进面部学习。不同的工作组研究了在面部匹配任务中提供多个图像的潜在好处。要求观看者判断目标面部是与同一个人的单个面部图像（如检查带有照片的身份证）相匹配还是与多个面部图像相匹配。这里的证据不太清楚，有些研究发现较小的多图像利益，而另一些则没有优势。在四个实验中，我们解决了学习和匹配研究中多张图片的好处之间的差异。我们显示了多图像阵列仅在阵列位于目标之前时才有助于面部匹配。与同时进行人脸匹配任务不同，顺序匹配和学习任务涉及内存，需要从数组中提取出稳定的人脸表示形式，以便随后与目标进行比较。我们的结果表明，只有在需要这种抽象时，才会从多图像阵列中受益，而当阵列和目标图像同时可用时，则不会。这些研究调和了人脸学习和人脸匹配之间的明显差异，并为人脸感知中人际变异性的研究提供了理论框架。