The surprisingly high human efficiency at learning to recognize faces.
The surprisingly high human efficiency at learning to recognize faces.
复制标题
人类学习识别面孔的效率惊人地高。
DOI：
10.1016/j.visres.2008.10.014
复制
发表时间：
2009
期刊：
影响因子：
1.8
通讯作者：
Eckstein,MiguelP
中科院分区：
文献类型：
--
作者：
Peterson,MatthewF;Abbey,CraigK;Eckstein,MiguelP
我们研究了人类通过快速学习个人相关特征来优化人脸识别性能的能力。我们创造了具有高度集中于单一特征（鼻子、眼睛、下巴或嘴巴）的识别视觉信息的人造面孔。在每2500个学习块中随机选择一个特征，并在四次试验中保留下来，在此期间，观察者识别随机抽样的有噪声的人脸图像。观察者通过间接反馈学习了鉴别特征，从而获得了很大的性能提升。表现与学习贝叶斯理想观察者相比，与先前的研究相比，在更简单的刺激下产生了意想不到的高学习效果。我们探索了各种解释，并得出结论，当观察者对人脸识别特征不确定时，适应性眼动策略不能驱动更高的学习，而主要可以由人脸识别的次优性来解释。我们表明，即使人类被告知四个特征中的每一个都同样有可能是歧视性特征，但人类使用特定特征来执行任务的初始偏见会导致看似超优的学习。我们还研究了人类在空间分布的面部特征中整合视觉信息的效率低下的可能性。总之，研究结果表明，当人类学会识别包含歧视性信息的特征时，他们在区分人脸方面可以表现出很大的性能提升效果。
We investigated the ability of humans to optimize face recognition performance through rapid learning of individual relevant features. We created artificial faces with discriminating visual information heavily concentrated in single features (nose, eyes, chin or mouth). In each of 2500 learning blocks a feature was randomly selected and retained over the course of four trials, during which observers identified randomly sampled, noisy face images. Observers learned the discriminating feature through indirect feedback, leading to large performance gains. Performance was compared to a learning Bayesian ideal observer, resulting in unexpectedly high learning compared to previous studies with simpler stimuli. We explore various explanations and conclude that the higher learning measured with faces cannot be driven by adaptive eye movement strategies but can be mostly accounted for by suboptimalities in human face discrimination when observers are uncertain about the discriminating feature. We show that an initial bias of humans to use specific features to perform the task even though they are informed that each of four features is equally likely to be the discriminatory feature would lead to seemingly supra-optimal learning. We also examine the possibility of inefficient human integration of visual information across the spatially distributed facial features. Together, the results suggest that humans can show large performance improvement effects in discriminating faces as they learn to identify the feature containing the discriminatory information.