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. 2012 Apr;23(4):427-34.
doi: 10.1177/0956797611427407. Epub 2012 Mar 5.

The perception of a face is no more than the sum of its parts

Jason M Gold  1 , Patrick J Mundy, Bosco S Tjan
Affiliations

The perception of a face is no more than the sum of its parts

Jason M Gold et al. Psychol Sci. 2012 Apr.

Abstract

When you see a person's face, how do you go about combining his or her facial features to make a decision about who that person is? Most current theories of face perception assert that the ability to recognize a human face is not simply the result of an independent analysis of individual features, but instead involves a holistic coding of the relationships among features. This coding is thought to enhance people's ability to recognize a face beyond what would be expected if each feature were shown in isolation. In the study reported here, we explicitly tested this idea by comparing human performance on facial-feature integration with that of an optimal Bayesian integrator. Contrary to the predictions of most current notions of face perception, our findings showed that human observers integrate facial features in a manner that is no better than would be predicted by their ability to use each individual feature when shown in isolation. That is, a face is perceived no better than the sum of its individual parts.

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Conflict of interest statement

Declaration of Conflicting Interests

The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article.

Figures

Fig. 1
Fig. 1
Examples of the stimuli used in Experiments 1 through 3. Four features were isolated from six different face images (each column shows features from a single face). In the combined condition, all four features were shown. In the other four conditions, only one feature from a face was shown. In Experiment 1, features were presented upright against a gray background. In Experiment 2, all features were presented upright inside of a background face that was created by averaging the whole faces from which the features were taken and leaving four holes for the features; the examples in the bottom row show the features from the combined condition presented against the average-face background. In Experiment 3, the five conditions were the same, but the features were inverted; half of the observers saw features against a gray background, and the other half saw features against the average-face background.
Fig. 2
Fig. 2
Results of Experiment 1 (a, b) and Experiment 2 (c, d). The graphs in the left column show contrast sensitivity as a function of condition for 5 human observers and an ideal observer. The graphs in the right column show the integration index as a function of observer, along with the mean across observers. Also shown is the integration index predicted by a suboptimal best-feature model observer (see the text). In Experiment 1, facial features were shown in an upright position against a gray background. In Experiment 2, facial features were shown in an upright position against a background face image. Error bars on all individual sensitivities and indices were obtained through bootstrap simulations (Efron & Tibshirani, 1993) and represent ±1 SD in the case of sensitivity and +1 SD in the case of the integration index. Error bars for the mean integration indices show +1 SEM. The optimal index is 1, which is highlighted by the dashed horizontal line.
Fig. 3
Fig. 3
Results of Experiment 3 for inverted facial features presented against a gray background (a, b) and against a background face image (c. d). The graphs in the left column show contrast sensitivity as a function of condition for human observers and the ideal observer. The graphs in the right column show the integration index as a function of observer, along with the mean across observers. Also shown is the integration index predicted by a suboptimal best-feature model observer (see the text). Error bars on all individual sensitivities and indices were obtained through bootstrap simulations (Efron & Tibshirani, 1993) and represent ±1 SD in the case of sensitivity and +1 SD in the case of the integration index. Error bars for the mean integration indices show +1 SEM. The optimal index is 1, which is highlighted by the dashed horizontal line.
Fig. 4
Fig. 4
Mean integration index across all three experiments as a function of stimulus type. Also shown is the integration index predicted by a suboptimal best-feature model observer (see the text). Facial features were presented either upright or inverted and against either a gray background or a background face image. Error bars show ±1 SEM. The optimal index is 1, which is highlighted by the dashed horizontal line.
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