The School of Psychological Sciences, Tel-Aviv University, Tel-Aviv 69978, Israel.
The School of Psychological Sciences, Tel-Aviv University, Tel-Aviv 69978, Israel; The Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv 69978, Israel.
Cognition. 2020 Dec;205:104445. doi: 10.1016/j.cognition.2020.104445. Epub 2020 Sep 10.
Studies on person recognition have primarily examined recognition of static faces, presented on a computer screen at a close distance. Nevertheless, in naturalistic situations we typically see the whole dynamic person, often approaching from a distance. In such cases, facial information may be less clear, and the motion pattern of an individual, their dynamic identity signature (DIS), may be used for person recognition. Studies that examined the role of motion in person recognition, presented videos of people in motion. However, such stimuli do not allow for the dissociation of gait from face and body form, as different identities differ both in their gait and static appearance. To examine the contribution of gait in person recognition, independently from static appearance, we used a virtual environment, and presented across participants, the same face and body form with different gaits. The virtual environment also enabled us to assess the distance at which a person is recognized as a continuous variable. Using this setting, we assessed the accuracy and distance at which identities are recognized based on their gait, as a function of gait distinctiveness. We find that the accuracy and distance at which people were recognized increased with gait distinctiveness. Importantly, these effects were found when recognizing identities in motion but not from static displays, indicating that DIS rather than attention, enabled more accurate person recognition. Overall these findings highlight that gait contributes to person recognition beyond the face and body and stress an important role for gait in real-life person recognition.
对人物识别的研究主要考察了对静态人脸的识别，这些人脸是在计算机屏幕上近距离呈现的。然而，在自然场景中，我们通常会看到整个动态的人，他们通常从远处走近。在这种情况下，面部信息可能不太清晰，个体的运动模式，即他们的动态身份特征（Dynamic Identity Signature，DIS），可能被用于人物识别。研究人员通过呈现动态人物的视频来考察运动在人物识别中的作用。然而，此类刺激并不能将步态与面部和身体形态区分开来，因为不同的身份在步态和静态外观上都有所不同。为了独立于静态外观来考察步态在人物识别中的贡献，我们使用了一个虚拟环境，向不同的参与者呈现相同的面部和身体形态，但具有不同的步态。虚拟环境还使我们能够评估被识别为连续变量的人的距离。使用这种设置，我们评估了基于步态识别身份的准确性和距离，以及步态的独特性的影响。我们发现，随着步态独特性的增加，人们被识别的准确性和距离也随之增加。重要的是，这些影响仅在识别运动中的身份时出现，而在识别静态显示中的身份时则没有出现，这表明 DIS 而不是注意力，使人物识别更加准确。总的来说，这些发现强调了步态在超越面部和身体的人物识别中起到的作用，并突出了步态在现实生活中人物识别中的重要作用。
