Suppr超能文献

马的人脸识别:支持整体过程的数据。

Human Face Recognition in Horses: Data in Favor of a Holistic Process.

作者信息

Lansade Léa, Colson Violaine, Parias Céline, Reigner Fabrice, Bertin Aline, Calandreau Ludovic

机构信息

PRC, INRAE, CNRS, IFCE, University Tours, Nouzilly, France.

LPGP, INRAE, UR1037 Fish Physiology and Genomics, Rennes, France.

出版信息

Front Psychol. 2020 Sep 15;11:575808. doi: 10.3389/fpsyg.2020.575808. eCollection 2020.

Abstract

Recent studies have demonstrated that horses can recognize humans based simply on visual information. However, none of these studies have investigated whether this involves the recognition of the face itself, or simply identifying people from non-complex external clues, such as hair color. To go beyond this we wanted to know whether certain features of the face were indispensable for this recognition (e.g., colors, hair or eyes). The 11 horses in this study had previously learned to identify four unfamiliar faces (portrait view and in color) presented repeatedly on a screen. We thus assessed whether they were able to identify these same faces spontaneously when they were presented in four other conditions: profile view, black and white, eyes hidden, changed hairstyle. The horses' performances remained higher than chance level for all the conditions. In a choice test under real conditions, they then approached the people whose face they had learned more often than unknown people. In conclusion, when considering all the individuals studied, no single facial element that we tested appears to be essential for recognition, suggesting holistic processing in face recognition. That means horses do not base their recognition solely on an easy clue such as hair color. They can also link faces from photographs with people in real life, indicating that horses do not process images of faces as simple abstract shapes.

摘要

最近的研究表明,马仅基于视觉信息就能识别人类。然而,这些研究都没有调查这是否涉及对面部本身的识别,或者仅仅是从非复杂的外部线索(如头发颜色)来识别人员。为了进一步探究这个问题,我们想知道面部的某些特征对于这种识别是否不可或缺(例如,颜色、头发或眼睛)。本研究中的11匹马此前已学会识别在屏幕上反复呈现的四张陌生面孔(正面视图且为彩色)。因此,我们评估了在其他四种条件下呈现这些面孔时,它们是否能够自发识别:侧面视图、黑白、眼睛遮挡、发型改变。在所有条件下,马的表现都高于随机水平。在实际条件下的选择测试中,它们随后更频繁地接近它们认识面孔的人,而不是陌生人。总之,考虑到所有研究的个体,我们测试的任何单一面部元素似乎都不是识别所必需的,这表明在面部识别中存在整体处理。这意味着马并非仅仅基于诸如头发颜色这样简单的线索进行识别。它们还能将照片中的面孔与现实生活中的人联系起来,这表明马不会将面部图像作为简单的抽象形状来处理。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a948/7522352/ca8c0390d6e1/fpsyg-11-575808-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验