Institute of Research in Psychology (IPSY), University of Louvain, Louvain-la-Neuve 1348, Belgium.
Institute of Neuroscience (IoNS), University of Louvain, Louvain-la-Neuve 1348, Belgium.
eNeuro. 2022 Oct 6;9(5). doi: 10.1523/ENEURO.0420-21.2022. Print 2022 Sep-Oct.
The ability to detect faces in the environment is of utmost ecological importance for human social adaptation. While face categorization is efficient, fast and robust to sensory degradation, it is massively impaired when the facial stimulus does not match the natural contrast statistics of this visual category, i.e., the typically experienced ordered alternation of relatively darker and lighter regions of the face. To clarify this phenomenon, we characterized the contribution of natural contrast statistics to face categorization. Specifically, 31 human adults viewed various natural images of nonface categories at a rate of 12 Hz, with highly variable images of faces occurring every eight stimuli (1.5 Hz). As in previous studies, neural responses at 1.5 Hz as measured with high-density electroencephalography (EEG) provided an objective neural index of face categorization. Here, when face images were shown in their naturally experienced contrast statistics, the 1.5-Hz face categorization response emerged over occipito-temporal electrodes at very low contrast [5.1%, or 0.009 root-mean-square (RMS) contrast], quickly reaching optimal amplitude at 22.6% of contrast (i.e., RMS contrast of 0.041). Despite contrast negation preserving an image's spectral and geometrical properties, negative contrast images required twice as much contrast to trigger a face categorization response, and three times as much to reach optimum. These observations characterize how the internally stored natural contrast statistics of the face category facilitate visual processing for the sake of fast and efficient face categorization.
环境中面孔的识别能力对人类的社会适应具有至关重要的生态意义。虽然面孔分类对感官退化具有高效、快速且稳健的特点，但当面部刺激与这种视觉类别自然对比度的统计数据不匹配时，它会受到极大的损害，即典型的经历过相对较暗和较亮区域的有序交替。为了阐明这一现象，我们描述了自然对比度统计数据对面孔分类的贡献。具体来说，31 名成年人类观看了各种非面孔类别的自然图像，以 12 Hz 的速率进行，而面孔的高度变化图像每八个刺激出现一次（1.5 Hz）。与之前的研究一样，高密度脑电图 (EEG) 测量的 1.5 Hz 时的神经反应提供了面孔分类的客观神经指标。在这里，当面孔图像以其自然经历的对比度统计数据呈现时，1.5 Hz 的面孔分类反应出现在枕颞电极上，对比度非常低[5.1%，或 0.009 均方根 (RMS) 对比度]，在 22.6%的对比度（即 RMS 对比度为 0.041）时迅速达到最佳幅度。尽管对比度否定保留了图像的光谱和几何特性，但负对比度图像需要两倍的对比度才能触发面孔分类反应，需要三倍的对比度才能达到最佳效果。这些观察结果描述了内部存储的面孔类别自然对比度统计数据如何促进视觉处理，以便快速高效地进行面孔分类。
