Kwon Miyoung, Legge Gordon E
Department of Psychology, University of Minnesota, Elliott Hall, 75 East River Rd., Minneapolis, MN 55455, USA.
Vision Res. 2011 Sep 15;51(18):1995-2007. doi: 10.1016/j.visres.2011.06.020. Epub 2011 Aug 9.
It is well known that object recognition requires spatial frequencies exceeding some critical cutoff value. People with central scotomas who rely on peripheral vision have substantial difficulty with reading and face recognition. Deficiencies of pattern recognition in peripheral vision, might result in higher cutoff requirements, and may contribute to the functional problems of people with central-field loss. Here we asked about differences in spatial-cutoff requirements in central and peripheral vision for letter and face recognition. The stimuli were the 26 letters of the English alphabet and 26 celebrity faces. Each image was blurred using a low-pass filter in the spatial frequency domain. Critical cutoffs (defined as the minimum low-pass filter cutoff yielding 80% accuracy) were obtained by measuring recognition accuracy as a function of cutoff frequency (in cycles per object). Our data showed that critical cutoffs increased from central to peripheral vision by 20% for letter recognition and by 50% for face recognition. We asked whether these differences could be accounted for by central/peripheral differences in the contrast sensitivity function (CSF). We addressed this question by implementing an ideal-observer model which incorporates empirical CSF measurements and tested the model on letter and face recognition. The success of the model indicates that central/peripheral differences in the cutoff requirements for letter and face recognition can be accounted for by the information content of the stimulus limited by the shape of the human CSF, combined with a source of internal noise and followed by an optimal decision rule.
众所周知，物体识别需要空间频率超过某个临界截止值。依赖周边视觉的中心暗点患者在阅读和人脸识别方面存在很大困难。周边视觉中模式识别的缺陷可能导致更高的截止要求，并可能导致中心视野丧失患者的功能问题。在这里，我们研究了中心视觉和周边视觉在字母和人脸识别方面空间截止要求的差异。刺激物是26个英文字母和26张名人脸。每个图像在空间频率域中使用低通滤波器进行模糊处理。通过测量识别准确率作为截止频率（每物体周期数）的函数来获得临界截止值（定义为产生80%准确率的最低低通滤波器截止值）。我们的数据表明，字母识别的临界截止值从中心视觉到周边视觉增加了20%，人脸识别增加了50%。我们询问这些差异是否可以由对比敏感度函数（CSF）的中心/周边差异来解释。我们通过实施一个理想观察者模型来解决这个问题，该模型纳入了经验性的CSF测量，并在字母和人脸识别上对该模型进行了测试。该模型的成功表明，字母和人脸识别截止要求的中心/周边差异可以由受人类CSF形状限制的刺激信息内容、内部噪声源以及最优决策规则来解释。
