Levi D M, Klein S A, Sharma V
College of Optometry, University of Houston, TX 77204-6052, USA.
Vision Res. 1999 Feb;39(3):445-65. doi: 10.1016/s0042-6989(98)00125-4.
The present paper addresses whether topographical jitter or undersampling might limit pattern perception in foveal, peripheral and strabismic amblyopic vision. In the first experiment, we measured contrast thresholds for detecting and identifying the orientation (up, down, left, right) of E-like patterns comprised of Gabor samples. We found that detection and identification thresholds were both degraded in peripheral and amblyopic vision; however, the orientation identification/detection threshold ratio was approximately the same in foveal, peripheral and amblyopic vision. This result is somewhat surprising, because we anticipated that a high degree of uncalibrated topographical jitter in peripheral and amblyopic vision would have affected orientation identification to a greater extent than detection. In the second experiment, we investigated the tolerance of human and model observers to perturbation of the positions of the samples defining the pattern when its contrast was suprathreshold, by measuring a 'jitter threshold' (the amount of jitter required to reduce performance from near perfect to 62.5% correct). The results and modeling of our jitter experiments suggest that pattern identification is highly robust to positional jitter. The positional tolerance of foveal, peripheral and amblyopic vision is equal to about half the separation of the features and the close similarity between the three visual systems argues against extreme topographical jitter. The effects of jitter on human performance are consistent with the predictions of a 'template' model. In the third experiment we determined what fraction of the 17 Gabor samples are needed to reliably identify the orientation of the E-patterns by measuring a 'sample threshold' (the proportion of samples required for 62.5% correct performance). In foveal vision, human observers are highly efficient requiring only about half the samples for reliable pattern identification. Relative to an ideal observer model, humans perform this task with 85% efficiency. In contrast, in both peripheral vision and strabismic amblyopia more samples are required. The increased number of features required in peripheral vision and strabismic amblyopia suggests that in these visual systems, the stimulus is underrepresented at the stage of feature integration.
本文探讨了地形抖动或欠采样是否会限制中央凹、周边和斜视性弱视视觉中的图案感知。在第一个实验中,我们测量了检测和识别由Gabor样本组成的E形图案方向(上、下、左、右)的对比度阈值。我们发现,周边视觉和弱视视觉中的检测和识别阈值均下降;然而,中央凹、周边和弱视视觉中的方向识别/检测阈值比大致相同。这一结果有些令人惊讶,因为我们预计周边和弱视视觉中高度未校准的地形抖动对方向识别的影响会比检测更大。在第二个实验中,我们通过测量“抖动阈值”(将性能从接近完美降低到62.5%正确所需的抖动量),研究了人类和模型观察者对定义图案的样本位置受到扰动时的耐受性,此时图案对比度高于阈值。我们抖动实验的结果和建模表明,图案识别对位置抖动具有高度鲁棒性。中央凹、周边和弱视视觉的位置耐受性约等于特征间距的一半,并且这三种视觉系统之间的高度相似性表明不存在极端的地形抖动。抖动对人类表现的影响与“模板”模型的预测一致。在第三个实验中,我们通过测量“样本阈值”(达到62.5%正确表现所需的样本比例)来确定可靠识别E形图案方向需要17个Gabor样本中的多少比例。在中央凹视觉中,人类观察者效率很高,可靠的图案识别仅需约一半的样本。相对于理想观察者模型,人类执行此任务的效率为85%。相比之下,在周边视觉和斜视性弱视中则需要更多样本。周边视觉和斜视性弱视中所需特征数量的增加表明,在这些视觉系统中,刺激在特征整合阶段的表征不足。