Cottaris Nicolas P, Jiang Haomiao, Ding Xiaomao, Wandell Brian A, Brainard David H
Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA.
Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
J Vis. 2019 Apr 1;19(4):8. doi: 10.1167/19.4.8.
We present a computational-observer model of the human spatial contrast-sensitivity function based on the Image Systems Engineering Toolbox for Biology (ISETBio) simulation framework. We demonstrate that ISETBio-derived contrast-sensitivity functions agree well with ones derived using traditional ideal-observer approaches, when the mosaic, optics, and inference engine are matched. Further simulations extend earlier work by considering more realistic cone mosaics, more recent measurements of human physiological optics, and the effect of varying the inference engine used to link visual representations to psychophysical performance. Relative to earlier calculations, our simulations show that the spatial structure of realistic cone mosaics reduces the upper bounds on performance at low spatial frequencies, whereas realistic optics derived from modern wave-front measurements lead to increased upper bounds at high spatial frequencies. Finally, we demonstrate that the type of inference engine used has a substantial effect on the absolute level of predicted performance. Indeed, the performance gap between an ideal observer with exact knowledge of the relevant signals and human observers is greatly reduced when the inference engine has to learn aspects of the visual task. ISETBio-derived estimates of stimulus representations at various stages along the visual pathway provide a powerful tool for computing the limits of human performance.
我们基于生物学图像系统工程工具箱（ISETBio）模拟框架，提出了一种人类空间对比敏感度函数的计算观测模型。我们证明，当视网膜镶嵌、光学系统和推理引擎匹配时，ISETBio衍生的对比敏感度函数与使用传统理想观测者方法得出的函数高度吻合。进一步的模拟扩展了早期的工作，考虑了更现实的视锥细胞镶嵌、人类生理光学的最新测量结果，以及改变用于将视觉表征与心理物理学表现联系起来的推理引擎的影响。相对于早期的计算，我们的模拟表明，现实视锥细胞镶嵌的空间结构降低了低空间频率下的性能上限，而从现代波前测量得出的现实光学系统则导致高空间频率下的性能上限增加。最后，我们证明所使用的推理引擎类型对预测性能的绝对水平有重大影响。实际上，当推理引擎必须学习视觉任务的各个方面时，具有相关信号确切知识的理想观测者与人类观测者之间的性能差距会大大缩小。ISETBio衍生的沿视觉通路各个阶段的刺激表征估计，为计算人类性能极限提供了一个强大的工具。
