A computational observer model of spatial contrast sensitivity: Effects of wavefront-based optics, cone mosaic structure, and inference engine

A computational observer model of spatial contrast sensitivity: Effects of wavefront-based optics, cone mosaic structure, and inference engine
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空间对比敏感度的计算观察者模型:基于波前的光学、锥形镶嵌结构和推理引擎的影响

DOI:
10.1101/378323
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发表时间:
2018
期刊:
bioRxiv
影响因子:
--
通讯作者:
D. Brainard
D. Brainard
中科院分区:
--
文献类型:
--
作者:
N. Cottaris;Haomiao Jiang;Xiaomao Ding;B. Wandell;D. Brainard

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我们提出了一个基于生物图像系统工程工具(ISETBio)仿真框架的人类空间对比敏感度(CSF)函数的计算观察者模型。当马赛克、光学和推理引擎匹配时,isetbio衍生的CSFs与使用传统理想观测器方法衍生的CSFs非常一致。进一步的模拟扩展了早期的工作,考虑了更真实的锥体马赛克,最近对人类生理光学的测量,以及用于将视觉表征与心理物理表现联系起来的不同推理引擎的影响。相对于之前的计算,我们的模拟表明,真实的锥体马赛克的空间结构降低了在低空间频率下的性能上限,而来自现代波前测量的真实光学导致高空间频率的上限增加。最后,我们证明了所使用的推理引擎类型对预测性能的绝对水平有实质性的影响。事实上,当推理引擎必须学习视觉任务的各个方面时,具有相关信号精确知识的理想观察者与人类观察者之间的性能差距大大减小。isetbio在视觉通路的不同阶段对刺激表征的估计为计算人类表现的极限提供了一个强大的工具。
We present a computational observer model of the human spatial contrast sensitivity (CSF) function based on the Image Systems EngineeringTools for Biology (ISETBio) simulation framework. We demonstrate that ISETBio-derived CSFs agree well with CSFs 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 psy-chohysical performance. Relative to earlier calculations, our simulations show that the spatial structure of realistic cone mosaics reduces upper bounds on performance at low spatial frequencies, whereas realistic optics derived from modern wavefront measurements lead to increased upper bounds 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 different stages along the visual pathway provide a powerful tool for computing the limits of human performance.
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