Murray Richard F
Department of Psychology and Centre for Vision Research, York University, Toronto, Ontario, Canada.
J Vis. 2011 May 2;11(5):2. doi: 10.1167/11.5.2.
Classification images have recently become a widely used tool in visual psychophysics. Here, I review the development of classification image methods over the past fifteen years. I provide some historical background, describing how classification images and related methods grew out of established statistical and mathematical frameworks and became common tools for studying biological systems. I describe key developments in classification image methods: use of optimal weighted sums based on the linear observer model, formulation of classification images in terms of the generalized linear model, development of statistical tests, use of priors to reduce dimensionality, methods for experiments with more than two response alternatives, a variant using multiplicative noise, and related methods for examining nonlinearities in visual processing, including second-order Volterra kernels and principal component analysis. I conclude with a selective review of how classification image methods have led to substantive findings in three representative areas of vision research, namely, spatial vision, perceptual organization, and visual search.
分类图像最近已成为视觉心理物理学中广泛使用的工具。在此，我回顾过去十五年中分类图像方法的发展。我提供一些历史背景，描述分类图像及相关方法如何从既定的统计和数学框架中发展而来，并成为研究生物系统的常用工具。我描述分类图像方法的关键发展：基于线性观察者模型的最优加权和的使用、根据广义线性模型对分类图像的公式化、统计检验的发展、使用先验来降低维度、具有两个以上响应选项的实验方法、使用乘性噪声的变体以及用于检查视觉处理中的非线性的相关方法，包括二阶Volterra核和主成分分析。最后，我选择性地回顾了分类图像方法如何在视觉研究的三个代表性领域，即空间视觉、知觉组织和视觉搜索中得出实质性发现。
