Classification image analysis: Estimation and statistical inference for two-alternative forced-choice experiments
Classification image analysis: Estimation and statistical inference for two-alternative forced-choice experiments
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DOI：
10.1167/2.1.5
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发表时间：
2002-01-01
影响因子：
1.8
通讯作者：
Eckstein, Miguel P.
中科院分区：
文献类型：
--
作者：
Abbey, Craig K.;Eckstein, Miguel P.
我们考虑对从两种替代强制选择实验范式获得的分类图像进行估计和统计假设检验。我们从简单的强制选择检测和区分任务的任务性能概率模型开始。特别关注一般线性滤波器模型，因为这些模型导致将分类图像直接解释为滤波器权重的估计。然后，我们描述了从观察者数据获取分类图像的估计过程。提出了许多统计测试，用于基于从分类图像派生的一些更紧凑的特征集来测试来自分类图像的各种假设。作为如何使用我们描述的方法的示例，我们提出了一个研究高斯凹凸轮廓检测的案例研究。
We consider estimation and statistical hypothesis testing on classification images obtained from the two-alternative forced-choice experimental paradigm. We begin with a probabilistic model of task performance for simple forced-choice detection and discrimination tasks. Particular attention is paid to general linear filter models because these models lead to a direct interpretation of the classification image as an estimate of the filter weights. We then describe an estimation procedure for obtaining classification images from observer data. A number of statistical tests are presented for testing various hypotheses from classification images based on some more compact set of features derived from them. As an example of how the methods we describe can be used, we present a case study investigating detection of a Gaussian bump profile.