Bayesian inference for psychometric functions
Bayesian inference for psychometric functions
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DOI：
10.1167/5.5.8
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发表时间：
2005-01-01
影响因子：
1.8
通讯作者：
Wichmann, FA
中科院分区：
文献类型：
--
作者：
Kuss, M;Jäkel, F;Wichmann, FA
在心理物理学研究中，心理测量函数用于模拟物理刺激强度与观察者检测或区分不同强度刺激的能力之间的关系。在本研究中，我们提出使用贝氏推论来撷取实验资料中所包含的资讯来估计心理测量函数的参数。由于贝叶斯推断不能进行分析，我们描述了如何马尔可夫链蒙特卡罗方法可以用来生成样本的后验分布参数。这些样本用于估计贝叶斯置信区间和后验分布的其他特征。此外，我们还讨论了心理测量函数的参数化和先验分布在分析中的作用。所提出的方法举例说明使用人工生成的数据和真实的实验数据的案例研究。此外，我们比较了我们的方法与传统的方法的基础上最大似然参数估计结合自举技术的置信区间估计，发现贝叶斯方法是上级。
In psychophysical studies, the psychometric function is used to model the relation between physical stimulus intensity and the observer's ability to detect or discriminate between stimuli of different intensities. In this study, we propose the use of Bayesian inference to extract the information contained in experimental data to estimate the parameters of psychometric functions. Because Bayesian inference cannot be performed analytically, we describe how a Markov chain Monte Carlo method can be used to generate samples from the posterior distribution over parameters. These samples are used to estimate Bayesian confidence intervals and other characteristics of the posterior distribution. In addition, we discuss the parameterization of psychometric functions and the role of prior distributions in the analysis. The proposed approach is exemplified using artificially generated data and in a case study for real experimental data. Furthermore, we compare our approach with traditional methods based on maximum likelihood parameter estimation combined with bootstrap techniques for confidence interval estimation and find the Bayesian approach to be superior.