Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data
Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data
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DOI：
10.1016/j.visres.2016.02.002
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发表时间：
2016-05-01
期刊：
影响因子：
1.8
通讯作者：
Wichmann, Felix A.
中科院分区：
文献类型：
--
作者：
Schutt, Heiko H.;Harmeling, Stefan;Wichmann, Felix A.
心理测量函数描述了一个实验变量，如刺激强度，如何影响观察者的行为。从实验数据中估计心理测量功能在心理物理学、实验心理学和行为神经科学等领域起着核心作用。实验数据可能表现出大量的过度分散，这可能是由于观察者行为的非平稳性。在这里，我们将通常用于心理测量功能估计的标准二项式模型扩展到β-二项式模型。我们表明，使用β-二项式模型，即使在数据表现出大量的过度分散，也可以确定准确的可信区间。这超越了经典的过度分散拟合优度的措施，可以检测过度分散，但没有提供方法来做正确的推断过度分散的数据。我们使用贝叶斯推断方法估计后验分布的心理测量函数的参数。与以前的贝叶斯心理测量推理方法不同，我们的软件实现psignifit 4在自动确定的范围内进行后验的数值积分。这避免了使用马尔可夫链蒙特卡罗（MCMC）方法，通常需要专业知识。大量的数值试验表明，该方法的有效性，我们讨论了过度分散实验设计的影响。实现该方法的综合MATLAB工具箱是免费提供的;提供基本功能的Python实现也是可用的。(C)2016作者爱思唯尔有限公司出版
The psychometric function describes how an experimental variable, such as stimulus strength, influences the behaviour of an observer. Estimation of psychometric functions from experimental data plays a central role in fields such as psychophysics, experimental psychology and in the behavioural neurosciences. Experimental data may exhibit substantial overdispersion, which may result from non-stationarity in the behaviour of observers. Here we extend the standard binomial model which is typically used for psychometric function estimation to a beta-binomial model. We show that the use of the beta-binomial model makes it possible to determine accurate credible intervals even in data which exhibit substantial overdispersion. This goes beyond classical measures for overdispersion goodness-of-fit which can detect overdispersion but provide no method to do correct inference for overdispersed data. We use Bayesian inference methods for estimating the posterior distribution of the parameters of the psychometric function. Unlike previous Bayesian psychometric inference methods our software implementation-psignifit 4 performs numerical integration of the posterior within automatically determined bounds. This avoids the use of Markov chain Monte Carlo (MCMC) methods typically requiring expert knowledge. Extensive numerical tests show the validity of the approach and we discuss implications of overdispersion for experimental design. A comprehensive MATLAB toolbox implementing the method is freely available; a python implementation providing the basic capabilities is also available. (C) 2016 The Authors. Published by Elsevier Ltd.