Bayesian adaptive estimation: The next dimension
Bayesian adaptive estimation: The next dimension
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DOI：
10.1016/j.jmp.2005.12.005
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发表时间：
2006-08-01
影响因子：
1.8
通讯作者：
Lukka, TJ
中科院分区：
文献类型：
--
作者：
Kujala, JV;Lukka, TJ
我们提出了一个新的心理测量模型的二维刺激，如色差，基于参数化的阈值的一维心理测量函数为所有椭圆。应用于该模型的P贝叶斯自适应估计方法产生同时在多个刺激维度上变化的试验。模拟表明，这种新的程序可以是更有效的比更传统的程序估计的心理功能的一维线独立，只需要四分之一或更少的试验次数在典型的情况下相同的性能。在一个真实的心理物理实验中，每个估计的阈值椭圆只有22次试验就足以一致地证明某些颜色外观现象。我们讨论的多维适应的实际意义。为了使该模型的应用实际，我们提出了两个显着更快的算法运行的P方法：一个离散化的算法，利用快速傅立叶变换更好地缩放与采样率和蒙特卡罗粒子滤波算法，应该能够扩展到更多的维度。(c)2006年爱思唯尔公司All rights reserved.
We propose a new psychometric model for two-dimensional stimuli, such as color differences, based on parameterizing the threshold of a one-dimensional psychometric function as ail ellipse. The P Bayesian adaptive estimation method applied to this model yields trials that vary in multiple stimulus dimensions simultaneously. Simulations indicate that this new procedure can be much more efficient than the more conventional procedure of estimating the psychometric function on one-dimensional lines independently, requiring only one-fourth or less the number of trials for equivalent performance in typical situations. In a real psychophysical experiment with a yes-no task, as few as 22 trials per estimated threshold ellipse were enough to consistently demonstrate certain color appearance phenomena. We discuss the practical implications of the multidimensional adaptation. In order to make the application of the model practical, we present two significantly faster algorithms for running the P method: a discretized algorithm utilizing the Fast Fourier Transform for better scaling with the sampling rates and a Monte Carlo particle filter algorithm that should be able to scale into even more dimensions. (c) 2006 Elsevier Inc. All rights reserved.