PsiMLE: A maximum-likelihood estimation approach to estimating psychophysical scaling and variability more reliably, efficiently, and flexibly
PsiMLE: A maximum-likelihood estimation approach to estimating psychophysical scaling and variability more reliably, efficiently, and flexibly
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PsiMLE：一种最大似然估计方法，可以更可靠、高效、灵活地估计心理物理尺度和变异性
DOI：
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发表时间：
2015
影响因子：
5.4
通讯作者：
Justin Halberda
中科院分区：
文献类型：
--
作者：
Darko Odic;Hee Yeon Im;Robert M. Eisinger;Ryan Ly;Justin Halberda
一个简单而流行的心理物理模型--通常被描述为沿有序的内部尺度排列的重叠的高斯调谐曲线--能够准确地描述大多数心理维度(例如，时间、空间、数字或亮度)在幅度估计、生产和繁殖任务中的人类和非人类的行为表现和神经编码。该模型传统上包括两个参数，用于确定物理刺激如何转换为心理大小：(1)描述物理信号压缩或扩展到相应心理标度(β)的指数，以及(2)沿心理标度(σ)对高斯激活的内在变化量(通常称为内部噪声)的估计。到目前为止，传统上使用对数-对数样地上的线性斜率来估计β，并使用一种完全不同的平均方差系数的方法来估计σ。我们提供了这些传统方法的尊重，但关键的审查，并提供了最大似然估计(MLE)和估计β和σ的贝叶斯估计方法[PSIMLE(β，σ)]的教程，以及自由软件，研究人员可以使用它来实现它，而不需要具备MLE或贝叶斯统计(R-PSIMLE)的背景。通过一系列的仿真和行为实验，验证了该方法的有效性、可靠性、高效性和灵活性，并发现新方法在各个方面都优于传统方法。
A simple and popular psychophysical model—usually described as overlapping Gaussian tuning curves arranged along an ordered internal scale—is capable of accurately describing both human and nonhuman behavioral performance and neural coding in magnitude estimation, production, and reproduction tasks for most psychological dimensions (e.g., time, space, number, or brightness). This model traditionally includes two parameters that determine how a physical stimulus is transformed into a psychological magnitude: (1) an exponent that describes the compression or expansion of the physical signal into the relevant psychological scale (β), and (2) an estimate of the amount of inherent variability (often called internal noise) in the Gaussian activations along the psychological scale (σ). To date, linear slopes on log–log plots have traditionally been used to estimate β, and a completely separate method of averaging coefficients of variance has been used to estimate σ. We provide a respectful, yet critical, review of these traditional methods, and offer a tutorial on a maximum-likelihood estimation (MLE) and a Bayesian estimation method for estimating both β and σ [PsiMLE(β,σ)], coupled with free software that researchers can use to implement it without a background in MLE or Bayesian statistics (R-PsiMLE). We demonstrate the validity, reliability, efficiency, and flexibility of this method through a series of simulations and behavioral experiments, and find the new method to be superior to the traditional methods in all respects.
理解数值近似和数字词之间的映射：威廉姆斯综合症和典型发展的证据。
影响因子：
3.7
作者：
Libertus,MelissaE;Feigenson,Lisa;Halberda,Justin;Landau,Barbara
通讯作者：
Landau,Barbara
影响因子：
4.6
作者：
Mazzocco MM;Feigenson L;Halberda J
通讯作者：
Halberda J
影响因子：
3.7
作者：
Libertus ME;Feigenson L;Halberda J
通讯作者：
Halberda J