The ROC Toolbox: A toolbox for analyzing receiver-operating characteristics derived from confidence ratings
The ROC Toolbox: A toolbox for analyzing receiver-operating characteristics derived from confidence ratings
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DOI：
10.3758/s13428-016-0796-z
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发表时间：
2017-08-01
影响因子：
5.4
通讯作者：
Yonelinas, Andrew P.
中科院分区：
文献类型：
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作者：
Koen, Joshua D.;Barrett, Frederick S.;Yonelinas, Andrew P.
信号检测理论和对受者操作特征的分析在情景记忆和知觉理论的发展中起着至关重要的作用。本论文的目的是介绍中华民国政府的经济政策。该工具箱是一组用Matlab编程语言编写的函数，可用于将各种常见的信号检测模型拟合到从置信度实验中获得的ROC数据。开发ROC模型的目标是创建一种工具（1）易于使用，研究人员易于使用自己的数据实现，（2）可以根据不同的研究参数灵活定义模型，例如响应选项的数量（例如，置信度）和实验条件，以及（3）提供最佳例程（例如，ROC工具箱允许各种不同的置信度尺度，目前包括识别记忆和感知中常用的模型：（1）不等方差信号检测（UVSD）模型，（2）双过程信号检测（DPSD）模型，以及（3）混合信号检测（MSD）模型。对于拟合给定数据集的每个模型，ROC工具箱绘制有关最佳拟合模型参数和各种拟合优度度量的汇总信息。在这里，我们将概述ROC工具箱，说明如何使用它来输入和分析真实的数据，并简要讨论可以添加到工具箱中的功能。
Signal-detection theory, and the analysis of receiver-operating characteristics (ROCs), has played a critical role in the development of theories of episodic memory and perception. The purpose of the current paper is to present the ROC Toolbox. This toolbox is a set of functions written in the Matlab programming language that can be used to fit various common signal detection models to ROC data obtained from confidence rating experiments. The goals for developing the ROC Toolbox were to create a tool (1) that is easy to use and easy for researchers to implement with their own data, (2) that can flexibly define models based on varying study parameters, such as the number of response options (e.g., confidence ratings) and experimental conditions, and (3) that provides optimal routines (e.g., Maximum Likelihood estimation) to obtain parameter estimates and numerous goodness-of-fit measures.The ROC toolbox allows for various different confidence scales and currently includes the models commonly used in recognition memory and perception: (1) the unequal variance signal detection (UVSD) model, (2) the dual process signal detection (DPSD) model, and (3) the mixture signal detection (MSD) model. For each model fit to a given data set the ROC toolbox plots summary information about the best fitting model parameters and various goodness-of-fit measures. Here, we present an overview of the ROC Toolbox, illustrate how it can be used to input and analyse real data, and finish with a brief discussion on features that can be added to the toolbox.