![]() This paper introduces the ROC Toolbox, which was developed to address this gap in the field and provide a standardized framework for the analysis of ROC data. However, there is no standard analysis package to fit different signal detection models to ROC data. Indeed, the use of ROC analysis is very popular in many different areas of cognitive psychology. Moreover, ROC analysis has been used to shed light on the cognitive process affected in numerous clinical populations, such as individuals with medial temporal lobe damage ( Bowles et al., 2007 Bowles et al., 2010 Yonelinas et al., 2002), Alzheimer's Disease ( Ally, Gold, & Budson, 2009 for review, see Koen & Yonelinas, 2014), and Schizophrenia ( Libby, Yonelinas, Ranganath, & Ragland, 2013). Signal-detection theory, and the analysis of receiver operating characteristic (ROC) curves in particular, plays an important role in understanding the processes supporting performance in different cognitive domains, such as perception (e.g., Aly & Yonelinas, 2012), working memory (e.g., Rouder et al., 2008), and episodic memory ( Egan, 1958 Green & Swets, 1988 Yonelinas, 1999 for review, see Yonelinas & Parks, 2007). 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. 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. 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. 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. 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 purpose of the current paper is to present the ROC Toolbox. 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.
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