TY - JOUR
T1 - The ROC Toolbox
T2 - A toolbox for analyzing receiver-operating characteristics derived from confidence ratings
AU - Koen, Joshua D.
AU - Barrett, Frederick S.
AU - Harlow, Iain M.
AU - Yonelinas, Andrew P.
N1 - Funding Information:
This work was supported by National Science Foundation Graduate Research Fellowship 1148897 awarded to Joshua D. Koen and by National Institute of Mental Health Grants R01-MH059352-13 and R01-MH083734-05 awarded to Andrew P. Yonelinas. JDK was supported by a Ruth L. Kirschstein National Research Service Award from the National Institute on Aging (F32-AG049583) during the preparation of this manuscript.
Publisher Copyright:
© 2016, Psychonomic Society, Inc.
PY - 2017/8/1
Y1 - 2017/8/1
N2 - 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.
AB - 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.
KW - Memory
KW - Open source software
KW - Perception
KW - Signal detection theory
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U2 - 10.3758/s13428-016-0796-z
DO - 10.3758/s13428-016-0796-z
M3 - Article
C2 - 27573007
AN - SCOPUS:84984652005
VL - 49
SP - 1399
EP - 1406
JO - Behavior Research Methods
JF - Behavior Research Methods
SN - 1554-351X
IS - 4
ER -