Signal detection theory analysis of type 1 and type 2 data: Meta-dâ², response-specific meta-dâ², and the unequal variance SDT model

© 2014 Springer-Verlag Berlin Heidelberg. All rights reserved. Previously we have proposed a signal detection theory (SDT) methodology for measuring metacognitive sensitivity (Maniscalco and Lau, Conscious Cogn 21:422-430, 2012). Our SDT measure, meta-dâ², provides a response-bias free measure of how well confidence ratings track task accuracy. Here we provide an overview of standard SDT and an extended formal treatment of meta-dâ². However, whereas meta-dâ² characterizes an observer's sensitivity in tracking overall accuracy, it may sometimes be of interest to assess metacognition for a particular kind of behavioral response. For instance, in a perceptual detection task, we may wish to characterize metacognition separately for reports of stimulus presence and absence. Here we discuss the methodology for computing such a response-specific meta-dâ² and provide corresponding Matlab code. This approach potentially offers an alternative explanation for data that are typically taken to support the unequal variance SDT (UV-SDT) model. We demonstrate that simulated data generated from UV-SDT can be well fit by an equal variance SDT model positing different metacognitive ability for each kind of behavioral response, and likewise that data generated by the latter model can be captured by UV-SDT. This ambiguity entails that caution is needed in interpreting the processes underlying relative operating characteristic (ROC) curve properties. Type 1 ROC curves generated by combining type 1 and type 2 judgments, traditionally interpreted in terms of low-level processes (UV), can potentially be interpreted in terms of high-level processes instead (response-specific metacognition). Similarly, differences in area under response-specific type 2 ROC curves may reflect the influence of low-level processes (UV) rather than high-level metacognitive processes.Link_to_subscribed_fulltex