Binary ROCs in perception and recognition memory are curved.

In recognition memory, a classic finding is that receiver operating characteristics (ROCs) are curvilinear. This has been taken to support the fundamental assumptions of signal detection theory (SDT) over discrete-state models such as the double high-threshold model (2HTM), which predicts linear ROCs. Recently, however, Bröder and Schütz (2009) challenged this argument by noting that most of the data on which support for SDT is based have involved confidence ratings. The authors argued that certain types of rating scale usage may result in curved ROCs even if the generating process is thresholded in nature. From this point of view, only ROCs constructed via experimental bias manipulations are useful for discriminating between the models. Bröder and Schütz conducted a meta-analysis and new experiments that compared SDT and the 2HTM using binary (yes-no) ROCs and found that many of these functions were linear, supporting 2HTM over SDT. We examine all the data reported by Bröder and Schütz, noting important limitations in their methodology, analyses, and conclusions. We report a new meta-analysis and 2 new experiments to examine the issue more closely while avoiding the limitations of Bröder and Schütz's study. These new data indicate that binary ROCs are curved in recognition, consistent with previous findings in perception and reasoning. Our results support classic arguments in favor of SDT and indicate that curvature in ratings ROCs is not task specific. We recommend the ratings procedure and suggest that analyses based on threshold models be treated with caution.