Recognition ROCs Are Curvilinear-or Are They? On Premature Arguments Against the Two-High-Threshold Model of Recognition

Recent reviews of recognition receiver operating characteristics (ROCs) claim that their curvilinear shape rules out threshold models of recognition. However, the shape of ROCs based on confidence ratings is not diagnostic to refute threshold models, whereas ROCs based on experimental bias manipulations are. Also, fitting predicted frequencies to actual data is a more sensitive method for model comparisons than ROC regressions. In a reanalysis of 59 published data sets, the 2-high-threshold model (2HTM) fit the data better than an unequal variance signal detection model in about half of the cases. Three recognition experiments with experimental bias manipulation were conducted that yielded linear ROCs and a better fit of the 2HTM in all cases. On the basis of actual data and a simulation, the authors argue that both models are at least equally valid as measurement tools and call perhaps be integrated theoretically.