Three models have been advanced to explain the asymmetrical ROCs that are commonly observed on recognition memory tasks. One model, the unequal-variance signal-detection (UVSD) model, assumes that recognition decisions result from a strength-based process that is governed by two unequal-variance Gaussian distributions. A second model, the dual-process signal-detection (DPSD) model, assumes that recognition decisions are sometimes based on a threshold-recollection process and otherwise rely on a strength-based (familiarity) process. A third model, the mixture signal-detection (MSD) model, holds that recognition memory decisions are based on a continuous memory strength variable, but the old item distribution consists of a mixture of two equal-variance Gaussian distributions with different means: the higher mean distribution for attended items and the lower mean distribution for partially attended items. We tested the ability of these three models to predict two-alternative forced-choice (2AFC) recognition performance based on an ROC analysis of yes/no recognition performance. While all three were able to predict 2AFC performance to some degree, the UVSD model explained more variance than either the DP or MSD model. In addition, the specific model-based parameter estimates were more sensible for the UVSD model than for the other two models. The issue on theoretical validity and model flexibility will be discussed. |