A stochastic detection and retrieval model for the study of metacognition

Abstract

We present a signal detection-like model termed the stochastic detection and retrieval model (SDRM) for use in studying metacognition. Focusing on paradigms that relate retrieval (e.g., recall or recognition) and confidence judgments, the SDRM measures (1) variance in the retrieval process, (2) variance in the confidence process, (3) the extent to which different sources of information underlie each response, (4) simple bias (i.e., increasing or decreasing confidence criteria across conditions), and (5) metacognitive bias (i.e., contraction or expansion of the confidence criteria across conditions). In the metacognition literature, gamma correlations have been used to measure the accuracy of confidence judgments. However, gamma cannot distinguish between the first 3 attributes, and it cannot measure either form of bias. In contrast, the SDRM can distinguish among the attributes, and it can measure both forms of bias. In this way, the SDRM can be used to test competing process theories by determining the attribute that best accounts for a change across conditions. To demonstrate the SDRM's usefulness, we investigated judgments of learning (JOLs) followed by cued-recall. Through a series of nested and non-nested model comparisons applied to a new experiment, the SDRM determined that a reduction in variance during the confidence process is the most likely explanation of the delayed-JOL effect, and a stronger relation between information underlying JOLs and recall is the most likely explanation of the testing-JOL effect. Following a brief discussion of implications for JOL theories, we conclude with a broader discussion of how the SDRM can benefit metacognition research.