How should we measure metacognitive sensitivity, i.e. the efficacy with which observers’ confidence ratings discriminate between their own correct and incorrect stimulus classifications? We argue that currently available methods are inadequate because they are influenced by factors such as response bias and type 1 sensitivity . Extending the signal detection theory approach of Galvin, Podd, Drga, and Whitmore , we propose a method of measuring type 2 sensitivity that is free from these confounds. We call our measure meta-d′, which reflects (...) how much information, in signal-to-noise units, is available for metacognition. Applying this novel method in a 2-interval forced choice visual task, we found that subjects’ metacognitive sensitivity was close to, but significantly below, optimality. We discuss the theoretical implications of these findings, as well as related computational issues of the method. We also provide free Matlab code for implementing the analysis. (shrink)

The medial prefrontal cortex (mPFC) has been the subject of intense interest as a locus of cognitive control. Several computational models have been proposed to account for a range of effects, including error detection, conflict monitoring, error likelihood prediction, and numerous other effects observed with single-unit neurophysiology, fMRI, and lesion studies. Here, we review the state of computational models of cognitive control and offer a new theoretical synthesis of the mPFC as signaling response–outcome predictions. This new synthesis has two interacting (...) components. The first component learns to predict the various possible outcomes of a planned action, and the second component detects discrepancies between the actual and intended responses; the detected discrepancies in turn update the outcome predictions. This single construct is consistent with a wide array of performance monitoring effects in mPFC and suggests a unifying account of the cognitive role of medial PFC in performance monitoring. (shrink)