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)

Human behavior depends on the ability to effectively introspect about our performance. For simple perceptual decisions, this introspective or metacognitive ability varies substantially across individuals and is correlated with the structure of focal areas in prefrontal cortex. This raises the possibility that the ability to introspect about different perceptual decisions might be mediated by a common cognitive process. To test this hypothesis, we examined whether inter-individual differences in metacognitive ability were correlated across two different perceptual tasks where individuals made judgments (...) about different and unrelated visual stimulus properties. We found that inter-individual differences were strongly correlated between the two tasks for metacognitive ability but not objective performance. Such stability of an individual’s metacognitive ability across different perceptual tasks indicates a general mechanism supporting metacognition independent of the specific task. (shrink)

Our subjective sensory experiences are thought to be heavily shaped by interactions between expectations and incoming sensory information. However, the neural mechanisms supporting these interactions remain poorly understood. By using combined psychophysical and functional MRI techniques, brain activation related to the intensity of expected pain and experienced pain was characterized. As the magnitude of expected pain increased, activation increased in the thalamus, insula, prefrontal cortex, anterior cingulate cortex (ACC) and other brain regions. Pain-intensity-related brain activation was identified in a widely (...) distributed set of brain regions but overlapped partially with expectation-related activation in regions, including the anterior insula and ACC. When expected pain was manipulated, expectations of decreased pain powerfully reduced both the subjective experience of pain and activation of pain-related brain regions, such as the primary somatosensory cortex, insular cortex, and ACC. These results confirm that a mental representation of an impending sensory event can significantly shape neural processes that underlie the formulation of the actual sensory experience and provide insight as to how positive expectations diminish the severity of chronic disease states. (shrink)