Metacognition has long been defined as “cognition about cognition”. One of its aspects is the error monitoring ability, which includes being aware of one’s own errors without external feedback. This ability is mostly investigated in two-alternative forced choice tasks, where the performance has all or none nature in terms of accuracy. The previous literature documents the effect of different influences on the error monitoring ability, such as working memory, feedback and sensorimotor involvement. However, these demonstrations fall short of generalizing to the real life scenarios where the errors often have a magnitude and a direction. To bridge this gap, recent studies showed that humans could keep track of the magnitude and the direction of their errors in temporal, spatial and numerical domains in two metrics: confidence and short-long/few-more judgements. This talk will cover how the documented effects that are obtained in the two alternative forced choices tasks apply to the temporal error monitoring ability. Finally, how magnitude and direction monitoring (i.e., confidence and short-long judgements) can be differentiated as the two indices of temporal error monitoring ability will be discussed.

Error monitoring refers to the ability to monitor one's own task performance without explicit feedback. This ability is studied typically in two-alternative forced-choice (2AFC) paradigms. Recent research showed that humans can also keep track of the magnitude and direction of errors in different magnitude domains (e.g., numerosity, duration, length). Based on the evidence that suggests a shared mechanism for magnitude representations, we aimed to investigate whether metric error monitoring ability is commonly governed across different magnitude domains. Participants reproduced/estimated temporal, numerical, and spatial magnitudes after which they rated their confidence regarding first order task performance and judged the direction of their reproduction/estimation errors. Participants were also tested in a 2AFC perceptual decision task and provided confidence ratings regarding their decisions. Results showed that variability in reproductions/estimations and metric error monitoring ability, as measured by combining confidence and error direction judgements, were positively related across temporal, spatial, and numerical domains. Metacognitive sensitivity in these metric domains was also positively associated with each other but not with metacognitive sensitivity in the 2AFC perceptual decision task. In conclusion, the current findings point at a general metric error monitoring ability that is shared across different metric domains with limited generalizability to perceptual decision-making.

Does that title put you to sleep or pique your interest? The goal of my presentation is to introduce a powerful yet under-utilized mathematical equation that is surprisingly effective at uncovering spatiotemporal patterns that are embedded in data -- but that might be inaccessible in traditional analysis methods due to low SNR or sparse spatial distribution. If you flunked calculus, then don't worry: the math is really easy, and I'll spend most of the time discussing intuition, simulations, and applications in real data. I will also spend some time in the beginning of the talk providing a bird's-eye-view of the empirical research in my lab, which focuses on mesoscale brain dynamics associated with error monitoring and response competition.