ABSTRACT
People form higher-level, metacognitive representations of their own abilities across a range of tasks. Here we ask how metacognitive confidence judgments of performance during motor learning are shaped by the learner’s recent history of errors. Across two motor adaptation experiments, our computational modeling approach demonstrated that people’s confidence judgments are best explained by a recency-weighted averaging of observed motor errors. Moreover, in the formation of these confidence estimates, people appear to re-weight observed motor errors according to a subjective cost function. Finally, confidence judgments appeared to incorporate recent motor errors in a manner that was sensitive to the volatility of the learning environment, integrating a shallower history when the environment was more volatile. Our study provides a novel descriptive model that successfully approximates the dynamics of metacognitive judgments during motor learning.
NEW & NOTEWORTHY This study examined how, during visuomotor learning, people’s confidence in their movement decisions is shaped by their recent history of errors. Using computational modeling, we found that confidence judgments incorporated recent error history, tracked subjective error costs, and were sensitive to environmental volatility. Together, these results provide a novel model of metacognitive judgments during motor learning that could be applied to future computational and neural studies at the interface of higher-order cognition and motor behavior.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
AUTHOR EMAILS, christopher.hewitson{at}yale.edu, naser.al-fawakhiri{at}yale.edu, samuel.mcdougle{at}yale.edu
AUTHOR FUNDING, Christopher Louis Hewitson was funded by Yale University’s Seesel Postdoctoral Fellowship.
