Summary
Humans can monitor their own mental lives and build representations that contain knowledge about themselves. This capacity to introspect and report one’s own mental states, or in other words “knowing how much one knows”, is termed metacognition. Although metacognition is crucial to behave adequately in a complex environment, metacognitive judgments are often suboptimal. Specifically for neurological and psychiatric diseases, metacognitive failures are highly prevalent, with severe consequences in terms of quality of life. This project proposes a new hypothesis to explain the determining factors of metacognitive failures: namely, that metacognition does not operate in a vacuum but relies on the monitoring of signals from the body, and more specifically, on motor signals involved during action execution. We suggest several experiments to test the motor hypothesis for self-monitoring, and propose a new remediation procedure to resolve metacognitive failures resulting from deficient action monitoring. We will start by assessing the contribution of motor signals to metacognition by identifying the behavioral and neural correlates for detecting self-committed vs. observed errors (WP1), and by using virtual reality and robotics to probe metacognition in a vacuum, operating in the complete absence of voluntary actions (WP2). Finally, we will use these results to develop and evaluate a method to train metacognition in healthy volunteers and individuals with schizophrenia in a bottom-up manner, using online feedback based on motor signals (WP3). This new metacognitive remediation procedure will be performed both in a clinical context and on mobile devices. The goal of this ambitious project is therefore twofold, theoretical in shedding new light on a cognitive process central to our most profound mental states, and clinical in establishing a new remediation method to tackle a major health and societal issue.
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| Web resources: | https://cordis.europa.eu/project/id/803122 |
| Start date: | 01-03-2019 |
| End date: | 31-01-2025 |
| Total budget - Public funding: | 1 389 500,00 Euro - 1 389 500,00 Euro |
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Original description
Humans can monitor their own mental lives and build representations that contain knowledge about themselves. This capacity to introspect and report one’s own mental states, or in other words “knowing how much one knows”, is termed metacognition. Although metacognition is crucial to behave adequately in a complex environment, metacognitive judgments are often suboptimal. Specifically for neurological and psychiatric diseases, metacognitive failures are highly prevalent, with severe consequences in terms of quality of life. This project proposes a new hypothesis to explain the determining factors of metacognitive failures: namely, that metacognition does not operate in a vacuum but relies on the monitoring of signals from the body, and more specifically, on motor signals involved during action execution. We suggest several experiments to test the motor hypothesis for self-monitoring, and propose a new remediation procedure to resolve metacognitive failures resulting from deficient action monitoring. We will start by assessing the contribution of motor signals to metacognition by identifying the behavioral and neural correlates for detecting self-committed vs. observed errors (WP1), and by using virtual reality and robotics to probe metacognition in a vacuum, operating in the complete absence of voluntary actions (WP2). Finally, we will use these results to develop and evaluate a method to train metacognition in healthy volunteers and individuals with schizophrenia in a bottom-up manner, using online feedback based on motor signals (WP3). This new metacognitive remediation procedure will be performed both in a clinical context and on mobile devices. The goal of this ambitious project is therefore twofold, theoretical in shedding new light on a cognitive process central to our most profound mental states, and clinical in establishing a new remediation method to tackle a major health and societal issue.Status
SIGNEDCall topic
ERC-2018-STGUpdate Date
27-04-2024
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