Predicting Future Errors During Skill Performance
National Institute of Neurological Disorders and Stroke (NINDS)
Summary
Background: Many tasks people do every day require a series of individual movements. Control over these movements is called motor skills. But even highly skilled people can make mistakes. Researchers have found that they can predict when a person will make a mistake 0.1 second before it happens. Now, they want to find out if they can increase that time up to 1 second-long enough to warn the person and prevent the mistake. Objective: To see if motor skill errors can be detected up to 1 second before they occur. Eligibility: Right-handed healthy adults aged 18 to 35. Design: Participants will have 2 to 5 study visits. Each visit will be 1 to 2 hours. They will have a physical and neurological exam. They will have 1 or 2 magnetic resonance imaging (MRI) scans. They will lie on a table that slides into a large cylinder. The MRI uses strong magnets to capture images of the inside of the body, including the brain. They will have another scan, called magnetoencephalography (MEG). Small metal disks attached to wires will be taped to their head. Participants will sit in a padded chair with their head inside of a helmet. The helmet will not cover their eyes or face. Participants will perform a series of typing tasks on a keyboard. They will have short breaks between each round. Their head movements will be tracked, and their eye and finger movements will be videotaped.
Description
Study Description: Human motor skills are composed of sequences of individual actions performed with utmost precision. However, even highly skilled human behavior is susceptible to errors. When these errors occur, they may have serious consequences, for example, when pilots are manually landing a plane or when surgeons control robotic devices during surgery. In such cases, the ability to predict and prevent these upcoming errors from occurring would clearly be advantageous. We recently utilized a withinindividual machine learning strategy to characterize brain activity predictive of future mo…