Motor learning is typically assessed in the lab, affording a high degree of control over the task environment. However, this level of control often comes at the cost of smaller sample sizes and a homogenous pool of participants (e.g. college students). To address this, we have designed a web-based motor learning experiment, making it possible to reach a larger, more diverse set of participants. As a proof-of-concept, we collected 1,581 participants completing a visuomotor rotation task, where participants controlled a visual cursor on the screen with their mouse and trackpad. Motor learning was indexed by how fast participants were able to compensate for a 45° rotation imposed between the cursor and their actual movement. Using a cross-validated LASSO regression, we found that motor learning varied significantly with the participant’s age and sex, and also strongly correlated with the location of the target, visual acuity, and satisfaction with the experiment. In contrast, participants' mouse and browser type were features eliminated by the model, indicating that motor performance was not influenced by variations in computer hardware and software. Together, this proof-of-concept study demonstrates how large datasets can generate important insights into the factors underlying motor learning.

The brain is remarkably flexible, and appears to instantly reconfigure its processing depending on what’s needed to solve a task at hand: fMRI studies indicate that distal brain areas appear to fluidly couple and decouple with one another depending on behavioral context. We investigated how the brain coordinates its activity across areas to inform complex, top-down control behaviors. Animals were trained to perform a novel brain machine interface task to guide a visual cursor to a reward zone, using activity recorded with widefield calcium imaging. This allowed us to screen for cortical areas implicated in causal neural control of the visual object. Animals could decorrelate normally highly-correlated areas to perform the task, and used an explore-exploit search in neural activity space to discover successful strategies. Higher visual and parietal areas were more active during the task in expert animals. Single unit recordings targeted to these areas indicated that the sensory representation of an object was sensitive to an animal’s subjective sense of controlling it.