A software tool for at-home measurement of sensorimotor adaptation.

Sensorimotor learning is traditionally studied in the laboratory, but recent public health emergencies have caused the community to consider at-home data collection. To accelerate this effort, we implemented a software tool that remotely tracks motor learning. Compared with previous remote data collection strategies, our software (1) generates experiments of arbitrary length that (2) run locally on a participant's laptop which (3) can be modified without any programming expertise in the research laboratory. Here we show a close correspondence between behaviors captured by our tool and those observed in laboratory environments including savings, interference, spontaneous recovery, and variations in implicit and explicit learning due to changes in perturbation variance, reaction time constraints, and feedback delay. Our software and its corresponding manuals are available here: https://osf.io/e8b63/ .