Every movement we make requires our brains to predict what forces–gravity, an object we’re holding, a strong gust of wind–each of our body parts will experience in order to move in a coordinated fashion. No movement is ever exactly the same and so it is remarkable that we are not constantly tripping over ourselves. It is well-known that humans learn based on previous errors in their movements. My work at the Harvard Neuromotor Control Lab was to investigate how the brain learns to “makes generalizations” about movements and learn from its mistakes.
Data were collected from participants playing a simple target acquisition video game during which they would need to move a cursor on a computer monitor using a joystick they held in their arm. In this game, this special joystick would introduce a novel force to deflect the participant’s aim as they moved. In order to play the game successfully, participants would need to compensate for the force by unconsciously learning the joystick’s dynamics. To make things even harder, the force the joystick generated depended on the direction of their arm movement. When the force was taken away, we could precisely measure the pattern of forces participants had learned for arm movements in different directions. We demonstrated that humans learn from their mistakes based on movements that actually happened rather than what was planned to happen–like how it’s hard to do a cartwheel until you know what it feels like to do a cartwheel. We further demonstrated that this knowledge may be used to develop novel training programs to help people recover more quickly from movement injuries such as stroke.
- Modeling movement dynamics of the human arm
- Designing a program of targets to allow participant’s to learn and unlearn a novel force field
- Statistical analysis of noisy movement data
- Gonzalez-Castro LN, Monsen CB, and Smith MA. The binding of learning to action in motor adaptation. PLoS Computational Biology. 2011.
- Smith MA, Gonzalez Castro LN, Monsen CB, Brayanov J (2008) Adaptive changes in arm dynamics are experience-dependent rather than goal-dependent. Washington, D.C. Society for Neuroscience.