The speed-accuracy tradeoff has been observed in both sensory and motor systems across a wide-range of animals. Fitts's law is well known to describe the trade-off between speed and accuracy of natural human movement across many tasks. Yet it remains difficult to explain what in our brain causes these fundamental limits. While certain limitations in robots and brain-computer interfaces are similar, they often are slower and noisier with different trade-offs. We want to develop better descriptions of why the neurons in our brain limit our ability to move both fast and precisely. Then we'll work on developing new decoding algorithms for brain-computer interfaces that can more closely mimic the speed and precision of natural human movement.

One area of motor precision we are particularly interested in is the relationship between target-independent and target-dependent neural signals. When we want to move our arm and hand, many neurons in the motor areas of the brain and spinal cord become active with any movement – a target-independent signal. Additionally, different neurons become more or less active to signal which direction you want to move – the target-dependent signal. Currently, it's often difficult to tell what part of any individual neuron's change in activity is linked to signaling a particular movement versus changes that happen for any movement just because the brain area became more active.

Recent results in our lab have shown the target-independent activity appears as a repeated pattern across a network of neurons with consistent timing that divides a movement into a set of submovements. We are further examining whether this is always the case or if there may be exceptions and how the timing of submovements influences one's ability to correct to incoming sensory information.