The nervous system circuitry that controls locomotion has evolved to intrinsically generate an optimal search pattern for resources.

A new method for analyzing continuous psychophysics experiments estimates not only perceptual uncertainty from tracking tasks but also action variability, intrinsic costs, and subjective internal models.

Humans can predict and optimally plan goal-directed dynamic walking tasks that have transient events such as negotiating a sidewalk curb.

A computational model shows that preparation arises as an optimal control strategy in input-driven recurrent neural networks performing a delayed-reaching task.

Neural computations necessary for efficient control of saccades capture the phenomenon of saccadic suppression, which suggests that neural resources are shared for perception and control.

The rhythmicity in upper-limb tracking movements and associated population dynamics in primary motor cortex is explained by a feedback controller incorporating optimal state estimation.

Humans often walk in relatively brief bouts whose speed and duration vary dynamically and satisfy an optimal trade-off between effort and time.

Theoretical models and experimental results reveal how the optimal integration of gravity cues fine tunes movement kinematics so that motor effort is minimized in the ubiquitous gravity field.

A virtual balancing task with parallel experiments on human and non-human primates revealed a spectrum of behaviors (classified by a computational model into position, velocity, or mixed control strategy) that can serve as basis for analyzing neural population dynamics.