Hansol X. Ryu

This study investigates how humans control force by examining trade-offs between error reduction and biases in force tracking tasks.

The paper investigates loss functions humans optimize for regression and classification tasks using inverse optimization, finding different exponents and functions describe human behavior under varying data conditions.

The paper proposes an optimality principle for combining central pattern generators and reflex circuits in locomotion, showing how uncertainty-driven feedback optimizes gait stability and energy efficiency.