Assistant Professor Meghan Huber of the UMass Amherst Mechanical and Industrial Engineering Department was a co-author and part of a research team that published an article titled “The role of path information in visual perception of joint stiffness” in the November 28 issue of PLOS Computational Biology. The paper’s research aimed to further understand the astonishing ability humans have to extract underlying information from visually observing the movement of others.

The other two members of the team were lead author A. Michael West Jr. and co-author Neville Hogan of the Department of Mechanical Engineering at the Massachusetts Institute of Technology.

The three researchers based their research on behavioral studies showing that humans learn from observing and imitating the motor behavior of others. “Even from sparse motion information,” the authors wrote, “humans can easily determine intention from arm movement, distinguish emotion from patterns in dancing, and identify individuals from gait patterns.” 

In the context of humans visually observing the movements of others, the PLOS Computational Biology paper concentrated on one specific correlation: that humans can correctly infer changes in limb stiffness from nontrivial changes in multi-joint limb motion.

As the three researchers explained, “Stiffness of the arms or legs, the force evoked by displacement, plays an important role in managing physical interaction with objects in the world.”

One might assume that measuring arm or leg stiffness might intuitively require physical contact. Nevertheless, as the researchers wrote, “Previous study showed that humans have a remarkable ability to estimate stiffness solely from visual observation of a computer simulation, with no physical contact. The present study extended that work and found that this ability was robust.”

In particular, the three authors found in their experiments that the ability to estimate simulated stiffness was largely unaffected by changing the time course of simulated motion. “This was surprising,” they explained, “given the extensive prior research reporting that distorting velocity patterns influences motion perception.”

According to the researchers, “The results presented in this paper indicate that geometric information (path) predominates over temporal information (velocity) in the perception of stiffness. Given the highly cited relationship between motor action and perception, it also suggests that the structure of the motor control system we used in the simulations is a reasonable approximation of the neural motor controller.”

In conclusion, as the researchers explained, “This work provides insight into humans’ representation of motor behavior and how humans interpret and learn from the motor actions of others.” (March 2023)

Article posted in Research