Perceiving latent dynamics: Innate and coachable visual estimation of limb damping

Humans are remarkably adept at extracting latent dynamic information from purely visual cues. Prior work shows that people can innately estimate differences in limb stiffness using solely their visual observation of movement, which suggests that components of mechanical impedance may be embedded within humans internal predictive models of movement. We tested whether humans can similarly perceive damping, a force-velocity relationship, and whether targeted coaching can enhance this visual ability. Specifically, 30 participants observed abstract two-link arm simulations with systematically varied elbow damping and rated their perceived level of damping for several trials. Participants completed two sessions separated by one of three brief coaching interventions: (1) no coaching, (2) coaching to attend to hand velocity, or (3) coaching to attend to elbow-angle velocity. Results reveal that (1) humans can innately perceive changes in arm damping using solely their visual observation of motion and (2) coaching further improved performance, with the elbow-angle coaching group showing a significantly greater increase in rating accuracy compared to the other two groups. This work extends our understanding of how action-perception coupling supports inference of mechanical impedance. Moreover, we demonstrated that perceptual strategies for estimating damping are malleable and can be systematically improved through coaching. We not only identified the visual cues observers relied on but also guided them toward more classifiable features, effectively strengthening their perceptual models of limb dynamics. Author summaryHumans are remarkably adept at understanding an objects latent dynamic properties simply by watching it move, even when the underlying forces are unseen. In this paper, we demonstrated that people can notice differences in how "damped" a moving limb is using vision alone. Moreover, we found that brief coaching helped participants focus on the most informative features, significantly improving their ability to differentiate the damping levels. These results demonstrate how people can visually infer aspects of movement that are normally thought to require physical interaction, offering insight into how the motor system links action and perception. They also show that strategies can be shaped and improved, supporting real-world healthcare applications. In stroke rehabilitation, physical therapists physically assess the resistance of a patients limb, so better guidance on the most relevant visual cues can help clinicians learn faster and even provide care remotely. In robot-assisted surgery, surgeons operate a console to perform procedures with limited or no force feedback, so they must estimate tissue dynamics properties largely from visual observation. Understanding how people visually estimate these dynamics can inform training for more precise surgical decisions. Overall, our findings clarify how humans interpret movement dynamics and how coaching can support more consistent and accurate perceptual decisions.