Model of rhythmic ball bouncing using a visually controlled neural oscillator

Abstract

The present paper investigates the sensory-driven modulations of central pattern generator dynamics that can be expected to reproduce human behavior during rhythmic hybrid tasks. We propose a theoretical model of human sensorimotor behavior able to account for the observed data from the ball-bouncing task. The novel control architecture is composed of a Matsuoka neural oscillator coupled with the environment through visual sensory feedback. The architecture's ability to reproduce human-like performance during the ball-bouncing task in the presence of perturbations is quantified by comparison of simulated and recorded trials. The results suggest that human visual control of the task is achieved online. The adaptive behavior is made possible by a parametric and state control of the limit cycle emerging from the interaction of the rhythmic pattern generator, the musculoskeletal system, and the environment. The study demonstrates that a behavioral model based on a neural oscillator controlled by visual information is able to accurately reproduce human modulations in a motor action with respect to sensory information during the rhythmic ball-bouncing task. The model attractor dynamics emerging from the interaction between the neuromusculoskeletal system and the environment met task requirements, environmental constraints, and human behavioral choices without relying on movement planning and explicit internal models of the environment.

The self-organization of ball bouncing

“…The neuromechanical model (CPG and arm) shared by the various models tested in this study is similar to the model presented in Avrin et al (2017b). A Matsuoka neural oscillator acts as a CPG and activates the arm flexor and extensor muscles to generate oscillating torque at the elbow (Matsuoka 1985(Matsuoka , 2011.…”

“…The information-movement coupling responsible for the paddle period adaptation, presented in Equations 5, is implemented in this operating mode via an adaptation of the oscillator natural frequency. As demonstrated in Avrin et al (2016Avrin et al ( , 2017b, this operating mode is robust only if the oscillator natural frequency is adapted directly after impact. This post-impact adaptation requires the ball period to be predicted based on the perception of the ball's post-impact velocity and the internal quantitative knowledge of the gravity acceleration value, as hypothesized in de Rugy et al (2003).…”

“…The paddle period adaptation would thus result from the oscillator entrainment by this informational input. However, this mode is only efficient when the oscillator natural frequency ω n is close to the ball frequency 2π/T b as explained in Avrin et al (2017b). As a consequence, the FO mode is completed by an adaptation of the oscillator natural frequency to equal the ball frequency at each cycle.…”

“…The hypothesis that humans predict the ball period based on an explicit internal representation of the gravity acceleration and use this information to achieve the task by relying on once-per-cycle intermittent coupling is tested in this study. In Avrin et al (2017b), the possibility that the paddle period was adapted only when the ball reaches its apex was investigated. In that case, the agent may deduce the ball period as the double of the half-period between impact and ball apex.…”

“…Instead of relying on internal models, it is possible that human control strategies rely on resonance phenomena resulting from continuous couplings between the NMS and the environment. This solution was implemented in Avrin et al (2017b) to synchronize the paddle trajectory with the ball trajectory. It demonstrated an accurate matching with the humans' bounce error series for trials including perturbations on the environmental conditions determined by the gravity acceleration and the ball-paddle restitution coefficient.…”