Learning new perception–action solutions in virtual ball bouncing

Abstract

How do humans discover stable solutions to perceptual-motor tasks as they interact with the physical environment? We investigate this question using the task of rhythmically bouncing a ball on a racket, for which a passively stable solution is defined. Previously, it was shown that participants exploit this passive stability but can also actively stabilize bouncing under perceptual control. Using a virtual ball-bouncing display, we created new behavioral solutions for rhythmic bouncing by introducing a temporal delay (45 degrees -180 degrees ) between the motion of the physical racket and that of the virtual racket. We then studied how participants searched for and realized a new solution. In all delay conditions, participants learned to maintain bouncing just outside the passively stable region, indicating a role for active stabilization. They recovered the approximate initial phase of ball impact in the virtual racket cycle (half-way through the upswing) by adjusting the impact phase with the physical racket. With short delays (45 degrees , 90 degrees ), the impact phase quickly shifted later in the physical racket upswing. With long delays (135 degrees , 180 degrees ), bouncing was destabilized and phase was widely visited before a new preferred phase gradually emerged, during the physical downswing. Destabilization was likely due to the loss of spatial symmetry between the ball and physical racket motion at impact. The results suggest that new behavioral solutions may be discovered and stabilized through broad irregular sampling of variable space rather than through a systematic search.

Action-perception patterns in virtual ball bouncing: Combating system latency and tracking functional validity

“…One cannot exclude the fact that the high performance of participants when ETEL values are kept within a limited range (up to 110 ms in ball bouncing) was obtained through a change in the behavior, with respect to "natural" behavior in the real world. Indeed, many experimental studies have shown that, when exposed to visually delayed feedback, sensori-motor adaptation (Cunningham et al, 2001a;Foulkes and Miall, 2000;Miall and Jackson, 2006) or motor learning (Morice et al, 2007) take place. Consequently, the functional fidelity of a VE cannot be tested solely through the analysis of average performance because an average high performance can be reached with subtly impaired behavioral responses.…”

“…When the racket hits the ball with negative acceleration -in what is called "a dynamical attractor" -small perturbations in ball motion (e.g., vertical velocity) do not need to be corrected, and ball trajectories relax back to a limit-cycle behavior within a few cycles without any active control of the racket. Two distinct virtual ball-bouncing set-ups have been recently developed in order to assess whether participants exploit the passive dynamical regime when performing the ball-bouncing task (de Rugy et al, 2003;Morice et al, 2007). However, in such ball-bouncing VEs, ETEL can induce wrong software computation of racket velocity and consequently wrong ball motion.…”

“…Recently, with one of the above-mentioned VEs, Morice et al (2007) have created new ball-bouncing conditions by introducing a temporal delay between the motion of the physical racket and the virtual racket. In all delay conditions (ranging from 83.75 to 335 ms), the behavior of participants was initially disrupted.…”

Intercepting real and simulated falling objects: What is the difference?

“…Indeed, numerous experiments have been conducted using virtual reality in the domain of TTC estimation, for example in interceptive actions (e.g., Gray, 2002, Zaal and Michaels, 2003, Takeichi et al, 2004, pedestrian street crossing (e.g., Seward et al, 2007, Lobjois andCavallo, 2007), and ball bouncing (e.g., Morice et al, 2007). All these studies have provided new results and new interpretations of human behavior that would become doubtful if the ecological validity of the simulation were challenged.…”

Implicit guidance to stable performance in a rhythmic perceptual-motor skill

“…As the ball bouncing task is performed in a virtual environment, the physical laws that generate the ball movements can be modified to manipulate the attractors in the task. Previously, Morice et al (2007) showed that shifting the position and velocity of the virtual racket shifted the attractor solutions, but this manipulation created a new perceptual-motor mapping that needed to be matched. Hence, like in visuo-motor adaptation studies, the learned behavior disappeared immediately after removal of the manipulation.…”