Ball Bouncing

"Ball Bouncing" Project (2003 – Present)

Acronym:  "Ball Bouncing"

Name: Learning to bounce a ball in a time-delayed virtual environment

Type: Fundamental

Funds: Doctoral Fellowship (Bourse doctorale, Ministère de l'Education Nationale de la Recherche et de Technologie)

key researchers: Antoine MORICE, Isabelle SIEGLER (Université Paris-Sud 11, Orsay, France), Benoit G. BARDY (Université Montpellier-1, Montpellier, France)

Collaborators: William H. Warren (University of Connecticut, providence, USA)


The project :

The task of rhythmically bouncing a ball on a racket provides a deceptively simple but conceptually rich model system that incorporates mechanical, and informational constraints, allowing us to investigate the dynamics of perception and action. Ball bouncing provides a case study for the general problem of how people learn stable behavioral solutions to everyday tasks as they interact with the physical environment, from manipulating tools and other objects to learning skills such as skiing or cycling. Within this framework we can address such questions as to what properties define a behavioral attractor, how agents capitalize on the physics of the world to organize behavior, whether behavior is passively stable or actively controlled, and how information is exploited in the process.

Theory and current research:

I seek to understand how humans interacting with a physical system discover a new attractor and use it to stabilize their behavior.

Ball bouncing layout

 The Ball Bouncing task layout. The upper right panel depict the experimental setup. Participant hold a physical tennis table racket and have to control the displacement left panel depict thof a virtual racket on the screen in order to perform regular bounce of a virtual ball. The upper left panel depict the content of the virtual scene. The virtual racket had to propel a virtual ball at a target height. The upper left pannel shows the dynamics of the virtual racket and ball positions signals over time. The target error is comptuted at each bounce. The lower right panel depict the first return map of the bouncing task at each trial, indicating how participant exploit the different active (light gray square) and passive dynamical passive regime (dark gray square) of the bouncing task (Schaal et al., 1996).

Past Experiments

Experiment #1 : Measuring delays and evaluating its consequences for human bouncing a ball in time-delayed virtual environemnts

How can we evaluate the spatio-temporal performance of virtual environments (VE) for research use? Here we show that end-to-end latency (ETEL) of VE can strongly damage users’ perceptual and perceptuo-motor behaviors and that it can be considered to be the key factor for evaluating face and functional fidelity of a VE. We used a virtual ball-bouncing task as a paradigmatic example. Ball bouncing is known to exhibit attractive and repelling states whose localization in the racket cycle is sufficiently thin to be changed by small variations of ETEL. We first present a simple test-bed to measure the intrinsic ETEL of research-related VE systems. We then report results of a psychophysical ball-bouncing experiment in which ETEL was manipulated. While face validity (i.e., subjective experience) was maintained with relatively high values, the results reveal that the perception-action behavior (performance) was damaged with smaller ETEL values. These results call for action-perception variables in order to test the fidelity of VE systems.



Experiment #2 :Dynamics of learning in time-delayed virtual environements

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˚ to 180˚) 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˚, 90˚), the impact phase quickly shifted later in the physical racket upswing. With long delays (135˚, 180˚), 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.


Key references (downloadable version in page Publications)

  1. Morice, A.H.P., Siegler I.A, Bardy, B.G. (2008) Action-perception patterns in virtual ball-bouncing: Combating system latency and tracking functional validity. Journal of Neuroscience Methods , 169, 255-266.
  2. Morice, A.H.P., Siegler I., Warren W., Bardy B. (2007) Learning new perception-action solutions in virtual ball bouncing. Experimental Brain Research, 181, 249-265.