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Abstraction of physical properties from complex object interactions: the case of elasticity

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84115

Nusseck,  M
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83913

Fleming,  R
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83839

Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Nusseck, M., Fleming, R., & Bülthoff, H. (2005). Abstraction of physical properties from complex object interactions: the case of elasticity. Poster presented at 8th Tübingen Perception Conference (TWK 2005), Tübingen, Germany.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D621-8
Abstract
For humans it is useful to be able to visually infer an object’s physical properties (e.g. weight, hardness or elasticity). One potentially important source of information is the way that an object moves and interacts with other objects in the environment. For example, the way that a ball bounces could inform us about its elasticity. There have been several explorations of what are the necessary and typical visual cues in a bouncing event. However, in most previous work the stimuli consisted of a ball bouncing repeatedly on a simple horizontal plane. Warren, Kim and Husney (1987) showed that under these circumstances, there are at least three heuristic cues to elasticity: relative height, relative period and relative velocity of the bounces. We wanted to test whether the visual system can interpret more complex bouncing events in which these simple cues are not present in the display. Can subjects abstract something more sophisticated from the trajectories of bouncing objects, or must they rely on these simple heuristics? To test this, we used the Virtools Physics Pack to simulate a ball falling through an array of horizontal cylindrical pegs housed in a vertical box. The ball fell from a random location above the box, bounced a number of times and finally fell out of a hole at the bottom. This stimulus allows us to completely randomize the duration that the ball needs to get through this box, the number of collisions, the velocity of the ball and the height of the rebounds, because the angle of the collisions with the pegs is always different. Subjects performed an elasticity matching task. Subjects were presented with two pegboxes simultaneously. The left-hand box was the Test ball, whose elasticity was chosen at random by the computer. The right-hand box contained the Match ball, whose elasticity could be adjusted by the subject. The subject?s task was to adjust this elasticity of the Match ball until it appeared to have the same behavior as the Test ball. The results show that subjects generally performed poorly in this task, despite dramatic variations in the elasticity of the ball. However, we found large individual differences, in which some subjects were able to perform the task above chance levels. Our results suggest that subjects normally rely on simple heuristics to estimate elasticity (e.g. bounce height), which, by design, were eliminated from our stimuli. Further research is needed to investigate which additional, complex cues were used in some cases to abstract the behaviour of the ball.