Dataset Information

Visually inferring elasticity from the motion trajectory of bouncing cubes.

ABSTRACT: Visually inferring the elasticity of a bouncing object poses a challenge to the visual system: The observable behavior of the object depends on its elasticity but also on extrinsic factors, such as its initial position and velocity. Estimating elasticity requires disentangling these different contributions to the observed motion. We created 2-second simulations of a cube bouncing in a room and varied the cube's elasticity in 10 steps. The cube's initial position, orientation, and velocity were varied randomly to gain three random samples for each level of elasticity. We systematically limited the visual information by creating three versions of each stimulus: (a) a full rendering of the scene, (b) the cube in a completely black environment, and (c) a rigid version of the cube following the same trajectories but without rotating or deforming (also in a completely black environment). Thirteen observers rated the apparent elasticity of the cubes and the typicality of their motion. Generally, stimuli were judged as less typical if they showed rigid motion without rotations, highly elastic cubes, or unlikely events. Overall, elasticity judgments correlated strongly with the true elasticity but did not show perfect constancy. Yet, importantly, we found similar results for all three stimulus conditions, despite significant differences in their apparent typicality. This suggests that the trajectory alone contains the information required to make elasticity judgments.

SUBMITTER: Paulun VC

PROVIDER: S-EPMC7416883 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Visually inferring elasticity from the motion trajectory of bouncing cubes.

Paulun Vivian C VC Fleming Roland W RW

Journal of vision 20200601 6

Visually inferring the elasticity of a bouncing object poses a challenge to the visual system: The observable behavior of the object depends on its elasticity but also on extrinsic factors, such as its initial position and velocity. Estimating elasticity requires disentangling these different contributions to the observed motion. We created 2-second simulations of a cube bouncing in a room and varied the cube's elasticity in 10 steps. The cube's initial position, orientation, and velocity were v ...[more]

PMID: 32516356

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Project description:To interact successfully with moving objects in our environment we need to be able to predict their behavior. Predicting the position of a moving object requires an estimate of its velocity. When flow parsing during self-motion is incomplete-that is, when some of the retinal motion created by self-motion is incorrectly attributed to object motion-object velocity estimates become biased. Further, the process of flow parsing should add noise and lead to object velocity judgements being more variable during self-motion. Biases and lowered precision in velocity estimation should then translate to biases and lowered precision in motion extrapolation. We investigate this relationship between self-motion, velocity estimation and motion extrapolation with two tasks performed in a realistic virtual reality (VR) environment: first, participants are shown a ball moving laterally which disappears after a certain time. They then indicate by button press when they think the ball would have hit a target rectangle positioned in the environment. While the ball is visible, participants sometimes experience simultaneous visual lateral self-motion in either the same or in the opposite direction of the ball. The second task is a two-interval forced choice task in which participants judge which of two motions is faster: in one interval they see the same ball they observed in the first task while in the other they see a ball cloud whose speed is controlled by a PEST staircase. While observing the single ball, they are again moved visually either in the same or opposite direction as the ball or they remain static. We expect participants to overestimate the speed of a ball that moves opposite to their simulated self-motion (speed estimation task), which should then lead them to underestimate the time it takes the ball to reach the target rectangle (prediction task). Seeing the ball during visually simulated self-motion should increase variability in both tasks. We expect to find performance in both tasks to be correlated, both in accuracy and precision.

Dataset Information

Visually inferring elasticity from the motion trajectory of bouncing cubes.

Publications

Visually inferring elasticity from the motion trajectory of bouncing cubes.

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