The predictive outfielder: a critical test across gravities

Area of Science:

• * Human motor control and visual perception.
• * Robotics and autonomous systems.
• * Biomechanics and computational neuroscience.

Background:

• * Human interception relies on optic variables and closed-loop systems.
• * Previous models often neglect environmental factors like gravity.
• * Comparing different interception models empirically is challenging due to similar trajectory predictions.

Purpose of the Study:

• * To present a novel model for predicting interception trajectories.
• * To incorporate environmental constants, specifically gravity, into interception models.
• * To empirically validate the model's predictions against human performance.

Main Methods:

• * Developed a model using optic variables to predict landing position and flight time.
• * Integrated environmental gravity into the model for adaptive predictions.
• * Conducted virtual reality experiments varying simulated gravity and object size.

Main Results:

• * The model's predictions diverged from optic-only heuristics when gravity varied.
• * Empirical trajectories and kinematic patterns closely matched the model's predictions.
• * Environmental constants significantly influence interception strategies.

Conclusions:

• * Models incorporating environmental constants, like gravity, are crucial for accurate interception prediction.
• * The proposed model offers a more realistic simulation of human interception.
• * Future research should consider environmental factors in understanding motor control.