# The predictive outfielder: a critical test across gravities

## TL;DR

The paper explores how humans predict moving object paths using visual cues and gravity, showing that incorporating gravity improves prediction accuracy.

## Contribution

A novel model that integrates optic variables and gravity to predict object trajectories in different gravitational environments.

## Key findings

- The model's predictions align with empirical data when gravity is factored in.
- Varying gravity in virtual experiments revealed distinct prediction patterns between models.
- Including environmental constants like gravity enhances interception accuracy.

## Abstract

Intercepting moving targets is a widespread challenge across many species. In humans, heuristics that use optic variables have excelled in guiding interception, relying on a closed-loop system to couple optic variables directly with direction of locomotion. This contrasts with models that explicitly recover final positions from initial trajectory conditions. However, comparing these different approaches using empirical data is challenging, as they often predict similar locomotion trajectories. We present a model based on optic variables that continuously updates predictions on the landing position in the three-dimensional scene and remaining flight time based on the outfielder’s real-time movements. A distinct feature is the model’s adaptability to different gravitational accelerations, making its predictions inherently tailored to specific environments. By actively integrating gravity, our model produces trajectory predictions that can be validated against actual paths. To compare our model with previous ones, we conducted experiments within virtual reality, strategically varying simulated gravity and physical size. The variation in gravity resulted in qualitatively distinct predictions between heuristics based solely on optic variables and our model, which incorporates gravity. The empirical trajectories, kinematic patterns and timing responses aligned well with our model’s predictions, emphasizing the importance of including environmental constants.