Gravity Prior and Temporal Horizon Shape Interceptive Behavior under Active Inference

TL;DR

This study explores how internal models of gravity and extended predictive horizons improve the performance of an active inference agent in intercepting moving objects, highlighting the importance of physical dynamics in motor control.

Contribution

It demonstrates that incorporating gravity priors and longer predictive horizons enhances interceptive accuracy within an active inference framework.

Findings

Gravity priors improve spatial and temporal accuracy.

Longer predictive horizons yield smoother trajectories.

All strategies successfully intercept objects, but some outperform others.

Abstract

Accurate interception of moving objects, such as catching a ball, requires the nervous system to overcome sensory delays, noise, and environmental dynamics. One key challenge is predicting future object motion in the presence of sensory uncertainty and inherent neural processing latencies. Theoretical frameworks such as internal models and optimal control have emphasized the role of predictive mechanisms in motor behavior. Active Inference extends these ideas by positing that perception and action arise from minimizing variational free energy under a generative model of the world. In this study, we investigate how different predictive strategies and the inclusion of environmental dynamics, specifically an internal model of gravity, influence interceptive control within an Active Inference agent. We simulate a simplified ball-catching task in which the agent moves a cursor horizontally to intercept a parabolically falling object. Four strategies are compared: short temporal horizon prediction of the next position or long horizon estimation of the interception point, each with or without a gravity prior. Performance is evaluated across diverse initial conditions using spatial and temporal error, action magnitude, and movement corrections. All strategies produce successful interception behavior, but those that incorporate gravity and longer temporal horizons outperform others. Including a gravity prior significantly improves spatial and temporal accuracy. Predicting the future interception point yields lower action values and smoother trajectories compared to short-horizon prediction. These findings suggest that internal models of physical dynamics and extended predictive horizons can enhance interceptive control, providing a unified computational account of how the brain may integrate sensory uncertainty, physical expectations, and motor planning.