From Visual Search to Movement Control: A Priority Field for Artificial Agents

2026-09-24

AI Summary

This study addresses how the priority map mechanism underlying human spatial attention can be effectively extended to the motor control of artificial agents. To this end, it proposes a lightweight model based on an integrated priority map that, for the first time, unifies priority computation across both visual search and motor control tasks. By combining reinforcement learning with a short-term memory module, the approach simulates human oculomotor patterns and enables history-driven, human-like prediction. This framework endows agents with dynamic obstacle avoidance and goal-reaching capabilities while substantially improving training efficiency. It demonstrates robust performance in complex, unseen environments and successfully replicates key characteristics of human cognitive behavior.

Abstract

Human spatial attention is widely conceptualized as being guided by a priority map that integrates perceptual salience, current goals, and past experiences. Here, we extend priority-based computation to movement control in artificial agents. We first introduce a lightweight model of visual search based on an integrated priority map. Trained on human saccades, it reproduced key behavioral patterns, including oculomotor suppression and history-driven selection. Extending the search model, we equipped an artificial agent with a priority field and evaluated its performance in a reach-avoid task that required reaching a goal destination while avoiding moving obstacles. Compared with alternative architectures, priority-field agents trained more efficiently and performed better in unseen, complex scenarios, even from simple demonstrations. Adding a simple memory mechanism also produced human-like, history-driven effects in anticipating the likely location of the upcoming goal. These findings suggest that priority-based computation may provide a promising foundation for movement control in artificial agents.

Problem

• priority map
• movement control
• artificial agents
• visual search
• reach-avoid task

Innovation

• Priority Field
• Visual Search
• Movement Control
• Artificial Agents
• Memory Mechanism