Interoceptive Attention as Dynamic Homeostatic Prioritization in a Foraging Agent

2026-08-04

AI Summary

This study addresses how biological systems dynamically allocate interoceptive precision under perceptual bandwidth constraints to simultaneously satisfy multiple physiological needs—a mechanism not yet well understood. The authors propose an active inference–based interoceptive attention mechanism that, within a fixed perceptual budget, prioritizes precision allocation to the most urgent need channel according to beliefs about bodily states, jointly informing perceptual updates and action planning. Evaluated in the four-channel AffectWorld grid environment, this approach more than doubles survival rates during learning compared to uniform allocation (0.414 vs. 0.199) and nearly doubles the learning speed of target-channel dynamics. This work provides the first evidence that demand-driven dynamic precision allocation can concurrently accelerate perceptual learning and enhance survival performance, contingent upon tight coupling between perception and planning.

Abstract

Biological systems must regulate competing needs under limited perceptual bandwidth, where sharpening one estimate costs the capacity to sharpen the others. Any fixed-budget system therefore has to decide where to allocate its perceptual precision. We study this in a foraging agent that must keep several bodily needs satisfied to survive, modelled with active inference. At each step it reads its own body-state beliefs, identifies the most-needed channel, and reallocates a fixed budget of interoceptive precision toward it, so that the same precision-shaped likelihood feeds both belief update and planning. In AffectWorld, a four-channel foraging gridworld, this selective allocation more than doubles learning-phase survival at matched budget against a uniform-precision agent ($0.414$ vs $0.199$ across 11 layouts, $n{=}32$ seeds each, paired cluster-bootstrap $p \leq 10^{-4}$). Two further results sharpen the mechanism. The benefit runs through planning as well as perception, since denying the shaped likelihood to the planner alone removes about half of it. It is also need-aligned, since aiming precision at the least-needed channel does worse than spreading it evenly. The attended channel additionally learns its own dynamics about twice as fast, and stays ahead even at matched observation count, a behavioural trace of the same precision routing, visible in learning speed, not survival.

Problem

• interoceptive attention
• perceptual bandwidth
• homeostatic prioritization
• foraging agent
• precision allocation

Innovation

• interoceptive attention
• active inference
• precision allocation
• dynamic homeostasis
• foraging agent