Interoceptive Attention as Dynamic Homeostatic Prioritization in a Foraging Agent
Quick Answer
The study presents a foraging agent utilizing active inference to dynamically allocate interoceptive precision, resulting in over double the survival rate during learning phases compared to uniform-precision agents (0.414 vs 0.199).
Quick Take
This selective attention enhances both planning and perception, with significant implications for artificial intelligence in resource allocation.
Key Points
- Foraging agent allocates interoceptive precision based on bodily needs.
- Survival rate during learning phases: 0.414 vs 0.199 against uniform-precision agents.
- Selective allocation improves both planning and perception capabilities.
- Attended channels learn dynamics twice as fast compared to others.
- Results indicate need-aligned precision allocation is crucial for performance.
DeepSignal Analysis
What happened
The study investigates a foraging agent that employs active inference to manage interoceptive precision for survival. This agent significantly outperformed uniform-precision agents in survival rates during learning phases, achieving 0.414 compared to 0.199. The research highlights the importance of selective attention in resource allocation for artificial intelligence.
Key evidence
- The foraging agent reallocates a fixed budget of interoceptive precision based on its bodily needs, enhancing both planning and perception.
- In the AffectWorld gridworld, the selective allocation of precision led to over double the survival rate during learning phases, with statistical significance (p ≤ 10^{-4}).
- The attended channel not only learns its dynamics faster but also maintains this advantage even when observation counts are matched.
Why it matters
This research provides insights into how artificial intelligence can mimic biological systems in managing limited resources. By demonstrating that dynamic allocation of perceptual precision can enhance survival rates, it opens avenues for developing more efficient AI systems. The findings could influence future designs in AI that require adaptive resource management.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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.
| Comments: | Accepted at SAB 2026 (From Animals to Animats 18), forthcoming in the Springer Lecture Notes in Artificial Intelligence proceedings. 20 pages, 11 numbered figures (12 graphics), 5 tables. The 12-page camera-ready paper is reproduced without alteration and followed by supplementary analyses that were not part of the proceedings paper. Code: this https URL |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.04232 [cs.AI] |
| (or arXiv:2608.04232v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.04232 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: St John Grimbly [view email]
[v1]
Tue, 4 Aug 2026 21:18:45 UTC (343 KB)
— Originally published at arxiv.org
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