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This paper investigates two distinct mechanisms for interoceptive prioritization in active inference agents: increasing the precision of sensory evidence for a need versus reweighting outcome preferences to favor need relief. By simulating a foraging agent with lethal needs, the study reveals that precision enhances survival and sharpens interoceptive posteriors, while preference reweighting alters learning dynamics, leading to a more focused but potentially lower-quality body model. The findings suggest that survival metrics alone are insufficient for fully understanding how agents learn to prioritize and model their internal states.
Prioritizing bodily needs via sensory precision sharpens interoceptive inference and boosts survival, but preference reweighting leads to a narrower, less comprehensive learned body model.
An embodied agent must prioritise among competing bodily needs. In active inference, it can respond to an urgent need by increasing the precision of information about that need or by increasing its preference for outcomes that relieve it. We compare these two interventions, separately and together, in a foraging gridworld with three lethal needs, and ask how each changes survival, perception and the body model the agent learns. Precision sharpens interoceptive posteriors and improves survival. Preference reweighting keeps bodily states further from their lethal boundaries and changes the states from which the agent learns, so its learned model covers fewer need levels, with lower quality across all levels but slightly higher quality on the levels it visits. An experiment on 72 further layouts does not establish the predicted preference benefit on difficult layouts, but both interventions improve average survival there and their interaction is positive. Survival alone therefore gives an incomplete account of how a model prioritises needs and learns about its body.