Predictive Allostatic Organization in Recurrent and Spiking Agents Under Partial Observability

Adaptive behavior under partial observability depends on internal organization that carries information beyond the current observation. Drawing on Barrett and Miller's account of categorization as predictive, compressive, functionally organized, and allostatically constrained, we test whether recurrent and spiking agents develop internal states with corresponding computational properties. Agents operate in an energy-constrained foraging task requiring resource acquisition, threat avoidance, contact-dependent consumption, and regulation of an internal energy variable. In a frozen benchmark, learned agents outperform random and heuristic baselines; the trace-augmented recurrent policy is strongest overall, while spiking variants show stress-specific differences. Early internal dynamics predict later full-safe-efficient success above permutation baseline, reaching a maximum ROC-AUC of 0.802. Reduced PCA subspaces retain behaviorally relevant information. Feature-family controls show that predictive signal is distributed across trace, policy-head, internal-dynamics, observation, and allostatic variables, and low-energy state remains strongly decodable after explicit energy-related features are removed. Evaluation-time perturbations to temporal state, sensory information, operating conditions, and allostatic mechanisms alter behavior and/or internal prediction. Seed-balanced event probes show weaker but measurable information about future contact, successful consumption, and threat events, alongside strong low-energy decoding. We interpret this pattern as a computational analogue of predictive allostatic organization: distributed control regimes that are predictive, energy-sensitive, action-relevant, and partly causally involved, without claiming biological validation or discrete symbolic categories.

Improvements to AI systems:

1. Add an allostatic objective to reinforcement learning (RL) reward functions. Instead of maximizing only task reward (e.g., food collected), include a secondary penalty for internal energy states that fall below a threshold, forcing the agent to learn anticipatory resource management. The improved system can sustain performance under prolonged resource scarcity without explicit external cues.

2. Use trace-augmented recurrent policies (e.g., GRU with an explicit short-term memory trace) as the default architecture for partially observable control tasks. The improved system outperforms both feedforward and standard recurrent baselines in tasks requiring sequential decisions (foraging, navigation, or inventory management) by retaining predictive information across time steps.

3. Implement a predictive internal state regularizer. During training, add a loss term that predicts future task-relevant events (e.g., contact, threat, success) from the hidden state at earlier timesteps. The improved system develops internal representations that are more causally entangled with future outcomes, leading to earlier and more accurate anticipation of rare events (e.g., equipment failure, predator appearance).

4. Design spiking neural network (SNN) policies with stress-specific activation thresholds. For energy-constrained or safety-critical deployments, use spiking agents that alter their firing rate or threshold based on internal energy level. The improved system shows differentiated behavior under low-resource vs. high-resource conditions, reducing wasteful computation during normal operation while becoming more sensitive during emergencies.

5. Introduce a PCA-based dimensionality reduction layer for interpretable internal state monitoring. The improved system can compress its hidden state into a low-dimensional subspace (e.g., 3–5 components) that retains behaviorally relevant information, enabling real-time human oversight of agent intentions without full state inspection.

6. Add an allostatic feature ablation robustness check during evaluation. After training, explicitly remove all energy-related features from the observation and internal state, then test whether the agent still decodes low-energy states. The improved system maintains a decodable low-energy signal even when direct energy inputs are masked, making it resilient to sensor loss or feature corruption.

7. Implement a seed-balanced event probe for rare-event prediction. Train a secondary classifier on the agent's internal state to predict future contact, consumption, or threat events, using balanced sampling to avoid class imbalance. The improved system can issue early warnings for rare but critical events with measurable AUC (e.g., 0.6–0.8) even when the primary policy is not explicitly trained for prediction.

8. Use evaluation-time perturbation testing as a standard validation protocol. Systematically perturb temporal state (e.g., shuffle hidden state order), sensory input (e.g., drop observations), operating conditions (e.g., change energy cost), and allostatic mechanisms (e.g., disable energy regulation). The improved system is validated to have causally involved internal dynamics—not just correlational—by showing that these perturbations degrade both behavior and internal prediction, enabling more trustworthy deployment.

9. Design distributed control regimes instead of monolithic policies. Split the policy into multiple specialized sub-modules (trace, policy-head, internal-dynamics, observation, allostatic) that each contribute predictive signal. The improved system exhibits graceful degradation: if one module is corrupted, others still carry enough information for partial task success, improving fault tolerance in real-world robotics.

10. Apply the categorization without discrete symbols principle to representation learning. Instead of forcing discrete latent codes, use continuous, compressive, and functionally organized representations that are allostatically constrained. The improved system learns flexible, context-dependent categories (e.g., safe to eat now vs. dangerous) without hard boundaries, enabling better generalization to novel but similar situations.