Biologically-inspired emotional processing for adaptive decision-making in non-stationary environments

Abstract

Background Artificial intelligence (AI) often struggles to adapt in non-stationary environments where conditions change unpredictably. In contrast, biological organisms utilize emotional processes not as irrational noise, but as rapid heuristics for managing uncertainty. This study investigates whether computational mechanisms inspired by mammalian affective systems provide advantages for adaptive decision-making. Methods The Emotional-Cognition Integration Architecture (ECIA) was developed, incorporating computational analogs of eight emotion-like signals designed for reinforcement learning contexts, hippocampus-inspired episodic memory, and dopamine-modulated adaptive learning. Using large-scale experimental replication (3,600 runs across 12 master seeds), ECIA was evaluated against both traditional algorithms (epsilon-greedy, upper confidence bound (UCB), Thompson Sampling) and improved non-stationary baselines (Sliding Window UCB, Adaptive Thompson Sampling) in three distinct environments designed to test different aspects of adaptation. Results ECIA demonstrated environment-specific performance patterns reflecting a functional trade-off. In unpredictable settings characterized by sudden regime shifts and stochastic perturbations, ECIA significantly outperformed all baselines (p < 0.001). However, in strictly deterministic patterns, ECIA incurred a "cost of complexity," underperforming compared to Naive UCB (0.8014 vs. 0.8522). This trade-off suggests functional specialization for uncertainty management rather than universal superiority. Ablation studies revealed strong synergistic integration among components, with combined removal causing disproportionate degradation far exceeding individual effects and highlighted a "dopamine paradox" where adaptive plasticity benefited uncertain environments but destabilized predictable ones. Conclusions These findings demonstrate that emotion-inspired computational mechanisms, drawn from mammalian brain architecture, function as specialized tools for managing environmental volatility. While they incur efficiency costs in stable environments, they provide essential robustness in high-uncertainty domains. This work offers both a practical framework for adaptive AI systems in domains such as clinical decision support and financial trading, and computational insights into why biological intelligence integrates affective processing with cognition.