Learning under constraints: a theoretical framework for comparing resource-constrained learning in biological and artificial systems.

Learning processes, cognitive architectures, available resources, and methods for sampling the environment and generating intelligent responses in complex sensory domains can differ significantly between natural and artificial systems. In this work, we present theoretical and modeling-based analysis of early-stage learning under resource constraints, comparing biological intelligence with a class of freely evolving, weakly constrained artificial systems (FEW), focusing on essential resource constraints such as computational capacity, memory, and energy. We develop quantitative models of sensory exploration and learning under strong and weak resource constraints, formalizing how limitations in energy, memory, and computational capacity shape sampling strategies and learning dynamics. For biologically constrained systems, we show that steep anisotropy in the cognitive cost gradient induces prioritized, depth-oriented exploration within limited sensory regions, leading to robust and resource-efficient learning. In contrast, we demonstrate that FEW systems, despite access to abundant resources, face a paradox of unconstrained learning: in the absence of intrinsic prioritization and evaluative feedback, uniform or random sampling leads to inefficient exploration of the sensory domain. To examine this challenge, we introduce a comparative framework for evaluating sensory traversal strategies and show that no single strategy dominates across prioritization accuracy, robustness, and resource efficiency. Instead, our analysis suggests a meta-strategy approach, in which adaptive selection among exploration strategies optimizes empirical success while preserving empirical accountability required for adaptive optimization. These results clarify the functional role of constraints in biological learning and provide principled guidance for the design of next-generation artificial learning systems operating in complex sensory environments.