Harnessing theoretical neuroscience to understand decision-making across aging

Understanding how decision-making changes across the lifespan is a central challenge for neuroscience, yet research on cognitive aging remains largely disconnected from the theoretical and computational advances shaping modern systems neuroscience. Over recent decades, theoretical frameworks have transformed how we study cognition in young, healthy brains. In contrast, aging research has relied on single-metric behavioral measures, cross-sectional comparisons, and descriptive neural analyses, limiting our ability to explain fundamental differences in individual aging trajectories. This gap represents a missed opportunity: aging offers a powerful platform for testing theories of neural computation, stability, and flexibility under changing biological constraints. We argue that closer integration between aging research and contemporary theoretical neuroscience will move the field from descriptive towards mechanistic insights. Here, we outline how recent advances in behavioral quantification, latent-state modeling, dynamical systems, encoding models, representational geometry, and recurrent neural networks offer a rich theoretical toolkit for studying decision-making across the lifespan.