A State-Dependent Drift Diffusion Model Reveals Mice Actively Explore A Speed-Accuracy Continuum During Decision-Making

Understanding how animals shift between different decision-making strategies is critical for bridging normative models with naturalistic behavior. While drift diffusion models (DDMs) provide a powerful framework for describing evidence accumulation in two-alternative forced choice (TAFC) tasks, they assume fixed parameters across trials--an assumption often violated in practice. Here, we intro- duce a state-dependent DDM framework in which discrete latent states modulate decision parameters from trial to trial. This approach reveals that mice dynamically switch between impulsive and deliberative decision states that differ in accuracy and response latency, suggesting active exploration of the speed-accuracy trade-off. We uncover rare high-bound states in which mice exhibit deliberation times and ac- curacies approaching those observed in humans. These results raise new questions about the cognitive flexibility of rodent decision-making and offer a foundation for studying how internal states and external variables--such as reward history or uncertainty--influence strategy selection. Our method provides a natural interface for integration with neural recordings and dynamical systems models, offering a path toward identifying the circuit-level mechanisms underlying adaptive decision behavior.