Diurnal rhythms of choice: a novel state-dependent drift diffusion model uncovers time-dependent changes in rat decision making

Time-of-day severely impacts human decision-making, with real-world consequences. Studying shifts in decision-making strategy requires controlled, long timescale behavioral measurement and analyses that can extract insight from time-varying behavior. We introduce two complementary advances to address this gap: an autonomous 24-hour training facility for continuous behavioral measurement during decision-making and an interpretable modeling framework that captures non-stationary decision dynamics from reaction times and choices. Rats were trained on a visual evidence accumulation task across months, generating over a half million trials spanning the circadian period. Our model revealed latent behavioral states characterized by distinct evidence accumulation parameters, including differences in drift rate, bias, and decision-commitment time. These states recur across days and align with feeding schedules and the light–dark cycle, producing periodic fluctuations in performance over 24 hours. Together, these results demonstrate how continuous behavioral sampling combined with generative modeling uncovers long-timescale structure in decision-making obscured by stationary analyses.