Validation and Comparison of Non-stationary Cognitive Models: A Diffusion Model Application

Cognitive processes naturally fluctuate over time, and superstatistics offer a flexible way to capture these non-stationary dynamics in cognitive models. We provide the first experimental validation of this approach by comparing four time-varying diffusion decision models in a perceptual task with controlled difficulty and speed-accuracy conditions. Using deep learning for efficient Bayesian estimation, we find that models allowing both gradual and abrupt parameter changes best match the data, suggesting that the inferred dynamics reflect genuine shifts in cognitive states.