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Mathematical models of cognition are often memoryless and ignore potentialfluctuations of their parameters. However, human cognition is inherentlydynamic. Thus, we propose to augment mechanistic cognitive models with atemporal dimension and estimate the resulting dynamics from a superstatisticsperspective. Such a model entails a hierarchy between a low-level observationmodel and a high-level transition model. The observation model describes thelocal behavior of a system, and the transition model specifies how theparameters of the observation model evolve over time. To overcome theestimation challenges resulting from the complexity of superstatistical models,we develop and validate a simulation-based deep learning method for Bayesianinference, which can recover both time-varying and time-invariant parameters.We first benchmark our method against two existing frameworks capable ofestimating time-varying parameters. We then apply our method to fit a dynamicversion of the diffusion decision model to long time series of human responsetimes data. Our results show that the deep learning approach is very efficientin capturing the temporal dynamics of the model. Furthermore, we show that theerroneous assumption of static or homogeneous parameters will hide importanttemporal information.

Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive Models