Neural superstatistics for Bayesian estimation of dynamic cognitive models.

ABSTRACT: Mathematical models of cognition are often memoryless and ignore potential fluctuations of their parameters. However, human cognition is inherently dynamic. Thus, we propose to augment mechanistic cognitive models with a temporal dimension and estimate the resulting dynamics from a superstatistics perspective. Such a model entails a hierarchy between a low-level observation model and a high-level transition model. The observation model describes the local behavior of a system, and the transition model specifies how the parameters of the observation model evolve over time. To overcome the estimation challenges resulting from the complexity of superstatistical models, we develop and validate a simulation-based deep learning method for Bayesian inference, which can recover both time-varying a

SUBMITTER: Schumacher L 

PROVIDER: S-EPMC10447473 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature