BibTeX

@article{2211.13165v4,
Author        = {Lukas Schumacher and Paul-Christian Bürkner and Andreas Voss and Ullrich Köthe and Stefan T. Radev},
Title         = {Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive
  Models},
Eprint        = {2211.13165v4},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ME},
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 and time-invariant parameters.
We first benchmark our method against two existing frameworks capable of
estimating time-varying parameters. We then apply our method to fit a dynamic
version of the diffusion decision model to long time series of human response
times data. Our results show that the deep learning approach is very efficient
in capturing the temporal dynamics of the model. Furthermore, we show that the
erroneous assumption of static or homogeneous parameters will hide important
temporal information.},
Year          = {2022},
Month         = {Nov},
Url           = {http://arxiv.org/abs/2211.13165v4},
File          = {2211.13165v4.pdf}
}