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Neural superstatistics for bayesian estimation of dynamic cognitive models

Mr. Lukas Schumacher
University of Basel ~ Economic Psychology
Stefan Radev
Rensselaer Polytechnic Institute ~ Cognitive Science
Andreas Voss
Heidelberg University ~ Institute of Psychology
Paul-Christian Bürkner
Aalto University, Finland

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.



Dynamic Cognitive Models
Bayesian Amortized Inference
Diffusion Decision Model

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Cite this as:

Schumacher, L., Radev, S. T., Voss, A., & Bürkner, P.-C. (2023, July). Neural superstatistics for bayesian estimation of dynamic cognitive models. Abstract published at MathPsych/ICCM/EMPG 2023. Via