ArticleScientific reports2023
Neural superstatistics for Bayesian estimation of dynamic cognitive models.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Quantifying uncertainty in drift diffusion models of decision making under temporal dependence and parameter variability.bioRxiv : the preprint server for biology · 2026Article
- Brunswik's fundamental principle explained: A diffusion lens model of vicarious functioning.Psychonomic bulletin & review · 2026Review
- Human Strategy Adaptation in Reinforcement Learning Resembles Policy Gradient Ascent.bioRxiv : the preprint server for biology · 2025Article
- Parameter estimation of hyper-spherical diffusion models with a time-dependent threshold: An integral equation method.Behavior research methods · 2025Article
- Latent variable sequence identification for cognitive models with neural network estimators.Behavior research methods · 2025Article
- Comparing likelihood-based and likelihood-free approaches to fitting and comparing models of intertemporal choice.Behavior research methods · 2025Article
- Validation and Comparison of Non-stationary Cognitive Models: A Diffusion Model Application.Computational brain & behavior · 2025Article
- Dynamics of task preparation processes revealed by effect course analysis on response times and error rates.Scientific reports · 2024Article
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Authors and funding
5 authors.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.