Neural superstatistics for Bayesian estimation of dynamic cognitive models

• Verfasst von: Schumacher, Lukas [VerfasserIn]
• Verfasst von: Bürkner, Paul-Christian [VerfasserIn]
• Verfasst von: Voß, Andreas [VerfasserIn]
• Verfasst von: Köthe, Ullrich [VerfasserIn]
• Verfasst von: Radev, Stefan [VerfasserIn]
• Titel: Neural superstatistics for Bayesian estimation of dynamic cognitive models
• Verf.angabe: Lukas Schumacher, Paul-Christian Bürkner, Andreas Voss, Ullrich Köthe & Stefan T. Radev
• Jahr: 2023
• Umfang: 16 S.
• Illustrationen: Illustrationen
• Fussnoten: Online veröffentlicht: 23. August 2023 ; Gesehen am 22.11.2023
• Titel Quelle: Enthalten in: Scientific reports
• Ort Quelle: [London] : Macmillan Publishers Limited, part of Springer Nature, 2011
• Jahr Quelle: 2023
• Band/Heft Quelle: 13(2023), Artikel-ID 13778, Seite 1-16
• ISSN Quelle: 2045-2322
• DOI: doi:10.1038/s41598-023-40278-3
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Human behaviour
• Sach-SW: Statistics
• K10plus-PPN: 1870876695

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.