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Honey, I shrunk the parameter space: Dimensionality reduction for hierarchical models.

Reilly Innes
University of Newcastle ~ School of Psychology
Niek Stevenson
University of Amsterdam ~ Brain & Cognition
Dr. Quentin Gronau
University of Newcastle
Andrew Heathcote
University of Tasmania ~ Division of Psychology
Prof. Birte Forstmann
University of Amsterdam ~ Psychology
Dr. Scott Brown
University of Newcastle ~ School of Psychology
Steven Miletić
University of Amsterdam ~ Department of Psychology

Joint modelling of behaviour and neural activation poses the potential for a significant advance to methods of linking brain and behaviour. However, methods of joint modelling have been limited by difficulties in estimation, often due to high dimensionality and simultaneous estimation challenges. In this talk, we present a method of model estimation which allows for a significant dimensionality reduction using factor analysis at the group level in a Bayesian hierarchical model based estimation framework. The method is based on the particle metropolis within Gibbs sampling algorithm (Gunawan, Hawkins, Tran, Kohn, & Brown, 2020) - which is robust and reliable - with changes implemented to the standard ‘pmwg’ R package. Additionally, we briefly highlight several alternate solutions to the dimensionality problem. Although we focus on joint modelling methods, this model based estimation approach could be used for any high dimensional modelling problem. We provide open source code and accompanying tutorial documentation to make the method accessible to any researchers.



Joint Modelling
Hierarchical Modelling
Dimensionality Reduction

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Cite this as:

Innes, R., Stevenson, N., Gronau, Q. F., Heathcote, A., Forstmann, B., Brown, S., & Miletić, S. (2022, July). Honey, I shrunk the parameter space: Dimensionality reduction for hierarchical models. Paper presented at Virtual MathPsych/ICCM 2022. Via