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Updated: Jul 18, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
Published on: March 1, 2022
Bayesian comparison of spatially regularised general linear models
Will Penny1, Guillaume Flandin, Nelson Trujillo-Barreto
1Wellcome Department of Imaging Neuroscience, University College, London WC1N 3BG, UK. wpenny@ion.ucl.ac.uk
Abstract:
In previous work (Penny et al., [2005]: Neuroimage 24:350-362) we have developed a spatially regularised General Linear Model for the analysis of functional magnetic resonance imaging data that allows for the characterisation of regionally specific effects using Posterior Probability Maps (PPMs). In this paper we show how it also provides an approximation to the model evidence. This is important as it is the basis of Bayesian model comparison and provides a unified framework for Bayesian Analysis of Variance, Cluster of Interest analyses and the principled selection of signal and noise models. We also provide extensions that implement spatial and anatomical regularisation of noise process parameters.
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