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Variational representational similarity analysis

This technical note describes a variational or Bayesian implementation of representational similarity analysis (RSA) and pattern component modelling (PCM). It considers RSA and PCM as Bayesian model comparison procedures that assess the evidence for stimulus or condition-specific patterns of respons...

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Detalles Bibliográficos
Autores principales: Friston, Karl J., Diedrichsen, Jörn, Holmes, Emma, Zeidman, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Academic Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6892264/
https://www.ncbi.nlm.nih.gov/pubmed/31255808
http://dx.doi.org/10.1016/j.neuroimage.2019.06.064
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author Friston, Karl J.
Diedrichsen, Jörn
Holmes, Emma
Zeidman, Peter
author_facet Friston, Karl J.
Diedrichsen, Jörn
Holmes, Emma
Zeidman, Peter
author_sort Friston, Karl J.
collection PubMed
description This technical note describes a variational or Bayesian implementation of representational similarity analysis (RSA) and pattern component modelling (PCM). It considers RSA and PCM as Bayesian model comparison procedures that assess the evidence for stimulus or condition-specific patterns of responses distributed over voxels or channels. On this view, one can use standard variational inference procedures to quantify the contributions of particular patterns to the data, by evaluating second-order parameters or hyperparameters. Crucially, this allows one to use parametric empirical Bayes (PEB) to infer which patterns are consistent among subjects. At the between-subject level, one can then assess the evidence for different (combinations of) hypotheses about condition-specific effects using Bayesian model comparison. Alternatively, one can select a single hypothesis that best explains the pattern of responses using Bayesian model selection. This note rehearses the technical aspects of within and between-subject RSA using a worked example, as implemented in the Statistical Parametric Mapping (SPM) software. En route, we highlight the connection between univariate and multivariate analyses of neuroimaging data and the sorts of analyses that are possible using component modelling and representational similarity analysis.
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spelling pubmed-68922642019-12-16 Variational representational similarity analysis Friston, Karl J. Diedrichsen, Jörn Holmes, Emma Zeidman, Peter Neuroimage Article This technical note describes a variational or Bayesian implementation of representational similarity analysis (RSA) and pattern component modelling (PCM). It considers RSA and PCM as Bayesian model comparison procedures that assess the evidence for stimulus or condition-specific patterns of responses distributed over voxels or channels. On this view, one can use standard variational inference procedures to quantify the contributions of particular patterns to the data, by evaluating second-order parameters or hyperparameters. Crucially, this allows one to use parametric empirical Bayes (PEB) to infer which patterns are consistent among subjects. At the between-subject level, one can then assess the evidence for different (combinations of) hypotheses about condition-specific effects using Bayesian model comparison. Alternatively, one can select a single hypothesis that best explains the pattern of responses using Bayesian model selection. This note rehearses the technical aspects of within and between-subject RSA using a worked example, as implemented in the Statistical Parametric Mapping (SPM) software. En route, we highlight the connection between univariate and multivariate analyses of neuroimaging data and the sorts of analyses that are possible using component modelling and representational similarity analysis. Academic Press 2019-11-01 /pmc/articles/PMC6892264/ /pubmed/31255808 http://dx.doi.org/10.1016/j.neuroimage.2019.06.064 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Friston, Karl J.
Diedrichsen, Jörn
Holmes, Emma
Zeidman, Peter
Variational representational similarity analysis
title Variational representational similarity analysis
title_full Variational representational similarity analysis
title_fullStr Variational representational similarity analysis
title_full_unstemmed Variational representational similarity analysis
title_short Variational representational similarity analysis
title_sort variational representational similarity analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6892264/
https://www.ncbi.nlm.nih.gov/pubmed/31255808
http://dx.doi.org/10.1016/j.neuroimage.2019.06.064
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