title: Model selection and gobbledygook: response to Lohmann et al.
abstract: Lohmann et al. (this issue) make three unremarkable observations about model selection and use them to critique dynamic causal modelling-a Bayesian model selection procedure based on causal models of dynamical systems (Marreiros et al., 2010). In this response, we unpack their misconceptions and try to answer their questions.
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK. Electronic address: k.friston@ucl.ac.uk.
authors.forename: Karl
authors.initials: K
authors.lastname: Friston
authors.affiliation: Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Switzerland.
authors.forename: Jean
authors.initials: J
authors.lastname: Daunizeau
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK; Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Switzerland.
authors.forename: Klaas Enno
authors.initials: KE
authors.lastname: Stephan
mesh_terms: D001921:Brain / Q000033:anatomy & histology*; D015984:Causality; D006801:Humans; D015233:Models, Statistical