title: Network mechanisms of intentional learning.
abstract: The ability to learn new tasks rapidly is a prominent characteristic of human behaviour. This ability relies on flexible cognitive systems that adapt in order to encode temporary programs for processing non-automated tasks. Previous functional imaging studies have revealed distinct roles for the lateral frontal cortices (LFCs) and the ventral striatum in intentional learning processes. However, the human LFCs are complex; they house multiple distinct sub-regions, each of which co-activates with a different functional network. It remains unclear how these LFC networks differ in their functions and how they coordinate with each other, and the ventral striatum, to support intentional learning. Here, we apply a suite of fMRI connectivity methods to determine how LFC networks activate and interact at different stages of two novel tasks, in which arbitrary stimulus-response rules are learnt either from explicit instruction or by trial-and-error. We report that the networks activate en masse and in synchrony when novel rules are being learnt from instruction. However, these networks are not homogeneous in their functions; instead, the directed connectivities between them vary asymmetrically across the learning timecourse and they disengage from the task sequentially along a rostro-caudal axis. Furthermore, when negative feedback indicates the need to switch to alternative stimulus-response rules, there is additional input to the LFC networks from the ventral striatum. These results support the hypotheses that LFC networks interact as a hierarchical system during intentional learning and that signals from the ventral striatum have a driving influence on this system when the internal program for processing the task is updated.
authors.affiliation: The Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London, UK. Electronic address: a.hampshire@imperial.ac.uk.
authors.forename: Adam
authors.initials: A
authors.lastname: Hampshire
authors.affiliation: The Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London, UK; Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Hellyer
authors.affiliation: Institute of Cognitive Neuroscience, University College London, London, UK.
authors.forename: Beth
authors.initials: B
authors.lastname: Parkin
authors.affiliation: The Brain and Mind Institute, University of Western Ontario, London, ON, Canada.
authors.forename: Nole
authors.initials: N
authors.lastname: Hiebert
authors.affiliation: The Brain and Mind Institute, University of Western Ontario, London, ON, Canada.
authors.forename: Penny
authors.initials: P
authors.lastname: MacDonald
authors.affiliation: The Brain and Mind Institute, University of Western Ontario, London, ON, Canada.
authors.forename: Adrian M
authors.initials: AM
authors.lastname: Owen
authors.affiliation: The Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London, UK.
authors.forename: Robert
authors.initials: R
authors.lastname: Leech
authors.affiliation: Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Medical Research Council Cognition and Brain Sciences Unit, Cambridge, UK.
authors.forename: James
authors.initials: J
authors.lastname: Rowe
mesh_terms: D000328:Adult; D001931:Brain Mapping; D005625:Frontal Lobe / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007858:Learning / Q000502:physiology; D008279:Magnetic Resonance Imaging; D009434:Neural Pathways / Q000502:physiology*
keywords: Caudate; Dynamic causal modelling; Frontal cortex; Functional connectivity; Learning