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Dynamic Causal Modelling of Active Vision This Accepted Manuscript has not been copyedited and formatted. The final version may differ from this version. Research Articles: Systems/Circuits Dynamic causal modelling of active vision Thomas Parr1, M. Berk Mirza1, Hayriye Cagnan2,3 and Karl J Friston1 1Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, WC1N 3BG, UK. 2MRC Brain Network Dynamics Unit at the University of Oxford 3Nuffield Department of Clinical Neurosciences, University of Oxford https://doi.org/10.1523/JNEUROSCI.2459-18.2019 Received: 24 September 2018 Revised: 8 March 2019 Accepted: 11 March 2019 Published: 10 June 2019 Author contributions: T.P. and K.F. designed research; T.P. performed research; T.P. analyzed data; T.P. wrote the first draft of the paper; T.P., M.B.M., H.C., and K.F. edited the paper; M.B.M., H.C., and K.F. wrote the paper. Conflict of Interest: The authors declare no competing financial interests. TP is supported by the Rosetrees Trust (Award Number 173346). KJF is a Wellcome Principal Research Fellow (Ref: 088130/Z/09/Z). MBM is a member of PACE (Perception and Action in Complex Environments) supported by the European Union's Horizon 2020 (Marie Sklodowska-Curie Grant Agreement 642961). HC is supported by the MRC (MR/R020418/1). We are grateful to David Bradbury and Eric Featherstone for their technical support and to the WCHN MEG group for helpful discussions. Correspondence: Thomas Parr, The Wellcome Centre for Human Neuroimaging, Institute of Neurology, 12 Queen Square, London, UK WC1N 3BG, [email protected] Cite as: J. Neurosci 2019; 10.1523/JNEUROSCI.2459-18.2019 Alerts: Sign up at www.jneurosci.org/alerts to receive customized email alerts when the fully formatted version of this article is published. Accepted manuscripts are peer-reviewed but have not been through the copyediting, formatting, or proofreading process. Copyright © 2019 Parr et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed. Dynamic causal modelling of active vision Thomas Parr1*, M. Berk Mirza1, Hayriye Cagnan2,3, Karl J Friston1 1 Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, WC1N 3BG, UK. 2 MRC Brain Network Dynamics Unit at the University of Oxford 2 Nuffield Department of Clinical Neurosciences, University of Oxford [email protected], [email protected], [email protected], [email protected] Correspondence: Thomas Parr The Wellcome Centre for Human Neuroimaging Institute of Neurology 12 Queen Square, London, UK WC1N 3BG [email protected] Abbreviated title: Active vision Pages: 25 Figures: 8 Wordcount – Abstract: 213; Introduction: 371; Discussion: 1333 Disclosure statement: The authors have no disclosures or conflict of interest. Acknowledgements: TP is supported by the Rosetrees Trust (Award Number 173346). KJF is a Wellcome Principal Research Fellow (Ref: 088130/Z/09/Z). MBM is a member of PACE (Perception and Action in Complex Environments) supported by the European Union's Horizon 2020 (Marie Sklodowska-Curie Grant Agreement 642961). HC is supported by the MRC (MR/R020418/1). We are grateful to David Bradbury and Eric Featherstone for their technical support and to the WCHN MEG group for helpful discussions. 1 Active vision 2 3 Dynamic causal modelling of active vision 4 5 Thomas Parr1*, M. Berk Mirza1, Hayriye Cagnan2,3, Karl J Friston1 6 1 Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, WC1N 3BG, UK. 7 2 MRC Brain Network Dynamics Unit at the University of Oxford 8 2 Nuffield Department of Clinical Neurosciences, University of Oxford 9 [email protected], [email protected], [email protected], [email protected] 10 11 Correspondence: Thomas Parr 12 The Wellcome Centre for Human Neuroimaging 13 Institute of Neurology 14 12 Queen Square, London, UK WC1N 3BG 15 [email protected] 16 17 18 Abstract 19 In this paper, we draw from recent theoretical work on active perception that suggests the brain 20 makes use of an internal (i.e., generative) model to make inferences about the causes of sensations. 21 This view treats visual sensations as consequent on action (i.e., saccades) and implies that visual 22 percepts must be actively constructed via a sequence of eye movements. Oculomotor control calls 23 on a distributed set of brain sources that includes the dorsal and ventral frontoparietal (attention) 24 networks. We argue that connections from the frontal eye fields to ventral parietal sources 25 represent the mapping from ‘where’, fixation location to information derived from ‘what’ 26 representations in the ventral visual stream. During scene construction, this mapping must be 1 Active vision 27 learned, putatively through changes in the effective connectivity of these synapses. Here, we test 28 the hypothesis that the coupling between the dorsal frontal cortex and the right temporoparietal 29 cortex is modulated during saccadic interrogation of a simple visual scene. Using dynamic causal 30 modelling for magnetoencephalography (MEG) with (male and female) human participants, we 31 assess the evidence for changes in effective connectivity by comparing models that allow for this 32 modulation with models that do not. We find strong evidence for modulation of connections 33 between the two attention networks; namely, a disinhibition of the ventral network by its dorsal 34 counterpart. 35 36 Keywords: Dynamic causal modelling; Active vision; Magnetoencephalography; Eye-tracking; 37 Attention; Visual neglect 38 39 Significance statement 40 This work draws from recent theoretical accounts of active vision and provides empirical evidence 41 for changes in synaptic efficacy consistent with these computational models. In brief, we used 42 magnetoencephalography in combination with eye-tracking to assess the neural correlates of a form 43 of short-term memory during a dot cancellation task. Using dynamic causal modelling to quantify 44 changes in effective connectivity, we found evidence that the coupling between the dorsal and 45 ventral attention networks changed during the saccadic interrogation of a simple visual scene. 46 Intuitively, this is consistent with the idea that these neuronal connections may encode beliefs about 47 “what I would see if I looked there”, and that this mapping is optimised as new data are obtained 48 with each fixation. 49 50 Introduction 2 Active vision 51 Perception is a fundamentally active process. While this is true across modalities, it is especially 52 obvious in the visual system, where what we see depends upon where we look (Andreopoulos & 53 Tsotsos, 2013; Ognibene & Baldassarre, 2014; Parr & Friston, 2017a; Wurtz, McAlonan, Cavanaugh, 54 & Berman, 2011). In this paper, we consider the anatomy that supports decisions about where to 55 look, and the fast plastic changes that underwrite effective saccadic interrogation of a visual scene. 56 We appeal to the metaphor of perception as hypothesis testing (Gregory, 1980), treating each 57 fixation as an experiment to garner new information about states of affairs in the world (Mirza, 58 Adams, Mathys, & Friston, 2018; Mirza, Adams, Mathys, & Friston, 2016; Parr & Friston, 2017c). 59 Building upon recent theoretical work (Parr & Friston, 2017b), which includes a formal model of the 60 task used here, we hypothesised that the configuration of a visual scene is best represented in terms 61 of expected visual sensations contingent upon a given saccade (‘what I would see if I looked there’) 62 (Zimmermann & Lappe, 2016). This implies a form of short-term plasticity following each fixation, as 63 the mapping from fixation to observation is optimised. 64 The purpose of this study is not to evaluate whether we engage in active vision, as there is already 65 substantial evidence in favour of this (Mirza et al., 2018; Yang, Lengyel, & Wolpert, 2016), but to try 66 to understand how the underlying computations manifest in terms of changes in effective 67 connectivity. Our aim is to establish whether there is neurobiological evidence in favour of 68 optimisation of a generative model (Yuille & Kersten, 2006) that represents visual consequences of 69 fixations as a series of eye-movements are performed. 70 In the following, we describe our experimental set-up, including our gaze-contingent cancellation 71 task. Through source reconstruction, we demonstrate the engagement of frontal, temporal, and 72 parietal sources, and note the right-lateralisation of the temporal component. We then detail the 73 hypothesis in terms of network models or architectures and use DCM to adjudicate between models 74 that do and do not allow for plastic changes in key connections. This model comparison revealed a 3 Active vision 75 decrease, from early to late fixations, in the inhibition of neuronal populations in the ventral 76 network by those in the dorsal network. 77 78 79 Network structure 80 The cortical anatomy of oculomotor control has been investigated through functional neuroimaging, 81 neuropsychological, and structural connectivity studies. Figure 1 summarises how their findings 82 converge upon a system that can be separated into a bilateral dorsal frontoparietal network, and a 83 right lateralised ventral network. In brief, functional imaging experiments (Corbetta & Shulman, 84 2002; Vossel, Weidner, Driver, Friston, & Fink, 2012) during visuospatial tasks reveal activation of the 85 frontal eye fields (FEF) and the intraparietal sulcus (IPS) in both hemispheres, but greater 86 involvement of the right temporoparietal junction (TPJ) than its contralateral homologue. The 87 volumes of the white matter tracts connecting the components of the dorsal attention network are 88 comparable, while those connecting the ventral network sources are of a significantly greater 89 volume in the right hemisphere (Thiebaut de Schotten et al., 2011). Neuropsychological asymmetries 90 reinforce this network structure, with right hemispheric lesions much more likely than left to give 91 rise to visual neglect (Halligan & Marshall, 1998).
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