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      Perceptual awareness and active inference

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          Perceptual awareness depends upon the way in which we engage with our sensorium. This notion is central to active inference, a theoretical framework that treats perception and action as inferential processes. This variational perspective on cognition formalizes the notion of perception as hypothesis testing and treats actions as experiments that are designed (in part) to gather evidence for or against alternative hypotheses. The common treatment of perception and action affords a useful interpretation of certain perceptual phenomena whose active component is often not acknowledged. In this article, we start by considering Troxler fading – the dissipation of a peripheral percept during maintenance of fixation, and its recovery during free (saccadic) exploration. This offers an important example of the failure to maintain a percept without actively interrogating a visual scene. We argue that this may be understood in terms of the accumulation of uncertainty about a hypothesized stimulus when free exploration is disrupted by experimental instructions or pathology. Once we take this view, we can generalize the idea of using bodily (oculomotor) action to resolve uncertainty to include the use of mental (attentional) actions for the same purpose. This affords a useful way to think about binocular rivalry paradigms, in which perceptual changes need not be associated with an overt movement.

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          Most cited references 143

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          Information Theory and Statistical Mechanics

           E. T. Jaynes (1957)
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            Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects.

            We describe a model of visual processing in which feedback connections from a higher- to a lower-order visual cortical area carry predictions of lower-level neural activities, whereas the feedforward connections carry the residual errors between the predictions and the actual lower-level activities. When exposed to natural images, a hierarchical network of model neurons implementing such a model developed simple-cell-like receptive fields. A subset of neurons responsible for carrying the residual errors showed endstopping and other extra-classical receptive-field effects. These results suggest that rather than being exclusively feedforward phenomena, nonclassical surround effects in the visual cortex may also result from cortico-cortical feedback as a consequence of the visual system using an efficient hierarchical strategy for encoding natural images.
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              Dynamic causal modelling.

              In this paper we present an approach to the identification of nonlinear input-state-output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters that mediate intrinsic coupling among the states, and (3) [bilinear] parameters that allow the inputs to modulate that coupling. Identification proceeds in a Bayesian framework given known, deterministic inputs and the observed responses of the system. We developed this approach for the analysis of effective connectivity using experimentally designed inputs and fMRI responses. In this context, the coupling parameters correspond to effective connectivity and the bilinear parameters reflect the changes in connectivity induced by inputs. The ensuing framework allows one to characterise fMRI experiments, conceptually, as an experimental manipulation of integration among brain regions (by contextual or trial-free inputs, like time or attentional set) that is revealed using evoked responses (to perturbations or trial-bound inputs, like stimuli). As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling (cf., psychophysiologic interactions). However, unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.

                Author and article information

                Neurosci Conscious
                Neurosci Conscious
                Neuroscience of Consciousness
                Oxford University Press
                10 September 2019
                10 September 2019
                : 2019
                : 1
                [1 ]Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London , Institute of Neurology, 12 Queen Square, London, UK
                [2 ]Cognition & Philosophy Laboratory, Department of Philosophy, Monash University , Melbourne, Australia
                Author notes
                Correspondence address. The Wellcome Centre for Human Neuroimaging, Institute of Neurology, 12 Queen Square, London WC1N 3BG, UK. Tel: +44 (0)20 3448 4362; Fax: +44 (0)20 7813 1420; E-mail: thomas.parr.12@
                © The Author(s) 2019. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                Page count
                Pages: 15
                Funded by: Rosetrees Trust 10.13039/501100000833
                Award ID: 173346
                Funded by: Australian Government Research Training Program
                Funded by: RTP
                Funded by: Wellcome Principal Research Fellow
                Award ID: 088130/Z/09/Z
                Funded by: Australian Research Council 10.13039/501100000923
                Award ID: DP160102770
                Award ID: DP190101805
                Research Article


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