33
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      The hippocampus extrapolates beyond the view in scenes: An fMRI study of boundary extension

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Boundary extension (BE) is a pervasive phenomenon whereby people remember seeing more of a scene than was present in the physical input, because they extrapolate beyond the borders of the original stimulus. This automatic embedding of a scene into a wider context supports our experience of a continuous and coherent world, and is therefore highly adaptive. BE, whilst occurring rapidly, is nevertheless thought to comprise two stages. The first involves the active extrapolation of the scene beyond its physical boundaries, and is constructive in nature. The second phase occurs at retrieval, where the initial extrapolation beyond the original scene borders is revealed by a subsequent memory error. The brain regions associated with the initial, and crucial, extrapolation of a scene beyond the view have never been investigated. Here, using functional MRI (fMRI) and a classic BE paradigm, we found that this extrapolation of scenes occurred rapidly around the time a scene was first viewed, and was associated with engagement of the hippocampus (HC) and parahippocampal cortex (PHC). Using connectivity analyses we determined that the HC in particular seemed to drive the BE effect, exerting top–down influence on PHC and indeed as far back down the processing stream as early visual cortex (VC). These cortical regions subsequently displayed activity profiles that tracked the trial-by-trial subjective perception of the scenes, rather than physical reality, thereby reflecting the behavioural expression of the BE error. Together our results show that the HC is involved in the active extrapolation of scenes beyond their physical borders. This information is then automatically and rapidly channelled through the scene processing hierarchy as far back as early VC. This suggests that the anticipation and construction of scenes is a pervasive and important aspect of our online perception, with the HC playing a central role.

          Related collections

          Most cited references38

          • Record: found
          • Abstract: found
          • Article: not found

          Repetition and the brain: neural models of stimulus-specific effects.

          One of the most robust experience-related cortical dynamics is reduced neural activity when stimuli are repeated. This reduction has been linked to performance improvements due to repetition and also used to probe functional characteristics of neural populations. However, the underlying neural mechanisms are as yet unknown. Here, we consider three models that have been proposed to account for repetition-related reductions in neural activity, and evaluate them in terms of their ability to account for the main properties of this phenomenon as measured with single-cell recordings and neuroimaging techniques. We also discuss future directions for distinguishing between these models, which will be important for understanding the neural consequences of repetition and for interpreting repetition-related effects in neuroimaging data.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            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.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Unified segmentation

                Bookmark

                Author and article information

                Journal
                Cortex
                Cortex
                Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
                Masson
                0010-9452
                1973-8102
                1 September 2013
                September 2013
                : 49
                : 8
                : 2067-2079
                Affiliations
                Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
                Author notes
                [] Corresponding author. Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK. e.maguire@ 123456ucl.ac.uk
                Article
                CORTEX941
                10.1016/j.cortex.2012.11.010
                3764338
                23276398
                51816c85-a9d1-483c-adc7-8fb48be66862
                © 2013 Elsevier Srl. All rights reserved.

                This document may be redistributed and reused, subject to certain conditions.

                History
                : 13 June 2012
                : 10 September 2012
                : 23 November 2012
                Categories
                Research Report

                Neurology
                hippocampus,scenes,boundary extension,fmri,parahippocampal cortex
                Neurology
                hippocampus, scenes, boundary extension, fmri, parahippocampal cortex

                Comments

                Comment on this article