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The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

The Dynamic Representation of Scenes

Open access
Rensink, Ronald A.·2000·Visual Cognition

Abstract

One of the more powerful impressions created by vision is that of a coherent, richly detailed world where everything is present simultaneously. Indeed, this impression is so compelling that we tend to ascribe these properties not only to the external world, but to our internal representations as well. But results from several recent experiments argue against this latter ascription. For example, changes in images of real-world scenes often go unnoticed when made during a saccade, flicker, blink, or movie cut. This “change blindness” provides strong evidence against the idea that our brains contain a picture-like representation of the scene that is everywhere detailed and coherent. How then do we represent a scene? It is argued here that focused attention provides spatiotemporal coherence for the stable representation of one object at a time. It is then argued that the allocation of attention can be co-ordinated to create a “virtual representation”. In such a scheme, a stable object representation is formed whenever needed, making it appear to higher levels as if all objects in the scene are represented in detail simultaneously.

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Metadata

Authors
Rensink, R. A.
Venue
Visual Cognition
Published
2000-01
Volume
7
Issue
1-3
Pages
17-42
ISSN
  • 1350-6285 (ISSN-L)
  • 1464-0716
OpenAlex
W2122710056
Cited by
1,078
References
4
FWCI
16.13
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: The Dynamic Representation of Scenes. https://app.cochat.ai/papers/10.1080/135062800394667
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Rensink, R. A. (2000). The Dynamic Representation of Scenes. Visual Cognition. https://doi.org/10.1080/135062800394667

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.