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 accessAbstract
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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Contextual Cueing: Implicit Learning and Memory of Visual Context Guides Spatial Attention
Chun, M. M., & Jiang, Y. (1998) · Cognitive Psychology · doi:10.1006/cogp.1998.0681 · Cited by 1,959
Neural mechanisms for visual memory and their role in attention
Desimone, R. (1996) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.93.24.13494 · Cited by 1,028
Solving the “real” mysteries of visual perception: The world as an outside memory
O'Regan, J. K. (1992) · Canadian Journal of Psychology / Revue canadienne de psychologie · doi:10.1037/h0084327 · Cited by 997
Cited by
CBAM: Convolutional Block Attention Module
Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_1 · Cited by 25,853
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248
A review on the attention mechanism of deep learning
Niu, Z., Zhong, G., & Yu, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.091 · Cited by 3,379
Attention mechanisms in computer vision: A survey
Guo, M.-H., Xu, T.-X., Liu, J.-J., Liu, Z.-N., Jiang, P.-T., Mu, T.-J., Zhang, S.-H., Martin, R. R., Cheng, M.-M., & Hu, S.-M. (2022) · Computational Visual Media · doi:10.1007/s41095-022-0271-y · Cited by 2,566
A taxonomy of external and internal attention.
Chun, M. M., Golomb, J. D., & Turk-Browne, N. B. (2011) · Annual Review of Psychology · doi:10.1146/annurev.psych.093008.100427 · Cited by 1,472
Metadata
- Authors
- Rensink, R. A.
- Venue
- Visual Cognition
- Publisher
- Informa UK Limited
- 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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