The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Modeling the role of salience in the allocation of overt visual attention
Open accessAbstract
A biologically motivated computational model of bottom-up visual selective attention was used to examine the degree to which stimulus salience guides the allocation of attention. Human eye movements were recorded while participants viewed a series of digitized images of complex natural and artificial scenes. Stimulus dependence of attention, as measured by the correlation between computed stimulus salience and fixation locations, was found to be significantly greater than that expected by chance alone and furthermore was greatest for eye movements that immediately follow stimulus onset. The ability to guide attention of three modeled stimulus features (color, intensity and orientation) was examined and found to vary with image type. Additionally, the effect of the drop in visual sensitivity as a function of eccentricity on stimulus salience was examined, modeled, and shown to be an important determiner of attentional allocation. Overall, the results indicate that stimulus-driven, bottom-up mechanisms contribute significantly to attentional guidance under natural viewing conditions.
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Citations
Cites
Posner, M. I. (1980) · Quarterly Journal of Experimental Psychology · doi:10.1080/00335558008248231 · Cited by 9,670 · cited 2×
“Observers can vo-litionally select regions of space (Posner, 1980) or individual objects (Rock & Gutman, 1981; Duncan, 1984; Tipper, Weaver, Jerreat, & Burak, 1994) to attend.”
BackgroundNeural Mechanisms of Selective Visual Attention
Desimone, R., & Duncan, J. (1995) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.18.030195.001205 · Cited by 8,388
The Laplacian pyramid as a compact image code
Burt, P., & Adelson, E. (1983) · IEEE Transactions on Communications · doi:10.1109/tcom.1983.1095851 · Cited by 6,077
Ferrier lecture. Functional architecture of macaque monkey visual cortex
Hubel, D. H., & Wiesel, T. N. (1977) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1977.0085 · Cited by 2,608
Guided search: An alternative to the feature integration model for visual search.
Wolfe, J. M., Cave, K. R., & Franzel, S. L. (1989) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037//0096-1523.15.3.419 · Cited by 2,147
The role of attention in the programming of saccades
Kowler, E., Anderson, E., Dosher, B., & Blaser, E. (1995) · Vision Research · doi:10.1016/0042-6989(94)00279-u · Cited by 1,302
Summation and Division by Neurons in Primate Visual Cortex
Carandini, M., & Heeger, D. J. (1994) · Science · doi:10.1126/science.8191289 · Cited by 649 · cited 1×
“The normalization scales the activations of a map to the squared difference between its global maximum and the average of the remaining (local) maxima in the map (such a normalization has been used successfully to explain single-cell behavior in primary visual cortex; Carandini & Heeger, 1994).”
Method
Cited by
Harel, J., Koch, C., & Perona, P. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0073 · Cited by 3,439
Rayner, K. (2009) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470210902816461 · Cited by 2,682
Bayesian surprise attracts human attention
Itti, L., & Baldi, P. (2009) · Vision Research · doi:10.1016/j.visres.2008.09.007 · Cited by 1,224
Carrasco, M., Ling, S., & Read, S. (2004) · Nature Neuroscience · doi:10.1038/nn1194 · Cited by 1,085
Attention and consciousness: two distinct brain processes
Koch, C., & Tsuchiya, N. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.10.012 · Cited by 1,046
Metadata
- Authors
- Parkhurst, D., Law, K., & Niebur, E.
- Venue
- Vision Research
- Publisher
- Elsevier BV
- Published
- 2002-01
- Volume
- 42
- Issue
- 1
- Pages
- 107-123
- ISSN
- 0042-6989 (ISSN-L)
- OpenAlex
- W1969258103
- Cited by
- 1,388
- References
- 7
- FWCI
- 36.69
- Type
- journal article
- Language
- en
- Open access
- bronze
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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.
