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A saliency-based search mechanism for overt and covert shifts of visual attention
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Cites
Neural 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
SPACE AND ATTENTION IN PARIETAL CORTEX
Colby, C. L., & Goldberg, M. E. (1999) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.22.1.319 · Cited by 1,710
Corbetta, M. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.3.831 · Cited by 995
Olshausen, B., Anderson, C., & Van Essen, D. (1993) · The Journal of Neuroscience · doi:10.1523/jneurosci.13-11-04700.1993 · Cited by 908
Constraints on cortical and thalamic projections: the no-strong-loops hypothesis
Crick, F., & Koch, C. (1998) · Nature · doi:10.1038/34584 · Cited by 353 · cited 1×
“Thus, visual attention is closely linked to 6isual awareness ( Crick & Koch, 1998 ).”
BackgroundPositron emission tomographic measurements of pulvinar activity during an attention task
LaBerge, D., & Buchsbaum, M. (1990) · The Journal of Neuroscience · doi:10.1523/jneurosci.10-02-00613.1990 · Cited by 353
Cited by
Computational modelling of visual attention
Itti, L., & Koch, C. (2001) · Nature Reviews Neuroscience · doi:10.1038/35058500 · Cited by 4,733
Self-citationHarel, J., Koch, C., & Perona, P. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0073 · Cited by 3,439
Self-citationSaliency Detection: A Spectral Residual Approach
Hou, X., & Zhang, L. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.383267 · Cited by 3,422
Rayner, K. (2009) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470210902816461 · Cited by 2,682
Bernal, J., Sánchez, F. J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., & Vilariño, F. (2015) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2015.02.007 · Cited by 2,191
Top-down versus bottom-up attentional control: a failed theoretical dichotomy
Awh, E., Belopolsky, A. V., & Theeuwes, J. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.06.010 · Cited by 1,557
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
Evaluating the Visualization of What a Deep Neural Network Has Learned
Samek, W., Binder, A., Montavon, G., Lapuschkin, S., & Muller, K.-R. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2599820 · Cited by 1,164
Attentional modulation of visual processing
Reynolds, J. H., & Chelazzi, L. (2004) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.26.041002.131039 · Cited by 1,158
Attention, intention, and priority in the parietal lobe
Bisley, J. W., & Goldberg, M. E. (2010) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-060909-152823 · Cited by 1,043
Attention and choice: A review on eye movements in decision making
Orquin, J. L., & Mueller Loose, S. (2013) · Acta Psychologica · doi:10.1016/j.actpsy.2013.06.003 · Cited by 940
Five factors that guide attention in visual search
Wolfe, J. M., & Horowitz, T. S. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0058 · Cited by 875
Eye guidance in natural vision: reinterpreting salience
Tatler, B. W., Hayhoe, M. M., Land, M. F., & Ballard, D. H. (2011) · Journal of Vision · doi:10.1167/11.5.5 · Cited by 775
Guided Search 6.0: an updated model of visual search
Wolfe, J. M. (2021) · Psychonomic Bulletin & Review · doi:10.3758/s13423-020-01859-9 · Cited by 703
Mather, M., Clewett, D., Sakaki, M., & Harley, C. W. (2016) · Behavioral and Brain Sciences · doi:10.1017/s0140525x15000667 · Cited by 695
Eye movements and the control of actions in everyday life
Land, M. F. (2006) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2006.01.002 · Cited by 667
Differential attention-dependent response modulation across cell classes in macaque visual area V4
Mitchell, J. F., Sundberg, K. A., & Reynolds, J. H. (2007) · Neuron · doi:10.1016/j.neuron.2007.06.018 · Cited by 665
UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
Zhang, J., Fan, D.-P., Dai, Y., Anwar, S., Saleh, F. S., Zhang, T., & Barnes, N. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00861 · Cited by 423
The brain circuitry of attention
Shipp, S. (2004) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2004.03.004 · Cited by 402
An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed
Navalpakkam, V., & Itti, L. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.54 · Cited by 369
Self-citationTask and context determine where you look
Rothkopf, C. A., Ballard, D. H., & Hayhoe, M. M. (2016) · Journal of Vision · doi:10.1167/7.14.16 · Cited by 366
Circuits for Action and Cognition: A View from the Superior Colliculus
Basso, M. A., & May, P. J. (2017) · Annual Review of Vision Science · doi:10.1146/annurev-vision-102016-061234 · Cited by 336
Search Goal Tunes Visual Features Optimally
Navalpakkam, V., & Itti, L. (2007) · Neuron · doi:10.1016/j.neuron.2007.01.018 · Cited by 331
Self-citationThe Role of Stimulus-Driven and Goal-Driven Control in Saccadic Visual Selection.
van Zoest, W., Donk, M., & Theeuwes, J. (2004) · Journal of Experimental Psychology: Human Perception and Performance · doi:10.1037/0096-1523.30.4.749 · Cited by 330
Wilson, A. L., Buckley, E., Buckley, J. D., & Bogomolova, S. (2016) · Food Quality and Preference · doi:10.1016/j.foodqual.2016.02.009 · Cited by 308
The development of face orienting mechanisms in infants at-risk for autism
Elsabbagh, M., Gliga, T., Pickles, A., Hudry, K., Charman, T., & Johnson, M. H. (2013) · Behavioural Brain Research · doi:10.1016/j.bbr.2012.07.030 · Cited by 257
Enhanced Emotional Memory: Cognitive and Neural Mechanisms
Talmi, D. (2013) · Current Directions in Psychological Science · doi:10.1177/0963721413498893 · Cited by 257
Hamker, F. H. (2005) · Cerebral Cortex · doi:10.1093/cercor/bhh146 · Cited by 217
Altering Spatial Priority Maps via Reward-Based Learning
Chelazzi, L., E to inova, J., Calletti, R., Lo Gerfo, E., Sani, I., Della Libera, C., & Santandrea, E. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0277-14.2014 · Cited by 195
Markov blankets, information geometry and stochastic thermodynamics
Parr, T., Da Costa, L., & Friston, K. (2020) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2019.0159 · Cited by 141
Can people identify original and manipulated photos of real-world scenes?
Nightingale, S. J., Wade, K. A., & Watson, D. G. (2017) · Cognitive Research: Principles and Implications · doi:10.1186/s41235-017-0067-2 · Cited by 135
Visual Salience Affects Performance in a Working Memory Task
Fine, M. S., & Minnery, B. S. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.5503-08.2009 · Cited by 115
Brain-Based Devices for the Study of Nervous Systems and the Development of Intelligent Machines
Krichmar, J. L., & Edelman, G. M. (2005) · Artificial Life · doi:10.1162/1064546053278946 · Cited by 106
Action perception as hypothesis testing
Donnarumma, F., Costantini, M., Ambrosini, E., Friston, K., & Pezzulo, G. (2017) · Cortex · doi:10.1016/j.cortex.2017.01.016 · Cited by 90
Aalto Interface Metrics (AIM): A Service and Codebase for Computational GUI Evaluation
Oulasvirta, A., De Pascale, S., Koch, J., Langerak, T., Jokinen, J., Todi, K., Laine, M., Kristhombuge, M., Zhu, Y., Miniukovich, A., et al. (2018) · Adjunct Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology · doi:10.1145/3266037.3266087 · Cited by 94
Shifting the spotlight of attention: evidence for discrete computations in cognition
Buschman, T. J., & Miller, E. K. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00194 · Cited by 87
Influence and limitations of popout in the selection of salient visual stimuli by area V4 neurons
Burrows, B. E., & Moore, T. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.3710-09.2009 · Cited by 85
Introducing a Bayesian model of selective attention based on active inference
Mirza, M. B., Adams, R. A., Friston, K., & Parr, T. (2019) · Scientific Reports · doi:10.1038/s41598-019-50138-8 · Cited by 83
Get real! Resolving the debate about equivalent social stimuli
Birmingham, E., Bischof, W. F., & Kingstone, A. (2009) · Visual Cognition · doi:10.1080/13506280902758044 · Cited by 81
Differential Gaze Behavior towards Sexually Preferred and Non-Preferred Human Figures
Hall, C., Hogue, T., & Guo, K. (2011) · The Journal of Sex Research · doi:10.1080/00224499.2010.521899 · Cited by 79
Performance of visually guided tasks using simulated prosthetic vision and saliency-based cues
Parikh, N., Itti, L., Humayun, M., & Weiland, J. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/2/026017 · Cited by 54
Self-citationGenerative models for active vision
Parr, T., Sajid, N., Da Costa, L., Mirza, M. B., & Friston, K. J. (2021) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2021.651432 · Cited by 46
Rewarded visual items capture attention only in heterogeneous contexts
Feldmann‐Wüstefeld, T., Brandhofer, R., & Schubö, A. (2016) · Psychophysiology · doi:10.1111/psyp.12641 · Cited by 49
Conditioned fear modulates visual selection.
Mulckhuyse, M., Crombez, G., & Van der Stigchel, S. (2013) · Emotion · doi:10.1037/a0031076 · Cited by 42
Schwartz, B. (2001) · Frontiers in bioscience · doi:10.2741/schwartz · Cited by 40
Does it look safe? An eye tracking study into the visual aspects of fear of crime
Crosby, F., & Hermens, F. (2019) · Quarterly Journal of Experimental Psychology · doi:10.1177/1747021818769203 · Cited by 35
A new look at novelty effects: guiding search away from old distractors
Yang, H., Chen, X., & Zelinsky, G. J. (2009) · Attention, Perception & Psychophysics · doi:10.3758/app.71.3.554 · Cited by 30
Nakayama, K., Moher, J., & Song, J.-H. (2023) · Annual Review of Psychology · doi:10.1146/annurev-psych-021422-043229 · Cited by 21
Gibboni, R. (2009) · Frontiers in Behavioral Neuroscience · doi:10.3389/neuro.08.050.2009 · Cited by 17
Metadata
- Authors
- Itti, L., & Koch, C.
- Venue
- Vision Research
- Publisher
- Elsevier BV
- Published
- 2000-06
- Volume
- 40
- Issue
- 10-12
- Pages
- 1489-1506
- ISSN
- 0042-6989 (ISSN-L)
- OpenAlex
- W2054802006
- Cited by
- 3,156
- References
- 7
- FWCI
- 40.77
- Type
- journal article
- Language
- en
- Open access
- bronze
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