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Modeling the Temporal Dynamics of IT Neurons in Visual Search: A Mechanism for Top-Down Selective Attention
Abstract
We propose a neural model for object-oriented attention in which various visual stimuli (shapes, colors, letters, etc.) are represented by competing, mutually inhibitory, cell assemblies. The model's response to a sequence of cue and target stimuli mimics the neural responses in infero temporal (IT) visual cortex of monkeys performing a visual search task: enhanced response during the display of the stimulus, which decays but remains above a spontaneous rate after the cue disappears. When, subsequently, a display consisting of the target and several distractors is presented, the activity of all stimulus-driven cells is initially enhanced. After a short period of time, however, the activity of the cell assembly representing the cue stimulus is enhanced while the activity of the distractors decays because of mutual competition and a small top-down “expectational” input. The model fits the measured delayed activity in IT-cortex, recently reported by Chelazzi, Miller, Duncan, and Desimone (1993a), and we suggest that such a process, which is largely independent of the number of distractors, may be used by the visual system for selecting an expected target (appearing at an uncertain location) among distractors.
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“In that proposal, certain bottom-up pathways carried error signals indicating when there was a mismatch between data and their reconstruction or prediction with contextual priors and that when there was no error, the lower area would be relatively inactive.”
From Hierarchical Bayesian inference in the visual cortex (2003) · cites this paper as [7]
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“These experiments support our earlier proposal 1 and related ideas 7,16,74 that top-down generative signals could explain away the earlier evidence based on efficient coding consideration.”
From Hierarchical Bayesian inference in the visual cortex (2003) · cites this paper as [7]
Citations
Cites
James, W. (1891) · Personality Culture Society · doi:10.1037/11059-000 · Cited by 25,876
A feature-integration theory of attention.
Treisman, A. M., & Gelade, G. (1980) · Cognitive Psychology · doi:10.1016/0010-0285(80)90005-5 · Cited by 12,366
Posner, M. I. (1980) · Quarterly Journal of Experimental Psychology · doi:10.1080/00335558008248231 · Cited by 9,670 · cited 1×
“It has been shown that orienting focal attention leads to an improvement in visual performance ( Posner, 1980 ), and it was proposed that it is required for the conjunction of features (Treisman & Gelade, 1980; Treisman & Schmidt, 1982) or for the scrutiny of targets composed of indistinguishable textons (such as ‘T”s…”
BackgroundVisual search and stimulus similarity.
Duncan, J. S., & Humphreys, G. (1989) · Psychology Review · doi:10.1037/0033-295x.96.3.433 · Cited by 3,919
Excitatory and Inhibitory Interactions in Localized Populations of Model Neurons
Wilson, H. R., & Cowan, J. D. (1972) · Biophysical Journal · doi:10.1016/s0006-3495(72)86068-5 · Cited by 3,579
A massively parallel architecture for a self-organizing neural pattern recognition machine
Carpenter, G. A., & Grossberg, S. (1987) · Computer Vision, Graphics, and Image Processing · doi:10.1016/s0734-189x(87)80014-2 · Cited by 2,525
Feature analysis in early vision: Evidence from search asymmetries.
Treisman, A., & Gormican, S. (1988) · Psychological Review · doi:10.1037/0033-295x.95.1.15 · Cited by 2,260
Cohen, J. D., Dunbar, K. N., & McClelland, J. L. (1990) · Psychological Review · doi:10.1037/0033-295x.97.3.332 · Cited by 2,192
Society for Neuroscience 2007 abstracts
Wishart, T. M., Paterson, J. M., Short, D. M., Meredith, S., Robertson, K., Sutherland, C. D., Cousin, M. A., Dutia, M. B., & Gillingwater, T. H. (2007) · Cited by 2,094
Visual attention within and around the field of focal attention: A zoom lens model
Eriksen, C. W., & St. James, J. D. (1986) · Perception & Psychophysics · doi:10.3758/bf03211502 · Cited by 1,907
Research works printed but not linked: 7
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Conflict monitoring and cognitive control
Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001) · Psychological Review · doi:10.1037/0033-295x.108.3.624 · Cited by 7,351
The time course of perceptual choice: The leaky, competing accumulator model.
Usher, M., & McClelland, J. L. (2001) · Psychological Review · doi:10.1037/0033-295x.108.3.550 · Cited by 2,152
Self-citationHierarchical Bayesian inference in the visual cortex
Lee, T. S., & Mumford, D. (2003) · Journal of the Optical Society of America A · doi:10.1364/josaa.20.001434 · Cited by 1,473
Top-Down Neural Attention by Excitation Backprop
Zhang, J., Bargal, S. A., Lin, Z., Brandt, J., Shen, X., & Sclaroff, S. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1059-x · Cited by 815
Responses of Neurons in Inferior Temporal Cortex During Memory-Guided Visual Search
Chelazzi, L., Duncan, J., Miller, E. K., & Desimone, R. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.80.6.2918 · Cited by 726
Visual attention mediated by biased competition in extrastriate visual cortex
Desimone, R. (1998) · Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rstb.1998.0280 · Cited by 696
A Neural Theory of Visual Attention: Bridging Cognition and Neurophysiology.
Bundesen, C., Habekost, T., & Kyllingsbæk, S. (2005) · Psychological Review · doi:10.1037/0033-295x.112.2.291 · Cited by 614
A symbolic-connectionist theory of relational inference and generalization.
Hummel, J. E., & Holyoak, K. J. (2003) · Psychological Review · doi:10.1037/0033-295x.110.2.220 · Cited by 567
Computational Neuroscience of Vision
Rolls, E. T., & Deco, G. (2001) · doi:10.1093/acprof:oso/9780198524885.001.0001 · Cited by 554
Luck, S. J., Girelli, M., McDermott, M. T., & Ford, M. A. (1997) · Cognitive Psychology · doi:10.1006/cogp.1997.0660 · Cited by 481
Loss Aversion and Inhibition in Dynamical Models of Multialternative Choice.
Usher, M., & McClelland, J. L. (2004) · Psychological Review · doi:10.1037/0033-295x.111.3.757 · Cited by 403
Self-citationComputational models of schizophrenia and dopamine modulation in the prefrontal cortex
Rolls, E. T., Loh, M., Deco, G., & Winterer, G. (2008) · Nature reviews. Neuroscience · doi:10.1038/nrn2462 · Cited by 396
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Doumas, L. A. A., Hummel, J. E., & Sandhofer, C. M. (2008) · Psychological Review · doi:10.1037/0033-295x.115.1.1 · Cited by 366
In search of common foundations for cortical computation
Phillips, W. A., & Singer, W. (1997) · Behavioral and Brain Sciences · doi:10.1017/s0140525x9700160x · Cited by 342
Attention, short-term memory, and action selection: A unifying theory
Deco, G., & Rolls, E. T. (2005) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2005.08.004 · Cited by 324
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Deco, G., & Rolls, E. T. (2003) · Vision Research · doi:10.1016/j.visres.2003.09.037 · Cited by 317
Control of Cognitive Processes
Monsell, S., & Driver, J. (2000) · The MIT Press eBooks · doi:10.7551/mitpress/1481.001.0001 · Cited by 1,650
Task reward structure shapes rapid receptive field plasticity in auditory cortex
David, S. V., Fritz, J. B., & Shamma, S. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1117717109 · Cited by 271
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Metadata
- Authors
- Usher, M., & Niebur, E.
- Affiliations
- Carnegie Mellon University (US)
- California Institute of Technology (US)
- Venue
- Journal of Cognitive Neuroscience
- Publisher
- MIT Press - Journals
- Published
- 1996
- Volume
- 8
- Issue
- 4
- Pages
- 311-327
- ISSN
- 0898-929X (ISSN-L)
- 1530-8898
- OpenAlex
- W2099555952
- Cited by
- 147
- References
- 68
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 1 · 2022: 1 · 2021: 1 · 2020: 2 · 2019: 2 · 2018: 2 · 2017: 2 · 2016: 1 · 2014: 3 · 2013: 4 · 2012: 2
- FWCI
- 2.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Cognitive Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- visual search, inferotemporal cortex, cell assemblies, reciprocal inhibition, distractor suppression, temporal dynamics of neural responses
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