The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Responses of Neurons in Inferior Temporal Cortex During Memory-Guided Visual Search
Abstract
Chelazzi, Leonardo, John Duncan, Earl K. Miller, and Robert Desimone. Responses of neurons in inferior temporal cortex during memory-guided visual search. J. Neurophysiol. 80: 2918–2940, 1998. A typical scene will contain many different objects, few of which are relevant to behavior at any given moment. Thus attentional mechanisms are needed to select relevant objects for visual processing and control over behavior. We examined this role of attention in the inferior temporal cortex of macaque monkeys, using a visual search paradigm. While the monkey maintained fixation, a cue stimulus was presented at the center of gaze, followed by a blank delay period. After the delay, an array of two to five choice stimuli was presented extrafoveally, and the monkey was rewarded for detecting a target stimulus matching the cue. The behavioral response was a saccadic eye movement to the target in one version of the task and a lever release in another. The array was composed of one “good” stimulus (effective in driving the cell when presented alone) and one or more “poor” stimuli (ineffective in driving the cell when presented alone). Most cells showed higher delay activity after a good stimulus used as the cue than after a poor stimulus. The baseline activity of cells was also higher preceding a good cue, if the animal expected it to occur. This activity may depend on a top-down bias in favor of cells coding the relevant stimulus. When the choice array was presented, most cells showed suppressive interactions between the stimuli as well as strong attention effects. When the choice array was presented in the contralateral visual field, most cells initially responded the same, regardless of which stimulus was the target. However, within 150–200 ms of array onset, responses were determined by the target stimulus. If the target was the good stimulus, the response to the array became equal to the response to the good stimulus presented alone. If the target was a poor stimulus, the response approached the response to that stimulus presented alone. Thus the influence of the nontarget stimulus was eliminated. These effects occurred well in advance of the behavioral response. When the array was positioned with stimuli on opposite sides of the vertical meridian, the contralateral stimulus appeared to dominate the response, and this dominant effect could not be overcome by attention. Overall, the results support a “biased competition” model of attention, according to which 1) objects in the visual field compete for representation in the cortex, and 2) this competition is biased in favor of the behaviorally relevant object by virtue of “top-down” feedback from structures involved in working memory.
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“Later in time, competition can be biased by top-down control—either by prioritizing a spatial location (Moran & Desimone, 1985) or the perceptual feature(s) summarized in an “attentional template” (Chelazzi, Duncan, Miller, & Desimone, 1998; Reynolds, Chelazzi, & Desimone, 1999).”
From Selection history: How reward modulates selectivity of visual attention. (2018) · cites this paper as Chelazzi, Duncan, Miller, & Desimone, 1998
Citations
Cites
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
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
Self-citationLane, S. D. (2016) · Routledge eBooks · doi:10.4324/9781315506173-8 · Cited by 4,707
Visual search and stimulus similarity.
Duncan, J. S., & Humphreys, G. (1989) · Psychology Review · doi:10.1037/0033-295x.96.3.433 · Cited by 3,919
Self-citationSelective Attention Gates Visual Processing in the Extrastriate Cortex
Moran, J., & Desimone, R. (1985) · Science · doi:10.1126/science.4023713 · Cited by 2,433
Self-citationA Method of Measuring Eye Movemnent Using a Scieral Search Coil in a Magnetic Field
Robinson, D. A. (1963) · IEEE Transactions on Bio-medical Electronics · doi:10.1109/tbmel.1963.4322822 · Cited by 2,234
Selective Attention Gates Visual Processing in the Extrastriate Cortex
Moran, J., & Desimone, R. (2003) · The MIT Press eBooks · doi:10.7551/mitpress/2834.003.0024 · Cited by 2,164
Self-citationGuided 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 2,150
A method of measuring eye movements using a scleral search coil in a magnetic field
Robinson, D. A. (1963) · Medical Entomology and Zoology · Cited by 1,709
Bundesen, C. (1990) · Psychological Review · doi:10.1037/0033-295x.97.4.523 · Cited by 1,693
Research works printed but not linked: 5
- Reynolds J., 1994, Soc. Neurosci. Abstr., 20, 1054
- Reynolds J., 1997, Soc. Neurosci. Abstr., 23, 302
- Reynolds J., 1995, Soc. Neurosci. Abstr., 21, 1759
- Reynolds J., 1996, Soc. Neurosci. Abstr., 22, 1197
- 10.1037/0096-1523.16.3.459 · DOI
Cited by
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Metadata
- Authors
- Chelazzi, L., Duncan, J., Miller, E. K., & Desimone, R.
- Affiliations
- University of Verona (IT)
- National Institutes of Health (US)
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 1998-12-01
- Volume
- 80
- Issue
- 6
- Pages
- 2918-2940
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- OpenAlex
- W2143532311
- Cited by
- 726
- References
- 54
- Citations by year
- 2026: 13 · 2025: 10 · 2024: 21 · 2023: 21 · 2022: 20 · 2021: 25 · 2020: 31 · 2019: 19 · 2018: 23 · 2017: 22 · 2016: 21 · 2015: 35 · 2014: 38 · 2013: 28 · 2012: 28
- FWCI
- 10.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Neurophysiology (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (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 and Behavioral Psychology Studies
- Keywords
- inferior temporal cortex, visual attention, biased competition model, top-down feedback, working memory, delay activity, object recognition, macaque, saccades
- MeSH terms
- Animals, Attention, Conditioning, Operant, Cues, Fixation, Ocular, Macaca mulatta, Male, Memory, Neurons, Photic Stimulation, Psychomotor Performance, Retina, Temporal Lobe, Visual Perception
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