The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Competitive Mechanisms Subserve Attention in Macaque Areas V2 and V4
Open accessAbstract
It is well established that attention modulates visual processing in extrastriate cortex. However, the underlying neural mechanisms are unknown. A consistent observation is that attention has its greatest impact on neuronal responses when multiple stimuli appear together within a cell’s receptive field. One way to explain this is to assume that multiple stimuli activate competing populations of neurons and that attention biases this competition in favor of the attended stimulus. In the absence of competing stimuli, there is no competition to be resolved. Accordingly, attention has a more limited effect on the neuronal response to a single stimulus. To test this interpretation, we measured the responses of neurons in macaque areas V2 and V4 using a behavioral paradigm that allowed us to isolate automatic sensory processing mechanisms from attentional effects. First, we measured each cell’s response to a single stimulus presented alone inside the receptive field or paired with a second receptive field stimulus, while the monkey attended to a location outside the receptive field. Adding the second stimulus typically caused the neuron’s response to move toward the response that was elicited by the second stimulus alone. Then, we directed the monkey’s attention to one element of the pair. This drove the neuron’s response toward the response elicited when the attended stimulus appeared alone. These findings are consistent with the idea that attention biases competitive interactions among neurons, causing them to respond primarily to the attended stimulus. A quantitative neural model of attention is proposed to account for these results.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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 Reynolds, Chelazzi, & Desimone, 1999
Experimental — accuracy not yet measuredIn the citing paper
“It also shows good agreement (23) with other data in V4 (28) about the response of neurons to combinations of simple two-bar stimuli (within the receptive field of the S 2 units)”
From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [28]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Self-citationShifts in Selective Visual Attention: Towards the Underlying Neural Circuitry
Koch, C., & Ullman, S. (1987) · Springer eBooks · doi:10.1007/978-94-009-3833-5_5 · Cited by 3,762
Selective Attention Gates Visual Processing in the Extrastriate Cortex
Moran, J., & Desimone, R. (1985) · Science · doi:10.1126/science.4023713 · Cited by 2,433
Self-citationSelective 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-citationGrossberg, S. (1976) · Biological Cybernetics · doi:10.1007/bf00344744 · Cited by 1,910
Neural mechanisms of spatial selective attention in areas V1, V2, and V4 of macaque visual cortex
Luck, S. J., Chelazzi, L., Hillyard, S. A., & Desimone, R. (1997) · Journal of Neurophysiology · doi:10.1152/jn.1997.77.1.24 · Cited by 1,615
Self-citationHow Does a Brain Build a Cognitive Code?
Grossberg, S. (1982) · Boston studies in the philosophy of science · doi:10.1007/978-94-009-7758-7_1 · Cited by 1,204
How does a brain build a cognitive code?
Grossberg, S. (1980) · Psychological Review · doi:10.1037/0033-295x.87.1.1 · Cited by 1,160
The representation of visual salience in monkey parietal cortex
Gottlieb, J. P., Kusunoki, M., & Goldberg, M. E. (1998) · Nature · doi:10.1038/35135 · Cited by 1,078
Motter, B. C. (1993) · Journal of Neurophysiology · doi:10.1152/jn.1993.70.3.909 · Cited by 1,055
Research works printed but not linked: 2
- Chelazzi, 1994, Responses of V4 neurons during visual search., Soc Neurosci Abstr, 20, 1054
- Snedecor G Cochran W (1967) Analysis of covariance. Statistical methods, Sixth Edition, pp 419–446. (Iowa State UP, Ames, IA).
Cited by
Sorted by citations
Items 801–900 of 999
The unconscious allocation of cognitive resources to task-relevant and task irrelevant thoughts.
Kuldas, S., Hashim, S., Nizam Ismail, H., Ali Samsudin, M., & Bakar, Z. A. (2023) · Figshare · doi:10.6084/m9.figshare.24851328 · Cited by 9
Liu, S.-H., Dosher, B. A., & Lu, Z. (2008) · Vision Research · doi:10.1016/j.visres.2008.07.025 · Cited by 10
Occlusion, Attention and Object Representations
Taylor, N. R., Panchev, C., Hartley, M., Kasderidis, S., & Taylor, J. G. (2006) · Lecture notes in computer science · doi:10.1007/11840817_62 · Cited by 10
Visual search and single-cell electrophysiology of attention: Area MT, from sensation to perception
Treue, S., & Martínez-Trujillo, J. (2006) · Visual Cognition · doi:10.1080/13506280500197256 · Cited by 10
Nonspatial attributes of stimuli can influence spatial limitations of attentional control
Moore, C. M., Lanagan‐Leitzel, L. K., Chen, P., Danek, R. H., & Fine, E. M. (2007) · Perception & Psychophysics · doi:10.3758/bf03193757 · Cited by 10
Combined expectancy effects: An accumulator model☆
Mattler, U. (2005) · Cognitive Psychology · doi:10.1016/j.cogpsych.2005.05.002 · Cited by 10
A review of brain-based neuro-cognitive models
Taylor, J. G. (2004) · Cognitive Processing · doi:10.1007/s10339-004-0033-z · Cited by 9
Surface segmentation cues influence negative priming for novel and familiar shapes.
Loula, F., Kourtzi, Z., & Shiffrar, M. (2000) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037//0278-7393.26.4.929 · Cited by 10
Inhibitory synchrony as a mechanism for attentional gain modulationPreprint
Tiesinga, P. H. E., Fellous, J., Salinas, E., José, J. V., & B, T. J. S. A. (2005) · arXiv (Cornell University) · doi:10.48550/arxiv.q-bio/0503019 · Cited by 10
Santos‐Mayo, A., & Moratti, S. (2024) · Cortex · doi:10.1016/j.cortex.2024.11.005 · Cited by 8
Shimazaki, H. (2017) · Springer eBooks · doi:10.1007/978-3-319-71976-4 · Cited by 9
Visual Stimulus Content in V4 Is Conveyed by Gamma-Rhythmic Information Packages
Lisitsyn, D., Grothe, I., Kreiter, A. K., & Ernst, U. A. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.0689-20.2020 · Cited by 9
Hu, Q., Hu, W., Liu, K., Bu, X., Hu, L., Li, L., Chai, X., & Chen, Y. (2021) · Neuroscience Bulletin · doi:10.1007/s12264-021-00790-w · Cited by 9
Attentional Sampling between Eye Channels
Re, D., Karvat, G., & Landau, A. N. (2023) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_02018 · Cited by 10
Electrophysiological and Cognitive Changes in Hard Coal Miners Associated with Working Underground
Çelik, S., Yıldırım, E., & Güntekin, B. (2024) · Clinical EEG and Neuroscience · doi:10.1177/15500594241237912 · Cited by 9
Wiesner, S., Baumgart, I. W., & Huang, X. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.1950-19.2020 · Cited by 9
Kim, N. Y., Pinsk, M. A., & Kästner, S. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab009 · Cited by 8
Prefrontal delay-period activity is affected by visual cues presented outside the memory field
Inoue, M., & Funahashi, S. (2002) · Neuroreport · doi:10.1097/00001756-200211150-00021 · Cited by 9
Spatial interference between attended items engenders serial visual processing
Yamani, Y., McCarley, J. S., Mounts, J. R. W., & Kramer, A. F. (2012) · Attention Perception & Psychophysics · doi:10.3758/s13414-012-0392-y · Cited by 9
Clutter Modulates the Representation of Target Objects in the Human Occipitotemporal Cortex
Erez, Y., & Yovel, G. (2013) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00505 · Cited by 9
Attention as Sigma-Pi controlled ACh-based feedback
Taylor, J., Hartley, M., & Taylor, N. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2005.1555839 · Cited by 9
Visual perception: Monkeys see things our way
Connor, C. E. (2000) · Current Biology · doi:10.1016/s0960-9822(00)00798-3 · Cited by 9
Selective Visual Attention in ADHD: A Narrative Review
Klein, J. L., Allen, H. A., Clibbens, J., Cook, A., Amanatidou, V., & Mavritsaki, E. (2025) · Current Neurology and Neuroscience Reports · doi:10.1007/s11910-025-01435-5 · Cited by 10
Chen, S., & Melara, R. D. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00585 · Cited by 8
Feature-based attention spreads preferentially in an object-specific manner
Festman, Y., & Braun, J. (2011) · Vision Research · doi:10.1016/j.visres.2011.12.003 · Cited by 8
Wykowska, A., & Schubö, A. (2011) · International Journal of Social Robotics · doi:10.1007/s12369-011-0127-6 · Cited by 8
Attention Gates the Selective Encoding of Duration
Maarseveen, J., Hogendoorn, H., Verstraten, F. A. J., & Paffen, C. L. E. (2018) · Scientific Reports · doi:10.1038/s41598-018-20850-y · Cited by 9
Spatially Specific Attention Mechanisms Are Sensitive to Competition during Visual Search
Yeh, L.-C., Yeh, Y., & Kuo, B.-C. (2019) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01418 · Cited by 8
Spatial Attention Enhances the Neural Representation of Invisible Signals Embedded in Noise
Smout, C., & Mattingley, J. B. (2018) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01283 · Cited by 8
Ouerfelli‐Ethier, J., Salemme, R., Fournet, R., Urquizar, C., Pisella, L., & Khan, A. Z. (2021) · Cerebral Cortex Communications · doi:10.1093/texcom/tgab054 · Cited by 9
Erez, Y., Kadohisa, M., Petrov, P., Sigala, N., Buckley, M. J., Kusunoki, M., & Duncan, J. (2022) · European Journal of Neuroscience · doi:10.1111/ejn.15757 · Cited by 9
Out of the spotlight: face to face with attention
Kästner, S., & McMains, S. A. (2007) · Nature Neuroscience · doi:10.1038/nn1107-1344 · Cited by 8
Exploring the mechanisms underlying surface-based stimulus selection
Stoner, G. R., & Blanc, G. (2009) · Vision Research · doi:10.1016/j.visres.2009.11.015 · Cited by 8
What roles can attention play in recognition?
Tsotsos, J. K. (2008) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/devlrn.2008.4640805 · Cited by 8
Plasticity in the Visual System
Pinaud, R., Tremere, L. A., & Weerd, P. D. (2006) · Springer eBooks · doi:10.1007/0-387-28190-8 · Cited by 60
Sensory processing and categorization in cortical and deep neural networks
Pinotsis, D. A., Siegel, M., & Miller, E. K. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116118 · Cited by 9
Cholinergic manipulations affect sensory responses but not attentional enhancement in macaque MT
Veith, V. K., Quigley, C., & Treue, S. (2021) · BMC Biology · doi:10.1186/s12915-021-00993-7 · Cited by 7
Texture segmentation influences the spatial profile of presaccadic attention
Ghahghaei, S., & Verghese, P. (2017) · Journal of Vision · doi:10.1167/17.2.10 · Cited by 9
Dynamic regulation of BDNF and NT-3 expression during visual system development
Lein, E. S., Hohn, A., & Shatz, C. J. (2000) · The Journal of Comparative Neurology · doi:10.1002/(sici)1096-9861(20000424)420:1 · Cited by 74
The What and Why of Binding: Review The Modeler's Perspective
von der Malsburg, C. (1999) · Cited by 8
Contrasting effects of exogenous and endogenous attention on size perception
Han, Y., Tan, Z.-H., Zhuang, H., & Qian, J. (2021) · British Journal of Psychology · doi:10.1111/bjop.12529 · Cited by 7
Ni, S. Y. K., Harris, B., & Gong, P. (2024) · Communications Biology · doi:10.1038/s42003-024-06228-z · Cited by 10
Attentional sampling resolves competition along the visual hierarchy
Re, D., Kusnir, F., & Landau, A. N. (2025) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2025.06.004 · Cited by 11
Mocz, V., Jeong, S. K., Chun, M. M., & Xu, Y. (2023) · Scientific Reports · doi:10.1038/s41598-023-36029-z · Cited by 9
Interareal Connections of the Macaque Cortex
Anderson, J. C., & Martin, K. A. C. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-801393-9.00006-2 · Cited by 7
Mechanisms for shaping receptive field in monkey area TE
Obara, K., O’Hashi, K., & Tanifuji, M. (2017) · Journal of Neurophysiology · doi:10.1152/jn.00348.2017 · Cited by 7
Long-range neural inhibition and stimulus competition in the archerfish optic tectum
Volotsky, S., Vinepinsky, E., Donchin, O., & Segev, R. (2019) · Journal of Comparative Physiology A · doi:10.1007/s00359-019-01345-1 · Cited by 5
Intrusions into the shadow of attention: A new take on illusory conjunctions
Henderson, C. M., & McClelland, J. L. (2020) · Attention Perception & Psychophysics · doi:10.3758/s13414-019-01893-3 · Cited by 7
Synchrony, flexible network configuration, and linking neural events to behavior
Kreiter, A. K. (2020) · Current Opinion in Physiology · doi:10.1016/j.cophys.2020.08.008 · Cited by 7
Increase in internetwork functional connectivity in the human brain with attention capture
Sun, H., Yue, Q., Sy, J. L., Godwin, D., Eaton, H. P., Raghavan, P., & Marois, R. (2020) · Journal of Neurophysiology · doi:10.1152/jn.00693.2019 · Cited by 6
Grossberg, S. (2021) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2021.650263 · Cited by 9
Attention along the cortical hierarchy: Development matters
Lynn, A., & Amso, D. (2021) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1575 · Cited by 7
Theiss, J. D., Bowen, J. D., & Silver, M. A. (2021) · Neural Computation · doi:10.1162/neco_a_01447 · Cited by 7
Behavioral and ERP evidence that object-based attention utilizes fine-grained spatial mechanisms
Çatak, E. N., Özkan, M., Kafalıgönül, H., & Stoner, G. R. (2022) · Cortex · doi:10.1016/j.cortex.2022.02.013 · Cited by 7
Visual Perception of Contextual Effect and Its Neural Correlates
Ejima, Y., Takahashi, S., Yamamoto, H., & Goda, N. (2007) · Springer eBooks · doi:10.1007/978-4-431-73021-7_1 · Cited by 7
An ERP investigation of the modulation of subliminal priming by exogenous cues
Marzouki, Y., Midgley, K. J., Holcomb, P. J., & Jonathan Grainger, B. (2008) · Brain Research · doi:10.1016/j.brainres.2008.06.113 · Cited by 7
Modelling attention in individual cells leads to a system with realistic saccade behaviours
Lanyon, L., & Denham, S. L. (2009) · Cognitive Neurodynamics · doi:10.1007/s11571-008-9073-x · Cited by 7
Normalization for probabilistic inference with neurons
Eliasmith, C., & Martens, J. (2011) · Biological Cybernetics · doi:10.1007/s00422-011-0433-y · Cited by 7
Sex Differences in Competition-Based Attentional Selection
Sänger, J., Schneider, D. A., Beste, C., & Wascher, E. (2012) · Zeitschrift für Psychologie · doi:10.1027/2151-2604/a000100 · Cited by 7
A Bayesian inference theory of attention: neuroscience and algorithms
Chikkerur, S. S., Serre, T., & Poggio, T. (2009) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 7
Attentional Selection: Top-Down, Bottom-Up and History-Based Biases
Theeuwes, J., & Failing, M. (2020) · Cited by 7
Sparse visual models for biologically inspired sensorimotor control
Li, Y., & Jabri, M. A. (2003) · CogPrints (University of Southampton) · Cited by 7
TarzaNN : A General Purpose Neural Network Simulator for Visual Attention Modeling
Rothenstein, A. L., Zaharescu, A., & Tsotsos, J. K. (2005) · Lecture notes in computer science · doi:10.1007/978-3-540-30572-9_12 · Cited by 7
Contrast Dependence of Smooth Pursuit Eye Movements following a Saccade to Superimposed Targets
Fallah, M., & Reynolds, J. H. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037888 · Cited by 7
Self-citationAttention-based long-lasting sensitization and suppression of colors
Tseng, C., Vidnyánszky, Z., Papathomas, T. V., & Sperling, G. (2009) · Vision Research · doi:10.1016/j.visres.2009.09.019 · Cited by 7
Rhythmic Neuronal Synchronization Subserves Selective Attentional Processing
Womelsdorf, T., & Fries, P. (2011) · Research and perspectives in neurosciences · doi:10.1007/978-3-642-18015-6_7 · Cited by 7
Task-dependent modulation of target-flanker lateral interactions in vision
Freeman, E. D., & Driver, J. (2005) · Perception & Psychophysics · doi:10.3758/bf03193519 · Cited by 7
Cognitive Control in Frontostriatal Disorders
Richer, F., & Chouinard, S. (2003) · Humana Press eBooks · doi:10.1007/978-1-59259-326-2_8 · Cited by 7
Attention modulation of neural tuning through peak and base rate in correlated firing
Nakahara, H., & Амари, Ш.-И. (2002) · Neural Networks · doi:10.1016/s0893-6080(01)00126-5 · Cited by 7
Dynamic NormalizationPreprint
Heeger, D. J., & Zemlianova, K. O. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.22.002634 · Cited by 6
Synchronization and pattern separation in spiking associative memories and visual cortical areas
Knoblauch, A. (2003) · OPen Access Repositorium der Universität Ulm (OPARU) (Ulm University) · doi:10.18725/oparu-38 · Cited by 7
Feature selectivity of corticocortical feedback along the primate dorsal visual pathway
Korkian, Y., Nakhla, N., & Pack, C. C. (2025) · Journal of Neurophysiology · doi:10.1152/jn.00278.2024 · Cited by 6
Wagatsuma, N., Nobukawa, S., & Kurikawa, T. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013306 · Cited by 6
Cues to move increase information in superior colliculus tuning curves
Li, X., & Basso, M. A. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00154.2011 · Cited by 6
Lanssens, A., Mantini, D., de Beeck, H. O., & Gillebert, C. R. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.838683 · Cited by 6
Sustained attention operates via dissociable neural mechanisms across different eccentric locations
Phangwiwat, T., Phunchongharn, P., Wongsawat, Y., Chatnuntawech, I., Wang, S., Chunharas, C., Sprague, T. C., Woodman, G. F., & Itthipuripat, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-61171-7 · Cited by 7
Optimal Coding Predicts Attentional Modulation of Activity in Neural Systems
Jaramillo, S., & Pearlmutter, B. A. (2007) · Neural Computation · doi:10.1162/neco.2007.19.5.1295 · Cited by 6
Mechanisms of attention: Psychophysics, cognitive psychology, and cognitive neuroscience.
Lu, Z. (2008) · PubMed · doi:10.14947/psychono.kj00005084867 · Cited by 8
Selective visual attention, visual search and visual awareness
Butter, C. M. (2004) · Progress in brain research · doi:10.1016/s0079-6123(03)14413-5 · Cited by 6
Studying Consciousness Using Direct Recording from Single Neurons in the Human Brain
Cerf, M., & Mackay, M. (2011) · Research and perspectives in neurosciences · doi:10.1007/978-3-642-18015-6_8 · Cited by 5
Daliri, M. R., Kozyrev, V., & Treue, S. (2016) · Scientific Reports · doi:10.1038/srep27666 · Cited by 7
A neurodynamic model of the interaction between color perception and color memory
Marić, M., & Domijan, D. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.06.008 · Cited by 6
A cellular and attentional network explanation of consciousness
Munévar, G. (2020) · Consciousness and Cognition · doi:10.1016/j.concog.2020.102982 · Cited by 7
Attention et perception de scènes visuelles
Boloix, E. (2007) · L’Année psychologique · doi:10.4074/s0003503307001066 · Cited by 7
Selective Visual Attention Modulates Oscillatory Neuronal Synchronization
Fries, P., & Desimone, R. (2005) · Elsevier eBooks · doi:10.1016/b978-012375731-9/50090-2 · Cited by 6
Self-citationSolutions to the Binding Problem: Review Progress through Controversy Summary and Convergence
Treisman, A. (1999) · Cited by 5
A hybrid neural network model for consciousness
Lin, J., Jin, X., & Yang, J.-G. (2004) · Journal of Zhejiang University. Science A · doi:10.1631/jzus.2004.1440 · Cited by 6
Soga, M., & Kashimori, Y. (2008) · Vision Research · doi:10.1016/j.visres.2008.10.023 · Cited by 6
Visual Attention and visual awareness
VanRullen, R., & Koch, C. (2005) · Cited by 6
What and where: a bayesian inference theory of visual attention
Poggio, T., & Chikkerur, S. S. (2010) · Cited by 6
Sellitto, M., & di Pellegrino, G. (2015) · Elsevier eBooks · doi:10.1016/b978-0-08-097086-8.55003-x · Cited by 6
Archie, K. A., & Mel, B. W. (2000) · neural information processing systems · Cited by 6
Systems-Level Neuronal Modeling of Visual Attentional Mechanisms
Corchs, S., Stetter, M., & Deco, G. (2003) · Artificial Intelligence Review · doi:10.1023/a:1026028429257 · Cited by 6
The micro-structure of attention
Taylor, N. R., Hartley, M., & Taylor, J. G. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.08.002 · Cited by 8
Nobre, A. C. (2018) · Wiley eBooks · doi:10.1002/9781119170174.epcn206 · Cited by 8
Borron, K. (2000) · Land-line · Cited by 7
Lateral asymmetry of voluntary attention orienting
Castro-Barros, B. A., Lacerda, A. M., Righi, L. L., & Ribeiro-do-Valle, L. E. (2010) · Brazilian Journal of Medical and Biological Research · doi:10.1590/s0100-879x2010007500070 · Cited by 5
Are neuronal mechanisms of attention universal across human sensory and motor brain maps?
DeYoe, E. A., Huddleston, W. E., & Greenberg, A. S. (2024) · Psychonomic Bulletin & Review · doi:10.3758/s13423-024-02495-3 · Cited by 5
Kupers, E. R., Kim, I., & Kalanit Grill-spector, A. (2024) · Nature Communications · doi:10.1038/s41467-024-51243-7 · Cited by 7
Simple action alters attention towards visual features
Wang, Z., Weidler, B. J., Sun, P., & Abrams, R. A. (2021) · Attention Perception & Psychophysics · doi:10.3758/s13414-021-02259-4 · Cited by 5
Metadata
- Authors
- Reynolds, J. H., Chelazzi, L., & Desimone, R.
- Affiliations
- National Institutes of Health (US)
- University of Verona (IT)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 1999-03-01
- Volume
- 19
- Issue
- 5
- Pages
- 1736-1753
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6782185
- OpenAlex
- W2170478537
- Cited by
- 1,236
- References
- 46
- Citations by year
- 2026: 22 · 2025: 20 · 2024: 26 · 2023: 28 · 2022: 23 · 2021: 38 · 2020: 31 · 2019: 28 · 2018: 35 · 2017: 26 · 2016: 43 · 2015: 49 · 2014: 55 · 2013: 54 · 2012: 56
- FWCI
- 24.65
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Mental Health
- 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
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Multisensory perception and integration
- Keywords
- visual attention, competition mechanism, V2, area V4, extrastriate cortex, receptive fields, macaque visual cortex
- MeSH terms
- Animals, Attention, Behavior, Animal, Electrodes, Implanted, Macaca, Male, Models, Neurological, Neurons, Photic Stimulation, Reaction Time, Task Performance and Analysis, Visual Cortex, Visual Pathways, Visual Perception
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review