One-dimensional dynamics of attention and decision making in LIP
Open accessAbstract
Where we allocate our visual spatial attention depends upon a continual competition between internally generated goals and external distractions. Recently it was shown that single neurons in the macaque lateral intraparietal area (LIP) can predict the amount of time a distractor can shift the locus of spatial attention away from a goal. We propose that this remarkable dynamical correspondence between single neurons and attention can be explained by a network model in which generically high-dimensional firing-rate vectors rapidly decay to a single mode. We find direct experimental evidence for this model, not only in the original attentional task, but also in a very different task involving perceptual decision making. These results confirm a theoretical prediction that slowly varying activity patterns are proportional to spontaneous activity, pose constraints on models of persistent activity, and suggest a network mechanism for the emergence of robust behavioral timing from heterogeneous neuronal populations.
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The neural population remains one-dimensional during the choice period between first touch and reaction time and starts to increase only at the onset of decision execution and later, in epoch 5.
“However, in our experiments, the neural population remains one-dimensional (46) during choice period between FT and RT and starts to increase only at the onset of decision execution and later, in epoch 5.”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [46]
Emergence of synchronous modulation, dimensionality collapse, and strong suppression locked to sensory stimulation are known neural correlates of top-down regulated attention and decision-making processes.
“Emergence of synchronous modulation (33, 34), dimensionality collapse (46), and strong suppression locked to sensory stimulation (47, 48) are known neural correlates of top–down regulated attention (35) and decision-making processes (3–5).”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [46]
In LIP data from the RDM experiment, the dynamics within each of the two selective neural pools is dominated by a slow mode.
“This is consistent with the finding that, in the LIP data recorded from the RDM experiment (Roitman and Shadlen, 2002), the dynamics within each of the two selective neural pools is dominated by a slow mode (Ganguli et al., 2008); thus, the description of two competing neural pools requires two dynamical variables.”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Ganguli et al., 2008
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“, 2002), and linear networks are common models for the dynamics of neural circuits (Dayan and Abbott, 2001; Shriki et al., 2003; Vogels et al., 2005; Rajan and Abbott, 2006; Ganguli et al., 2008; Ganguli et al., 2008; Murphy and Miller, 2009; Miri et al., 2011).”
BackgroundNeural state space alignment for magnitude generalization in humans and recurrent networks
Sheahan, H., Luyckx, F., Nelli, S., Teupe, C., & Summerfield, C. (2021) · Neuron · doi:10.1016/j.neuron.2021.02.004 · Cited by 67 · see citing works
Herrington, T. M., & Assad, J. A. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.6025-09.2010 · Cited by 63 · see citing works
“In support of this idea, a recent study demonstrated that sustained activity in LIP can be modeled by recurrent excitatory networks, perhaps flexibly spanning multiple cortical areas (Ganguli et al., 2008).”
BackgroundBiased Associative Representations in Parietal Cortex
Fitzgerald, J. K., Freedman, D. J., Fanini, A., Bennur, S., Gold, J. I., & Assad, J. A. (2013) · Neuron · doi:10.1016/j.neuron.2012.11.014 · Cited by 62 · see citing works
“…mutual scaling of delay and spontaneous activity (Figures 7B and 7C) was not due to simple differences in excitability among LIP neurons: if the scaling were due to excitability differences, we would have also expected a linear relationship with the transient visual response (Ganguli et al., 2008).”
InfluentialResponse times from ensembles of accumulators
Zandbelt, B. B., Purcell, B. A., Palmeri, T. J., Logan, G. D., & Schall, J. D. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1310577111 · Cited by 60 · see citing works
“Though the neurobiology of the transition from preparation to execution is vaguely understood, outside recent modeling efforts (26, 27), a threshold mechanism for evidence accumulation triggering response preparation is even less understood.”
BackgroundNovelty Enhances Visual Salience Independently of Reward in the Parietal Lobe
Foley, N. C., Jangraw, D. C., Peck, C., & Gottlieb, J. P. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.4171-13.2014 · Cited by 63 · see citing works
“However, it may be explained by the fact that LIP encodes relatively early stages of planning and target selection, and its saccade responses may reflect feedback from downstream motor areas (Ganguli et al., 2008; Suzuki and Gottlieb, 2013) that may be influenced by additional factors and relatively dissociated from t…”
BackgroundChaudhuri, R., He, B. J., & Wang, X. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx233 · Cited by 59 · see citing works
“The eigenvalues of the corresponding random matrix form a cloud of points around the origin (with radius σ), along with a single outlying eigenvalue located at μ (Rajan and Abbott, 2006; Ganguli et al., 2008; Tao, 2011).”
BackgroundMethodResultInfluentialCausal Inference of Body Ownership in the Posterior Parietal Cortex
Chancel, M., Iriye, H., & Ehrsson, H. H. (2022) · The Journal of Neuroscience · doi:10.1523/jneurosci.0656-22.2022 · Cited by 55 · see citing works
“, 2019) where parietal regions implement multisensory estimates of body ownership, regardless of the context (Ganguli et al., 2008; Suzuki and Gottlieb, 2013), and frontal regions integrate contextual cues, and prior expectations, taking into account sensory uncertainty (Gau and Noppeney, 2016; Kayser and Kayser, 2018…”
BackgroundGoal-seeking compresses neural codes for space in the human hippocampus and orbitofrontal cortex
Muhle-Karbe, P. S., Sheahan, H., Pezzulo, G., Spiers, H. J., Chien, S., Schuck, N. W., & Summerfield, C. (2023) · Neuron · doi:10.1016/j.neuron.2023.08.021 · Cited by 50 · see citing works
Neural Correlates of Visual?Spatial Attention in Electrocorticographic Signals in Humans
Gunduz, A., Brunner, P., Daitch, A. L., Leuthardt, E. C., Ritaccio, A. L., Pesaran, B., & Schalk, G. (2011) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2011.00089 · Cited by 49 · see citing works
Direct observation of the neural computations underlying a single decisionPreprint
Steinemann, N. A., Stine, G. M., Trautmann, E. M., Zylberberg, A., Wolpert, D. M., & Shadlen, M. N. (2023) · eLife · doi:10.7554/elife.90859.3 · Cited by 82 · see citing works
Dimensionality, information and learning in prefrontal cortex
Bartolo, R., Saunders, R. C., Mitz, A. R., & Averbeck, B. B. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007514 · Cited by 53 · see citing works
“Surprisingly, it is often the case that relatively low dimensional approximations can capture much of the variance in high dimensional datasets [2, 3, 23, 24, 30, 31].”
BackgroundFlexible motor sequence generation during stereotyped escape responses
Wang, Y., Zhang, X., Qi, X., Hung, W., Florman, J., Huo, J., Xu, T., Xie, Y., Alkema, M. J., Zhen, M., et al. (2020) · eLife · doi:10.7554/elife.56942 · Cited by 49 · see citing works
“On the other hand, neural networks, whether adaptive or hard-wired, have structures that shape population neural dynamics onto a low dimensional manifold, where nonrandom and ordered activity patterns emerge (Ganguli et al., 2008; Harvey et al., 2012; Inagaki et al., 2018).”
BackgroundOptimal information loading into working memory explains dynamic coding in the prefrontal cortex
Stroud, J. P., Watanabe, K., Suzuki, T., Stokes, M. G., & Lengyel, M. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2307991120 · Cited by 50 · see citing works
Neuroscience out of control: control-theoretic perspectives on neural circuit dynamics
Kao, T.-C., & Hennequin, G. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.09.001 · Cited by 47 · see citing works
“…models have also shed light on a variety of circuit computations, including short-term memory [30–32], selective amplification and surround suppression in primary visual cortex [33 •• , 34], movement generation[26 •• , 20], attention and decision-making [35], and probabilistic inference [36, 37].”
BackgroundPerturbing low dimensional activity manifolds in spiking neuronal networks
Wärnberg, E., & Kumar, A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007074 · Cited by 51 · see citing works
Mysore, S. P., & Knudsen, E. I. (2014) · Neuron · doi:10.1016/j.neuron.2014.08.019 · Cited by 41 · see citing works
“Computational modeling work has characterized selection in terms of a race-to-bound process or one-dimensional decay process to account for the dynamics of neural responses during selection tasks (Ganguli et al., 2008; Gold and Shadlen, 2007; Smith and Ratcliff, 2004; Wang, 2012).”
Tang, E., Mattar, M. G., Giusti, C., Lydon‐Staley, D. M., Thompson‐Schill, S. L., & Bassett, D. S. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0400-9 · Cited by 43 · see citing works
“For example, data from the lateral intraparietal area in macaques suggests that neuronal spiking maps onto a one-dimensional dynamical trajectory [22].”
BackgroundWang, T., Chen, Y., & Cui, H. (2022) · Neuroscience Bulletin · doi:10.1007/s12264-022-00832-x · Cited by 38 · see citing works
“It has been shown that one-dimensional dynamics are enough to model the transition from spontaneous activity to delay activity in the macaque lateral intraparietal area for spatial attention diversion [79], but things get more complicated for motor control.”
BackgroundMultiplexed subspaces route neural activity across brain-wide networks
MacDowell, C. J., Libby, A., Jahn, C. I., Tafazoli, S., Ardalan, A., & Buschman, T. J. (2025) · Nature Communications · doi:10.1038/s41467-025-58698-2 · Cited by 39 · see citing works
Parietal Cortical Neuronal Activity Is Selective for Express Saccades
Chen, M., Liu, Y., Wei, L., & Zhang, M. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2675-12.2013 · Cited by 32 · see citing works
“…Snyder et al., 1997; Thier and Andersen, 1998; Colby and Goldberg, 1999; Bisley and Goldberg, 2003; Li et al., 2004; Andersen and Cui, 2009; , 2010; Guan et al., 2012) (Platt and Glimcher, 1999; Shadlen and Newsome, 2001; Gold and Shadlen, 2007; Ganguli et al., 2008; Kiani and Shadlen, 2009).”
Ecological expected utility and the mythical neural code
Feldman, J. A. (2009) · Cognitive Neurodynamics · doi:10.1007/s11571-009-9090-4 · Cited by 29 · see citing works
“Timing fit is crucial in a variety of ways, including LTP (Long Term Potentiation, Gerstner et al. 1997) and STDP (spike timing dependent plasticity; Bender et al. 2006; Izhikevich 2007), but development and learning are beyond the scope of this review.”
BackgroundMohan, K., Zhu, O., & Freedman, D. J. (2020) · Neuron · doi:10.1016/j.neuron.2020.11.022 · Cited by 27 · see citing works
Variational Online Learning of Neural Dynamics
Zhao, Y., & Park, I. M. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.00071 · Cited by 25 · see citing works
“Although the absolute arrangement is dissimilar, the topology and relation of the five identified fixed points show correspondence (indicated by purple lines). theories have been proposed (Wang, 2002; Wong and Wang, 2006; Ganguli et al., 2008; Barak et al., 2013; Mante et al., 2013).”
BackgroundBiased Neural Representation of Feature-Based Attention in the Human Frontoparietal Network
Gong, M., & Liu, T. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.0690-20.2020 · Cited by 24 · see citing works
Noise Tolerance of Attractor and Feedforward Memory Models
Lim, S., & Goldman, M. S. (2011) · Neural Computation · doi:10.1162/neco_a_00234 · Cited by 24 · see citing works
Colas, J. T. (2017) · PLoS ONE · doi:10.1371/journal.pone.0186822 · Cited by 19 · see citing works
Berlemont, K., & Nadal, J. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.1015-18.2018 · Cited by 19 · see citing works
“This type of time course is in agreement with the experimental findings of Roitman and Shadlen (2002) and Ganguli et al. (2008), who measure the activity of LIP neurons during a decision task.”
Learning shapes neural geometry in the prefrontal cortexPreprint
Wójcik, M. J., Stroud, J. P., Wasmuht, D., Makoto Kusunoki, A., Kadohisa, M., Buckley, M. J., Costa, R. P., Myers, N. E., Hunt, L. T., Duncan, J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.24.538054 · Cited by 21 · see citing works
Decision making and perceptual bistability in spike-based neuromorphic VLSI systems
Corradi, F., You, H., Giulioni, M., & Indiveri, G. (2015) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2015.7169245 · Cited by 20 · see citing works
“In particular, reverberating states of cortical activity are thought to underlie important cognitive processes and functions: it has been shown for example that attractor networks in cerebral cortex are important for long-term memory [1], [2], short-term memory [3]–[5], contextual mental states [6], attention [7], bis…”
BackgroundComputational models of decision making: integration, stability, and noise
Cain, N., & Shea‐Brown, E. (2012) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.04.013 · Cited by 20 · see citing works
Nishida, S., Tanaka, T., Shibata, T., Ikeda, K., Aso, T., & Ogawa, T. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht031 · Cited by 19 · see citing works
“Ganguli et al. (2008) have proposed a theoretical attractor network model that could explain the neuronal behaviors in the LIP during an attentional or perceptual decision-making task.”
BackgroundFunctional network properties of the auditory cortex
Lestang, J.-H., Cai, H., Averbeck, B. B., & Cohen, Y. E. (2023) · Hearing Research · doi:10.1016/j.heares.2023.108768 · Cited by 13 · see citing works
Impact of network topology on decision-making
Lu, S., Fang, J., Guo, A., & Peng, Y. (2008) · Neural Networks · doi:10.1016/j.neunet.2008.09.012 · Cited by 14 · see citing works
Chien, J. M., Wallis, J. D., & Rich, E. L. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.0292-23.2023 · Cited by 18 · see citing works
Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex
Logiaco, L., Quilodran, R., Procyk, E., & Arleo, A. (2015) · PLoS Biology · doi:10.1371/journal.pbio.1002222 · Cited by 13 · see citing works
“In addition, evidence suggests that neurons in MT are decoded through integration, a process that could be reflected in LIP (lateral intraparietal cortex) activity [61] and that appears to have onedimensional dynamics [62].”
BackgroundFunctional Implications of Dale's Law in Balanced Neuronal Network Dynamics and Decision Making
Barranca, V. J., Bhuiyan, A., Sundgren, M., & Xing, F. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.801847 · Cited by 17 · see citing works
“It is also possible to similarly engineer one-population networks with the goal of effectively choosing among a large number of alternatives (Ganguli et al., 2008; Heekeren et al., 2008; Barranca et al., 2019a), and we expect such networks exhibit similar functional properties as we have demonstrated in the case of tw…”
BackgroundChafee, M. V. (2013) · Neuron · doi:10.1016/j.neuron.2012.12.025 · Cited by 12 · see citing works
“Fitzgerald et al. (2013) found that spontaneous and evoked activity levels were correlated in single neurons, as did Ganguli and colleagues (Ganguli et al., 2008).”
BackgroundResultNetwork structure and input integration in competing firing rate models for decision-making
Barranca, V. J., Huang, H. P., & Kawakita, G. (2019) · Journal of Computational Neuroscience · doi:10.1007/s10827-018-0708-6 · Cited by 12 · see citing works
Quantifying the impact of network structure on speed and accuracy in collective decision-making
Daniels, B. C., & Romańczuk, P. (2021) · Theory in Biosciences · doi:10.1007/s12064-020-00335-1 · Cited by 12 · see citing works
Streaming Variational Monte Carlo
Zhao, Y., Nassar, J., Jordan, I., Bugallo, M. F., & Park, I. M. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3153225 · Cited by 12 · see citing works
Dynamics of Multiple-Choice Decision Making
You, H., & Wang, D. (2013) · Neural Computation · doi:10.1162/neco_a_00473 · Cited by 11 · see citing works
Spatial perception during pursuit initiation
Blanke, M., Harsch, L., Knöll, J., & Bremmer, F. (2010) · Vision Research · doi:10.1016/j.visres.2010.08.037 · Cited by 11 · see citing works
“Physiological (Ganguli et al., 2008) as well as psychophysical (Adam, Davelaar, van der Gouw, & Willems, 2008) studies have shown that briefly presented distractors diminish spatial perception in a certain temporal and spatial window.”
New methods for localizing and manipulating neuronal dynamics in behaving animals
Fee, M. S., & Long, M. A. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.06.010 · Cited by 10 · see citing works
Summerfield, C., & Blangero, A. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-805308-9.00012-9 · Cited by 16 · see citing works
Change-based population coding
Moazzezi, R. (2011) · UCL Discovery (University College London) · Cited by 11 · see citing works
Coding of attention across the human intraparietal sulcus
Connolly, J. D., Kentridge, R. W., & Cavina‐Pratesi, C. (2015) · Experimental Brain Research · doi:10.1007/s00221-015-4507-2 · Cited by 9 · see citing works
“…Andersen 2011; Mountcastle et al. 1975; Quian Quiroga et al. 2006; Snyder et al. 1997, 2000), others have stressed a relatively greater role in visuospatial attention (Bisley and Goldberg 2003, 2010; Bushnell et al. 1981; Colby and Goldberg 1999; Ganguli et al. 2008; Goldberg and Bushnell 1981).”
BackgroundInfluentialStructure Learning and the Parietal CortexPreprint
Summerfield, C., Luyckx, F., & Sheahan, H. (2019) · doi:10.31234/osf.io/zfxj2 · Cited by 9 · see citing works
Dynamics of robust pattern separability in the hippocampal dentate gyrus
Zylberberg, J., Hyde, R. A., & Strowbridge, B. W. (2015) · Hippocampus · doi:10.1002/hipo.22546 · Cited by 8 · see citing works
“Relatedly, there are many prior reports of neural responses being constrained to low-dimensional regions of the space of possible neural responses (Seung, 1996; Wimmer et al., 2014; Ganguli et al., 2008; Yoon et al., 2013).”
BackgroundSpatiotemporal discrimination in attractor networks with short-term synaptic plasticity
Ballintyn, B., Shlaer, B., & Miller, P. C. (2019) · Journal of Computational Neuroscience · doi:10.1007/s10827-019-00717-5 · Cited by 9 · see citing works
Neuronal pattern separation of motion-relevant input in LIP activity
Berberian, N., MacPherson, A., Giraud, É., Richardson, L., & Thivierge, J. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00145.2016 · Cited by 8 · see citing works
“Related work shows that models with sparse intracortical connectivity offer a robust fit to experimental data in LIP (Ganguli et al. 2008).”
BackgroundRowland, J. M., van der Plas, T. L., Loidolt, M., Lees, R. M., Keeling, J., Dehning, J., Akam, T. E., Priesemann, V., & Packer, A. M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.28.474343 · Cited by 8 · see citing works
Decoding Task-Specific Cognitive States with Slow, Directed Functional Networks in the Human Brain
Sridharan, D., Jain, H., Jain, H., & Sundaresan, M. (2020) · eNeuro · doi:10.1523/eneuro.0512-19.2019 · Cited by 8 · see citing works
“Connectivity in randomly connected excitatory-inhibitory (E-I) networks of neurons can produce slow timescales, without fine-tuning of network parameters (Rajan and Abbott, 2006; Ganguli et al., 2008; Friston et al., 2014).”
BackgroundMethodInfluentialOptimal Readout of Correlated Neural Activity in a Decision-Making Circuit
Calderini, M., Zhang, S., Berberian, N., & Thivierge, J. (2018) · Neural Computation · doi:10.1162/neco_a_01083 · Cited by 7 · see citing works
Adaptive erasure of spurious sequences in sensory cortical circuits
Bernacchia, A., Fiser, J., Hennequin, G., & Lengyel, M. (2022) · Neuron · doi:10.1016/j.neuron.2022.03.006 · Cited by 6 · see citing works
“This was particularly notable in networks with connections strong enough as to induce long (but finite) timescales in their dynamics (Ganguli et al., 2008), as networks with random connectivity produced strongly sequential inputs in that case (Figure S3C).”
Zhang, W., Falkner, A. L., Krishna, B. S., Goldberg, M. E., & Miller, K. D. (2016) · Neuron · doi:10.1016/j.neuron.2016.11.023 · Cited by 5 · see citing works
Evolution of neural activity in circuits bridging sensory and abstract knowledge
Mastrogiuseppe, F., Hiratani, N., & Latham, P. E. (2023) · eLife · doi:10.7554/elife.79908 · Cited by 7 · see citing works
Uncovering Dynamical Equations of Stochastic Decision Models Using Data-Driven SINDy Algorithm
Lenfesty, B., Bhattacharyya, S., & Wong‐Lin, K. (2025) · Neural Computation · doi:10.1162/neco_a_01736 · Cited by 5 · see citing works
“…Silva et al., 2020) to identify parsimonious linear or nonlinear equations of low-dimensional single-trial decision dynamics, given existing evidence suggesting that decision neural dynamics are embedded in low-dimensional space (Ganguli et al., 2008; Shenoy & Kao, 2021; Steinemann et al., 2023).”
- Authors
- Ganguli, S., Bisley, J. W., Roitman, J. D., Shadlen, M. N., Goldberg, M. E., & Miller, K. D.
- Affiliations
- University of California, San Francisco (US)
- University of California, Los Angeles (US)
- University of Illinois Chicago (US)
- Howard Hughes Medical Institute (US)
- New York State Psychiatric Institute (US)
- Columbia University (US)
- Corresponding author
- Ganguli, Surya
- ORCID
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2008-04
- Volume
- 58
- Issue
- 1
- Pages
- 15-25
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC7204626
- OpenAlex
- W2142146307
- Cited by
- 152
- References
- 50
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 11 · 2023: 12 · 2022: 9 · 2021: 8 · 2020: 10 · 2019: 17 · 2018: 3 · 2017: 10 · 2016: 8 · 2015: 4 · 2014: 8 · 2013: 9 · 2012: 5
- FWCI
- 0.64
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Howard Hughes Medical Institute
- National Institutes of Health (5r24ey015634-05)
- National Eye Institute (EY11378, R24 EY-015634, EY 05603, R01EY011001, EY-014978, P30EY019007, EY11001, R01EY017039, R01EY011378, R37EY005603, EY-15634, R01EY005603, R01EY014978)
- National Center for Research Resources (RR00166, P51RR000166)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- lateral intraparietal area, spatial attention, distractor competition, top-down attention, single-unit activity, network models, firing rate dynamics, persistent activity, perceptual decision making, spontaneous activity, macaque
- MeSH terms
- Animals, Attention, Decision Making, Humans, Macaca, Nerve Net, Parietal Lobe
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