Perceptual decision making in less than 30 milliseconds
Open accessAbstract
In perceptual discrimination tasks, a subject's response time is determined by both sensory and motor processes. Measuring the time consumed by the perceptual evaluation step alone is therefore complicated by factors such as motor preparation, task difficulty and speed-accuracy tradeoffs. Here we present a task design that minimizes these confounding factors and allows us to track a subject's perceptual performance with unprecedented temporal resolution. We find that monkeys can make accurate color discriminations in less than 30 ms. Furthermore, our simple task design provides a tool for elucidating how neuronal activity relates to sensory as opposed to motor processing, as demonstrated with neural data from cortical oculomotor neurons. In these cells, perceptual information acts by accelerating and decelerating the ongoing motor plans associated with correct and incorrect choices, as predicted by a race-to-threshold model, and the time course of these neural events parallels the time course of the subject's choice accuracy.
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A shift of attention can remain covert or be accompanied by saccades generated by a network including portions of the basal ganglia, superior colliculus, and frontal eye field.
“A shift of attention can remain covert – generating only an improvement in perceptual discrimination – or can be accompanied by saccades – rapid eye movements that are generated by a network of cortical and subcortical structures that includes portions of the basal ganglia, the superior colliculus and the frontal eye field (Schall et al., 2011; Stanford et al., 2010).”
From Attention, learning, and the value of information (2012) · cites this paper as Stanford et al., 2010
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Nonparametric Statistics for the Behavioral Sciences
Guitton, H., & Siegel, S. (1958) · Revue économique · doi:10.2307/3498751 · Cited by 35,281 · see citing works
The Jackknife, the Bootstrap and Other Resampling Plans
Efron, B. (1982) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970319 · Cited by 8,120 · see citing works
Bootstrap Methods and Their Application
Loughin, T. M., Davison, A. C., & HINKLEY, D. V. (1999) · Journal of the American Statistical Association · doi:10.2307/2669707 · Cited by 5,478 · see citing works
Humans integrate visual and haptic information in a statistically optimal fashion
Ernst, M. O., & Banks, M. S. (2002) · Nature · doi:10.1038/415429a · Cited by 4,844 · see citing works
High-speed scanning in human memory
Sternberg, S. (1966) · Science · doi:10.1126/science.153.3736.652 · Cited by 3,576 · see citing works
Chronometric explorations of mind
Posner, M. I. (1978) · Cited by 2,441 · see citing works
Herrnstein, R. J. (1961) · Journal of the Experimental Analysis of Behavior · doi:10.1901/jeab.1961.4-267 · Cited by 2,281 · see citing works
(1986) · Cited by 2,022 · see citing works
The analysis of visual motion: a comparison of neuronal and psychophysical performance
Britten, K., Shadlen, M., Newsome, W., & Movshon, J. (1992) · The Journal of Neuroscience · doi:10.1523/jneurosci.12-12-04745.1992 · Cited by 1,958 · see citing works
On the speed of mental processes
Donders, F. C. (1969) · Acta Psychologica · doi:10.1016/0001-6918(69)90065-1 · Cited by 1,784 · see citing works
Primate frontal eye fields. I. Single neurons discharging before saccades.
Bruce, C. J., & Goldberg, M. E. (1985) · Journal of Neurophysiology · doi:10.1152/jn.1985.53.3.603 · Cited by 1,584 · see citing works
Probabilistic decision making by slow reverberation in cortical circuits
Wang, X.-J. (2002) · Neuron · doi:10.1016/s0896-6273(02)01092-9 · Cited by 1,242 · see citing works
Neural Control of Voluntary Movement Initiation
Hanes, D. P., & Schall, J. D. (1996) · Science · doi:10.1126/science.274.5286.427 · Cited by 1,145 · see citing works
The simplest complete model of choice response time: Linear ballistic accumulation
Brown, S. D., & Heathcote, A. (2008) · Cognitive Psychology · doi:10.1016/j.cogpsych.2007.12.002 · Cited by 1,130 · see citing works
Psychology and neurobiology of simple decisions
Smith, P. L., & Ratcliff, R. (2004) · Trends in Neurosciences · doi:10.1016/j.tins.2004.01.006 · Cited by 1,051 · see citing works
Matching Behavior and the Representation of Value in the Parietal Cortex
Sugrue, L. P., Corrado, G. S., & Newsome, W. T. (2004) · Science · doi:10.1126/science.1094765 · Cited by 1,030 · see citing works
A Recurrent Network Mechanism of Time Integration in Perceptual Decisions
Wong, K.-F., & Wang, X.-J. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.3733-05.2006 · Cited by 1,029 · see citing works
Neural computation of log likelihood in control of saccadic eye movements
Carpenter, R. H. S., & Williams, M. L. L. (1995) · Nature · doi:10.1038/377059a0 · Cited by 823 · see citing works
The effect of stimulus strength on the speed and accuracy of a perceptual decision
Palmer, J., Huk, A. C., & Shadlen, M. N. (2005) · Journal of Vision · doi:10.1167/5.5.1 · Cited by 691 · see citing works
Decision-making with multiple alternatives
Churchland, A. K., Kiani, R., & Shadlen, M. N. (2008) · Nature Neuroscience · doi:10.1038/nn.2123 · Cited by 671 · see citing works
Probabilistic population codes for Bayesian decision making
Beck, J. M., Ma, W. J., Kiani, R., Hanks, T., Churchland, A. K., Roitman, J., Shadlen, M. N., Latham, P. E., & Pouget, A. (2008) · Neuron · doi:10.1016/j.neuron.2008.09.021 · Cited by 645 · see citing works
Thompson, K. G., Hanes, D. P., Bichot, N. P., & Schall, J. D. (1996) · Journal of Neurophysiology · doi:10.1152/jn.1996.76.6.4040 · Cited by 618 · see citing works
Neural correlates of multisensory cue integration in macaque MSTd
Gu, Y., Angelaki, D. E., & DeAngelis, G. C. (2008) · Nature Neuroscience · doi:10.1038/nn.2191 · Cited by 554 · see citing works
Recent models and findings in visual backward masking: A comparison, review, and update
Breitmeyer, B. G., & Öğmen, H. (2000) · Perception & Psychophysics · doi:10.3758/bf03212157 · Cited by 532 · see citing works
Cortico–basal ganglia circuit mechanism for a decision threshold in reaction time tasks
Lo, C.-C., & Wang, X.-J. (2006) · Nature Neuroscience · doi:10.1038/nn1722 · Cited by 505 · see citing works
Kiani, R., Hanks, T. D., & Shadlen, M. N. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4761-07.2008 · Cited by 500 · see citing works
Parallel versus serial processing in rapid pattern discrimination
Bergen, J. R., & Julesz, B. (1983) · Nature · doi:10.1038/303696a0 · Cited by 490 · see citing works
Inhibitory control in mind and brain: An interactive race model of countermanding saccades.
Boucher, L., Palmeri, T. J., Logan, G. D., & Schall, J. D. (2007) · Psychological Review · doi:10.1037/0033-295x.114.2.376 · Cited by 485 · see citing works
Trajectory control in targeted force impulses
Hening, W., Vicario, D. S., & Ghez, C. P. (1988) · Experimental Brain Research · doi:10.1007/bf00247526 · Cited by 478 · see citing works
Risk-sensitive neurons in macaque posterior cingulate cortex
McCoy, A. N., & Platt, M. L. (2005) · Nature Neuroscience · doi:10.1038/nn1523 · Cited by 380 · see citing works
Separate Signals for Target Selection and Movement Specification in the Superior Colliculus
Horwitz, G. D., & Newsome, W. T. (1999) · Science · doi:10.1126/science.284.5417.1158 · Cited by 346 · see citing works
Meyer, D. E., Osman, A. M., Irwin, D. E., & Yantis, S. (1988) · Biological Psychology · doi:10.1016/0301-0511(88)90013-0 · Cited by 344 · see citing works
Neuronal correlates of subjective sensory experience
de Lafuente, V., & Romo, R. (2005) · Nature Neuroscience · doi:10.1038/nn1587 · Cited by 319 · see citing works
Perceptually Bistable Three-Dimensional Figures Evoke High Choice Probabilities in Cortical Area MT
Dodd, J., Krug, K., Cumming, B. G., & Parker, A. J. (2001) · Journal of Neuroscience · doi:10.1523/jneurosci.21-13-04809.2001 · Cited by 306 · see citing works
A diffusion model account of masking in two-choice letter identification.
Ratcliff, R., & Rouder, J. N. (2000) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037//0096-1523.26.1.127 · Cited by 238 · see citing works
Ratcliff, R., Hasegawa, Y. T., Hasegawa, R. P., Smith, P. L., & Segraves, M. A. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00393.2006 · Cited by 236 · see citing works
Paré, M., & Munoz, D. P. (1996) · Journal of Neurophysiology · doi:10.1152/jn.1996.76.6.3666 · Cited by 230 · see citing works
A Labeled-Line Code for Small and Large Numerosities in the Monkey Prefrontal Cortex
Nieder, A., & Merten, K. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.1056-07.2007 · Cited by 195 · see citing works
A diffusion model account of masking in two-choice letter identification.
Ratcliff, R., & Rouder, J. N. (2000) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/0096-1523.26.1.127 · Cited by 182 · see citing works
Neural correlates of decision processes: neural and mental chronometry
Schall, J. D. (2003) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(03)00039-4 · Cited by 163 · see citing works
Trajectory control in targeted force impulses
Hening, W., Favilla, M., & Ghez, C. P. (1988) · Experimental Brain Research · doi:10.1007/bf00247527 · Cited by 134 · see citing works
A Biophysically Based Neural Model of Matching Law Behavior: Melioration by Stochastic Synapses
Soltani, A., & Wang, X. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.5159-05.2006 · Cited by 127 · see citing works
Can Monkeys Choose Optimally When Faced with Noisy Stimuli and Unequal Rewards?
Feng, S. F., Holmes, P. J., Rorie, A. E., & Newsome, W. T. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000284 · Cited by 105 · see citing works
The Temporal Impulse Response Underlying Saccadic Decisions
Ludwig, C. J. H., Gilchrist, I. D., McSorley, E., & Baddeley, R. J. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.2197-05.2005 · Cited by 100 · see citing works
Gradual Specification of Response Amplitude in Human Tracking Performance
Ghez, C., Hening, W., & Favilla, M. (2008) · Brain, Behavior and Evolution · doi:10.1159/000115902 · Cited by 62 · cited 1× · see citing works
“Part of its success depends on simultaneously controlling the timing of the sensory information (cue) and the response trigger (go), as suggested by earlier studies in motor contro”
Breitmeyer, B. G., Ro, T., & Öğmen, H. (2004) · Consciousness and Cognition · doi:10.1016/j.concog.2004.08.007 · Cited by 55 · see citing works
Temporal Resolution for the Perception of Features and Conjunctions
Bodelón, C., Fallah, M., & Reynolds, J. H. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.3860-06.2007 · Cited by 52 · see citing works
Matsumora, T., Koida, K., & Komatsu, H. (2008) · Journal of Neurophysiology · doi:10.1152/jn.90551.2008 · Cited by 46 · see citing works
Strategies optimize the detection of motion transients
Ghose, G. M. (2006) · Journal of Vision · doi:10.1167/6.4.10 · Cited by 30 · see citing works
So Many Choices: What Computational Models Reveal about Decision-Making Mechanisms
Salinas, E. (2008) · Neuron · doi:10.1016/j.neuron.2008.12.011 · Cited by 12 · see citing works
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Normalization as a canonical neural computation
Carandini, M., & Heeger, D. J. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3136 · Cited by 1,906 · see citing works
Fast and sensitive GCaMP calcium indicators for imaging neural populations
Zhang, Y., Rózsa, M., Liang, Y., Bushey, D., Wei, Z., Zheng, J., Reep, D., Broussard, G. J., Tsang, A., Tsegaye, G., et al. (2023) · Nature · doi:10.1038/s41586-023-05828-9 · Cited by 887 · see citing works
Representative learning design and functionality of research and practice in sport
Pinder, R. A., Davids, K., Renshaw, I., & Araújo, D. (2011) · Journal of Sport and Exercise Psychology · doi:10.1123/jsep.33.1.146 · Cited by 692 · see citing works
Not Noisy, Just Wrong: The Role of Suboptimal Inference in Behavioral Variability
Beck, J. M., Ma, W. J., Pitkow, X., Latham, P. E., & Pouget, A. (2012) · Neuron · doi:10.1016/j.neuron.2012.03.016 · Cited by 316 · see citing works
Deliberation and Commitment in the Premotor and Primary Motor Cortex during Dynamic Decision Making
Thura, D., & Cisek, P. (2014) · Neuron · doi:10.1016/j.neuron.2014.01.031 · Cited by 284 · see citing works
Decision making by urgency gating: theory and experimental support
Thura, D., Beauregard-Racine, J., Fradet, C.-W., & Cisek, P. (2012) · Journal of Neurophysiology · doi:10.1152/jn.01071.2011 · Cited by 267 · see citing works
Attention, learning, and the value of information
Gottlieb, J. (2012) · Neuron · doi:10.1016/j.neuron.2012.09.034 · Cited by 241 · see citing works
“The oculomotor component of an attentional response is generated by a network of cortical and subcortical structures that includes portions of the basal ganglia, the superior colliculus, and the frontal eye field (Schall et al., 2011; Stanford et al., 2010).”
BackgroundLearning by the Dendritic Prediction of Somatic Spiking
Urbanczik, R., & Senn, W. (2014) · Neuron · doi:10.1016/j.neuron.2013.11.030 · Cited by 230 · see citing works
Independence of Movement Preparation and Movement Initiation
Haith, A. M., Pakpoor, J., & Krakauer, J. W. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.3245-15.2016 · Cited by 225 · see citing works
Decision-Related Activity in Sensory Neurons: Correlations Among Neurons and with Behavior
Nienborg, H., Cohen, M. R., & Cumming, B. G. (2012) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062111-150403 · Cited by 199 · see citing works
Bridging Neural and Computational Viewpoints on Perceptual Decision-Making
O’Connell, R. G., Shadlen, M. N., Wong‐Lin, K., & Kelly, S. P. (2018) · Trends in Neurosciences · doi:10.1016/j.tins.2018.06.005 · Cited by 214 · see citing works
Working Memory and Decision-Making in a Frontoparietal Circuit Model
Murray, J. D., Jaramillo, J., & Wang, X. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0343-17.2017 · Cited by 199 · see citing works
Decision Making: The Neuroethological Turn
Pearson, J. M., Watson, K. K., & Platt, M. L. (2014) · Neuron · doi:10.1016/j.neuron.2014.04.037 · Cited by 184 · see citing works
Behavior genetics and postgenomics
Charney, E. (2012) · Behavioral and Brain Sciences · doi:10.1017/s0140525x11002226 · Cited by 207 · see citing works
From salience to saccades: multiple-alternative gated stochastic accumulator model of visual search
Purcell, B. A., Schall, J. D., Logan, G. D., & Palmeri, T. J. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.4622-11.2012 · Cited by 179 · see citing works
Kiani, R., Cueva, C. J., Reppas, J. B., & Newsome, W. T. (2014) · Current Biology · doi:10.1016/j.cub.2014.05.049 · Cited by 158 · see citing works
The LATER model of reaction time and decision
Noorani, I., & Carpenter, R. H. S. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.02.018 · Cited by 144 · see citing works
Neurons in Dorsal Anterior Cingulate Cortex Signal Postdecisional Variables in a Foraging Task
Blanchard, T. C., & Hayden, B. Y. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.3151-13.2014 · Cited by 141 · see citing works
Optimal multisensory decision-making in a reaction-time task
Drugowitsch, J., DeAngelis, G. C., Klier, E. M., Angelaki, D. E., & Pouget, A. (2014) · eLife · doi:10.7554/elife.03005 · Cited by 133 · see citing works
Neurocomputational mechanisms of prior-informed perceptual decision-making in humans
Kelly, S. P., Corbett, E. A., & O’Connell, R. G. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-00967-9 · Cited by 145 · see citing works
Decision Threshold Modulation in the Human Brain
Domenech, P., & Dreher, J. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.2371-10.2010 · Cited by 123 · see citing works
Vacillation, indecision and hesitation in moment-by-moment decoding of monkey motor cortex
Kaufman, M. T., Churchland, M. M., Ryu, S. I., & Shenoy, K. V. (2015) · eLife · doi:10.7554/elife.04677 · Cited by 125 · see citing works
The sifting of visual information in the superior colliculus
Lee, K. H., Tran, A., Turan, Z., & Meister, M. (2020) · eLife · doi:10.7554/elife.50678 · Cited by 121 · see citing works
Dynamic multisensory integration: somatosensory speed trumps visual accuracy during feedback control
Crevecoeur, F., Munoz, D. P., & Scott, S. H. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.0184-16.2016 · Cited by 114 · see citing works
Thura, D., Cos, I., Trung, J., & Cisek, P. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0162-14.2014 · Cited by 109 · see citing works
Effects of Altered Excitation-Inhibition Balance on Decision Making in a Cortical Circuit Model
Lam, N. H., Borduqui, T., Hallak, J. E. C., Roque, A. C., Anticevic, A., Krystal, J. H., Wang, X., & Murray, J. D. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.1371-20.2021 · Cited by 115 · see citing works
Gill, J. V., Lerman, G. M., Zhao, H., Stetler, B. J., Rinberg, D. A., & Shoham, S. (2020) · Neuron · doi:10.1016/j.neuron.2020.07.034 · Cited by 105 · see citing works
Noise and Correlations in Parallel Perceptual Decision Making
Otto, T. U., & Mamassian, P. (2012) · Current Biology · doi:10.1016/j.cub.2012.05.031 · Cited by 110 · see citing works
Ultrafast Population Encoding by Cortical Neurons
Tchumatchenko, T., Malyshev, A. Y., Wolf, F., & Volgushev, M. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.2182-11.2011 · Cited by 99 · see citing works
Salinas, E., & Stanford, T. R. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.3977-12.2013 · Cited by 96 · see citing works
Opriș, I. C., Lebedev, M., & Nelson, R. J. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00061 · Cited by 94 · see citing works
Comparing fixed and collapsing boundary versions of the diffusion model
Voskuilen, C., Ratcliff, R., & Smith, P. L. (2016) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2016.04.008 · Cited by 100 · see citing works
Population-wide distributions of neural activity during perceptual decision-making
Wohrer, A., Humphries, M. D., & Machens, C. K. (2012) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2012.09.004 · Cited by 89 · see citing works
Reaction times can reflect habits rather than computations
Wong, A. L., Goldsmith, J., Forrence, A. D., Haith, A. M., & Krakauer, J. W. (2017) · eLife · doi:10.7554/elife.28075 · Cited by 89 · see citing works
The Urge to Decide and Act: Implications for Brain Function and Dysfunction
Carland, M. A., Thura, D., & Cisek, P. (2019) · The Neuroscientist · doi:10.1177/1073858419841553 · Cited by 77 · see citing works
Emerging principles of population coding: in search for the neural code
Shamir, M. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.01.002 · Cited by 79 · see citing works
Haith, A. M., Huberdeau, D. M., & Krakauer, J. W. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004171 · Cited by 82 · see citing works
The Limits of Deliberation in a Perceptual Decision Task
Zariwala, H. A., Kepecs, A., Uchida, N., Hirokawa, J., & Mainen, Z. (2013) · Neuron · doi:10.1016/j.neuron.2013.02.010 · Cited by 85 · see citing works
Integrated neural dynamics of sensorimotor decisions and actions
Thura, D., Cabana, J., Feghaly, A., & Cisek, P. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001861 · Cited by 83 · see citing works
Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials
Pfister, J.-P., Dayan, P., & Lengyel, M. (2010) · Nature Neuroscience · doi:10.1038/nn.2640 · Cited by 74 · see citing works
Saccade metrics reflect decision-making dynamics during urgent choices
Seideman, J. A., Stanford, T. R., & Salinas, E. (2018) · Nature Communications · doi:10.1038/s41467-018-05319-w · Cited by 72 · see citing works
Ultrafast population coding and axo-somatic compartmentalization
Zhang, C., Hofmann, D., Neef, A., & Wolf, F. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009775 · Cited by 71 · see citing works
The latency of a visual evoked potential tracks the onset of decision making
Nunez, M. D., Gosai, A. K., Vandekerckhove, J., & Srinivasan, R. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.052 · Cited by 70 · see citing works
Accumulators, Neurons, and Response Time
Schall, J. D. (2019) · Trends in Neurosciences · doi:10.1016/j.tins.2019.10.001 · Cited by 71 · see citing works
Costello, M. G., Zhu, D., Salinas, E., & Stanford, T. R. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.1899-13.2013 · Cited by 60 · see citing works
Eye Movements as a Window into Decision-Making
Spering, M. (2022) · Annual Review of Vision Science · doi:10.1146/annurev-vision-100720-125029 · Cited by 63 · see citing works
Computational models of spatial updating in peri-saccadic perception
Hamker, F. H., Zirnsak, M., Ziesche, A., & Lappe, M. (2011) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2010.0229 · Cited by 61 · see citing works
Learning rational temporal eye movement strategies
Hoppe, D., & Rothkopf, C. A. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1601305113 · Cited by 57 · see citing works
Salinas, E., Scerra, V. E., Hauser, C. K., Costello, M. G., & Stanford, T. R. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00085 · Cited by 58 · see citing works
Synchronization between the end stages of the dorsal and the ventral visual stream
Verhoef, B.-E., Vogels, R., & Janssen, P. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00924.2010 · Cited by 59 · see citing works
Reassessing optimal neural population codes with neurometric functions
Berens, P., Ecker, A. S., Gerwinn, S., Tolias, A. S., & Bethge, M. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1015904108 · Cited by 58 · see citing works
Predation and the Origin of Neurones
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Tracking the Temporal Evolution of a Perceptual Judgment Using a Compelled-Response Task
Shankar, S., Massoglia, D. P., Zhu, D., Costello, M. G., Stanford, T. R., & Salinas, E. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.1419-11.2011 · Cited by 54 · see citing works
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Miller, P. C., & Katz, D. B. (2013) · Journal of Computational Neuroscience · doi:10.1007/s10827-013-0452-x · Cited by 46 · see citing works
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Saccadic body turns in walking Drosophila
Geurten, B. R. H., Jähde, P., Corthals, K., & Göpfert, M. C. (2014) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2014.00365 · Cited by 41 · see citing works
All-or-None Context Dependence Delineates Limits of FEF Visual Target Selection
Scerra, V. E., Costello, M. G., Salinas, E., & Stanford, T. R. (2019) · Current Biology · doi:10.1016/j.cub.2018.12.013 · Cited by 39 · see citing works
Wang, M., Montanède, C., Chandrasekaran, C., Peixoto, D., Shenoy, K. V., & Kalaska, J. F. (2019) · Nature Communications · doi:10.1038/s41467-019-09460-y · Cited by 39 · see citing works
Motor selection dynamics in FEF explain the reaction time variance of saccades to single targets
Hauser, C. K., Zhu, D., Stanford, T. R., & Salinas, E. (2018) · eLife · doi:10.7554/elife.33456 · Cited by 39 · see citing works
Ziesche, A., & Hamker, F. H. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.3407-11.2011 · Cited by 38 · see citing works
Salinas, E., Shankar, S., Costello, M. G., Zhu, D., & Stanford, T. R. (2010) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2010.00153 · Cited by 33 · see citing works
The Functional Link between Area MT Neural Fluctuations and Detection of a Brief Motion Stimulus
Smith, J. E. T., Zhan, C. A., & Cook, E. P. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.1347-11.2011 · Cited by 34 · see citing works
Dynamics of visual receptive fields in the macaque frontal eye field
Mayo, J. P., DiTomasso, A., Sommer, M. A., & Smith, M. A. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00746.2015 · Cited by 32 · see citing works
A spiking neural network based on the basal ganglia functional anatomy
Baladron, J., & Hamker, F. H. (2015) · Neural Networks · doi:10.1016/j.neunet.2015.03.002 · Cited by 31 · see citing works
Salinas, E., Steinberg, B. R., Sussman, L. A., Fry, S. M., Hauser, C. K., Anderson, D. D., & Stanford, T. R. (2019) · eLife · doi:10.7554/elife.46359 · Cited by 29 · see citing works
Deciding Not to Decide: Computational and Neural Evidence for Hidden Behavior in Sequential Choice
Gluth, S., Rieskamp, J., & Büchel, C. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003309 · Cited by 26 · see citing works
Globus pallidus dynamics reveal covert strategies for behavioral inhibition
Gu, B.-M., Schmidt, R., & Berke, J. D. (2020) · eLife · doi:10.7554/elife.57215 · Cited by 25 · see citing works
Dynamics of Visual Perceptual Decision-Making in Freely Behaving Mice
You, W.-K., & Mysore, S. P. (2022) · eNeuro · doi:10.1523/eneuro.0161-21.2022 · Cited by 26 · see citing works
Proactive Action Preparation: Seeing Action Preparation as a Continuous and Proactive Process
Pezzulo, G., & Ognibene, D. (2012) · Motor Control · doi:10.1123/mcj.16.3.386 · Cited by 27 · see citing works
Ziesche, A., & Hamker, F. H. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00025 · Cited by 27 · see citing works
Fetsch, C. R. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2015.12.002 · Cited by 26 · see citing works
Fast Coding of Orientation in Primary Visual Cortex
Shriki, O., Kohn, A., & Shamir, M. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002536 · Cited by 27 · see citing works
Salinas, E., & Stanford, T. R. (2018) · Scientific Reports · doi:10.1038/s41598-018-32224-5 · Cited by 24 · see citing works
Urgent Decision Making: Resolving Visuomotor Interactions at High Temporal Resolution
Stanford, T. R., & Salinas, E. (2021) · Annual Review of Vision Science · doi:10.1146/annurev-vision-100419-103842 · Cited by 30 · see citing works
Predicting the timing of wrong decisions with LATER
Noorani, I., Gao, M. J., Pearson, B., & Carpenter, R. H. S. (2011) · Experimental Brain Research · doi:10.1007/s00221-011-2587-1 · Cited by 25 · see citing works
Ziesche, A., Bergelt, J., Deubel, H., & Hamker, F. H. (2017) · Vision Research · doi:10.1016/j.visres.2017.06.007 · Cited by 24 · see citing works
The importance of decision onset
Teichert, T., Grinband, J., & Ferrera, V. P. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00274.2015 · Cited by 24 · see citing works
Dynamic combination of sensory and reward information under time pressure
Farashahi, S. G., Ting, C.-C., Kao, C.-H., Wu, S., & Soltani, A. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006070 · Cited by 22 · see citing works
Urgency forces stimulus-driven action by overcoming cognitive control
Poth, C. H. (2021) · eLife · doi:10.7554/elife.73682 · Cited by 23 · see citing works
Fast-starts in hunting fish: decision-making in small networks of identified neurons
Schuster, S. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.12.004 · Cited by 23 · see citing works
Paradoxical Evidence Integration in Rapid Decision Processes
Rüter, J., Marcille, N., Sprekeler, H., Gerstner, W., & Herzog, M. H. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002382 · Cited by 22 · see citing works
Ruiz, O., & Paradiso, M. A. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00733.2011 · Cited by 23 · see citing works
Shao, Y.-Q., Fan, L., Wu, W., Zhu, Y.-J., & Xu, H. (2022) · Current Biology · doi:10.1016/j.cub.2022.05.029 · Cited by 21 · see citing works
Multiphasic value biases in fast-paced decisions
Corbett, E. A., Martinez-Rodriguez, L. A., Judd, C., O’Connell, R. G., & Kelly, S. P. (2023) · eLife · doi:10.7554/elife.67711 · Cited by 23 · see citing works
Barbosa, P., Kaski, D. N., Castro, P., Lees, A. J., Warner, T. T., & Djamshidian, A. (2019) · Journal of Parkinson s Disease · doi:10.3233/jpd-181460 · Cited by 21 · see citing works
Proactive and reactive accumulation-to-bound processes compete during perceptual decisions
Hernández-Navarro, L., Hermoso-Mendizabal, A., Duque, D., de la Rocha, J., & Hyafil, A. (2021) · Nature Communications · doi:10.1038/s41467-021-27302-8 · Cited by 20 · see citing works
Predicting Visual Consciousness Electrophysiologically from Intermittent Binocular Rivalry
O’Shea, R. P., Kornmeier, J., & Roeber, U. (2013) · PLoS ONE · doi:10.1371/journal.pone.0076134 · Cited by 20 · see citing works
The Perceptual Cues that Reshape Expert Reasoning
Harré, M., Bossomaier, T. R. J., & Snyder, A. W. (2012) · Scientific Reports · doi:10.1038/srep00502 · Cited by 19 · see citing works
Detection and identification of speech sounds using cortical activity patterns
Centanni, T. M., Sloan, A. M., Reed, A., Engineer, C. T., Rennaker, R. L., & Kilgard, M. P. (2013) · Neuroscience · doi:10.1016/j.neuroscience.2013.11.030 · Cited by 18 · see citing works
Towards a unifying mechanism for cancelling movements
Noorani, I. (2017) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2016.0191 · Cited by 16 · see citing works
A conflict between spatial selection and evidence accumulation in area LIP
Seideman, J. A., Stanford, T. R., & Salinas, E. (2022) · Nature Communications · doi:10.1038/s41467-022-32209-z · Cited by 17 · see citing works
Fast perception of binocular disparity.
Caziot, B., Valsecchi, M., Gegenfurtner, K. R., & Backus, B. T. (2015) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/xhp0000066 · Cited by 16 · see citing works
Eye movements reset visual perception
Paradiso, M. A., Meshi, D., Pisarcik, J., & Levine, S. (2012) · Journal of Vision · doi:10.1167/12.13.11 · Cited by 16 · see citing works
Exogenous capture accounts for fundamental differences between pro- and antisaccade performance
Goldstein, A. T., Stanford, T. R., & Salinas, E. (2022) · eLife · doi:10.7554/elife.76964 · Cited by 14 · see citing works
Stimulus Salience Conflicts and Colludes with Endogenous Goals During Urgent Choices
Oor, E. E., Stanford, T. R., & Salinas, E. (2022) · iScience · doi:10.1016/j.isci.2023.106253 · Cited by 15 · see citing works
- Authors
- Stanford, T. R., Shankar, S., Massoglia, D. P., Costello, M. G., & Salinas, E.
- Affiliations
- Wake Forest University Health Sciences (US)
- Corresponding author
- Salinas, Emilio
- Venue
- Nature Neuroscience
- Published
- 2010-01-24
- Volume
- 13
- Issue
- 3
- Pages
- 379-385
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC2834559
- OpenAlex
- W2031658574
- Cited by
- 228
- References
- 49
- Citations by year
- 2026: 4 · 2025: 12 · 2024: 14 · 2023: 12 · 2022: 17 · 2021: 9 · 2020: 10 · 2019: 15 · 2018: 12 · 2017: 12 · 2016: 11 · 2015: 22 · 2014: 20 · 2013: 16 · 2012: 20
- FWCI
- 11.09
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Drug Abuse (R01DA030750)
- National Eye Institute (R01EY012389)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Keywords
- perceptual decision making, perceptual discrimination, color discrimination, reaction time (psychology), motor reaction time, oculomotor neurons, choice accuracy, monkeys
- MeSH terms
- Action Potentials, Animals, Haplorhini, Color, Decision Making, Microelectrodes, Models, Neurological, Neurons, Neuropsychological Tests, Photic Stimulation, Psychometrics, Psychomotor Performance, Reaction Time, Saccades, Video Recording, Visual Perception, Eye Movement Measurements
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