The DOI matches its Crossref record; 97 of the 97 references it cites were found in a registry or an index.
Uncertainty, Neuromodulation, and Attention
Open accessAbstract
Uncertainty in various forms plagues our interactions with the environment. In a Bayesian statistical framework, optimal inference and prediction, based on unreliable observations in changing contexts, require the representation and manipulation of different forms of uncertainty. We propose that the neuromodulators acetylcholine and norepinephrine play a major role in the brain's implementation of these uncertainty computations. Acetylcholine signals expected uncertainty, coming from known unreliability of predictive cues within a context. Norepinephrine signals unexpected uncertainty, as when unsignaled context switches produce strongly unexpected observations. These uncertainty signals interact to enable optimal inference and learning in noisy and changeable environments. This formulation is consistent with a wealth of physiological, pharmacological, and behavioral data implicating acetylcholine and norepinephrine in specific aspects of a range of cognitive processes. Moreover, the model suggests a class of attentional cueing tasks that involve both neuromodulators and shows how their interactions may be part-antagonistic, part-synergistic.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The text we found for this paper looks like another document, so the self-check wasn’t run.
Citations
Cites
An Integrative Theory of Prefrontal Cortex Function
Miller, E. K., & Cohen, J. D. (2001) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.24.1.167 · Cited by 12,803
Advances in Neural Information Processing Systems 14
(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 9,187
The attention system of the human brain
Posner, M. I., & Petersen, S. E. (1990) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.13.030190.000325 · Cited by 8,070
Information theory, inference, and learning algorithms
(2004) · Choice Reviews Online · doi:10.5860/choice.41-5949 · Cited by 6,536
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
(2005) · Choice Reviews Online · doi:10.5860/choice.43-0626 · Cited by 4,726
Probability theory the logic of science
Jaynes, E. T., & Bretthorst, G. L. (2003) · Cited by 4,600
Alzheimer's Disease and Senile Dementia: Loss of Neurons in the Basal Forebrain
Whitehouse, P., Price, D., Struble, R., Clark, A., Coyle, J., & DeLong, M. R. (1982) · Science · doi:10.1126/science.7058341 · Cited by 3,627
Pearce, J. M., & Hall, G. (1980) · Psychological Review · doi:10.1037/0033-295x.87.6.532 · Cited by 3,023
(1995) · Choice Reviews Online · doi:10.5860/choice.32-5931 · Cited by 2,337
Cited by
Sorted by citations
Items 401–500 of 1,008
The neurobiology of uncertainty: implications for statistical learning
Hasson, U. (2016) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2016.0048 · Cited by 77
Pupil size and social vigilance in rhesus macaques
Ebitz, R. B., Pearson, J. M., & Platt, M. L. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00100 · Cited by 71
Wong, R., Lénárt, N., Hill, L., Toms, L., Coutts, G., Martinecz, B., Császár, E., Nyíri, G., Papaemmanouil, A., Waisman, A., et al. (2018) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2018.11.012 · Cited by 82
Neuromodulator and Emotion Biomarker for Stress Induced Mental Disorders
Gu, S., Wang, W., Wang, F., & Huang, J. H. (2016) · Neural Plasticity · doi:10.1155/2016/2609128 · Cited by 78
Noradrenergic neuromodulation of human attention for emotional and neutral stimuli
De Martino, B., Strange, B. A., & Dolan, R. J. (2008) · Psychopharmacology · doi:10.1007/s00213-007-1015-5 · Cited by 103
Noradrenergic and cholinergic systems take centre stage in neuropsychiatric diseases of ageing
Orlando, I. F., Shine, J. M., Robbins, T. W., Rowe, J. B., & O’Callaghan, C. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105167 · Cited by 81
Relation of Locus Coeruleus Neurons in Monkeys to Pavlovian and Operant Behaviors
Bouret, S., & Richmond, B. J. (2008) · Journal of Neurophysiology · doi:10.1152/jn.91048.2008 · Cited by 78
Boosting visual cortex function and plasticity with acetylcholine to enhance visual perception
Kang, J. I., Huppé‐Gourgues, F., & Vaucher, E. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00172 · Cited by 75
Monosov, I. E., & Hikosaka, O. (2013) · Nature Neuroscience · doi:10.1038/nn.3398 · Cited by 82
Irrational Choice under Uncertainty Correlates with Lower Striatal D2/3Receptor Binding in Rats
Cocker, P. J., Dinelle, K., Kornelson, R., Sossi, V., & Winstanley, C. A. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.0626-12.2012 · Cited by 78
Tea With Milk? A Hierarchical Generative Framework of Sequential Event Comprehension
Kuperberg, G. R. (2020) · Topics in Cognitive Science · doi:10.1111/tops.12518 · Cited by 90
Human complex exploration strategies are enriched by noradrenaline-modulated heuristics
Dubois, M., Habicht, J., Michely, J., Moran, R., Dolan, R. J., & Hauser, T. U. (2021) · eLife · doi:10.7554/elife.59907 · Cited by 76
Pfeffer, T. A., Ponce‐Alvarez, A., Meindertsma, T., Gahnstrom, C. J., van den Brink, R. L., Nolte, G., Tsetsos, K., Engel, A. K., Deco, G., & Donner, T. H. (2021) · Science Advances · doi:10.1126/sciadv.abf5620 · Cited by 73
Koot, S., Zoratto, F., Cassano, T., Colangeli, R., Laviola, G., van den Bos, R., & Adriani, W. (2011) · Neuropharmacology · doi:10.1016/j.neuropharm.2011.11.002 · Cited by 81
Combined effects of spatial and feature-based attention on responses of V4 neurons
Hayden, B. Y., & Gallant, J. L. (2008) · Vision Research · doi:10.1016/j.visres.2008.06.011 · Cited by 76
Kwisthout, J., Bekkering, H., & van Rooij, I. (2017) · Brain and Cognition · doi:10.1016/j.bandc.2016.02.008 · Cited by 73
Memory, modelling and Marr: a commentary on Marr (1971) ‘Simple memory: a theory of archicortex’
Willshaw, D. J., Dayan, P., & Morris, R. (2015) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2014.0383 · Cited by 81
Self-citationJang, A. I., Costa, V. D., Rudebeck, P. H., Chudasama, Y., Murray, E. A., & Averbeck, B. B. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1594-15.2015 · Cited by 81
Perceptual Classification in a Rapidly Changing Environment
Summerfield, C., Behrens, T. E. J., & Koechlin, É. (2011) · Neuron · doi:10.1016/j.neuron.2011.06.022 · Cited by 76
Stress, genotype and norepinephrine in the prediction of mouse behavior using reinforcement learning
Lukšys, G., Wulfram Gerstner, B., & Sandi, C. (2009) · Nature Neuroscience · doi:10.1038/nn.2374 · Cited by 79
The uncertainty processing theory of motivation
Anselme, P. (2009) · Behavioural Brain Research · doi:10.1016/j.bbr.2009.12.020 · Cited by 77
Fetsch, C. R., Odean, N. N., Jeurissen, D., El-Shamayleh, Y., Horwitz, G. D., & Shadlen, M. N. (2018) · eLife · doi:10.7554/elife.36523 · Cited by 74
Pupil diameter encodes the idiosyncratic, cognitive complexity of belief updating
Filipowicz, A. L. S., Glaze, C. M., Kable, J. W., & Gold, J. I. (2020) · eLife · doi:10.7554/elife.57872 · Cited by 88
Krichmar, J. L. (2013) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2013.00001 · Cited by 79
Silvetti, M., Seurinck, R., & Verguts, T. (2012) · Cortex · doi:10.1016/j.cortex.2012.05.008 · Cited by 81
Large-scale dynamics of perceptual decision information across human cortex
Wilming, N., Murphy, P. R., Meyniel, F., & Donner, T. H. (2020) · Nature Communications · doi:10.1038/s41467-020-18826-6 · Cited by 77
Statistical decision theory to relate neurons to behavior in the study of covert visual attention
Eckstein, M. P., Peterson, M., Pham, B. T., & Droll, J. A. (2009) · Vision Research · doi:10.1016/j.visres.2008.12.008 · Cited by 76
Inference in the Brain: Statistics Flowing in Redundant Population Codes
Pitkow, X., & Angelaki, D. E. (2017) · Neuron · doi:10.1016/j.neuron.2017.05.028 · Cited by 79
Salvi, C., Simoncini, C., Grafman, J. H., & Beeman, M. J. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116933 · Cited by 73
Newman, L. A., & McGaughy, J. A. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.5112-07.2008 · Cited by 71
Balconi, M., Finocchiaro, R., & Canavesio, Y. (2014) · Addictive Behaviors · doi:10.1016/j.addbeh.2014.02.007 · Cited by 74
Sensory processing in autism across exteroceptive and interoceptive domains.
Proff, I., Williams, G. L., Quadt, L., & Garfinkel, S. N. (2022) · Psychology & Neuroscience · doi:10.1037/pne0000262 · Cited by 69
Control of entropy in neural models of environmental state
Müller, T., Mars, R. B., Behrens, T. E. J., & O’Reilly, J. X. (2019) · eLife · doi:10.7554/elife.39404 · Cited by 70
The locus coeruleus as a global model failure system
Jordan, R. (2023) · Trends in Neurosciences · doi:10.1016/j.tins.2023.11.006 · Cited by 82
Optimality and Robustness of a Biophysical Decision-Making Model under Norepinephrine Modulation
Eckhoff, P. A., Wong‐Lin, K., & Holmes, P. J. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.5024-08.2009 · Cited by 67
Representations of uncertainty in sensorimotor control
Orbán, G., & Wolpert, D. M. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.05.026 · Cited by 72
Balancing new against old information: the role of puzzlement surprise in learning
Faraji, M., Preuschoff, K., & Gerstner, W. (2018) · Neural Computation · doi:10.1162/neco_a_01025 · Cited by 68
Dissociable forms of uncertainty-driven representational change across the human brain
Nassar, M. R., McGuire, J. T., Ritz, H., & Kable, J. W. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.1713-18.2018 · Cited by 65
Janitzky, K., Lippert, M., Engelhorn, A., Tegtmeier, J., Goldschmidt, J., Heinze, H., & Ohl, F. W. (2015) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2015.00286 · Cited by 70
Neural evidence for Bayesian trial-by-trial adaptation on the N400 during semantic priming
Delaney-Busch, N., Morgan, E., Lau, E. F., & Kuperberg, G. R. (2019) · Cognition · doi:10.1016/j.cognition.2019.01.001 · Cited by 73
Pupil-Linked Arousal Responds to Unconscious Surprisal
Alamia, A., VanRullen, R., Pasqualotto, E., Mouraux, A., & Zénon, A. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.3010-18.2019 · Cited by 75
Complementary neural correlates of motivation in dopaminergic and noradrenergic neurons of monkeys
Bouret, S., Ravel, S., & Richmond, B. J. (2012) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2012.00040 · Cited by 73
Posterior Cingulate Neurons Dynamically Signal Decisions to Disengage during Foraging
Barack, D. L., Chang, S. W. C., & Platt, M. L. (2017) · Neuron · doi:10.1016/j.neuron.2017.09.048 · Cited by 61
Why and how the brain weights contributions from a mixture of experts
O’Doherty, J. P., Lee, S. W., Tadayonnejad, R., Cockburn, J., Iigaya, K., & Charpentier, C. J. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.10.022 · Cited by 70
Eckstein, M. K., Master, S. L., Dahl, R. E., Wilbrecht, L., & Collins, A. G. E. (2022) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2022.101106 · Cited by 69
The effect of atomoxetine on random and directed exploration in humans
Warren, C. M., Wilson, R. C., van der Wee, N. J., Giltay, E. J., van Noorden, M. S., Cohen, J. D., & Nieuwenhuis, S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0176034 · Cited by 68
What makes the dorsomedial frontal cortex active during reading the mental states of others?
Isoda, M., & Noritake, A. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00232 · Cited by 64
Generalization of Dynamics Learning Across Changes in Movement Amplitude
Mattar, A. A. G., & Ostry, D. J. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00886.2009 · Cited by 62
Edeline, J. (2012) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2012.00045 · Cited by 65
Cholinergic Stimulation Enhances Bayesian Belief Updating in the Deployment of Spatial Attention
Vossel, S., Bauer, M., Mathys, C. D., Adams, R. A., Dolan, R. J., Stephan, K. E., & Friston, K. J. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0091-14.2014 · Cited by 66
Task-evoked pupil responses reflect internal belief states
Colizoli, O., de Gee, J. W., Urai, A. E., & Donner, T. H. (2018) · Scientific Reports · doi:10.1038/s41598-018-31985-3 · Cited by 67
Behavioral specifications of reward-associated long-term memory enhancement in humans
Wittmann, B. C., Dolan, R. J., & Düzel, E. (2011) · Learning & Memory · doi:10.1101/lm.1996811 · Cited by 62
Tonic exploration governs both flexibility and lapses
Ebitz, R. B., Sleezer, B. J., Jedema, H. P., Bradberry, C. W., & Hayden, B. Y. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007475 · Cited by 58
David, M., Giovane, M. D., Liu, K. Y., Gostick, B., Rowe, J. B., Oboh, I., Howard, R. J., & Malhotra, P. (2022) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2022-329136 · Cited by 66
Insight and the selection of ideas
Laukkonen, R., Webb, M. E., Salvi, C., Tangen, J. M., Slagter, H. A., & Schooler, J. W. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105363 · Cited by 72
Theoretical and Empirical Studies of Learning
Niv, Y., & Montague, P. R. (2009) · Elsevier eBooks · doi:10.1016/b978-0-12-374176-9.00022-1 · Cited by 65
Response-based outcome predictions and confidence regulate feedback processing and learning
Frömer, R., Nassar, M. R., Bruckner, R., Stürmer, B., Sommer, W., & Yeung, N. (2021) · eLife · doi:10.7554/elife.62825 · Cited by 67
Learning in a changing environment.
Speekenbrink, M., & Shanks, D. R. (2010) · Journal of Experimental Psychology General · doi:10.1037/a0018620 · Cited by 61
Context Learning in the Rodent Hippocampus
Fuhs, M. C., & Touretzky, D. S. (2007) · Neural Computation · doi:10.1162/neco.2007.19.12.3173 · Cited by 59
Sadacca, B. F., Wikenheiser, A. M., & Schoenbaum, G. M. (2016) · Neuroscience · doi:10.1016/j.neuroscience.2016.04.017 · Cited by 59
Volatility Facilitates Value Updating in the Prefrontal Cortex
Massi, B., Donahue, C. H., & Lee, D. (2018) · Neuron · doi:10.1016/j.neuron.2018.06.033 · Cited by 64
Dezza, I. C., Axel Cleeremans, B., & Alexander, W. (2019) · Journal of Experimental Psychology General · doi:10.1037/xge0000546 · Cited by 55
The ecology of action selection: insights from artificial life
Seth, A. K. (2007) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2007.2052 · Cited by 64
Fouragnan, E., Queirazza, F., Retzler, C., Mullinger, K. J., & Philiastides, M. G. (2017) · Scientific Reports · doi:10.1038/s41598-017-04507-w · Cited by 61
Nicotinic receptors in the ventral tegmental area promote uncertainty-seeking
Naudé, J., Tolu, S., Dongelmans, M., Torquet, N., Valverde, S., Rodriguez, G., Pons, S., Maskos, U., Mourot, A., Marti, F., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4223 · Cited by 61
Hierarchical models of pain: Inference, information-seeking, and adaptive control.
Seymour, B., & Mancini, F. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117212 · Cited by 59
Piray, P., Ly, V., Roelofs, K., Cools, R., & Toni, I. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.1394-18.2018 · Cited by 61
The neural correlates of hierarchical predictions for perceptual decisions
Weilnhammer, V. A., Stuke, H., Sterzer, P., & Schmack, K. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.2901-17.2018 · Cited by 57
Neurochemistry of Visual Attention
Lockhofen, D. E. L., & Mulert, C. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.643597 · Cited by 66
Ferrier, J., Bayet‐Robert, M., Dalmann, R., Guerrab, A. E., Aissouni, Y., Graveron‐Demilly, D., Chalus, M., Pinguet, J., Eschalier, A., Richard, D., et al. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1537-15.2015 · Cited by 60
Dual contributions of noradrenaline to behavioural flexibility and motivation
Jahn, C. I., Gilardeau, S., Varazzani, C., Blain, B., Sallet, J., Walton, M. E., & Bouret, S. (2018) · Psychopharmacology · doi:10.1007/s00213-018-4963-z · Cited by 68
Brain-wide representations of ongoing behavior: a universal principle?
Kaplan, H. S., & Zimmer, M. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.02.008 · Cited by 59
An exploration-exploitation model based on norepinepherine and dopamine activity
McClure, S. M., Gilzenrat, M. S., & Cohen, J. D. (2005) · neural information processing systems · Cited by 61
Effects of acetylcholine on neuronal properties in entorhinal cortex
Heys, J. G., Schultheiss, N. W., Shay, C. F., Tsuno, Y., & Hasselmo, M. E. (2012) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2012.00032 · Cited by 61
Priming of actions increases sense of control over unexpected outcomes
Sidarus, N., Chambon, V., & Haggard, P. (2013) · Consciousness and Cognition · doi:10.1016/j.concog.2013.09.008 · Cited by 67
DREADD-mediated modulation of locus coeruleus inputs to mPFC improves strategy set-shifting
Cope, Z. A., Vazey, E. M., Floresco, S., & Jones, G. S. A. (2019) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2019.02.009 · Cited by 59
Cortical Signals for Rewarded Actions and Strategic Exploration
Donahue, C. H., Seo, H., & Lee, D. (2013) · Neuron · doi:10.1016/j.neuron.2013.07.040 · Cited by 56
Eckstein, M. P., Mack, S. C., Liston, D. B., Bogusch, L., Menzel, R., & Krauzlis, R. J. (2013) · Vision Research · doi:10.1016/j.visres.2012.12.011 · Cited by 63
Aminihajibashi, S., Hagen, T., Foldal, M. D., Laeng, B., & Espeseth, T. (2019) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2019.03.007 · Cited by 63
Cholinergic suppression of visual responses in primate V1 is mediated by GABAergic inhibition
Disney, A. A., Aoki, C., & Hawken, M. J. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00188.2012 · Cited by 57
Noradrenergic and cholinergic modulation of late ERP responses to deviant stimuli
Brown, S. B. R. E., van der Wee, N. J. A., van Noorden, M. S., Giltay, E. J., & Nieuwenhuis, S. (2015) · Psychophysiology · doi:10.1111/psyp.12544 · Cited by 59
A taxonomy of surprise definitions
Modirshanechi, A., Brea, J., & Wulfram Gerstner, B. (2022) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2022.102712 · Cited by 67
Monosov, I. E., Leopold, D. A., & Hikosaka, O. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0051-15.2015 · Cited by 56
Atomoxetine facilitates attentional set shifting in adolescent rats
Cain, R. E., Wasserman, M. C., Waterhouse, B. D., & McGaughy, J. A. (2011) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2011.04.003 · Cited by 55
Violations of Core Knowledge Shape Early Learning
Stahl, A. E., & Feigenson, L. (2018) · Topics in Cognitive Science · doi:10.1111/tops.12389 · Cited by 58
Wen, W., & Haggard, P. (2019) · Cognition · doi:10.1016/j.cognition.2019.104074 · Cited by 56
Actor-critic reinforcement learning in the songbird
Chen, R., & Goldberg, J. H. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.08.005 · Cited by 56
Curiosity, information demand and attentional priority
Gottlieb, J. P., Cohanpour, M., Li, Y., Singletary, N. M., & Zabeh, E. (2020) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2020.07.016 · Cited by 54
Harris, D., Arthur, T., Broadbent, D. P., Wilson, M., Vine, S. J., & Runswick, O. R. (2022) · Sports Medicine · doi:10.1007/s40279-022-01689-w · Cited by 65
Polymorphism of the COMT, MAO, DAT, NET and 5-HTT Genes, and Biogenic Amines in Parkinson’s Disease
Dorszewska, J., Prendecki, M., Oczkowska, A., Różycka, A., Lianeri, M., & Kozubski, W. P. (2014) · Current Genomics · doi:10.2174/1389202914666131210210241 · Cited by 56
The peripheral effect of direct current stimulation on brain circuits involving memory
Vanneste, S., Mohan, A., Yoo, H. B., Huang, Y., Luckey, A. M., McLeod, S. L., Tabet, M. N., de Souza, R. R., McIntyre, C. K., Chapman, S. B., et al. (2020) · Science Advances · doi:10.1126/sciadv.aax9538 · Cited by 60
Adaptive learning is structure learning in time
Yu, L. Q., Wilson, R. C., & Nassar, M. R. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.06.024 · Cited by 60
Medial prefrontal cortex and the adaptive regulation of reinforcement learning parameters
Khamassi, M., Enel, P., Dominey, P. F., & Procyk, E. (2013) · Progress in brain research · doi:10.1016/b978-0-444-62604-2.00022-8 · Cited by 67
Supplementary Eye Field Encodes Reward Prediction Error
So, N., & Stuphorn, V. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.4419-11.2012 · Cited by 59
Llewellyn, S. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12003135 · Cited by 95
Neuromodulation as a robot controller
Cox, B., & Krichmar, J. L. (2009) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2009.933628 · Cited by 55
Electrophysiological correlates of decision making under varying levels of uncertainty
Bland, A. R., & Schaefer, A. (2011) · Brain Research · doi:10.1016/j.brainres.2011.08.031 · Cited by 58
Rommers, J., & Federmeier, K. D. (2018) · Cortex · doi:10.1016/j.cortex.2017.12.018 · Cited by 55
Age-related loss of noradrenergic neurons in the brains of triple transgenic mice
Manaye, K. F., Mouton, P. R., Xu, G., Drew, A., Lei, D.-L., Sharma, Y., Rebeck, G. W., & Turner, S. (2013) · AGE · doi:10.1007/s11357-011-9343-0 · Cited by 54
Payzan-LeNestour, É., & Bossaerts, P. L. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00150 · Cited by 54
Metadata
- Authors
- Yu, A. J., & Dayan, P.
- Affiliations
- Gatsby Computational Neuroscience Unit (GB)
- Corresponding author
- Yu, Angela J.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2005-05
- Volume
- 46
- Issue
- 4
- Pages
- 681-692
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W2154562678
- Cited by
- 1,774
- References
- 97
- Citations by year
- 2026: 111 · 2025: 127 · 2024: 105 · 2023: 115 · 2022: 105 · 2021: 121 · 2020: 118 · 2019: 103 · 2018: 83 · 2017: 91 · 2016: 81 · 2015: 66 · 2014: 108 · 2013: 76 · 2012: 85
- FWCI
- 19.61
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 2999 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- TUbilio (Technical University of Darmstadt) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- uncertainty, neuromodulation, acetylcholine, norepinephrine, attention, Bayesian inference, context switching, attentional cueing, prediction, noisy environments, learning
- MeSH terms
- Acetylcholine, Algorithms, Animals, Attention, Bayes Theorem, Computer Simulation, Dose-Response Relationship, Drug, Generalization, Psychological, Humans, Learning, Models, Neurological, Nicotine, Norepinephrine, Scopolamine, Time Factors, Likelihood Functions, Signal Detection, Psychological, Nicotinic Agonists, Muscarinic Antagonists, Maze Learning, Uncertainty, Rats
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