Cited by 1,774Neural and Behavioral Psychology StudiesNeural dynamics and brain functionMemory and Neural Mechanisms

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 access

Yu, Angela J., Dayan, Peter·2005·Neuron

DOI 10.1016/j.neuron.2005.04.026

Open this paper in CoChat — free account Open PDF

Abstract

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.

Integrity signals

Registry

Crossref · 10.1016/j.neuron.2005.04.026

Cites

97 of 97 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

The text we found for this paper looks like another document, so its claims and self-check weren’t run. The registry and citation checks still apply.

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.

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
The Cognitive neurosciences
(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
A model for Pavlovian learning: Variations in the effectiveness of conditioned but not of unconditioned stimuli.
Pearce, J. M., & Hall, G. (1980) · Psychological Review · doi:10.1037/0033-295x.87.6.532 · Cited by 3,023
The Cognitive neurosciences
(1995) · Choice Reviews Online · doi:10.5860/choice.32-5931 · Cited by 2,337

Flagged first, then by citations

Show all 97 references

Sorted by citations

Items 301–400 of 1,008

Eye pupil signals information gain
Zénon, A. (2019) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2019.1593 · Cited by 110
Pupil diameter is not an accurate real-time readout of locus coeruleus activity
Megemont, M., McBurney-Lin, J., & Yang, H. (2022) · eLife · doi:10.7554/elife.70510 · Cited by 127
Demands on response inhibition processes determine modulations of theta band activity in superior frontal areas and correlations with pupillometry – Implications for the norepinephrine system during inhibitory control
Dippel, G., Mückschel, M., Ziemssen, T., & Beste, C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.037 · Cited by 107
The Prediction of Impact of a Looming Stimulus onto the Body Is Subserved by Multisensory Integration Mechanisms
Cléry, J. C., Guipponi, O., Odouard, S., Pinède, S., Wardak, C., & Hamed, S. B. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0610-17.2017 · Cited by 111
The perception of probability.
Gallistel, C. R., Krishan, M., Liu, Y., Miller, R., & Latham, P. E. (2014) · Psychological Review · doi:10.1037/a0035232 · Cited by 114
Hierarchical decision processes that operate over distinct timescales underlie choice and changes in strategy
Purcell, B. A., & Kiani, R. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1524685113 · Cited by 106
Dopamine, Prediction Error and Beyond
Diederen, K. M. J., & Fletcher, P. C. (2021) · The Neuroscientist · doi:10.1177/1073858420907591 · Cited by 106
Exploration Disrupts Choice-Predictive Signals and Alters Dynamics in Prefrontal Cortex
Ebitz, R. B., Albarran, E., & Moore, T. (2017) · Neuron · doi:10.1016/j.neuron.2017.12.007 · Cited by 105
A postdecisional neural marker of confidence predicts information-seeking in decision-making
Desender, K., Murphy, P., Boldt, A., Verguts, T., & Yeung, N. (2019) · The Journal of Neuroscience · doi:10.1523/jneurosci.2620-18.2019 · Cited by 88
The modulatory effects of nicotine on parietal cortex activity in a cued target detection task depend on cue reliability
Giessing, C., Thiel, C. M., Rösler, F., & Fink, G. R. (2006) · Neuroscience · doi:10.1016/j.neuroscience.2005.10.005 · Cited by 104
Bayesian modeling of flexible cognitive control
Jiang, J., Heller, K., & Egner, T. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.06.001 · Cited by 112
APOE e4 polymorphism in young adults is associated with improved attention and indexed by distinct neural signatures
Rusted, J. M., Evans, S., King, S. L., Dowell, N. G., Tabet, N., & Tofts, P. S. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.010 · Cited by 109
Time Slows Down during Accidents
Arstila, V. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00196 · Cited by 108
Octopamine Neuromodulatory Effects on a Social Behavior Decision-Making Network in Drosophila Males
Certel, S. J., Leung, A. K.-W., Lin, C.-Y., Pérez, P. L., Chiang, A., & Kravitz, E. A. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013248 · Cited by 102
From relief to surprise: Dual control of epistemic curiosity in the human brain
Ligneul, R., Mermillod, M., & Morisseau, T. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.07.038 · Cited by 105
Opposite Neural Signatures of Motion-Induced Blindness in Human Dorsal and Ventral Visual Cortex
Donner, T. H., Sagi, D., Bonneh, Y. S., & Heeger, D. J. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.2371-08.2008 · Cited by 111
Organizing memories for generalization in complementary learning systems
Sun, W., Advani, M., Spruston, N., Saxe, A., & Fitzgerald, J. E. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01382-9 · Cited by 103
Heuristics, learning and the business angel investment decision-making process
Harrison, R., Mason, C. M., & Smith, D. (2015) · Entrepreneurship and Regional Development · doi:10.1080/08985626.2015.1066875 · Cited by 117
States of curiosity and interest enhance memory differently in adolescents and in children
Fandakova, Y., & Gruber, M. J. (2020) · Developmental Science · doi:10.1111/desc.13005 · Cited by 120
Statistical computations underlying the dynamics of memory updating
Gershman, S. J., Radulescu, A., Norman, K. A., & Niv, Y. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003939 · Cited by 101
Ambiguity aversion in rhesus macaques
Hayden, B. Y. (2010) · Frontiers in Neuroscience · doi:10.3389/fnins.2010.00166 · Cited by 108
Metaplasticity as a Neural Substrate for Adaptive Learning and Choice under Uncertainty
Farashahi, S. G., Donahue, C. H., Khorsand, P., Seo, H., Lee, D., & Soltani, A. (2017) · Neuron · doi:10.1016/j.neuron.2017.03.044 · Cited by 106
Thalamocortical excitability modulation guides human perception under uncertainty
Kosciessa, J. Q., Lindenberger, U., & Garrett, D. D. (2021) · Nature Communications · doi:10.1038/s41467-021-22511-7 · Cited by 102
Contextual inference in learning and memory
Heald, J. B., Lengyel, M., & Wolpert, D. M. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.10.004 · Cited by 120
Prompt but inefficient: nicotine differentially modulates discrete components of attention
Vangkilde, S., Bundesen, C., & Coull, J. T. (2011) · Psychopharmacology · doi:10.1007/s00213-011-2361-x · Cited by 108
Catecholamines and cognition after traumatic brain injury
Jenkins, P. O., Mehta, M. A., & Sharp, D. J. (2016) · Brain · doi:10.1093/brain/aww128 · Cited by 102
Evidence for Broad Versus Segregated Projections from Cholinergic and Noradrenergic Nuclei to Functionally and Anatomically Discrete Subregions of Prefrontal Cortex
Chandler, D. J., & Waterhouse, B. D. (2012) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2012.00020 · Cited by 110
Differential expression of muscarinic acetylcholine receptors across excitatory and inhibitory cells in visual cortical areas V1 and V2 of the macaque monkey
Disney, A. A., Domakonda, K. V., & Aoki, C. (2006) · The Journal of Comparative Neurology · doi:10.1002/cne.21096 · Cited by 99
Statistical learning attenuates visual activity only for attended stimuli
Richter, D., & de Lange, F. P. (2019) · eLife · doi:10.7554/elife.47869 · Cited by 110
State anxiety biases estimates of uncertainty and impairs reward learning in volatile environments
Hein, T. P., de Fockert, J., & Ruiz, M. H. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117424 · Cited by 99
Neural computations of threat
Levy, I., & Schiller, D. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.11.007 · Cited by 95
A simple model for learning in volatile environments
Piray, P., & Daw, N. D. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007963 · Cited by 104
Deterministic functions of cortical acetylcholine
Sarter, M. F., Lustig, C., Howe, W. M., Gritton, H. J., & Berry, A. S. (2014) · European Journal of Neuroscience · doi:10.1111/ejn.12515 · Cited by 104
Effects of the cholinergic agonist nicotine on reorienting of visual spatial attention and top-down attentional control
Thiel, C. M., & Fink, G. R. (2008) · Neuroscience · doi:10.1016/j.neuroscience.2007.10.061 · Cited by 97
Gender-specific neuroanatomical basis of behavioral inhibition/approach systems (BIS/BAS) in a large sample of young adults: A voxel-based morphometric investigation
Li, Y., Qiao, L., Sun, J., Wei, D., Li, W., Qiu, J., Zhang, Q., & Shi, H. (2014) · Behavioural Brain Research · doi:10.1016/j.bbr.2014.08.041 · Cited by 102
Sequential neuromodulation of Hebbian plasticity offers mechanism for effective reward-based navigation
Brzosko, Z., Zannone, S., Schultz, W., Clopath, C., & Paulsen, O. (2017) · eLife · doi:10.7554/elife.27756 · Cited by 101
Anxiety Impedes Adaptive Social Learning Under Uncertainty
Lamba, A., Frank, M. J., & FeldmanHall, O. (2020) · Psychological Science · doi:10.1177/0956797620910993 · Cited by 94
Intelligent problem-solving as integrated hierarchical reinforcement learning
Eppe, M., Gumbsch, C., Kerzel, M., Nguyen, P. D. H., Butz, M. V., & Wermter, S. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-021-00433-9 · Cited by 110
Calcium Imaging of Basal Forebrain Activity during Innate and Learned Behaviors
Harrison, T. C., Pinto, L., Brock, J. R., & Dan, Y. (2016) · Frontiers in Neural Circuits · doi:10.3389/fncir.2016.00036 · Cited by 101
Catecholamines alter the intrinsic variability of cortical population activity and perception
Pfeffer, T. A., Avrămiea, A.-E., Nolte, G., Engel, A. K., Linkenkaer‐Hansen, K., & Donner, T. H. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2003453 · Cited by 98
Representation Wars: Enacting an Armistice Through Active Inference
Constant, A., Clark, A., & Friston, K. J. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.598733 · Cited by 102
A Bayesian Model of Conditioned Perception.
Stocker, A. A., & Simoncelli, E. P. (2007) · PubMed · Cited by 98
Relating Pupil Dilation and Metacognitive Confidence during Auditory Decision-Making
Lempert, K. M., Chen, Y. L., & Fleming, S. M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0126588 · Cited by 94
Searching for an anchor in an unpredictable world: A computational model of obsessive-compulsive disorder
Fradkin, I., Adams, R. A., Parr, T., Roiser, J. P., & Huppert, J. D. (2020) · Psychological Review · doi:10.1037/rev0000188 · Cited by 89
Pattern Classification of Working Memory Networks Reveals Differential Effects of Methylphenidate, Atomoxetine, and Placebo in Healthy Volunteers
Marquand, A. F., Simoni, S. D., O’Daly, O., Williams, S., Mourão-Miranda, J., & Mehta, M. A. (2011) · Neuropsychopharmacology · doi:10.1038/npp.2011.9 · Cited by 96
Robot Cognitive Control with a Neurophysiologically Inspired Reinforcement Learning Model
Khamassi, M., Lallée, S., Enel, P., Procyk, E., & Dominey, P. F. (2011) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2011.00001 · Cited by 104
Learning Reward Uncertainty in the Basal Ganglia
Mikhael, J. G., & Bogacz, R. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005062 · Cited by 99
Going the Extra Mile: Effects of Discourse Context on Two Late Positivities During Language Comprehension
Brothers, T. A., Wlotko, E. W., Warnke, L., & Kuperberg, G. R. (2020) · Neurobiology of Language · doi:10.1162/nol_a_00006 · Cited by 104
An in vivo probabilistic atlas of the human locus coeruleus at ultra-high field
Ye, R., Rua, C., O’Callaghan, C., Jones, P. S., Hezemans, F. H., Kaalund, S. S., Tsvetanov, K. A., Rodgers, C. T., Williams, G. B., Passamonti, L., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117487 · Cited by 97
The locus coeruleus broadcasts prediction errors across the cortex to promote sensorimotor plasticity
Jordan, R., & Keller, G. B. (2023) · eLife · doi:10.7554/elife.85111 · Cited by 127
Projection specificity in heterogeneous locus coeruleus cell populations: implications for learning and memoryPreprint
Uematsu, A., Tan, B. Z., & Johansen, J. P. (2015) · Learning & Memory · doi:10.1101/lm.037283.114 · Cited by 89
BIS and BAS correlates with psychophysiological and cortical response systems during aversive and appetitive emotional stimuli processing
Balconi, M., Falbo, L., & Conte, V. A. (2011) · Motivation and Emotion · doi:10.1007/s11031-011-9244-7 · Cited by 94
Mapping collective behavior in the big-data era
Bentley, R. A., O’Brien, M. J., & Brock, W. A. (2014) · Behavioral and Brain Sciences · doi:10.1017/s0140525x13000289 · Cited by 95
Understanding the buffering effect of social media use on anxiety during the COVID-19 pandemic lockdown
Marzouki, Y., Aldossari, F. S., & Veltri, G. (2021) · Humanities and Social Sciences Communications · doi:10.1057/s41599-021-00724-x · Cited by 94
Evidences from Rewarding System, FRN and P300 Effect in Internet-Addiction in Young People
Balconi, M., Venturella, I., & Finocchiaro, R. (2017) · Brain Sciences · doi:10.3390/brainsci7070081 · Cited by 96
Affective bias as a rational response to the statistics of rewards and punishments
Pulcu, E., & Browning, M. D. (2017) · eLife · doi:10.7554/elife.27879 · Cited by 88
Pupil-linked phasic arousal evoked by violation but not emergence of regularity within rapid sound sequences
Zhao, S., Maria Chait, A., Dick, F., Dayan, P., Furukawa, S., & Liao, H. (2019) · Nature Communications · doi:10.1038/s41467-019-12048-1 · Cited by 104
Self-citation
Long-term expression of human contextual fear and extinction memories involves amygdala, hippocampus and ventromedial prefrontal cortex: a reinstatement study in two independent samples
Lonsdorf, T. B., Haaker, J., & Kalisch, R. (2014) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsu018 · Cited by 89
Trigeminal, Visceral and Vestibular Inputs May Improve Cognitive Functions by Acting through the Locus Coeruleus and the Ascending Reticular Activating System: A New Hypothesis
Cicco, V. D., Fantozzi, M. P. T., Cataldo, E., Barresi, M., Bruschini, L., Faraguna, U., & Manzoni, D. (2018) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2017.00130 · Cited by 95
Adaptive circuit dynamics across human cortex during evidence accumulation in changing environments
Murphy, P. R., Wilming, N., Hernandez-Bocanegra, D. C., Prat-Ortega, G., & Donner, T. H. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00839-z · Cited by 105
A cholinergic feedback circuit to regulate striatal population uncertainty and optimize reinforcement learning
Franklin, N. T., & Frank, M. J. (2015) · eLife · doi:10.7554/elife.12029 · Cited by 88
Brain substrates of behavioral programs associated with self-regulation
Tops, M. (2010) · Frontiers in Psychology · doi:10.3389/fpsyg.2010.00152 · Cited by 89
Locus coeruleus activation accelerates perceptual learning
Glennon, E., Carcea, I., Martins, A. R. O., Multani, J., Shehu, I., Svirsky, M. A., & Froemke, R. C. (2018) · Brain Research · doi:10.1016/j.brainres.2018.05.048 · Cited by 92
How Outcome Uncertainty Mediates Attention, Learning, and Decision-Making
Monosov, I. E. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.06.009 · Cited by 95
The Computational, Pharmacological, and Physiological Determinants of Sensory Learning under Uncertainty
Lawson, R. P., Bisby, J. A., Nord, C. L., Burgess, N., & Rees, G. E. (2020) · Current Biology · doi:10.1016/j.cub.2020.10.043 · Cited by 88
Reward Sensitivity, Decisional Bias, and Metacognitive Deficits in Cocaine Drug Addiction
Balconi, M., Finocchiaro, R., & Campanella, S. (2014) · Journal of Addiction Medicine · doi:10.1097/adm.0000000000000065 · Cited by 91
Uncertainty Makes Me Emotional: Uncertainty as an Elicitor and Modulator of Emotional States
Morriss, J., Tupitsa, E., Dodd, H. F., & Hirsch, C. R. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.777025 · Cited by 98
Electroencephalographic correlates of target and outcome errors
Krigolson, O. E., Holroyd, C. B., Gyn, G. H. V., & Heath, M. (2008) · Experimental Brain Research · doi:10.1007/s00221-008-1482-x · Cited by 91
FRN and P300 ERP effect modulation in response to feedback sensitivity: The contribution of punishment-reward system (BIS/BAS) and Behaviour Identification of action
Balconi, M., & Crivelli, D. (2009) · Neuroscience Research · doi:10.1016/j.neures.2009.10.011 · Cited by 93
A brain basis for musical hallucinations
Kumar, S., Sedley, W., Barnes, G. R., Teki, S., Friston, K. J., & Griffiths, T. D. (2013) · Cortex · doi:10.1016/j.cortex.2013.12.002 · Cited by 89
Acute psilocybin enhances cognitive flexibility in rats
Pacheco, A. T., Olson, R. J., Garza, G., & Moghaddam, B. (2023) · Neuropsychopharmacology · doi:10.1038/s41386-023-01545-z · Cited by 94
How can a Bayesian approach inform neuroscience?
O’Reilly, J. X., Jbabdi, S., & Behrens, T. E. J. (2012) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2012.08010.x · Cited by 98
Making Decisions with Unknown Sensory Reliability
Denève, S. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00075 · Cited by 91
Handbook of Reward and Decision Making
Dreher, J., & Tremblay, L. (2009) · Elsevier eBooks · doi:10.1016/b978-0-12-374620-7.x0001-5 · Cited by 88
Confidence in beliefs about pain predicts expectancy effects on pain perception and anticipatory processing in right anterior insula
Brown, C. A., Seymour, B., El-Deredy, W., & Jones, A. K. P. (2008) · Pain · doi:10.1016/j.pain.2008.04.028 · Cited by 83
Precision and False Perceptual Inference
Parr, T., Benrimoh, D. A., Vincent, P., & Friston, K. J. (2018) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2018.00039 · Cited by 87
Safety Needs Mediate Stressful Events Induced Mental Disorders
Zheng, Z., Gu, S., Yu, L., Lu, S., Wang, W., Li, Y., & Wang, F. (2016) · Neural Plasticity · doi:10.1155/2016/8058093 · Cited by 87
Modulation of Hippocampal Circuits by Muscarinic and Nicotinic Receptors
Dannenberg, H., Young, K., & Hasselmo, M. E. (2017) · Frontiers in Neural Circuits · doi:10.3389/fncir.2017.00102 · Cited by 89
Not All Predictions Are Equal: “What” and “When” Predictions Modulate Activity in Auditory Cortex through Different Mechanisms
Auksztulewicz, R., Schwiedrzik, C. M., Thesen, T., Doyle, W., Devinsky, O., Nobre, A. C., Schroeder, C. E., Friston, K. J., & Melloni, L. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.0369-18.2018 · Cited by 83
Rapid Brain Responses Independently Predict Gain Maximization and Loss Minimization during Economic Decision Making
Martín, R. S., Appelbaum, L. G., Pearson, J. M., Huettel, S. A., & Woldorff, M. G. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.4242-12.2013 · Cited by 90
Noradrenaline blockade specifically enhances metacognitive performance
Hauser, T. U., Allen, M., Purg, N., Moutoussis, M., Rees, G., & Dolan, R. J. (2017) · eLife · doi:10.7554/elife.24901 · Cited by 87
Dopaminergic and cholinergic modulations of visual-spatial attention and working memory: Insights from molecular genetic research and implications for adult cognitive development.
Störmer, V. S., Passow, S., Biesenack, J., & Li, S. (2011) · Developmental Psychology · doi:10.1037/a0026198 · Cited by 90
Scaling prediction errors to reward variability benefits error-driven learning in humans
Diederen, K. M. J., & Schultz, W. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00483.2015 · Cited by 69
Computational models of adaptive behavior and prefrontal cortex
Soltani, A., & Koechlin, É. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01123-1 · Cited by 86
The grammar of visual narrative: Neural evidence for constituent structure in sequential image comprehension
Cohn, N., Jackendoff, R. S., Holcomb, P. J., & Kuperberg, G. R. (2014) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2014.09.018 · Cited by 140
Effects of acute administration of nicotine, amphetamine, diazepam, morphine, and ethanol on risky decision-making in rats
Mitchell, M. R., Vokes, C. M., Blankenship, A. L., Simon, N. W., & Setlow, B. (2011) · Psychopharmacology · doi:10.1007/s00213-011-2363-8 · Cited by 86
Integration of Reinforcement Learning and Optimal Decision-Making Theories of the Basal Ganglia
Bogacz, R., & Larsen, T. (2011) · Neural Computation · doi:10.1162/neco_a_00103 · Cited by 86
Frontopolar cortex and decision-making efficiency: comparing brain activity of experts with different professional background during an exploration-exploitation task
Martinez, D. L., Canessa, N., Brusoni, S., Zollo, M., Hare, T. A., Alemanno, F., & Cappa, S. F. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00927 · Cited by 84
Stress attenuates the flexible updating of aversive value
Raio, C. M., Hartley, C. A., Orederu, T. A., Li, J., & Phelps, E. A. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1702565114 · Cited by 84
State-Dependent Performance of Optic-Flow Processing Interneurons
Longden, K. D., & Krapp, H. G. (2009) · Journal of Neurophysiology · doi:10.1152/jn.00395.2009 · Cited by 79
An evolutionary computational theory of prefrontal executive function in decision-making
Koechlin, É. (2014) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2013.0474 · Cited by 94
Embodied inference: or "I think therefore I am, if I am what I think"
Friston, K. J. (2011) · Cited by 84
Behavioral and Neural Effects of Nicotine on Visuospatial Attentional Reorienting in Non-Smoking Subjects
Vossel, S., Thiel, C. M., & Fink, G. R. (2008) · Neuropsychopharmacology · doi:10.1038/sj.npp.1301469 · Cited by 81
Contextual influence on confidence judgments in human reinforcement learning
Lebreton, M. P., Bacily, K., Palminteri, S., & Engelmann, J. B. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006973 · Cited by 91
Reframing PTSD for computational psychiatry with the active inference framework
Linson, A., & Friston, K. (2019) · Cognitive Neuropsychiatry · doi:10.1080/13546805.2019.1665994 · Cited by 81
A predictive account of how novelty influences declarative memory
Quent, J. A., Henson, R. N., & Greve, A. (2021) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2021.107382 · Cited by 84
The Computational and Neural Bases of Context-Dependent Learning
Heald, J. B., Wolpert, D. M., & Lengyel, M. (2023) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-092322-100402 · Cited by 86
Stochastic variational learning in recurrent spiking networks
Rezende, D. J., & Gerstner, W. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00038 · Cited by 80
The P300, the LPP, context updating, and memory: What is the functional significance of the emotion-related late positive potential?
Fields, E. C. (2023) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2023.08.005 · Cited by 92
Disentangling signal and noise in autism spectrum disorder
de Cruys, S. V., der Hallen, R. V., & Wagemans, J. P. (2016) · Brain and Cognition · doi:10.1016/j.bandc.2016.08.004 · Cited by 83

Authors: Yu, A. J., & Dayan, P.
Affiliations: Gatsby Computational Neuroscience Unit (GB)
Corresponding author: Yu, Angela J.
ORCID: Yu, Angela J.
Dayan, Peter
Venue: Neuron
Publisher: Elsevier BV
Published: 2005-05
Volume: 46
Issue: 4
Pages: 681-692
ISSN: 0896-6273 (ISSN-L)
DOI: 10.1016/j.neuron.2005.04.026
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

Uncertainty, Neuromodulation, and Attention

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks