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 601–700 of 1,008

Investigating the Prospective Sense of Agency: Effects of Processing Fluency, Stimulus Ambiguity, and Response Conflict
Sidarus, N., Vuorre, M., Metcalfe, J., & Haggard, P. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.00545 · Cited by 40
Complementary contributions of basolateral amygdala and orbitofrontal cortex to value learning under uncertainty
Stolyarova, A., & Izquierdo, A. (2017) · eLife · doi:10.7554/elife.27483 · Cited by 39
Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making
He, X., Modirshanechi, A., Lehmann, M. P., Wulfram Gerstner, B., & Herzog, M. H. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009070 · Cited by 49
Informing deep neural networks by multiscale principles of neuromodulatory systems
Mei, J., Muller, E. B., & Ramaswamy, S. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2021.12.008 · Cited by 47
Challenges of Interpreting Frontal Neurons during Value-Based Decision-Making
Wallis, J. D., & Rich, E. L. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00124 · Cited by 41
Local plasticity rules can learn deep representations using self-supervised contrastive predictionsPreprint
Illing, B., Ventura, J., Bellec, G., & Wulfram Gerstner, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.08262 · Cited by 42
Noradrenergic and Cholinergic Modulation of Belief Updating
Jepma, M., Brown, S. B. R. E., Murphy, P. R., Koelewijn, S. C., de Vries, B., van den Maagdenberg, A. M., & Nieuwenhuis, S. (2018) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01317 · Cited by 39
Nicotinic alteration of decision-making
Naudé, J., Dongelmans, M., & Faure, P. (2015) · Neuropharmacology · doi:10.1016/j.neuropharm.2014.11.021 · Cited by 39
Electroencephalographic correlates of temporal Bayesian belief updating and surprise
Visalli, A., Capizzi, M., Ambrosini, E., Kopp, B., & Vallesi, A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117867 · Cited by 44
Bayesian Surprise Predicts Human Event Segmentation in Story Listening
Kumar, M., Goldstein, A., Michelmann, S., Zacks, J. M., Hasson, U., & Norman, K. A. (2023) · Cognitive Science · doi:10.1111/cogs.13343 · Cited by 51
Altered Neural Processing of the Need to Stop in Young Adults at Risk for Stimulant Dependence
Harlé, K. M., Shenoy, P., Stewart, J. L., Tapert, S. F., Yu, A. J., & Paulus, M. P. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.2297-13.2014 · Cited by 39
Self-citation
Neural Evidence for Adaptive Strategy Selection in Value-Based Decision-Making
Gluth, S., Rieskamp, J., & Büchel, C. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht049 · Cited by 41
Neuromodulation and a Reconceptualization of Autism Spectrum Disorders: Using the Locus Coeruleus Functioning as an Exemplar
London, E. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.01120 · Cited by 40
Context-dependent control of behavior in Drosophila
Oram, T. B., & Card, G. M. (2022) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2022.02.003 · Cited by 41
Response Flexibility: The Role of the Lateral Habenula
Hones, V. I., & Mizumori, S. J. Y. (2022) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2022.852235 · Cited by 41
Shared Mechanisms for Confidence Judgements and Error Detection in Human Decision Making
Yeung, N., & Summerfield, C. (2014) · Springer eBooks · doi:10.1007/978-3-642-45190-4_7 · Cited by 38
Expected Reward Value and Reward Uncertainty Have Temporally Dissociable Effects on Memory Formation
Stanek, J. K., Dickerson, K. C., Chiew, K. S., Clement, N. J., & Adcock, R. A. (2019) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01411 · Cited by 38
A flexible and generalizable model of online latent-state learning
Cochran, A. L., & Cisler, J. M. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007331 · Cited by 37
Bayesian Filtering with Multiple Internal Models: Toward a Theory of Social Intelligence
Isomura, T., Parr, T., & Friston, K. J. (2019) · Neural Computation · doi:10.1162/neco_a_01239 · Cited by 43
Learning in Volatile Environments With the Bayes Factor Surprise
Liakoni, V., Modirshanechi, A., Wulfram Gerstner, B., & Brea, J. (2021) · Neural Computation · doi:10.1162/neco_a_01352 · Cited by 40
Stochastic reinforcement benefits skill acquisitionPreprint
Dayan, E., Averbeck, B. B., Richmond, B. J., & Cohen, L. G. (2014) · Learning & Memory · doi:10.1101/lm.032417.113 · Cited by 39
Uncertainty alters the balance between incremental learning and episodic memory
Nicholas, J., Daw, N. D., & Shohamy, D. (2022) · eLife · doi:10.7554/elife.81679 · Cited by 42
Neurogenetic Variations in Norepinephrine Availability Enhance Perceptual Vividness
Todd, R. M., Ehlers, M. R., Müller, D. J., Robertson, A., Palombo, D. J., Freeman, N., Levine, B., & Anderson, A. K. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.4489-14.2015 · Cited by 41
A computational model of altered gait patterns in parkinson's disease patients negotiating narrow doorways
Muralidharan, V. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00190 · Cited by 38
Visual Prediction Error Spreads Across Object Features in Human Visual Cortex
Jiang, J., Summerfield, C., & Egner, T. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.1546-16.2016 · Cited by 38
Tonic noradrenergic activity modulates explorative behavior and attentional set shifting: Evidence from pupillometry and gaze pattern analysis
Pajkossy, P., Szöllősi, Á., Demeter, G., & Racsmány, M. (2017) · Psychophysiology · doi:10.1111/psyp.12964 · Cited by 38
Individual Neurons in the Cingulate Cortex Encode Action Monitoring, Not Selection, during Adaptive Decision-Making
Li, Y., Nassar, M. R., Kable, J. W., & Gold, J. I. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.0159-19.2019 · Cited by 37
Acute stress enhances tolerance of uncertainty during decision-making
Byrne, K. A., Peters, C., Willis, H. C., Phan, D., Cornwall, A. C., & Worthy, D. A. (2020) · Cognition · doi:10.1016/j.cognition.2020.104448 · Cited by 41
Estimates of locus coeruleus function with functional magnetic resonance imaging are influenced by localization approaches and the use of multi-echo data
Turker, H. B., Riley, E., Luh, W., Colcombe, S. J., & Swallow, K. M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118047 · Cited by 42
State anxiety alters the neural oscillatory correlates of predictions and prediction errors during reward-based learning
Hein, T. P., & Ruiz, M. H. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118895 · Cited by 37
Smoking and the bandit: A preliminary study of smoker and nonsmoker differences in exploratory behavior measured with a multiarmed bandit task.
Addicott, M. A., Pearson, J. M., Wilson, J., Platt, M. L., & McClernon, F. J. (2012) · Experimental and Clinical Psychopharmacology · doi:10.1037/a0030843 · Cited by 39
Noradrenergic but not cholinergic modulation of olfactory bulb during processing of near threshold concentration stimuli.
Escanilla, O., Alperin, S., Youssef, M., Ennis, M. J., & Linster, C. (2012) · Behavioral Neuroscience · doi:10.1037/a0030006 · Cited by 37
Neuromodulatory role of acetylcholine in visually-induced cortical activation: Behavioral and neuroanatomical correlates
Dotigny, F., Amor, A. Y. B., Burke, M., & Vaucher, E. (2008) · Neuroscience · doi:10.1016/j.neuroscience.2008.04.030 · Cited by 37
A pavlovian model of the amygdala and its influence within the medial temporal lobe
Carrere, M., & Alexandre, F. (2015) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00041 · Cited by 38
Optimal configurations of spatial scale for grid cell firing under noise and uncertainty
Towse, B. W., Barry, C., Bush, D., & Burgess, N. (2013) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2013.0290 · Cited by 38
Active Inference, Belief Propagation, and the Bethe Approximation
Schwöbel, S., Kiebel, S. J., & Marković, D. (2018) · Neural Computation · doi:10.1162/neco_a_01108 · Cited by 37
Increased random exploration in schizophrenia is associated with inflammation
Cathomas, F., Klaus, F., Guetter, K., Chung, H.-K., Beharelle, A. R., Spiller, T. R., Schlegel, R., Seifritz, E., Hartmann-Riemer, M. N., Tobler, P. N., et al. (2021) · Schizophrenia · doi:10.1038/s41537-020-00133-0 · Cited by 38
Prediction learning in adults with autism and its molecular correlates
Sapey‐Triomphe, L., Temmerman, J., Puts, N. A. J., & Wagemans, J. P. (2021) · Molecular Autism · doi:10.1186/s13229-021-00470-6 · Cited by 38
Measures of tonic and phasic activity of the locus coeruleus—norepinephrine system in children with autism spectrum disorder: An event‐related potential and pupillometry study
Kim, Y., Kadlaskar, G., Keehn, R. M., & Keehn, B. (2022) · Autism Research · doi:10.1002/aur.2820 · Cited by 35
Attentional Disengagement and the Locus Coeruleus – Norepinephrine System in Children With Autism Spectrum Disorder
Keehn, B., Kadlaskar, G., Bergmann, S., McNally Keehn, R., & Francis, A. (2021) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2021.716447 · Cited by 35
Bayesian Models of Brain and Behaviour
Penny, W. D. (2012) · ISRN Biomathematics · doi:10.5402/2012/785791 · Cited by 38
Internal signal correlates neural populations and biases perceptual decision reports
Carnevale, F., de Lafuente, V., Romo, R., & Parga, N. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1216799109 · Cited by 35
Simulation of cholinergic and noradrenergic modulation of behavior in uncertain environments
Avery, M. C., Nitz, D. A., Chiba, A. A., & Krichmar, J. L. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00005 · Cited by 36
Neural and neurochemical basis of reinforcement-guided decision making
Khani, A., & Rainer, G. (2016) · Journal of Neurophysiology · doi:10.1152/jn.01113.2015 · Cited by 38
Regulation of evidence accumulation by pupil-linked arousal processes
Keung, W., Hagen, T. A., & Wilson, R. C. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0551-4 · Cited by 48
A Neuroeconomic Framework for Creative Cognition
Lin, H., & Vartanian, O. A. (2018) · Perspectives on Psychological Science · doi:10.1177/1745691618794945 · Cited by 43
Abnormal dynamic functional connectivity of hippocampal subregions associated with working memory impairment in melancholic depression
Shunkai, L., Su, T., Zhong, S., Chen, G., Zhang, Y., Zhao, H., Chen, P., Tang, G., Qi, Z., He, J., et al. (2021) · Psychological Medicine · doi:10.1017/s0033291721004906 · Cited by 37
Reward-Related Activity in Ventral Striatum Is Action Contingent and Modulated by Behavioral Relevance
FitzGerald, T. H. B., Schwartenbeck, P., & Dolan, R. J. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.4389-13.2014 · Cited by 36
Micro-analysis of infant looking in a naturalistic social setting: insights from biologically based models of attention
de Barbaro, K., Chiba, A., & Deák, G. O. (2011) · Developmental Science · doi:10.1111/j.1467-7687.2011.01066.x · Cited by 34
Pain anticipation recruits the mesolimbic system and differentially modulates subsequent recognition memory
Bauch, E. M., Rausch, V. H., & Bunzeck, N. (2014) · Human Brain Mapping · doi:10.1002/hbm.22497 · Cited by 36
The Role of Dopamine in Motor Flexibility
Bestmann, S., Ruge, D., Rothwell, J. C., & Galea, J. M. (2014) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00706 · Cited by 34
Spatial Attention and Temporal Expectation Under Timed Uncertainty Predictably Modulate Neuronal Responses in Monkey V1
Sharma, J., 杉原, 弘., Yarden Katz, A., Schummers, J., Tenenbaum, J. B., & Sur, M. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhu086 · Cited by 37
Blocking muscarinic receptors in the olfactory bulb impairs performance on an olfactory short-term memory task
Devore, S., Manella, L. C., & Linster, C. (2012) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2012.00059 · Cited by 34
Uncertainty in learning, choice, and visual fixation
Stojić, H., Orquin, J. L., Dayan, P., Dolan, R. J., & Speekenbrink, M. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1911348117 · Cited by 34
Self-citation
Facilitating the propagation of spiking activity in feedforward networks by including feedback
Rezaei, H., Aertsen, A., Kumar, A., & Valizadeh, A. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008033 · Cited by 33
Sustained Pupil Responses Are Modulated by Predictability of Auditory Sequences
Milne, A. E., Zhao, S., Tampakaki, C., Bury, G., & Maria Chait, A. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.2879-20.2021 · Cited by 42
Adaptive Learning through Temporal Dynamics of State Representation
Razmi, N., & Nassar, M. R. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.0387-21.2022 · Cited by 32
The Construction of Semantic Memory: Grammar-Based Representations Learned from Relational Episodic Information
Battaglia, F. P., & Pennartz, C. M. A. (2011) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2011.00036 · Cited by 38
Effects of the noradrenergic agonist clonidine on temporal and spatial attention
Nieuwenhuis, S., van Nieuwpoort, I. C., Veltman, D. J., & Drent, M. L. (2007) · Psychopharmacology · doi:10.1007/s00213-007-0770-7 · Cited by 34
A Computational Analysis of Aberrant Delay Discounting in Psychiatric Disorders
Story, G. W., Moutoussis, M., & Dolan, R. J. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01948 · Cited by 35
Nonaggressive and adapted social cognition is controlled by the interplay between noradrenergic and nicotinic receptor mechanisms in the prefrontal cortex
Coura, R. S., Cressant, A., Xia, J., de Chaumont, F., Olivo‐Marin, J. C., Pelloux, Y., Dalley, J. W., & Granon, S. (2013) · The FASEB Journal · doi:10.1096/fj.13-231084 · Cited by 33
Social anxiety and dynamic social reinforcement learning in a volatile environment
Beltzer, M. L., Adams, S., Beling, P. A., & Teachman, B. A. (2019) · Clinical Psychological Science · doi:10.1177/2167702619858425 · Cited by 32
Personality correlates (BAS-BIS), self-perception of social ranking, and cortical (alpha frequency band) modulation in peer-group comparison
Balconi, M., & Pagani, S. (2014) · Physiology & Behavior · doi:10.1016/j.physbeh.2014.05.043 · Cited by 33
Doubting what you already know: Uncertainty regarding state transitions is associated with obsessive compulsive symptoms
Fradkin, I., Ludwig, C. J. H., Eldar, E., & Huppert, J. D. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007634 · Cited by 35
Uncertain instruction: effects on curiosity, learning, and transfer
Lamnina, M., & Chase, C. C. (2021) · Instructional Science · doi:10.1007/s11251-021-09557-2 · Cited by 31
Pupil diameter tracked during motor adaptation in humans
Yokoi, A., & Weiler, J. (2022) · Journal of Neurophysiology · doi:10.1152/jn.00021.2022 · Cited by 35
Humans and Insects Decide in Similar Ways
Louâpre, P., van Alphen, J. J. M., & Pierre, J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0014251 · Cited by 36
Dynamics of Prefrontal and Cingulate Activity during a Reward-Based Logical Deduction Task
Landmann, C., Dehaene, S., Pappatà, S., Jobert, A., Bottlaender, M., Roumenov, D., & Bihan, D. L. (2006) · Cerebral Cortex · doi:10.1093/cercor/bhk028 · Cited by 35
Multiple Mechanisms for Processing Reward Uncertainty in the Primate Basal Forebrain
Ledbetter, N. M., Chen, C. D., & Monosov, I. E. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.1123-16.2016 · Cited by 34
Flexible updating of dynamic knowledge structures
Richter, F. R., Bays, P. M., Jeyarathnarajah, P., & Simons, J. S. (2019) · Scientific Reports · doi:10.1038/s41598-019-39468-9 · Cited by 33
Event-Related Desynchronization/Synchronization in Spinocerebellar Ataxia Type 3
Aoh, Y., Hsiao, H.-J., Lu, M., Macerollo, A., Huang, H., Hamada, M., Tsai, C., & Chen, J. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00822 · Cited by 35
Pupil size as a window on neural substrates of cognitionPreprint
Joshi, S., & Gold, J. I. (2019) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/dvsme · Cited by 52
Visual hallucinations in Parkinson’s disease: spotlight on central cholinergic dysfunction
Ignatavicius, A., Matar, E., & Lewis, S. J. G. (2025) · Brain · doi:10.1093/brain/awae289 · Cited by 32
Deficient reinforcement learning in medial frontal cortex as a model of dopamine-related motivational deficits in ADHD
Silvetti, M., Wiersema, J. R., Sonuga-Barke, E. J. S., & Verguts, T. (2013) · Neural Networks · doi:10.1016/j.neunet.2013.05.008 · Cited by 36
Older adults fail to form stable task representations during model-based reversal inference
Hämmerer, D., Schwartenbeck, P., Gallagher, M., FitzGerald, T. H. B., Düzel, E., & Dolan, R. J. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.10.009 · Cited by 28
Neural arbitration between social and individual learning systems
Diaconescu, A. O., Stecy, M., Kasper, L., Burke, C. J., Nagy, Z., Mathys, C. D., & Tobler, P. N. (2020) · eLife · doi:10.7554/elife.54051 · Cited by 32
Bilingualism: A Neurocognitive Exercise in Managing Uncertainty
Gullifer, J. W., & Titone, D. A. (2021) · Neurobiology of Language · doi:10.1162/nol_a_00044 · Cited by 36
A Reinforcement Meta-Learning Framework of Executive Function and Information Demand
Silvetti, M., Lasaponara, S., Daddaoua, N., Horan, M., & Gottlieb, J. P. (2021) · Neural Networks · doi:10.1016/j.neunet.2022.10.004 · Cited by 30
Opioid withdrawal results in an increased local and remote functional connectivity at EEG alpha and beta frequency bands
Fingelkurts, A. A., Fingelkurts, A. A., Kivisaari, R., Autti, T. H., Борисов, С., Puuskari, V., Jokela, O., & Kähkönen, S. (2007) · Neuroscience Research · doi:10.1016/j.neures.2007.01.011 · Cited by 33
Thorndike's Law 2.0: Dopamine and the Regulation of Thrift
Beeler, J. A. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00116 · Cited by 40
Optimality and Some of Its Discontents: Successes and Shortcomings of Existing Models for Binary Decisions
Holmes, P. J., & Cohen, J. D. (2014) · Topics in Cognitive Science · doi:10.1111/tops.12084 · Cited by 33
Preparing humanity for change and artificial intelligencePreprint
Laukkonen, R., Biddell, H., & Gallagher, R. (2019) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/g5qwc · Cited by 37
Scopolamine and amphetamine produce similar decision-making deficits on a rat gambling task via independent pathways
Silveira, M. M., Malcolm, E., Shoaib, M., & Winstanley, C. A. (2015) · Behavioural Brain Research · doi:10.1016/j.bbr.2014.12.029 · Cited by 31
Predictive sensorimotor control in autism
Arthur, T., Vine, S., Brosnan, M., & Buckingham, G. (2020) · Brain · doi:10.1093/brain/awaa243 · Cited by 29
Arousal state affects perceptual decision-making by modulating hierarchical sensory processing in a large-scale visual system model
Sörensen, L. K. A., Bohté, S. M., Slagter, H. A., & Scholte, H. S. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009976 · Cited by 35
Sequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule
Saponati, M., & Vinck, M. A. (2023) · Nature Communications · doi:10.1038/s41467-023-40651-w · Cited by 35
Visual learning for perceptual and categorical decisions in the human brain
Kourtzi, Z. (2009) · Vision Research · doi:10.1016/j.visres.2009.09.025 · Cited by 33
Is Task-Irrelevant Learning Really Task-Irrelevant?
Seitz, A. R., & Watanabe, T. (2008) · PLoS ONE · doi:10.1371/journal.pone.0003792 · Cited by 31
Could LC-NE-Dependent Adjustment of Neural Gain Drive Functional Brain Network Reorganization?
Guedj, C., David Meunier, A., Meunier, M., & Hadj‐Bouziane, F. (2017) · Neural Plasticity · doi:10.1155/2017/4328015 · Cited by 29
The Influence of Feedback on Task-Switching Performance: A Drift Diffusion Modeling Account
Hoffing, R. C., Karvelis, P., Rupprechter, S., Seriès, P., & Seitz, A. R. (2018) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2018.00001 · Cited by 31
What is dopamine doing in model-based reinforcement learning?
Akam, T., & Walton, M. E. (2020) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2020.10.010 · Cited by 36
Rational inattention and tonic dopamine
Mikhael, J. G., Lai, L., & Gershman, S. J. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008659 · Cited by 33
Effects of novelty on activity of lateral and medial prefrontal neurons.
Matsumoto, M., Matsumoto, K., & Tanaka, K. (2007) · Neurosciences research · doi:10.1016/j.neures.2006.10.017 · Cited by 27
Sequences learned without awareness can orient attention during the perception of human activity
Swallow, K. M., & Zacks, J. M. (2008) · Psychonomic Bulletin & Review · doi:10.3758/pbr.15.1.116 · Cited by 29
Anticipatory emotions in decision tasks: Covert markers of value or attentional processes?
Davis, T. H., Love, B. C., & Maddox, W. T. (2009) · Cognition · doi:10.1016/j.cognition.2009.04.002 · Cited by 31
Expecting the unexpected: a review of learning under uncertainty across development
Topel, S., Ma, I., Sleutels, J. J. M., van Steenbergen, H., de Bruijn, E. R. A., & van Duijvenvoorde, A. C. K. (2023) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-023-01098-0 · Cited by 37
Using brain potentials to understand prism adaptation: the error-related negativity and the P300
MacLean, S. J., Hassall, C. D., Ishigami, Y., Krigolson, O. E., & Eskes, G. A. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00335 · Cited by 35
Evaluation of ambiguous associations in the amygdala by learning the structure of the environment
Madarász, T. J., Díaz-Mataix, L., Akhand, O., Ycu, E. A., LeDoux, J. E., & Johansen, J. P. (2016) · Nature Neuroscience · doi:10.1038/nn.4308 · Cited by 31
The control of tonic pain by active relief learning
Zhang, S., Mano, H., Lee, M., Yoshida, W., Kawato, M., Robbins, T. W., & Seymour, B. (2018) · eLife · doi:10.7554/elife.31949 · Cited by 37
Two distinct profiles of fMRI and neurophysiological activity elicited by acetylcholine in visual cortex
Zaldivar, D., Rauch, A., Logothetis, N. K., & Goense, J. B. M. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1808507115 · Cited by 27

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