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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.
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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
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The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120
The reorienting system of the human brain: from environment to theory of mind
Corbetta, M., Patel, G., & Shulman, G. L. (2008) · Neuron · doi:10.1016/j.neuron.2008.04.017 · Cited by 3,889
Daw, N. D., Niv, Y., & Dayan, P. (2005) · Nature Neuroscience · doi:10.1038/nn1560 · Cited by 2,533
Self-citationLearning the value of information in an uncertain world
Behrens, T. E. J., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. S. (2007) · Nature Neuroscience · doi:10.1038/nn1954 · Cited by 2,053
Inhibition and impulsivity: behavioral and neural basis of response control
Bari, A., & Robbins, T. W. (2013) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2013.06.005 · Cited by 1,921
The free-energy principle: a rough guide to the brain?
Friston, K. (2009) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2009.04.005 · Cited by 1,831
The locus coeruleus and noradrenergic modulation of cognition
Sara, S. J. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2573 · Cited by 1,848
Joshi, S., Li, Y., Kalwani, R. M., & Gold, J. I. (2016) · Neuron · doi:10.1016/j.neuron.2015.11.028 · Cited by 1,387
Perceiving is believing: a Bayesian approach to explaining the positive symptoms of schizophrenia
Fletcher, P. C., & Frith, C. D. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2536 · Cited by 1,462
An opportunity cost model of subjective effort and task performance
Kurzban, R., Duckworth, A., Kable, J. W., & Myers, J. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12003196 · Cited by 1,381
Attention, uncertainty, and free-energy
Feldman, H., & Friston, K. J. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00215 · Cited by 1,365
A free energy principle for the brain
Friston, K., Kilner, J., & Harrison, L. (2006) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2006.10.001 · Cited by 1,369
Acetylcholine as a Neuromodulator: Cholinergic Signaling Shapes Nervous System Function and Behavior
Picciotto, M. R., Higley, M. J., & Mineur, Y. S. (2012) · Neuron · doi:10.1016/j.neuron.2012.08.036 · Cited by 1,404
Spectral fingerprints of large-scale neuronal interactions
Siegel, M., Donner, T. H., & Engel, A. K. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3137 · Cited by 1,295
REBUS and the anarchic brain: Toward a unified model of the brain action of psychedelics
Carhart-Harris, R. L., & Friston, K. J. (2019) · Pharmacological Reviews · doi:10.1124/pr.118.017160 · Cited by 1,180
Dysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring
Stephan, K. E., Friston, K. J., & Frith, C. D. (2009) · Schizophrenia Bulletin · doi:10.1093/schbul/sbn176 · Cited by 1,134
Cohen, J. D., McClure, S. M., & Yu, A. J. (2007) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2007.2098 · Cited by 1,121
Self-citationMedial prefrontal cortex as an action-outcome predictor
Alexander, W. H., & Brown, J. W. (2011) · Nature Neuroscience · doi:10.1038/nn.2921 · Cited by 1,000
Orienting and reorienting: the locus coeruleus mediates cognition through arousal
Sara, S. J., & Bouret, S. (2012) · Neuron · doi:10.1016/j.neuron.2012.09.011 · Cited by 1,056
Hierarchical Models in the Brain
Friston, K. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000211 · Cited by 952
“The physiological aspects of these dynamics provide an interesting substrate for attentional mechanisms in the brain (see Schroeder et al., [62] for review) and are not unrelated to the ideas in [63].”
BackgroundResultInfluentialAn Interoceptive Predictive Coding Model of Conscious Presence
Seth, A. K., Suzuki, K., & Critchley, H. D. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2011.00395 · Cited by 950
“While for exteroception this may involve cholingeric neurotransmission via attention (Yu and Dayan, 2005); for interoception, proprioception, and value-learning, prediction error precision is suggested to be encoded by dopamine (Fiorillo et al.”
BackgroundActive interoceptive inference and the emotional brain
Seth, A. K., & Friston, K. J. (2016) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2016.0007 · Cited by 945
“Estimating precision speaks to a fundamental aspect of inference; namely, the encoding of precision or expected uncertainty (38-40).”
BackgroundOptimal decision making and the anterior cingulate cortex
Kennerley, S. W., Walton, M. E., Behrens, T. E. J., Buckley, M. J., & Rushworth, M. F. S. (2006) · Nature Neuroscience · doi:10.1038/nn1724 · Cited by 948
Precise minds in uncertain worlds: Predictive coding in autism.
Van de Cruys, S., Evers, K., Van der Hallen, R., Van Eylen, L., Boets, B., de-Wit, L., & Wagemans, J. (2014) · Psychological Review · doi:10.1037/a0037665 · Cited by 885
Pupillometry: A Window to the Preconscious?
Laeng, B., Sirois, S., & Gredebäck, G. (2012) · Perspectives on Psychological Science · doi:10.1177/1745691611427305 · Cited by 921
Neural correlates, computation and behavioural impact of decision confidence
Kepecs, A., Uchida, N., Zariwala, H. A., & Mainen, Z. F. (2008) · Nature · doi:10.1038/nature07200 · Cited by 809
Bayesian decision theory in sensorimotor control
Kording, K. P., & Wolpert, D. M. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.003 · Cited by 916
Action and behavior: a free-energy formulation
Friston, K. J., Daunizeau, J., Kilner, J., & Kiebel, S. J. (2010) · Biological Cybernetics · doi:10.1007/s00422-010-0364-z · Cited by 866
“In a perceptual setting, they may mediate attentional gain (cf., Abbott et al. 1997; Yu and Dayan 2005; Friston 2008).”
BackgroundPredictive Processing: A Canonical Cortical Computation
Keller, G. B., & Mrsic-Flogel, T. D. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.003 · Cited by 836
Pupil diameter covaries with BOLD activity in human locus coeruleus
Murphy, P. R., O'Connell, R. G., O'Sullivan, M., Robertson, I. H., & Balsters, J. H. (2014) · Human Brain Mapping · doi:10.1002/hbm.22466 · Cited by 819
Network reset: a simplified overarching theory of locus coeruleus noradrenaline function
Bouret, S., & Sara, S. J. (2005) · Trends in Neurosciences · doi:10.1016/j.tins.2005.09.002 · Cited by 862
Gilzenrat, M. S., Nieuwenhuis, S., Jepma, M., & Cohen, J. D. (2010) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/cabn.10.2.252 · Cited by 873
Rational regulation of learning dynamics by pupil-linked arousal systems
Nassar, M. R., Rumsey, K. M., Wilson, R. C., Parikh, K., Heasly, B., & Gold, J. I. (2012) · Nature Neuroscience · doi:10.1038/nn.3130 · Cited by 722
Anterior insular cortex and emotional awareness
Gu, X., Hof, P. R., Friston, K. J., & Fan, J. (2013) · Journal of Comparative Neurology · doi:10.1002/cne.23368 · Cited by 712
“Another study showed that AIC is activated by not only prediction of risk but also the prediction error of risk (Preuschoff et al., 2008), which relates to unexpected uncertainty and state transition (Yu and Dayan, 2005).”
A Bayesian foundation for individual learning under uncertainty
Mathys, C. (2011) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2011.00039 · Cited by 655
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Mather, M., Clewett, D., Sakaki, M., & Harley, C. W. (2016) · Behavioral and Brain Sciences · doi:10.1017/s0140525x15000667 · Cited by 695
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Eckstein, M. K., Guerra-Carrillo, B., Singley, A. T. M., & Bunge, S. A. (2016) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2016.11.001 · Cited by 765
Decision Making in Recurrent Neuronal Circuits
Wang, X.-J. (2008) · Neuron · doi:10.1016/j.neuron.2008.09.034 · Cited by 679
“Some studies aimed at identifying distinct brain systems that are recruited by different types of uncertainty (Yu and Dayan, 2005; Hsu et al., 2005; Huettel et al., 2006; Behrens et al., 2007).”
BackgroundAn aberrant precision account of autism
Lawson, R. P., Rees, G., & Friston, K. J. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00302 · Cited by 641
Friston, K. J., & Stephan, K. E. (2007) · Synthese · doi:10.1007/s11229-007-9237-y · Cited by 673
Neuromodulation of Brain States
Lee, S., & Dan, Y. (2012) · Neuron · doi:10.1016/j.neuron.2012.09.012 · Cited by 697
Doya, K. (2008) · Nature Neuroscience · doi:10.1038/nn2077 · Cited by 642
Psychological entropy: a framework for understanding uncertainty-related anxiety
Hirsh, J. B., Mar, R. A., & Peterson, J. B. (2012) · Psychological Review · doi:10.1037/a0026767 · Cited by 606
Metacognition in human decision-making: confidence and error monitoring
Yeung, N., & Summerfield, C. (2012) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2011.0416 · Cited by 667
A synaptic memory trace for cortical receptive field plasticity
Froemke, R. C., Merzenich, M. M., & Schreiner, C. E. (2007) · Nature · doi:10.1038/nature06289 · Cited by 600
Fast modulation of visual perception by basal forebrain cholinergic neurons
Pinto, L., Goard, M. J., Estandian, D., Xu, M., Kwan, A. C., Lee, S.-H., Harrison, T. C., Feng, G., & Dan, Y. (2013) · Nature Neuroscience · doi:10.1038/nn.3552 · Cited by 529
The dysconnection hypothesis (2016)
Friston, K., Brown, H. R., Siemerkus, J., & Stephan, K. E. (2016) · Schizophrenia Research · doi:10.1016/j.schres.2016.07.014 · Cited by 561
Computational psychiatry: the brain as a phantastic organ
Friston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014) · The Lancet Psychiatry · doi:10.1016/s2215-0366(14)70275-5 · Cited by 531
Nassar, M. R., Wilson, R. C., Heasly, B. S., & Gold, J. I. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.0822-10.2010 · Cited by 607
Neuroeconomics: cross-currents in research on decision-making
Sanfey, A. G., Loewenstein, G. F., McClure, S. M., & Cohen, J. D. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.01.009 · Cited by 571
Predictive coding explains binocular rivalry: an epistemological review
Hohwy, J., Roepstorff, A., & Friston, K. (2008) · Cognition · doi:10.1016/j.cognition.2008.05.010 · Cited by 546
Reasoning, Learning, and Creativity: Frontal Lobe Function and Human Decision-Making
Collins, A., & Koechlin, E. (2012) · PLoS Biology · doi:10.1371/journal.pbio.1001293 · Cited by 558
“The actor predictive mapping simply regularizes action outcome likelihood given stimulus [13].”
BackgroundMethodInfluentialBasal forebrain activation enhances cortical coding of natural scenes
Goard, M. J., & Dan, Y. (2009) · Nature Neuroscience · doi:10.1038/nn.2402 · Cited by 558
Exploration versus exploitation in space, mind, and society
Hills, T. T., Todd, P. M., Lazer, D. M. J., Redish, A. D., & Couzin, I. D. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.10.004 · Cited by 600
Neural Basis of Reinforcement Learning and Decision Making
Lee, D., Seo, H., & Jung, M. W. (2012) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062111-150512 · Cited by 581
Anxious individuals have difficulty learning the causal statistics of aversive environments
Browning, M., Behrens, T. E., Jocham, G., O'Reilly, J. X., & Bishop, S. J. (2015) · Nature Neuroscience · doi:10.1038/nn.3961 · Cited by 476
Uncertainty in perception and the Hierarchical Gaussian Filter
Mathys, C. D., Lomakina, E. I., Daunizeau, J., Iglesias, S., Brodersen, K. H., Friston, K. J., & Stephan, K. E. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00825 · Cited by 465
Pupil dilation signals surprise: evidence for noradrenaline’s role in decision making
Preuschoff, K. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00115 · Cited by 538
The ecology of human fear: survival optimization and the nervous system
Mobbs, D., Hagan, C. C., Dalgleish, T., Silston, B., & Prévost, C. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00055 · Cited by 498
Neuronal basis of sequential foraging decisions in a patchy environment
Hayden, B. Y., Pearson, J. M., & Platt, M. L. (2011) · Nature Neuroscience · doi:10.1038/nn.2856 · Cited by 547
A unified framework for addiction: Vulnerabilities in the decision process
Redish, A. D., Jensen, S., & Johnson, A. (2008) · Behavioral and Brain Sciences · doi:10.1017/s0140525x0800472x · Cited by 501
Posterior cingulate cortex: adapting behavior to a changing world
Pearson, J. M., Heilbronner, S. R., Barack, D. L., Hayden, B. Y., & Platt, M. L. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.02.002 · Cited by 504
Conscious perception of errors and its relation to the anterior insula
Ullsperger, M., Harsay, H. A., Wessel, J. R., & Ridderinkhof, K. R. (2010) · Brain Structure and Function · doi:10.1007/s00429-010-0261-1 · Cited by 463
Great Expectations: Is there Evidence for Predictive Coding in Auditory Cortex?
Heilbron, M., & Chait, M. (2018) · Neuroscience · doi:10.1016/j.neuroscience.2017.07.061 · Cited by 428
Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis
Huys, Q. J., Pizzagalli, D. A., Bogdan, R., & Dayan, P. (2013) · Biology of Mood & Anxiety Disorders · doi:10.1186/2045-5380-3-12 · Cited by 480
Self-citationPlasticity of Cortical Excitatory-Inhibitory Balance
Froemke, R. C. (2015) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071714-034002 · Cited by 458
Amodio, D. M., Master, S. L., Yee, C. M., & Taylor, S. E. (2008) · Psychophysiology · doi:10.1111/j.1469-8986.2007.00609.x · Cited by 463
The computational neurobiology of learning and reward
Daw, N. D., & Doya, K. (2006) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2006.03.006 · Cited by 492
Pupil-linked arousal is driven by decision uncertainty and alters serial choice bias
Urai, A. E., Braun, A., & Donner, T. H. (2017) · Nature Communications · doi:10.1038/ncomms14637 · Cited by 466
Acetylcholine contributes through muscarinic receptors to attentional modulation in V1
Herrero, J. L., Roberts, M. J., Delicato, L. S., Gieselmann, M. A., Dayan, P., & Thiele, A. (2008) · Nature · doi:10.1038/nature07141 · Cited by 459
Self-citationBayesian theories of conditioning in a changing world
Courville, A. C., Daw, N. D., & Touretzky, D. S. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.004 · Cited by 457
Varazzani, C., San‐Galli, A., Gilardeau, S., & Bouret, S. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0454-15.2015 · Cited by 482
Nieuwenhuis, S., de Geus, E. J. C., & Aston‐Jones, G. (2010) · Psychophysiology · doi:10.1111/j.1469-8986.2010.01057.x · Cited by 479
A framework for local cortical oscillation patterns
Donner, T. H., & Siegel, M. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.03.007 · Cited by 451
Reinforcement learning or active inference?
Friston, K. J., Daunizeau, J., & Kiebel, S. J. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006421 · Cited by 443
Jepma, M., & Nieuwenhuis, S. (2010) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2010.21548 · Cited by 473
Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules
Frémaux, N., & Wulfram Gerstner, B. (2016) · Frontiers in Neural Circuits · doi:10.3389/fncir.2015.00085 · Cited by 449
Hayden, B. Y., Heilbronner, S. R., Pearson, J. M., & Platt, M. L. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.4652-10.2011 · Cited by 379
Toward a neurobiology of delusions
Corlett, P. R., Taylor, J. R., Wang, X.-J., Fletcher, P. C., & Krystal, J. H. (2010) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2010.06.007 · Cited by 394
Autism, oxytocin and interoception
Quattrocki, E., & Friston, K. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.09.012 · Cited by 393
Central Cholinergic Neurons Are Rapidly Recruited by Reinforcement Feedback
Hangya, B., Ranade, S. P., Lorenc, M., & Kepecs, A. (2015) · Cell · doi:10.1016/j.cell.2015.07.057 · Cited by 399
Foundations of human reasoning in the prefrontal cortex
Donoso, M., Collins, A. G. E., & Koechlin, É. (2014) · Science · doi:10.1126/science.1252254 · Cited by 407
The neuromodulator of exploration: A unifying theory of the role of dopamine in personality
DeYoung, C. G. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00762 · Cited by 387
Causal inference in perception
Shams, L., & Beierholm, U. R. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.07.001 · Cited by 403
Confidence as Bayesian probability: From neural origins to behavior
Meyniel, F., Sigman, M., & Mainen, Z. F. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.039 · Cited by 349
EARLY EVENTS IN OLFACTORY PROCESSING
Wilson, R. I., & Mainen, Z. F. (2006) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.112950 · Cited by 425
From drugs to deprivation: A Bayesian framework for understanding models of psychosis
Corlett, P. R., Frith, C. D., & Fletcher, P. C. (2009) · Psychopharmacology · doi:10.1007/s00213-009-1561-0 · Cited by 367
“This uncertainty signalling role has been ascribed to various neuromodulators, including dopamine (Fiorillo et al. 2003) and acetylcholine (Yu and Dayan 2002, 2005).”
BackgroundThe dynamics of memory as a consequence of optimal adaptation to a changing body
Kording, K. P., Tenenbaum, J. B., & Shadmehr, R. (2007) · Nature Neuroscience · doi:10.1038/nn1901 · Cited by 393
Hierarchical Prediction Errors in Midbrain and Basal Forebrain during Sensory Learning
Iglesias, S., Mathys, C., Brodersen, K. H., Kasper, L., Piccirelli, M., den Ouden, H. E. M., & Stephan, K. E. (2013) · Neuron · doi:10.1016/j.neuron.2013.09.009 · Cited by 342
Serotonin in Affective Control
Dayan, P., & Huys, Q. J. M. (2009) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.051508.135607 · Cited by 381
Self-citationThe functional role of the inferior parietal lobe in the dorsal and ventral stream dichotomy
Singh‐Curry, V., & Husain, M. (2008) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2008.11.033 · Cited by 399
Dissociable effects of surprise and model update in parietal and anterior cingulate cortex
O’Reilly, J. X., Schüffelgen, U., Cuell, S. F., Behrens, T. E. J., Mars, R. B., & Rushworth, M. F. S. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1305373110 · Cited by 400
Redish, A. D., Jensen, S., Johnson, A., & Kurth-Nelson, Z. (2007) · Psychological Review · doi:10.1037/0033-295x.114.3.784 · Cited by 346
Computations of uncertainty mediate acute stress responses in humans
de Berker, A. O., Rutledge, R. B., Mathys, C., Marshall, L., Cross, G. F., Dolan, R. J., & Bestmann, S. (2016) · Nature Communications · doi:10.1038/ncomms10996 · Cited by 330
IMAGING VALUATION MODELS IN HUMAN CHOICE
Montague, P. R., King‐Casas, B., & Cohen, J. D. (2006) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.112903 · Cited by 382
Knowing how much you don't know: a neural organization of uncertainty estimates
Bach, D. R., & Dolan, R. J. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3289 · Cited by 339
Event-related potential studies of outcome processing and feedback-guided learning
San Martín, R. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00304 · Cited by 354
Hassani, S. A., Oemisch, M., Balcarras, M., Westendorff, S., Ardid, S., van der Meer, M. A. A., Tiesinga, P. H. E., & Womelsdorf, T. (2017) · Scientific Reports · doi:10.1038/srep40606 · Cited by 364
Thiele, A., & Bellgrove, M. A. (2018) · Neuron · doi:10.1016/j.neuron.2018.01.008 · Cited by 350
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 ›
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