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
Flagged first, then by citations
Cited by
Sorted by citations
Items 701–800 of 1,008
Walentowska, W., Severo, M. C., Moors, A., & Pourtois, G. (2019) · Psychophysiology · doi:10.1111/psyp.13456 · Cited by 33
Active inference, morphogenesis, and computational psychiatry
Pio‐Lopez, L., Kuchling, F., Tung, A., Pezzulo, G., & Levin, M. G. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.988977 · Cited by 29
Stocco, A. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00018 · Cited by 31
Craig, A. B., Phillips, M. E., Zaldivar, A., Bhattacharyya, R., & Krichmar, J. L. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.00017 · Cited by 29
Ecological expected utility and the mythical neural code
Feldman, J. A. (2009) · Cognitive Neurodynamics · doi:10.1007/s11571-009-9090-4 · Cited by 29
Easdale, L. C., Pelley, M. E. L., & Beesley, T. (2017) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470218.2017.1363257 · Cited by 34
The basal ganglia in reward and decision making
Doll, B. B., & Frank, M. J. (2009) · Elsevier eBooks · doi:10.1016/b978-0-12-374620-7.00019-4 · Cited by 31
Schriver, B. J., Perkins, S. M., Sajda, P., & Wang, Q. (2020) · Psychophysiology · doi:10.1111/psyp.13565 · Cited by 29
Tackling Scientific Misconceptions: The Element of Surprise
Theobald, M., & Brod, G. (2021) · Child Development · doi:10.1111/cdev.13582 · Cited by 32
Neuromodulation and Neurophysiology on the Timescale of Learning and Decision-Making
Grossman, C. D., & Cohen, J. Y. (2022) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-092021-125059 · Cited by 30
He, H., Hong, L., & Sajda, P. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011081 · Cited by 30
Adaptive Behavior: Humans Act as Bayesian Learners
Yu, A. J. (2007) · Current Biology · doi:10.1016/j.cub.2007.09.007 · Cited by 29
Self-citationBalconi, M., & Crivelli, D. (2010) · Clinical Neurophysiology · doi:10.1016/j.clinph.2010.03.015 · Cited by 29
Zhang, D., Gao, M., Xu, D., Shi, W., Gutkin, B. S., Steffensen, S. C., Lukas, R. J., & Wu, J. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.5411-11.2012 · Cited by 31
Left Hemispheric Imbalance and Reward Mechanisms Affect Gambling Behavior
Balconi, M., Finocchiaro, R., & Canavesio, Y. (2014) · Clinical EEG and Neuroscience · doi:10.1177/1550059413513261 · Cited by 28
Psychosis as a disorder of muscarinic signalling: psychopathology and pharmacology
McCutcheon, R. A., Weber, L. A. E., Nour, M. M., Cragg, S. J., & McGuire, P. K. (2024) · The Lancet Psychiatry · doi:10.1016/s2215-0366(24)00100-7 · Cited by 29
André, M. A. ̈. E., Wolf, O. T., & Manahan‐Vaughan, D. (2015) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2015.00125 · Cited by 29
Nicotinic Receptor Gene CHRNA4 Interacts with Processing Load in Attention
Espeseth, T., Sneve, M. H., Rootwelt, H., & Laeng, B. (2010) · PLoS ONE · doi:10.1371/journal.pone.0014407 · Cited by 30
Zaldivar, D., Goense, J. B. M., Lowe, S., Logothetis, N. K., & Panzeri, S. (2018) · Current Biology · doi:10.1016/j.cub.2017.12.006 · Cited by 28
The principle of consistency and the cause and function of behaviour
KAMPEN, H. S. V. (2018) · Behavioural Processes · doi:10.1016/j.beproc.2018.12.013 · Cited by 34
A Control Theoretic Model of Adaptive Learning in Dynamic Environments
Ritz, H., Nassar, M. R., Frank, M. J., & Shenhav, A. (2018) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01289 · Cited by 31
The stability flexibility tradeoff and the dark side of detail
Nassar, M. R., & Troiani, V. (2020) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-020-00848-8 · Cited by 30
Graphene-electrode array for brain map remodeling of the cortical surface
Lee, M.-S., Lee, S., Kim, J., Lim, J., Lee, J., Masri, S., Bao, S., Yang, S., Ahn, J., & Yang, S. (2021) · NPG Asia Materials · doi:10.1038/s41427-021-00334-8 · Cited by 33
Neural responses in macaque prefrontal cortex are linked to strategic exploration
Jahn, C. I., Grohn, J., Cuell, S. F., Emberton, A., Bouret, S., Walton, M. E., Kolling, N., & Sallet, J. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3001985 · Cited by 31
Learning‐dependent dynamics of beta‐frequency oscillations in the basal forebrain of rats
Quinn, L. K., Nitz, D. A., & Chiba, A. A. (2010) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2010.07422.x · Cited by 28
Graupner, M., Maex, R., & Gutkin, B. S. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003183 · Cited by 30
Cohen, J. D., & Aston‐Jones, G. (2005) · Nature · doi:10.1038/436471a · Cited by 27
Schwalm, M., & Jubal, E. R. (2017) · eNeuro · doi:10.1523/eneuro.0293-16.2017 · Cited by 29
Choice History Biases Subsequent Evidence AccumulationPreprint
Urai, A. E., de Gee, J. W., Tsetsos, K., & Donner, T. H. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/251595 · Cited by 32
Bayesian reinforcement learning: A basic overview
Kang, P., Tobler, P. N., & Dayan, P. (2024) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2024.107924 · Cited by 32
Self-citationA Computational Account of the Development and Evolution of Psychotic Symptoms
Powers, A. R., Angelos, P. A., Bond, A., Farina, E., Fredericks, C. A., Gandhi, J., Greenwald, M. S., Hernández-Busot, G., Hosein, G. X., Kelley, M., et al. (2024) · Biological Psychiatry · doi:10.1016/j.biopsych.2024.08.026 · Cited by 29
Greenwood, P. M., Sundararajan, R., Lin, M., Kumar, R., Fryxell, K. J., & Parasuraman, R. (2008) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2008.21164 · Cited by 29
Harris, D., Arthur, T., Vine, S. J., Liu, J., Rahman, H. R. A., Han, F., & Wilson, M. (2022) · Scientific Reports · doi:10.1038/s41598-022-26544-w · Cited by 37
Hierarchical predictive coding in distributed pain circuits
Chen, Z. S. (2023) · Frontiers in Neural Circuits · doi:10.3389/fncir.2023.1073537 · Cited by 35
Spiking neural predictive coding for continually learning from data streams
Ororbia, A. G. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126292 · Cited by 43
Locus coeruleus neuromodulation of memories encoded during negative or unexpected action outcomes
Clewett, D., Schoeke, A., & Mather, M. (2014) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2014.03.006 · Cited by 27
Bourgeois, J., Meas‐Yedid, V., Sourd, A. L., Fauré, P., Pons, S., Maskos, U., Changeux, J., Olivo‐Marín, J., & Granon, S. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr159 · Cited by 34
Neuropsychology of Communication
Canal, P., Vespignani, F., Molinaro, N., & Cacciari, C. (2010) · Springer eBooks · doi:10.1007/978-88-470-1584-5 · Cited by 27
Metacognition and confidence: A review and synthesisPreprint
Fleming, S. M. (2023) · doi:10.31234/osf.io/ge7tz · Cited by 27
Altered orientation of spatial attention in depersonalization disorder
Adler, J., Beutel, M. E., Knebel, A., Berti, S., Unterrainer, J. M., & Michal, M. (2014) · Psychiatry Research · doi:10.1016/j.psychres.2014.02.021 · Cited by 26
Brown, S. B. R. E., van Steenbergen, H., Kedar, T., & Nieuwenhuis, S. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00023 · Cited by 27
Models of neuromodulation for computational psychiatry
Iglesias, S., Tomiello, S., Schneebeli, M., & Stephan, K. E. (2016) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1420 · Cited by 28
Ali, A. A., Kamal, M. M., Khalil, M. G., Ali, S. A., Elariny, H. A., Bekhit, A. A., & Wahid, A. M. (2021) · Experimental Aging Research · doi:10.1080/0361073x.2021.1959823 · Cited by 28
Martin, J. T., Whittaker, A. H., & Johnston, S. J. (2022) · European Journal of Neuroscience · doi:10.1111/ejn.15585 · Cited by 28
Surprise and novelty in the brain
Modirshanechi, A., Becker, S., Brea, J., & Wulfram Gerstner, B. (2023) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2023.102758 · Cited by 31
A Bayesian Theory of Sequential Causal Learning and Abstract Transfer
Lu, H., Rojas, R. R., Beckers, T., & Yuille, A. (2015) · Cognitive Science · doi:10.1111/cogs.12236 · Cited by 26
Balconi, M., Finocchiaro, R., & Canavesio, Y. (2015) · Journal of Neuropsychiatry · doi:10.1176/appi.neuropsych.14070165 · Cited by 26
Population Code Dynamics in Categorical Perception
Tajima, C. I., Tajima, S., Koida, K., Komatsu, H., Aihara, K., & Suzuki, H. (2016) · Scientific Reports · doi:10.1038/srep22536 · Cited by 26
Change is in the eye of the beholder
Yu, A. J. (2012) · Nature Neuroscience · doi:10.1038/nn.3150 · Cited by 24
Self-citationCognitive effects of subdiaphragmatic vagal deafferentation in rats
Klarer, M., Weber-Stadlbauer, U., Arnold, M., Langhans, W., & Meyer, U. (2017) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2017.05.006 · Cited by 25
A predictive coding account of psychotic symptoms in autism spectrum disorder
van Schalkwyk, G. I., Volkmar, F. R., & Corlett, P. R. (2017) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-017-3065-9 · Cited by 25
Grazioplene, R. G., DeYoung, C. G., Rogosch, F. A., & Cicchetti, D. (2012) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12031 · Cited by 26
Experimentally induced and real-world anxiety have no demonstrable effect on goal-directed behaviour
Gillan, C. M., Vaghi, M. M. S., Hezemans, F. H., Grothe, S. V. G., Dafflon, J., Brühl, A. B., Savulich, G., & Robbins, T. W. (2020) · Psychological Medicine · doi:10.1017/s0033291720000203 · Cited by 32
Modelling delusions as temporally-evolving beliefs (Commentary on Coltheart and Davies)
Corlett, P. R., & Fletcher, P. C. (2021) · Cognitive Neuropsychiatry · doi:10.1080/13546805.2021.1938984 · Cited by 26
Schadenberg, B. R., Reidsma, D., Heylen, D., & Evers, V. (2021) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3461534 · Cited by 29
Organization and engagement of a prefrontal-olfactory network during olfactory selective attention
Cansler, H. L., Zandt, E. E. I. ’., Carlson, K. S., Khan, W. T., Ma, M., & Wesson, D. W. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac153 · Cited by 28
Hallock, H. L., Adiraju, S. S., Miranda-Barrientos, J. A., McInerney, J. M., Oh, S., DeBrosse, A. C., Li, Y., Carr, G. V., & Martinowich, K. (2023) · Neuropsychopharmacology · doi:10.1038/s41386-023-01692-3 · Cited by 23
Dopamine and Norepinephrine Differentially Mediate the Exploration–Exploitation Tradeoff
Chen, C. S., Mueller, D., Knep, E., Ebitz, R. B., & Grissom, N. M. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.1194-23.2024 · Cited by 27
Schreiber, W. B., Asok, A., Jablonski, S. A., Rosen, J. B., & Stanton, M. E. (2014) · Brain Research · doi:10.1016/j.brainres.2014.06.007 · Cited by 25
Psychopathy-related traits and the use of reward and social information: a computational approach
Brazil, I. A., Hunt, L. T., Bulten, B. H., Kessels, R. P. C., de Bruijn, E. R. A., & Mars, R. B. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00952 · Cited by 27
The amygdala instructs insular feedback for affective learning
Kargl, D., Kaczanowska, J., Ulonska, S., Groessl, F., Piszczek, Ł., Lazović, J., Buehler, K., & Haubensak, W. E. (2020) · eLife · doi:10.7554/elife.60336 · Cited by 28
Updating of Attentional and Premotor Allocation Resources as function of previous trial outcome
Arjona, A., Escudero, M., & Gómez, C. M. (2014) · Scientific Reports · doi:10.1038/srep04526 · Cited by 24
A Theoretical Framework for Evaluating Psychiatric Research Strategies
Katahira, K., & Yamashita, Y. (2017) · Computational Psychiatry · doi:10.1162/cpsy_a_00008 · Cited by 22
Kreis, I. V., Zhang, L., Moritz, S., & Pfuhl, G. (2021) · Schizophrenia Research · doi:10.1016/j.schres.2021.06.038 · Cited by 21
Acetylcholine and noradrenaline enhance foraging optimality in humans
Doren, N., Chung, H.-K., Grueschow, M., Quednow, B. B., Hayward-Koennecke, H. K., Jetter, A., & Tobler, P. N. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2305596120 · Cited by 23
Funamizu, A., Ito, M., Doya, K., Kanzaki, R., & Takahashi, H. (2012) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2012.08025.x · Cited by 26
Shimozaki, S. S., Schoonveld, W., & Eckstein, M. P. (2012) · Journal of Vision · doi:10.1167/12.6.27 · Cited by 23
Exploration and Exploitation During Sequential Search
Dam, G., & Kording, K. P. (2009) · Cognitive Science · doi:10.1111/j.1551-6709.2009.01021.x · Cited by 24
A biologically inspired action selection algorithm based on principles of neuromodulation
Krichmar, J. L. (2012) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2012.6252633 · Cited by 24
Nosjean, A., Cressant, A., de Chaumont, F., Olivo‐Marín, J., Chauveau, F., & Granon, S. (2015) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2014.00447 · Cited by 26
GANEing traction: The broad applicability of NE hotspots to diverse cognitive and arousal phenomena
Mather, M., Clewett, D., Sakaki, M., & Harley, C. W. (2016) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16000017 · Cited by 20
Neuromodulation as a Robot Controller: A Brain Inspired Strategy for Controlling Autonomous Robots
Cox, B. R., & Krichmar, J. L. (2009) · Cited by 24
Learning and forgetting using reinforced Bayesian change detection
Moens, V., & Zénon, A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006713 · Cited by 25
Hoffing, R. C., Lauharatanahirun, N., Forster, D. E., Garcia, J. O., Vettel, J. M., & Thurman, S. M. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230517 · Cited by 26
Testing predictive coding theories of autism spectrum disorder using models of active inference
Arthur, T., Vine, S., Buckingham, G., Brosnan, M., Wilson, M., & Harris, D. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011473 · Cited by 21
“This is achieved through hierarchical representations of probability that encode beliefs about the world, the (un)certainty of those beliefs, and how likely the world is to change [54].”
BackgroundImpact of Media-Induced Uncertainty on Mental Health: Narrative-Based Perspective.
Kesner, L., Juríčková, V., Grygarová, D., & Horáček, J. (2025) · JMIR Mental Health · doi:10.2196/68640 · Cited by 23
“…particularly Bayesian computational models, posit the uncertainty to arise from discrepancies between top-down expectations and sensory outcomes [56,57], it bears emphasizing that the sociopolitical and environmental adversities are to a large extent (or to many people) hidden to direct sensory…”
Pupillo, F., Ortiz-Tudela, J., Bruckner, R., & Shing, Y. L. (2023) · npj Science of Learning · doi:10.1038/s41539-023-00166-x · Cited by 27
Pajkossy, P., Szöllősi, Á., Demeter, G., & Racsmány, M. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00506 · Cited by 21
Auditory Attentional Disengagement in Children with Autism Spectrum Disorder
Keehn, B., Kadlaskar, G., Keehn, R. M., & Francis, A. L. (2019) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-019-04111-z · Cited by 23
Arthur, T., & Harris, D. (2021) · Cortex · doi:10.1016/j.cortex.2021.09.017 · Cited by 24
The modulation of brain network integration and arousal during exploration
Tardiff, N., Medaglia, J. D., Bassett, D. S., & Thompson‐Schill, S. L. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118369 · Cited by 22
Hamelin, H., Poizat, G., Florian, C., Kursa, M. B., Pittaras, E., Callebert, J., Rampon, C., Taouis, M., Hamed, A., & Granon, S. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab274 · Cited by 25
Morriss, J., Bradford, D. E., Wake, S. J., Biagi, N., Tanovic, E., Kaye, J. T., & Joormann, J. (2021) · Biological Psychology · doi:10.1016/j.biopsycho.2021.108223 · Cited by 23
Eyes wide open: Regulation of arousal by temporal expectations
Shalev, N., & Nobre, A. C. (2022) · Cognition · doi:10.1016/j.cognition.2022.105062 · Cited by 23
Emergence of sensory attenuation based upon the free-energy principle
Idei, H., Ohata, W., Yamashita, Y., Ogata, T., & Tani, J. (2022) · Scientific Reports · doi:10.1038/s41598-022-18207-7 · Cited by 27
How celebrity status and gaze direction in ads drive visual attention to shape consumer decisions
D’Ambrogio, S., Werksman, N., Platt, M. L., & Johnson, E. N. (2022) · Psychology and Marketing · doi:10.1002/mar.21772 · Cited by 29
Acetylcholine modulates the precision of prediction error in the auditory cortex
Pérez‐González, D., Lao-Rodríguez, A. B., Aedo, C., & Malmierca, M. S. (2023) · eLife · doi:10.7554/elife.91475 · Cited by 35
Frontal Norepinephrine Represents a Threat Prediction Error Under Uncertainty
Basu, A., Yang, J.-H., Yu, A. L., Glaeser-Khan, S., Rondeau, J. A., Feng, J., Krystal, J. H., Li, Y., & Kaye, A. P. (2024) · Biological Psychiatry · doi:10.1016/j.biopsych.2024.01.025 · Cited by 37
Self-citationSchmidt, S. L., Chew, E. Y., Bennett, D., Hammad, M. A., & Frӧhlich, F. (2013) · Neuropharmacology · doi:10.1016/j.neuropharm.2013.04.045 · Cited by 24
Las redes bayesianas como herramientas de modelado en psicología
Puga, J. L., García, J. G., Sánchez, L. D. L. F., & Solana, E. I. D. L. F. (2007) · Institutional Repository of the University of Granada (University of Granada) · Cited by 23
Carvalho, F. M., Chaim, K. T., Sanchez, T. A., & de Araújo, D. B. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00268 · Cited by 23
Oemisch, M., Watson, M. R., Womelsdorf, T., & Schubö, A. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00540 · Cited by 25
Uncertainty-based modulation for lifelong learning
Brna, A., Brown, R. C., Connolly, P. M., Simons, S. B., Shimizu, R. E., & Aguilar, M. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.09.011 · Cited by 23
Hoang, K., Watt, H., Golemme, M., Perry, R. J., Ritchie, C. W., Wilson, D., Pickett, J., Fox, C., Howard, R. J., & Malhotra, P. (2022) · Trials · doi:10.1186/s13063-022-06190-3 · Cited by 23
Learning to Perceive and Perceiving to Learn
Press, C., Kok, P., & Yon, D. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.01.002 · Cited by 24
Neuropsychopharmacology of Cognitive Flexibility
Cools, R. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00253-0 · Cited by 23
Mechanisms underlying the basal forebrain enhancement of top‐down and bottom‐up attention
Avery, M. C., Dutt, N. D., & Krichmar, J. L. (2013) · European Journal of Neuroscience · doi:10.1111/ejn.12433 · Cited by 21
Evans, S., Gray, M. A., Dowell, N. G., Tabet, N., Tofts, P. S., King, S. L., & Rusted, J. M. (2012) · Neuropsychopharmacology · doi:10.1038/npp.2012.230 · Cited by 23
Chase, H. W., Fournier, J. C. J., Greenberg, T., Almeida, J., Stiffler, R. S., Zevallos, C. R., Aslam, H. A., Cooper, C. M., Deckersbach, T., Weyandt, S., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0126326 · Cited by 22
Jahn, C. I., Varazzani, C., Sallet, J., Walton, M. E., & Bouret, S. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa089 · Cited by 24
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