Noradrenergic and Cholinergic Modulation of Belief Updating
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To make optimal predictions in a dynamic environment, the impact of new observations on existing beliefs—that is, the learning rate—should be guided by ongoing estimates of change and uncertainty. Theoretical work has proposed specific computational roles for various neuromodulatory systems in the control of learning rate, but empirical evidence is still sparse. The aim of the current research was to examine the role of the noradrenergic and cholinergic systems in learning rate regulation. First, we replicated our recent findings that the centroparietal P3 component of the EEG—an index of phasic catecholamine release in the cortex—predicts trial-to-trial variability in learning rate and mediates the effects of surprise and belief uncertainty on learning rate (Study 1, n = 17). Second, we found that pharmacological suppression of either norepinephrine or acetylcholine activity produced baseline-dependent effects on learning rate following nonobvious changes in an outcome-generating process (Study 1). Third, we identified two genes, coding for α2A receptor sensitivity ( ADRA2A) and norepinephrine reuptake ( NET), as promising targets for future research on the genetic basis of individual differences in learning rate (Study 2, n = 137). Our findings suggest a role for the noradrenergic and cholinergic systems in belief updating and underline the importance of studying interactions between different neuromodulatory systems.
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Baseline task performance in a placebo condition frequently predicts the behavioral effects of catecholaminergic manipulations.
“Second, baseline task performance (in a placebo condition) was considered, as it frequently predicts the behavioral effects of catecholaminergic manipulations (Cools & Robbins, 2004; Jepma et al., 2016, 2018) and therefore may indicate an individual’s baseline placement on the inverted-U curve relating dopamine activity and WM performance.”
From Baseline-dependent effect of dopamine’s precursor L-tyrosine on working memory gating but not updating (2020) · cites this paper as Jepma et al., 2018
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Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371 · see citing works
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388 · see citing works
Nonparametric statistical testing of EEG- and MEG-data
Maris, E., & Oostenveld, R. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.03.024 · Cited by 8,496 · see citing works
The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120 · cited 1× · see citing works
“According to contemporary ideas in neuroscience, the brain’s overarching function is to construct a model of its environment to optimally predict its sensory inputs and thus minimize the amount of surprise or “prediction error” (Friston, 2010; Doya, 2007; Rao & Ballard, 1999).”
BackgroundUpdating P300: An integrative theory of P3a and P3b
Polich, J. (2007) · Clinical Neurophysiology · doi:10.1016/j.clinph.2007.04.019 · Cited by 7,919 · see citing works
Rao, R. P. N., & Ballard, D. H. (1999) · Nature Neuroscience · doi:10.1038/4580 · Cited by 5,483 · cited 1× · see citing works
“According to contemporary ideas in neuroscience, the brain’s overarching function is to construct a model of its environment to optimally predict its sensory inputs and thus minimize the amount of surprise or “prediction error” (Friston, 2010; Doya, 2007; Rao & Ballard, 1999).”
BackgroundA new method for off-line removal of ocular artifact
Gratton, G., Coles, M. G. H., & Donchin, E. (1983) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(83)90135-9 · Cited by 5,055 · see citing works
Aston-Jones, G., & Cohen, J. D. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.28.061604.135709 · Cited by 4,472 · see citing works
Is the P300 component a manifestation of context updating?
Donchin, E., & Coles, M. G. H. (1988) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00058027 · Cited by 3,585 · see citing works
Learning 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 · see citing works
The locus coeruleus and noradrenergic modulation of cognition
Sara, S. J. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2573 · Cited by 1,848 · see citing works
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 · see citing works
Uncertainty, Neuromodulation, and Attention
Yu, A. J., & Dayan, P. (2005) · Neuron · doi:10.1016/j.neuron.2005.04.026 · Cited by 1,774 · see citing works
The role of acetylcholine in learning and memory
Hasselmo, M. E. (2006) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2006.09.002 · Cited by 1,693 · see citing works
Decision making, the P3, and the locus coeruleus–norepinephrine system
Nieuwenhuis, S., Aston-Jones, G., & Cohen, J. D. (2005) · Psychological Bulletin · doi:10.1037/0033-2909.131.4.510 · Cited by 1,678 · see citing works
Self-citationSvensson, T. H., Bunney, B. S., & Aghajanian, G. K. (1975) · Brain Research · doi:10.1016/0006-8993(75)90276-0 · Cited by 1,009 · see citing works
A Network Model of Catecholamine Effects: Gain, Signal-to-Noise Ratio, and Behavior
Servan‐Schreiber, D., Printz, H., & Cohen, J. D. (1990) · Science · doi:10.1126/science.2392679 · Cited by 770 · see citing works
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 · see citing works
Metalearning and neuromodulation
Doya, K. (2002) · Neural Networks · doi:10.1016/s0893-6080(02)00044-8 · Cited by 704 · see citing works
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 564 · see citing works
Neuromodulation and cortical function: modeling the physiological basis of behavior
Hasselmo, M. E. (1995) · Behavioural Brain Research · doi:10.1016/0166-4328(94)00113-t · Cited by 552 · see citing works
PITMAN, E. J. G. (1939) · Biometrika · doi:10.1093/biomet/31.1-2.9 · Cited by 480 · see citing works
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 · see citing works
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 462 · see citing works
Brain mediators of cardiovascular responses to social threat
Wager, T. D., Waugh, C. E., Lindquist, M., Noll, D. C., Fredrickson, B. L., & Taylor, S. F. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.05.043 · Cited by 402 · see citing works
Brain mediators of predictive cue effects on perceived pain
Atlas, L. Y., Bolger, N., Lindquist, M. A., & Wager, T. D. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.0057-10.2010 · Cited by 389 · see citing works
Dopamine, affordance and active inference
Friston, K. J., Shiner, T., FitzGerald, T., Galea, J. M., Adams, R., Brown, H., Dolan, R. J., Moran, R., Stephan, K. E., & Bestmann, S. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002327 · Cited by 381 · see citing works
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 381 · see citing works
Cools, R., Frank, M. J., Gibbs, S. E. B., Miyakawa, A., Jagust, W. J., & D’Esposito, M. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.4467-08.2009 · Cited by 365 · see citing works
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 · see citing works
Bluhm, M. D. L., & Morilak, D. A. (2005) · Neuroscience · doi:10.1016/j.neuroscience.2005.09.031 · Cited by 325 · see citing works
Free Energy, Precision and Learning: The Role of Cholinergic Neuromodulation
Moran, R. J., Campo, P., Symmonds, M., Stephan, K. E., Dolan, R. J., & Friston, K. J. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.4255-12.2013 · Cited by 312 · see citing works
Functionally dissociable influences on learning rate in a dynamic environment
McGuire, J. T., Nassar, M. R., Gold, J. I., & Kable, J. W. (2014) · Neuron · doi:10.1016/j.neuron.2014.10.013 · Cited by 299 · see citing works
Wager, T. D., van Ast, V. A., Hughes, B. L., Davidson, M. L., Lindquist, M. A., & Ochsner, K. N. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.05.044 · Cited by 264 · see citing works
Acetylcholine acts on m2‐muscarinic receptors to excite rat locus coeruleus neurones
Egan, T. M., & North, R. A. (1985) · British Journal of Pharmacology · doi:10.1111/j.1476-5381.1985.tb11070.x · Cited by 263 · see citing works
Chemistry of the adaptive mind
Cools, R., & Robbins, T. W. (2004) · Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2004.1468 · Cited by 257 · see citing works
The neural representation of unexpected uncertainty during value-based decision making
Payzan-LeNestour, E., Dunne, S., Bossaerts, P., & O’Doherty, J. P. (2013) · Neuron · doi:10.1016/j.neuron.2013.04.037 · Cited by 257 · see citing works
Krugel, L. K., Biele, G., Mohr, P. N. C., Li, S.-C., & Heekeren, H. R. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905191106 · Cited by 229 · see citing works
McGaughy, J., Ross, R. S., & Eichenbaum, H. (2008) · Neuroscience · doi:10.1016/j.neuroscience.2008.01.064 · Cited by 226 · see citing works
Modulators in concert for cognition: Modulator interactions in the prefrontal cortex
Briand, L. A., Gritton, H., Howe, W. M., Young, D. A., & Sarter, M. (2007) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2007.06.007 · Cited by 216 · see citing works
Activation of the noradrenergic system facilitates an attentional shift in the rat
Devauges, V., & Sara, S. J. (1990) · Behavioural Brain Research · doi:10.1016/0166-4328(90)90118-x · Cited by 202 · see citing works
Acetylcholine in cortical inference
Yu, A. J., & Dayan, P. (2002) · Neural Networks · doi:10.1016/s0893-6080(02)00058-8 · Cited by 195 · see citing works
Neurogenetics and Pharmacology of Learning, Motivation, and Cognition
Frank, M. J., & Fossella, J. (2010) · Neuropsychopharmacology · doi:10.1038/npp.2010.96 · Cited by 191 · see citing works
Effects of locus coeruleus lesions on auditory, long-latency, event-related potentials in monkey
Pineda, J., Foote, S., & Neville, H. (1989) · The Journal of Neuroscience · doi:10.1523/jneurosci.09-01-00081.1989 · Cited by 184 · see citing works
Lesions of the dorsal noradrenergic bundle impair attentional set-shifting in the rat
Tait, D. S., Brown, V. J., Farovik, A., Theobald, D. E., Dalley, J. W., & Robbins, T. W. (2007) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2007.05612.x · Cited by 179 · see citing works
Kimura, F., Fukuda, M., & Tsumoto, T. (1999) · European Journal of Neuroscience · doi:10.1046/j.1460-9568.1999.00779.x · Cited by 165 · see citing works
Hsieh, C. Y., Cruikshank, S. J., & Metherate, R. (2000) · Brain Research · doi:10.1016/s0006-8993(00)02766-9 · Cited by 165 · see citing works
Correcting for Regression to the Mean in Behavior and Ecology
Kelly, C., & Price, T. D. (2005) · The American Naturalist · doi:10.1086/497402 · Cited by 163 · see citing works
Pharmacological Fingerprints of Contextual Uncertainty
Marshall, L., Mathys, C., Ruge, D., de Berker, A. O., Dayan, P., Stephan, K. E., & Bestmann, S. (2016) · PLOS Biology · doi:10.1371/journal.pbio.1002575 · Cited by 158 · see citing works
Involvement of Direct and Indirect Pathways in Electrocorticographic Activation
Dringenberg, H. C. (1998) · Neuroscience & Biobehavioral Reviews · doi:10.1016/s0149-7634(97)00012-2 · Cited by 140 · see citing works
Newman, L. A., Darling, J., & McGaughy, J. (2008) · Psychopharmacology · doi:10.1007/s00213-008-1097-8 · Cited by 137 · see citing works
On the Origin of Cortical Dopamine: Is it a Co-Transmitter in Noradrenergic Neurons?
Devoto, P., & Flore, G. (2006) · Current Neuropharmacology · doi:10.2174/157015906776359559 · Cited by 137 · see citing works
Dopamine modulates involuntary attention shifting and reorienting: an electromagnetic study
Kähkönen, S., Ahveninen, J., Pekkonen, E., Kaakkola, S., Huttunen, J., Ilmoniemi, R. J., & Jääskeläinen, I. P. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00305-x · Cited by 123 · see citing works
Nassar, M. R., Bruckner, R., Gold, J. I., Li, S., Heekeren, H. R., & Eppinger, B. (2016) · Nature Communications · doi:10.1038/ncomms11609 · Cited by 123 · see citing works
Inhibition of the norepinephrine transporter improves behavioral flexibility in rats and monkeys
Seu, E., Lang, A., Rivera, R. J., & Jentsch, J. D. (2008) · Psychopharmacology · doi:10.1007/s00213-008-1250-4 · Cited by 121 · see citing works
A computational analysis of the neural bases of Bayesian inference
Kolossa, A., Kopp, B., & Fingscheidt, T. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.11.007 · Cited by 114 · see citing works
Le potentiel évoqué cognitif P300 (I) : aspects théorique et psychobiologique
Hansenne, M. (2000) · Neurophysiologie Clinique · doi:10.1016/s0987-7053(00)00223-9 · Cited by 99 · see citing works
Lapiz, M. D. S., Bondi, C. O., & Morilak, D. A. (2007) · Neuropsychopharmacology · doi:10.1038/sj.npp.1301235 · Cited by 95 · see citing works
Catecholaminergic Regulation of Learning Rate in a Dynamic Environment
Jepma, M., Murphy, P. R., Nassar, M. R., Rangel-Gomez, M., Meeter, M., & Nieuwenhuis, S. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005171 · Cited by 91 · see citing works
Self-citationBari, A., & Aston-Jones, G. (2013) · Neuropharmacology · doi:10.1016/j.neuropharm.2012.07.020 · Cited by 85 · see citing works
Taeye, L. D., Vonck, K. E. J., van Bochove, M. E., Boon, P., Roost, D. V., Mollet, L., Meurs, A., Herdt, V. D., Carrette, E., Dauwe, I., et al. (2014) · Neurotherapeutics · doi:10.1007/s13311-014-0272-3 · Cited by 84 · see citing works
Single-Trial Event-Related Potential Correlates of Belief Updating
Bennett, D., Murawski, C., & Bode, S. (2015) · eNeuro · doi:10.1523/eneuro.0076-15.2015 · Cited by 78 · see citing works
Kwisthout, J., Bekkering, H., & van Rooij, I. (2017) · Brain and Cognition · doi:10.1016/j.bandc.2016.02.008 · Cited by 73 · see citing works
Stress, genotype and norepinephrine in the prediction of mouse behavior using reinforcement learning
Lukšys, G., Gerstner, W., & Sandi, C. (2009) · Nature Neuroscience · doi:10.1038/nn.2374 · Cited by 72 · see citing works
Adams, L. M., & Foote, S. L. (1988) · Brain Research Bulletin · doi:10.1016/0361-9230(88)90151-7 · Cited by 71 · see citing works
Cholinergic Stimulation Enhances Bayesian Belief Updating in the Deployment of Spatial Attention
Vossel, S., Bauer, M., Mathys, C. D., Adams, R. A., Dolan, R. J., Stephan, K. E., & Friston, K. J. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0091-14.2014 · Cited by 65 · see citing works
Liu, J., Kiehl, K. A., Pearlson, G. D., Perrone‐Bizzozero, N. I., Eichele, T., & Calhoun, V. D. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.02.045 · Cited by 62 · see citing works
Noradrenergic and cholinergic modulation of late ERP responses to deviant stimuli
Brown, S. B. R. E., van der Wee, N. J. A., van Noorden, M. S., Giltay, E. J., & Nieuwenhuis, S. (2015) · Psychophysiology · doi:10.1111/psyp.12544 · Cited by 57 · see citing works
Self-citationThe influence of the noradrenergic system on optimal control of neural plasticity
Silvetti, M., Seurinck, R., van Bochove, M. E., & Verguts, T. (2013) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2013.00160 · Cited by 50 · see citing works
Neural Mechanisms of Updating under Reducible and Irreducible Uncertainty
Kobayashi, K., & Hsu, M. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0535-17.2017 · Cited by 50 · see citing works
Learning, the P3, and the Locus Coeruleus-Norepinephrine System
Nieuwenhuis, S. (2011) · The MIT Press eBooks · doi:10.7551/mitpress/9780262016438.003.0012 · Cited by 44 · see citing works
Self-citationPharmacology of sensory stimulation-evoked increases in frontal cortical acetylcholine release
Acquas, E., Wilson, C. M., & Fibiger, H. C. (1998) · Neuroscience · doi:10.1016/s0306-4522(97)00546-0 · Cited by 44 · see citing works
Dissociable contribution of prefrontal and striatal dopaminergic genes to learning in economic games
Set, E., Sáez, I., Zhu, L., Houser, D. E., Myung, N., Zhong, S., Ebstein, R. P., Chew, S. H., & Hsu, M. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1316259111 · Cited by 35 · see citing works
Beta receptor-mediated modulation of the late positive potential in humans
de Rover, M., Brown, S. B. R. E., Boot, N. C., Hajcak, G., van Noorden, M. S., van der Wee, N. J. A., & Nieuwenhuis, S. (2011) · Psychopharmacology · doi:10.1007/s00213-011-2426-x · Cited by 35 · see citing works
Self-citationRangel‐Gomez, M., Hickey, C., van Amelsvoort, T., Bet, P. M., & Meeter, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066469 · Cited by 31 · see citing works
Learning and Generalization under Ambiguity: An fMRI Study
Chumbley, J. R., Flandin, G., Bach, D. R., Jean Daunizeau, A., Fehr, E., Dolan, R. J., & Friston, K. J. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002346 · Cited by 20 · see citing works
Noradrenergic and cholinergic effects on speed and sensitivity measures of phasic alerting.
Brown, S. B. R. E., Tona, K., van Noorden, M. S., Giltay, E. J., van der Wee, N. J. A., & Nieuwenhuis, S. (2015) · Behavioral Neuroscience · doi:10.1037/bne0000030 · Cited by 20 · see citing works
Self-citationBrown, S. B. R. E., Slagter, H. A., van Noorden, M. S., Giltay, E. J., van der Wee, N. J. A., & Nieuwenhuis, S. (2015) · Psychopharmacology · doi:10.1007/s00213-015-4111-y · Cited by 18 · see citing works
Self-citationModulation of attention by noradrenergic alpha2-agents varies according to arousal level
Coull, J. T. (2001) · Drug News & Perspectives · doi:10.1358/dnp.2001.14.1.858386 · Cited by 15 · see citing works
Gibbs, A. A., Bautista, C. E., Mowlem, F., Naudts, K., & Duka, D. (2014) · Journal of Psychiatry & Neuroscience · doi:10.1503/jpn.130131 · Cited by 11 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Figure A1. (This figure is related to Figure 6 in the main text.) Baseline-dependent drug effects on learning rate for different levels of change point probability. Trials were divided in nine bins, using the change point probabilities indicated on the left side of each plot as cutoff values. Note that, as change point probability was not equally distributed, the different bins contained different numbers of trials. The gray distributions are permutation distributions of the correlation coefficients predicted by regression to the mean in each bin, and the red lines indicate the observed correlations. 1818 Journal of Cognitive Neuroscience Volume 30, Number 12
- Doya, K. (2007). Bayesian brain: Probabilistic approaches to neural coding. Cambridge, MA: MIT Press.
Cited by
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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 96 · see citing works
Statistical context dictates the relationship between feedback-related EEG signals and learning
Nassar, M. R., Bruckner, R., & Frank, M. J. (2019) · eLife · doi:10.7554/elife.46975 · Cited by 94 · see citing works
With an eye on uncertainty: Modelling pupillary responses to environmental volatility
Vincent, P., Parr, T., Benrimoh, D. A., & Friston, K. J. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007126 · Cited by 54 · see citing works
Brain dynamics for confidence-weighted learning
Meyniel, F. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007935 · Cited by 49 · see citing works
Hezemans, F. H., Wolpe, N., O’Callaghan, C., Ye, R., Rua, C., Jones, P. S., Murley, A. G., Holland, N., Regenthal, R., Tsvetanov, K. A., et al. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010079 · Cited by 48 · see citing works
Disentangling performance-monitoring signals encoded in feedback-related EEG dynamics
Kirsch, F., Kirschner, H., Fischer, A. G., Klein, T. A., & Ullsperger, M. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119322 · Cited by 35 · see citing works
Pupil diameter tracked during motor adaptation in humans
Yokoi, A., & Weiler, J. (2022) · Journal of Neurophysiology · doi:10.1152/jn.00021.2022 · Cited by 34 · see citing works
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 · see citing works
Jongkees, B. J. (2020) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/s13415-020-00783-8 · Cited by 29 · see citing works
Weber, L. A. E., Tomiello, S., Schöbi, D., Wellstein, K. V., Mueller, D. R., Iglesias, S., & Stephan, K. E. (2022) · eLife · doi:10.7554/elife.74835 · Cited by 26 · see citing works
Jahn, C. I., Varazzani, C., Sallet, J., Walton, M. E., & Bouret, S. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa089 · Cited by 21 · see citing works
Cerpa, J.-C., Coutureau, É., & Parkes, S. L. (2021) · Behavioral Neuroscience · doi:10.1037/bne0000426 · Cited by 20 · see citing works
Pupil Size Encodes Uncertainty during Exploration
Fan, H., Burke, T. D., Sambrano, D. C., Dial, E., Phelps, E. A., & Gershman, S. J. (2023) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_02025 · Cited by 18 · see citing works
Tona, K., Revers, H., Verkuil, B., & Nieuwenhuis, S. (2020) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01603 · Cited by 18 · see citing works
Self-citationFeedback-related EEG dynamics separately reflect decision parameters, biases, and future choices
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Experience-driven recalibration of learning from surprising events
Bakst, L., & McGuire, J. T. (2022) · Cognition · doi:10.1016/j.cognition.2022.105343 · Cited by 16 · see citing works
Warren, C. V., Kroll, C., & Kopp, B. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105221 · Cited by 11 · see citing works
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Wurm, F., Walentowska, W., Ernst, B., Severo, M. C., Pourtois, G., & Steinhauser, M. (2021) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01777 · Cited by 10 · see citing works
Catecholamines reduce choice history biases in perceptual decision making
de Gee, J. W., Kloosterman, N. A., Braun, A., & Donner, T. H. (2025) · PLoS Biology · doi:10.1371/journal.pbio.3003361 · Cited by 6 · see citing works
Systematic review: Effects of cholinergic signaling on cognition in human pharmacological studies
Dan, Y. R., Christakou, A., & Karin Roelofs, A. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106408 · Cited by 5 · see citing works
MacCormack, J. K., Armstrong‐Carter, E., Humphreys, K. L., & Muscatell, K. A. (2021) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsab047 · Cited by 5 · see citing works
Narasimhan, S., Schriver, B. J., & Wang, Q. (2023) · Journal of Neurophysiology · doi:10.1152/jn.00309.2022 · Cited by 4 · see citing works
A dynamical systems model of arousal-driven behavioural state transitionsPreprint
Johnson, P. A., Nieuwenhuis, S., Mejías, J. F., & Urai, A. E. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.31.685593 · Cited by 4 · see citing works
Self-citationPrediction errors indexed by the P3 track the updating of complex long-term memory schemasPreprint
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Jongkees, B. J. (2019) · doi:10.31234/osf.io/bpv2y · Cited by 3 · see citing works
Brancaccio, A., Tagliaferri, M., Miniussi, C., & Cattaneo, L. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121335 · Cited by 2 · see citing works
Fluctuations in arousal reflect latent state transitions that facilitate behavioural optimization
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An Integrative and Mechanistic Model of Impaired Belief Updating in Schizophrenia
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Electrophysiological correlates of (mis)judging social information
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Absence of Systematic Effects of Internalizing Psychopathology on Learning Under UncertaintyPreprint
Satti, M. H., Wille, K., Nassar, M. R., Cichy, R. M., Schuck, N. W., Dayan, P., & Bruckner, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.12.653409 · Cited by 1 · see citing works
Absence of Systematic Effects of Internalizing Psychopathology on Learning Under UncertaintyPreprint
Satti, M. H., Wille, K., Nassar, M. R., Cichy, R. M., Schuck, N. W., Dayan, P., & Bruckner, R. (2025) · eLife · doi:10.7554/elife.107611.1 · Cited by 1 · see citing works
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Greenhouse‐Tucknott, A., Foucault, C., Buot, A., & Meyniel, F. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.26.708148 · Cited by 1 · see citing works
Absence of Systematic Effects of Internalizing Psychopathology on Learning Under UncertaintyPreprint
Satti, M. H., Wille, K., Nassar, M. R., Cichy, R. M., Schuck, N. W., Dayan, P., & Bruckner, R. (2025) · eLife · doi:10.7554/elife.107611
Implication of the locus coeruleus in effort processing : electrophysiological studies
Bornert, P. (2021) · theses.fr (ABES) · doi:10.70675/665d6bd7ze2f5z41c9z93f8z764be3c6d822
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Plat, H., Chevallier, C., Piccin, A., Marchand, A. R., Naudé, J., & Coutureau, É. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2536535123
Change-point oddball elicits pupil-linked signatures of Bayesian belief updatingPreprint
Kopel, L., Hoomans, J. M., van Gastel, E., Tijhuis, J. V., de Graaf, M., Shafik, M., Matthijsse, S., Murphy, P. R., van Gaal, S., Mototaka Suzuki, A., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.25.754382
Self-citation
- Authors
- Jepma, M., Brown, S. B. R. E., Murphy, P. R., Koelewijn, S. C., de Vries, B., van den Maagdenberg, A. M., & Nieuwenhuis, S.
- Affiliations
- Leiden University (NL)
- Leiden University Medical Center (NL)
- Leiden University Medical Centre
- Venue
- Journal of Cognitive Neuroscience
- Publisher
- MIT Press
- Published
- 2018-12
- Volume
- 30
- Issue
- 12
- Pages
- 1803-1820
- ISSN
- 0898-929X (ISSN-L)
- 1530-8898
- OpenAlex
- W2886901971
- Cited by
- 39
- References
- 80
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 2 · 2023: 3 · 2022: 6 · 2021: 5 · 2020: 5 · 2019: 6
- FWCI
- 2.38
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Cognitive Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- UvA-DARE (University of Amsterdam) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- Universiteit van Amsterdam
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- belief updating, learning rate, noradrenergic system, cholinergic system, P300, catecholamine release, surprise, belief uncertainty, norepinephrine, acetylcholine, ADRA2A, .NET, individual differences, neuromodulation
- MeSH terms
- Acetylcholine, Adolescent, Adult, Brain, Clonidine, Double-Blind Method, Electroencephalography, Female, Humans, Learning, Male, Norepinephrine, Scopolamine, Receptors, Adrenergic, alpha-2, Cross-Over Studies, Cholinergic Antagonists, Uncertainty, Norepinephrine Plasma Membrane Transport Proteins, Young Adult, Genetic Association Studies, Adrenergic alpha-2 Receptor Agonists, Anticipation, Psychological
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