Cited by 286Neural and Behavioral Psychology StudiesMemory and Neural MechanismsDecision-Making and Behavioral Economics

The DOI matches its Crossref record; 118 of the 118 references it cites were found in a registry or an index.

Adaptive learning under expected and unexpected uncertainty

Open access

Soltani, Alireza, Izquierdo, Alicia·2019·Nature reviews. Neuroscience

DOI 10.1038/s41583-019-0180-yPMC PMC6752962

Open this paper in CoChat — free account

Abstract

The outcome of a decision is often uncertain, and outcomes can vary over repeated decisions. Whether decision outcomes should substantially affect behaviour and learning depends on whether they are representative of a typically experienced range of outcomes or signal a change in the reward environment. Successful learning and decision-making therefore require the ability to estimate expected uncertainty (related to the variability of outcomes) and unexpected uncertainty (related to the variability of the environment). Understanding the bases and effects of these two types of uncertainty and the interactions between them - at the computational and the neural level - is crucial for understanding adaptive learning. Here, we examine computational models and experimental findings to distil computational principles and neural mechanisms for adaptive learning under uncertainty.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1038/s41583-019-0180-y

Cites

118 of 118 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
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
What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience?
Berridge, K. C., & Robinson, T. E. (1998) · Brain Research Reviews · doi:10.1016/s0165-0173(98)00019-8 · Cited by 4,127
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 expected value of control: an integrative theory of anterior cingulate cortex function
Shenhav, A., Botvinick, M. M., & Cohen, J. D. (2013) · Neuron · doi:10.1016/j.neuron.2013.07.007 · Cited by 2,321
Cortical substrates for exploratory decisions in humans
Daw, N. D., O'Doherty, J. P., Dayan, P., Seymour, B., & Dolan, R. J. (2006) · Nature · doi:10.1038/nature04766 · Cited by 2,224
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
Uncertainty and anticipation in anxiety: an integrated neurobiological and psychological perspective
Grupe, D. W., & Nitschke, J. B. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3524 · Cited by 2,007
Uncertainty, Neuromodulation, and Attention
Yu, A. J., & Dayan, P. (2005) · Neuron · doi:10.1016/j.neuron.2005.04.026 · Cited by 1,774

Show all 118 references

Research works printed but not linked: 2

Dayan, P. & Long, T. in Advances in Neural Information Processing Systems 10: Proceedings of the 1997 Conference (eds Jordan, M. I., Kearns, M. J. & Solla, S. A.) 117–123 (MIT Press, 1998).
Soltani, A. C., W. & Wang, X. J. in Decision Neuroscience: An Integrative Perspective (eds Dreher, J.-C. & Tremblay, L.) 163–222 (Elsevier Academic Press, 2017).

Sorted by citations

Items 101–200 of 285

Distinct Computational Mechanisms of Uncertainty Processing Explain Opposing Exploratory Behaviors in Anxiety and Apathy
Yan, X., Ebitz, R. B., Grissom, N. M., Darrow, D., & Herman, A. B. (2025) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2025.01.005 · Cited by 13
Adaptive reinforcement learning is causally supported by anterior cingulate cortex and striatum
Treuting, R. L., Boroujeni, K. B., Gerrity, C. G., Neumann, A. R., Tiesinga, P. H. E., & Womelsdorf, T. (2025) · Neuron · doi:10.1016/j.neuron.2025.05.018 · Cited by 18
Violated predictions enhance the representational fidelity of visual features in perception
Rideaux, R., Dang, P., Jackel-David, L., Buhmann, Z., Rangelov, D., & Mattingley, J. B. (2025) · Journal of Vision · doi:10.1167/jov.25.4.14 · Cited by 10
Adaptation of utility functions to reward distribution in rhesus monkeys
Bujold, P. M., Ferrari-Toniolo, S., & Schultz, W. (2021) · Cognition · doi:10.1016/j.cognition.2021.104764 · Cited by 11
Contributions of Attention to Learning in Multidimensional Reward Environments
Wang, M. C., & Soltani, A. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.2300-23.2024 · Cited by 13
Self-citation
Multiscale Computation and Dynamic Attention in Biological and Artificial Intelligence
Badman, R. P., Hills, T. T., & Akaishi, R. (2020) · Brain Sciences · doi:10.3390/brainsci10060396 · Cited by 9
Modulation of Dopamine for Adaptive Learning: a Neurocomputational Model
Inglis, J. B., Valentin, V. V., & Ashby, F. G. (2020) · Computational Brain & Behavior · doi:10.1007/s42113-020-00083-x · Cited by 10
Neuronal Representation of a Working Memory-Based Decision Strategy in the Motor and Prefrontal Cortico-Basal Ganglia Loops
Yoshizawa, T., Ito, M., & Doya, K. (2023) · eNeuro · doi:10.1523/eneuro.0413-22.2023 · Cited by 9
Prenatal Stress and Maternal Role in Neurodevelopment
Thomason, M. E., & Hendrix, C. L. (2024) · Annual Review of Developmental Psychology · doi:10.1146/annurev-devpsych-120321-011905 · Cited by 13
Distributed Intracranial Activity Underlying Human Decision-making Behavior
Overton, J. A., Moxon, K. A., Stickle, M. P., Peters, L. M., Lin, J. J., Chang, E. F., Knight, R. T., Hsu, M., & Sáez, I. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.0572-24.2024 · Cited by 13
Exploring replay
Antonov, G. K., & Dayan, P. (2025) · Nature Communications · doi:10.1038/s41467-025-56731-y · Cited by 10
Neural signals implicated in the processing of appetitive and aversive events in social and non-social contexts
Vázquez, D., Schneider, K. N., & Roesch, M. R. (2022) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2022.926388 · Cited by 11
A characterization of the neural representation of confidence during probabilistic learning
Bounmy, T., Eger, E., & Meyniel, F. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2022.119849 · Cited by 16
Unpredictability as a modulator of drug self-administration: Relevance for substance-use disorders
Huskinson, S. L. (2020) · Behavioural Processes · doi:10.1016/j.beproc.2020.104156 · Cited by 9
Reinforcement learning modeling reveals a reward-history-dependent strategy underlying reversal learning in squirrel monkeys.
Bari, B. A., Moerke, M. J., Jedema, H. P., Effinger, D. P., Cohen, J. Y., & Bradberry, C. W. (2021) · Behavioral Neuroscience · doi:10.1037/bne0000492 · Cited by 10
Flexible learning in complex worlds
Leimar, O., Quiñones, A. E., & Bshary, R. (2023) · Behavioral Ecology · doi:10.1093/beheco/arad109 · Cited by 11
Romantic Unions and Mental Health: The Role of Relationship Churning
Halpern‐Meekin, S., & Turney, K. (2022) · Journal of Health and Social Behavior · doi:10.1177/00221465221126091 · Cited by 8
Persistent decision-making in mice, monkeys and humans
Laurie, V.-J., Shourkeshti, A., Chen, C. S., Herman, A. B., Grissom, N. M., & Ebitz, R. B. (2026) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2025.1983 · Cited by 7
A simple model for learning in volatile environmentsPreprint
Piray, P., & Daw, N. D. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/701466 · Cited by 8
Hierarchy of prediction errors shapes the learning of context-dependent sensory representationsPreprint
Tsai, M. C., Teutsch, J., Wybo, W. A. M., Helmchen, F., Banerjee, A., & Senn, W. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.30.615819 · Cited by 10
Prior Expectations of Volatility Following Psychotherapy for Delusions
Sheffield, J. M., Sloan, A. F., Corlett, P. R., Rogers, B. P., Vandekar, S., Liu, J., Beals, K., Hall, L. M., Gautier, T., Moussa‐Tooks, A. B., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.17132 · Cited by 7
Theoretical Review of Research to Date on Competences 4.0—What Do We Know about Competences in Industry 4.0? A Status Quo Analysis
Poszytek, P., Hyrcza-Michalska, M., Brodny, J., Wawrzała, P., Gębal, P. E., Lisok, J., KRUSZEWSKA, J., Sigurðardóttir, A. G., Bugnova, M., & Dobrowolska, M. (2023) · Sustainability · doi:10.3390/su151612267 · Cited by 7
Sustained EEG responses to rapidly unfolding stochastic sounds reflect Bayesian inferred reliability tracking
Zhao, S., Skerritt-Davis, B., Elhilali, M., Dick, F., & Maria Chait, A. (2024) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2024.102696 · Cited by 11
Broadscale dampening of uncertainty adjustment in the aging brain
Kosciessa, J. Q., Mayr, U., Lindenberger, U., & Garrett, D. D. (2024) · Nature Communications · doi:10.1038/s41467-024-55416-2 · Cited by 7
Combinations of low-level and high-level neural processes account for distinct patterns of context-dependent choice
Spitmaan, M., Horno, O., Chu, E. K., & Soltani, A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007427 · Cited by 7
Self-citation
Synaptic development of layer V pyramidal neurons in the prenatal human prefrontal neocortex: a Neurolucida-aided Golgi study
He, L.-X., Wan, L., Xiang, W., Li, J.-M., Pan, A.-H., & Lu, D.-H. (2020) · Neural Regeneration Research · doi:10.4103/1673-5374.274345 · Cited by 7
Knowing what you don’t know: Estimating the uncertainty of feedforward and feedback inputs with prediction-error circuitsPreprint
Hertäg, L., Wilmes, K. A., & Claudia Clopath, A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.13.571410 · Cited by 7
Neural correlates of reward valuation in individuals with nonsuicidal self-injury under uncertainty
Lee, J. O., Moon, H., Zoh, S.-M., Jo, E., & Hur, J. (2024) · Psychological Medicine · doi:10.1017/s0033291724001363 · Cited by 7
Neural coding of choice and outcome are modulated by uncertainty in orbitofrontal but not secondary motor cortex
Romero-Sosa, J. L., Yeghikian, A., Wikenheiser, A. M., Blair, H. T., & Izquierdo, A. (2025) · Nature Communications · doi:10.1038/s41467-025-63866-5 · Cited by 6
Self-citation
Longitudinal trajectories of anterior cingulate glutamate and subclinical psychotic experiences in early adolescence: the impact of bullying victimization
Okada, N., Yahata, N., Koshiyama, D., Morita, K., Sawada, K., Kanata, S., Fujikawa, S., Sugimoto, N., Toriyama, R., Masaoka, M., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-023-02382-8 · Cited by 7
Controlling human causal inference through in silico task design
Lee, J. H., Heo, S., & Lee, S. W. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.113702 · Cited by 6
Secondary motor cortex tracks decision value during the learning of a non-instructed task
Augusto, E., Kouskoff, V., Chenouard, N., Giraudet, M., Peltier, L., de Miranda, A., Louis, A., Alonso, L., & Gambino, F. (2025) · Cell Reports · doi:10.1016/j.celrep.2024.115152 · Cited by 7
Uncertainty estimation with prediction-error circuits
Hertäg, L., Wilmes, K. A., & Clopath, C. (2025) · Nature Communications · doi:10.1038/s41467-025-58311-6 · Cited by 18
There Is More to AI than Meets the Eye: Aligning Man-made Algorithms with Nature-inspired Mechanisms
Hamid, O. H. (2022) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa56895.2022.10017523 · Cited by 7
Temporal dynamics of the semantic versus affective representations of valence during reversal learning
Heimer, O., Kron, A., & Hertz, U. (2023) · Cognition · doi:10.1016/j.cognition.2023.105423 · Cited by 5
Predictive event segmentation and representation with neural networks: A self-supervised model assessed by psychological experiments
Başgöl, H., Ayhan, İ., & Uğur, E. (2023) · Cognitive Systems Research · doi:10.1016/j.cogsys.2023.101167 · Cited by 7
Compulsivity is linked to maladaptive choice variability but unaltered reinforcement learning under uncertaintyPreprint
Lee, J. K., Rouault, M., & Wyart, V. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.05.522867 · Cited by 6
Foraging Under Uncertainty Follows the Marginal Value Theorem with Bayesian Updating of Environment RepresentationsPreprint
Webb, J., Steffan, P., Hayden, B. Y., Lee, D., Kemere, C. T., & McGinley, M. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.30.587253 · Cited by 6
Dynamic Foraging Behavior Performance Is Not Affected byScn2aHaploinsufficiency
Schamiloglu, S., Wu, H., Zhou, M., Kwan, A. C., & Bender, K. J. (2023) · eNeuro · doi:10.1523/eneuro.0367-23.2023 · Cited by 4
Uncertainty of treatment efficacy moderates placebo effects on reinforcement learning
Augustat, N., Endres, D., & Mueller, E. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-64240-z · Cited by 5
Foraging animals use dynamic Bayesian updating to model meta-uncertainty in environment representations
Webb, J., Steffan, P., Hayden, B. Y., Lee, D., Kemere, C. T., & McGinley, M. J. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012989 · Cited by 10
Locus coeruleus tonic upregulation increases selectivity to inconspicuous auditory information in autistic compared to non-autistic individuals: a combined pupillometry and electroencephalography study
Bast, N., Ahmad, J., Mason, L., Jones, E. J. H., Matyjek, M., Polzer, L., Luckhardt, C., Müller, A. K., McAlonan, G. M., Banaschewski, T., et al. (2025) · Molecular Autism · doi:10.1186/s13229-025-00678-w · Cited by 7
Discovery, Interruption, and Updating of Auditory Regularities in Memory: Evidence from Low-Frequency Brain Dynamics in Human MEG
Bianco, R., Magami, K., Pearce, M. T., & Maria Chait, A. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.0629-25.2025 · Cited by 5
Psychological factors motivating the choice of university entrants
Derkach, T. M., Kolodiazhna, A. V., & Shuhailo, Y. (2021) · SHS Web of Conferences · doi:10.1051/shsconf/202110402001 · Cited by 5
Exploring how healthcare teams balance the neurodynamics of autonomous and collaborative behaviors: a proof of concept
Stevens, R. H., & Galloway, T. L. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.932468 · Cited by 5
Neuronal Sequence Models for Bayesian Online Inference
Frölich, S., Marković, D., & Kiebel, S. J. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.530937 · Cited by 5
Expecting the unexpected: the paranoid style of belief updating across speciesPreprint
Reed, E. J., Uddenberg, S., Mathys, C. D., Taylor, J. R., Groman, S. M., & Corlett, P. R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.24.963298 · Cited by 5
Novelty is not Surprise: Human exploratory and adaptive behavior in sequential decision-makingPreprint
He, X., Modirshanechi, A., Lehmann, M. P., Wulfram Gerstner, B., & Herzog, M. H. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.24.311084 · Cited by 7
Clown Doctors in the Lento Tempo of Pediatric Oncology: A Dialogue between Psychology and Art on the Meaning of an Encounter When Life Meaning Is DisruptedPreprint
Peceguina, I., Alcântara, I., & González, A. J. (2023) · Preprints.org · doi:10.20944/preprints202309.0377.v1 · Cited by 5
Sequential recruitment of accumbal dopamine and acetylcholine during learningPreprint
Zhang, Z., Costa, K. M., Zong, W., Zhuo, Y., Li, Y., Li, Y., & Schoenbaum, G. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.06.631529 · Cited by 5
Two Determinants of Dynamic Adaptive Learning for Magnitudes and Probabilities
Foucault, C., & Meyniel, F. (2024) · Open Mind · doi:10.1162/opmi_a_00139 · Cited by 6
Superior colliculus encodes the visual cue associated with rewarding behavior in sucrose self-administration in mice
Cun, X.-F., Zhu, H., Wu, N., Jing, M., & Song, R. (2025) · Scientific Reports · doi:10.1038/s41598-025-19192-3 · Cited by 4
Uncertainty and reward histories have distinct effects on decisions after wins and losses
Kalhan, S., Magnard, R., Zhang, Z., Cheng, Y., & Janak, P. H. (2026) · Scientific Reports · doi:10.1038/s41598-026-37554-3 · Cited by 5
Prefrontal to ventral tegmental area dynamics drive contingency degradation
Hjort, M. M., Garrett, Z. Q., Gordon-Fennell, A., Ancell, E., Trzeciak, M., Lu, P.-Y., Bruchas, M. R., Witten, D., Steinmetz, N. A., & Stuber, G. D. (2026) · Nature · doi:10.1038/s41586-026-10443-5 · Cited by 5
Same Same, But Different: Brain Areas Underlying the Learning from Repetitive Episodic Prediction Errors
Jainta, B., Zahedi, A., & Schubotz, R. I. (2024) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_02204 · Cited by 4
Anterior cingulate cortex lesions impair multiple facets of task engagement not mediated by dorsomedial striatum neuron firing
Vázquez, D., Peña-Flores, N., Maulhardt, S., Solway, A., Charpentier, C. J., & Roesch, M. R. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae332 · Cited by 5
Trust-Based Decision-Making in the Health Context Discriminates Biological Risk Profiles in Type 1 Diabetes
Jorge, H., Duarte, I. C., Baptista, C. F., Relvas, A. P., & Castelo-Branco, M. S. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12081236 · Cited by 4
A nonlinear relationship between prediction errors and learning rates in human reinforcement-learning
Ikwunne, B., Parham, J., & Pulcu, E. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013445 · Cited by 4
Parsing Neurodynamic Information Streams to Estimate the Frequency, Magnitude and Duration of Team Uncertainty
Stevens, R. H., & Galloway, T. L. (2021) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2021.606823 · Cited by 4
Networks of spike oscillators and their application for creating autonomous intelligent devices
Ванаг, В. К. (2021) · Physics-Uspekhi · doi:10.3367/ufne.2021.05.038978 · Cited by 4
Learning at variable attentional load requires cooperation between working memory, meta-learning and attention-augmented reinforcement learningPreprint
Womelsdorf, T., Watson, M. R., & Tiesinga, P. H. E. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.27.315432 · Cited by 4
Uncertainty in learning and decision-making: Introduction to the special issue
Levy, I., & Schiller, D. (2023) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-023-01111-6 · Cited by 4
Cholinergic modulation of reinforcement learning and prefrontal value computations under uncertaintyPreprint
Kurtenbach, H., Froböse, M. I., Ort, E., Bahners, B. H., Butz, M., Schnitzler, A., Hirschmann, J., & Jocham, G. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.20.614105 · Cited by 4
A frontal motor circuit for economic decisions and actionsPreprint
Gauld, O. M., Bao, C., Li, J., Boeshertz, G., Sit, T. P. H., Warren, J., Tutt, J. O., Pan, Y., Zervogiannis, N., Clopath, C., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.11.693624 · Cited by 9
Stimulus uncertainty and relative reward rates determine adaptive responding in perceptual decision-making
de la Cuesta‐Ferrer, L., Koß, C., Starosta, S., Kasties, N., Lengersdorf, D., Jäkel, F., & Stüttgen, M. C. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012636 · Cited by 3
Suppression of the dorsomedial striatum impairs reversal learning in response to probabilistic uncertainty and disrupts corticostriatal function in mice
Conn, K., Das, J., Alexander, S., Burne, T. H. J., & Kesby, J. P. (2025) · Neuropharmacology · doi:10.1016/j.neuropharm.2025.110568 · Cited by 3
Neural variability in the medial prefrontal cortex contributes to efficient adaptive behavior
Findling, C., Romand-Monnier, M., Skvortsova, V., & Koechlin, É. (2025) · Nature Communications · doi:10.1038/s41467-025-66444-x · Cited by 4
Towards dynamic computing: Multi-dynamic neuronal-synaptic transistors for visual intelligence
Xu, C., Zhu, Z., Zeng, H., Xue, J., Yang, Q., & Chen, H. (2025) · Nano Energy · doi:10.1016/j.nanoen.2025.111656 · Cited by 3
What the eyes, confidence, and partner’s identity can tell about change of mind
Sanchez, R., Tomei, A.-C., Mamassian, P., Vidal, M., & Desantis, A. (2024) · Neuroscience of Consciousness · doi:10.1093/nc/niae018 · Cited by 7
Violation of auditory regularities is reflected in pupil dynamics
Başgöl, H., Dayan, P., & Franz, V. H. (2024) · Cortex · doi:10.1016/j.cortex.2024.10.023 · Cited by 5
Curiosity and the Regulation of Affective Memory
Ham, J., Murty, V. P., & Helion, C. (2024) · Emotion Review · doi:10.1177/17540739241303500 · Cited by 3
Antifragile control systems in neuronal processing: a sensorimotor perspective
Axenie, C. (2025) · Biological Cybernetics · doi:10.1007/s00422-025-01003-7 · Cited by 4
Adaptive reward representations integrate expected uncertainty signals in orbitofrontal cortex
Zhang, Q., & Zhou, J. (2025) · Science Advances · doi:10.1126/sciadv.adv9590 · Cited by 3
Contribution of amygdala to dynamic model arbitration under uncertainty
Woo, J. H., Costa, V. D., Taswell, C. A., Rothenhoefer, K. M., Averbeck, B. B., & Soltani, A. (2025) · Nature Communications · doi:10.1038/s41467-025-66745-1 · Cited by 3
Self-citation
The computational and neural substrates underlying social learning
Li, S., Chen, X., Zhai, Y., Zhang, Y., Zhang, Z., & Chunliang, F. E. N. G. (2021) · Advances in Psychological Science · doi:10.3724/sp.j.1042.2021.00677 · Cited by 3
Weaker situations: Uncertainty reveals individual differences in learning: Implications for PTSD
Allen, M. T. (2023) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-023-01077-5 · Cited by 5
Reinforcement learning of altruistic punishment differs between cultures and across the lifespan
Guo, Z., Yu, J., Wang, W., Lockwood, P. L., & Wu, Z. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012274 · Cited by 3
Exploring the relationship between frequent cannabis use, belief updating under uncertainty and psychotic-like symptoms
Liang, X., Avram, M.-M., Gibbs-Dean, T., Chesney, E., Oliver, D., Wang, S., Obreshkova, S., Spencer, T., Englund, A., & Diederen, K. M. J. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1309868 · Cited by 3
Compulsivity is linked to suboptimal choice variability but unaltered reinforcement learning under uncertainty
Lee, J. K., Rouault, M., & Wyart, V. (2025) · Nature Mental Health · doi:10.1038/s44220-024-00364-5 · Cited by 13
Cognitive Learning Assessment Based on FAHP and RSM: A Case Study of Introduction to Network course
Lin, C., Chang, S.-J., & Chen, Y. (2021) · Journal of Educational Computing Research · doi:10.1177/07356331211012685 · Cited by 3
A Decision Architecture for Safety ComputationsPreprint
Tashjian, S. M., Zbozinek, T. D., & Mobbs, D. (2020) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/4s8zb · Cited by 3
Multiple and subject-specific roles of uncertainty in reward-guided decision-makingPreprint
Paunov, A. M., L’Hôtellier, M., Guo, D., He, Z., Yu, A. J., & Meyniel, F. (2024) · eLife · doi:10.7554/elife.103363 · Cited by 5
Neurochemical markers of uncertainty processing in humansPreprint
Jassim, N., Waade, P. T., Parsons, O., Petzschner, F. H., Rua, C., Rodgers, C. T., Baron‐Cohen, S., John Suckling, A., Mathys, C. D., & Lawson, R. P. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.19.639013 · Cited by 3
Anterior cingulate cortex mixes retrospective cognitive signals and ongoing movement signatures during decision-makingPreprint
Oesch, L. T., Thomas, M. C., Sandberg, D., Couto, J., & Churchland, A. K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.11.648398 · Cited by 3
Neural signatures of model-based and model-free reinforcement learning across prefrontal cortex and striatum
Miranda, B., Butler, J. L., Malalasekera, W. M. N., Behrens, T. E. J., Dayan, P., & Kennerley, S. W. (2026) · eLife · doi:10.7554/elife.106032.3 · Cited by 2
Acetylcholine modulates prefrontal outcome coding during threat learning under uncertainty
Tu, G., Wen, P., Halawa, A., & Takehara‐Nishiuchi, K. (2024) · eLife · doi:10.7554/elife.102986.2 · Cited by 9
Exposure to unpredictable childhood environments is associated with amygdala activation during early extinction in adulthood
Duda, J. M., Keding, T. J., Kribakaran, S., Odriozola, P., Kitt, E. R., Cohodes, E. M., Zacharek, S. J., McCauley, S., Haberman, J. T., Joormann, J., et al. (2025) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2025.101578 · Cited by 3
Future shapes present: autonomous goal-directed and sensory-focused mode switching in a Bayesian allostatic network model
Idei, H., Tani, J., Ogata, T., & Yamashita, Y. (2025) · npj Complexity · doi:10.1038/s44260-025-00046-y · Cited by 3
Memory Constraints in Uncertainty Misestimation: A Computational Model of Working Memory and Environmental Change Detection
Lim, L. X., Akaishi, R., & Hélie, S. (2025) · Mathematics · doi:10.3390/math13152431 · Cited by 2
Cognitive and Neural Mechanisms of Motivation, Reward, and Self-Control
Goschke, T. (2025) · Springer eBooks · doi:10.1007/978-3-031-87947-0_10 · Cited by 2
The utility of early life adversity questionnaires in neuroimaging research: Challenges in uncovering neurobiological mechanisms
Mitchell, O., Roddy, D. W., & Connaughton, M. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106434 · Cited by 2
Anterior cingulate neurons combine outcome monitoring of past decisions with ongoing movement signals
Oesch, L. T., Thomas, M. C., Sandberg, D., Couto, J., & Churchland, A. K. (2026) · Nature Communications · doi:10.1038/s41467-026-70639-1 · Cited by 11
Similar network compositions, but distinct neural dynamics underlying belief updating in environments with and without explicit outcomes
Fiore, V. G., & Gu, X. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118821 · Cited by 2
Conceptual Framework for Adversarial Thinking Adoption in Experiential Learning Model for Robotics Learning
Adnan, N., Abdullah, S. N. H. S., Yusof, R. J. R., & Zainal, N. F. A. (2022) · International Conference on Business Analytics for Technology and Security (ICBATS) · doi:10.1109/icbats54253.2022.9758998 · Cited by 2
Migrainous vertigo impairs adaptive learning as a function of uncertainty
Sharif, M., Rea, O., Burling, R., Miraval, M. E., Patel, R. P., Saman, Y., Rea, P. A., Yoon, H.-J., Kheradmand, A., & Arshad, Q. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1436127 · Cited by 2
Grasp context-dependent uncertainty alters the relative contribution of anticipatory and feedback-based mechanisms in object manipulation
Dutta, S., & Varadhan, S. K. M. (2024) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2024.108996 · Cited by 2
A roadmap for transformative translational research on gambling disorder in the UK
Hynes, T. J., Bowden‐Jones, H., Chamberlain, S. R., & Belin, D. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106071 · Cited by 1
Mixing memory and desire: How memory reactivation supports deliberative decision‐making
(2022) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1601 · Cited by 2
Introduction of Adaptive Learning at the University: UrFU Case of Implementing “Foreign Language” Discipline
Larionova, V. A., Гончарова, Н., Daineko, L. V., & Kovalev, F. (2024) · Education & Self Development · doi:10.26907/esd.19.1.09 · Cited by 2
Paranoia and Belief Updating During a CrisisPreprint
Suthaharan, P., Reed, E. J., Leptourgos, P., Kenney, J. G., Uddenberg, S., Mathys, C. D., Litman, L., Robinson, J., Moss, A. J., Taylor, J. R., et al. (2020) · doi:10.31234/osf.io/mtces · Cited by 2

Authors: Soltani, A., & Izquierdo, A.
Affiliations: Dartmouth College (US)
University of California, Los Angeles (US)
Corresponding author: Soltani, Alireza
Izquierdo, Alicia
ORCID: Soltani, Alireza
Izquierdo, Alicia
Venue: Nature reviews. Neuroscience
Publisher: Springer Science and Business Media LLC
Published: 2019-05-30
Volume: 20
Issue: 10
Pages: 635-644
ISSN: 1471-003X (ISSN-L)
1471-0048
DOI: 10.1038/s41583-019-0180-y
PMC: PMC6752962
OpenAlex: W2946961766
Cited by: 286
References: 118
Citations by year: 2026: 31 · 2025: 63 · 2024: 51 · 2023: 42 · 2022: 29 · 2021: 36 · 2020: 26 · 2019: 8
FWCI: 3.10
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature reviews. Neuroscience (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
eScholarship (California Digital Library) (submittedVersion)
Funders: National Institute on Drug Abuse (R01DA047870)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
Keywords: adaptive learning, uncertainty estimation, decision-making, computational model, neural mechanisms, outcome variability, reinforcement learning
MeSH terms: Adaptation, Biological, Animals, Brain, Choice Behavior, Decision Making, Humans, Learning, Nerve Net, Uncertainty

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

Adaptive learning under expected and unexpected uncertainty

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks