Learned uncertainty: The free energy principle in anxiety
Open accessAbstract
Generalized anxiety disorder is among the world’s most prevalent psychiatric disorders and often manifests as persistent and difficult to control apprehension. Despite its prevalence, there is no integrative, formal model of how anxiety and anxiety disorders arise. Here, we offer a perspective derived from the free energy principle; one that shares similarities with established constructs such as learned helplessness . Our account is simple: anxiety can be formalized as learned uncertainty . A biological system, having had persistent uncertainty in its past, will expect uncertainty in its future, irrespective of whether uncertainty truly persists. Despite our account’s intuitive simplicity—which can be illustrated with the mere flip of a coin—it is grounded within the free energy principle and hence situates the formation of anxiety within a broader explanatory framework of biological self-organization and self-evidencing. We conclude that, through conceptualizing anxiety within a framework of working generative models, our perspective might afford novel approaches in the clinical treatment of anxiety and its key symptoms.
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Psychological and neural mechanisms through which uncertainty-inducing stressors elicit negative mental states, particularly anxiety, and may lead to psychopathology have been extensively described in the literature.
“Psychological and neural mechanisms through which uncertainty-inducing stressors elicit negative mental states, particularly anxiety, and may lead to psychopathology have been extensively described in the literature [31,35,36,57,119,120], and these models are broadly relevant in the present context.”
From Impact of Media-Induced Uncertainty on Mental Health: Narrative-Based Perspective. (2025) · cites this paper as [57]
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Compton, R. J., Dainer‐Best, J., Fineman, S. L., Freedman, G., Mutso, A., & Rohwer, J. (2009) · Cognition & Emotion · doi:10.1080/02699930802696856 · Cited by 9 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
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Cited by
Sorted by citations
Individual differences in computational psychiatry: A review of current challenges
Karvelis, P., Paulus, M. P., & Diaconescu, A. O. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105137 · Cited by 113 · see citing works
An, H., Gu, X., Obrenovic, B., & Godinic, D. (2023) · Psychology Research and Behavior Management · doi:10.2147/prbm.s416100 · Cited by 59 · see citing works
A new predictive coding model for a more comprehensive account of delusions
Harding, J. N., Wolpe, N., Brugger, S. P., Navarro, V., Teufel, C., & Fletcher, P. C. (2024) · The Lancet Psychiatry · doi:10.1016/s2215-0366(23)00411-x · Cited by 45 · see citing works
The free energy principle induces neuromorphic development
Fields, C., Friston, K. J., Glazebrook, J. F., Levin, M. G., & Marcianò, A. (2022) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/aca7de · Cited by 29 · see citing works
Impact 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 · see citing works
“Psychological and neural mechanisms through which uncertainty-inducing stressors elicit negative mental states, particularly anxiety, and may lead to psychopathology have been extensively described in the literature [31,35,36,57,119,120], and these models are broadly relevant in the present context.”
Barbieri, P., Sarasso, P., Lodico, F., Aliverti, A., Murayama, K., Sacco, K., & Ronga, I. (2023) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2022.0413 · Cited by 18 · see citing works
Ruffini, G., Castaldo, F., Lopez-Sola, E., Sanchez-Todo, R., & Vohryzek, J. (2024) · Entropy · doi:10.3390/e26110953 · Cited by 18 · see citing works
Priors and prejudice: hierarchical predictive processing in intergroup perception
McGovern, H. T., & Otten, M. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1386370 · Cited by 12 · see citing works
Self-citationArousal coherence, uncertainty, and well-being: an active inference account
Biddell, H., Solms, M., Slagter, H. A., & Laukkonen, R. (2024) · Neuroscience of Consciousness · doi:10.1093/nc/niae011 · Cited by 11 · see citing works
Self-citationSarasso, P., Tschacher, W., Schoeller, F., Francesetti, G., Roubal, J., Gecele, M., Sacco, K., & Ronga, I. (2024) · Physics of Life Reviews · doi:10.1016/j.plrev.2024.09.005 · Cited by 12 · see citing works
Consciousness, Sapience and Sentience—A Metacybernetic View
Yolles, M. I. (2022) · Systems · doi:10.3390/systems10060254 · Cited by 11 · see citing works
Crandall, A., Castaneda, G. L., Barlow, M. J., & Magnusson, B. M. (2024) · Frontiers in Child and Adolescent Psychiatry · doi:10.3389/frcha.2023.1249529 · Cited by 10 · see citing works
Harris, D., Arthur, T., Wilson, M., Gallais, B. L., Parsons, T. D., Dill, A., & Vine, S. (2025) · Biological Cybernetics · doi:10.1007/s00422-025-01006-4 · Cited by 5 · see citing works
McGovern, H. T., Aqil, M., Atasoy, S., & Carhart-Harris, R. L. (2025) · Neuroscience of Consciousness · doi:10.1093/nc/niaf039 · Cited by 5 · see citing works
Self-citationSimonetti, A., Bernardi, E., Kurian, S. R., Restaino, A., Calderoni, C., Chiara, E. D., Bardi, F., Sani, G., Soares, J. C., & Saxena, K. (2025) · Brain Sciences · doi:10.3390/brainsci15020152 · Cited by 4 · see citing works
jin, W., & Jue, Z. (2026) · Learning and Motivation · doi:10.1016/j.lmot.2026.102241 · Cited by 3 · see citing works
Human regulatory systems in the age of abundance: A predictive processing perspective
Muzik, O., & Diwadkar, V. A. (2025) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.15302 · Cited by 3 · see citing works
A constructionist predictive processing account of anxiety
Bae, S. Y. (2025) · Synthese · doi:10.1007/s11229-025-05330-x · Cited by 2 · see citing works
From symptoms to signatures: a transdiagnostic predictive coding framework for precision psychiatry
Lyndon, S. (2026) · Nature Mental Health · doi:10.1038/s44220-026-00607-7 · Cited by 2 · see citing works
Free Energy Projective Simulation (FEPS): Active inference with interpretability
Pazem, J., Krumm, M., Vining, A. Q., Fiderer, L. J., & Briegel, H. J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0331047 · Cited by 2 · see citing works
McGovern, H. T., Wellman, N., Hutchinson, B. T., Oestreich, L., Cooper, S., Fonzo, G. A., & Doss, M. K. (2026) · Psychopharmacology · doi:10.1007/s00213-026-07163-z · Cited by 2 · see citing works
Self-citationSauer, K. S., Pohl, A., den Bergh, O. V., & Witthöft, M. (2025) · Psychosomatic Medicine · doi:10.1097/psy.0000000000001437 · Cited by 1 · see citing works
The significance of absence: A contextual-relational perspective
Zamengo, L. (2025) · Theory & Psychology · doi:10.1177/09593543251355117 · Cited by 1 · see citing works
McGovern, H. T., Hutchinson, B. T., Jackson, J., Wells, E., Bandara, K. H., & Foe, A. D. (2025) · International Journal of Jungian Studies · doi:10.1163/19409060-bja10054 · Cited by 2 · see citing works
Self-citationHarris, D., Arthur, T., Wilson, M., Gallais, B. L., Parsons, T. D., Dill, A., & Vine, S. J. (2023) · doi:10.31234/osf.io/654h3
Burdening the Predictive Mind: A Predictive Processing Approach to Health-Related BurdensPreprint
Smart, P. R., Fair, N., Fraser, S. D., & Boniface, M. (2024) · doi:10.31234/osf.io/h8scp
Waggoner, M. (2025) · Philosophical Psychology · doi:10.1080/09515089.2025.2537896
Emotion and allostatic control: An active inference account of emotion regulation
Carìa, A., & Pezzulo, G. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106639 · Cited by 1 · see citing works
Bailey, R., Carmichael, T. D., & Penney, A. M. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2026.1766275
Diagnostic uncertainty as a public health imperative: a call for R69 code adoption
Hernandez, H. W., Epstein, H. M., Alimohamed, M. Z., Haskell, H. W., Mentis, A. A., Pearce, D. A., & Kanter, M. (2026) · Diagnosis · doi:10.1515/dx-2026-0049
Ullah, N. (2026) · Studies in systems, decision and control · doi:10.1007/978-3-032-16544-2_31
Математична формалізація терапевтичних інтервенцій
Черненко, В. (2026) · Research Notes · doi:10.31654/2663-4902-2026-pp-3-317-324
Reframing the lived experience of persistent dizziness: insights from active inference
Broberg, M. A., Thacker, M., Friston, K. J., & Esteves, J. E. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1867836
- Authors
- McGovern, H. T., De Foe, A., Biddell, H., Leptourgos, P., Corlett, P., Bandara, K., & Hutchinson, B. T.
- Affiliations
- The University of Queensland (AU)
- Yale University (US)
- The University of Melbourne (AU)
- Australian National University (AU)
- Corresponding author
- Hutchinson, Brendan T.
- ORCID
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2022-09-06
- Volume
- 13
- Pages
- 943785-943785
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC9559819
- OpenAlex
- W4294793588
- Cited by
- 33
- References
- 111
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 7 · 2023: 4 · 2022: 2
- FWCI
- 3.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Psychology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Monash University Research Portal (Monash University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Social Sciences › Arts and Humanities › Philosophy › Mental Health and Psychiatry
- Keywords
- anxiety, generalized anxiety disorder, free energy principle, learned helplessness, generative models, biological self-organization, anxiety treatment
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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